WorldWideScience

Sample records for optimal learning content

  1. Optimizing top precision performance measure of content-based image retrieval by learning similarity function

    KAUST Repository

    Liang, Ru-Ze

    2017-04-24

    In this paper we study the problem of content-based image retrieval. In this problem, the most popular performance measure is the top precision measure, and the most important component of a retrieval system is the similarity function used to compare a query image against a database image. However, up to now, there is no existing similarity learning method proposed to optimize the top precision measure. To fill this gap, in this paper, we propose a novel similarity learning method to maximize the top precision measure. We model this problem as a minimization problem with an objective function as the combination of the losses of the relevant images ranked behind the top-ranked irrelevant image, and the squared Frobenius norm of the similarity function parameter. This minimization problem is solved as a quadratic programming problem. The experiments over two benchmark data sets show the advantages of the proposed method over other similarity learning methods when the top precision is used as the performance measure.

  2. Optimizing top precision performance measure of content-based image retrieval by learning similarity function

    KAUST Repository

    Liang, Ru-Ze; Shi, Lihui; Wang, Haoxiang; Meng, Jiandong; Wang, Jim Jing-Yan; Sun, Qingquan; Gu, Yi

    2017-01-01

    In this paper we study the problem of content-based image retrieval. In this problem, the most popular performance measure is the top precision measure, and the most important component of a retrieval system is the similarity function used to compare a query image against a database image. However, up to now, there is no existing similarity learning method proposed to optimize the top precision measure. To fill this gap, in this paper, we propose a novel similarity learning method to maximize the top precision measure. We model this problem as a minimization problem with an objective function as the combination of the losses of the relevant images ranked behind the top-ranked irrelevant image, and the squared Frobenius norm of the similarity function parameter. This minimization problem is solved as a quadratic programming problem. The experiments over two benchmark data sets show the advantages of the proposed method over other similarity learning methods when the top precision is used as the performance measure.

  3. Incorporating technology-based learning tools into teaching and learning of optimization problems

    Science.gov (United States)

    Yang, Irene

    2014-07-01

    The traditional approach of teaching optimization problems in calculus emphasizes more on teaching the students using analytical approach through a series of procedural steps. However, optimization normally involves problem solving in real life problems and most students fail to translate the problems into mathematic models and have difficulties to visualize the concept underlying. As an educator, it is essential to embed technology in suitable content areas to engage students in construction of meaningful learning by creating a technology-based learning environment. This paper presents the applications of technology-based learning tool in designing optimization learning activities with illustrative examples, as well as to address the challenges in the implementation of using technology in teaching and learning optimization. The suggestion activities in this paper allow flexibility for educator to modify their teaching strategy and apply technology to accommodate different level of studies for the topic of optimization. Hence, this provides great potential for a wide range of learners to enhance their understanding of the concept of optimization.

  4. "UML Quiz": Automatic Conversion of Web-Based E-Learning Content in Mobile Applications

    Science.gov (United States)

    von Franqué, Alexander; Tellioglu, Hilda

    2014-01-01

    Many educational institutions use Learning Management Systems to provide e-learning content to their students. This often includes quizzes that can help students to prepare for exams. However, the content is usually web-optimized and not very usable on mobile devices. In this work a native mobile application ("UML Quiz") that imports…

  5. Learning Content Management Systems

    Directory of Open Access Journals (Sweden)

    Tache JURUBESCU

    2008-01-01

    Full Text Available The paper explains the evolution of e-Learning and related concepts and tools and its connection with other concepts such as Knowledge Management, Human Resources Management, Enterprise Resource Planning, and Information Technology. The paper also distinguished Learning Content Management Systems from Learning Management Systems and Content Management Systems used for general web-based content. The newest Learning Content Management System, very expensive and yet very little implemented is one of the best tools that helps us to cope with the realities of the 21st Century in what learning concerns. The debates over how beneficial one or another system is for an organization, can be driven by costs involved, efficiency envisaged, and availability of the product on the market.

  6. Optimization of internet content filtering-Combined with KNN and OCAT algorithms

    Science.gov (United States)

    Guo, Tianze; Wu, Lingjing; Liu, Jiaming

    2018-04-01

    The face of the status quo that rampant illegal content in the Internet, the result of traditional way to filter information, keyword recognition and manual screening, is getting worse. Based on this, this paper uses OCAT algorithm nested by KNN classification algorithm to construct a corpus training library that can dynamically learn and update, which can be improved on the filter corpus for constantly updated illegal content of the network, including text and pictures, and thus can better filter and investigate illegal content and its source. After that, the research direction will focus on the simplified updating of recognition and comparison algorithms and the optimization of the corpus learning ability in order to improve the efficiency of filtering, save time and resources.

  7. The Optimization by Using the Learning Styles in the Adaptive Hypermedia Applications

    Science.gov (United States)

    Hamza, Lamia; Tlili, Guiassa Yamina

    2018-01-01

    This article addresses the learning style as a criterion for optimization of adaptive content in hypermedia applications. First, the authors present the different optimization approaches proposed in the area of adaptive hypermedia systems whose goal is to define the optimization problem in this type of system. Then, they present the architecture…

  8. Machine learning meliorates computing and robustness in discrete combinatorial optimization problems.

    Directory of Open Access Journals (Sweden)

    Fushing Hsieh

    2016-11-01

    Full Text Available Discrete combinatorial optimization problems in real world are typically defined via an ensemble of potentially high dimensional measurements pertaining to all subjects of a system under study. We point out that such a data ensemble in fact embeds with system's information content that is not directly used in defining the combinatorial optimization problems. Can machine learning algorithms extract such information content and make combinatorial optimizing tasks more efficient? Would such algorithmic computations bring new perspectives into this classic topic of Applied Mathematics and Theoretical Computer Science? We show that answers to both questions are positive. One key reason is due to permutation invariance. That is, the data ensemble of subjects' measurement vectors is permutation invariant when it is represented through a subject-vs-measurement matrix. An unsupervised machine learning algorithm, called Data Mechanics (DM, is applied to find optimal permutations on row and column axes such that the permuted matrix reveals coupled deterministic and stochastic structures as the system's information content. The deterministic structures are shown to facilitate geometry-based divide-and-conquer scheme that helps optimizing task, while stochastic structures are used to generate an ensemble of mimicries retaining the deterministic structures, and then reveal the robustness pertaining to the original version of optimal solution. Two simulated systems, Assignment problem and Traveling Salesman problem, are considered. Beyond demonstrating computational advantages and intrinsic robustness in the two systems, we propose brand new robust optimal solutions. We believe such robust versions of optimal solutions are potentially more realistic and practical in real world settings.

  9. Contribution of Content Knowledge and Learning Ability to the Learning of Facts.

    Science.gov (United States)

    Kuhara-Kojima, Keiko; Hatano, Giyoo

    1991-01-01

    In 3 experiments, 1,598 Japanese college students were examined concerning the learning of facts in 2 content domains, baseball and music. Content knowledge facilitated fact learning only in the relevant domain; learning ability facilitated fact learning in both domains. Effects of content knowledge and learning ability were additive. (SLD)

  10. Adaptation of mathematical educational content in e-learning resources

    Directory of Open Access Journals (Sweden)

    Yuliya V. Vainshtein

    2017-01-01

    Full Text Available Modern trends in the world electronic educational system development determine the necessity of adaptive learning intellectual environments and resources’ development and implementation. An upcoming trend in improvement the quality of studying mathematical disciplines is the development and application of adaptive electronic educational resources. However, the development and application experience of adaptive technologies in higher education is currently extremely limited and does not imply the usage flexibility. Adaptive educational resources in the electronic environment are electronic educational resources that provide the student with a personal educational space, filled with educational content that “adapts” to the individual characteristics of the students and provides them with the necessary information.This article focuses on the mathematical educational content adaptation algorithms development and their implementation in the e-learning system. The peculiarity of the proposed algorithms is the possibility of their application and distribution for adaptive e-learning resources construction. The novelty of the proposed approach is the three-step content organization of the adaptive algorithms for the educational content: “introductory adaptation of content”, “the current adaptation of content”, “estimative and a corrective adaptation”. For each stage of the proposed system, mathematical algorithms for educational content adaptation in adaptive e-learning resources are presented.Due to the high level of abstraction and complexity perception of mathematical disciplines, educational content is represented in the various editions of presentation that correspond to the levels of assimilation of the course material. Adaptation consists in the selection of the optimal edition of the material that best matches the individual characteristics of the student. The introduction of a three-step content organization of the adaptive

  11. E-Learning Optimization: The Relative and Combined Effects of Mental Practice and Modeling on Enhanced Podcast-Based Learning--A Randomized Controlled Trial

    Science.gov (United States)

    Alam, Fahad; Boet, Sylvain; Piquette, Dominique; Lai, Anita; Perkes, Christopher P.; LeBlanc, Vicki R.

    2016-01-01

    Enhanced podcasts increase learning, but evidence is lacking on how they should be designed to optimize their effectiveness. This study assessed the impact two learning instructional design methods (mental practice and modeling), either on their own or in combination, for teaching complex cognitive medical content when incorporated into enhanced…

  12. Bare-Bones Teaching-Learning-Based Optimization

    Directory of Open Access Journals (Sweden)

    Feng Zou

    2014-01-01

    Full Text Available Teaching-learning-based optimization (TLBO algorithm which simulates the teaching-learning process of the class room is one of the recently proposed swarm intelligent (SI algorithms. In this paper, a new TLBO variant called bare-bones teaching-learning-based optimization (BBTLBO is presented to solve the global optimization problems. In this method, each learner of teacher phase employs an interactive learning strategy, which is the hybridization of the learning strategy of teacher phase in the standard TLBO and Gaussian sampling learning based on neighborhood search, and each learner of learner phase employs the learning strategy of learner phase in the standard TLBO or the new neighborhood search strategy. To verify the performance of our approaches, 20 benchmark functions and two real-world problems are utilized. Conducted experiments can been observed that the BBTLBO performs significantly better than, or at least comparable to, TLBO and some existing bare-bones algorithms. The results indicate that the proposed algorithm is competitive to some other optimization algorithms.

  13. Simulation-based optimization parametric optimization techniques and reinforcement learning

    CERN Document Server

    Gosavi, Abhijit

    2003-01-01

    Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduces the evolving area of simulation-based optimization. The book's objective is two-fold: (1) It examines the mathematical governing principles of simulation-based optimization, thereby providing the reader with the ability to model relevant real-life problems using these techniques. (2) It outlines the computational technology underlying these methods. Taken together these two aspects demonstrate that the mathematical and computational methods discussed in this book do work. Broadly speaking, the book has two parts: (1) parametric (static) optimization and (2) control (dynamic) optimization. Some of the book's special features are: *An accessible introduction to reinforcement learning and parametric-optimization techniques. *A step-by-step description of several algorithms of simulation-based optimization. *A clear and simple introduction to the methodology of neural networks. *A gentle introduction to converg...

  14. Optimal quantum learning of a unitary transformation

    International Nuclear Information System (INIS)

    Bisio, Alessandro; Chiribella, Giulio; D'Ariano, Giacomo Mauro; Facchini, Stefano; Perinotti, Paolo

    2010-01-01

    We address the problem of learning an unknown unitary transformation from a finite number of examples. The problem consists in finding the learning machine that optimally emulates the examples, thus reproducing the unknown unitary with maximum fidelity. Learning a unitary is equivalent to storing it in the state of a quantum memory (the memory of the learning machine) and subsequently retrieving it. We prove that, whenever the unknown unitary is drawn from a group, the optimal strategy consists in a parallel call of the available uses followed by a 'measure-and-rotate' retrieving. Differing from the case of quantum cloning, where the incoherent 'measure-and-prepare' strategies are typically suboptimal, in the case of learning the 'measure-and-rotate' strategy is optimal even when the learning machine is asked to reproduce a single copy of the unknown unitary. We finally address the problem of the optimal inversion of an unknown unitary evolution, showing also in this case the optimality of the 'measure-and-rotate' strategies and applying our result to the optimal approximate realignment of reference frames for quantum communication.

  15. Multimedia Based E-learning : Design and Integration of Multimedia Content in E-learning

    Directory of Open Access Journals (Sweden)

    Abdulaziz Omar Alsadhan

    2014-05-01

    Full Text Available The advancement in multimedia and information technologies also have impacted the way of imparting education. This advancement has led to rapid use of e learning systems and has enabled greater integration of multimedia content into e learning systems. This paper present a model for development of e learning systems based on multimedia content. The model is called “Multimedia based e learning” and is loosely based on waterfall software development model. This model consists of three distinct phases; Multimedia Content Modelling, Multimedia content Development, Multimedia content Integration. These three phases are further sub divided into 7 different activities which are analysis, design, technical requirements, content development, content production & integration, implementation and evaluation. This model defines a general framework that can be applied for the development of e learning systems across all disciplines and subjects.

  16. Development of Efficient Authoring Software for e-Learning Contents

    Science.gov (United States)

    Kozono, Kazutake; Teramoto, Akemi; Akiyama, Hidenori

    The contents creation in e-Learning system becomes an important problem. The contents of e-Learning should include figure and voice media for a high-level educational effect. However, the use of figure and voice complicates the operation of authoring software considerably. A new authoring software, which can build e-Learning contents efficiently, has been developed to solve this problem. This paper reports development results of the authoring software.

  17. My Kind of Town (Chicago Is): Content Collections Optimize Learning Related to the 2018 AANA Annual Meeting.

    Science.gov (United States)

    Hunt, Timothy J; Brand, Jefferson C; Rossi, Michael J; Lubowitz, James H

    2018-04-01

    The 2018 Arthroscopy Association of North America Annual Meeting represents an opportunity to deepen one's understanding of a wide variety of topics. Arthroscopy journal readers have diverse practices and interests, and the meeting is designed to accommodate individual needs. The constructivist learning theory provides that scholars learn in many different ways. Thus, to enrich your learning experience, selected recently published Arthroscopy articles are suggested to supplement material presented at the meeting. The articles are collated on our web site in Content Collections, to allow meeting participants to prepare and to allow those unable to attend to remain engaged. We offer suggestions and encourage readers to customize their own learning experience. Copyright © 2018 Arthroscopy Association of North America. Published by Elsevier Inc. All rights reserved.

  18. Machine Learning meets Mathematical Optimization to predict the optimal production of offshore wind parks

    DEFF Research Database (Denmark)

    Fischetti, Martina; Fraccaro, Marco

    2018-01-01

    In this paper we propose a combination of Mathematical Optimization and Machine Learning to estimate the value of optimized solutions. In particular, we investigate if a machine, trained on a large number of optimized solutions, could accurately estimate the value of the optimized solution for new...... in production between optimized/non optimized solutions, it is not trivial to understand the potential value of a new site without running a complete optimization. This could be too time consuming if a lot of sites need to be evaluated, therefore we propose to use Machine Learning to quickly estimate...... the potential of new sites (i.e., to estimate the optimized production of a site without explicitly running the optimization). To do so, we trained and tested different Machine Learning models on a dataset of 3000+ optimized layouts found by the optimizer. Thanks to the close collaboration with a leading...

  19. Sparse Learning with Stochastic Composite Optimization.

    Science.gov (United States)

    Zhang, Weizhong; Zhang, Lijun; Jin, Zhongming; Jin, Rong; Cai, Deng; Li, Xuelong; Liang, Ronghua; He, Xiaofei

    2017-06-01

    In this paper, we study Stochastic Composite Optimization (SCO) for sparse learning that aims to learn a sparse solution from a composite function. Most of the recent SCO algorithms have already reached the optimal expected convergence rate O(1/λT), but they often fail to deliver sparse solutions at the end either due to the limited sparsity regularization during stochastic optimization (SO) or due to the limitation in online-to-batch conversion. Even when the objective function is strongly convex, their high probability bounds can only attain O(√{log(1/δ)/T}) with δ is the failure probability, which is much worse than the expected convergence rate. To address these limitations, we propose a simple yet effective two-phase Stochastic Composite Optimization scheme by adding a novel powerful sparse online-to-batch conversion to the general Stochastic Optimization algorithms. We further develop three concrete algorithms, OptimalSL, LastSL and AverageSL, directly under our scheme to prove the effectiveness of the proposed scheme. Both the theoretical analysis and the experiment results show that our methods can really outperform the existing methods at the ability of sparse learning and at the meantime we can improve the high probability bound to approximately O(log(log(T)/δ)/λT).

  20. Machine Learning Optimization of Evolvable Artificial Cells

    DEFF Research Database (Denmark)

    Caschera, F.; Rasmussen, S.; Hanczyc, M.

    2011-01-01

    can be explored. A machine learning approach (Evo-DoE) could be applied to explore this experimental space and define optimal interactions according to a specific fitness function. Herein an implementation of an evolutionary design of experiments to optimize chemical and biochemical systems based...... on a machine learning process is presented. The optimization proceeds over generations of experiments in iterative loop until optimal compositions are discovered. The fitness function is experimentally measured every time the loop is closed. Two examples of complex systems, namely a liposomal drug formulation...

  1. Resolving the Problem of Intelligent Learning Content in Learning Management Systems

    Science.gov (United States)

    Rey-Lopez, Marta; Brusilovsky, Peter; Meccawy, Maram; Diaz-Redondo, Rebeca; Fernandez-Vilas, Ana; Ashman, Helen

    2008-01-01

    Current e-learning standardization initiatives have put much effort into easing interoperability between systems and the reusability of contents. For this to be possible, one of the most relevant areas is the definition of a run-time environment, which allows Learning Management Systems to launch, track and communicate with learning objects.…

  2. Online Pedagogical Tutorial Tactics Optimization Using Genetic-Based Reinforcement Learning.

    Science.gov (United States)

    Lin, Hsuan-Ta; Lee, Po-Ming; Hsiao, Tzu-Chien

    2015-01-01

    Tutorial tactics are policies for an Intelligent Tutoring System (ITS) to decide the next action when there are multiple actions available. Recent research has demonstrated that when the learning contents were controlled so as to be the same, different tutorial tactics would make difference in students' learning gains. However, the Reinforcement Learning (RL) techniques that were used in previous studies to induce tutorial tactics are insufficient when encountering large problems and hence were used in offline manners. Therefore, we introduced a Genetic-Based Reinforcement Learning (GBML) approach to induce tutorial tactics in an online-learning manner without basing on any preexisting dataset. The introduced method can learn a set of rules from the environment in a manner similar to RL. It includes a genetic-based optimizer for rule discovery task by generating new rules from the old ones. This increases the scalability of a RL learner for larger problems. The results support our hypothesis about the capability of the GBML method to induce tutorial tactics. This suggests that the GBML method should be favorable in developing real-world ITS applications in the domain of tutorial tactics induction.

  3. The power of associative learning and the ontogeny of optimal behaviour.

    Science.gov (United States)

    Enquist, Magnus; Lind, Johan; Ghirlanda, Stefano

    2016-11-01

    Behaving efficiently (optimally or near-optimally) is central to animals' adaptation to their environment. Much evolutionary biology assumes, implicitly or explicitly, that optimal behavioural strategies are genetically inherited, yet the behaviour of many animals depends crucially on learning. The question of how learning contributes to optimal behaviour is largely open. Here we propose an associative learning model that can learn optimal behaviour in a wide variety of ecologically relevant circumstances. The model learns through chaining, a term introduced by Skinner to indicate learning of behaviour sequences by linking together shorter sequences or single behaviours. Our model formalizes the concept of conditioned reinforcement (the learning process that underlies chaining) and is closely related to optimization algorithms from machine learning. Our analysis dispels the common belief that associative learning is too limited to produce 'intelligent' behaviour such as tool use, social learning, self-control or expectations of the future. Furthermore, the model readily accounts for both instinctual and learned aspects of behaviour, clarifying how genetic evolution and individual learning complement each other, and bridging a long-standing divide between ethology and psychology. We conclude that associative learning, supported by genetic predispositions and including the oft-neglected phenomenon of conditioned reinforcement, may suffice to explain the ontogeny of optimal behaviour in most, if not all, non-human animals. Our results establish associative learning as a more powerful optimizing mechanism than acknowledged by current opinion.

  4. Recall Performance for Content-Addressable Memory Using Adiabatic Quantum Optimization

    Energy Technology Data Exchange (ETDEWEB)

    Imam, Neena [ORNL; Humble, Travis S. [ORNL; McCaskey, Alex [ORNL; Schrock, Jonathan [ORNL; Hamilton, Kathleen E. [ORNL

    2017-09-01

    A content-addressable memory (CAM) stores key-value associations such that the key is recalled by providing its associated value. While CAM recall is traditionally performed using recurrent neural network models, we show how to solve this problem using adiabatic quantum optimization. Our approach maps the recurrent neural network to a commercially available quantum processing unit by taking advantage of the common underlying Ising spin model. We then assess the accuracy of the quantum processor to store key-value associations by quantifying recall performance against an ensemble of problem sets. We observe that different learning rules from the neural network community influence recall accuracy but performance appears to be limited by potential noise in the processor. The strong connection established between quantum processors and neural network problems supports the growing intersection of these two ideas.

  5. SOFTWARE IMPLEMENTATION FOR SCORM CONTENT MIGRATION IN THE LEARNING MANAGEMENT SYSTEM

    Directory of Open Access Journals (Sweden)

    Y. B. Popova

    2017-01-01

    Full Text Available Using of learning management systems increases the possibility of teachers and students in achieving their goals in education. Such systems provide learning content, help to organize and to monitor training progress, help to collect statistics. However, the transition from one LMS to another there is a problem of content migration, because all training materials and tests should either be recreated, or somehow be migrated to the new system. Content migration by hand is a very time-consuming process, so the leading developers of the learning management systems developed a standard for the organization and storage of content, called SCORM (Eng., Sharable Content Object Reference Model. Created by this standard, the content must migrate to the learning management system provided its support for these systems. SCORM standard allows you to create training content that is not dependent on the learning management system, but the loosely embedded in it. This approach enables teachers to develop unique courses and put them free available or for sale in the Internet for all interested persons, and to use educational content created by the best specialists around the world to carry out their activities. The content on the SCORM standard imposes certain requirements on the learning management systems, as they do not distort the training content and properly interact with the tests. The aim of this article is a software implementation of a content migration by SCORM standard from other learning management systems in its own development used at the Software Department of the Faculty of Information Technology and Robotics of the Belarusian National Technical University.

  6. Data Science and Optimal Learning for Material Discovery and Design

    Science.gov (United States)

    ; Optimal Learning for Material Discovery & Design Data Science and Optimal Learning for Material inference and optimization methods that can constrain predictions using insights and results from theory directions in the application of information theoretic tools to materials problems related to learning from

  7. Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality Tightening

    OpenAIRE

    He, Frank S.; Liu, Yang; Schwing, Alexander G.; Peng, Jian

    2016-01-01

    We propose a novel training algorithm for reinforcement learning which combines the strength of deep Q-learning with a constrained optimization approach to tighten optimality and encourage faster reward propagation. Our novel technique makes deep reinforcement learning more practical by drastically reducing the training time. We evaluate the performance of our approach on the 49 games of the challenging Arcade Learning Environment, and report significant improvements in both training time and...

  8. Effects of WOE Presentation Types Used in Pre-Training on the Cognitive Load and Comprehension of Content in Animation-Based Learning Environments

    Science.gov (United States)

    Jung, Jung,; Kim, Dongsik; Na, Chungsoo

    2016-01-01

    This study investigated the effectiveness of various types of worked-out examples used in pre-training to optimize the cognitive load and enhance learners' comprehension of the content in an animation-based learning environment. An animation-based learning environment was developed specifically for this study. The participants were divided into…

  9. The power of associative learning and the ontogeny of optimal behaviour

    Science.gov (United States)

    Enquist, Magnus; Lind, Johan

    2016-01-01

    Behaving efficiently (optimally or near-optimally) is central to animals' adaptation to their environment. Much evolutionary biology assumes, implicitly or explicitly, that optimal behavioural strategies are genetically inherited, yet the behaviour of many animals depends crucially on learning. The question of how learning contributes to optimal behaviour is largely open. Here we propose an associative learning model that can learn optimal behaviour in a wide variety of ecologically relevant circumstances. The model learns through chaining, a term introduced by Skinner to indicate learning of behaviour sequences by linking together shorter sequences or single behaviours. Our model formalizes the concept of conditioned reinforcement (the learning process that underlies chaining) and is closely related to optimization algorithms from machine learning. Our analysis dispels the common belief that associative learning is too limited to produce ‘intelligent’ behaviour such as tool use, social learning, self-control or expectations of the future. Furthermore, the model readily accounts for both instinctual and learned aspects of behaviour, clarifying how genetic evolution and individual learning complement each other, and bridging a long-standing divide between ethology and psychology. We conclude that associative learning, supported by genetic predispositions and including the oft-neglected phenomenon of conditioned reinforcement, may suffice to explain the ontogeny of optimal behaviour in most, if not all, non-human animals. Our results establish associative learning as a more powerful optimizing mechanism than acknowledged by current opinion. PMID:28018662

  10. Modeling and optimization of cloud-ready and content-oriented networks

    CERN Document Server

    Walkowiak, Krzysztof

    2016-01-01

    This book focuses on modeling and optimization of cloud-ready and content-oriented networks in the context of different layers and accounts for specific constraints following from protocols and technologies used in a particular layer. It addresses a wide range of additional constraints important in contemporary networks, including various types of network flows, survivability issues, multi-layer networking, and resource location. The book presents recent existing and new results in a comprehensive and cohesive way. The contents of the book are organized in five chapters, which are mostly self-contained. Chapter 1 briefly presents information on cloud computing and content-oriented services, and introduces basic notions and concepts of network modeling and optimization. Chapter 2 covers various optimization problems that arise in the context of connection-oriented networks. Chapter 3 focuses on modeling and optimization of Elastic Optical Networks. Chapter 4 is devoted to overlay networks. The book concludes w...

  11. Content Production for E-Learning in Engineering

    Directory of Open Access Journals (Sweden)

    Andreas Auinger

    2007-06-01

    Full Text Available The didactic quality of lear0ning materialscan be improved by enriching learning material with didacticinformation. Such content elements assist selfdirectedlearning processes in virtual learningenvironments effectively. In order to develop didacticallymotivated for flexible use, e.g., at different terminaldevices such as PC or PDA, a structured procedure isrequired. We propose the selection and identification ofdidactically relevant information prior to enrichment ofhighly structured content with didactical information. Itcan be achieved by using the CoDEx method (ContentDidactically Explicit, and a mapping scheme to thelearning-technology standard conform XML contentstructures. Furthermore, aspects for multi-channel contentdelivery in the application field of engineering have to betaken into account. In this paper we refer to the objectivesand results of the EU-funded ELIE project (E-Learning InEngineering to demonstrate the proposed procedure’seffectiveness for content engineering.

  12. The power of associative learning and the ontogeny of optimal behaviour

    OpenAIRE

    Enquist, Magnus; Lind, Johan; Ghirlanda, Stefano

    2016-01-01

    Behaving efficiently (optimally or near-optimally) is central to animals' adaptation to their environment. Much evolutionary biology assumes, implicitly or explicitly, that optimal behavioural strategies are genetically inherited, yet the behaviour of many animals depends crucially on learning. The question of how learning contributes to optimal behaviour is largely open. Here we propose an associative learning model that can learn optimal behaviour in a wide variety of ecologically relevant ...

  13. Transforming existing content into reusable Learning Objects

    NARCIS (Netherlands)

    Doorten, Monique; Giesbers, Bas; Janssen, José; Daniels, Jan; Koper, Rob

    2003-01-01

    Please cite as: Doorten, M., Giesbers, B., Janssen, J., Daniëls, J, & Koper, E.J.R., (2004). Transforming existing content into reusable learning objects. In R. McGreal, Online Education using Learning Objects (pp. 116-127). London: RoutledgeFalmer.

  14. Negotiating Content with Learners Using Technology Enhanced Teaching and Learning Solutions

    Directory of Open Access Journals (Sweden)

    Richard Smith

    2011-09-01

    Full Text Available This paper examines issues around learning ‘content’ and its place in the new digital learning culture. We focus on the increasing demands of digital learners for content that is relevant and the challenges this poses if educators are to stay relevant to them. We say ‘relevance’ is best achieved when content is negotiated with learners in collaboration with instructors. We describe strategies in which technology enhanced teaching and learning solutions have enabled learners to negotiate and create digitised learning content that is educationally, culturally and socially relevant. We cite two case studies that exemplify this approach: a trial of negotiated content with primary school aged digital learners at Brisbane School of Distance Education (BSDE, Australia, and the content decision-making processes used for the development of e-learning courses for hearing health professionals and Auditory-Verbal Therapy at Hear and Say WorldWide Brisbane, Australia. We focus on the changing demands and skill sets of digital learners, their learning managers and subject matter experts, and the use of technology enhanced teaching and learning solutions as the negotiating tool in the development of digital content that is academically rigorous and also learner friendly.

  15. Parallel strategy for optimal learning in perceptrons

    International Nuclear Information System (INIS)

    Neirotti, J P

    2010-01-01

    We developed a parallel strategy for learning optimally specific realizable rules by perceptrons, in an online learning scenario. Our result is a generalization of the Caticha-Kinouchi (CK) algorithm developed for learning a perceptron with a synaptic vector drawn from a uniform distribution over the N-dimensional sphere, so called the typical case. Our method outperforms the CK algorithm in almost all possible situations, failing only in a denumerable set of cases. The algorithm is optimal in the sense that it saturates Bayesian bounds when it succeeds.

  16. Multi-instance dictionary learning via multivariate performance measure optimization

    KAUST Repository

    Wang, Jim Jing-Yan

    2016-12-29

    The multi-instance dictionary plays a critical role in multi-instance data representation. Meanwhile, different multi-instance learning applications are evaluated by specific multivariate performance measures. For example, multi-instance ranking reports the precision and recall. It is not difficult to see that to obtain different optimal performance measures, different dictionaries are needed. This observation motives us to learn performance-optimal dictionaries for this problem. In this paper, we propose a novel joint framework for learning the multi-instance dictionary and the classifier to optimize a given multivariate performance measure, such as the F1 score and precision at rank k. We propose to represent the bags as bag-level features via the bag-instance similarity, and learn a classifier in the bag-level feature space to optimize the given performance measure. We propose to minimize the upper bound of a multivariate loss corresponding to the performance measure, the complexity of the classifier, and the complexity of the dictionary, simultaneously, with regard to both the dictionary and the classifier parameters. In this way, the dictionary learning is regularized by the performance optimization, and a performance-optimal dictionary is obtained. We develop an iterative algorithm to solve this minimization problem efficiently using a cutting-plane algorithm and a coordinate descent method. Experiments on multi-instance benchmark data sets show its advantage over both traditional multi-instance learning and performance optimization methods.

  17. Multi-instance dictionary learning via multivariate performance measure optimization

    KAUST Repository

    Wang, Jim Jing-Yan; Tsang, Ivor Wai-Hung; Cui, Xuefeng; Lu, Zhiwu; Gao, Xin

    2016-01-01

    The multi-instance dictionary plays a critical role in multi-instance data representation. Meanwhile, different multi-instance learning applications are evaluated by specific multivariate performance measures. For example, multi-instance ranking reports the precision and recall. It is not difficult to see that to obtain different optimal performance measures, different dictionaries are needed. This observation motives us to learn performance-optimal dictionaries for this problem. In this paper, we propose a novel joint framework for learning the multi-instance dictionary and the classifier to optimize a given multivariate performance measure, such as the F1 score and precision at rank k. We propose to represent the bags as bag-level features via the bag-instance similarity, and learn a classifier in the bag-level feature space to optimize the given performance measure. We propose to minimize the upper bound of a multivariate loss corresponding to the performance measure, the complexity of the classifier, and the complexity of the dictionary, simultaneously, with regard to both the dictionary and the classifier parameters. In this way, the dictionary learning is regularized by the performance optimization, and a performance-optimal dictionary is obtained. We develop an iterative algorithm to solve this minimization problem efficiently using a cutting-plane algorithm and a coordinate descent method. Experiments on multi-instance benchmark data sets show its advantage over both traditional multi-instance learning and performance optimization methods.

  18. Sinkronisasi Content E-learning Terdistribusi Berbasis Model Komunikasi Indirect Menggunakan Sistem Publish-Subscribe

    Directory of Open Access Journals (Sweden)

    Sufrendo Saputra

    2017-01-01

    Full Text Available Sinkronisasi content antar e-learning memungkinkan beberapa e-learning memiliki content yang sama secara konsisten. Perubahan content pada salah satu e-learning akan membuat sistem memastikan e-learning lain mengetahui perubahan tersebut. Model komunikasi yang memungkinkan adanya sinkronisasi ini merupakan komunikasi indirect berbasis publish-subscribe. Setiap e-learning memiliki content-nya masing-masing yang secara otomatis akan di-publish oleh sistem. E-learning lain yang tergabung dalam sistem sinkronisasi kemudian dapat memilih content mana yang ingin di-subscribe. Jika terdapat perubahan pada sebuah content, dan content tersebut memiliki subscriber, maka sistem akan memberitahu subscriber bahwa telah terjadi perubahan pada content. Teknologi utama yang digunakan dalam sistem ini adalah Moodle, PHP, dan Java. Moodle sebagai modul yang digunakan untuk mensimulasikan e-learning. PHP dan Java sebagai framework dari sistem sinkronisasi. Model komunikasi yang digunakan merupakan komunikasi indirect berbasis publish-subscribe. Model komunikasi ini menempatkan sebuah perantara bagi komunikasi antar e-learning.

  19. Adaptive Multimedia Content Delivery for Context-Aware U-Learning

    Science.gov (United States)

    Zhao, Xinyou; Okamoto, Toshio

    2011-01-01

    Empowered by mobile computing, teachers and students can benefit from computing in more scenarios beyond the traditional computer classroom. But because of the much diversity of device specification, learning contents and mobile context existing today, the learners get a bad learning experience (e.g. rich contents cannot be displayed correctly)…

  20. Evaluation Criterion for Quality Assessment of E-Learning Content

    Science.gov (United States)

    Al-Alwani, Abdulkareem

    2014-01-01

    Research trends related to e-learning systems are oriented towards increasing the efficiency and capacity of the systems, thus they reflect a large variance in performance when considering content conformity and quality standards. The Framework related to standardisation of digital content for e-learning systems is likely to play a significant…

  1. Learning content and the creative cloud

    NARCIS (Netherlands)

    Specht, Marcus

    2012-01-01

    Specht, M. (2012, 18 April). Learning content and the creative cloud. Presentation given at the workshop for creative cloud CLICK workshop Faculteit Bouwkunde & Architectuur TU Delft, Delft, The Netherlands.

  2. Adolescent literacy: learning and understanding content.

    Science.gov (United States)

    Goldman, Susan R

    2012-01-01

    Learning to read--amazing as it is to small children and their parents--is one thing. Reading to learn, explains Susan Goldman of the University of Illinois at Chicago, is quite another. Are today's students able to use reading and writing to acquire knowledge, solve problems, and make decisions in academic, personal, and professional arenas? Do they have the literacy skills necessary to meet the demands of the twenty-first century? To answer these questions, Goldman describes the increasingly complex comprehension, reasoning skills, and knowledge that students need as they progress through school and surveys what researchers and educators know about how to teach those skills. Successfully reading to learn requires the ability to analyze, synthesize, and evaluate information from multiple sources, Goldman writes. Effective readers must be able to apply different knowledge, reading, and reasoning processes to different types of content, from fiction to history and science, to news accounts and user manuals. They must assess sources of information for relevance, reliability, impartiality, and completeness. And they must connect information across multiple sources. In short, successful readers must not only use general reading skills but also pay close attention to discipline-specific processes. Goldman reviews the evidence on three different instructional approaches to reading to learn: general comprehension strategies, classroom discussion, and disciplinary content instruction. She argues that building the literacy skills necessary for U.S. students to read comprehensively and critically and to learn content in a variety of disciplines should be a primary responsibility for all of the nation's teachers. But outside of English, few subject-area teachers are aware of the need to teach subject-area reading comprehension skills, nor have they had opportunities to learn them themselves. Building the capacity of all teachers to meet the literacy needs of today's students

  3. Optimal long-term contracting with learning

    OpenAIRE

    He, Zhiguo; Wei, Bin; Yu, Jianfeng; Gao, Feng

    2016-01-01

    We introduce uncertainty into Holmstrom and Milgrom (1987) to study optimal long-term contracting with learning. In a dynamic relationship, the agent's shirking not only reduces current performance but also increases the agent's information rent due to the persistent belief manipulation effect. We characterize the optimal contract using the dynamic programming technique in which information rent is the unique state variable. In the optimal contract, the optimal effort is front-loaded and decr...

  4. Modularization and Structured Markup for Learning Content in an Academic Environment

    Science.gov (United States)

    Schluep, Samuel; Bettoni, Marco; Schar, Sissel Guttormsen

    2006-01-01

    This article aims to present a flexible component model for modular, web-based learning content, and a simple structured markup schema for the separation of content and presentation. The article will also contain an overview of the dynamic Learning Content Management System (dLCMS) project, which implements these concepts. Content authors are a…

  5. Patient learning of treatment contents in cognitive therapy.

    Science.gov (United States)

    Gumport, Nicole B; Dong, Lu; Lee, Jason Y; Harvey, Allison G

    2018-03-01

    Research has demonstrated that both memory and learning for treatment contents are poor, and that both are associated with worse treatment outcome. The Memory Support Intervention has been shown to improve memory for treatment, but it has not yet been established if this intervention can also improve learning of treatment contents. This study was designed to document the number of times participants exhibited each of the indices of learning, to examine the indices of learning and their relationship to recall of treatment points, and to investigate the association between the indices of learning and depression outcome. Adults diagnosed with major depressive disorder (N = 48) were randomly assigned to 14 sessions of cognitive therapy-as-usual (CT-as-usual) or cognitive therapy plus the Memory Support Intervention (CT + Memory Support). Measures of learning, memory, and depressive symptomatology were taken at mid-treatment, post-treatment, and at 6-month follow-up. Relative to the CT-as-usual group, participants in the CT + Memory Support group reported more accurate thoughts and applications of treatment points at mid-treatment, post-treatment, and 6-month follow-up. Patient recall was significantly correlated with application and cognitive generalization. Thoughts and application at mid-treatment were associated with increased odds of treatment response at post-treatment. The learning measure for this study has not yet been psychometrically validated. The results are based on a small sample. Learning during treatment is poor, but modifiable via the Memory Support Intervention. These results provide encouraging data that improving learning of treatment contents can reduce symptoms during and following treatment. Copyright © 2017 Elsevier Ltd. All rights reserved.

  6. Optimal Long-term Contracting with Learning

    OpenAIRE

    Jianfeng Yu; Bin Wei; Zhiguo He

    2012-01-01

    This paper introduces profitability uncertainty into an infinite-horizon variation of the classic Holmstrom and Milgrom (1987) model, and studies optimal dynamic contracting with endogenous learning. The agent's potential belief manipulation leads to the hidden information problem, which makes incentive provisions intertemporally linked in the optimal contract. We reduce the contracting problem into a dynamic programming problem with one state variable, and characterize the optimal contract w...

  7. Football coaches’ development in Brazil: a focus on the content of learning

    Directory of Open Access Journals (Sweden)

    Alexandre Vinicius Bobato Tozetto

    2017-12-01

    Full Text Available Abstract AIM The aim of the study was to analyze the lifelong content of learning of coaches. METHODS Eight coaches inserted in an Elite Football Club participated. Rappaport Time Line and semi-structured interviews were used to obtain the data. The coaches’ learning was organized according to the theory of Lifelong Learning.1-4 RESULTS The coaches presented in their personal experiences, with their families and as athletes, content of learning such as “leadership development” and “formation of values”. In professional experiences, such as in academic training, coach assistants and even coaching, they are also reported as essential in obtaining content of learning (general and specific knowledge, training methods, leadership development, self-control. Finally, the reflexive process is considered by most coaches as a potentiator of learning, with interference on the “coach-athlete relationship”, “activity adjustment,” among other content of learning. CONCLUSION The content learned throughout the life were defined in certain episodes for presenting different meanings in the life of the coaches, in which they related to a new experience according to their biographies. Therefore, the various episodes offer coaches new experiences, in which they can incorporate, reinforce or renew the content about the coaching process and are responsible for the development of the coach.

  8. Sizing optimization of skeletal structures using teaching-learning based optimization

    Directory of Open Access Journals (Sweden)

    Vedat Toğan

    2017-03-01

    Full Text Available Teaching Learning Based Optimization (TLBO is one of the non-traditional techniques to simulate natural phenomena into a numerical algorithm. TLBO mimics teaching learning process occurring between a teacher and students in a classroom. A parameter named as teaching factor, TF, seems to be the only tuning parameter in TLBO. Although the value of the teaching factor, TF, is determined by an equation, the value of 1 or 2 has been used by the researchers for TF. This study intends to explore the effect of the variation of teaching factor TF on the performances of TLBO. This effect is demonstrated in solving structural optimization problems including truss and frame structures under the stress and displacement constraints. The results indicate that the variation of TF in the TLBO process does not change the results obtained at the end of the optimization procedure when the computational cost of TLBO is ignored.

  9. Retail Executives’ Professional Learning Contents

    Directory of Open Access Journals (Sweden)

    Eduardo de Aquino Lucena

    2014-05-01

    Full Text Available The research question that is addressed in this article is the following: what do the executives from small retailing firms learn in their work environment? The theoretical framework of the study is based on texts from the field of learning. This is a qualitative investigation. Ten interviews with clothing retail executives were carried out. Later, these interviews were transcribed and analyzed. In the data analysis stage, two categories were established in response to the research question. Regarding the first, respondents perceived difficulties (problems in their work environments and obtained specific information and knowledge in order to deal with these situations. So as to overcome different professional difficulties, respondents learned about colors and types of fabric and about certain manufacturing process aspects referring to the clothing sold by their companies. They also declared to have learned about their companies’ suppliers and about certain issues referring to sales, and to the accounting and the financial management of their companies. The second category refers to a change in some of the respondents’ habits. This learning content refers to predispositions to respond and/ or act in situations at their work environments. Respondents reported having changed the way they related to other people. They emphasized that they had learned how to interact with the employees at their stores and how to carry out supervision. Differently from other studies, we found that the retailers’ learning (individual learning affected their companies’ learning (organizational learning through changes in certain aspects of the analyzed companies’ organizational structures.

  10. Optimizing performance through intrinsic motivation and attention for learning: The OPTIMAL theory of motor learning.

    Science.gov (United States)

    Wulf, Gabriele; Lewthwaite, Rebecca

    2016-10-01

    Effective motor performance is important for surviving and thriving, and skilled movement is critical in many activities. Much theorizing over the past few decades has focused on how certain practice conditions affect the processing of task-related information to affect learning. Yet, existing theoretical perspectives do not accommodate significant recent lines of evidence demonstrating motivational and attentional effects on performance and learning. These include research on (a) conditions that enhance expectancies for future performance, (b) variables that influence learners' autonomy, and (c) an external focus of attention on the intended movement effect. We propose the OPTIMAL (Optimizing Performance through Intrinsic Motivation and Attention for Learning) theory of motor learning. We suggest that motivational and attentional factors contribute to performance and learning by strengthening the coupling of goals to actions. We provide explanations for the performance and learning advantages of these variables on psychological and neuroscientific grounds. We describe a plausible mechanism for expectancy effects rooted in responses of dopamine to the anticipation of positive experience and temporally associated with skill practice. Learner autonomy acts perhaps largely through an enhanced expectancy pathway. Furthermore, we consider the influence of an external focus for the establishment of efficient functional connections across brain networks that subserve skilled movement. We speculate that enhanced expectancies and an external focus propel performers' cognitive and motor systems in productive "forward" directions and prevent "backsliding" into self- and non-task focused states. Expected success presumably breeds further success and helps consolidate memories. We discuss practical implications and future research directions.

  11. Genetic Learning Particle Swarm Optimization.

    Science.gov (United States)

    Gong, Yue-Jiao; Li, Jing-Jing; Zhou, Yicong; Li, Yun; Chung, Henry Shu-Hung; Shi, Yu-Hui; Zhang, Jun

    2016-10-01

    Social learning in particle swarm optimization (PSO) helps collective efficiency, whereas individual reproduction in genetic algorithm (GA) facilitates global effectiveness. This observation recently leads to hybridizing PSO with GA for performance enhancement. However, existing work uses a mechanistic parallel superposition and research has shown that construction of superior exemplars in PSO is more effective. Hence, this paper first develops a new framework so as to organically hybridize PSO with another optimization technique for "learning." This leads to a generalized "learning PSO" paradigm, the *L-PSO. The paradigm is composed of two cascading layers, the first for exemplar generation and the second for particle updates as per a normal PSO algorithm. Using genetic evolution to breed promising exemplars for PSO, a specific novel *L-PSO algorithm is proposed in the paper, termed genetic learning PSO (GL-PSO). In particular, genetic operators are used to generate exemplars from which particles learn and, in turn, historical search information of particles provides guidance to the evolution of the exemplars. By performing crossover, mutation, and selection on the historical information of particles, the constructed exemplars are not only well diversified, but also high qualified. Under such guidance, the global search ability and search efficiency of PSO are both enhanced. The proposed GL-PSO is tested on 42 benchmark functions widely adopted in the literature. Experimental results verify the effectiveness, efficiency, robustness, and scalability of the GL-PSO.

  12. Language Learning of Gifted Individuals: A Content Analysis Study

    Directory of Open Access Journals (Sweden)

    Beria Gokaydin

    2017-11-01

    Full Text Available This study aims to carry out a content analysis of the studies on language learning of gifted individuals and determine the trends in this field. Articles on language learning of gifted individuals published in the Scopus database were examined based on certain criteria including type of publication, year of publication, language, research discipline, countries of research, institutions of authors, key words, and resources. Data were analyzed with the content analysis method. Results showed that the number of studies on language learning of gifted individuals has increased throughout the years. Recommendations for further research and practices are provided.

  13. Fast and Epsilon-Optimal Discretized Pursuit Learning Automata.

    Science.gov (United States)

    Zhang, JunQi; Wang, Cheng; Zhou, MengChu

    2015-10-01

    Learning automata (LA) are powerful tools for reinforcement learning. A discretized pursuit LA is the most popular one among them. During an iteration its operation consists of three basic phases: 1) selecting the next action; 2) finding the optimal estimated action; and 3) updating the state probability. However, when the number of actions is large, the learning becomes extremely slow because there are too many updates to be made at each iteration. The increased updates are mostly from phases 1 and 3. A new fast discretized pursuit LA with assured ε -optimality is proposed to perform both phases 1 and 3 with the computational complexity independent of the number of actions. Apart from its low computational complexity, it achieves faster convergence speed than the classical one when operating in stationary environments. This paper can promote the applications of LA toward the large-scale-action oriented area that requires efficient reinforcement learning tools with assured ε -optimality, fast convergence speed, and low computational complexity for each iteration.

  14. Optimal and Autonomous Control Using Reinforcement Learning: A Survey.

    Science.gov (United States)

    Kiumarsi, Bahare; Vamvoudakis, Kyriakos G; Modares, Hamidreza; Lewis, Frank L

    2018-06-01

    This paper reviews the current state of the art on reinforcement learning (RL)-based feedback control solutions to optimal regulation and tracking of single and multiagent systems. Existing RL solutions to both optimal and control problems, as well as graphical games, will be reviewed. RL methods learn the solution to optimal control and game problems online and using measured data along the system trajectories. We discuss Q-learning and the integral RL algorithm as core algorithms for discrete-time (DT) and continuous-time (CT) systems, respectively. Moreover, we discuss a new direction of off-policy RL for both CT and DT systems. Finally, we review several applications.

  15. Can a student learn optimally from two different teachers?

    International Nuclear Information System (INIS)

    Neirotti, J P

    2010-01-01

    We explore the effects of over-specificity in learning algorithms by investigating the behavior of a student, suited to learn optimally from a teacher B, learning from a teacher B' ≠ B. We only considered the supervised, on-line learning scenario with teachers selected from a particular family. We found that, in the general case, the application of the optimal algorithm to the wrong teacher produces a residual generalization error, even if the right teacher is harder. By imposing mild conditions to the learning algorithm form, we obtained an approximation for the residual generalization error. Simulations carried out in finite networks validate the estimate found.

  16. Content and language integrated learning: principles and perspectives

    OpenAIRE

    BAKLAGOVA J.

    2014-01-01

    This article is devoted to the innovative model for language education Content and Language Integrated Learning (CLIL) which has gained in immense popularity all over the world. Based on communicative approach, CLIL provides progress in language and in the content subject, creativity and independence in language using, developing higher order thinking skills. A successful CLIL lesson should combine such elements as content, communication, cognition and culture

  17. Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-Time Popularities

    Science.gov (United States)

    Sadeghi, Alireza; Sheikholeslami, Fatemeh; Giannakis, Georgios B.

    2018-02-01

    Small basestations (SBs) equipped with caching units have potential to handle the unprecedented demand growth in heterogeneous networks. Through low-rate, backhaul connections with the backbone, SBs can prefetch popular files during off-peak traffic hours, and service them to the edge at peak periods. To intelligently prefetch, each SB must learn what and when to cache, while taking into account SB memory limitations, the massive number of available contents, the unknown popularity profiles, as well as the space-time popularity dynamics of user file requests. In this work, local and global Markov processes model user requests, and a reinforcement learning (RL) framework is put forth for finding the optimal caching policy when the transition probabilities involved are unknown. Joint consideration of global and local popularity demands along with cache-refreshing costs allow for a simple, yet practical asynchronous caching approach. The novel RL-based caching relies on a Q-learning algorithm to implement the optimal policy in an online fashion, thus enabling the cache control unit at the SB to learn, track, and possibly adapt to the underlying dynamics. To endow the algorithm with scalability, a linear function approximation of the proposed Q-learning scheme is introduced, offering faster convergence as well as reduced complexity and memory requirements. Numerical tests corroborate the merits of the proposed approach in various realistic settings.

  18. Optimal learning with Bernstein online aggregation

    DEFF Research Database (Denmark)

    Wintenberger, Olivier

    2017-01-01

    batch version achieves the fast rate of convergence log (M) / n in deviation. The BOA procedure is the first online algorithm that satisfies this optimal fast rate. The second order refinement is required to achieve the optimality in deviation as the classical exponential weights cannot be optimal, see...... is shown to be sufficiently small to assert the fast rate in the iid setting when the loss is Lipschitz and strongly convex. We also introduce a multiple learning rates version of BOA. This fully adaptive BOA procedure is also optimal, up to a log log (n) factor....

  19. Rich media content adaptation in e-learning systems

    OpenAIRE

    Mirri, Silvia

    2007-01-01

    The wide use of e-technologies represents a great opportunity for underserved segments of the population, especially with the aim of reintegrating excluded individuals back into society through education. This is particularly true for people with different types of disabilities who may have difficulties while attending traditional on-site learning programs that are typically based on printed learning resources. The creation and provision of accessible e-learning contents may therefore become ...

  20. A novel comprehensive learning artificial bee colony optimizer for dynamic optimization biological problems.

    Science.gov (United States)

    Su, Weixing; Chen, Hanning; Liu, Fang; Lin, Na; Jing, Shikai; Liang, Xiaodan; Liu, Wei

    2017-03-01

    There are many dynamic optimization problems in the real world, whose convergence and searching ability is cautiously desired, obviously different from static optimization cases. This requires an optimization algorithm adaptively seek the changing optima over dynamic environments, instead of only finding the global optimal solution in the static environment. This paper proposes a novel comprehensive learning artificial bee colony optimizer (CLABC) for optimization in dynamic environments problems, which employs a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff. The main motive of CLABC is to enrich artificial bee foraging behaviors in the ABC model by combining Powell's pattern search method, life-cycle, and crossover-based social learning strategy. The proposed CLABC is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. The experiments for evaluating CLABC are conducted on the dynamic moving peak benchmarks. Furthermore, the proposed algorithm is applied to a real-world application of dynamic RFID network optimization. Statistical analysis of all these cases highlights the significant performance improvement due to the beneficial combination and demonstrates the performance superiority of the proposed algorithm.

  1. E-LEARNING TOOLS: STRUCTURE, CONTENT, CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    Yuliya H. Loboda

    2012-05-01

    Full Text Available The article analyses the problems of organization of educational process with use of electronic means of education. Specifies the definition of "electronic learning", their structure and content. Didactic principles are considered, which are the basis of their creation and use. Given the detailed characteristics of e-learning tools for methodological purposes. On the basis of the allocated pedagogical problems of the use of electronic means of education presented and complemented by their classification, namely the means of theoretical and technological training, means of practical training, support tools, and comprehensive facilities.

  2. Simultaneously learning and optimizing using controlled variance pricing

    NARCIS (Netherlands)

    Boer, den A.V.; Zwart, B.

    2014-01-01

    Price experimentation is an important tool for firms to find the optimal selling price of their products. It should be conducted properly, since experimenting with selling prices can be costly. A firm, therefore, needs to find a pricing policy that optimally balances between learning the optimal

  3. Modelling and Optimizing Mathematics Learning in Children

    Science.gov (United States)

    Käser, Tanja; Busetto, Alberto Giovanni; Solenthaler, Barbara; Baschera, Gian-Marco; Kohn, Juliane; Kucian, Karin; von Aster, Michael; Gross, Markus

    2013-01-01

    This study introduces a student model and control algorithm, optimizing mathematics learning in children. The adaptive system is integrated into a computer-based training system for enhancing numerical cognition aimed at children with developmental dyscalculia or difficulties in learning mathematics. The student model consists of a dynamic…

  4. Awareness for Contextualized Digital Contents in Ubiquitous Learning Environments

    NARCIS (Netherlands)

    Börner, Dirk

    2010-01-01

    Börner, D. (2009). Awareness for Contextualized Digital Contents in Ubiquitous Learning Environments. Presented at the Doctoral Consortium of the Fourth European Conference on Technology Enhanced Learning (EC-TEL 2009). September, 29-October, 2, 2009, Nice, France.

  5. The Usage of E-Learning Model To Optimize Learning System In Higher Education by Using Dick and Carey Design Approach

    Directory of Open Access Journals (Sweden)

    Anak Agung Gde Satia Utama

    2016-04-01

    Full Text Available Nowadays many universities in the world apply technology enhanced learning in order to help learning activities. Due to the potentials technology enhanced learning offers, recent education using it and universities in particular are trying to apply it. One of the subjects of this research is The Accounting Department of Airlangga University in Surabaya. The idea of this research is to investigate the students about how they know deeply about e-learning system and learning objectives as a first step to conduct e-learning model. After the model completed, the next step is to prepare database learning. Entity Relationship Diagram (ERD can help to explain the model. The purpose of this research was done by using Dick and Carey Design Model. There are nine steps to conduct e-learning model. All steps can be categorized into three steps research: first is the introduction or empirical study, the next step is the design and the last is the feedback after the implementation. The methodology used in this research is using Qualitative Exploratory, by using questionnaire and interviews as data collection techniques. The analysis of the data shows organization requires information about e-learning content, user as a learning subject and information technology infrastructures. E-learning model as one of the alternative learning can help users to optimized learning.

  6. A hybrid bird mating optimizer algorithm with teaching-learning-based optimization for global numerical optimization

    Directory of Open Access Journals (Sweden)

    Qingyang Zhang

    2015-02-01

    Full Text Available Bird Mating Optimizer (BMO is a novel meta-heuristic optimization algorithm inspired by intelligent mating behavior of birds. However, it is still insufficient in convergence of speed and quality of solution. To overcome these drawbacks, this paper proposes a hybrid algorithm (TLBMO, which is established by combining the advantages of Teaching-learning-based optimization (TLBO and Bird Mating Optimizer (BMO. The performance of TLBMO is evaluated on 23 benchmark functions, and compared with seven state-of-the-art approaches, namely BMO, TLBO, Artificial Bee Bolony (ABC, Particle Swarm Optimization (PSO, Fast Evolution Programming (FEP, Differential Evolution (DE, Group Search Optimization (GSO. Experimental results indicate that the proposed method performs better than other existing algorithms for global numerical optimization.

  7. A Generational Opportunity: A 21st Century Learning Content Delivery System

    Science.gov (United States)

    McElroy, Patrick

    2007-01-01

    This paper describes a collaboratively developed, open marketplace for network-based learning and research content for the higher education community. It explores how available technologies and standards can facilitate a new knowledge creation industry for higher education learning content that engages all stakeholders in new ways. The Advisory…

  8. From the social learning theory to a social learning algorithm for global optimization

    OpenAIRE

    Gong, Yue-Jiao; Zhang, Jun; Li, Yun

    2014-01-01

    Traditionally, the Evolutionary Computation (EC) paradigm is inspired by Darwinian evolution or the swarm intelligence of animals. Bandura's Social Learning Theory pointed out that the social learning behavior of humans indicates a high level of intelligence in nature. We found that such intelligence of human society can be implemented by numerical computing and be utilized in computational algorithms for solving optimization problems. In this paper, we design a novel and generic optimization...

  9. Awareness for Contextualized Digital Contents in Ubiquitous Learning Environments

    NARCIS (Netherlands)

    Börner, Dirk; Specht, Marcus

    2010-01-01

    Börner, D., & Specht, M. (2009). Awareness for Contextualized Digital Contents in Ubiquitous Learning Environments. Proceedings of the Doctoral Consortium of the Fourth European Conference on Technology Enhanced Learning (EC-TEL 2009). September, 29-October, 2, 2009, Nice, France. [unpublished

  10. Multiswarm comprehensive learning particle swarm optimization for solving multiobjective optimization problems.

    Science.gov (United States)

    Yu, Xiang; Zhang, Xueqing

    2017-01-01

    Comprehensive learning particle swarm optimization (CLPSO) is a powerful state-of-the-art single-objective metaheuristic. Extending from CLPSO, this paper proposes multiswarm CLPSO (MSCLPSO) for multiobjective optimization. MSCLPSO involves multiple swarms, with each swarm associated with a separate original objective. Each particle's personal best position is determined just according to the corresponding single objective. Elitists are stored externally. MSCLPSO differs from existing multiobjective particle swarm optimizers in three aspects. First, each swarm focuses on optimizing the associated objective using CLPSO, without learning from the elitists or any other swarm. Second, mutation is applied to the elitists and the mutation strategy appropriately exploits the personal best positions and elitists. Third, a modified differential evolution (DE) strategy is applied to some extreme and least crowded elitists. The DE strategy updates an elitist based on the differences of the elitists. The personal best positions carry useful information about the Pareto set, and the mutation and DE strategies help MSCLPSO discover the true Pareto front. Experiments conducted on various benchmark problems demonstrate that MSCLPSO can find nondominated solutions distributed reasonably over the true Pareto front in a single run.

  11. Integrating language and content learning objectives : the Bilkent University adjunct model

    OpenAIRE

    Doğan, Egemen Barış

    2003-01-01

    Cataloged from PDF version of article. In response to a global interest in learning English, many instructional approaches, methods, and techniques have been developed. Some have been short-lived, and others have sustained themselves for longer periods of time. Content-based instruction (CBI) — a particular approach to CBI involving a pairing of language and content classes with shared language and content learning objectives — have been considered as viable ways to teach la...

  12. A novel comprehensive learning artificial bee colony optimizer for dynamic optimization biological problems

    Directory of Open Access Journals (Sweden)

    Weixing Su

    2017-03-01

    Full Text Available There are many dynamic optimization problems in the real world, whose convergence and searching ability is cautiously desired, obviously different from static optimization cases. This requires an optimization algorithm adaptively seek the changing optima over dynamic environments, instead of only finding the global optimal solution in the static environment. This paper proposes a novel comprehensive learning artificial bee colony optimizer (CLABC for optimization in dynamic environments problems, which employs a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff. The main motive of CLABC is to enrich artificial bee foraging behaviors in the ABC model by combining Powell’s pattern search method, life-cycle, and crossover-based social learning strategy. The proposed CLABC is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. The experiments for evaluating CLABC are conducted on the dynamic moving peak benchmarks. Furthermore, the proposed algorithm is applied to a real-world application of dynamic RFID network optimization. Statistical analysis of all these cases highlights the significant performance improvement due to the beneficial combination and demonstrates the performance superiority of the proposed algorithm.

  13. Active Learning Strategies for Phenotypic Profiling of High-Content Screens.

    Science.gov (United States)

    Smith, Kevin; Horvath, Peter

    2014-06-01

    High-content screening is a powerful method to discover new drugs and carry out basic biological research. Increasingly, high-content screens have come to rely on supervised machine learning (SML) to perform automatic phenotypic classification as an essential step of the analysis. However, this comes at a cost, namely, the labeled examples required to train the predictive model. Classification performance increases with the number of labeled examples, and because labeling examples demands time from an expert, the training process represents a significant time investment. Active learning strategies attempt to overcome this bottleneck by presenting the most relevant examples to the annotator, thereby achieving high accuracy while minimizing the cost of obtaining labeled data. In this article, we investigate the impact of active learning on single-cell-based phenotype recognition, using data from three large-scale RNA interference high-content screens representing diverse phenotypic profiling problems. We consider several combinations of active learning strategies and popular SML methods. Our results show that active learning significantly reduces the time cost and can be used to reveal the same phenotypic targets identified using SML. We also identify combinations of active learning strategies and SML methods which perform better than others on the phenotypic profiling problems we studied. © 2014 Society for Laboratory Automation and Screening.

  14. Optimizing Classroom Instruction through Self-Paced Learning Prototype

    Science.gov (United States)

    Bautista, Romiro G.

    2015-01-01

    This study investigated the learning impact of self-paced learning prototype in optimizing classroom instruction towards students' learning in Chemistry. Two sections of 64 Laboratory High School students in Chemistry were used as subjects of the study. The Quasi-Experimental and Correlation Research Design was used in the study: a pre-test was…

  15. Towards Optimality in Online Learning – The OLeCenT Approach

    Directory of Open Access Journals (Sweden)

    Carl Beckford

    2017-06-01

    Full Text Available Higher Education Institutions (HEIs employ Learning Management Systems (LMSs primarily for greater efficiency, profitability, technological advancement or survival. The predominantly used LMSs, Moodle and Blackboard account for in excess of 60% usage by the top HEIs. However, the individual international regions do not necessarily bear the percentages of the overall total. Gaps are identified in optimality in course delivery within online learning when one studies LMSs and their functionalities. Advanced Distributed Learning (ADL Initiative which was established to standardize and modernize training and education management and delivery, developed and recommended usage of Sharable Content Object Reference Model (SCORM 2004 and later versions. SCORM 2004 which provides for flexibility in sequencing and navigation for learner-centric course delivery is not supported in any version of the more prevalently used LMSs. It is believed that most people have a preferred way in processing information. We propose codifying one or more Learning Style Instruments (LSIs, diagnosing the preferred teaching approach(es and dominant/existing learning styles within a batch of learners, then providing course delivery as a best-fit per learner. As a proof of concept, OLeCenT allows the input of one or more course learning paths with real-time learning and automatic reconfiguration of the course path where a new trend or pattern is identified. OLeCenT identified disparity in teaching-learning and provided a mechanism towards improving online learner-centric course delivery. OLeCenT also identified comparative levels of similarities among learners and instructors even where they are deemed to be of different teaching-learning styles/mechanisms.

  16. Optimizing Chemical Reactions with Deep Reinforcement Learning.

    Science.gov (United States)

    Zhou, Zhenpeng; Li, Xiaocheng; Zare, Richard N

    2017-12-27

    Deep reinforcement learning was employed to optimize chemical reactions. Our model iteratively records the results of a chemical reaction and chooses new experimental conditions to improve the reaction outcome. This model outperformed a state-of-the-art blackbox optimization algorithm by using 71% fewer steps on both simulations and real reactions. Furthermore, we introduced an efficient exploration strategy by drawing the reaction conditions from certain probability distributions, which resulted in an improvement on regret from 0.062 to 0.039 compared with a deterministic policy. Combining the efficient exploration policy with accelerated microdroplet reactions, optimal reaction conditions were determined in 30 min for the four reactions considered, and a better understanding of the factors that control microdroplet reactions was reached. Moreover, our model showed a better performance after training on reactions with similar or even dissimilar underlying mechanisms, which demonstrates its learning ability.

  17. A new evolutionary algorithm with LQV learning for combinatorial problems optimization

    International Nuclear Information System (INIS)

    Machado, Marcelo Dornellas; Schirru, Roberto

    2000-01-01

    Genetic algorithms are biologically motivated adaptive systems which have been used, with good results, for combinatorial problems optimization. In this work, a new learning mode, to be used by the population-based incremental learning algorithm, has the aim to build a new evolutionary algorithm to be used in optimization of numerical problems and combinatorial problems. This new learning mode uses a variable learning rate during the optimization process, constituting a process known as proportional reward. The development of this new algorithm aims its application in the optimization of reload problem of PWR nuclear reactors, in order to increase the useful life of the nuclear fuel. For the test, two classes of problems are used: numerical problems and combinatorial problems. Due to the fact that the reload problem is a combinatorial problem, the major interest relies on the last class. The results achieved with the tests indicate the applicability of the new learning mode, showing its potential as a developing tool in the solution of reload problem. (author)

  18. Teaching learning based optimization algorithm and its engineering applications

    CERN Document Server

    Rao, R Venkata

    2016-01-01

    Describing a new optimization algorithm, the “Teaching-Learning-Based Optimization (TLBO),” in a clear and lucid style, this book maximizes reader insights into how the TLBO algorithm can be used to solve continuous and discrete optimization problems involving single or multiple objectives. As the algorithm operates on the principle of teaching and learning, where teachers influence the quality of learners’ results, the elitist version of TLBO algorithm (ETLBO) is described along with applications of the TLBO algorithm in the fields of electrical engineering, mechanical design, thermal engineering, manufacturing engineering, civil engineering, structural engineering, computer engineering, electronics engineering, physics and biotechnology. The book offers a valuable resource for scientists, engineers and practitioners involved in the development and usage of advanced optimization algorithms.

  19. Language Learning of Gifted Individuals: A Content Analysis Study

    Science.gov (United States)

    Gokaydin, Beria; Baglama, Basak; Uzunboylu, Huseyin

    2017-01-01

    This study aims to carry out a content analysis of the studies on language learning of gifted individuals and determine the trends in this field. Articles on language learning of gifted individuals published in the Scopus database were examined based on certain criteria including type of publication, year of publication, language, research…

  20. Particle Swarm Optimization with Double Learning Patterns.

    Science.gov (United States)

    Shen, Yuanxia; Wei, Linna; Zeng, Chuanhua; Chen, Jian

    2016-01-01

    Particle Swarm Optimization (PSO) is an effective tool in solving optimization problems. However, PSO usually suffers from the premature convergence due to the quick losing of the swarm diversity. In this paper, we first analyze the motion behavior of the swarm based on the probability characteristic of learning parameters. Then a PSO with double learning patterns (PSO-DLP) is developed, which employs the master swarm and the slave swarm with different learning patterns to achieve a trade-off between the convergence speed and the swarm diversity. The particles in the master swarm and the slave swarm are encouraged to explore search for keeping the swarm diversity and to learn from the global best particle for refining a promising solution, respectively. When the evolutionary states of two swarms interact, an interaction mechanism is enabled. This mechanism can help the slave swarm in jumping out of the local optima and improve the convergence precision of the master swarm. The proposed PSO-DLP is evaluated on 20 benchmark functions, including rotated multimodal and complex shifted problems. The simulation results and statistical analysis show that PSO-DLP obtains a promising performance and outperforms eight PSO variants.

  1. Particle Swarm Optimization with Double Learning Patterns

    Science.gov (United States)

    Shen, Yuanxia; Wei, Linna; Zeng, Chuanhua; Chen, Jian

    2016-01-01

    Particle Swarm Optimization (PSO) is an effective tool in solving optimization problems. However, PSO usually suffers from the premature convergence due to the quick losing of the swarm diversity. In this paper, we first analyze the motion behavior of the swarm based on the probability characteristic of learning parameters. Then a PSO with double learning patterns (PSO-DLP) is developed, which employs the master swarm and the slave swarm with different learning patterns to achieve a trade-off between the convergence speed and the swarm diversity. The particles in the master swarm and the slave swarm are encouraged to explore search for keeping the swarm diversity and to learn from the global best particle for refining a promising solution, respectively. When the evolutionary states of two swarms interact, an interaction mechanism is enabled. This mechanism can help the slave swarm in jumping out of the local optima and improve the convergence precision of the master swarm. The proposed PSO-DLP is evaluated on 20 benchmark functions, including rotated multimodal and complex shifted problems. The simulation results and statistical analysis show that PSO-DLP obtains a promising performance and outperforms eight PSO variants. PMID:26858747

  2. Proposing an Optimal Learning Architecture for the Digital Enterprise.

    Science.gov (United States)

    O'Driscoll, Tony

    2003-01-01

    Discusses the strategic role of learning in information age organizations; analyzes parallels between the application of technology to business and the application of technology to learning; and proposes a learning architecture that aligns with the knowledge-based view of the firm and optimizes the application of technology to achieve proficiency…

  3. Machine learning paradigms in design optimization: Applications in turbine aerodynamic design

    Science.gov (United States)

    Goel, Sanjay

    Mechanisms of incorporating machine learning paradigms in design optimization have been investigated in the current research. The primary focus of the work is on machine learning algorithms which use computational models that are analogous to the hypothesized principles of natural or biological learning. Examples from structural and aerodynamic optimization have been used to demonstrate the potential of the proposed schemes. The first strategy examined in the current work seeks to improve the convergence of optimization problems by pruning the search space of weak variables. Such variables are identified by learning from a database of existing designs using neural networks. By using clustering techniques, different sets of weak variables are identified in different regions of the design space. Parameter sensitivity information obtained in the process of identifying weak variables provides accurate heuristics for formulating design rules. The impact of this methodology on obtaining converged designs has been investigated for a turbine design problem. Optimization results from a three-stage power turbine and an aircraft engine turbine are presented in this thesis. The second scheme is an evolutionary design optimization technique which gets progressively 'smarter' during the optimization process by learning from computed domain knowledge. This technique employs adaptive learning mechanisms (classifiers) which recognize the influence of the design variables on the problem solution and then generalize them to dynamically create or change design rules during optimization. This technique, when applied to a constrained optimization problem, shows progressive improvement in convergence of search, as successive generations of rules evolve by learning from the environment. To investigate this methodology, a truss optimization problem is solved with an objective of minimizing the truss weight subject to stress constraints in the truss members. A distinct convergent trend is

  4. Optimal design of the heat pipe using TLBO (teaching–learning-based optimization) algorithm

    International Nuclear Information System (INIS)

    Rao, R.V.; More, K.C.

    2015-01-01

    Heat pipe is a highly efficient and reliable heat transfer component. It is a closed container designed to transfer a large amount of heat in system. Since the heat pipe operates on a closed two-phase cycle, the heat transfer capacity is greater than for solid conductors. Also, the thermal response time is less than with solid conductors. The three major elemental parts of the rotating heat pipe are: a cylindrical evaporator, a truncated cone condenser, and a fixed amount of working fluid. In this paper, a recently proposed new stochastic advanced optimization algorithm called TLBO (Teaching–Learning-Based Optimization) algorithm is used for single objective as well as multi-objective design optimization of heat pipe. It is easy to implement, does not make use of derivatives and it can be applied to unconstrained or constrained problems. Two examples of heat pipe are presented in this paper. The results of application of TLBO algorithm for the design optimization of heat pipe are compared with the NPGA (Niched Pareto Genetic Algorithm), GEM (Grenade Explosion Method) and GEO (Generalized External optimization). It is found that the TLBO algorithm has produced better results as compared to those obtained by using NPGA, GEM and GEO algorithms. - Highlights: • The TLBO (Teaching–Learning-Based Optimization) algorithm is used for the design and optimization of a heat pipe. • Two examples of heat pipe design and optimization are presented. • The TLBO algorithm is proved better than the other optimization algorithms in terms of results and the convergence

  5. Optimizing the number of steps in learning tasks for complex skills.

    Science.gov (United States)

    Nadolski, Rob J; Kirschner, Paul A; van Merriënboer, Jeroen J G

    2005-06-01

    Carrying out whole tasks is often too difficult for novice learners attempting to acquire complex skills. The common solution is to split up the tasks into a number of smaller steps. The number of steps must be optimized for efficient and effective learning. The aim of the study is to investigate the relation between the number of steps provided to learners and the quality of their learning of complex skills. It is hypothesized that students receiving an optimized number of steps will learn better than those receiving either the whole task in only one step or those receiving a large number of steps. Participants were 35 sophomore law students studying at Dutch universities, mean age=22.8 years (SD=3.5), 63% were female. Participants were randomly assigned to 1 of 3 computer-delivered versions of a multimedia programme on how to prepare and carry out a law plea. The versions differed only in the number of learning steps provided. Videotaped plea-performance results were determined, various related learning measures were acquired and all computer actions were logged and analyzed. Participants exposed to an intermediate (i.e. optimized) number of steps outperformed all others on the compulsory learning task. No differences in performance on a transfer task were found. A high number of steps proved to be less efficient for carrying out the learning task. An intermediate number of steps is the most effective, proving that the number of steps can be optimized for improving learning.

  6. Video Content Search System for Better Students Engagement in the Learning Process

    Directory of Open Access Journals (Sweden)

    Alanoud Alotaibi

    2014-12-01

    Full Text Available As a component of the e-learning educational process, content plays an essential role. Increasingly, the video-recorded lectures in e-learning systems are becoming more important to learners. In most cases, a single video-recorded lecture contains more than one topic or sub-topic. Therefore, to enable learners to find the desired topic and reduce learning time, e-learning systems need to provide a search capability for searching within the video content. This can be accomplished by enabling learners to identify the video or portion that contains a keyword they are looking for. This research aims to develop Video Content Search system to facilitate searching in educational videos and its contents. Preliminary results of an experimentation were conducted on a selected university course. All students needed a system to avoid time-wasting problem of watching long videos with no significant benefit. The statistics showed that the number of learners increased during the experiment. Future work will include studying impact of VCS system on students’ performance and satisfaction.

  7. Optimizing area under the ROC curve using semi-supervised learning.

    Science.gov (United States)

    Wang, Shijun; Li, Diana; Petrick, Nicholas; Sahiner, Berkman; Linguraru, Marius George; Summers, Ronald M

    2015-01-01

    Receiver operating characteristic (ROC) analysis is a standard methodology to evaluate the performance of a binary classification system. The area under the ROC curve (AUC) is a performance metric that summarizes how well a classifier separates two classes. Traditional AUC optimization techniques are supervised learning methods that utilize only labeled data (i.e., the true class is known for all data) to train the classifiers. In this work, inspired by semi-supervised and transductive learning, we propose two new AUC optimization algorithms hereby referred to as semi-supervised learning receiver operating characteristic (SSLROC) algorithms, which utilize unlabeled test samples in classifier training to maximize AUC. Unlabeled samples are incorporated into the AUC optimization process, and their ranking relationships to labeled positive and negative training samples are considered as optimization constraints. The introduced test samples will cause the learned decision boundary in a multidimensional feature space to adapt not only to the distribution of labeled training data, but also to the distribution of unlabeled test data. We formulate the semi-supervised AUC optimization problem as a semi-definite programming problem based on the margin maximization theory. The proposed methods SSLROC1 (1-norm) and SSLROC2 (2-norm) were evaluated using 34 (determined by power analysis) randomly selected datasets from the University of California, Irvine machine learning repository. Wilcoxon signed rank tests showed that the proposed methods achieved significant improvement compared with state-of-the-art methods. The proposed methods were also applied to a CT colonography dataset for colonic polyp classification and showed promising results.

  8. Building a Smart E-Portfolio Platform for Optimal E-Learning Objects Acquisition

    Directory of Open Access Journals (Sweden)

    Chih-Kun Ke

    2013-01-01

    Full Text Available In modern education, an e-portfolio platform helps students in acquiring e-learning objects in a learning activity. Quality is an important consideration in evaluating the desirable e-learning object. Finding a means of determining a high quality e-learning object from a large number of candidate e-learning objects is an important requirement. To assist student learning in a modern e-portfolio platform, this work proposed an optimal selection approach determining a reasonable e-learning object from various candidate e-learning objects. An optimal selection approach which uses advanced information techniques is proposed. Each e-learning object undergoes a formalization process. An Information Retrieval (IR technique extracts and analyses key concepts from the student’s previous learning contexts. A context-based utility model computes the expected utility values of various e-learning objects based on the extracted key concepts. The expected utility values of e-learning objects are used in a multicriteria decision analysis to determine the optimal selection order of the candidate e-learning objects. The main contribution of this work is the demonstration of an effective e-learning object selection method which is easy to implement within an e-portfolio platform and which makes it smarter.

  9. Learning About Semi Conductors for Teaching—the Role Played by Content Knowledge in Pedagogical Content Knowledge (PCK) Development

    Science.gov (United States)

    Rollnick, Marissa

    2017-08-01

    This study focuses on how teachers learn to teach a new topic and the role played by their developing content knowledge as they teach. The paper is based on seven high school science teachers' studies on the teaching of semiconductors, at the time a new topic in the curriculum. Analysis of artefacts such as teacher concept maps, video recordings of lessons, journals and other classroom-based evidence shows how the extent and type of teachers' content knowledge informed their choice of teaching approaches and how their learning of content took place alongside the development of teaching strategies. The development of content knowledge was combined with increased understanding of how to teach the topic in almost all cases. Evidence of development of teachers' PCK was found in their increased ability to design teaching strategies, and their use of representations and suitable assessment tasks for their lessons. Some specific common teaching strategies were identified across the teachers. These strategies could add to the canon of teachers' topic - specific professional knowledge for semiconductors. The study provides increased understanding of how teachers simultaneously master content and its teaching and how mediated self-reflection is a fruitful approach for assisting teachers to learn to teach a new topic.

  10. The Challenge of Content Creation to Facilitate Personalized E-Learning Experiences

    Science.gov (United States)

    Turker, Ali; Gorgun, Ilhami; Conlan, Owen

    2006-01-01

    The runtime creation of pedagogically coherent learning content for an individual learner's needs and preferences is a considerable challenge. By selecting and combining appropriate learning assets into a new learning object such needs and preferences may be accounted for. However, to assure coherence, these objects should be consumed within…

  11. A multi-objective improved teaching-learning based optimization algorithm for unconstrained and constrained optimization problems

    Directory of Open Access Journals (Sweden)

    R. Venkata Rao

    2014-01-01

    Full Text Available The present work proposes a multi-objective improved teaching-learning based optimization (MO-ITLBO algorithm for unconstrained and constrained multi-objective function optimization. The MO-ITLBO algorithm is the improved version of basic teaching-learning based optimization (TLBO algorithm adapted for multi-objective problems. The basic TLBO algorithm is improved to enhance its exploration and exploitation capacities by introducing the concept of number of teachers, adaptive teaching factor, tutorial training and self-motivated learning. The MO-ITLBO algorithm uses a grid-based approach to adaptively assess the non-dominated solutions (i.e. Pareto front maintained in an external archive. The performance of the MO-ITLBO algorithm is assessed by implementing it on unconstrained and constrained test problems proposed for the Congress on Evolutionary Computation 2009 (CEC 2009 competition. The performance assessment is done by using the inverted generational distance (IGD measure. The IGD measures obtained by using the MO-ITLBO algorithm are compared with the IGD measures of the other state-of-the-art algorithms available in the literature. Finally, Lexicographic ordering is used to assess the overall performance of competitive algorithms. Results have shown that the proposed MO-ITLBO algorithm has obtained the 1st rank in the optimization of unconstrained test functions and the 3rd rank in the optimization of constrained test functions.

  12. Personalized learning: From neurogenetics of behaviors to designing optimal language training.

    Science.gov (United States)

    Wong, Patrick C M; Vuong, Loan C; Liu, Kevin

    2017-04-01

    Variability in drug responsivity has prompted the development of Personalized Medicine, which has shown great promise in utilizing genotypic information to develop safer and more effective drug regimens for patients. Similarly, individual variability in learning outcomes has puzzled researchers who seek to create optimal learning environments for students. "Personalized Learning" seeks to identify genetic, neural and behavioral predictors of individual differences in learning and aims to use predictors to help create optimal teaching paradigms. Evidence for Personalized Learning can be observed by connecting research in pharmacogenomics, cognitive genetics and behavioral experiments across domains of learning, which provides a framework for conducting empirical studies from the laboratory to the classroom and holds promise for addressing learning effectiveness in the individual learners. Evidence can also be seen in the subdomain of speech learning, thus providing initial support for the applicability of Personalized Learning to language. Copyright © 2016 Elsevier Ltd. All rights reserved.

  13. Interactive Preference Learning of Utility Functions for Multi-Objective Optimization

    OpenAIRE

    Dewancker, Ian; McCourt, Michael; Ainsworth, Samuel

    2016-01-01

    Real-world engineering systems are typically compared and contrasted using multiple metrics. For practical machine learning systems, performance tuning is often more nuanced than minimizing a single expected loss objective, and it may be more realistically discussed as a multi-objective optimization problem. We propose a novel generative model for scalar-valued utility functions to capture human preferences in a multi-objective optimization setting. We also outline an interactive active learn...

  14. An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems

    Directory of Open Access Journals (Sweden)

    Vivek Patel

    2012-08-01

    Full Text Available Nature inspired population based algorithms is a research field which simulates different natural phenomena to solve a wide range of problems. Researchers have proposed several algorithms considering different natural phenomena. Teaching-Learning-based optimization (TLBO is one of the recently proposed population based algorithm which simulates the teaching-learning process of the class room. This algorithm does not require any algorithm-specific control parameters. In this paper, elitism concept is introduced in the TLBO algorithm and its effect on the performance of the algorithm is investigated. The effects of common controlling parameters such as the population size and the number of generations on the performance of the algorithm are also investigated. The proposed algorithm is tested on 35 constrained benchmark functions with different characteristics and the performance of the algorithm is compared with that of other well known optimization algorithms. The proposed algorithm can be applied to various optimization problems of the industrial environment.

  15. Optimal calibration of variable biofuel blend dual-injection engines using sparse Bayesian extreme learning machine and metaheuristic optimization

    International Nuclear Information System (INIS)

    Wong, Ka In; Wong, Pak Kin

    2017-01-01

    Highlights: • A new calibration method is proposed for dual-injection engines under biofuel blends. • Sparse Bayesian extreme learning machine and flower pollination algorithm are employed in the proposed method. • An SI engine is retrofitted for operating under dual-injection strategy. • The proposed method is verified experimentally under the two idle speed conditions. • Comparison with other machine learning methods and optimization algorithms is conducted. - Abstract: Although many combinations of biofuel blends are available in the market, it is more beneficial to vary the ratio of biofuel blends at different engine operating conditions for optimal engine performance. Dual-injection engines have the potential to implement such function. However, while optimal engine calibration is critical for achieving high performance, the use of two injection systems, together with other modern engine technologies, leads the calibration of the dual-injection engines to a very complicated task. Traditional trial-and-error-based calibration approach can no longer be adopted as it would be time-, fuel- and labor-consuming. Therefore, a new and fast calibration method based on sparse Bayesian extreme learning machine (SBELM) and metaheuristic optimization is proposed to optimize the dual-injection engines operating with biofuels. A dual-injection spark-ignition engine fueled with ethanol and gasoline is employed for demonstration purpose. The engine response for various parameters is firstly acquired, and an engine model is then constructed using SBELM. With the engine model, the optimal engine settings are determined based on recently proposed metaheuristic optimization methods. Experimental results validate the optimal settings obtained with the proposed methodology, indicating that the use of machine learning and metaheuristic optimization for dual-injection engine calibration is effective and promising.

  16. Interactive ontology-based user modelling for personalized learning content management

    NARCIS (Netherlands)

    Denaux, R.O.; Dimitrova, V.; Aroyo, L.M.; Aroyo, L.; Tasso, C.

    2004-01-01

    This position paper discusses the need for using interactive ontology-based user modeling to empower on the fly adaptation in learning information systems. We outline several open issues related to adaptive learning content delivery and present an approach to deal with these issues based on the

  17. Application of Multi-Objective Human Learning Optimization Method to Solve AC/DC Multi-Objective Optimal Power Flow Problem

    Science.gov (United States)

    Cao, Jia; Yan, Zheng; He, Guangyu

    2016-06-01

    This paper introduces an efficient algorithm, multi-objective human learning optimization method (MOHLO), to solve AC/DC multi-objective optimal power flow problem (MOPF). Firstly, the model of AC/DC MOPF including wind farms is constructed, where includes three objective functions, operating cost, power loss, and pollutant emission. Combining the non-dominated sorting technique and the crowding distance index, the MOHLO method can be derived, which involves individual learning operator, social learning operator, random exploration learning operator and adaptive strategies. Both the proposed MOHLO method and non-dominated sorting genetic algorithm II (NSGAII) are tested on an improved IEEE 30-bus AC/DC hybrid system. Simulation results show that MOHLO method has excellent search efficiency and the powerful ability of searching optimal. Above all, MOHLO method can obtain more complete pareto front than that by NSGAII method. However, how to choose the optimal solution from pareto front depends mainly on the decision makers who stand from the economic point of view or from the energy saving and emission reduction point of view.

  18. Genetic algorithm enhanced by machine learning in dynamic aperture optimization

    Science.gov (United States)

    Li, Yongjun; Cheng, Weixing; Yu, Li Hua; Rainer, Robert

    2018-05-01

    With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given "elite" status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitness of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. The machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.

  19. Optimal critic learning for robot control in time-varying environments.

    Science.gov (United States)

    Wang, Chen; Li, Yanan; Ge, Shuzhi Sam; Lee, Tong Heng

    2015-10-01

    In this paper, optimal critic learning is developed for robot control in a time-varying environment. The unknown environment is described as a linear system with time-varying parameters, and impedance control is employed for the interaction control. Desired impedance parameters are obtained in the sense of an optimal realization of the composite of trajectory tracking and force regulation. Q -function-based critic learning is developed to determine the optimal impedance parameters without the knowledge of the system dynamics. The simulation results are presented and compared with existing methods, and the efficacy of the proposed method is verified.

  20. Learning decision trees with flexible constraints and objectives using integer optimization

    NARCIS (Netherlands)

    Verwer, S.; Zhang, Y.

    2017-01-01

    We encode the problem of learning the optimal decision tree of a given depth as an integer optimization problem. We show experimentally that our method (DTIP) can be used to learn good trees up to depth 5 from data sets of size up to 1000. In addition to being efficient, our new formulation allows

  1. A modified teaching–learning based optimization for multi-objective optimal power flow problem

    International Nuclear Information System (INIS)

    Shabanpour-Haghighi, Amin; Seifi, Ali Reza; Niknam, Taher

    2014-01-01

    Highlights: • A new modified teaching–learning based algorithm is proposed. • A self-adaptive wavelet mutation strategy is used to enhance the performance. • To avoid reaching a large repository size, a fuzzy clustering technique is used. • An efficiently smart population selection is utilized. • Simulations show the superiority of this algorithm compared with other ones. - Abstract: In this paper, a modified teaching–learning based optimization algorithm is analyzed to solve the multi-objective optimal power flow problem considering the total fuel cost and total emission of the units. The modified phase of the optimization algorithm utilizes a self-adapting wavelet mutation strategy. Moreover, a fuzzy clustering technique is proposed to avoid extremely large repository size besides a smart population selection for the next iteration. These techniques make the algorithm searching a larger space to find the optimal solutions while speed of the convergence remains good. The IEEE 30-Bus and 57-Bus systems are used to illustrate performance of the proposed algorithm and results are compared with those in literatures. It is verified that the proposed approach has better performance over other techniques

  2. The Development of SCORM-Conformant Learning Content Based on the Learning Cycle Using Participatory Design

    Science.gov (United States)

    Su, C. Y.; Chiu, C. H.; Wang, T. I.

    2010-01-01

    This study incorporates the 5E learning cycle strategy to design and develop Sharable Content Object Reference Model-conformant materials for elementary science education. The 5E learning cycle that supports the constructivist approach has been widely applied in science education. The strategy consists of five phases: engagement, exploration,…

  3. Personalized Learning: From Neurogenetics of Behaviors to Designing Optimal Language Training

    Science.gov (United States)

    Wong, Patrick C. M.; Vuong, Loan; Liu, Kevin

    2016-01-01

    Variability in drug responsivity has prompted the development of Personalized Medicine, which has shown great promise in utilizing genotypic information to develop safer and more effective drug regimens for patients. Similarly, individual variability in learning outcomes has puzzled researchers who seek to create optimal learning environments for students. “Personalized Learning” seeks to identify genetic, neural and behavioral predictors of individual differences in learning and aims to use predictors to help create optimal teaching paradigms. Evidence for Personalized Learning can be observed by connecting research in pharmacogenomics, cognitive genetics and behavioral experiments across domains of learning, which provides a framework for conducting empirical studies from the laboratory to the classroom and holds promise for addressing learning effectiveness in the individual learners. Evidence can also be seen in the subdomain of speech learning, thus providing initial support for the applicability of Personalized Learning to language. PMID:27720749

  4. The right time to learn: mechanisms and optimization of spaced learning

    Science.gov (United States)

    Smolen, Paul; Zhang, Yili; Byrne, John H.

    2016-01-01

    For many types of learning, spaced training, which involves repeated long inter-trial intervals, leads to more robust memory formation than does massed training, which involves short or no intervals. Several cognitive theories have been proposed to explain this superiority, but only recently have data begun to delineate the underlying cellular and molecular mechanisms of spaced training, and we review these theories and data here. Computational models of the implicated signalling cascades have predicted that spaced training with irregular inter-trial intervals can enhance learning. This strategy of using models to predict optimal spaced training protocols, combined with pharmacotherapy, suggests novel ways to rescue impaired synaptic plasticity and learning. PMID:26806627

  5. Assimilation of contents and learning through the use of video tutorials

    Directory of Open Access Journals (Sweden)

    David JIMÉNEZ CASTILLO

    2013-01-01

    Full Text Available The need for a change in the university educational model promoted by the establishment of the European Higher Education Area (EHEA has promoted the implementation of numerous proposals for innovation in university teaching. These innovative practices that are based on a process of reflection and analysis of past teaching experience, are helping to improve qualitatively the teaching practice and, consequently, the learning process and outcomes of students, from a process of reflection and analysis of the teaching experience. In this context, this paper focuses on analyzing a specific teaching tool for innovation, the video tutorial, in order to assess its influence on the processes of assimilation of contents and self-learning. In particular, we attempt to show if the video tutorial allows reinforcing the understanding of practical contents that have been previously given by the classical method of masterly exposition. From the analysis of data obtained through a survey directed to a sample of students after experimenting with the teaching tool, it is shown that the video tutorial is considered a very suitable tool to improve the assimilation capacity of the contents taught previously and to acquire higher learning. After performing a regression analysis, the research also shows that students’ attitudes toward multimedia tools and the perceived utility of video tutorial positively influence these capacities. On the contrary, we find that the attitude towards individual learning and the attention paid by the student to the contents of the video tutorial do not affect the level of learning obtained from this tool.

  6. Improved object optimal synthetic description, modeling, learning, and discrimination by GEOGINE computational kernel

    Science.gov (United States)

    Fiorini, Rodolfo A.; Dacquino, Gianfranco

    2005-03-01

    GEOGINE (GEOmetrical enGINE), a state-of-the-art OMG (Ontological Model Generator) based on n-D Tensor Invariants for n-Dimensional shape/texture optimal synthetic representation, description and learning, was presented in previous conferences elsewhere recently. Improved computational algorithms based on the computational invariant theory of finite groups in Euclidean space and a demo application is presented. Progressive model automatic generation is discussed. GEOGINE can be used as an efficient computational kernel for fast reliable application development and delivery in advanced biomedical engineering, biometric, intelligent computing, target recognition, content image retrieval, data mining technological areas mainly. Ontology can be regarded as a logical theory accounting for the intended meaning of a formal dictionary, i.e., its ontological commitment to a particular conceptualization of the world object. According to this approach, "n-D Tensor Calculus" can be considered a "Formal Language" to reliably compute optimized "n-Dimensional Tensor Invariants" as specific object "invariant parameter and attribute words" for automated n-Dimensional shape/texture optimal synthetic object description by incremental model generation. The class of those "invariant parameter and attribute words" can be thought as a specific "Formal Vocabulary" learned from a "Generalized Formal Dictionary" of the "Computational Tensor Invariants" language. Even object chromatic attributes can be effectively and reliably computed from object geometric parameters into robust colour shape invariant characteristics. As a matter of fact, any highly sophisticated application needing effective, robust object geometric/colour invariant attribute capture and parameterization features, for reliable automated object learning and discrimination can deeply benefit from GEOGINE progressive automated model generation computational kernel performance. Main operational advantages over previous

  7. Optimizing Knowledge Sharing In Learning Networks Through Peer Tutoring

    NARCIS (Netherlands)

    Hsiao, Amy; Brouns, Francis; Kester, Liesbeth; Sloep, Peter

    2009-01-01

    Hsiao, Y. P., Brouns, F., Kester, L., & Sloep, P. B. (2009). Optimizing Knowledge Sharing In Learning Networks Through Peer Tutoring. In D. Kinshuk, J. Sampson, J. Spector, P. Isaías, P. Barbosa & D. Ifenthaler (Eds.). Proceedings of IADIS International Conference Cognition and Exploratory Learning

  8. Content-based VLE designs improve learning efficiency in constructivist statistics education.

    Science.gov (United States)

    Wessa, Patrick; De Rycker, Antoon; Holliday, Ian Edward

    2011-01-01

    We introduced a series of computer-supported workshops in our undergraduate statistics courses, in the hope that it would help students to gain a deeper understanding of statistical concepts. This raised questions about the appropriate design of the Virtual Learning Environment (VLE) in which such an approach had to be implemented. Therefore, we investigated two competing software design models for VLEs. In the first system, all learning features were a function of the classical VLE. The second system was designed from the perspective that learning features should be a function of the course's core content (statistical analyses), which required us to develop a specific-purpose Statistical Learning Environment (SLE) based on Reproducible Computing and newly developed Peer Review (PR) technology. The main research question is whether the second VLE design improved learning efficiency as compared to the standard type of VLE design that is commonly used in education. As a secondary objective we provide empirical evidence about the usefulness of PR as a constructivist learning activity which supports non-rote learning. Finally, this paper illustrates that it is possible to introduce a constructivist learning approach in large student populations, based on adequately designed educational technology, without subsuming educational content to technological convenience. Both VLE systems were tested within a two-year quasi-experiment based on a Reliable Nonequivalent Group Design. This approach allowed us to draw valid conclusions about the treatment effect of the changed VLE design, even though the systems were implemented in successive years. The methodological aspects about the experiment's internal validity are explained extensively. The effect of the design change is shown to have substantially increased the efficiency of constructivist, computer-assisted learning activities for all cohorts of the student population under investigation. The findings demonstrate that a

  9. Content-based VLE designs improve learning efficiency in constructivist statistics education.

    Directory of Open Access Journals (Sweden)

    Patrick Wessa

    Full Text Available BACKGROUND: We introduced a series of computer-supported workshops in our undergraduate statistics courses, in the hope that it would help students to gain a deeper understanding of statistical concepts. This raised questions about the appropriate design of the Virtual Learning Environment (VLE in which such an approach had to be implemented. Therefore, we investigated two competing software design models for VLEs. In the first system, all learning features were a function of the classical VLE. The second system was designed from the perspective that learning features should be a function of the course's core content (statistical analyses, which required us to develop a specific-purpose Statistical Learning Environment (SLE based on Reproducible Computing and newly developed Peer Review (PR technology. OBJECTIVES: The main research question is whether the second VLE design improved learning efficiency as compared to the standard type of VLE design that is commonly used in education. As a secondary objective we provide empirical evidence about the usefulness of PR as a constructivist learning activity which supports non-rote learning. Finally, this paper illustrates that it is possible to introduce a constructivist learning approach in large student populations, based on adequately designed educational technology, without subsuming educational content to technological convenience. METHODS: Both VLE systems were tested within a two-year quasi-experiment based on a Reliable Nonequivalent Group Design. This approach allowed us to draw valid conclusions about the treatment effect of the changed VLE design, even though the systems were implemented in successive years. The methodological aspects about the experiment's internal validity are explained extensively. RESULTS: The effect of the design change is shown to have substantially increased the efficiency of constructivist, computer-assisted learning activities for all cohorts of the student

  10. Development of Usability Criteria for E-Learning Content Development Software

    Science.gov (United States)

    Celik, Serkan

    2012-01-01

    Revolutionary advancements have been observed in e-learning technologies though an amalgamated evaluation methodology for new generation e-learning content development tools is not available. The evaluation of educational software for online use must consider its usability and as well as its pedagogic effectiveness. This study is a first step…

  11. Analysis of Documents Published in Scopus Database on Foreign Language Learning through Mobile Learning: A Content Analysis

    Science.gov (United States)

    Uzunboylu, Huseyin; Genc, Zeynep

    2017-01-01

    The purpose of this study is to determine the recent trends in foreign language learning through mobile learning. The study was conducted employing document analysis and related content analysis among the qualitative research methodology. Through the search conducted on Scopus database with the key words "mobile learning and foreign language…

  12. Statistical and optimal learning with applications in business analytics

    Science.gov (United States)

    Han, Bin

    Statistical learning is widely used in business analytics to discover structure or exploit patterns from historical data, and build models that capture relationships between an outcome of interest and a set of variables. Optimal learning on the other hand, solves the operational side of the problem, by iterating between decision making and data acquisition/learning. All too often the two problems go hand-in-hand, which exhibit a feedback loop between statistics and optimization. We apply this statistical/optimal learning concept on a context of fundraising marketing campaign problem arising in many non-profit organizations. Many such organizations use direct-mail marketing to cultivate one-time donors and convert them into recurring contributors. Cultivated donors generate much more revenue than new donors, but also lapse with time, making it important to steadily draw in new cultivations. The direct-mail budget is limited, but better-designed mailings can improve success rates without increasing costs. We first apply statistical learning to analyze the effectiveness of several design approaches used in practice, based on a massive dataset covering 8.6 million direct-mail communications with donors to the American Red Cross during 2009-2011. We find evidence that mailed appeals are more effective when they emphasize disaster preparedness and training efforts over post-disaster cleanup. Including small cards that affirm donors' identity as Red Cross supporters is an effective strategy, while including gift items such as address labels is not. Finally, very recent acquisitions are more likely to respond to appeals that ask them to contribute an amount similar to their most recent donation, but this approach has an adverse effect on donors with a longer history. We show via simulation that a simple design strategy based on these insights has potential to improve success rates from 5.4% to 8.1%. Given these findings, when new scenario arises, however, new data need to

  13. Extreme Learning Machine and Particle Swarm Optimization in optimizing CNC turning operation

    Science.gov (United States)

    Janahiraman, Tiagrajah V.; Ahmad, Nooraziah; Hani Nordin, Farah

    2018-04-01

    The CNC machine is controlled by manipulating cutting parameters that could directly influence the process performance. Many optimization methods has been applied to obtain the optimal cutting parameters for the desired performance function. Nonetheless, the industry still uses the traditional technique to obtain those values. Lack of knowledge on optimization techniques is the main reason for this issue to be prolonged. Therefore, the simple yet easy to implement, Optimal Cutting Parameters Selection System is introduced to help the manufacturer to easily understand and determine the best optimal parameters for their turning operation. This new system consists of two stages which are modelling and optimization. In modelling of input-output and in-process parameters, the hybrid of Extreme Learning Machine and Particle Swarm Optimization is applied. This modelling technique tend to converge faster than other artificial intelligent technique and give accurate result. For the optimization stage, again the Particle Swarm Optimization is used to get the optimal cutting parameters based on the performance function preferred by the manufacturer. Overall, the system can reduce the gap between academic world and the industry by introducing a simple yet easy to implement optimization technique. This novel optimization technique can give accurate result besides being the fastest technique.

  14. Content-Based Instruction Approach In Instructional Multimedia For English Learning

    OpenAIRE

    Farani, Rizki

    2016-01-01

    Content-based Instruction (CBI) is an approach in English learning that integrates certain topic and English learning objectives. This approach focuses on using English competencies as a “bridge” to comprehend certain topic or theme in English. Nowadays, this approach can be used in instructional multimedia to support English learning by using computer. Instructional multimedia with computer system refers to the sequential or simultaneous use of variety of media formats in a given presentatio...

  15. Design of Open Content Social Learning Based on the Activities of Learner and Similar Learners

    Science.gov (United States)

    John, Benneaser; Jayakumar, J.; Thavavel, V.; Arumugam, Muthukumar; Poornaselvan, K. J.

    2017-01-01

    Teaching and learning are increasingly taking advantage of the rapid growth in Internet resources, open content, mobile technologies and social media platforms. However, due to the generally unstructured nature and overwhelming quantity of learning content, effective learning remains challenging. In an effort to close this gap, the authors…

  16. E-learning in radiology - the practical use of the content management system ILIAS

    International Nuclear Information System (INIS)

    Schuetze, B.; Mildenberger, P.; Kaemmerer, M.

    2006-01-01

    Purpose: Due to the possibility of using different kinds of visualization, e-learning has the advantage of allowing individualized learning. A check should be performed to determine whether the use of the web-based content management system ILIAS simplifies the writing and production of electronic learning modules in radiology. Materials and methods: Internet-based e-learning provides access to existing learning modules regardless of time and location, since fast Internet connections are readily available. Results: Web Content Management Systems (WCMS) are suitable platforms for imparting radiology-related information (visual abilities like the recognition of patterns as well as interdisciplinary specialized knowledge). The open source product ILIAS is a free WCMS. It is used by many universities and is accepted by both students and lecturers. Its modular and object-oriented software architecture makes it easy to adapt and enlarge the platform. The employment of e-learning standards such as LOM and SCORM within ILIAS makes it possible to reuse contents, even if the platform has to be changed. Conclusion: ILIAS renders it possible to provide students with texts, images, or files of any other kind within a learning context which is defined by the lecturer. Students can check their acquired knowledge via online testing and receive direct performance feedback. The significant interest that students have shown in ILIAS proves that e-learning can be a useful addition to conventional learning methods. (orig.)

  17. [E-Learning in radiology; the practical use of the content management system ILIAS].

    Science.gov (United States)

    Schütze, B; Mildenberger, P; Kämmerer, M

    2006-05-01

    Due to the possibility of using different kinds of visualization, e-learning has the advantage of allowing individualized learning. A check should be performed to determine whether the use of the web-based content management system ILIAS simplifies the writing and production of electronic learning modules in radiology. Internet-based e-learning provides access to existing learning modules regardless of time and location, since fast Internet connections are readily available. Web Content Management Systems (WCMS) are suitable platforms for imparting radiology-related information (visual abilities like the recognition of patterns as well as interdisciplinary specialized knowledge). The open source product ILIAS is a free WCMS. It is used by many universities and is accepted by both students and lecturers. Its modular and object-oriented software architecture makes it easy to adapt and enlarge the platform. The employment of e-learning standards such as LOM and SCORM within ILIAS makes it possible to reuse contents, even if the platform has to be changed. ILIAS renders it possible to provide students with texts, images, or files of any other kind within a learning context which is defined by the lecturer. Students can check their acquired knowledge via online testing and receive direct performance feedback. The significant interest that students have shown in ILIAS proves that e-learning can be a useful addition to conventional learning methods.

  18. Automated analysis of high-content microscopy data with deep learning.

    Science.gov (United States)

    Kraus, Oren Z; Grys, Ben T; Ba, Jimmy; Chong, Yolanda; Frey, Brendan J; Boone, Charles; Andrews, Brenda J

    2017-04-18

    Existing computational pipelines for quantitative analysis of high-content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. Here, we demonstrate that the application of deep learning to biological image data can overcome the pitfalls associated with conventional machine learning classifiers. Using a deep convolutional neural network (DeepLoc) to analyze yeast cell images, we show improved performance over traditional approaches in the automated classification of protein subcellular localization. We also demonstrate the ability of DeepLoc to classify highly divergent image sets, including images of pheromone-arrested cells with abnormal cellular morphology, as well as images generated in different genetic backgrounds and in different laboratories. We offer an open-source implementation that enables updating DeepLoc on new microscopy datasets. This study highlights deep learning as an important tool for the expedited analysis of high-content microscopy data. © 2017 The Authors. Published under the terms of the CC BY 4.0 license.

  19. Magnetic Resonance Super-resolution Imaging Measurement with Dictionary-optimized Sparse Learning

    Directory of Open Access Journals (Sweden)

    Li Jun-Bao

    2017-06-01

    Full Text Available Magnetic Resonance Super-resolution Imaging Measurement (MRIM is an effective way of measuring materials. MRIM has wide applications in physics, chemistry, biology, geology, medical and material science, especially in medical diagnosis. It is feasible to improve the resolution of MR imaging through increasing radiation intensity, but the high radiation intensity and the longtime of magnetic field harm the human body. Thus, in the practical applications the resolution of hardware imaging reaches the limitation of resolution. Software-based super-resolution technology is effective to improve the resolution of image. This work proposes a framework of dictionary-optimized sparse learning based MR super-resolution method. The framework is to solve the problem of sample selection for dictionary learning of sparse reconstruction. The textural complexity-based image quality representation is proposed to choose the optimal samples for dictionary learning. Comprehensive experiments show that the dictionary-optimized sparse learning improves the performance of sparse representation.

  20. Reply : Collective Action and the Empirical Content of Stochastic Learning Models

    NARCIS (Netherlands)

    Macy, M.W.; Flache, A.

    2007-01-01

    We are grateful for the opportunity that Bendor, Diermeier, and Ting (hereafter BDT) have provided to address important questions about the empirical content of learning theoretic solutions to the collective action problem. They discuss two well-known classes of adaptive models— stochastic learning

  1. Teaching-Learning-Based Optimization with Learning Enthusiasm Mechanism and Its Application in Chemical Engineering

    Directory of Open Access Journals (Sweden)

    Xu Chen

    2018-01-01

    Full Text Available Teaching-learning-based optimization (TLBO is a population-based metaheuristic search algorithm inspired by the teaching and learning process in a classroom. It has been successfully applied to many scientific and engineering applications in the past few years. In the basic TLBO and most of its variants, all the learners have the same probability of getting knowledge from others. However, in the real world, learners are different, and each learner’s learning enthusiasm is not the same, resulting in different probabilities of acquiring knowledge. Motivated by this phenomenon, this study introduces a learning enthusiasm mechanism into the basic TLBO and proposes a learning enthusiasm based TLBO (LebTLBO. In the LebTLBO, learners with good grades have high learning enthusiasm, and they have large probabilities of acquiring knowledge from others; by contrast, learners with bad grades have low learning enthusiasm, and they have relative small probabilities of acquiring knowledge from others. In addition, a poor student tutoring phase is introduced to improve the quality of the poor learners. The proposed method is evaluated on the CEC2014 benchmark functions, and the computational results demonstrate that it offers promising results compared with other efficient TLBO and non-TLBO algorithms. Finally, LebTLBO is applied to solve three optimal control problems in chemical engineering, and the competitive results show its potential for real-world problems.

  2. Reinforcement active learning in the vibrissae system: optimal object localization.

    Science.gov (United States)

    Gordon, Goren; Dorfman, Nimrod; Ahissar, Ehud

    2013-01-01

    Rats move their whiskers to acquire information about their environment. It has been observed that they palpate novel objects and objects they are required to localize in space. We analyze whisker-based object localization using two complementary paradigms, namely, active learning and intrinsic-reward reinforcement learning. Active learning algorithms select the next training samples according to the hypothesized solution in order to better discriminate between correct and incorrect labels. Intrinsic-reward reinforcement learning uses prediction errors as the reward to an actor-critic design, such that behavior converges to the one that optimizes the learning process. We show that in the context of object localization, the two paradigms result in palpation whisking as their respective optimal solution. These results suggest that rats may employ principles of active learning and/or intrinsic reward in tactile exploration and can guide future research to seek the underlying neuronal mechanisms that implement them. Furthermore, these paradigms are easily transferable to biomimetic whisker-based artificial sensors and can improve the active exploration of their environment. Copyright © 2012 Elsevier Ltd. All rights reserved.

  3. Combining Digital Archives Content with Serious Game Approach to Create a Gamified Learning Experience

    Directory of Open Access Journals (Sweden)

    D.-T. Shih

    2015-08-01

    Full Text Available This paper presents an interdisciplinary to develop content-aware application that combines game with learning on specific categories of digital archives. The employment of content-oriented game enhances the gamification and efficacy of learning in culture education on architectures and history of Hsinchu County, Taiwan. The gamified form of the application is used as a backbone to support and provide a strong stimulation to engage users in learning art and culture, therefore this research is implementing under the goal of “The Digital ARt/ARchitecture Project”. The purpose of the abovementioned project is to develop interactive serious game approaches and applications for Hsinchu County historical archives and architectures. Therefore, we present two applications, “3D AR for Hukou Old ” and “Hsinchu County History Museum AR Tour” which are in form of augmented reality (AR. By using AR imaging techniques to blend real object and virtual content, the users can immerse in virtual exhibitions of Hukou Old Street and Hsinchu County History Museum, and to learn in ubiquitous computing environment. This paper proposes a content system that includes tools and materials used to create representations of digitized cultural archives including historical artifacts, documents, customs, religion, and architectures. The Digital ARt / ARchitecture Project is based on the concept of serious game and consists of three aspects: content creation, target management, and AR presentation. The project focuses on developing a proper approach to serve as an interactive game, and to offer a learning opportunity for appreciating historic architectures by playing AR cards. Furthermore, the card game aims to provide multi-faceted understanding and learning experience to help user learning through 3D objects, hyperlinked web data, and the manipulation of learning mode, and then effectively developing their learning levels on cultural and historical archives in

  4. Combining Digital Archives Content with Serious Game Approach to Create a Gamified Learning Experience

    Science.gov (United States)

    Shih, D.-T.; Lin, C. L.; Tseng, C.-Y.

    2015-08-01

    This paper presents an interdisciplinary to develop content-aware application that combines game with learning on specific categories of digital archives. The employment of content-oriented game enhances the gamification and efficacy of learning in culture education on architectures and history of Hsinchu County, Taiwan. The gamified form of the application is used as a backbone to support and provide a strong stimulation to engage users in learning art and culture, therefore this research is implementing under the goal of "The Digital ARt/ARchitecture Project". The purpose of the abovementioned project is to develop interactive serious game approaches and applications for Hsinchu County historical archives and architectures. Therefore, we present two applications, "3D AR for Hukou Old " and "Hsinchu County History Museum AR Tour" which are in form of augmented reality (AR). By using AR imaging techniques to blend real object and virtual content, the users can immerse in virtual exhibitions of Hukou Old Street and Hsinchu County History Museum, and to learn in ubiquitous computing environment. This paper proposes a content system that includes tools and materials used to create representations of digitized cultural archives including historical artifacts, documents, customs, religion, and architectures. The Digital ARt / ARchitecture Project is based on the concept of serious game and consists of three aspects: content creation, target management, and AR presentation. The project focuses on developing a proper approach to serve as an interactive game, and to offer a learning opportunity for appreciating historic architectures by playing AR cards. Furthermore, the card game aims to provide multi-faceted understanding and learning experience to help user learning through 3D objects, hyperlinked web data, and the manipulation of learning mode, and then effectively developing their learning levels on cultural and historical archives in Hsinchu County.

  5. Practice of the Education for the Principle of Otto Cycle by the E-Learning CG-Content

    Science.gov (United States)

    Sato, Tomoaki; Nagaoka, Keizo; Oguchi, Kosei

    A CG-animation content which supports the learning of the Otto cycle was developed. This content has a piston assembly and the diagrams of PV, VS, TP and TS. The each diagram has a pointer which moves along the line of the graph and they are synchronized with the movement of the piston. The learners can operate this content directly on the e-learning system. While watching the movements of the piston assembly, the learners can confirm the state of the engine about temperature, pressure, volume, and entropy by the synchronized pointer on the diagrams. This content was used for the class of the machining practice exercise. The learning effect of the content was examined by the score of the short test. As the result of this examination, the CG-animation content was effective in the learning of the Otto cycle.

  6. Nurse practitioner preferences for distance education methods related to learning style, course content, and achievement.

    Science.gov (United States)

    Andrusyszyn, M A; Cragg, C E; Humbert, J

    2001-04-01

    The relationships among multiple distance delivery methods, preferred learning style, content, and achievement was sought for primary care nurse practitioner students. A researcher-designed questionnaire was completed by 86 (71%) participants, while 6 engaged in follow-up interviews. The results of the study included: participants preferred learning by "considering the big picture"; "setting own learning plans"; and "focusing on concrete examples." Several positive associations were found: learning on own with learning by reading, and setting own learning plans; small group with learning through discussion; large group with learning new things through hearing and with having learning plans set by others. The most preferred method was print-based material and the least preferred method was audio tape. The most suited method for content included video teleconferencing for counseling, political action, and transcultural issues; and video tape for physical assessment. Convenience, self-direction, and timing of learning were more important than delivery method or learning style. Preferred order of learning was reading, discussing, observing, doing, and reflecting. Recommended considerations when designing distance courses include a mix of delivery methods, specific content, outcomes, learner characteristics, and state of technology.

  7. A Particle Swarm Optimization Variant with an Inner Variable Learning Strategy

    Directory of Open Access Journals (Sweden)

    Guohua Wu

    2014-01-01

    Full Text Available Although Particle Swarm Optimization (PSO has demonstrated competitive performance in solving global optimization problems, it exhibits some limitations when dealing with optimization problems with high dimensionality and complex landscape. In this paper, we integrate some problem-oriented knowledge into the design of a certain PSO variant. The resulting novel PSO algorithm with an inner variable learning strategy (PSO-IVL is particularly efficient for optimizing functions with symmetric variables. Symmetric variables of the optimized function have to satisfy a certain quantitative relation. Based on this knowledge, the inner variable learning (IVL strategy helps the particle to inspect the relation among its inner variables, determine the exemplar variable for all other variables, and then make each variable learn from the exemplar variable in terms of their quantitative relations. In addition, we design a new trap detection and jumping out strategy to help particles escape from local optima. The trap detection operation is employed at the level of individual particles whereas the trap jumping out strategy is adaptive in its nature. Experimental simulations completed for some representative optimization functions demonstrate the excellent performance of PSO-IVL. The effectiveness of the PSO-IVL stresses a usefulness of augmenting evolutionary algorithms by problem-oriented domain knowledge.

  8. Optimal control in microgrid using multi-agent reinforcement learning.

    Science.gov (United States)

    Li, Fu-Dong; Wu, Min; He, Yong; Chen, Xin

    2012-11-01

    This paper presents an improved reinforcement learning method to minimize electricity costs on the premise of satisfying the power balance and generation limit of units in a microgrid with grid-connected mode. Firstly, the microgrid control requirements are analyzed and the objective function of optimal control for microgrid is proposed. Then, a state variable "Average Electricity Price Trend" which is used to express the most possible transitions of the system is developed so as to reduce the complexity and randomicity of the microgrid, and a multi-agent architecture including agents, state variables, action variables and reward function is formulated. Furthermore, dynamic hierarchical reinforcement learning, based on change rate of key state variable, is established to carry out optimal policy exploration. The analysis shows that the proposed method is beneficial to handle the problem of "curse of dimensionality" and speed up learning in the unknown large-scale world. Finally, the simulation results under JADE (Java Agent Development Framework) demonstrate the validity of the presented method in optimal control for a microgrid with grid-connected mode. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

  9. Integrating Curriculum through the Learning Cycle: Content-Based Reading and Vocabulary Instruction

    Science.gov (United States)

    Spencer, Brenda H.; Guillaume, Andrea M.

    2006-01-01

    The content areas provide rich contexts for developing vocabulary. This article presents some principles and a lesson model--the learning cycle--that can be used to develop vocabulary while building understanding in science. Because science instruction and the learning cycle model promote learning in real-world contexts, they provide students with…

  10. Supervised learning of tools for content-based search of image databases

    Science.gov (United States)

    Delanoy, Richard L.

    1996-03-01

    A computer environment, called the Toolkit for Image Mining (TIM), is being developed with the goal of enabling users with diverse interests and varied computer skills to create search tools for content-based image retrieval and other pattern matching tasks. Search tools are generated using a simple paradigm of supervised learning that is based on the user pointing at mistakes of classification made by the current search tool. As mistakes are identified, a learning algorithm uses the identified mistakes to build up a model of the user's intentions, construct a new search tool, apply the search tool to a test image, display the match results as feedback to the user, and accept new inputs from the user. Search tools are constructed in the form of functional templates, which are generalized matched filters capable of knowledge- based image processing. The ability of this system to learn the user's intentions from experience contrasts with other existing approaches to content-based image retrieval that base searches on the characteristics of a single input example or on a predefined and semantically- constrained textual query. Currently, TIM is capable of learning spectral and textural patterns, but should be adaptable to the learning of shapes, as well. Possible applications of TIM include not only content-based image retrieval, but also quantitative image analysis, the generation of metadata for annotating images, data prioritization or data reduction in bandwidth-limited situations, and the construction of components for larger, more complex computer vision algorithms.

  11. Incorporation of Socio-scientific Content into Active Learning Activities

    Science.gov (United States)

    King, D. B.; Lewis, J. E.; Anderson, K.; Latch, D.; Sutheimer, S.; Webster, G.; Moog, R.

    2014-12-01

    Active learning has gained increasing support as an effective pedagogical technique to improve student learning. One way to promote active learning in the classroom is the use of in-class activities in place of lecturing. As part of an NSF-funded project, a set of in-class activities have been created that use climate change topics to teach chemistry content. These activities use the Process Oriented Guided Inquiry Learning (POGIL) methodology. In this pedagogical approach a set of models and a series of critical thinking questions are used to guide students through the introduction to or application of course content. Students complete the activities in their groups, with the faculty member as a facilitator of learning. Through assigned group roles and intentionally designed activity structure, process skills, such as teamwork, communication, and information processing, are developed during completion of the activity. Each of these climate change activities contains a socio-scientific component, e.g., social, ethical and economic data. In one activity, greenhouse gases are used to explain the concept of dipole moment. Data about natural and anthropogenic production rates, global warming potential and atmospheric lifetimes for a list of greenhouse gases are presented. The students are asked to identify which greenhouse gas they would regulate, with a corresponding explanation for their choice. They are also asked to identify the disadvantages of regulating the gas they chose in the previous question. In another activity, where carbon sequestration is used to demonstrate the utility of a phase diagram, students use economic and environmental data to choose the best location for sequestration. Too often discussions about climate change (both in and outside the classroom) consist of purely emotional responses. These activities force students to use data to support their arguments and hypothesize about what other data could be used in the corresponding discussion to

  12. Scaffolding the design of accessible eLearning content: a user-centered approach and cognitive perspective.

    Science.gov (United States)

    Catarci, Tiziana; De Giovanni, Loredana; Gabrielli, Silvia; Kimani, Stephen; Mirabella, Valeria

    2008-08-01

    There exist various guidelines for facilitating the design, preparation, and deployment of accessible eLearning applications and contents. However, such guidelines prevalently address accessibility in a rather technical sense, without giving sufficient consideration to the cognitive aspects and issues related to the use of eLearning materials by learners with disabilities. In this paper we describe how a user-centered design process was applied to develop a method and set of guidelines for didactical experts to scaffold their creation of accessible eLearning content, based on a more sound approach to accessibility. The paper also discusses possible design solutions for tools supporting eLearning content authors in the adoption and application of the proposed approach.

  13. An M-Learning Content Recommendation Service by Exploiting Mobile Social Interactions

    Science.gov (United States)

    Chao, Han-Chieh; Lai, Chin-Feng; Chen, Shih-Yeh; Huang, Yueh-Min

    2014-01-01

    With the rapid development of the Internet and the popularization of mobile devices, participating in a mobile community becomes a part of daily life. This study aims the influence impact of social interactions on mobile learning communities. With m-learning content recommendation services developed from mobile devices and mobile network…

  14. Machine learning a Bayesian and optimization perspective

    CERN Document Server

    Theodoridis, Sergios

    2015-01-01

    This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches, which rely on optimization techniques, as well as Bayesian inference, which is based on a hierarchy of probabilistic models. The book presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing and computer science. Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. The book builds carefully from the basic classical methods to the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as shor...

  15. An Analysis of Learning Activities in a Technology Education Textbook for Teachers : Learning Process Based on Contents Framework and Learning Scene to Develop Technological Literacy

    OpenAIRE

    Yata, Chikahiko; Hamamoto, Kengo; Oguri, Takenori

    2014-01-01

    This study analyzed the learning activities in a textbook on technology education for teachers, in order to examine the learning processes and learning scenes detailed therein. Results of analyzing learning process, primary learning activity found each contents framework. Other learning activities designated to be related to complementary in learning process. Results of analyzing learning scene, 14 learning scenes, among them "Scene to recognize the impact on social life and progress of techn...

  16. Learning through Hallmark People in the Content Areas

    Science.gov (United States)

    Ciecierski, Lisa M.; Bintz, William P.

    2018-01-01

    This article shares aspects to consider when designing and implementing content area instruction through the study of people. It begins with a description of an inquiry that investigated students' learning with the use of authentic literature and meaningful writing in social studies, and then extends to a discussion of how to apply these same…

  17. An Attentional Goldilocks Effect: An Optimal Amount of Social Interactivity Promotes Word Learning from Video.

    Science.gov (United States)

    Nussenbaum, Kate; Amso, Dima

    2016-01-01

    Television can be a powerful education tool; however, content-makers must understand the factors that engage attention and promote learning from screen media. Prior research suggests that social engagement is critical for learning and that interactivity may enhance the educational quality of children's media. The present study examined the effects of increasing the social interactivity of television on children's visual attention and word learning. Three- to 5-year-old ( M Age = 4;5 years, SD = 9 months) children completed a task in which they viewed videos of an actress teaching them the Swahili label for an on-screen image. Each child viewed these video clips in four conditions that parametrically manipulated social engagement and interactivity. We then tested whether each child had successfully learned the Swahili labels. Though 5-year-old children were able to learn words in all conditions, we found that there was an optimal level of social engagement that best supported learning for all participants, defined by engaging the child but not distracting from word labeling. Our eye-tracking data indicated that children in this condition spent more time looking at the target image and less time looking at the actress's face as compared to the most interactive condition. These findings suggest that social interactivity is critical to engaging attention and promoting learning from screen media up until a certain point, after which social stimuli may draw attention away from target images and impair children's word learning.

  18. Study on optimal fat content in total parenteral nutrition in partially hepatectomized rats.

    Science.gov (United States)

    Abe, S; Sakabe, S; Hirata, M; Kamuro, H; Asahara, N; Watanabe, M

    1997-04-01

    In order to investigate the optimal fat content for total parenteral nutrition (TPN) solutions, male Wistar rats were subjected to 70% hepatectomy and then placed, for five days, on one of five TPN regimens in which fat represented 0%, 10%, 20%, 30% and 40%, respectively, of the total calorie content. As serum triglyceride levels in the fat-treated groups were lower than those in the non-treated normal rats, it was concluded that the administered fat was sufficiently hydrolyzed. The greater the fat content, the higher the regeneration rate of the remnant liver. Significant differences were found between the 0%-fat group and 20%-plus fat groups. Hepatic triglyceride level was significantly lower in the 20%-fat group. Hepatic protein level was significantly elevated in all fat-treated groups. Serum phospholipids and total cholesterol due to the lecithin contained in fat emulsion were significantly elevated in the 30 and 40%-fat groups, indicating that fat content of 30 and 40% was excessive. The results suggest that TPN containing fat is superior to fat-free TPN for liver regeneration after partial hepatectomy, and that optimal fat content is estimated to be about 20% of total calorie content in the case of this fat emulsion.

  19. Optimization of information properties of NAA with respect to information content and profitability of results

    International Nuclear Information System (INIS)

    Obrusnik, I.; Eckschlager, K.

    1986-01-01

    Information properties of analytical results together with other important parameters especially economic ones can be used for the optimization of analytical procedures. Therefore, we have proposed a computational technique for the optimization of multielement neutron activation analysis (NAA) based on the information content and profitability. The optimization starts with the prediction of the γ-ray spectra to be expected during analysis under given experimental conditions (sample size, irradiation, decay and counting times etc.) and with the calculation of detection and determination limits. In the next step, the information contents for the determination of particular elements and for the simultaneous determination of element groups are computed. The information content depends or is closely connected with such properties of the method as selectivity, snesitivity, precision, accuracy and, as in the other cases of trace analysis, also with the detection limit. Then, the information profitability (IP) taking into account the information content and relevance (appreciation of specific information according to its contribution to the solution of a given problem) together wit economic aspects can be calculated. This function can be used for the optimization of a particular NAA procedure, for the mutual comparison of different variants of NAA and also for the comparison with other analytical methods. The use of information profitability for the optimization of NAA is shown on a practical example of the INAA analysis of urban particulate matter SRN 1648 produced by NBS (USA). (author)

  20. Symbiosis-Based Alternative Learning Multi-Swarm Particle Swarm Optimization.

    Science.gov (United States)

    Niu, Ben; Huang, Huali; Tan, Lijing; Duan, Qiqi

    2017-01-01

    Inspired by the ideas from the mutual cooperation of symbiosis in natural ecosystem, this paper proposes a new variant of PSO, named Symbiosis-based Alternative Learning Multi-swarm Particle Swarm Optimization (SALMPSO). A learning probability to select one exemplar out of the center positions, the local best position, and the historical best position including the experience of internal and external multiple swarms, is used to keep the diversity of the population. Two different levels of social interaction within and between multiple swarms are proposed. In the search process, particles not only exchange social experience with others that are from their own sub-swarms, but also are influenced by the experience of particles from other fellow sub-swarms. According to the different exemplars and learning strategy, this model is instantiated as four variants of SALMPSO and a set of 15 test functions are conducted to compare with some variants of PSO including 10, 30 and 50 dimensions, respectively. Experimental results demonstrate that the alternative learning strategy in each SALMPSO version can exhibit better performance in terms of the convergence speed and optimal values on most multimodal functions in our simulation.

  1. A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors.

    Science.gov (United States)

    Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

    2017-09-21

    In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors.

  2. When increasing distraction helps learning: Distractor number and content interact in their effects on memory.

    Science.gov (United States)

    Nussenbaum, Kate; Amso, Dima; Markant, Julie

    2017-11-01

    Previous work has demonstrated that increasing the number of distractors in a search array can reduce interference from distractor content during target processing. However, it is unclear how this reduced interference influences learning of target information. Here, we investigated how varying the amount and content of distraction present in a learning environment affects visual search and subsequent memory for target items. In two experiments, we demonstrate that the number and content of competing distractors interact in their influence on target selection and memory. Specifically, while increasing the number of distractors present in a search array made target detection more effortful, it did not impair learning and memory for target content. Instead, when the distractors contained category information that conflicted with the target, increasing the number of distractors from one to three actually benefitted learning and memory. These data suggest that increasing numbers of distractors may reduce interference from conflicting conceptual information during encoding.

  3. ICT-supported language learning tools for Chinese as a foreign Language: a content review

    Directory of Open Access Journals (Sweden)

    Tina Čok

    2016-06-01

    Full Text Available The paper presents a meta-analysis of 37 scientific papers dealing with the use and adoption of ICT for learning and teaching Chinese as a foreign language. It has shown that systematic content reviews providing overall insight into the nature and level of development in the field are rare. The author tries to fill this content gap by answering three research questions: 1 What is the overall state of research in the field of ICT-assisted learning of CFL in terms of language teaching methods? 2 Which learning technologies are in use for the specific teaching and learning methods for Chinese as a foreign language? 3 Are some learning technologies used more often for practis ng specific language skills than others?

  4. Optimal design of planar slider-crank mechanism using teaching-learning-based optimization algorithm

    International Nuclear Information System (INIS)

    Chaudhary, Kailash; Chaudhary, Himanshu

    2015-01-01

    In this paper, a two stage optimization technique is presented for optimum design of planar slider-crank mechanism. The slider crank mechanism needs to be dynamically balanced to reduce vibrations and noise in the engine and to improve the vehicle performance. For dynamic balancing, minimization of the shaking force and the shaking moment is achieved by finding optimum mass distribution of crank and connecting rod using the equipemental system of point-masses in the first stage of the optimization. In the second stage, their shapes are synthesized systematically by closed parametric curve, i.e., cubic B-spline curve corresponding to the optimum inertial parameters found in the first stage. The multi-objective optimization problem to minimize both the shaking force and the shaking moment is solved using Teaching-learning-based optimization algorithm (TLBO) and its computational performance is compared with Genetic algorithm (GA).

  5. Optimal design of planar slider-crank mechanism using teaching-learning-based optimization algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Chaudhary, Kailash; Chaudhary, Himanshu [Malaviya National Institute of Technology, Jaipur (Malaysia)

    2015-11-15

    In this paper, a two stage optimization technique is presented for optimum design of planar slider-crank mechanism. The slider crank mechanism needs to be dynamically balanced to reduce vibrations and noise in the engine and to improve the vehicle performance. For dynamic balancing, minimization of the shaking force and the shaking moment is achieved by finding optimum mass distribution of crank and connecting rod using the equipemental system of point-masses in the first stage of the optimization. In the second stage, their shapes are synthesized systematically by closed parametric curve, i.e., cubic B-spline curve corresponding to the optimum inertial parameters found in the first stage. The multi-objective optimization problem to minimize both the shaking force and the shaking moment is solved using Teaching-learning-based optimization algorithm (TLBO) and its computational performance is compared with Genetic algorithm (GA).

  6. Influences of Multimedia Lesson Contents On Effective Learning

    Directory of Open Access Journals (Sweden)

    Tuncay Yavuz Ozdemir

    2013-11-01

    Full Text Available In the information era that we experience today, there is a rapid change in the methods, techniques and materials used for education and teaching. The usage of information and communication technology-assisted teaching materials are becoming more commonplace. Parallel to these developments, the Ministry of National Education took steps to develop IT substructures of all schools in the country and implemented many projects. The purpose of this study is to determine whether or not the multimedia lesson content used by teachers affect effective learning. This study is a qualitative study, conducted with 45 teachers working in primary schools during the 2011-2012 academic year. According to the study findings, participants believe that using multimedia lesson content during lectures increases student motivation, makes students more curious and interested, and think that using multimedia lesson content has positive effects.

  7. Using Selective Redundancy and Testing to Optimize Learning from Multimedia Lessons

    OpenAIRE

    Yue, Carole Leigh

    2014-01-01

    Multimedia learning refers to learning from a combination of words and images. In the present dissertation, a multimedia lesson is defined as an animated, narrated educational video that depicts a scientific process--a format of instructional material becoming increasingly common in online, hybrid, and traditional classrooms. The overarching goal of the present research was to investigate how to optimize learning from multimedia lessons using two related theories of multimedia learning (the...

  8. Strategic and Organisational Considerations in Planning Content and Language Integrated Learning: A Study on the Coordination between Content and Language Teachers

    Science.gov (United States)

    Pavón Vázquez, Víctor; Ávila López, Javier; Gallego Segador, Arturo; Espejo Mohedano, Roberto

    2015-01-01

    Content and language integrated learning (CLIL) is generally recognised as a fruitful example of bilingual education. However, success in CLIL may not be straightforward and may require the establishment of coordination between content and language teachers. The aim of this study is to investigate if content and language teachers are able to plan…

  9. The influence of inquiry learning model on additives theme with ethnoscience content to cultural awareness of students

    Science.gov (United States)

    Sudarmin, S.; Selia, E.; Taufiq, M.

    2018-03-01

    The purpose of this research is to determine the influence of inquiry learning model on additives theme with ethnoscience content to cultural awareness of students and how the students’ responses to learning. The method applied in this research is a quasi-experimental with non-equivalent control group design. The sampling technique applied in this research is the technique of random sampling. The samples were eight grade students of one of junior high schools in Semarang. The results of this research were (1) thestudents’ cultural awareness of the experiment class is better than the control class (2) inquiry learning model with ethnoscience content strongly influencing the cultural awareness of students by 78% and (3) students gave positive responses to inquiry learning model with ethnoscience content. The conclusions of this research are inquiry-learning model with ethnoscience content has positive influence on students’ cultural awareness.

  10. Synergetic motor control paradigm for optimizing energy efficiency of multijoint reaching via tacit learning.

    Science.gov (United States)

    Hayashibe, Mitsuhiro; Shimoda, Shingo

    2014-01-01

    A human motor system can improve its behavior toward optimal movement. The skeletal system has more degrees of freedom than the task dimensions, which incurs an ill-posed problem. The multijoint system involves complex interaction torques between joints. To produce optimal motion in terms of energy consumption, the so-called cost function based optimization has been commonly used in previous works.Even if it is a fact that an optimal motor pattern is employed phenomenologically, there is no evidence that shows the existence of a physiological process that is similar to such a mathematical optimization in our central nervous system.In this study, we aim to find a more primitive computational mechanism with a modular configuration to realize adaptability and optimality without prior knowledge of system dynamics.We propose a novel motor control paradigm based on tacit learning with task space feedback. The motor command accumulation during repetitive environmental interactions, play a major role in the learning process. It is applied to a vertical cyclic reaching which involves complex interaction torques.We evaluated whether the proposed paradigm can learn how to optimize solutions with a 3-joint, planar biomechanical model. The results demonstrate that the proposed method was valid for acquiring motor synergy and resulted in energy efficient solutions for different load conditions. The case in feedback control is largely affected by the interaction torques. In contrast, the trajectory is corrected over time with tacit learning toward optimal solutions.Energy efficient solutions were obtained by the emergence of motor synergy. During learning, the contribution from feedforward controller is augmented and the one from the feedback controller is significantly minimized down to 12% for no load at hand, 16% for a 0.5 kg load condition.The proposed paradigm could provide an optimization process in redundant system with dynamic-model-free and cost-function-free approach.

  11. Synergetic motor control paradigm for optimizing energy efficiency of multijoint reaching via tacit learning

    Science.gov (United States)

    Hayashibe, Mitsuhiro; Shimoda, Shingo

    2014-01-01

    A human motor system can improve its behavior toward optimal movement. The skeletal system has more degrees of freedom than the task dimensions, which incurs an ill-posed problem. The multijoint system involves complex interaction torques between joints. To produce optimal motion in terms of energy consumption, the so-called cost function based optimization has been commonly used in previous works.Even if it is a fact that an optimal motor pattern is employed phenomenologically, there is no evidence that shows the existence of a physiological process that is similar to such a mathematical optimization in our central nervous system.In this study, we aim to find a more primitive computational mechanism with a modular configuration to realize adaptability and optimality without prior knowledge of system dynamics.We propose a novel motor control paradigm based on tacit learning with task space feedback. The motor command accumulation during repetitive environmental interactions, play a major role in the learning process. It is applied to a vertical cyclic reaching which involves complex interaction torques.We evaluated whether the proposed paradigm can learn how to optimize solutions with a 3-joint, planar biomechanical model. The results demonstrate that the proposed method was valid for acquiring motor synergy and resulted in energy efficient solutions for different load conditions. The case in feedback control is largely affected by the interaction torques. In contrast, the trajectory is corrected over time with tacit learning toward optimal solutions.Energy efficient solutions were obtained by the emergence of motor synergy. During learning, the contribution from feedforward controller is augmented and the one from the feedback controller is significantly minimized down to 12% for no load at hand, 16% for a 0.5 kg load condition.The proposed paradigm could provide an optimization process in redundant system with dynamic-model-free and cost-function-free approach

  12. Promoting autonomous learning in English through the implementation of Content and Language Integrated Learning (CLIL in science and maths subjects

    Directory of Open Access Journals (Sweden)

    Andriani Putu Fika

    2018-01-01

    Full Text Available Autonomous learning is a concept in which the learner has the ability to take charge of their own learning. It becomes a notable aspect that should be perceived by students. The aim of this research is for finding out the strategies used by grade two teachers in Bali Kiddy Primary School to promote autonomous learning in English through the implementation of Content and Language Integrated Learning in science and maths subjects. This study was designed in the form of descriptive qualitative study. The data were collected through observation, interview, and document study. The result of the study shows that there are some strategies of promoting autonomous learning in English through the implementation of CLIL in Science and Maths subjects. Those strategies are table of content training, questioning & presenting, journal writing, choosing activities, and using online activity. Those strategies can be adopted or even adapted as the way to promote autonomous learning in English subject.

  13. Electronic Dictionary as a Tool for Integration of Additional Learning Content

    Directory of Open Access Journals (Sweden)

    Stefka Kovacheva

    2015-12-01

    Full Text Available Electronic Dictionary as a Tool for Integration of Additional Learning Content This article discusses electronic dictionary as an element of the „Bulgarian cultural and historical heritage under the protection of UNESCO” database developed in IMI (BAS, that will be used to integrate additional learning content. The electronic dictionary is described as an easily accessible book of reference, offering information to the shape, meaning, usage and the origin of words in connection to the cultural-historical heritage sites in Bulgaria, protected by UNESCO. The dictionary targets 9–11 year old students from Bulgarian schools, who study the subjects “Man and Society” in 4th grade and “History and Civilization” in 5th grade.

  14. Runtime Optimizations for Tree-Based Machine Learning Models

    NARCIS (Netherlands)

    N. Asadi; J.J.P. Lin (Jimmy); A.P. de Vries (Arjen)

    2014-01-01

    htmlabstractTree-based models have proven to be an effective solution for web ranking as well as other machine learning problems in diverse domains. This paper focuses on optimizing the runtime performance of applying such models to make predictions, specifically using gradient-boosted regression

  15. Optimization and validation of Folin-Ciocalteu method for the determination of total polyphenol content of Pu-erh tea.

    Science.gov (United States)

    Musci, Marilena; Yao, Shicong

    2017-12-01

    Pu-erh tea is a post-fermented tea that has recently gained popularity worldwide, due to potential health benefits related to the antioxidant activity resulting from its high polyphenolic content. The Folin-Ciocalteu method is a simple, rapid, and inexpensive assay widely applied for the determination of total polyphenol content. Over the past years, it has been subjected to many modifications, often without any systematic optimization or validation. In our study, we sought to optimize the Folin-Ciocalteu method, evaluate quality parameters including linearity, precision and stability, and then apply the optimized model to determine the total polyphenol content of 57 Chinese teas, including green tea, aged and ripened Pu-erh tea. Our optimized Folin-Ciocalteu method reduced analysis time, allowed for the analysis of a large number of samples, to discriminate among the different teas, and to assess the effect of the post-fermentation process on polyphenol content.

  16. Comparing Efficiency of Web Based Learning Contents on Different Media

    Directory of Open Access Journals (Sweden)

    Julija Lapuh Bele

    2009-11-01

    Full Text Available The purpose of the research was to find out what kind of multimedia learning materials gave the most efficient and effective results with regards to learning time and knowledge gained. Different web based learning materials were used as regards presentation mode: static pictures, animations with online text and animations with narrated text. Although the research results showed that learners from WBL contents with static graphics learnt less time than learners from animations, we did not find significant differences in learning time between experimental groups. However, we proved significant differences between three experimental groups in terms of gained knowledge. The learners using learning materials with static graphics performed worse than learners using materials with animations. Furthermore, we did not prove significant differences in gained knowledge between groups that learnt from audio animations and the animations with online text.

  17. Improving the quality of learning in science through optimization of lesson study for learning community

    Science.gov (United States)

    Setyaningsih, S.

    2018-03-01

    Lesson Study for Learning Community is one of lecturer profession building system through collaborative and continuous learning study based on the principles of openness, collegiality, and mutual learning to build learning community in order to form professional learning community. To achieve the above, we need a strategy and learning method with specific subscription technique. This paper provides a description of how the quality of learning in the field of science can be improved by implementing strategies and methods accordingly, namely by applying lesson study for learning community optimally. Initially this research was focused on the study of instructional techniques. Learning method used is learning model Contextual teaching and Learning (CTL) and model of Problem Based Learning (PBL). The results showed that there was a significant increase in competence, attitudes, and psychomotor in the four study programs that were modelled. Therefore, it can be concluded that the implementation of learning strategies in Lesson study for Learning Community is needed to be used to improve the competence, attitude and psychomotor of science students.

  18. Using Technology to Support Experiential Learning in Extension Nutrition and Health Programs

    Science.gov (United States)

    Schuster, Ellen

    2013-01-01

    Much has been written about hybrid or blended learning in K-12 and higher education. In hybrid, or blended learning, face-to-face and online delivery of content are provided. The challenge is how best to use each delivery mode to optimize learning. For example, students may view videos or other multimedia content outside of class, with class time…

  19. Implementation of Chaotic Gaussian Particle Swarm Optimization for Optimize Learning-to-Rank Software Defect Prediction Model Construction

    Science.gov (United States)

    Buchari, M. A.; Mardiyanto, S.; Hendradjaya, B.

    2018-03-01

    Finding the existence of software defect as early as possible is the purpose of research about software defect prediction. Software defect prediction activity is required to not only state the existence of defects, but also to be able to give a list of priorities which modules require a more intensive test. Therefore, the allocation of test resources can be managed efficiently. Learning to rank is one of the approach that can provide defect module ranking data for the purposes of software testing. In this study, we propose a meta-heuristic chaotic Gaussian particle swarm optimization to improve the accuracy of learning to rank software defect prediction approach. We have used 11 public benchmark data sets as experimental data. Our overall results has demonstrated that the prediction models construct using Chaotic Gaussian Particle Swarm Optimization gets better accuracy on 5 data sets, ties in 5 data sets and gets worse in 1 data sets. Thus, we conclude that the application of Chaotic Gaussian Particle Swarm Optimization in Learning-to-Rank approach can improve the accuracy of the defect module ranking in data sets that have high-dimensional features.

  20. Content-based retrieval of brain tumor in contrast-enhanced MRI images using tumor margin information and learned distance metric.

    Science.gov (United States)

    Yang, Wei; Feng, Qianjin; Yu, Mei; Lu, Zhentai; Gao, Yang; Xu, Yikai; Chen, Wufan

    2012-11-01

    A content-based image retrieval (CBIR) method for T1-weighted contrast-enhanced MRI (CE-MRI) images of brain tumors is presented for diagnosis aid. The method is thoroughly evaluated on a large image dataset. Using the tumor region as a query, the authors' CBIR system attempts to retrieve tumors of the same pathological category. Aside from commonly used features such as intensity, texture, and shape features, the authors use a margin information descriptor (MID), which is capable of describing the characteristics of tissue surrounding a tumor, for representing image contents. In addition, the authors designed a distance metric learning algorithm called Maximum mean average Precision Projection (MPP) to maximize the smooth approximated mean average precision (mAP) to optimize retrieval performance. The effectiveness of MID and MPP algorithms was evaluated using a brain CE-MRI dataset consisting of 3108 2D scans acquired from 235 patients with three categories of brain tumors (meningioma, glioma, and pituitary tumor). By combining MID and other features, the mAP of retrieval increased by more than 6% with the learned distance metrics. The distance metric learned by MPP significantly outperformed the other two existing distance metric learning methods in terms of mAP. The CBIR system using the proposed strategies achieved a mAP of 87.3% and a precision of 89.3% when top 10 images were returned by the system. Compared with scale-invariant feature transform, the MID, which uses the intensity profile as descriptor, achieves better retrieval performance. Incorporating tumor margin information represented by MID with the distance metric learned by the MPP algorithm can substantially improve the retrieval performance for brain tumors in CE-MRI.

  1. Optimal robustness of supervised learning from a noniterative point of view

    Science.gov (United States)

    Hu, Chia-Lun J.

    1995-08-01

    In most artificial neural network applications, (e.g. pattern recognition) if the dimension of the input vectors is much larger than the number of patterns to be recognized, generally, a one- layered, hard-limited perceptron is sufficient to do the recognition job. As long as the training input-output mapping set is numerically given, and as long as this given set satisfies a special linear-independency relation, the connection matrix to meet the supervised learning requirements can be solved by a noniterative, one-step, algebra method. The learning of this noniterative scheme is very fast (close to real-time learning) because the learning is one-step and noniterative. The recognition of the untrained patterns is very robust because a universal geometrical optimization process of selecting the solution can be applied to the learning process. This paper reports the theoretical foundation of this noniterative learning scheme and focuses the result at the optimal robustness analysis. A real-time character recognition scheme is then designed along this line. This character recognition scheme will be used (in a movie presentation) to demonstrate the experimental results of some theoretical parts reported in this paper.

  2. Language Alternation and Language Norm in Vocational Content and Language Integrated Learning

    Science.gov (United States)

    Kontio, Janne; Sylvén, Liss Kerstin

    2015-01-01

    The present article deals with language choice as communicative strategies in the language learning environment of an English-medium content and language integrated learning (CLIL) workshop at an auto mechanics class in a Swedish upper secondary school. The article presents the organisation and functions of language alternations (LAs) which are…

  3. Content-Based VLE Designs Improve Learning Efficiency in Constructivist Statistics Education

    Science.gov (United States)

    Wessa, Patrick; De Rycker, Antoon; Holliday, Ian Edward

    2011-01-01

    Background We introduced a series of computer-supported workshops in our undergraduate statistics courses, in the hope that it would help students to gain a deeper understanding of statistical concepts. This raised questions about the appropriate design of the Virtual Learning Environment (VLE) in which such an approach had to be implemented. Therefore, we investigated two competing software design models for VLEs. In the first system, all learning features were a function of the classical VLE. The second system was designed from the perspective that learning features should be a function of the course's core content (statistical analyses), which required us to develop a specific–purpose Statistical Learning Environment (SLE) based on Reproducible Computing and newly developed Peer Review (PR) technology. Objectives The main research question is whether the second VLE design improved learning efficiency as compared to the standard type of VLE design that is commonly used in education. As a secondary objective we provide empirical evidence about the usefulness of PR as a constructivist learning activity which supports non-rote learning. Finally, this paper illustrates that it is possible to introduce a constructivist learning approach in large student populations, based on adequately designed educational technology, without subsuming educational content to technological convenience. Methods Both VLE systems were tested within a two-year quasi-experiment based on a Reliable Nonequivalent Group Design. This approach allowed us to draw valid conclusions about the treatment effect of the changed VLE design, even though the systems were implemented in successive years. The methodological aspects about the experiment's internal validity are explained extensively. Results The effect of the design change is shown to have substantially increased the efficiency of constructivist, computer-assisted learning activities for all cohorts of the student population under

  4. A deep learning and novelty detection framework for rapid phenotyping in high-content screening

    Science.gov (United States)

    Sommer, Christoph; Hoefler, Rudolf; Samwer, Matthias; Gerlich, Daniel W.

    2017-01-01

    Supervised machine learning is a powerful and widely used method for analyzing high-content screening data. Despite its accuracy, efficiency, and versatility, supervised machine learning has drawbacks, most notably its dependence on a priori knowledge of expected phenotypes and time-consuming classifier training. We provide a solution to these limitations with CellCognition Explorer, a generic novelty detection and deep learning framework. Application to several large-scale screening data sets on nuclear and mitotic cell morphologies demonstrates that CellCognition Explorer enables discovery of rare phenotypes without user training, which has broad implications for improved assay development in high-content screening. PMID:28954863

  5. A Fast Optimization Method for General Binary Code Learning.

    Science.gov (United States)

    Shen, Fumin; Zhou, Xiang; Yang, Yang; Song, Jingkuan; Shen, Heng; Tao, Dacheng

    2016-09-22

    Hashing or binary code learning has been recognized to accomplish efficient near neighbor search, and has thus attracted broad interests in recent retrieval, vision and learning studies. One main challenge of learning to hash arises from the involvement of discrete variables in binary code optimization. While the widely-used continuous relaxation may achieve high learning efficiency, the pursued codes are typically less effective due to accumulated quantization error. In this work, we propose a novel binary code optimization method, dubbed Discrete Proximal Linearized Minimization (DPLM), which directly handles the discrete constraints during the learning process. Specifically, the discrete (thus nonsmooth nonconvex) problem is reformulated as minimizing the sum of a smooth loss term with a nonsmooth indicator function. The obtained problem is then efficiently solved by an iterative procedure with each iteration admitting an analytical discrete solution, which is thus shown to converge very fast. In addition, the proposed method supports a large family of empirical loss functions, which is particularly instantiated in this work by both a supervised and an unsupervised hashing losses, together with the bits uncorrelation and balance constraints. In particular, the proposed DPLM with a supervised `2 loss encodes the whole NUS-WIDE database into 64-bit binary codes within 10 seconds on a standard desktop computer. The proposed approach is extensively evaluated on several large-scale datasets and the generated binary codes are shown to achieve very promising results on both retrieval and classification tasks.

  6. Design Optimization of Mechanical Components Using an Enhanced Teaching-Learning Based Optimization Algorithm with Differential Operator

    Directory of Open Access Journals (Sweden)

    B. Thamaraikannan

    2014-01-01

    Full Text Available This paper studies in detail the background and implementation of a teaching-learning based optimization (TLBO algorithm with differential operator for optimization task of a few mechanical components, which are essential for most of the mechanical engineering applications. Like most of the other heuristic techniques, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. A differential operator is incorporated into the TLBO for effective search of better solutions. To validate the effectiveness of the proposed method, three typical optimization problems are considered in this research: firstly, to optimize the weight in a belt-pulley drive, secondly, to optimize the volume in a closed coil helical spring, and finally to optimize the weight in a hollow shaft. have been demonstrated. Simulation result on the optimization (mechanical components problems reveals the ability of the proposed methodology to find better optimal solutions compared to other optimization algorithms.

  7. Reinforcement learning for optimal control of low exergy buildings

    International Nuclear Information System (INIS)

    Yang, Lei; Nagy, Zoltan; Goffin, Philippe; Schlueter, Arno

    2015-01-01

    Highlights: • Implementation of reinforcement learning control for LowEx Building systems. • Learning allows adaptation to local environment without prior knowledge. • Presentation of reinforcement learning control for real-life applications. • Discussion of the applicability for real-life situations. - Abstract: Over a third of the anthropogenic greenhouse gas (GHG) emissions stem from cooling and heating buildings, due to their fossil fuel based operation. Low exergy building systems are a promising approach to reduce energy consumption as well as GHG emissions. They consists of renewable energy technologies, such as PV, PV/T and heat pumps. Since careful tuning of parameters is required, a manual setup may result in sub-optimal operation. A model predictive control approach is unnecessarily complex due to the required model identification. Therefore, in this work we present a reinforcement learning control (RLC) approach. The studied building consists of a PV/T array for solar heat and electricity generation, as well as geothermal heat pumps. We present RLC for the PV/T array, and the full building model. Two methods, Tabular Q-learning and Batch Q-learning with Memory Replay, are implemented with real building settings and actual weather conditions in a Matlab/Simulink framework. The performance is evaluated against standard rule-based control (RBC). We investigated different neural network structures and find that some outperformed RBC already during the learning phase. Overall, every RLC strategy for PV/T outperformed RBC by over 10% after the third year. Likewise, for the full building, RLC outperforms RBC in terms of meeting the heating demand, maintaining the optimal operation temperature and compensating more effectively for ground heat. This allows to reduce engineering costs associated with the setup of these systems, as well as decrease the return-of-invest period, both of which are necessary to create a sustainable, zero-emission building

  8. Comparative performance of an elitist teaching-learning-based optimization algorithm for solving unconstrained optimization problems

    Directory of Open Access Journals (Sweden)

    R. Venkata Rao

    2013-01-01

    Full Text Available Teaching-Learning-based optimization (TLBO is a recently proposed population based algorithm, which simulates the teaching-learning process of the class room. This algorithm requires only the common control parameters and does not require any algorithm-specific control parameters. In this paper, the effect of elitism on the performance of the TLBO algorithm is investigated while solving unconstrained benchmark problems. The effects of common control parameters such as the population size and the number of generations on the performance of the algorithm are also investigated. The proposed algorithm is tested on 76 unconstrained benchmark functions with different characteristics and the performance of the algorithm is compared with that of other well known optimization algorithms. A statistical test is also performed to investigate the results obtained using different algorithms. The results have proved the effectiveness of the proposed elitist TLBO algorithm.

  9. Homepage to distribute the anatomy learning contents including Visible Korean products, comics, and books.

    Science.gov (United States)

    Chung, Beom Sun; Chung, Min Suk

    2018-03-01

    The authors have operated the homepage (http://anatomy.co.kr) to provide the learning contents of anatomy. From the homepage, sectioned images, volume models, and surface models-all Visible Korean products-can be downloaded. The realistic images can be interactively manipulated, which will give rise to the interest in anatomy. The various anatomy comics (learning comics, comic strips, plastination comics, etc.) are approachable. Visitors can obtain the regional anatomy book with concise contents, mnemonics, and schematics as well as the simplified dissection manual and the pleasant anatomy essay. Medical students, health allied professional students, and even laypeople are expected to utilize the easy and comforting anatomy contents. It is hoped that other anatomists successively produce and distribute their own informative contents.

  10. Homepage to distribute the anatomy learning contents including Visible Korean products, comics, and books

    Science.gov (United States)

    Chung, Beom Sun

    2018-01-01

    The authors have operated the homepage (http://anatomy.co.kr) to provide the learning contents of anatomy. From the homepage, sectioned images, volume models, and surface models—all Visible Korean products—can be downloaded. The realistic images can be interactively manipulated, which will give rise to the interest in anatomy. The various anatomy comics (learning comics, comic strips, plastination comics, etc.) are approachable. Visitors can obtain the regional anatomy book with concise contents, mnemonics, and schematics as well as the simplified dissection manual and the pleasant anatomy essay. Medical students, health allied professional students, and even laypeople are expected to utilize the easy and comforting anatomy contents. It is hoped that other anatomists successively produce and distribute their own informative contents. PMID:29644104

  11. E-Learning Content Design Standards Based on Interactive Digital Concepts Maps in the Light of Meaningful and Constructivist Learning Theory

    Science.gov (United States)

    Afify, Mohammed Kamal

    2018-01-01

    The present study aims to identify standards of interactive digital concepts maps design and their measurement indicators as a tool to develop, organize and administer e-learning content in the light of Meaningful Learning Theory and Constructivist Learning Theory. To achieve the objective of the research, the author prepared a list of E-learning…

  12. Learning Based Approach for Optimal Clustering of Distributed Program's Call Flow Graph

    Science.gov (United States)

    Abofathi, Yousef; Zarei, Bager; Parsa, Saeed

    Optimal clustering of call flow graph for reaching maximum concurrency in execution of distributable components is one of the NP-Complete problems. Learning automatas (LAs) are search tools which are used for solving many NP-Complete problems. In this paper a learning based algorithm is proposed to optimal clustering of call flow graph and appropriate distributing of programs in network level. The algorithm uses learning feature of LAs to search in state space. It has been shown that the speed of reaching to solution increases remarkably using LA in search process, and it also prevents algorithm from being trapped in local minimums. Experimental results show the superiority of proposed algorithm over others.

  13. Memory reactivation during rest supports upcoming learning of related content

    Science.gov (United States)

    Schlichting, Margaret L.; Preston, Alison R.

    2014-01-01

    Although a number of studies have highlighted the importance of offline processes for memory, how these mechanisms influence future learning remains unknown. Participants with established memories for a set of initial face–object associations were scanned during passive rest and during encoding of new related and unrelated pairs of objects. Spontaneous reactivation of established memories and enhanced hippocampal–neocortical functional connectivity during rest was related to better subsequent learning, specifically of related content. Moreover, the degree of functional coupling during rest was predictive of neural engagement during the new learning experience itself. These results suggest that through rest-phase reactivation and hippocampal–neocortical interactions, existing memories may come to facilitate encoding during subsequent related episodes. PMID:25331890

  14. Memory reactivation during rest supports upcoming learning of related content.

    Science.gov (United States)

    Schlichting, Margaret L; Preston, Alison R

    2014-11-04

    Although a number of studies have highlighted the importance of offline processes for memory, how these mechanisms influence future learning remains unknown. Participants with established memories for a set of initial face-object associations were scanned during passive rest and during encoding of new related and unrelated pairs of objects. Spontaneous reactivation of established memories and enhanced hippocampal-neocortical functional connectivity during rest was related to better subsequent learning, specifically of related content. Moreover, the degree of functional coupling during rest was predictive of neural engagement during the new learning experience itself. These results suggest that through rest-phase reactivation and hippocampal-neocortical interactions, existing memories may come to facilitate encoding during subsequent related episodes.

  15. Cooperative Learning in Turkey: A Content Analysis of Theses

    Science.gov (United States)

    Dirlikli, Murat

    2016-01-01

    This study is a content analysis of theses concerning cooperative learning prepared in Turkey between the years 1993 and 2014. A total of 220 theses which were accessible online (open access) at the site of Council of Higher Education (CoHE) were analyzed. The publishing classification form used in this study was prepared analyzing similar forms…

  16. Attending Globally or Locally: Incidental Learning of Optimal Visual Attention Allocation

    Science.gov (United States)

    Beck, Melissa R.; Goldstein, Rebecca R.; van Lamsweerde, Amanda E.; Ericson, Justin M.

    2018-01-01

    Attention allocation determines the information that is encoded into memory. Can participants learn to optimally allocate attention based on what types of information are most likely to change? The current study examined whether participants could incidentally learn that changes to either high spatial frequency (HSF) or low spatial frequency (LSF)…

  17. Optimizing Knowledge Sharing in Learning Networks through Peer Tutoring

    NARCIS (Netherlands)

    Hsiao, Amy; Brouns, Francis; Kester, Liesbeth; Sloep, Peter

    2009-01-01

    Hsiao, Y. P., Brouns, F., Kester, L., & Sloep, P. (2009). Optimizing Knowledge Sharing in Learning Networks through Peer Tutoring. Presentation at the IADIS international conference on Cognition and Exploratory in Digital Age (CELDA 2009). November, 20-22, 2009, Rome, Italy.

  18. Road Artery Traffic Light Optimization with Use of the Reinforcement Learning

    Directory of Open Access Journals (Sweden)

    Rok Marsetič

    2014-04-01

    Full Text Available The basic principle of optimal traffic control is the appropriate real-time response to dynamic traffic flow changes. Signal plan efficiency depends on a large number of input parameters. An actuated signal system can adjust very well to traffic conditions, but cannot fully adjust to stochastic traffic volume oscillation. Due to the complexity of the problem analytical methods are not applicable for use in real time, therefore the purpose of this paper is to introduce heuristic method suitable for traffic light optimization in real time. With the evolution of artificial intelligence new possibilities for solving complex problems have been introduced. The goal of this paper is to demonstrate that the use of the Q learning algorithm for traffic lights optimization is suitable. The Q learning algorithm was verified on a road artery with three intersections. For estimation of the effectiveness and efficiency of the proposed algorithm comparison with an actuated signal plan was carried out. The results (average delay per vehicle and the number of vehicles that left road network show that Q learning algorithm outperforms the actuated signal controllers. The proposed algorithm converges to the minimal delay per vehicle regardless of the stochastic nature of traffic. In this research the impact of the model parameters (learning rate, exploration rate, influence of communication between agents and reward type on algorithm effectiveness were analysed as well.

  19. Optimizing classroom instruction through self-paced learning prototype

    Directory of Open Access Journals (Sweden)

    Romiro Gordo Bautista

    2015-09-01

    Full Text Available This study investigated the learning impact of self-paced learning prototype in optimizing classroom instruction towards students’ learning in Chemistry. Two sections of 64 Laboratory High School students in Chemistry were used as subjects of the study. The Quasi-Experimental and Correlation Research Design was used in the study: a pre-test was conducted, scored and analyzed which served as the basis in determining the initial learning schema of the respondents. A questionnaire was adopted to find the learning motivation of the students in science. Using Pearson-r correlation, it was found out that there is a highly significant relationship between their internal drive and their academic performance. Moreover, a post-test was conducted after self-paced learning prototype was used in the development of select topics in their curricular plot. It was found out that the students who experienced the self-paced learning prototype performed better in their academic performance as evidenced by the difference of their mean post-test results. ANCOVA results on the post-test mean scores of the respondents were utilized in establishing the causal-effect of the learning prototype to the academic performance of the students in Chemistry. A highly significant effect on their academic performance (R-square value of 70.7% and significant interaction of the models to the experimental grouping and mental abilities of the respondents are concluded in the study.

  20. Research into Financial Position of Listed Companies following Classification via Extreme Learning Machine Based upon DE Optimization

    OpenAIRE

    Fu Yu; Mu Jiong; Duan Xu Liang

    2016-01-01

    By means of the model of extreme learning machine based upon DE optimization, this article particularly centers on the optimization thinking of such a model as well as its application effect in the field of listed company’s financial position classification. It proves that the improved extreme learning machine algorithm based upon DE optimization eclipses the traditional extreme learning machine algorithm following comparison. Meanwhile, this article also intends to introduce certain research...

  1. Iterative learning control an optimization paradigm

    CERN Document Server

    Owens, David H

    2016-01-01

    This book develops a coherent theoretical approach to algorithm design for iterative learning control based on the use of optimization concepts. Concentrating initially on linear, discrete-time systems, the author gives the reader access to theories based on either signal or parameter optimization. Although the two approaches are shown to be related in a formal mathematical sense, the text presents them separately because their relevant algorithm design issues are distinct and give rise to different performance capabilities. Together with algorithm design, the text demonstrates that there are new algorithms that are capable of incorporating input and output constraints, enable the algorithm to reconfigure systematically in order to meet the requirements of different reference signals and also to support new algorithms for local convergence of nonlinear iterative control. Simulation and application studies are used to illustrate algorithm properties and performance in systems like gantry robots and other elect...

  2. Optimal execution in high-frequency trading with Bayesian learning

    Science.gov (United States)

    Du, Bian; Zhu, Hongliang; Zhao, Jingdong

    2016-11-01

    We consider optimal trading strategies in which traders submit bid and ask quotes to maximize the expected quadratic utility of total terminal wealth in a limit order book. The trader's bid and ask quotes will be changed by the Poisson arrival of market orders. Meanwhile, the trader may update his estimate of other traders' target sizes and directions by Bayesian learning. The solution of optimal execution in the limit order book is a two-step procedure. First, we model an inactive trading with no limit order in the market. The dealer simply holds dollars and shares of stocks until terminal time. Second, he calibrates his bid and ask quotes to the limit order book. The optimal solutions are given by dynamic programming and in fact they are globally optimal. We also give numerical simulation to the value function and optimal quotes at the last part of the article.

  3. Science Teacher Educators’ Engagement with Pedagogical Content Knowledge and Scientific Inquiry in Predominantly Paper-Based Distance Learning Programs

    Directory of Open Access Journals (Sweden)

    William J. FRASER

    2017-10-01

    Full Text Available This article focuses on the dilemmas science educators face when having to introduce Pedagogical Content Knowledge (PCK to science student teachers in a predominantly paper-based distance learning environment. It draws on the premise that science education is bound by the Nature of Science (NOS, and by the Nature of Scientific Inquiry (NOSI. Furthermore, science educators’ own PCK, and the limitations of a predominantly paper-based distance education (DE model of delivery are challenges that they have to face when introducing PCK and authentic inquiry-based learning experiences. It deprives them and their students from optimal engagement in a science-oriented community of practice, and leaves little opportunity to establish flourishing communities of inquiry. This study carried out a contextual analysis of the tutorial material to assess the PCK that the student teachers had been exposed to. This comprised the ideas of a community of inquiry, a community of science, the conceptualization of PCK, scientific inquiry, and the 5E Instructional Model of the Biological Sciences Curriculum Study. The analysis confirmed that the lecturers had a good understanding of NOS, NOSI and science process skills, but found it difficult to design interventions to optimize the PCK development of students through communities of inquiry. Paper-based tutorials are ideal to share theory, policies and practices, but fail to monitor the engagement of learners in communities of inquiry. The article concludes with a number of suggestions to address the apparent lack of impact power of the paper-based mode of delivery, specifically in relation to inquiry-based teaching and learning (IBTL.

  4. Study of Nd-Fe-B alloys with nonstoichiometric Nd content in optimal magnetic state

    Directory of Open Access Journals (Sweden)

    Ćosović V.

    2009-01-01

    Full Text Available Characterization of two rapid-quenched Nd-Fe-B alloys with nonstoichiometric Nd content in the optimized magnetic state was carried out using the X-ray diffractometry (XRD, 57Fe Mössbauer spectroscopic phase analysis (MS, electron microscopy (TEM, high resolution TEM (HREM and Superconducting Quantum Interference Device (SQUID magnetometer. The experimental results demonstrate the fundamental difference in the structure and magnetic properties of the two investigated alloys in the optimized magnetic state. The Nd-Fe-B alloy with the reduced Nd content (Nd4.5Fe77B18.5 was found to have the nanocomposite structure of Fe3B/Nd2Fe14B and partly α-Fe/Nd2Fe14B, with mean grain size below 30 nm. On the other side, the overstoichiometric Nd14Fe79B7 alloy has almost a monophase structure with the dominant content of the hard magnetic phase Nd2Fe14B (up to 95 wt. % and a mean crystallite size about 60 nm, as determined by XRD and TEM analysis. The results of magnetic measurements on SQUID magnetometer also suggest the nanocomposite structure of the Nd-low alloy and nanocrystalline decoupled structure of the Nd-rich alloy after the optimal heat treatment.

  5. EFFORTS TO IMPROVE LEARNING MOTIVATION OF STUDENT WITH CONTENT MASTERY IN SMP NEGERI 1 METRO

    Directory of Open Access Journals (Sweden)

    Hadi Pranoto

    2013-09-01

    Full Text Available Abstract: The study design using action research applied in guidance and counseling services (follow-services research. Subjects in this study, researchers took VII.4 grade students of SMP Negeri 1 Metro Odd Semester Lesson Tabun 2012/2013. Of the 24 students, there are 10 students who experience a lack of motivation to learn to 41.66%. The method used in collecting data by observation and field notes. Analysis of the data used is the analysis of qualitative and quantitative data. Validity test is done through assessment experts /specialists ie counseling teachers SMP Negeri 1 Metro, other friends peer discussions that instrument with other friends FKIP students with courses in counseling. The results of this study, it can be concluded that the results obtained through the implementation of the procurement of content services in increasing the motivation of learners class VII.4 SMP Negeri 1 Metro Tabun Odd Semester Lesson 20 12/20 13 is visible from the change in behavior and ability of learners in learners become more willing to meet the needs of achievement, students can understand or have confidence in learning, learners have the ability to overcome failure in learning, and learners have a good competitiveness in the service learning. Through mastery of content supplied by BK teacher can increase the motivation of learners class VII.4 SMP Negeri 1 Metro Odd Semester Academic Year 201212013. There is increased the motivation of learners in the first cycle seen from the average percentage that is equal to 27.5% and in the second cycle of 75 %, resulting in an increase of 47.5%. Response and activity VIl.4 grade students of SMP Negeri 1 Metro Odd Semester Lesson Tabun 2012/2013 on the service in the content mastery enhance learning motivation is very positive, it is shown by the participation of learners in the service following the mastery of content, learner motivation and enthusiasm in participating services as well as content mastery

  6. Optimal Control via Reinforcement Learning with Symbolic Policy Approximation

    NARCIS (Netherlands)

    Kubalìk, Jiřì; Alibekov, Eduard; Babuska, R.; Dochain, Denis; Henrion, Didier; Peaucelle, Dimitri

    2017-01-01

    Model-based reinforcement learning (RL) algorithms can be used to derive optimal control laws for nonlinear dynamic systems. With continuous-valued state and input variables, RL algorithms have to rely on function approximators to represent the value function and policy mappings. This paper

  7. Optimization of extraction of phenolic content from conyza bonariensis

    International Nuclear Information System (INIS)

    Thabit, R.A.S.; Cheng, X.R.

    2014-01-01

    This study aims to find out the effects of solvent type (ethanol, water, and ionic liquid (1-butyl-3-methylimidazolium bromide)(BMIM)Br), time (30-90min), and microwave power (200 - 600w) on extraction rate, antioxidant activity, and total phenolic content (TPC) of Conyza bonariensis. The functional components from C. bonariensis were extracted using high efficient microwave-assisted extraction technology. The experiments were carried out according to 17 runs with 3 variables and three levels for the optimization in response surface methodology (RSM) system. The extracts were analyzed by spectrophotometeric methods for the antioxidant and TPC. The optimal conditions for extraction rate, antioxidant and TPC were determined by RSM. The Box Behnken design (BBD) showed the polynomial. The optimal conditions, including (BMIM)Br as the solvent with 45.47 min and 300.60 w power, lead to the best extraction rate (25.94%), maximum DPPH radical scavenging (95.90%) and maximum TPC (174.18 mg GAE/g). Under these conditions, the experimental extraction rate was 25.13 ± 0.85 %, DPPH radical scavenging was 93.8 ± 1.67% and TPC was 171.5 ± 1.06mg GAE/g of the C. bonariensis extract, which matched with the predicted values. (author)

  8. Towards a Robuster Interpretive Parsing: learning from overt forms in Optimality Theory

    NARCIS (Netherlands)

    Biró, T.

    2013-01-01

    The input data to grammar learning algorithms often consist of overt forms that do not contain full structural descriptions. This lack of information may contribute to the failure of learning. Past work on Optimality Theory introduced Robust Interpretive Parsing (RIP) as a partial solution to this

  9. Localized Multiple Kernel Learning Via Sample-Wise Alternating Optimization.

    Science.gov (United States)

    Han, Yina; Yang, Kunde; Ma, Yuanliang; Liu, Guizhong

    2014-01-01

    Our objective is to train support vector machines (SVM)-based localized multiple kernel learning (LMKL), using the alternating optimization between the standard SVM solvers with the local combination of base kernels and the sample-specific kernel weights. The advantage of alternating optimization developed from the state-of-the-art MKL is the SVM-tied overall complexity and the simultaneous optimization on both the kernel weights and the classifier. Unfortunately, in LMKL, the sample-specific character makes the updating of kernel weights a difficult quadratic nonconvex problem. In this paper, starting from a new primal-dual equivalence, the canonical objective on which state-of-the-art methods are based is first decomposed into an ensemble of objectives corresponding to each sample, namely, sample-wise objectives. Then, the associated sample-wise alternating optimization method is conducted, in which the localized kernel weights can be independently obtained by solving their exclusive sample-wise objectives, either linear programming (for l1-norm) or with closed-form solutions (for lp-norm). At test time, the learnt kernel weights for the training data are deployed based on the nearest-neighbor rule. Hence, to guarantee their generality among the test part, we introduce the neighborhood information and incorporate it into the empirical loss when deriving the sample-wise objectives. Extensive experiments on four benchmark machine learning datasets and two real-world computer vision datasets demonstrate the effectiveness and efficiency of the proposed algorithm.

  10. The Effect of Scaffolded Strategies on Content Learning in a Designed Science Cyberlearning Environment

    Science.gov (United States)

    Kern, Cynthia Lee

    2013-01-01

    Scientific inscriptions--graphs, diagrams, and data--and argumentation are integral to generating and communicating scientific understanding. Scientific inscriptions and argumentation are also important to learning science. However, previous research has indicated that learners struggle to understand and learn science content represented in…

  11. Dynamic optimal strategies in transboundary pollution game under learning by doing

    Science.gov (United States)

    Chang, Shuhua; Qin, Weihua; Wang, Xinyu

    2018-01-01

    In this paper, we present a transboundary pollution game, in which emission permits trading and pollution abatement costs under learning by doing are considered. In this model, the abatement cost mainly depends on the level of pollution abatement and the experience of using pollution abatement technology. We use optimal control theory to investigate the optimal emission paths and the optimal pollution abatement strategies under cooperative and noncooperative games, respectively. Additionally, the effects of parameters on the results have been examined.

  12. Student-Generated Content: Enhancing Learning through Sharing Multiple-Choice Questions

    Science.gov (United States)

    Hardy, Judy; Bates, Simon P.; Casey, Morag M.; Galloway, Kyle W.; Galloway, Ross K.; Kay, Alison E.; Kirsop, Peter; McQueen, Heather A.

    2014-01-01

    The relationship between students' use of PeerWise, an online tool that facilitates peer learning through student-generated content in the form of multiple-choice questions (MCQs), and achievement, as measured by their performance in the end-of-module examinations, was investigated in 5 large early-years science modules (in physics, chemistry and…

  13. Optimization of Graphene Conductive Ink with 73 wt% Graphene Contents.

    Science.gov (United States)

    Xu, Chang-Yan; Shi, Xiao-Mei; Guo, Lu; Wang, Xi; Wang, Xin-Yi; Li, Jian-Yu

    2018-06-01

    With the pace of development accelerating in printed electronics, the fabrication and application of conductive ink have been brought into sharp focus in recent years. The discovery of graphene also unfolded a vigorous research campaign. In this paper, we prepared graphene conductive ink and explored the feasibility of applying the ink to flexible paper-based circuit. Since experimental study concentrating upon ink formulation was insufficient, orthogonal test design was used in the optimization of preparation formula of conductive ink for the first time. The purpose of this study was to determine the effect of constituent dosage on conductivity of graphene conductive ink, so as to obtain the optimized formula and prepare graphene conductive ink with good conductivity. Characterization of optimized graphene conductive ink we fabricated showed good adhesion to substrate and good resistance to acid and water. The graphene concentration of the optimized ink reached 73.17 wt% solid content. Particle size distribution of graphene conductive ink was uniform, which was about 1940 nm. Static surface tension was 28.9 mN/m and equilibrium contact angle was 23°, demonstrating that conductive ink had good wettability. Scanning Electron Microscope (SEM) analysis was also investigated, moreover, the feasibility of lightening a light-emitting diode (LED) light was verified. The graphene conductive ink with optimized formula can be stored for almost eight months, which had potential applications in flexible paper-based circuit in the future.

  14. Interactive Learning Environment for Bio-Inspired Optimization Algorithms for UAV Path Planning

    Science.gov (United States)

    Duan, Haibin; Li, Pei; Shi, Yuhui; Zhang, Xiangyin; Sun, Changhao

    2015-01-01

    This paper describes the development of BOLE, a MATLAB-based interactive learning environment, that facilitates the process of learning bio-inspired optimization algorithms, and that is dedicated exclusively to unmanned aerial vehicle path planning. As a complement to conventional teaching methods, BOLE is designed to help students consolidate the…

  15. Dispositional optimism and perceived risk interact to predict intentions to learn genome sequencing results.

    Science.gov (United States)

    Taber, Jennifer M; Klein, William M P; Ferrer, Rebecca A; Lewis, Katie L; Biesecker, Leslie G; Biesecker, Barbara B

    2015-07-01

    Dispositional optimism and risk perceptions are each associated with health-related behaviors and decisions and other outcomes, but little research has examined how these constructs interact, particularly in consequential health contexts. The predictive validity of risk perceptions for health-related information seeking and intentions may be improved by examining dispositional optimism as a moderator, and by testing alternate types of risk perceptions, such as comparative and experiential risk. Participants (n = 496) had their genomes sequenced as part of a National Institutes of Health pilot cohort study (ClinSeq®). Participants completed a cross-sectional baseline survey of various types of risk perceptions and intentions to learn genome sequencing results for differing disease risks (e.g., medically actionable, nonmedically actionable, carrier status) and to use this information to change their lifestyle/health behaviors. Risk perceptions (absolute, comparative, and experiential) were largely unassociated with intentions to learn sequencing results. Dispositional optimism and comparative risk perceptions interacted, however, such that individuals higher in optimism reported greater intentions to learn all 3 types of sequencing results when comparative risk was perceived to be higher than when it was perceived to be lower. This interaction was inconsistent for experiential risk and absent for absolute risk. Independent of perceived risk, participants high in dispositional optimism reported greater interest in learning risks for nonmedically actionable disease and carrier status, and greater intentions to use genome information to change their lifestyle/health behaviors. The relationship between risk perceptions and intentions may depend on how risk perceptions are assessed and on degree of optimism. (c) 2015 APA, all rights reserved.

  16. Nursing students' views of sociocultural factors in clinical learning: a qualitative content analysis.

    Science.gov (United States)

    Dadgaran, Ideh; Parvizy, Soroor; Peyrovi, Hamid

    2013-06-01

    The aim of this study is description of nursing students' views of sociocultural factors in clinical learning. A qualitative content analysis was conducted to describe nursing students' views of sociocultural factors in clinical learning. The participants consisted of 21 nursing students. Semi-structured and interactive interviews were used to collect data. All the interviews were recorded and transcribed, and then, they were analyzed using Qualitative Content Analysis and Max Qualitative Data Analysis 2010. From the transcripts, a remarkable number of primary themes, main themes, and sub-themes emerged. The main themes consisted of elements related to "society and culture", "family", "staff", and "classmates". The themes encompassed a spectrum of facilitators of and impediments to clinical learning. The findings showed that the administrators of nursing education should coordinate with faculty and staff by adopting interactive and participatory solutions, including the establishment of clinical learning teams and the transformation of hospitals into suitable sociocultural environments for education. © 2012 The Authors. Japan Journal of Nursing Science © 2012 Japan Academy of Nursing Science.

  17. Research into Financial Position of Listed Companies following Classification via Extreme Learning Machine Based upon DE Optimization

    Directory of Open Access Journals (Sweden)

    Fu Yu

    2016-01-01

    Full Text Available By means of the model of extreme learning machine based upon DE optimization, this article particularly centers on the optimization thinking of such a model as well as its application effect in the field of listed company’s financial position classification. It proves that the improved extreme learning machine algorithm based upon DE optimization eclipses the traditional extreme learning machine algorithm following comparison. Meanwhile, this article also intends to introduce certain research thinking concerning extreme learning machine into the economics classification area so as to fulfill the purpose of computerizing the speedy but effective evaluation of massive financial statements of listed companies pertain to different classes

  18. The Effect of Learned Optimism on Achievement Motivation and Academic Resilience in Female Adolescents

    Directory of Open Access Journals (Sweden)

    M Khademi

    2015-08-01

    Full Text Available The aim of this study was to investigate the effect of learned optimism on achievement motivation and academic resilience in female adolescents. This study was a quasi design, pre- and post-test control group and the subjects were selected among adolescents who were members of the Center for Intellectual Development of Children and Adolescents in Isfahan. These subjects selected by randomly style and divided into two experimental and control groups. They were 20 female adolescents aged between 13 to 15 years. The experimental group received optimism training in 7 sessions. Measuring tools were Hermance Achievement motivation questionnaire and Samuel’s academic resilience questionnaire. Data were analyzed by multivariate analysis of covariance (MANCOVA. The results showed that learned optimism had a significant effect on achievement motivation and it’s subscales (confidence and perseverance but it had no effect on other subscales (foresight and hard working. As well as learned optimism had no effect on academic resilience and it’s subscales (communication skills, orientation for the future, orientation for the problem-based. Based on these results focus on emotional and optimism in educational system leads to increase motivation in students and prevent failure and school drop.

  19. Relaxations to Sparse Optimization Problems and Applications

    Science.gov (United States)

    Skau, Erik West

    Parsimony is a fundamental property that is applied to many characteristics in a variety of fields. Of particular interest are optimization problems that apply rank, dimensionality, or support in a parsimonious manner. In this thesis we study some optimization problems and their relaxations, and focus on properties and qualities of the solutions of these problems. The Gramian tensor decomposition problem attempts to decompose a symmetric tensor as a sum of rank one tensors.We approach the Gramian tensor decomposition problem with a relaxation to a semidefinite program. We study conditions which ensure that the solution of the relaxed semidefinite problem gives the minimal Gramian rank decomposition. Sparse representations with learned dictionaries are one of the leading image modeling techniques for image restoration. When learning these dictionaries from a set of training images, the sparsity parameter of the dictionary learning algorithm strongly influences the content of the dictionary atoms.We describe geometrically the content of trained dictionaries and how it changes with the sparsity parameter.We use statistical analysis to characterize how the different content is used in sparse representations. Finally, a method to control the structure of the dictionaries is demonstrated, allowing us to learn a dictionary which can later be tailored for specific applications. Variations of dictionary learning can be broadly applied to a variety of applications.We explore a pansharpening problem with a triple factorization variant of coupled dictionary learning. Another application of dictionary learning is computer vision. Computer vision relies heavily on object detection, which we explore with a hierarchical convolutional dictionary learning model. Data fusion of disparate modalities is a growing topic of interest.We do a case study to demonstrate the benefit of using social media data with satellite imagery to estimate hazard extents. In this case study analysis we

  20. Do Gains in Secondary Teachers’ Content Knowledge Provide an ASSET to Student Learning?

    Science.gov (United States)

    Hites, Travis

    2015-01-01

    During the Summer of 2013, a group of East Texas middle and high school science teachers attended the first year of the Astronomy Summer School of East Texas (ASSET), a two-week NASA funded workshop. This workshop focused on providing area teachers with a rigorous two-week experience loaded with interactive content lessons combined with hands-on activities, all relating to the universal laws of astronomy as well as solar system concepts.The effectiveness of this workshop was gauged in part through a series of content surveys given to each participating educator at the beginning and end of the workshop. Similar content surveys were also administered to each teacher's students as pre/post-content surveys in an effort to determine the extent to which teacher gains were transferred into student gains, as well as to judge the effectiveness of the teachers' lessons in conveying these concepts to the students.Overall, students performed best on concepts where teachers exhibited the highest gains in their learning and focused most of their emphasis. A question-by-question analysis, though, suggests that a broad analysis paints an incomplete picture of student learning. We will present an item analysis of student gains by topic along with a comparison of content coverage and teacher gains. Looking beyond these numbers will present results that demonstrate that giving secondary teachers professional development opportunities to increase content knowledge, and tools to present such knowledge to their students, can improve student learning and performance, but is dependent on teacher confidence and level of coverage.This project is supported by the NASA Science Mission Directorate Education and Public Outreach for Earth and Space Science (EPOESS), which is part of the Research Opportunities in Space and Earth Sciences (ROSES), Grant Number NNX12AH11G.

  1. Optimization of Water Content for the Cryopreservation Of Allium sativum In Vitro Cultures by Encapsulation-Dehydration.

    Science.gov (United States)

    Lynch, P T; Souch, G R; Zamecnik, J; Harding, K

    There is a general requirement to determine and correlate water content to viability for the standardization of conservation protocols to facilitate effective cryostorage of plant germplasm. This study examined water content as a critical factor to optimize the cryostorage of Allium sativum. Stem discs were excised from post-harvest, stored bulbs prior to cryopreservation by encapsulation-dehydration and water content was determined gravimetrically. Survival of cryopreserved stem discs was 42.5 %, with 22.5 % exhibiting shoot regrowth following 6 h desiccation. Gravimetric data demonstrated a correlation between water content corresponding with survival / regrowth from desiccated, cryopreserved stem discs. For encapsulated stem discs a 25 % residual moisture and corresponding water content of 0.36 g H2O g -1 d.wt correlated with maximal survival following ~6.5 h of desiccation. The data concurs with the literature suggesting the formation of a stable vitrified state and a 'window' for optimal survival and regrowth that is between 6 - 10 h desiccation. Further studies using differential scanning calorimetry (DSC) are suggested to substantiate these findings.

  2. Social Learning and Optimal Advertising in the Motion Picture Industry

    OpenAIRE

    Ohio University; Department of Economics; Hailey Hayeon Joo

    2009-01-01

    Social learning is thought to be a key determinant of the demand for movies. This can be a double-edged sword for motion picture distributors, because when a movie is good, social learning can enhance the effectiveness of movie advertising, but when a movie is bad, it can mitigate this effectiveness. This paper develops an equilibrium model of consumers' movie-going choices and movie distributors' advertising decisions. First, we develop a structural model for studios' optimal advertising str...

  3. Bidirectional Nonnegative Deep Model and Its Optimization in Learning

    Directory of Open Access Journals (Sweden)

    Xianhua Zeng

    2016-01-01

    Full Text Available Nonnegative matrix factorization (NMF has been successfully applied in signal processing as a simple two-layer nonnegative neural network. Projective NMF (PNMF with fewer parameters was proposed, which projects a high-dimensional nonnegative data onto a lower-dimensional nonnegative subspace. Although PNMF overcomes the problem of out-of-sample of NMF, it does not consider the nonlinear characteristic of data and is only a kind of narrow signal decomposition method. In this paper, we combine the PNMF with deep learning and nonlinear fitting to propose a bidirectional nonnegative deep learning (BNDL model and its optimization learning algorithm, which can obtain nonlinear multilayer deep nonnegative feature representation. Experiments show that the proposed model can not only solve the problem of out-of-sample of NMF but also learn hierarchical nonnegative feature representations with better clustering performance than classical NMF, PNMF, and Deep Semi-NMF algorithms.

  4. Learning effective color features for content based image retrieval in dermatology

    NARCIS (Netherlands)

    Bunte, Kerstin; Biehl, Michael; Jonkman, Marcel F.; Petkov, Nicolai

    We investigate the extraction of effective color features for a content-based image retrieval (CBIR) application in dermatology. Effectiveness is measured by the rate of correct retrieval of images from four color classes of skin lesions. We employ and compare two different methods to learn

  5. Learning Design of Problem Based Learning Model Based on Recommendations of Sintax Study and Contents Issues on Physics Impulse Materials with Experimental Activities

    Directory of Open Access Journals (Sweden)

    Kristia Agustina

    2017-08-01

    Full Text Available This study aims to design learning Problem Based Learning Model based on syntax study recommendations and content issues on Physics Impulse materials through experiments. This research is a development research with Kemp model. The reference for making the learning design is the result of the syntax study and the content of existing PBL implementation problems from Agustina research. This instructional design is applied to the physics material about Impulse done through experimental activity. Limited trials were conducted on the SWCU Physics Education Study Program students group Salatiga, while the validity test was conducted by high school teachers and physics education lecturers. The results of the trial evaluation are limited and the validity test is used to improve the designs that have been made. The conclusion of this research is the design of learning by using PBL model on Impuls material by referring the result of syntax study and the problem content of existing PBL implementation can be produced by learning activity designed in laboratory experiment activity. The actual problem for Impuls material can be used car crash test video at factory. The results of validation tests and limited trials conducted by researchers assessed that the design of learning made by researchers can be used with small revisions. Suggestions from this research are in making learning design by using PBL model to get actual problem can by collecting news that come from newspaper, YouTube, internet, and television.

  6. Optimal and Learning-Based Demand Response Mechanism for Electric Water Heater System

    Directory of Open Access Journals (Sweden)

    Bo Lin

    2017-10-01

    Full Text Available This paper investigates how to develop a learning-based demand response approach for electric water heater in a smart home that can minimize the energy cost of the water heater while meeting the comfort requirements of energy consumers. First, a learning-based, data-driven model of an electric water heater is developed by using a nonlinear autoregressive network with external input (NARX using neural network. The model is updated daily so that it can more accurately capture the actual thermal dynamic characteristics of the water heater especially in real-life conditions. Then, an optimization problem, based on the NARX water heater model, is formulated to optimize energy management of the water heater in a day-ahead, dynamic electricity price framework. A genetic algorithm is proposed in order to solve the optimization problem more efficiently. MATLAB (R2016a is used to evaluate the proposed learning-based demand response approach through a computational experiment strategy. The proposed approach is compared with conventional method for operation of an electric water heater. Cost saving and benefits of the proposed water heater energy management strategy are explored.

  7. Fast machine-learning online optimization of ultra-cold-atom experiments.

    Science.gov (United States)

    Wigley, P B; Everitt, P J; van den Hengel, A; Bastian, J W; Sooriyabandara, M A; McDonald, G D; Hardman, K S; Quinlivan, C D; Manju, P; Kuhn, C C N; Petersen, I R; Luiten, A N; Hope, J J; Robins, N P; Hush, M R

    2016-05-16

    We apply an online optimization process based on machine learning to the production of Bose-Einstein condensates (BEC). BEC is typically created with an exponential evaporation ramp that is optimal for ergodic dynamics with two-body s-wave interactions and no other loss rates, but likely sub-optimal for real experiments. Through repeated machine-controlled scientific experimentation and observations our 'learner' discovers an optimal evaporation ramp for BEC production. In contrast to previous work, our learner uses a Gaussian process to develop a statistical model of the relationship between the parameters it controls and the quality of the BEC produced. We demonstrate that the Gaussian process machine learner is able to discover a ramp that produces high quality BECs in 10 times fewer iterations than a previously used online optimization technique. Furthermore, we show the internal model developed can be used to determine which parameters are essential in BEC creation and which are unimportant, providing insight into the optimization process of the system.

  8. Social Web Content Enhancement in a Distance Learning Environment: Intelligent Metadata Generation for Resources

    Science.gov (United States)

    García-Floriano, Andrés; Ferreira-Santiago, Angel; Yáñez-Márquez, Cornelio; Camacho-Nieto, Oscar; Aldape-Pérez, Mario; Villuendas-Rey, Yenny

    2017-01-01

    Social networking potentially offers improved distance learning environments by enabling the exchange of resources between learners. The existence of properly classified content results in an enhanced distance learning experience in which appropriate materials can be retrieved efficiently; however, for this to happen, metadata needs to be present.…

  9. Optimal Learning in Schools--Theoretical Evidence: Part 4 Metacognition

    Science.gov (United States)

    Crossland, John

    2017-01-01

    Parts 1 and 2 in this four-part series of articles (Crossland, 2016, 2017a) discussed the recent research from neuroscience linked to concepts from cognitive development that brought Piaget's theories into the 21st century and showed the most effective provision towards more optimal learning strategies. Part 2 reviewed Demetriou's latest thinking…

  10. Educational Information Quantization for Improving Content Quality in Learning Management Systems

    Science.gov (United States)

    Rybanov, Alexander Aleksandrovich

    2014-01-01

    The article offers the educational information quantization method for improving content quality in Learning Management Systems. The paper considers questions concerning analysis of quality of quantized presentation of educational information, based on quantitative text parameters: average frequencies of parts of speech, used in the text; formal…

  11. Interactive Multimodal Molecular Set – Designing Ludic Engaging Science Learning Content

    DEFF Research Database (Denmark)

    Thorsen, Tine Pinholt; Christiansen, Kasper Holm Bonde; Jakobsen Sillesen, Kristian

    2014-01-01

    This paper reports on an exploratory study investigating 10 primary school students’ interaction with an interactive multimodal molecular set fostering ludic engaging science learning content in primary schools (8th and 9th grade). The concept of the prototype design was to bridge the physical...... and virtual worlds with electronic tags and, through this, blend the familiarity of the computer and toys, to create a tool that provided a ludic approach to learning about atoms and molecules. The study was inspired by the participatory design and informant design methodologies and included design...

  12. Quantum learning: asymptotically optimal classification of qubit states

    International Nuclear Information System (INIS)

    Guta, Madalin; Kotlowski, Wojciech

    2010-01-01

    Pattern recognition is a central topic in learning theory, with numerous applications such as voice and text recognition, image analysis and computer diagnosis. The statistical setup in classification is the following: we are given an i.i.d. training set (X 1 , Y 1 ), ... , (X n , Y n ), where X i represents a feature and Y i in{0, 1} is a label attached to that feature. The underlying joint distribution of (X, Y) is unknown, but we can learn about it from the training set, and we aim at devising low error classifiers f: X→Y used to predict the label of new incoming features. In this paper, we solve a quantum analogue of this problem, namely the classification of two arbitrary unknown mixed qubit states. Given a number of 'training' copies from each of the states, we would like to 'learn' about them by performing a measurement on the training set. The outcome is then used to design measurements for the classification of future systems with unknown labels. We found the asymptotically optimal classification strategy and show that typically it performs strictly better than a plug-in strategy, which consists of estimating the states separately and then discriminating between them using the Helstrom measurement. The figure of merit is given by the excess risk equal to the difference between the probability of error and the probability of error of the optimal measurement for known states. We show that the excess risk scales as n -1 and compute the exact constant of the rate.

  13. Recommendation of standardized health learning contents using archetypes and semantic web technologies.

    Science.gov (United States)

    Legaz-García, María del Carmen; Martínez-Costa, Catalina; Menárguez-Tortosa, Marcos; Fernández-Breis, Jesualdo Tomás

    2012-01-01

    Linking Electronic Healthcare Records (EHR) content to educational materials has been considered a key international recommendation to enable clinical engagement and to promote patient safety. This would suggest citizens to access reliable information available on the web and to guide them properly. In this paper, we describe an approach in that direction, based on the use of dual model EHR standards and standardized educational contents. The recommendation method will be based on the semantic coverage of the learning content repository for a particular archetype, which will be calculated by applying semantic web technologies like ontologies and semantic annotations.

  14. Analysis of different thermal processing methods of foodstuffs to optimize protein, calcium, and phosphorus content for dialysis patients.

    Science.gov (United States)

    Vrdoljak, Ivica; Panjkota Krbavčić, Ines; Bituh, Martina; Vrdoljak, Tea; Dujmić, Zoran

    2015-05-01

    To analyze how different thermal processing methods affect the protein, calcium, and phosphorus content of hospital food served to dialysis patients and to generate recommendations for preparing menus that optimize nutritional content while minimizing the risk of hyperphosphatemia. Standard Official Methods of Analysis (AOAC) methods were used to determine dry matter, protein, calcium, and phosphorus content in potatoes, fresh and frozen carrots, frozen green beans, chicken, beef and pork, frozen hake, pasta, and rice. These levels were determined both before and after boiling in water, steaming, stewing in oil or water, or roasting. Most of the thermal processing methods did not significantly reduce protein content. Boiling increased calcium content in all foodstuffs because of calcium absorption from the hard water. In contrast, stewing in oil containing a small amount of water decreased the calcium content of vegetables by 8% to 35% and of chicken meat by 12% to 40% on a dry weight basis. Some types of thermal processing significantly reduced the phosphorus content of the various foodstuffs, with levels decreasing by 27% to 43% for fresh and frozen vegetables, 10% to 49% for meat, 7% for pasta, and 22.8% for rice on a dry weight basis. On the basis of these results, we modified the thermal processing methods used to prepare a standard hospital menu for dialysis patients. Foodstuffs prepared according to the optimized menu were similar in protein content, higher in calcium, and significantly lower in phosphorus than foodstuffs prepared according to the standard menu. Boiling in water and stewing in oil containing some water significantly reduced phosphorus content without affecting protein content. Soaking meat in cold water for 1 h before thermal processing reduced phosphorus content even more. These results may help optimize the design of menus for dialysis patients. Copyright © 2015 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights

  15. Online gaming for learning optimal team strategies in real time

    Science.gov (United States)

    Hudas, Gregory; Lewis, F. L.; Vamvoudakis, K. G.

    2010-04-01

    This paper first presents an overall view for dynamical decision-making in teams, both cooperative and competitive. Strategies for team decision problems, including optimal control, zero-sum 2-player games (H-infinity control) and so on are normally solved for off-line by solving associated matrix equations such as the Riccati equation. However, using that approach, players cannot change their objectives online in real time without calling for a completely new off-line solution for the new strategies. Therefore, in this paper we give a method for learning optimal team strategies online in real time as team dynamical play unfolds. In the linear quadratic regulator case, for instance, the method learns the Riccati equation solution online without ever solving the Riccati equation. This allows for truly dynamical team decisions where objective functions can change in real time and the system dynamics can be time-varying.

  16. Coupling Matched Molecular Pairs with Machine Learning for Virtual Compound Optimization.

    Science.gov (United States)

    Turk, Samo; Merget, Benjamin; Rippmann, Friedrich; Fulle, Simone

    2017-12-26

    Matched molecular pair (MMP) analyses are widely used in compound optimization projects to gain insights into structure-activity relationships (SAR). The analysis is traditionally done via statistical methods but can also be employed together with machine learning (ML) approaches to extrapolate to novel compounds. The here introduced MMP/ML method combines a fragment-based MMP implementation with different machine learning methods to obtain automated SAR decomposition and prediction. To test the prediction capabilities and model transferability, two different compound optimization scenarios were designed: (1) "new fragments" which occurs when exploring new fragments for a defined compound series and (2) "new static core and transformations" which resembles for instance the identification of a new compound series. Very good results were achieved by all employed machine learning methods especially for the new fragments case, but overall deep neural network models performed best, allowing reliable predictions also for the new static core and transformations scenario, where comprehensive SAR knowledge of the compound series is missing. Furthermore, we show that models trained on all available data have a higher generalizability compared to models trained on focused series and can extend beyond chemical space covered in the training data. Thus, coupling MMP with deep neural networks provides a promising approach to make high quality predictions on various data sets and in different compound optimization scenarios.

  17. Optimized Structure of the Traffic Flow Forecasting Model With a Deep Learning Approach.

    Science.gov (United States)

    Yang, Hao-Fan; Dillon, Tharam S; Chen, Yi-Ping Phoebe

    2017-10-01

    Forecasting accuracy is an important issue for successful intelligent traffic management, especially in the domain of traffic efficiency and congestion reduction. The dawning of the big data era brings opportunities to greatly improve prediction accuracy. In this paper, we propose a novel model, stacked autoencoder Levenberg-Marquardt model, which is a type of deep architecture of neural network approach aiming to improve forecasting accuracy. The proposed model is designed using the Taguchi method to develop an optimized structure and to learn traffic flow features through layer-by-layer feature granulation with a greedy layerwise unsupervised learning algorithm. It is applied to real-world data collected from the M6 freeway in the U.K. and is compared with three existing traffic predictors. To the best of our knowledge, this is the first time that an optimized structure of the traffic flow forecasting model with a deep learning approach is presented. The evaluation results demonstrate that the proposed model with an optimized structure has superior performance in traffic flow forecasting.

  18. Particle Swarm Optimization With Interswarm Interactive Learning Strategy.

    Science.gov (United States)

    Qin, Quande; Cheng, Shi; Zhang, Qingyu; Li, Li; Shi, Yuhui

    2016-10-01

    The learning strategy in the canonical particle swarm optimization (PSO) algorithm is often blamed for being the primary reason for loss of diversity. Population diversity maintenance is crucial for preventing particles from being stuck into local optima. In this paper, we present an improved PSO algorithm with an interswarm interactive learning strategy (IILPSO) by overcoming the drawbacks of the canonical PSO algorithm's learning strategy. IILPSO is inspired by the phenomenon in human society that the interactive learning behavior takes place among different groups. Particles in IILPSO are divided into two swarms. The interswarm interactive learning (IIL) behavior is triggered when the best particle's fitness value of both the swarms does not improve for a certain number of iterations. According to the best particle's fitness value of each swarm, the softmax method and roulette method are used to determine the roles of the two swarms as the learning swarm and the learned swarm. In addition, the velocity mutation operator and global best vibration strategy are used to improve the algorithm's global search capability. The IIL strategy is applied to PSO with global star and local ring structures, which are termed as IILPSO-G and IILPSO-L algorithm, respectively. Numerical experiments are conducted to compare the proposed algorithms with eight popular PSO variants. From the experimental results, IILPSO demonstrates the good performance in terms of solution accuracy, convergence speed, and reliability. Finally, the variations of the population diversity in the entire search process provide an explanation why IILPSO performs effectively.

  19. Fast Gaussian kernel learning for classification tasks based on specially structured global optimization.

    Science.gov (United States)

    Zhong, Shangping; Chen, Tianshun; He, Fengying; Niu, Yuzhen

    2014-09-01

    For a practical pattern classification task solved by kernel methods, the computing time is mainly spent on kernel learning (or training). However, the current kernel learning approaches are based on local optimization techniques, and hard to have good time performances, especially for large datasets. Thus the existing algorithms cannot be easily extended to large-scale tasks. In this paper, we present a fast Gaussian kernel learning method by solving a specially structured global optimization (SSGO) problem. We optimize the Gaussian kernel function by using the formulated kernel target alignment criterion, which is a difference of increasing (d.i.) functions. Through using a power-transformation based convexification method, the objective criterion can be represented as a difference of convex (d.c.) functions with a fixed power-transformation parameter. And the objective programming problem can then be converted to a SSGO problem: globally minimizing a concave function over a convex set. The SSGO problem is classical and has good solvability. Thus, to find the global optimal solution efficiently, we can adopt the improved Hoffman's outer approximation method, which need not repeat the searching procedure with different starting points to locate the best local minimum. Also, the proposed method can be proven to converge to the global solution for any classification task. We evaluate the proposed method on twenty benchmark datasets, and compare it with four other Gaussian kernel learning methods. Experimental results show that the proposed method stably achieves both good time-efficiency performance and good classification performance. Copyright © 2014 Elsevier Ltd. All rights reserved.

  20. 21st Century Pedagogical Content Knowledge and Science Teaching and Learning

    Science.gov (United States)

    Slough, Scott; Chamblee, Gregory

    2017-01-01

    Technological Pedagogical Content Knowledge (TPACK) is a theoretical framework that has enjoyed widespread applications as it applies to the integration of technology in the teaching and learning process. This paper reviews the background for TPACK, discusses some of its limitations, and reviews and introduces a new theoretical framework, 21st…

  1. Trans/Languaging and the Triadic Dialogue in Content and Language Integrated Learning (CLIL) Classrooms

    Science.gov (United States)

    Lin, Angel M. Y.; Lo, Yuen Yi

    2017-01-01

    There has been a rich literature on the role of language in learning and on its role in knowledge (co-)construction in the science classroom. This literature, rooted in social semiotics theories and sociocultural theories, discussed research conducted largely in contexts where students are learning content in their first language (L1). In this…

  2. The relationship between student engagement with online content and achievement in a blended learning anatomy course.

    Science.gov (United States)

    Green, Rodney A; Whitburn, Laura Y; Zacharias, Anita; Byrne, Graeme; Hughes, Diane L

    2017-12-13

    Blended learning has become increasingly common in higher education. Recent findings suggest that blended learning achieves better student outcomes than traditional face-to-face teaching in gross anatomy courses. While face-to-face content is perceived as important to learning there is less evidence for the significance of online content in improving student outcomes. Students enrolled in a second-year anatomy course from the physiotherapy (PT), exercise physiology (EP), and exercise science (ES) programs across two campuses were included (n = 500). A structural equation model was used to evaluate the relationship of prior student ability (represented by grade in prerequisite anatomy course) and final course grade and whether the relationship was mediated by program, campus or engagement with the online elements of the learning management system (LMS; proportion of documents and video segments viewed and number of interactions with discussion forums). PT students obtained higher grades and were more likely to engage with online course materials than EP and ES students. Prerequisite grade made a direct contribution to course final grade (P learning outcomes in a blended anatomy course can be predicted the by level of engagement with online content. Anat Sci Educ. © 2017 American Association of Anatomists. © 2017 American Association of Anatomists.

  3. Regularized spherical polar fourier diffusion MRI with optimal dictionary learning.

    Science.gov (United States)

    Cheng, Jian; Jiang, Tianzi; Deriche, Rachid; Shen, Dinggang; Yap, Pew-Thian

    2013-01-01

    Compressed Sensing (CS) takes advantage of signal sparsity or compressibility and allows superb signal reconstruction from relatively few measurements. Based on CS theory, a suitable dictionary for sparse representation of the signal is required. In diffusion MRI (dMRI), CS methods proposed for reconstruction of diffusion-weighted signal and the Ensemble Average Propagator (EAP) utilize two kinds of Dictionary Learning (DL) methods: 1) Discrete Representation DL (DR-DL), and 2) Continuous Representation DL (CR-DL). DR-DL is susceptible to numerical inaccuracy owing to interpolation and regridding errors in a discretized q-space. In this paper, we propose a novel CR-DL approach, called Dictionary Learning - Spherical Polar Fourier Imaging (DL-SPFI) for effective compressed-sensing reconstruction of the q-space diffusion-weighted signal and the EAP. In DL-SPFI, a dictionary that sparsifies the signal is learned from the space of continuous Gaussian diffusion signals. The learned dictionary is then adaptively applied to different voxels using a weighted LASSO framework for robust signal reconstruction. Compared with the start-of-the-art CR-DL and DR-DL methods proposed by Merlet et al. and Bilgic et al., respectively, our work offers the following advantages. First, the learned dictionary is proved to be optimal for Gaussian diffusion signals. Second, to our knowledge, this is the first work to learn a voxel-adaptive dictionary. The importance of the adaptive dictionary in EAP reconstruction will be demonstrated theoretically and empirically. Third, optimization in DL-SPFI is only performed in a small subspace resided by the SPF coefficients, as opposed to the q-space approach utilized by Merlet et al. We experimentally evaluated DL-SPFI with respect to L1-norm regularized SPFI (L1-SPFI), which uses the original SPF basis, and the DR-DL method proposed by Bilgic et al. The experiment results on synthetic and real data indicate that the learned dictionary produces

  4. LEAN-GREEN MANUFACTURING: COLLABORATIVE CONTENT AND LANGUAGE INTEGRATED LEARNING IN HIGHER EDUCATION AND ENGINEERING COURSES

    Directory of Open Access Journals (Sweden)

    MARCELO RUDOLFO CALVETE GASPAR

    2017-09-01

    Full Text Available Lean and Green manufacturing processes aim at achieving lower material and labour costs, while reducing impacts on the environment, and promoting sustainability as a whole. This paper reports on a pilot experiment with higher education and engineering students, exploring the full potential of a collaborative approach on courses integrating the Portuguese Polytechnic of Castelo Branco engineering studies curricula, while simultaneously improving their proficiency in English. Content and Language Integrated Learning (CLIL has become a key area of curricular innovation since it is known for improving both language and content teacher and student motivation. In this context, instructional design for CLIL entailed tandem work of content (engineering and language (English teacher to design learning sequences and strategies. This allowed students to improve not only their language skills in English but also their knowledge in the specific engineering domain content on green and lean manufacturing processes.

  5. DEVELOPMENT OF USABILITY CRITERIA FOR E-LEARNING CONTENT DEVELOPMENT SOFTWARE

    Directory of Open Access Journals (Sweden)

    Serkan ÇELIK

    2012-04-01

    Full Text Available Revolutionary advancements have been observed in e-learning technologies though an amalgamated evaluation methodology for new generation e-learning content development tools is not available. The evaluation of educational software for online use must consider its usability and as well as its pedagogic effectiveness. This study is a first step towards the definition of criteria for evaluating e-learning tools. A preliminary user study involving a group of pre-service instructional designers, observed during their interaction with e-learning tools, is reported. Throughout the study, specific usability attributes of these e-learning tools were identified. Participants were assigned to rate the importance of functional and pedagogical competences proposed during the criteria development phase. The findings of the study revealed 31 evaluation criteria under the headings of technical, media, and assessment competences. Among the groups of benchmarks proposed and rated by the users, assessment was considered as the most important one while technical and media features were even.The following step was actual implemention of the usability criteria into evaluation of fifteen leading software used in e-learning across the world. Mostly, tools were observed as having limitations in terms of capabilities. Comparing to the other software, Captivate, Softchalk, and Lectora were regarded as outstanding tools by the participants. Following the discussion on the limitations of the study, some implications for further research were proposed.

  6. A hypothesis on improving foreign accents by optimizing variability in vocal learning brain circuits

    OpenAIRE

    Simmonds, Anna J.

    2015-01-01

    Rapid vocal motor learning is observed when acquiring a language in early childhood, or learning to speak another language later in life. Accurate pronunciation is one of the hardest things for late learners to master and they are almost always left with a non-native accent. Here, I propose a novel hypothesis that this accent could be improved by optimizing variability in vocal learning brain circuits during learning. Much of the neurobiology of human vocal motor learning has been inferred fr...

  7. Optimization in Quaternion Dynamic Systems: Gradient, Hessian, and Learning Algorithms.

    Science.gov (United States)

    Xu, Dongpo; Xia, Yili; Mandic, Danilo P

    2016-02-01

    The optimization of real scalar functions of quaternion variables, such as the mean square error or array output power, underpins many practical applications. Solutions typically require the calculation of the gradient and Hessian. However, real functions of quaternion variables are essentially nonanalytic, which are prohibitive to the development of quaternion-valued learning systems. To address this issue, we propose new definitions of quaternion gradient and Hessian, based on the novel generalized Hamilton-real (GHR) calculus, thus making a possible efficient derivation of general optimization algorithms directly in the quaternion field, rather than using the isomorphism with the real domain, as is current practice. In addition, unlike the existing quaternion gradients, the GHR calculus allows for the product and chain rule, and for a one-to-one correspondence of the novel quaternion gradient and Hessian with their real counterparts. Properties of the quaternion gradient and Hessian relevant to numerical applications are also introduced, opening a new avenue of research in quaternion optimization and greatly simplified the derivations of learning algorithms. The proposed GHR calculus is shown to yield the same generic algorithm forms as the corresponding real- and complex-valued algorithms. Advantages of the proposed framework are illuminated over illustrative simulations in quaternion signal processing and neural networks.

  8. Optimizing T-Learning Course Scheduling Based on Genetic Algorithm in Benefit-Oriented Data Broadcast Environments

    Science.gov (United States)

    Huang, Yong-Ming; Chen, Chao-Chun; Wang, Ding-Chau

    2012-01-01

    Ubiquitous learning receives much attention in these few years due to its wide spectrum of applications, such as the T-learning application. The learner can use mobile devices to watch the digital TV based course content, and thus, the T-learning provides the ubiquitous learning environment. However, in real-world data broadcast environments, the…

  9. Optimization of thermal performance of a smooth flat-plate solar air heater using teaching–learning-based optimization algorithm

    Directory of Open Access Journals (Sweden)

    R. Venkata Rao

    2015-12-01

    Full Text Available This paper presents the performance of teaching–learning-based optimization (TLBO algorithm to obtain the optimum set of design and operating parameters for a smooth flat plate solar air heater (SFPSAH. The TLBO algorithm is a recently proposed population-based algorithm, which simulates the teaching–learning process of the classroom. Maximization of thermal efficiency is considered as an objective function for the thermal performance of SFPSAH. The number of glass plates, irradiance, and the Reynolds number are considered as the design parameters and wind velocity, tilt angle, ambient temperature, and emissivity of the plate are considered as the operating parameters to obtain the thermal performance of the SFPSAH using the TLBO algorithm. The computational results have shown that the TLBO algorithm is better or competitive to other optimization algorithms recently reported in the literature for the considered problem.

  10. Structure, Content, Delivery, Service, and Outcomes: Quality e-Learning in higher education

    Directory of Open Access Journals (Sweden)

    Colla J. MacDonald

    2005-07-01

    Full Text Available This paper addresses the need for quality e-Learning experiences. We used the Demand-Driven Learning Model (MacDonald, Stodel, Farres, Breithaupt, and Gabriel, 2001 to evaluate an online Masters in Education course. Multiple data collection methods were used to understand the experiences of stakeholders in this case study: the learners, design team, and facilitators. We found that all five dimensions of the model (structure, content, delivery, service, and outcomes must work in concert to implement a quality e-Learning course. Key themes include evolving learner needs, the search for connection, becoming an able e-participant, valued interactions, social construction of content, integration of delivery partners, and mindful weighing of benefits and trade-offs. By sharing insights into what is needed to design and deliver an e-Learning experience, our findings add to the growing knowledge of online learning. Using this model to evaluate perceptions of quality by key stakeholders has led to insights and recommendations on the Demand Driven Learning Model itself which may be useful for researchers in this area and strengthen the model. Quality has been defined in terms of the design of the e-Learning experience, the contextualized experience of learners, and evidence of learning outcomes (Carr and Carr, 2000; Jung 2000; Salmon, 2000. Quality and design of e-Learning courses, however, are sometimes compromised in an “ . . . effort to simply get something up and running��� in response to pressing consumer demands (Dick, 1996, p. 59. Educators and researchers have voiced concern over the lack of rigorous evaluation studies of e-Learning programs (e.g., Arbaugh, 2000; Howell, Saba, Lindsay, and Williams, 2004; Lockyer, Patterson, and Harper, 1999; Robinson, 2001. McGorry (2003 adds, “although the number of courses being delivered via the Internet is increasing rapidly, our knowledge of what makes these courses effective learning experiences

  11. Surveying In-Service Teachers' Beliefs about Game-Based Learning and Perceptions of Technological Pedagogical and Content Knowledge of Games

    Science.gov (United States)

    Hsu, Chung-Yuan; Tsai, Meng-Jung; Chang, Yu-Hsuan; Liang, Jyh-Chong

    2017-01-01

    Using the Game-based-learning Teaching Belief Scale (GTBS) and the Technological Pedagogical Content Knowledge--Games questionnaire (TPACK-G), this study investigated 316 Taiwanese in-service teachers' teaching beliefs about game-based learning and their perceptions of game-based pedagogical content knowledge (GPCK). Both t-tests and ANOVA…

  12. The Adaptation of Contents for the Creation of Foreign Language Learning Exams for Mobile Devices

    Directory of Open Access Journals (Sweden)

    Gimenez López Jose Luis

    2009-07-01

    Full Text Available This article describes the process of adaptation of online digital contents for the realization of foreign language learning tests through mobile devices. Taking into account the need detected in relation to the quick development of mobile technologies, the development and adaptation of existing online exams for mobile devices will be studied. We will do that by considering the possible navigation limits when using multiplatforms, and the aspects related to the formal and technical conditions which the audiovisual contents shown by the device must fulfil. The existing online language learning tests can be adapted to mobile devices through the programming XHTML language. But, the limitations of navigability in relation to contents and the handling of interaction devices available for users to do the tests must also be considered.

  13. The Developing Infant Creates a Curriculum for Statistical Learning.

    Science.gov (United States)

    Smith, Linda B; Jayaraman, Swapnaa; Clerkin, Elizabeth; Yu, Chen

    2018-04-01

    New efforts are using head cameras and eye-trackers worn by infants to capture everyday visual environments from the point of view of the infant learner. From this vantage point, the training sets for statistical learning develop as the sensorimotor abilities of the infant develop, yielding a series of ordered datasets for visual learning that differ in content and structure between timepoints but are highly selective at each timepoint. These changing environments may constitute a developmentally ordered curriculum that optimizes learning across many domains. Future advances in computational models will be necessary to connect the developmentally changing content and statistics of infant experience to the internal machinery that does the learning. Copyright © 2018 Elsevier Ltd. All rights reserved.

  14. The island model for parallel implementation of evolutionary algorithm of Population-Based Incremental Learning (PBIL) optimization

    International Nuclear Information System (INIS)

    Lima, Alan M.M. de; Schirru, Roberto

    2000-01-01

    Genetic algorithms are biologically motivated adaptive systems which have been used, with good results, for function optimization. The purpose of this work is to introduce a new parallelization method to be applied to the Population-Based Incremental Learning (PBIL) algorithm. PBIL combines standard genetic algorithm mechanisms with simple competitive learning and has ben successfully used in combinatorial optimization problems. The development of this algorithm aims its application to the reload optimization of PWR nuclear reactors. Tests have been performed with combinatorial optimization problems similar to the reload problem. Results are compared to the serial PBIL ones, showing the new method's superiority and its viability as a tool for the nuclear core reload problem solution. (author)

  15. “GENIT” AS EFFECTIVE DESIGN OF LEARNING MEDIA

    Directory of Open Access Journals (Sweden)

    Supriadi Mardiki

    2018-01-01

    Full Text Available There are two main approaches to using media in schools: students can learn "from" Media and technology, and they can learn "with" media and technology. The basis for The use of media and technology as a "tutor" in schools is "educational communication," that is the deliberate act of communicating content (teaching content for students by assuming that they will learn something "from" this communication, so communication is not again free but controlled and conditioned for educational purposes. Findings on the impact of technologybased instruction in education can be concluded that technology as a tutor has a positive effect on learning, one of them is the student can complete a set of educational goals in less time than necessary in a traditional approach. However, these two approaches only make the task become easier but do not activate and facilitate them to think critical and higher learning. “Media Genit” uses a new approach as an approach that can optimize perspective-based cognitive processes, constructivism, which constitutes an environment in which the student as a designer thinks creative about content combined with real-world tasks, student learning content, enjoy the learning process, and recognize that they have created something that is valuable.

  16. Optimal Learning in Schools--Theoretical Evidence: Part 3 Individual Differences

    Science.gov (United States)

    Crossland, John

    2017-01-01

    Parts 1 and 2 in this four-part series of articles (Crossland, 2016, 2017) discussed the recent research from neuroscience linked to concepts from cognitive development that brought Piaget's theories into the 21st century and showed the most effective provision towards more optimal learning strategies. Then the discussion moved onto Demetriou's…

  17. Assessment of the Optimization of E-Learning Facilities to Lecturers ...

    African Journals Online (AJOL)

    The study assessed the optimization of e-learning facilities to lecturers and students in Federal Colleges of Education in North West Zone of Nigeria. A descriptive survey was used. The population comprised all the lecturers and students in five (5) Federal Colleges of Education in the zone – numbering about 3,650 ...

  18. Optimizing Cognitive Load for Learning from Computer-Based Science Simulations

    Science.gov (United States)

    Lee, Hyunjeong; Plass, Jan L.; Homer, Bruce D.

    2006-01-01

    How can cognitive load in visual displays of computer simulations be optimized? Middle-school chemistry students (N = 257) learned with a simulation of the ideal gas law. Visual complexity was manipulated by separating the display of the simulations in two screens (low complexity) or presenting all information on one screen (high complexity). The…

  19. Pareto Optimal Solutions for Network Defense Strategy Selection Simulator in Multi-Objective Reinforcement Learning

    Directory of Open Access Journals (Sweden)

    Yang Sun

    2018-01-01

    Full Text Available Using Pareto optimization in Multi-Objective Reinforcement Learning (MORL leads to better learning results for network defense games. This is particularly useful for network security agents, who must often balance several goals when choosing what action to take in defense of a network. If the defender knows his preferred reward distribution, the advantages of Pareto optimization can be retained by using a scalarization algorithm prior to the implementation of the MORL. In this paper, we simulate a network defense scenario by creating a multi-objective zero-sum game and using Pareto optimization and MORL to determine optimal solutions and compare those solutions to different scalarization approaches. We build a Pareto Defense Strategy Selection Simulator (PDSSS system for assisting network administrators on decision-making, specifically, on defense strategy selection, and the experiment results show that the Satisficing Trade-Off Method (STOM scalarization approach performs better than linear scalarization or GUESS method. The results of this paper can aid network security agents attempting to find an optimal defense policy for network security games.

  20. NASA SMD STEM Activation: Enabling NASA Science Experts and Content into the Learning Environment

    Science.gov (United States)

    Hasan, Hashima; Erickson, Kristen

    2018-01-01

    The NASA Science Mission Directorate (SMD) restructured its efforts to enhance learning in science, technology, engineering, and mathematics (STEM) content areas through a cooperative agreement notice issued in 2015. This effort resulted in the competitive selection of 27 organizations to implement a strategic approach that leverages SMD’s unique assets. Three of these are exclusively directed towards Astrophysics. These unique assets include SMD’s science and engineering content and Science Discipline Subject Matter Experts. Awardees began their work during 2016 and span all areas of Earth and space science and the audiences NASA SMD intends to reach. The goal of the restructured STEM Activation program is to further enable NASA science experts and content into the learning environment more effectively and efficiently with learners of all ages. The objectives are to enable STEM education, improve US scientific literacy, advance national educational goals, and leverage efforts through partnerships. This presentation will provide an overview of the NASA SMD STEM Activation landscape and its commitment to meeting user needs.

  1. Content and Language Integrated Learning through an Online Game in Primary School: A Case Study

    Science.gov (United States)

    Dourda, Kyriaki; Bratitsis, Tharrenos; Griva, Eleni; Papadopoulou, Penelope

    2014-01-01

    In this paper an educational design proposal is presented which combines two well established teaching approaches, that of Game-based Learning (GBL) and Content and Language Integrated Learning (CLIL). The context of the proposal was the design of an educational geography computer game, utilizing QR Codes and Google Earth for teaching English…

  2. International Student Carbon Footprint Challenge--Social Media as a Content and Language Integrated Learning Environment

    Science.gov (United States)

    Fauville, Géraldine; Lantz-Andersson, Annika; Säljö, Roger

    2012-01-01

    Environmental education (EE) is now clearly specified in educational standards in many parts of the world, and at the same time the view of language learning is moving towards a content and language integrated learning (CLIL) strategy, to make English lessons more relevant and attractive for students (Eurydice, 2006). In this respect,…

  3. Relationship between the grades of a learned aversive-feeding response and the dopamine contents in Lymnaea

    Directory of Open Access Journals (Sweden)

    Hitoshi Aonuma

    2016-12-01

    Full Text Available The pond snail Lymnaea learns conditioned taste aversion (CTA and remembers not to respond to food substances that initially cause a feeding response. The possible relationship between how well snails learn to follow taste-aversion training and brain dopamine contents is not known. We examined this relationship and found the following: first, snails in the act of eating just before the commencement of CTA training were poor learners and had the highest dopamine contents in the brain; second, snails which had an ad libitum access to food, but were not eating just before training, were average learners and had lower dopamine contents; third, snails food-deprived for one day before training were the best learners and had significantly lower contents of dopamine compared to the previous two cohorts. There was a negative correlation between the CTA grades and the brain dopamine contents in these three cohorts. Fourth, snails food-deprived for five days before training were poor learners and had higher dopamine contents. Thus, severe hunger increased the dopamine content in the brain. Because dopamine functions as a reward transmitter, CTA in the severely deprived snails (i.e. the fourth cohort was thought to be mitigated by a high dopamine content.

  4. Textbooks for Content and Language Integrated Learning: policy, market and appropriate didactics?

    Directory of Open Access Journals (Sweden)

    María Ángeles Martín del Pozo

    2015-02-01

    Full Text Available The paper begins by approaching the concept of CLIL (Content and Language Integrated Learning providing a brief overview of the history of bilingual education. The influence of the linguistic policies of the European Union is discussed along with some beliefs about language teaching and how both have influenced the celerity of CLIL implementation, momentum and expansion. There are some indicators of the lack of a theoretical framework for CLIL, of insufficient teacher education and or inadequacy of materials. It is necessary to reflect systematically on to what extent commercially published textbooks match the demands of bilingual education. The second section centers on CLIL textbooks, mainly those commercialized by publishers, by referring to some recent studies which attempt to approach systematically their design and use. Since, by definition CLIL includes both content and language, our research question is if content books (in English also include content and language objectives. A corpus of 25 books from different subjects, years, and publishers is analyzed. The analysis shows an insufficient presence of linguistic objectives. Some reflections are made about this scarcity with the warning that this lack could hindrance an efficient implementation of CLIL. Thus, it could be said that these textbooks are not the product of discipline or didactic considerations but the result of the logic of market, publishers and linguistic policy.How to reference this articleMartín del Pozo, M. A., Rascón Estébanez, D. (2015. Textbooks for Content and Language Integrated Learning: policy, market and appropriate didactics?. Foro de Educación, 13(18, pp. 123-141. doi: http://dx.doi.org/10.14516/fde.2015.013.018.007 

  5. Teaching and Learning Numerical Analysis and Optimization: A Didactic Framework and Applications of Inquiry-Based Learning

    Science.gov (United States)

    Lappas, Pantelis Z.; Kritikos, Manolis N.

    2018-01-01

    The main objective of this paper is to propose a didactic framework for teaching Applied Mathematics in higher education. After describing the structure of the framework, several applications of inquiry-based learning in teaching numerical analysis and optimization are provided to illustrate the potential of the proposed framework. The framework…

  6. An Analysis of Learning Objectives and Content Coverage in Introductory Psychology Syllabi

    Science.gov (United States)

    Homa, Natalie; Hackathorn, Jana; Brown, Carrie M.; Garczynski, Amy; Solomon, Erin D.; Tennial, Rachel; Sanborn, Ursula A.; Gurung, Regan A. R.

    2013-01-01

    Introductory psychology is one of the most popular undergraduate courses and often serves as the gateway to choosing psychology as an academic major. However, little research has examined the typical structure of introductory psychology courses. The current study examined student learning objectives (SLOs) and course content in introductory…

  7. Effect of collaborative testing on learning and retention of course content in nursing students.

    Science.gov (United States)

    Rivaz, Mozhgan; Momennasab, Marzieh; Shokrollahi, Paymaneh

    2015-10-01

    Collaborative testing is a learning strategy that provides students with the opportunity to learn and practice collaboration. This study aimed to determine the effect of collaborative testing on test performance and retention of course content in nursing students of Shiraz University of Medical Sciences, Shiraz, Iran. This quasi-experimental study was carried out on 84 students enrolled in the course of Medical-Surgical 2 in Spring 2013 and Fall 2013 semesters. The control group consisting of 39 students participated in the first mid-term exam in an individual format. The intervention group, on the other hand, consisted of 45 students who took the test in a two-stage process. The first stage included an individual testing, while the second stage was a collaborative one given in groups of five individuals chosen randomly. Four weeks later, in order to investigate retention of the course content, both groups took part in the second mid-term exam held individually. The study findings showed significant difference between the mean scores in the intervention group in the Fall 2013 semester (p=0.001). Besides, a statistically significant difference was found between the two groups regarding the tests mean scores (p=0.001). Moreover, retention of course content improved in the collaborative group (p=0.001). The results indicated an increase in test performance and a long-term learning enhancement in collaborative testing compared with the traditional method. Collaborative testing, as an active learning technique and a valuable assessment method, can help nursing instructors provide the alumni with strong problem-solving and critical thinking abilities at healthcare environments.

  8. Machine Learning-Based Content Analysis: Automating the analysis of frames and agendas in political communication research

    NARCIS (Netherlands)

    Burscher, B.

    2016-01-01

    We used machine learning to study policy issues and frames in political messages. With regard to frames, we investigated the automation of two content-analytical tasks: frame coding and frame identification. We found that both tasks can be successfully automated by means of machine learning

  9. A new evolutionary algorithm with LVQ learning for the optimization of combinatory problems as a reload of nuclear reactors

    International Nuclear Information System (INIS)

    Machado, Marcelo Dornellas

    1999-04-01

    Genetic algorithms are biologically motivated adaptive systems which have been used, with good results, for function optimization. In this work, a new learning mode, to be used by the Population-Based Incremental Learning (PBIL) algorithm, who combines mechanisms of standard genetic algorithm with simple competitive learning, has the aim to build a new evolutionary algorithm to be used in optimization of numerical problems and combinatorial problems. This new learning mode uses a variable learning rate during the optimization process, constituting a process know as proportional reward. The development of this new algorithm aims its application in the optimization of reload problem of PWR nuclear reactors. This problem can be interpreted as search of a load pattern to be used in the nucleus of the reactor in order to increase the useful life of the nuclear fuel. For the test, two classes of problems are used: numerical problems and combinatorial problem, the major interest relies on the last class. The results achieved with the tests indicate the applicability of the new learning mode, showing its potential as a developing tool in the solution of reload problem. (author)

  10. Augmented Reality and Mobile Pedestrian Navigation with Heritage thematic contents: Perception of learning

    Directory of Open Access Journals (Sweden)

    Jorge Joo Nagata

    2017-03-01

    Full Text Available By creating a mobile learning app about heritage elements, related to the implementation of resources such as Augmented Reality (AR and Mobile Pedestrian Navigation (MPN, some training process has been developed in mobility contexts, linked to the territorial information on the historical and cultural patrimony corresponding to the cities of Salamanca (Spain and Santiago (Chile. The software development focuses on two major areas: the first is the determination of the territorial scenarios, generating a database that can be used in mobile contexts; the second is focused on the design and the development of the AR-MPN application, defining its architecture, functionality, interface and implementation. The results are the construction of flexible software in a mobile environment that allows the presentation of contents on the historical heritage of the selected cities. In a complementary way, the determination of the effectiveness of the application is carried out within a context of situated and mobile learning. From the students’ perception, both the mobile application and the developed learning are evaluated, using an instrument (questionnaire, consulting dimensions such as the hardware, the software and the patrimonial contents as part of the educational process in a mobile and localized context. The results establish that there is a positive evaluation around the tools and the implemented experiences, allowing the generation of new learning methodologies mediated in mobile contexts.

  11. Optimal interference code based on machine learning

    Science.gov (United States)

    Qian, Ye; Chen, Qian; Hu, Xiaobo; Cao, Ercong; Qian, Weixian; Gu, Guohua

    2016-10-01

    In this paper, we analyze the characteristics of pseudo-random code, by the case of m sequence. Depending on the description of coding theory, we introduce the jamming methods. We simulate the interference effect or probability model by the means of MATLAB to consolidate. In accordance with the length of decoding time the adversary spends, we find out the optimal formula and optimal coefficients based on machine learning, then we get the new optimal interference code. First, when it comes to the phase of recognition, this study judges the effect of interference by the way of simulating the length of time over the decoding period of laser seeker. Then, we use laser active deception jamming simulate interference process in the tracking phase in the next block. In this study we choose the method of laser active deception jamming. In order to improve the performance of the interference, this paper simulates the model by MATLAB software. We find out the least number of pulse intervals which must be received, then we can make the conclusion that the precise interval number of the laser pointer for m sequence encoding. In order to find the shortest space, we make the choice of the greatest common divisor method. Then, combining with the coding regularity that has been found before, we restore pulse interval of pseudo-random code, which has been already received. Finally, we can control the time period of laser interference, get the optimal interference code, and also increase the probability of interference as well.

  12. Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier

    Directory of Open Access Journals (Sweden)

    C. V. Subbulakshmi

    2015-01-01

    Full Text Available Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes a hybrid methodology based on machine learning paradigm. This paradigm integrates the successful exploration mechanism called self-regulated learning capability of the particle swarm optimization (PSO algorithm with the extreme learning machine (ELM classifier. As a recent off-line learning method, ELM is a single-hidden layer feedforward neural network (FFNN, proved to be an excellent classifier with large number of hidden layer neurons. In this research, PSO is used to determine the optimum set of parameters for the ELM, thus reducing the number of hidden layer neurons, and it further improves the network generalization performance. The proposed method is experimented on five benchmarked datasets of the UCI Machine Learning Repository for handling medical dataset classification. Simulation results show that the proposed approach is able to achieve good generalization performance, compared to the results of other classifiers.

  13. Optimized Extreme Learning Machine for Power System Transient Stability Prediction Using Synchrophasors

    Directory of Open Access Journals (Sweden)

    Yanjun Zhang

    2015-01-01

    Full Text Available A new optimized extreme learning machine- (ELM- based method for power system transient stability prediction (TSP using synchrophasors is presented in this paper. First, the input features symbolizing the transient stability of power systems are extracted from synchronized measurements. Then, an ELM classifier is employed to build the TSP model. And finally, the optimal parameters of the model are optimized by using the improved particle swarm optimization (IPSO algorithm. The novelty of the proposal is in the fact that it improves the prediction performance of the ELM-based TSP model by using IPSO to optimize the parameters of the model with synchrophasors. And finally, based on the test results on both IEEE 39-bus system and a large-scale real power system, the correctness and validity of the presented approach are verified.

  14. Web-Based Learning Support System

    Science.gov (United States)

    Fan, Lisa

    Web-based learning support system offers many benefits over traditional learning environments and has become very popular. The Web is a powerful environment for distributing information and delivering knowledge to an increasingly wide and diverse audience. Typical Web-based learning environments, such as Web-CT, Blackboard, include course content delivery tools, quiz modules, grade reporting systems, assignment submission components, etc. They are powerful integrated learning management systems (LMS) that support a number of activities performed by teachers and students during the learning process [1]. However, students who study a course on the Internet tend to be more heterogeneously distributed than those found in a traditional classroom situation. In order to achieve optimal efficiency in a learning process, an individual learner needs his or her own personalized assistance. For a web-based open and dynamic learning environment, personalized support for learners becomes more important. This chapter demonstrates how to realize personalized learning support in dynamic and heterogeneous learning environments by utilizing Adaptive Web technologies. It focuses on course personalization in terms of contents and teaching materials that is according to each student's needs and capabilities. An example of using Rough Set to analyze student personal information to assist students with effective learning and predict student performance is presented.

  15. A Course Wiki: Challenges in Facilitating and Assessing Student-Generated Learning Content for the Humanities Classroom

    Science.gov (United States)

    Lazda-Cazers, Rasma

    2010-01-01

    New Web technology allows for the design of traditionally lecture-centered humanities courses by fostering active learning and engaging students as producers of learning content. The article presents the experiences with a student-generated wiki for a Germanic Mythology course. Evaluations indicated an overwhelmingly positive student experience…

  16. Brain Based Learning in Science Education in Turkey: Descriptive Content and Meta Analysis of Dissertations

    Science.gov (United States)

    Yasar, M. Diyaddin

    2017-01-01

    This study aimed at performing content analysis and meta-analysis on dissertations related to brain-based learning in science education to find out the general trend and tendency of brain-based learning in science education and find out the effect of such studies on achievement and attitude of learners with the ultimate aim of raising awareness…

  17. TEACHER’S PROFESSIONAL COMPETENCE IN E-LEARNING ENVIRONMENT: CONTENT AND SPHERES OF APPLICATION

    Directory of Open Access Journals (Sweden)

    Irene Stetsenko

    2015-09-01

    Full Text Available The article lays bare the notion “e-learning”. It presents and reasons the advantages of using e-learning tools and technologies. It shows the contents of teachers’ professional competence in the field of information and communication technologies. The problems and ways to compile and update the skills required to work effectively in e-learning environment in the context of continuous pedagogical educational system (pedagogical high school student-teacher-teacher educational organization are discussed in the article as well.

  18. Synthesis of LaNiO3 perovskite type by chelating precursor method using EDTA: optimization of chelating content

    International Nuclear Information System (INIS)

    Santos, Jose Carlos dos; Pedrosa, Anne Michelle Garrido; Mesquita, Maria Eliane; Souza, Marcelo Jose Barros de

    2011-01-01

    The perovskites are strategic materials due their catalytic, electronic and magnetic properties. These properties are influenced by the calcination and synthesis conditions. In this work was carried out the synthesis of LaNiO 3 perovskite type by chelating precursor method using EDTA and also was studied the optimization of the EDTA content in the synthesis. The synthesized materials were characterized by X-ray diffraction (XRD), thermal gravimetric analysis (TG) and Infrared Spectroscopy (FTIR). In the optimization of the EDTA content the lowest ratio of metal / EDTA used was 1.0 / 0.1, where it was possible to obtain monophasic perovskite. (author)

  19. Solar photovoltaic power forecasting using optimized modified extreme learning machine technique

    Directory of Open Access Journals (Sweden)

    Manoja Kumar Behera

    2018-06-01

    Full Text Available Prediction of photovoltaic power is a significant research area using different forecasting techniques mitigating the effects of the uncertainty of the photovoltaic generation. Increasingly high penetration level of photovoltaic (PV generation arises in smart grid and microgrid concept. Solar source is irregular in nature as a result PV power is intermittent and is highly dependent on irradiance, temperature level and other atmospheric parameters. Large scale photovoltaic generation and penetration to the conventional power system introduces the significant challenges to microgrid a smart grid energy management. It is very critical to do exact forecasting of solar power/irradiance in order to secure the economic operation of the microgrid and smart grid. In this paper an extreme learning machine (ELM technique is used for PV power forecasting of a real time model whose location is given in the Table 1. Here the model is associated with the incremental conductance (IC maximum power point tracking (MPPT technique that is based on proportional integral (PI controller which is simulated in MATLAB/SIMULINK software. To train single layer feed-forward network (SLFN, ELM algorithm is implemented whose weights are updated by different particle swarm optimization (PSO techniques and their performance are compared with existing models like back propagation (BP forecasting model. Keywords: PV array, Extreme learning machine, Maximum power point tracking, Particle swarm optimization, Craziness particle swarm optimization, Accelerate particle swarm optimization, Single layer feed-forward network

  20. Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods

    Directory of Open Access Journals (Sweden)

    Felix F. Gonzalez-Navarro

    2016-10-01

    Full Text Available Biosensors are small analytical devices incorporating a biological recognition element and a physico-chemical transducer to convert a biological signal into an electrical reading. Nowadays, their technological appeal resides in their fast performance, high sensitivity and continuous measuring capabilities; however, a full understanding is still under research. This paper aims to contribute to this growing field of biotechnology, with a focus on Glucose-Oxidase Biosensor (GOB modeling through statistical learning methods from a regression perspective. We model the amperometric response of a GOB with dependent variables under different conditions, such as temperature, benzoquinone, pH and glucose concentrations, by means of several machine learning algorithms. Since the sensitivity of a GOB response is strongly related to these dependent variables, their interactions should be optimized to maximize the output signal, for which a genetic algorithm and simulated annealing are used. We report a model that shows a good generalization error and is consistent with the optimization.

  1. Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods.

    Science.gov (United States)

    Gonzalez-Navarro, Felix F; Stilianova-Stoytcheva, Margarita; Renteria-Gutierrez, Livier; Belanche-Muñoz, Lluís A; Flores-Rios, Brenda L; Ibarra-Esquer, Jorge E

    2016-10-26

    Biosensors are small analytical devices incorporating a biological recognition element and a physico-chemical transducer to convert a biological signal into an electrical reading. Nowadays, their technological appeal resides in their fast performance, high sensitivity and continuous measuring capabilities; however, a full understanding is still under research. This paper aims to contribute to this growing field of biotechnology, with a focus on Glucose-Oxidase Biosensor (GOB) modeling through statistical learning methods from a regression perspective. We model the amperometric response of a GOB with dependent variables under different conditions, such as temperature, benzoquinone, pH and glucose concentrations, by means of several machine learning algorithms. Since the sensitivity of a GOB response is strongly related to these dependent variables, their interactions should be optimized to maximize the output signal, for which a genetic algorithm and simulated annealing are used. We report a model that shows a good generalization error and is consistent with the optimization.

  2. Optimal Search Strategy of Robotic Assembly Based on Neural Vibration Learning

    Directory of Open Access Journals (Sweden)

    Lejla Banjanovic-Mehmedovic

    2011-01-01

    Full Text Available This paper presents implementation of optimal search strategy (OSS in verification of assembly process based on neural vibration learning. The application problem is the complex robot assembly of miniature parts in the example of mating the gears of one multistage planetary speed reducer. Assembly of tube over the planetary gears was noticed as the most difficult problem of overall assembly. The favourable influence of vibration and rotation movement on compensation of tolerance was also observed. With the proposed neural-network-based learning algorithm, it is possible to find extended scope of vibration state parameter. Using optimal search strategy based on minimal distance path between vibration parameter stage sets (amplitude and frequencies of robots gripe vibration and recovery parameter algorithm, we can improve the robot assembly behaviour, that is, allow the fastest possible way of mating. We have verified by using simulation programs that search strategy is suitable for the situation of unexpected events due to uncertainties.

  3. Optimizing learning of a locomotor task: amplifying errors as needed.

    Science.gov (United States)

    Marchal-Crespo, Laura; López-Olóriz, Jorge; Jaeger, Lukas; Riener, Robert

    2014-01-01

    Research on motor learning has emphasized that errors drive motor adaptation. Thereby, several researchers have proposed robotic training strategies that amplify movement errors rather than decrease them. In this study, the effect of different robotic training strategies that amplify errors on learning a complex locomotor task was investigated. The experiment was conducted with a one degree-of freedom robotic stepper (MARCOS). Subjects were requested to actively coordinate their legs in a desired gait-like pattern in order to track a Lissajous figure presented on a visual display. Learning with three different training strategies was evaluated: (i) No perturbation: the robot follows the subjects' movement without applying any perturbation, (ii) Error amplification: existing errors were amplified with repulsive forces proportional to errors, (iii) Noise disturbance: errors were evoked with a randomly-varying force disturbance. Results showed that training without perturbations was especially suitable for a subset of initially less-skilled subjects, while error amplification seemed to benefit more skilled subjects. Training with error amplification, however, limited transfer of learning. Random disturbing forces benefited learning and promoted transfer in all subjects, probably because it increased attention. These results suggest that learning a locomotor task can be optimized when errors are randomly evoked or amplified based on subjects' initial skill level.

  4. Construction and validation of clinical contents for development of learning objects.

    Science.gov (United States)

    Hortense, Flávia Tatiana Pedrolo; Bergerot, Cristiane Decat; Domenico, Edvane Birelo Lopes de

    2018-01-01

    to describe the process of construction and validation of clinical contents for health learning objects, aimed at patients in the treatment of head and neck cancer. descriptive, methodological study. The development of the script and the storyboard were based on scientific evidence and submitted to the appreciation of specialists for validation of content. The agreement index was checked quantitatively and the suggestions were qualitatively evaluated. The items described in the roadmap were approved by 99% of expert experts. The suggestions for adjustments were inserted in their entirety in the final version. The free-marginal kappa statistical test, for multiple evaluators, presented value equal to 0.68%, granting a substantial agreement. The steps taken in the construction and validation of the content for the production of educational material for patients with head and neck cancer were adequate, relevant and suitable for use in other subjects.

  5. Assessing STEM content learning: using the Arctic's changing climate to develop 21st century learner

    Science.gov (United States)

    Henderson, G. R.; Durkin, S.; Moran, A.

    2016-12-01

    In recent years the U.S. federal government has called for an increased focus on science, technology, engineering, and mathematics (STEM) in the educational system to ensure that there will be sufficient technical expertise to meet the needs of business and industry. As a direct result of this STEM emphasis, the number of outreach activities aimed at actively engaging these students in STEM learning has surged. Such activities, frequently in the form of summer camps led by university faculty, have targeted primary and secondary school students with the goal of growing student interest in STEM majors and STEM careers. This study assesses short-term content learning using a climate module that highlights rapidly changing Arctic climate conditions to illustrate concepts of radiative energy balance and climate feedback. Hands-on measurement of short and longwave radiation using simple instrumentation is used to demonstrate concepts that are then related back to the "big picture" Arctic issue. Pre and post module questionnaires were used to assess content learning, as this learning type has been identified as the basis for STEM literacy and the vehicle by which 21st century learning skills are usually developed. In this instance, students applied subject knowledge they gained by taking radiation measurements to better understand the real-world problem of climate change.

  6. The role of service-learning in college students' environmental literacy: Content knowledge, attitudes, and behaviors

    Science.gov (United States)

    Singletary, Joanna Lynn Bush

    This study evaluated the relationship of environmental service-learning on environmental literacy in undergraduates. The subjects were 36 undergraduates at a small liberal arts university enrolled in an environmental biology course. To determine the role of environmental service-learning on college students' environmental knowledge, attitudes, behaviors, and environmental literacy, this study utilized concurrent mixed methods approach for qualitative and quantitative analysis. A quasi-experimental repeated measures approach was the design of the quantitative component of the study. Data were collected on attitude, behavior, and content knowledge aspects of environmental literacy as measured by the Environmental Literacy Survey (Kibert, 2000). Hypotheses were tested by independent samples ttests and repeated measures ANOVA. Repeated measures ANOVA conducted on participants' three subscales scores for the Environmental Literacy Survey (attitude, behavior, and knowledge) indicated that students who participated in environmental service-learning scored statistically significantly higher than those that did not initially participate in service-learning. Qualitative data collected in the form of journal reflections and portfolios were evaluated for themes of environmental attitudes or affective statements, environmentally positive behaviors and skills, and ecological content. Quantitative and qualitative data support the positive role of environmental service-learning in the development of environmental literacy in undergraduate students.

  7. Optimizing the process of making sweet wines to minimize the content of ochratoxin A.

    Science.gov (United States)

    Ruíz Bejarano, M Jesús; Rodríguez Dodero, M Carmen; García Barroso, Carmelo

    2010-12-22

    During the drying process of raisins, the grapes are subjected to climatic variations that can result in heavy infections of some fungal species that produce ochratoxin A (OTA), a powerful toxic metabolite, whose maximum permitted content is set by the European Union at 2.0 μg/L for grapes, wine and other drinks derived from the grape. The aim of this paper is to optimize the process of making sweet wines in order to minimize the content of ochratoxin A. The results reflect a reduction of the OTA content in grapes dried under controlled conditions in a climatic chamber up to 24% compared to those sunned in the traditional way. A decrease of the concentrations of OTA is also observed during the processes of vinification. Those wines with prefermentative maceration reached a higher OTA content than the wines without maceration, but unexpectedly were not those preferred from a sensorial point of view. In addition, the process of aging in oak casks has been shown to serve as a natural method for reducing the OTA content of these wines.

  8. Exploring Changes to a Teacher's Teaching Practices and Student Learning through a Volleyball Content Knowledge Workshop

    Science.gov (United States)

    Kim, Insook

    2016-01-01

    This paper describes how improving a teacher's content knowledge changes his teaching practices and its subsequent effects on student learning during a middle school volleyball instructional unit. The study was designed to challenge teacher educators' thinking about the importance of in-depth content knowledge for effective teaching by…

  9. A hypothesis on improving foreign accents by optimizing variability in vocal learning brain circuits.

    Science.gov (United States)

    Simmonds, Anna J

    2015-01-01

    Rapid vocal motor learning is observed when acquiring a language in early childhood, or learning to speak another language later in life. Accurate pronunciation is one of the hardest things for late learners to master and they are almost always left with a non-native accent. Here, I propose a novel hypothesis that this accent could be improved by optimizing variability in vocal learning brain circuits during learning. Much of the neurobiology of human vocal motor learning has been inferred from studies on songbirds. Jarvis (2004) proposed the hypothesis that as in songbirds there are two pathways in humans: one for learning speech (the striatal vocal learning pathway), and one for production of previously learnt speech (the motor pathway). Learning new motor sequences necessary for accurate non-native pronunciation is challenging and I argue that in late learners of a foreign language the vocal learning pathway becomes inactive prematurely. The motor pathway is engaged once again and learners maintain their original native motor patterns for producing speech, resulting in speaking with a foreign accent. Further, I argue that variability in neural activity within vocal motor circuitry generates vocal variability that supports accurate non-native pronunciation. Recent theoretical and experimental work on motor learning suggests that variability in the motor movement is necessary for the development of expertise. I propose that there is little trial-by-trial variability when using the motor pathway. When using the vocal learning pathway variability gradually increases, reflecting an exploratory phase in which learners try out different ways of pronouncing words, before decreasing and stabilizing once the "best" performance has been identified. The hypothesis proposed here could be tested using behavioral interventions that optimize variability and engage the vocal learning pathway for longer, with the prediction that this would allow learners to develop new motor

  10. Gamification: Questing to Integrate Content Knowledge, Literacy, and 21st-Century Learning

    Science.gov (United States)

    Kingsley, Tara L.; Grabner-Hagen, Melissa M.

    2015-01-01

    This article showcases the use of gamification as a means to turn an existing curriculum into a game-based learning environment. The purpose of this article is to examine how gamification, coupled with effective pedagogy, can support the acquisition of 21st-century skills. Gamifying content allows students to earn experience points, badges, and…

  11. Learning-based 3D surface optimization from medical image reconstruction

    Science.gov (United States)

    Wei, Mingqiang; Wang, Jun; Guo, Xianglin; Wu, Huisi; Xie, Haoran; Wang, Fu Lee; Qin, Jing

    2018-04-01

    Mesh optimization has been studied from the graphical point of view: It often focuses on 3D surfaces obtained by optical and laser scanners. This is despite the fact that isosurfaced meshes of medical image reconstruction suffer from both staircases and noise: Isotropic filters lead to shape distortion, while anisotropic ones maintain pseudo-features. We present a data-driven method for automatically removing these medical artifacts while not introducing additional ones. We consider mesh optimization as a combination of vertex filtering and facet filtering in two stages: Offline training and runtime optimization. In specific, we first detect staircases based on the scanning direction of CT/MRI scanners, and design a staircase-sensitive Laplacian filter (vertex-based) to remove them; and then design a unilateral filtered facet normal descriptor (uFND) for measuring the geometry features around each facet of a given mesh, and learn the regression functions from a set of medical meshes and their high-resolution reference counterparts for mapping the uFNDs to the facet normals of the reference meshes (facet-based). At runtime, we first perform staircase-sensitive Laplacian filter on an input MC (Marching Cubes) mesh, and then filter the mesh facet normal field using the learned regression functions, and finally deform it to match the new normal field for obtaining a compact approximation of the high-resolution reference model. Tests show that our algorithm achieves higher quality results than previous approaches regarding surface smoothness and surface accuracy.

  12. Trade-off between learning and exploitation: the Pareto-optimal versus evolutionarily stable learning schedule in cumulative cultural evolution.

    Science.gov (United States)

    Wakano, Joe Yuichiro; Miura, Chiaki

    2014-02-01

    Inheritance of culture is achieved by social learning and improvement is achieved by individual learning. To realize cumulative cultural evolution, social and individual learning should be performed in this order in one's life. However, it is not clear whether such a learning schedule can evolve by the maximization of individual fitness. Here we study optimal allocation of lifetime to learning and exploitation in a two-stage life history model under a constant environment. We show that the learning schedule by which high cultural level is achieved through cumulative cultural evolution is unlikely to evolve as a result of the maximization of individual fitness, if there exists a trade-off between the time spent in learning and the time spent in exploiting the knowledge that has been learned in earlier stages of one's life. Collapse of a fully developed culture is predicted by a game-theoretical analysis where individuals behave selfishly, e.g., less learning and more exploiting. The present study suggests that such factors as group selection, the ability of learning-while-working ("on the job training"), or environmental fluctuation might be important in the realization of rapid and cumulative cultural evolution that is observed in humans. Copyright © 2013 Elsevier Inc. All rights reserved.

  13. Learning-Based Adaptive Optimal Tracking Control of Strict-Feedback Nonlinear Systems.

    Science.gov (United States)

    Gao, Weinan; Jiang, Zhong-Ping; Weinan Gao; Zhong-Ping Jiang; Gao, Weinan; Jiang, Zhong-Ping

    2018-06-01

    This paper proposes a novel data-driven control approach to address the problem of adaptive optimal tracking for a class of nonlinear systems taking the strict-feedback form. Adaptive dynamic programming (ADP) and nonlinear output regulation theories are integrated for the first time to compute an adaptive near-optimal tracker without any a priori knowledge of the system dynamics. Fundamentally different from adaptive optimal stabilization problems, the solution to a Hamilton-Jacobi-Bellman (HJB) equation, not necessarily a positive definite function, cannot be approximated through the existing iterative methods. This paper proposes a novel policy iteration technique for solving positive semidefinite HJB equations with rigorous convergence analysis. A two-phase data-driven learning method is developed and implemented online by ADP. The efficacy of the proposed adaptive optimal tracking control methodology is demonstrated via a Van der Pol oscillator with time-varying exogenous signals.

  14. Optimal Channel Selection Based on Online Decision and Offline Learning in Multichannel Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Mu Qiao

    2017-01-01

    Full Text Available We propose a channel selection strategy with hybrid architecture, which combines the centralized method and the distributed method to alleviate the overhead of access point and at the same time provide more flexibility in network deployment. By this architecture, we make use of game theory and reinforcement learning to fulfill the optimal channel selection under different communication scenarios. Particularly, when the network can satisfy the requirements of energy and computational costs, the online decision algorithm based on noncooperative game can help each individual sensor node immediately select the optimal channel. Alternatively, when the network cannot satisfy the requirements of energy and computational costs, the offline learning algorithm based on reinforcement learning can help each individual sensor node to learn from its experience and iteratively adjust its behavior toward the expected target. Extensive simulation results validate the effectiveness of our proposal and also prove that higher system throughput can be achieved by our channel selection strategy over the conventional off-policy channel selection approaches.

  15. Optimization and control of a continuous stirred tank fermenter using learning system

    Energy Technology Data Exchange (ETDEWEB)

    Thibault, J [Dept. of Chemical Engineering, Laval Univ., Quebec City, PQ (Canada); Najim, K [CNRS, URA 192, GRECO SARTA, Ecole Nationale Superieure d' Ingenieurs de Genie Chimique, 31 - Toulouse (France)

    1993-05-01

    A variable structure learning automaton is used as an optimization and control of a continuous stirred tank fermenter. The alogrithm requires no modelling of the process. The use of appropriate learning rules enables to locate the optimum dilution rate in order to maximize an objective cost function. It is shown that a hierarchical structure of automata can adapt to environmental changes and can also modify efficiently the domain of variation of the control variable in order to encompass the optimum value. (orig.)

  16. Learning a Language and Studying Content in an Additional Language: Student Opinions

    Science.gov (United States)

    Ger, Ugur; Bahar, Mustafa

    2018-01-01

    This study aims to understand the opinions of middle school and high school students about language learning and studying other content in an additional language in the school settings where English is used as the medium of instruction to teach more than 50% of the curriculum. For this end, 261 students from three different schools were…

  17. Construction and validation of clinical contents for development of learning objects

    Directory of Open Access Journals (Sweden)

    Flávia Tatiana Pedrolo Hortense

    Full Text Available ABSTRACT Objective: to describe the process of construction and validation of clinical contents for health learning objects, aimed at patients in the treatment of head and neck cancer. Method: descriptive, methodological study. The development of the script and the storyboard were based on scientific evidence and submitted to the appreciation of specialists for validation of content. The agreement index was checked quantitatively and the suggestions were qualitatively evaluated. Results: The items described in the roadmap were approved by 99% of expert experts. The suggestions for adjustments were inserted in their entirety in the final version. The free-marginal kappa statistical test, for multiple evaluators, presented value equal to 0.68%, granting a substantial agreement. Conclusion: The steps taken in the construction and validation of the content for the production of educational material for patients with head and neck cancer were adequate, relevant and suitable for use in other subjects.

  18. A reward optimization method based on action subrewards in hierarchical reinforcement learning.

    Science.gov (United States)

    Fu, Yuchen; Liu, Quan; Ling, Xionghong; Cui, Zhiming

    2014-01-01

    Reinforcement learning (RL) is one kind of interactive learning methods. Its main characteristics are "trial and error" and "related reward." A hierarchical reinforcement learning method based on action subrewards is proposed to solve the problem of "curse of dimensionality," which means that the states space will grow exponentially in the number of features and low convergence speed. The method can reduce state spaces greatly and choose actions with favorable purpose and efficiency so as to optimize reward function and enhance convergence speed. Apply it to the online learning in Tetris game, and the experiment result shows that the convergence speed of this algorithm can be enhanced evidently based on the new method which combines hierarchical reinforcement learning algorithm and action subrewards. The "curse of dimensionality" problem is also solved to a certain extent with hierarchical method. All the performance with different parameters is compared and analyzed as well.

  19. Optimal Learning for Efficient Experimentation in Nanotechnology and Biochemistry

    Science.gov (United States)

    2015-12-22

    AFRL-AFOSR-VA-TR-2016-0018 Optimal Learning for Efficient Experimentation in Nanotechnology, Biochemistry Warren Powell TRUSTEES OF PRINCETON... Biochemistry 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER FA9550-12-1-0200 5c.  PROGRAM ELEMENT NUMBER 61102F 6. AUTHOR(S) Warren Powell 5d.  PROJECT NUMBER 5e...scientists. 15. SUBJECT TERMS Biochemistry 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF 19a.  NAME OF RESPONSIBLE PERSON Warren

  20. Teaching with Stories as the Content and Context for Learning

    Directory of Open Access Journals (Sweden)

    Frances Vitali

    2016-02-01

    Full Text Available Undergraduate teacher education program students have the opportunity to work with diverse student populations in a local school district in the Four Corners Area in the Northwest part of New Mexico. The family oral history practicum is a way to connect theory and practice while recognizing the issue that language is not a neutral landscape. What better way to demonstrate this complementarity than through stories. The goal is to bring an awareness of respect for oral language in relationship to literate language and explore how to balance both perspectives in school culture as prospective teachers. Preservice teacher candidates become storytelling coaches and team up with third graders in semester long storytelling projects, collaborating with local elementary school teachers. Students' family stories become the content and context for teaching and learning. With a diverse classroom population of Navajo, Hispanic, Mexican, and White students, family stories are the heart and central theme of the project. Storytelling coaches learn the nuances of diversity when theory is massaged with authentic experience of students as they share what they have learned beside their young storytellers and authors.

  1. Reinforcement learning solution for HJB equation arising in constrained optimal control problem.

    Science.gov (United States)

    Luo, Biao; Wu, Huai-Ning; Huang, Tingwen; Liu, Derong

    2015-11-01

    The constrained optimal control problem depends on the solution of the complicated Hamilton-Jacobi-Bellman equation (HJBE). In this paper, a data-based off-policy reinforcement learning (RL) method is proposed, which learns the solution of the HJBE and the optimal control policy from real system data. One important feature of the off-policy RL is that its policy evaluation can be realized with data generated by other behavior policies, not necessarily the target policy, which solves the insufficient exploration problem. The convergence of the off-policy RL is proved by demonstrating its equivalence to the successive approximation approach. Its implementation procedure is based on the actor-critic neural networks structure, where the function approximation is conducted with linearly independent basis functions. Subsequently, the convergence of the implementation procedure with function approximation is also proved. Finally, its effectiveness is verified through computer simulations. Copyright © 2015 Elsevier Ltd. All rights reserved.

  2. Distance Learning Skills and Responsibilities: A Content Analysis of Job Announcements 1996-2010

    Science.gov (United States)

    Rebmann, Kristen Radsliff; Molitor, Simone; Rainey, Bonnie

    2012-01-01

    Archived job advertisements from the "International Federation of Library Associations and Institutions (IFLA) LIBJOBS" mailing list (1996-2010) were examined using content analysis. Findings suggest that distance learning (DL) skillsets as job qualifications emerged in the late 1990's and continue to be relevant today. Jobs with DL…

  3. The effects of CLIL on mathematical content learning: A longitudinal study

    Directory of Open Access Journals (Sweden)

    Jill Surmont

    2016-06-01

    Full Text Available Previous research has shown that content and language integrated learning (CLIL, an educational approach that offers content courses through more than one educational language, increases metalinguistic awareness. This improved insight into language structures is supposed to extend beyond the linguistic domain. In the present study, the question whether pupils who learn in a CLIL environment outperform their traditionally schooled peers in mathematics is investigated. In total, 107 pupils entered the study. All participants were in the first year of secondary education at a school in Ostend, in Flanders, the Dutch-speaking part of Belgium. Thirty-five pupils followed CLIL education in a foreign language (French and 72 followed traditional education that was given in the native language (Dutch. All participants were tested using a mathematical test at the beginning of the year, after three months, and after ten months. The first measurement of the mathematical scores showed that the two groups did not differ. In accordance with our hypothesis, the CLIL group scored higher than the non-CLIL group after ten months. Surprisingly, an effect was also found after three months. To conclude, CLIL appears to have a positive impact on the mathematical performance of pupils even after a short period of time.

  4. Learning Styles, Online Content Usage and Exam Performance in a Mixed-Format Introductory Computer Information Systems Course

    Science.gov (United States)

    Lang, Guido; O'Connell, Stephen D.

    2015-01-01

    We investigate the relationship between learning styles, online content usage and exam performance in an undergraduate introductory Computer Information Systems class comprised of both online video tutorials and in-person classes. Our findings suggest that, across students, (1) traditional learning style classification methodologies do not predict…

  5. An Improved Harmony Search Based on Teaching-Learning Strategy for Unconstrained Optimization Problems

    Directory of Open Access Journals (Sweden)

    Shouheng Tuo

    2013-01-01

    Full Text Available Harmony search (HS algorithm is an emerging population-based metaheuristic algorithm, which is inspired by the music improvisation process. The HS method has been developed rapidly and applied widely during the past decade. In this paper, an improved global harmony search algorithm, named harmony search based on teaching-learning (HSTL, is presented for high dimension complex optimization problems. In HSTL algorithm, four strategies (harmony memory consideration, teaching-learning strategy, local pitch adjusting, and random mutation are employed to maintain the proper balance between convergence and population diversity, and dynamic strategy is adopted to change the parameters. The proposed HSTL algorithm is investigated and compared with three other state-of-the-art HS optimization algorithms. Furthermore, to demonstrate the robustness and convergence, the success rate and convergence analysis is also studied. The experimental results of 31 complex benchmark functions demonstrate that the HSTL method has strong convergence and robustness and has better balance capacity of space exploration and local exploitation on high dimension complex optimization problems.

  6. Machine Learning Techniques in Optimal Design

    Science.gov (United States)

    Cerbone, Giuseppe

    1992-01-01

    to the problem, is then obtained by solving in parallel each of the sub-problems in the set and computing the one with the minimum cost. In addition to speeding up the optimization process, our use of learning methods also relieves the expert from the burden of identifying rules that exactly pinpoint optimal candidate sub-problems. In real engineering tasks it is usually too costly to the engineers to derive such rules. Therefore, this paper also contributes to a further step towards the solution of the knowledge acquisition bottleneck [Feigenbaum, 1977] which has somewhat impaired the construction of rulebased expert systems.

  7. Advanced Cell Classifier: User-Friendly Machine-Learning-Based Software for Discovering Phenotypes in High-Content Imaging Data.

    Science.gov (United States)

    Piccinini, Filippo; Balassa, Tamas; Szkalisity, Abel; Molnar, Csaba; Paavolainen, Lassi; Kujala, Kaisa; Buzas, Krisztina; Sarazova, Marie; Pietiainen, Vilja; Kutay, Ulrike; Smith, Kevin; Horvath, Peter

    2017-06-28

    High-content, imaging-based screens now routinely generate data on a scale that precludes manual verification and interrogation. Software applying machine learning has become an essential tool to automate analysis, but these methods require annotated examples to learn from. Efficiently exploring large datasets to find relevant examples remains a challenging bottleneck. Here, we present Advanced Cell Classifier (ACC), a graphical software package for phenotypic analysis that addresses these difficulties. ACC applies machine-learning and image-analysis methods to high-content data generated by large-scale, cell-based experiments. It features methods to mine microscopic image data, discover new phenotypes, and improve recognition performance. We demonstrate that these features substantially expedite the training process, successfully uncover rare phenotypes, and improve the accuracy of the analysis. ACC is extensively documented, designed to be user-friendly for researchers without machine-learning expertise, and distributed as a free open-source tool at www.cellclassifier.org. Copyright © 2017 Elsevier Inc. All rights reserved.

  8. Online EEG-Based Workload Adaptation of an Arithmetic Learning Environment.

    Science.gov (United States)

    Walter, Carina; Rosenstiel, Wolfgang; Bogdan, Martin; Gerjets, Peter; Spüler, Martin

    2017-01-01

    In this paper, we demonstrate a closed-loop EEG-based learning environment, that adapts instructional learning material online, to improve learning success in students during arithmetic learning. The amount of cognitive workload during learning is crucial for successful learning and should be held in the optimal range for each learner. Based on EEG data from 10 subjects, we created a prediction model that estimates the learner's workload to obtain an unobtrusive workload measure. Furthermore, we developed an interactive learning environment that uses the prediction model to estimate the learner's workload online based on the EEG data and adapt the difficulty of the learning material to keep the learner's workload in an optimal range. The EEG-based learning environment was used by 13 subjects to learn arithmetic addition in the octal number system, leading to a significant learning effect. The results suggest that it is feasible to use EEG as an unobtrusive measure of cognitive workload to adapt the learning content. Further it demonstrates that a promptly workload prediction is possible using a generalized prediction model without the need for a user-specific calibration.

  9. Aggregate-then-Curate: how digital learning champions help communities nurture online content

    Directory of Open Access Journals (Sweden)

    Andrew Whitworth

    2012-12-01

    Full Text Available Informational resources are essential for communities, rooting them in their own history, helping them learn and solve problems, giving them a voice in decision-making and so on. For digital inclusion – and inclusion in the informational and democratic processes of society more generally – it is essential that communities retain the skills, awareness and motivation to create and manage their own informational resources.This article explores a model for the creation of online content that incorporates the different ways in which the quality and relevance of information can be assured. This model, “Aggregate-then-Curate” (A/C, was developed from earlier work concerning digital inclusion in UK online centres, models of informal e-learning and ecologies of resources. A/C shows how creating online content can be viewed as a 7-step process, initiated by individuals but bringing in “digital learning champions”, other community members and formal educational institutions at different stages. A/C can be used to design training to help build the capacity to manage community informational resources in an inclusive way. The article then discusses and evaluates MOSI-ALONG, a Joint Information Systems Committee (JISC funded project founded on these ideas, which illustrates how A/C can be used to design training to help build the capacity to manage community informational resources in an inclusive way. This conclusion is supported by evaluations of the work done so far in MOSI-ALONG.

  10. Learning with distribution of optimized features for recognizing common CT imaging signs of lung diseases

    Science.gov (United States)

    Ma, Ling; Liu, Xiabi; Fei, Baowei

    2017-01-01

    Common CT imaging signs of lung diseases (CISLs) are defined as the imaging signs that frequently appear in lung CT images from patients. CISLs play important roles in the diagnosis of lung diseases. This paper proposes a novel learning method, namely learning with distribution of optimized feature (DOF), to effectively recognize the characteristics of CISLs. We improve the classification performance by learning the optimized features under different distributions. Specifically, we adopt the minimum spanning tree algorithm to capture the relationship between features and discriminant ability of features for selecting the most important features. To overcome the problem of various distributions in one CISL, we propose a hierarchical learning method. First, we use an unsupervised learning method to cluster samples into groups based on their distribution. Second, in each group, we use a supervised learning method to train a model based on their categories of CISLs. Finally, we obtain multiple classification decisions from multiple trained models and use majority voting to achieve the final decision. The proposed approach has been implemented on a set of 511 samples captured from human lung CT images and achieves a classification accuracy of 91.96%. The proposed DOF method is effective and can provide a useful tool for computer-aided diagnosis of lung diseases on CT images.

  11. Workforce Optimization for Bank Operation Centers: A Machine Learning Approach

    Directory of Open Access Journals (Sweden)

    Sefik Ilkin Serengil

    2017-12-01

    Full Text Available Online Banking Systems evolved and improved in recent years with the use of mobile and online technologies, performing money transfer transactions on these channels can be done without delay and human interaction, however commercial customers still tend to transfer money on bank branches due to several concerns. Bank Operation Centers serve to reduce the operational workload of branches. Centralized management also offers personalized service by appointed expert employees in these centers. Inherently, workload volume of money transfer transactions changes dramatically in hours. Therefore, work-force should be planned instantly or early to save labor force and increase operational efficiency. This paper introduces a hybrid multi stage approach for workforce planning in bank operation centers by the application of supervised and unsu-pervised learning algorithms. Expected workload would be predicted as supervised learning whereas employees are clus-tered into different skill groups as unsupervised learning to match transactions and proper employees. Finally, workforce optimization is analyzed for proposed approach on production data.

  12. An Improved Bacterial-Foraging Optimization-Based Machine Learning Framework for Predicting the Severity of Somatization Disorder

    Directory of Open Access Journals (Sweden)

    Xinen Lv

    2018-02-01

    Full Text Available It is of great clinical significance to establish an accurate intelligent model to diagnose the somatization disorder of community correctional personnel. In this study, a novel machine learning framework is proposed to predict the severity of somatization disorder in community correction personnel. The core of this framework is to adopt the improved bacterial foraging optimization (IBFO to optimize two key parameters (penalty coefficient and the kernel width of a kernel extreme learning machine (KELM and build an IBFO-based KELM (IBFO-KELM for the diagnosis of somatization disorder patients. The main innovation point of the IBFO-KELM model is the introduction of opposition-based learning strategies in traditional bacteria foraging optimization, which increases the diversity of bacterial species, keeps a uniform distribution of individuals of initial population, and improves the convergence rate of the BFO optimization process as well as the probability of escaping from the local optimal solution. In order to verify the effectiveness of the method proposed in this study, a 10-fold cross-validation method based on data from a symptom self-assessment scale (SCL-90 is used to make comparison among IBFO-KELM, BFO-KELM (model based on the original bacterial foraging optimization model, GA-KELM (model based on genetic algorithm, PSO-KELM (model based on particle swarm optimization algorithm and Grid-KELM (model based on grid search method. The experimental results show that the proposed IBFO-KELM prediction model has better performance than other methods in terms of classification accuracy, Matthews correlation coefficient (MCC, sensitivity and specificity. It can distinguish very well between severe somatization disorder and mild somatization and assist the psychological doctor with clinical diagnosis.

  13. Optimization and modeling of reduction of wastewater sludge water content and turbidity removal using magnetic iron oxide nanoparticles (MION).

    Science.gov (United States)

    Hwang, Jeong-Ha; Han, Dong-Woo

    2015-01-01

    Economic and rapid reduction of sludge water content in sewage wastewater is difficult and requires special advanced treatment technologies. This study focused on optimizing and modeling decreased sludge water content (Y1) and removing turbidity (Y2) with magnetic iron oxide nanoparticles (Fe3O4, MION) using a central composite design (CCD) and response surface methodology (RSM). CCD and RSM were applied to evaluate and optimize the interactive effects of mixing time (X1) and MION concentration (X2) on chemical flocculent performance. The results show that the optimum conditions were 14.1 min and 22.1 mg L(-1) for response Y1 and 16.8 min and 8.85 mg L(-1) for response Y2, respectively. The two responses were obtained experimentally under this optimal scheme and fit the model predictions well (R(2) = 97.2% for Y1 and R(2) = 96.9% for Y2). A 90.8% decrease in sludge water content and turbidity removal of 29.4% were demonstrated. These results confirm that the statistical models were reliable, and that the magnetic flocculation conditions for decreasing sludge water content and removing turbidity from sewage wastewater were appropriate. The results reveal that MION are efficient for rapid separation and are a suitable alterative to sediment sludge during the wastewater treatment process.

  14. The Effect of Content Representation Design Principles on Users' Intuitive Beliefs and Use of E-Learning Systems

    Science.gov (United States)

    Al-Samarraie, Hosam; Selim, Hassan; Zaqout, Fahed

    2016-01-01

    A model is proposed to assess the effect of different content representation design principles on learners' intuitive beliefs about using e-learning. We hypothesized that the impact of the representation of course contents is mediated by the design principles of alignment, quantity, clarity, simplicity, and affordance, which influence the…

  15. Applying Mathematical Optimization Methods to an ACT-R Instance-Based Learning Model.

    Science.gov (United States)

    Said, Nadia; Engelhart, Michael; Kirches, Christian; Körkel, Stefan; Holt, Daniel V

    2016-01-01

    Computational models of cognition provide an interface to connect advanced mathematical tools and methods to empirically supported theories of behavior in psychology, cognitive science, and neuroscience. In this article, we consider a computational model of instance-based learning, implemented in the ACT-R cognitive architecture. We propose an approach for obtaining mathematical reformulations of such cognitive models that improve their computational tractability. For the well-established Sugar Factory dynamic decision making task, we conduct a simulation study to analyze central model parameters. We show how mathematical optimization techniques can be applied to efficiently identify optimal parameter values with respect to different optimization goals. Beyond these methodological contributions, our analysis reveals the sensitivity of this particular task with respect to initial settings and yields new insights into how average human performance deviates from potential optimal performance. We conclude by discussing possible extensions of our approach as well as future steps towards applying more powerful derivative-based optimization methods.

  16. Applying Mathematical Optimization Methods to an ACT-R Instance-Based Learning Model.

    Directory of Open Access Journals (Sweden)

    Nadia Said

    Full Text Available Computational models of cognition provide an interface to connect advanced mathematical tools and methods to empirically supported theories of behavior in psychology, cognitive science, and neuroscience. In this article, we consider a computational model of instance-based learning, implemented in the ACT-R cognitive architecture. We propose an approach for obtaining mathematical reformulations of such cognitive models that improve their computational tractability. For the well-established Sugar Factory dynamic decision making task, we conduct a simulation study to analyze central model parameters. We show how mathematical optimization techniques can be applied to efficiently identify optimal parameter values with respect to different optimization goals. Beyond these methodological contributions, our analysis reveals the sensitivity of this particular task with respect to initial settings and yields new insights into how average human performance deviates from potential optimal performance. We conclude by discussing possible extensions of our approach as well as future steps towards applying more powerful derivative-based optimization methods.

  17. Learning partial differential equations via data discovery and sparse optimization.

    Science.gov (United States)

    Schaeffer, Hayden

    2017-01-01

    We investigate the problem of learning an evolution equation directly from some given data. This work develops a learning algorithm to identify the terms in the underlying partial differential equations and to approximate the coefficients of the terms only using data. The algorithm uses sparse optimization in order to perform feature selection and parameter estimation. The features are data driven in the sense that they are constructed using nonlinear algebraic equations on the spatial derivatives of the data. Several numerical experiments show the proposed method's robustness to data noise and size, its ability to capture the true features of the data, and its capability of performing additional analytics. Examples include shock equations, pattern formation, fluid flow and turbulence, and oscillatory convection.

  18. How organizational learning is associated with patient rights: a qualitative content analysis.

    Science.gov (United States)

    Heidari, Shahin; Nayeri, Nahid Dehghan; Ravari, Ali; Sabzevari, Sakineh

    2016-01-01

    Nowadays, patient rights, particularly receiving favorable health care based on modern knowledge, informed consent, and privacy, are important issues in health care delivery systems. Organizational learning is considered an important factor influencing health care quality and patient rights. However, there is little evidence regarding this issue. The present study was conducted to explore the role of organizational learning in patient rights from clinical nurses' viewpoint. This qualitative study was conducted through conventional content analysis. In total, 18 nurses who met the inclusion criteria participated in this study through purposive sampling with maximum variation. Data were gathered through 20 in-depth, semi-structured interviews, which continued until data saturation was achieved. Data collection also included constant and simultaneous comparative analyses. Data analysis led to four major themes: conservation of patient safety, providing favorable care, being the patient's advocate, and informing the patients. All the participants believed that organizational learning could play a vital role in respecting patient rights and interests. Participants believed that their efforts to conduct organizational learning, tried to improve respecting the patient rights via conservation of patient safety, trying to improve quality of care, being an advocate, and informing the patient. It would be appreciable if nursing managers honored the commitment of the nurses for learning, highlight their role as defenders of patient rights, and encourage them to initiate organizational learning.

  19. How organizational learning is associated with patient rights: a qualitative content analysis

    Directory of Open Access Journals (Sweden)

    Shahin Heidari

    2016-07-01

    Full Text Available Background: Nowadays, patient rights, particularly receiving favorable health care based on modern knowledge, informed consent, and privacy, are important issues in health care delivery systems. Organizational learning is considered an important factor influencing health care quality and patient rights. However, there is little evidence regarding this issue. Objective: The present study was conducted to explore the role of organizational learning in patient rights from clinical nurses’ viewpoint. Design: This qualitative study was conducted through conventional content analysis. In total, 18 nurses who met the inclusion criteria participated in this study through purposive sampling with maximum variation. Data were gathered through 20 in-depth, semi-structured interviews, which continued until data saturation was achieved. Data collection also included constant and simultaneous comparative analyses. Results: Data analysis led to four major themes: conservation of patient safety, providing favorable care, being the patient's advocate, and informing the patients. All the participants believed that organizational learning could play a vital role in respecting patient rights and interests. Conclusions: Participants believed that their efforts to conduct organizational learning, tried to improve respecting the patient rights via conservation of patient safety, trying to improve quality of care, being an advocate, and informing the patient. It would be appreciable if nursing managers honored the commitment of the nurses for learning, highlight their role as defenders of patient rights, and encourage them to initiate organizational learning.

  20. Spiking neural networks for handwritten digit recognition-Supervised learning and network optimization.

    Science.gov (United States)

    Kulkarni, Shruti R; Rajendran, Bipin

    2018-07-01

    We demonstrate supervised learning in Spiking Neural Networks (SNNs) for the problem of handwritten digit recognition using the spike triggered Normalized Approximate Descent (NormAD) algorithm. Our network that employs neurons operating at sparse biological spike rates below 300Hz achieves a classification accuracy of 98.17% on the MNIST test database with four times fewer parameters compared to the state-of-the-art. We present several insights from extensive numerical experiments regarding optimization of learning parameters and network configuration to improve its accuracy. We also describe a number of strategies to optimize the SNN for implementation in memory and energy constrained hardware, including approximations in computing the neuronal dynamics and reduced precision in storing the synaptic weights. Experiments reveal that even with 3-bit synaptic weights, the classification accuracy of the designed SNN does not degrade beyond 1% as compared to the floating-point baseline. Further, the proposed SNN, which is trained based on the precise spike timing information outperforms an equivalent non-spiking artificial neural network (ANN) trained using back propagation, especially at low bit precision. Thus, our study shows the potential for realizing efficient neuromorphic systems that use spike based information encoding and learning for real-world applications. Copyright © 2018 Elsevier Ltd. All rights reserved.

  1. Optimized extreme learning machine for urban land cover classification using hyperspectral imagery

    Science.gov (United States)

    Su, Hongjun; Tian, Shufang; Cai, Yue; Sheng, Yehua; Chen, Chen; Najafian, Maryam

    2017-12-01

    This work presents a new urban land cover classification framework using the firefly algorithm (FA) optimized extreme learning machine (ELM). FA is adopted to optimize the regularization coefficient C and Gaussian kernel σ for kernel ELM. Additionally, effectiveness of spectral features derived from an FA-based band selection algorithm is studied for the proposed classification task. Three sets of hyperspectral databases were recorded using different sensors, namely HYDICE, HyMap, and AVIRIS. Our study shows that the proposed method outperforms traditional classification algorithms such as SVM and reduces computational cost significantly.

  2. Optimization of supercritical carbon dioxide extraction of Piper Betel Linn leaves oil and total phenolic content

    Science.gov (United States)

    Aziz, A. H. A.; Yunus, M. A. C.; Arsad, N. H.; Lee, N. Y.; Idham, Z.; Razak, A. Q. A.

    2016-11-01

    Supercritical Carbon Dioxide (SC-CO2) Extraction was applied to extract piper betel linn leaves. The piper betel leaves oil was used antioxidant, anti-diabetic, anticancer and antistroke. The aim of this study was to optimize the conditions of pressure, temperature and flowrate for oil yield and total phenolic content. The operational conditions of SC-CO2 studied were pressure (10, 20, 30 MPa), temperature (40, 60, 80 °C) and flowrate carbon dioxide (4, 6, 8 mL/min). The constant parameters were average particle size and extraction regime, 355pm and 3.5 hours respectively. First order polynomial expression was used to express the extracted oil while second order polynomial expression was used to express the total phenolic content and the both results were satisfactory. The best conditions to maximize the total extraction oil yields and total phenolic content were 30 MPa, 80 °C and 4.42 mL/min leading to 7.32% of oil and 29.72 MPa, 67.53 °C and 7.98 mL/min leading to 845.085 mg GAE/g sample. In terms of optimum condition with high extraction yield and high total phenolic content in the extracts, the best operating conditions were 30 MPa, 78 °C and 8 mL/min with 7.05% yield and 791.709 mg gallic acid equivalent (GAE)/g sample. The most dominant condition for extraction of oil yield and phenolic content were pressure and CO2 flowrate. The results show a good fit to the proposed model and the optimal conditions obtained were within the experimental range with the value of R2 was 96.13% for percentage yield and 98.52% for total phenolic content.

  3. Optimized Assistive Human-Robot Interaction Using Reinforcement Learning.

    Science.gov (United States)

    Modares, Hamidreza; Ranatunga, Isura; Lewis, Frank L; Popa, Dan O

    2016-03-01

    An intelligent human-robot interaction (HRI) system with adjustable robot behavior is presented. The proposed HRI system assists the human operator to perform a given task with minimum workload demands and optimizes the overall human-robot system performance. Motivated by human factor studies, the presented control structure consists of two control loops. First, a robot-specific neuro-adaptive controller is designed in the inner loop to make the unknown nonlinear robot behave like a prescribed robot impedance model as perceived by a human operator. In contrast to existing neural network and adaptive impedance-based control methods, no information of the task performance or the prescribed robot impedance model parameters is required in the inner loop. Then, a task-specific outer-loop controller is designed to find the optimal parameters of the prescribed robot impedance model to adjust the robot's dynamics to the operator skills and minimize the tracking error. The outer loop includes the human operator, the robot, and the task performance details. The problem of finding the optimal parameters of the prescribed robot impedance model is transformed into a linear quadratic regulator (LQR) problem which minimizes the human effort and optimizes the closed-loop behavior of the HRI system for a given task. To obviate the requirement of the knowledge of the human model, integral reinforcement learning is used to solve the given LQR problem. Simulation results on an x - y table and a robot arm, and experimental implementation results on a PR2 robot confirm the suitability of the proposed method.

  4. Aero Engine Component Fault Diagnosis Using Multi-Hidden-Layer Extreme Learning Machine with Optimized Structure

    Directory of Open Access Journals (Sweden)

    Shan Pang

    2016-01-01

    Full Text Available A new aero gas turbine engine gas path component fault diagnosis method based on multi-hidden-layer extreme learning machine with optimized structure (OM-ELM was proposed. OM-ELM employs quantum-behaved particle swarm optimization to automatically obtain the optimal network structure according to both the root mean square error on training data set and the norm of output weights. The proposed method is applied to handwritten recognition data set and a gas turbine engine diagnostic application and is compared with basic ELM, multi-hidden-layer ELM, and two state-of-the-art deep learning algorithms: deep belief network and the stacked denoising autoencoder. Results show that, with optimized network structure, OM-ELM obtains better test accuracy in both applications and is more robust to sensor noise. Meanwhile it controls the model complexity and needs far less hidden nodes than multi-hidden-layer ELM, thus saving computer memory and making it more efficient to implement. All these advantages make our method an effective and reliable tool for engine component fault diagnosis tool.

  5. Pressure Prediction of Coal Slurry Transportation Pipeline Based on Particle Swarm Optimization Kernel Function Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Xue-cun Yang

    2015-01-01

    Full Text Available For coal slurry pipeline blockage prediction problem, through the analysis of actual scene, it is determined that the pressure prediction from each measuring point is the premise of pipeline blockage prediction. Kernel function of support vector machine is introduced into extreme learning machine, the parameters are optimized by particle swarm algorithm, and blockage prediction method based on particle swarm optimization kernel function extreme learning machine (PSOKELM is put forward. The actual test data from HuangLing coal gangue power plant are used for simulation experiments and compared with support vector machine prediction model optimized by particle swarm algorithm (PSOSVM and kernel function extreme learning machine prediction model (KELM. The results prove that mean square error (MSE for the prediction model based on PSOKELM is 0.0038 and the correlation coefficient is 0.9955, which is superior to prediction model based on PSOSVM in speed and accuracy and superior to KELM prediction model in accuracy.

  6. Image Segmentation using a Refined Comprehensive Learning Particle Swarm Optimizer for Maximum Tsallis Entropy Thresholding

    OpenAIRE

    L. Jubair Ahmed; A. Ebenezer Jeyakumar

    2013-01-01

    Thresholding is one of the most important techniques for performing image segmentation. In this paper to compute optimum thresholds for Maximum Tsallis entropy thresholding (MTET) model, a new hybrid algorithm is proposed by integrating the Comprehensive Learning Particle Swarm Optimizer (CPSO) with the Powell’s Conjugate Gradient (PCG) method. Here the CPSO will act as the main optimizer for searching the near-optimal thresholds while the PCG method will be used to fine tune the best solutio...

  7. Optimization of Protein Extraction from Spirulina platensis to Generate a Potential Co-Product and a Biofuel Feedstock with Reduced Nitrogen Content

    Energy Technology Data Exchange (ETDEWEB)

    Parimi, Naga Sirisha; Singh, Manjinder; Kastner, James R.; Das, Keshav C., E-mail: kdas@engr.uga.edu [College of Engineering, The University of Georgia, Athens, GA (United States); Forsberg, Lennart S.; Azadi, Parastoo [Complex Carbohydrate Research Center, The University of Georgia, Athens, GA (United States)

    2015-06-23

    The current work reports protein extraction from Spirulina platensis cyanobacterial biomass in order to simultaneously generate a potential co-product and a biofuel feedstock with reduced nitrogen content. S. platensis cells were subjected to cell disruption by high-pressure homogenization and subsequent protein isolation by solubilization at alkaline pH followed by precipitation at acidic pH. Response surface methodology was used to optimize the process parameters – pH, extraction (solubilization/precipitation) time and biomass concentration for obtaining maximum protein yield. The optimized process conditions were found to be pH 11.38, solubilization time of 35 min and biomass concentration of 3.6% (w/w) solids for the solubilization step, and pH 4.01 and precipitation time of 60 min for the precipitation step. At the optimized conditions, a high protein yield of 60.7% (w/w) was obtained. The protein isolate (co-product) had a higher protein content [80.6% (w/w)], lower ash [1.9% (w/w)] and mineral content and was enriched in essential amino acids, the nutritious γ-linolenic acid and other high-value unsaturated fatty acids compared to the original biomass. The residual biomass obtained after protein extraction had lower nitrogen content and higher total non-protein content than the original biomass. The loss of about 50% of the total lipids from this fraction did not impact its composition significantly owing to the low lipid content of S. platensis (8.03%).

  8. Optimization of Protein Extraction from Spirulina platensis to Generate a Potential Co-Product and a Biofuel Feedstock with Reduced Nitrogen Content

    International Nuclear Information System (INIS)

    Parimi, Naga Sirisha; Singh, Manjinder; Kastner, James R.; Das, Keshav C.; Forsberg, Lennart S.; Azadi, Parastoo

    2015-01-01

    The current work reports protein extraction from Spirulina platensis cyanobacterial biomass in order to simultaneously generate a potential co-product and a biofuel feedstock with reduced nitrogen content. S. platensis cells were subjected to cell disruption by high-pressure homogenization and subsequent protein isolation by solubilization at alkaline pH followed by precipitation at acidic pH. Response surface methodology was used to optimize the process parameters – pH, extraction (solubilization/precipitation) time and biomass concentration for obtaining maximum protein yield. The optimized process conditions were found to be pH 11.38, solubilization time of 35 min and biomass concentration of 3.6% (w/w) solids for the solubilization step, and pH 4.01 and precipitation time of 60 min for the precipitation step. At the optimized conditions, a high protein yield of 60.7% (w/w) was obtained. The protein isolate (co-product) had a higher protein content [80.6% (w/w)], lower ash [1.9% (w/w)] and mineral content and was enriched in essential amino acids, the nutritious γ-linolenic acid and other high-value unsaturated fatty acids compared to the original biomass. The residual biomass obtained after protein extraction had lower nitrogen content and higher total non-protein content than the original biomass. The loss of about 50% of the total lipids from this fraction did not impact its composition significantly owing to the low lipid content of S. platensis (8.03%).

  9. BP neural network optimized by genetic algorithm approach for titanium and iron content prediction in EDXRF

    International Nuclear Information System (INIS)

    Wang Jun; Liu Mingzhe; Li Zhe; Li Lei; Shi Rui; Tuo Xianguo

    2015-01-01

    The quantitative elemental content analysis is difficult due to the uniform effect, particle effect and the element matrix effect, etc, when using energy dispersive X-ray fluorescence (EDXRF) technique. In this paper, a hybrid approach of genetic algorithm (GA) and back propagation (BP) neural network was proposed without considering the complex relationship between the concentration and intensity. The aim of GA optimized BP was to get better network initial weights and thresholds. The basic idea was that the reciprocal of the mean square error of the initialization BP neural network was set as the fitness value of the individual in GA, and the initial weights and thresholds were replaced by individuals, and then the optimal individual was sought by selection, crossover and mutation operations, finally a new BP neural network model was created with the optimal initial weights and thresholds. The calculation results of quantitative analysis of titanium and iron contents for five types of ore bodies in Panzhihua Mine show that the results of classification prediction are far better than that of overall forecasting, and relative errors of 76.7% samples are less than 2% compared with chemical analysis values, which demonstrates the effectiveness of the proposed method. (authors)

  10. #13ReasonsWhy Health Professionals and Educators are Tweeting: A Systematic Analysis of Uses and Perceptions of Show Content and Learning Outcomes.

    Science.gov (United States)

    Walker, Kimberly K; Burns, Kelli

    2018-04-27

    This study is a content analysis of health professionals' and educators' tweets about a popular Netflix show that depicts teen suicide: 13 Reasons Why. A content analysis of 740 tweets was conducted to determine the main themes associated with professionals' and educators' tweets about the show, as well as the valence of the tweets. Additionally, a thematic analysis of linked content in tweets (n = 178) was conducted to explore additional content shared about the show and modeling outcomes. Results indicated the largest percentage of tweets was related to social learning, particularly about outcomes that could occur from viewing the show. The valence of the tweets about outcomes was more positive than negative. However, linked materials commonly circulated in tweets signified greater concern with unintended learning outcomes. Some of the linked content included media guidelines for reporting on suicide with recommendations that entertainment producers follow the guidelines. This study emphasizes the importance of including social learning objectives in future typologies of Twitter uses and demonstrates the importance of examining linked content in Twitter studies.

  11. An Attentional Goldilocks Effect: An Optimal Amount of Social Interactivity Promotes Word Learning from Video

    OpenAIRE

    Nussenbaum, Kate; Amso, Dima

    2015-01-01

    Television can be a powerful education tool; however, content-makers must understand the factors that engage attention and promote learning from screen media. Prior research suggests that social engagement is critical for learning and that interactivity may enhance the educational quality of children’s media. The present study examined the effects of increasing the social interactivity of television on children’s visual attention and word learning. Three- to 5-year-old (MAge = 4;5 years, SD =...

  12. Manifold optimization-based analysis dictionary learning with an ℓ1∕2-norm regularizer.

    Science.gov (United States)

    Li, Zhenni; Ding, Shuxue; Li, Yujie; Yang, Zuyuan; Xie, Shengli; Chen, Wuhui

    2018-02-01

    Recently there has been increasing attention towards analysis dictionary learning. In analysis dictionary learning, it is an open problem to obtain the strong sparsity-promoting solutions efficiently while simultaneously avoiding the trivial solutions of the dictionary. In this paper, to obtain the strong sparsity-promoting solutions, we employ the ℓ 1∕2 norm as a regularizer. The very recent study on ℓ 1∕2 norm regularization theory in compressive sensing shows that its solutions can give sparser results than using the ℓ 1 norm. We transform a complex nonconvex optimization into a number of one-dimensional minimization problems. Then the closed-form solutions can be obtained efficiently. To avoid trivial solutions, we apply manifold optimization to update the dictionary directly on the manifold satisfying the orthonormality constraint, so that the dictionary can avoid the trivial solutions well while simultaneously capturing the intrinsic properties of the dictionary. The experiments with synthetic and real-world data verify that the proposed algorithm for analysis dictionary learning can not only obtain strong sparsity-promoting solutions efficiently, but also learn more accurate dictionary in terms of dictionary recovery and image processing than the state-of-the-art algorithms. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. A novel model of motor learning capable of developing an optimal movement control law online from scratch.

    Science.gov (United States)

    Shimansky, Yury P; Kang, Tao; He, Jiping

    2004-02-01

    A computational model of a learning system (LS) is described that acquires knowledge and skill necessary for optimal control of a multisegmental limb dynamics (controlled object or CO), starting from "knowing" only the dimensionality of the object's state space. It is based on an optimal control problem setup different from that of reinforcement learning. The LS solves the optimal control problem online while practicing the manipulation of CO. The system's functional architecture comprises several adaptive components, each of which incorporates a number of mapping functions approximated based on artificial neural nets. Besides the internal model of the CO's dynamics and adaptive controller that computes the control law, the LS includes a new type of internal model, the minimal cost (IM(mc)) of moving the controlled object between a pair of states. That internal model appears critical for the LS's capacity to develop an optimal movement trajectory. The IM(mc) interacts with the adaptive controller in a cooperative manner. The controller provides an initial approximation of an optimal control action, which is further optimized in real time based on the IM(mc). The IM(mc) in turn provides information for updating the controller. The LS's performance was tested on the task of center-out reaching to eight randomly selected targets with a 2DOF limb model. The LS reached an optimal level of performance in a few tens of trials. It also quickly adapted to movement perturbations produced by two different types of external force field. The results suggest that the proposed design of a self-optimized control system can serve as a basis for the modeling of motor learning that includes the formation and adaptive modification of the plan of a goal-directed movement.

  14. Thai EFL Learners' Attitudes and Motivation towards Learning English through Content-Based Instruction

    Science.gov (United States)

    Lai Yuanxing; Aksornjarung, Prachamon

    2018-01-01

    This study examined EFL learners' attitudes and motivation towards learning English through content-based instruction (CBI) at a university in Thailand. Seventy-one (71) university students, the majority sophomores, answered a 6-point Likert scale questionnaire on attitudes and motivation together with six open-ended questions regarding learning…

  15. Learning optimal embedded cascades.

    Science.gov (United States)

    Saberian, Mohammad Javad; Vasconcelos, Nuno

    2012-10-01

    The problem of automatic and optimal design of embedded object detector cascades is considered. Two main challenges are identified: optimization of the cascade configuration and optimization of individual cascade stages, so as to achieve the best tradeoff between classification accuracy and speed, under a detection rate constraint. Two novel boosting algorithms are proposed to address these problems. The first, RCBoost, formulates boosting as a constrained optimization problem which is solved with a barrier penalty method. The constraint is the target detection rate, which is met at all iterations of the boosting process. This enables the design of embedded cascades of known configuration without extensive cross validation or heuristics. The second, ECBoost, searches over cascade configurations to achieve the optimal tradeoff between classification risk and speed. The two algorithms are combined into an overall boosting procedure, RCECBoost, which optimizes both the cascade configuration and its stages under a detection rate constraint, in a fully automated manner. Extensive experiments in face, car, pedestrian, and panda detection show that the resulting detectors achieve an accuracy versus speed tradeoff superior to those of previous methods.

  16. Not Utilized Learning Potentials

    DEFF Research Database (Denmark)

    Kragelund, Linda

    2007-01-01

      When the Danish Nursing Education in 2002 became a Bachelor Degree the clinical part of the education was reduced. Therefore, it was necessary to optimize learning in practice.       I made a qualitative investigation to describe student nurses' learning processes in non-routine situations where...... they interact with psychiatric patients. The theoretical framework includes primarily P. Jarvis' concept disjuncture and A. Heller's theory about everyday life. The empirical part of the study is primarily based on qualitative semi-structured interviews with, observations of and obser-views with a volunteer......-conscious disjuncture, in development of the concept pseudo-everyday life activities and in a categorizing mo­del for and a theory about student nurses' learning processes. The theory includes relations between 4 types of  disjuncture, 3 types of content in the learning processes, and factors that provoke...

  17. Service learning in Guatemala: using qualitative content analysis to explore an interdisciplinary learning experience among students in health care professional programs

    Directory of Open Access Journals (Sweden)

    Fries KS

    2013-02-01

    Full Text Available Kathleen S Fries,1 Donna M Bowers,2 Margo Gross,3 Lenore Frost31Nursing Program, 2Department of Physical Therapy and Human Movement Science, 3Graduate Program in Occupational Therapy, College of Health Professions, Sacred Heart University, Fairfield, CT, USAIntroduction: Interprofessional collaboration among health care professionals yields improved patient outcomes, yet many students in health care programs have limited exposure to interprofessional collaboration in the classroom and in clinical and service-learning experiences. This practice gap implies that students enter their professions without valuing interprofessional collaboration and the impact it has on promoting positive patient outcomes.Aim: The aim of this study was to describe the interprofessional experiences of students in health care professional programs as they collaborated to provide health care to Guatemalan citizens over a 7-day period.Methods: In light of the identified practice gap and a commitment by college administration to fund interprofessional initiatives, faculty educators from nursing, occupational therapy, and physical therapy conducted a qualitative study to explore a service-learning initiative focused on promoting interprofessional collaboration. Students collaborated in triads (one student from each of the three disciplines to provide supervised health care to underserved Guatemalan men, women, children, and infants across a variety of community and health care settings. Eighteen students participated in a qualitative research project by describing their experience of interprofessional collaboration in a service-learning environment. Twice before arriving in Guatemala, and on three occasions during the trip, participants reflected on their experiences and provided narrative responses to open-ended questions. Qualitative content analysis methodology was used to describe their experiences of interprofessional collaboration.Results: An interprofessional service-learning

  18. Content and Language Integrated Learning in the Netherlands: Teachers' Self-Reported Pedagogical Practices

    Science.gov (United States)

    van Kampen, Evelyn; Admiraal, Wilfried; Berry, Amanda

    2018-01-01

    In recent years, a surging uptake of content and language integrated learning (CLIL) has permeated the European context. This article presents the outcomes of a study about the self-reported pedagogical practices of CLIL teachers in the Netherlands. To investigate these teachers' pedagogies, a questionnaire was designed, validated and,…

  19. Learning Science Content through Socio-Scientific Issues-Based Instruction: A Multi-Level Assessment Study

    Science.gov (United States)

    Sadler, Troy D.; Romine, William L.; Topçu, Mustafa Sami

    2016-01-01

    Science educators have presented numerous conceptual and theoretical arguments in favor of teaching science through the exploration of socio-scientific issues (SSI). However, the empirical knowledge base regarding the extent to which SSI-based instruction supports student learning of science content is limited both in terms of the number of…

  20. A Novel Optimization-Based Approach for Content-Based Image Retrieval

    Directory of Open Access Journals (Sweden)

    Manyu Xiao

    2013-01-01

    Full Text Available Content-based image retrieval is nowadays one of the possible and promising solutions to manage image databases effectively. However, with the large number of images, there still exists a great discrepancy between the users’ expectations (accuracy and efficiency and the real performance in image retrieval. In this work, new optimization strategies are proposed on vocabulary tree building, retrieval, and matching methods. More precisely, a new clustering strategy combining classification and conventional K-Means method is firstly redefined. Then a new matching technique is built to eliminate the error caused by large-scaled scale-invariant feature transform (SIFT. Additionally, a new unit mechanism is proposed to reduce the cost of indexing time. Finally, the numerical results show that excellent performances are obtained in both accuracy and efficiency based on the proposed improvements for image retrieval.

  1. But science is international! Finding time and space to encourage intercultural learning in a content-driven physiology unit.

    Science.gov (United States)

    Etherington, Sarah J

    2014-06-01

    Internationalization of the curriculum is central to the strategic direction of many modern universities and has widespread benefits for student learning. However, these clear aspirations for internationalization of the curriculum have not been widely translated into more internationalized course content and teaching methods in the classroom, particularly in scientific disciplines. This study addressed one major challenge to promoting intercultural competence among undergraduate science students: finding time to scaffold such learning within the context of content-heavy, time-poor units. Small changes to enhance global and intercultural awareness were incorporated into existing assessments and teaching activities within a second-year biomedical physiology unit. Interventions were designed to start a conversation about global and intercultural perspectives on physiology, to embed the development of global awareness into the assessment and to promote cultural exchanges through peer interactions. In student surveys, 40% of domestic and 60% of international student respondents articulated specific learning about interactions in cross-cultural groups resulting from unit activities. Many students also identified specific examples of how cultural beliefs would impact on the place of biomedical physiology within the global community. In addition, staff observed more widespread benefits for student engagement and learning. It is concluded that a significant development of intercultural awareness and a more global perspective on scientific understanding can be supported among undergraduates with relatively modest, easy to implement adaptations to course content.

  2. The Pedagogical, Linguistic, and Content Features of Popular English Language Learning Websites in China: A Framework for Analysis and Design

    Science.gov (United States)

    Kettle, Margaret; Yuan, Yifeng; Luke, Allan; Ewing, Robyn; Shen, Huizhong

    2012-01-01

    As increasing numbers of Chinese language learners choose to learn English online, there is a need to investigate popular websites and their language learning designs. This paper reports on the first stage of a study that analyzed the pedagogical, linguistic, and content features of 25 Chinese English Language Learning (ELL) websites ranked…

  3. The effect of Content and Language Integrated Learning (CLIL) on English performance and self-confidence

    NARCIS (Netherlands)

    Jansma, Marrit; Minnaert, Alexander; Klinkenberg, Edwin

    2015-01-01

    In this study, it was investigated whether third language teaching through Content and Language Integrated Learning (CLIL) was more effective than teaching a third language as an isolated subject. By means of a cross-sectional study design, English vocabulary, speaking performance and

  4. Students using mobile phones in the classroom: Can the phones increase content learning

    Science.gov (United States)

    Rinehart, David Lee

    A study was conducted at a high-performing school in Southern California to explore the effects on learning content from students using their own smart phones in and out of the classroom. The study used a Switching Replications design format which allowed two independent analyses of posttest scores between a group using e-flash cards on smart phones and a group using paper flash cards. Quantitative data was collected via two tailed, t-tests and qualitative data was collected through observations and interviews. Results suggest that knowledge level learning may be increased with mobile phone use, but no effect on comprehension level learning was found. Students found the phones to be convenient in accessing flash cards anytime and anywhere. Enthusiasm for using the phones in class while initially high waned over the 1 month study duration. Students perceived the phones to not be a significant source of distraction outside of class.

  5. Deep ECGNet: An Optimal Deep Learning Framework for Monitoring Mental Stress Using Ultra Short-Term ECG Signals.

    Science.gov (United States)

    Hwang, Bosun; You, Jiwoo; Vaessen, Thomas; Myin-Germeys, Inez; Park, Cheolsoo; Zhang, Byoung-Tak

    2018-02-08

    Stress recognition using electrocardiogram (ECG) signals requires the intractable long-term heart rate variability (HRV) parameter extraction process. This study proposes a novel deep learning framework to recognize the stressful states, the Deep ECGNet, using ultra short-term raw ECG signals without any feature engineering methods. The Deep ECGNet was developed through various experiments and analysis of ECG waveforms. We proposed the optimal recurrent and convolutional neural networks architecture, and also the optimal convolution filter length (related to the P, Q, R, S, and T wave durations of ECG) and pooling length (related to the heart beat period) based on the optimization experiments and analysis on the waveform characteristics of ECG signals. The experiments were also conducted with conventional methods using HRV parameters and frequency features as a benchmark test. The data used in this study were obtained from Kwangwoon University in Korea (13 subjects, Case 1) and KU Leuven University in Belgium (9 subjects, Case 2). Experiments were designed according to various experimental protocols to elicit stressful conditions. The proposed framework to recognize stress conditions, the Deep ECGNet, outperformed the conventional approaches with the highest accuracy of 87.39% for Case 1 and 73.96% for Case 2, respectively, that is, 16.22% and 10.98% improvements compared with those of the conventional HRV method. We proposed an optimal deep learning architecture and its parameters for stress recognition, and the theoretical consideration on how to design the deep learning structure based on the periodic patterns of the raw ECG data. Experimental results in this study have proved that the proposed deep learning model, the Deep ECGNet, is an optimal structure to recognize the stress conditions using ultra short-term ECG data.

  6. Off-Policy Reinforcement Learning: Optimal Operational Control for Two-Time-Scale Industrial Processes.

    Science.gov (United States)

    Li, Jinna; Kiumarsi, Bahare; Chai, Tianyou; Lewis, Frank L; Fan, Jialu

    2017-12-01

    Industrial flow lines are composed of unit processes operating on a fast time scale and performance measurements known as operational indices measured at a slower time scale. This paper presents a model-free optimal solution to a class of two time-scale industrial processes using off-policy reinforcement learning (RL). First, the lower-layer unit process control loop with a fast sampling period and the upper-layer operational index dynamics at a slow time scale are modeled. Second, a general optimal operational control problem is formulated to optimally prescribe the set-points for the unit industrial process. Then, a zero-sum game off-policy RL algorithm is developed to find the optimal set-points by using data measured in real-time. Finally, a simulation experiment is employed for an industrial flotation process to show the effectiveness of the proposed method.

  7. New Dandelion Algorithm Optimizes Extreme Learning Machine for Biomedical Classification Problems

    Directory of Open Access Journals (Sweden)

    Xiguang Li

    2017-01-01

    Full Text Available Inspired by the behavior of dandelion sowing, a new novel swarm intelligence algorithm, namely, dandelion algorithm (DA, is proposed for global optimization of complex functions in this paper. In DA, the dandelion population will be divided into two subpopulations, and different subpopulations will undergo different sowing behaviors. Moreover, another sowing method is designed to jump out of local optimum. In order to demonstrate the validation of DA, we compare the proposed algorithm with other existing algorithms, including bat algorithm, particle swarm optimization, and enhanced fireworks algorithm. Simulations show that the proposed algorithm seems much superior to other algorithms. At the same time, the proposed algorithm can be applied to optimize extreme learning machine (ELM for biomedical classification problems, and the effect is considerable. At last, we use different fusion methods to form different fusion classifiers, and the fusion classifiers can achieve higher accuracy and better stability to some extent.

  8. A Spectral Reconstruction Algorithm of Miniature Spectrometer Based on Sparse Optimization and Dictionary Learning.

    Science.gov (United States)

    Zhang, Shang; Dong, Yuhan; Fu, Hongyan; Huang, Shao-Lun; Zhang, Lin

    2018-02-22

    The miniaturization of spectrometer can broaden the application area of spectrometry, which has huge academic and industrial value. Among various miniaturization approaches, filter-based miniaturization is a promising implementation by utilizing broadband filters with distinct transmission functions. Mathematically, filter-based spectral reconstruction can be modeled as solving a system of linear equations. In this paper, we propose an algorithm of spectral reconstruction based on sparse optimization and dictionary learning. To verify the feasibility of the reconstruction algorithm, we design and implement a simple prototype of a filter-based miniature spectrometer. The experimental results demonstrate that sparse optimization is well applicable to spectral reconstruction whether the spectra are directly sparse or not. As for the non-directly sparse spectra, their sparsity can be enhanced by dictionary learning. In conclusion, the proposed approach has a bright application prospect in fabricating a practical miniature spectrometer.

  9. Optimization of polyphenols extraction using response surface methodology and application of near infrared spectroscopy to phenolic content analysis of pine bark

    International Nuclear Information System (INIS)

    Derkyi, Nana Sarfo Agyemang

    2010-04-01

    The utilization of pine bark for processing water resistant phenol-formaldehyde adhesive for plywood production encounters difficulties due to the very high reactivity of the formaldehyde condensable phenolics and other un-intended compounds (sugars) extracted into solution, as well as time consuming and costly chemical analysis. The potential of near infrared reflectance spectroscopy (NIRS) for rapidly and accurately determining the polyphenolic contents in Pinus caribaea bark extracts was assessed by means of multivariate calibration techniques. To optimize the polyphenol content, four different solvents (aqueous acetone, aqueous ethanol, aqueous NaOH and water) were used in the extractions. Batch experiments were performed at different solvent concentrations, time, temperature and liquid-solid ratio. Mathematical polynomial models were proposed to identify the effects of individual interactions of these variables on the extraction of polyphenols and optimum content using response surface methodology (RSM). The optimized conditions were used to extract polyphenols which were used in the formulation of resol resins for plywood manufacture. The first derivative spectra with PLS regression were found to provide the best prediction of the tannin content and stiasny number of pine bark with a SECV = 0.14 and 1.26 and r"2 = 0.97 and 0.95 respectively. The predicted values were thus highly correlated with costly measured values of tannin content and Stiasny number. The highest extraction model efficiency (78.98%) was observed for aqueous extraction when only tannin content was maximized in the numerical optimization process. This corresponded to optimum extraction conditions of 69°C extraction temperature, 126 min extraction time and 23:1 liquid-solid ratio. The RSM model that gave a high tannin content (18.85%) with a corresponding good quality resin (shear strength = 2.4 MPa, 10% delamination) was found for aqueous ethanol extraction when the objective function was

  10. Design strategy for optimal iterative learning control applied on a deep drawing process

    DEFF Research Database (Denmark)

    Endelt, Benny Ørtoft

    2017-01-01

    Metal forming processes in general can be characterised as repetitive processes; this work will take advantage of this characteristic by developing an algorithm or control system which transfers process information from part to part, reducing the impact of repetitive uncertainties, e.g. a gradual...... changes in the material properties. The process is highly non-linear and the system plant is modelled using a non-linear finite element and the gain factors for the iterative learning controller is identified solving a non-linear optimal control problem. The optimal control problem is formulated as a non...

  11. Learning with Admixture: Modeling, Optimization, and Applications in Population Genetics

    DEFF Research Database (Denmark)

    Cheng, Jade Yu

    2016-01-01

    the foundation for both CoalHMM and Ohana. Optimization modeling has been the main theme throughout my PhD, and it will continue to shape my work for the years to come. The algorithms and software I developed to study historical admixture and population evolution fall into a larger family of machine learning...... geneticists strive to establish working solutions to extract information from massive volumes of biological data. The steep increase in the quantity and quality of genomic data during the past decades provides a unique opportunity but also calls for new and improved algorithms and software to cope...... including population splits, effective population sizes, gene flow, etc. Since joining the CoalHMM development team in 2014, I have mainly contributed in two directions: 1) improving optimizations through heuristic-based evolutionary algorithms and 2) modeling of historical admixture events. Ohana, meaning...

  12. Approximate ideal multi-objective solution Q(λ) learning for optimal carbon-energy combined-flow in multi-energy power systems

    International Nuclear Information System (INIS)

    Zhang, Xiaoshun; Yu, Tao; Yang, Bo; Zheng, Limin; Huang, Linni

    2015-01-01

    Highlights: • A novel optimal carbon-energy combined-flow (OCECF) model is firstly established. • A novel approximate ideal multi-objective solution Q(λ) learning is designed. • The proposed algorithm has a high convergence stability and reliability. • The proposed algorithm can be applied for OCECF in a large-scale power grid. - Abstract: This paper proposes a novel approximate ideal multi-objective solution Q(λ) learning for optimal carbon-energy combined-flow in multi-energy power systems. The carbon emissions, fuel cost, active power loss, voltage deviation and carbon emission loss are chosen as the optimization objectives, which are simultaneously optimized by five different Q-value matrices. The dynamic optimal weight of each objective is calculated online from the entire Q-value matrices such that the greedy action policy can be obtained. Case studies are carried out to evaluate the optimization performance for carbon-energy combined-flow in an IEEE 118-bus system and the regional power grid of southern China.

  13. DPT Student Perceptions of the Physical Therapist Assistant's Role: Effect of Collaborative Case-Based Learning Compared to Traditional Content Delivery and Clinical Experience.

    Science.gov (United States)

    Colgrove, Yvonne M; VanHoose, Lisa D

    2017-01-01

    Doctor of physical therapy (DPT) student learning about role delineation of physical therapist assistants (PTAs) is essential to ethical and legal practice. Survey assessment of three DPT student cohorts compared collaborative interprofessional case-based learning with PTA students to traditional curriculum delivery strategies. Control cohorts were assessed one time. The intervention group was assessed pre-intervention, immediately post-intervention, and after completing a full-time clinical experience. The case-based learning covered 46% of survey content, allowing for the assessment of content-specific material and potential learning through collaboration. Following the educational intervention, the intervention group improved significantly in areas inside and outside the case-based study content, outscoring both control groups on 25-34% of the survey items. Following the clinical experience, the intervention group declined answer accuracy for patient evaluation and treatment implementation, suggesting unlearning. Improvement in the administrative section was observed after the clinical experience. Perceptions of the tasks within the PTA role were diminished while tasks outside the scope of practice appeared clarified following the clinical experience. While case-based collaborative intraprofessional learning proves effective in student learning about the PTA role, changes following the clinical experience raise questions about the influence of the clinical environment on learning and the practical application of recently learned knowledge.

  14. Particle swarm optimization-based support vector machine for forecasting dissolved gases content in power transformer oil

    Energy Technology Data Exchange (ETDEWEB)

    Fei, Sheng-wei; Wang, Ming-Jun; Miao, Yu-bin; Tu, Jun; Liu, Cheng-liang [School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240 (China)

    2009-06-15

    Forecasting of dissolved gases content in power transformer oil is a complicated problem due to its nonlinearity and the small quantity of training data. Support vector machine (SVM) has been successfully employed to solve regression problem of nonlinearity and small sample. However, the practicability of SVM is effected due to the difficulty of selecting appropriate SVM parameters. Particle swarm optimization (PSO) is a new optimization method, which is motivated by social behaviour of organisms such as bird flocking and fish schooling. The method not only has strong global search capability, but also is very easy to implement. Thus, the proposed PSO-SVM model is applied to forecast dissolved gases content in power transformer oil in this paper, among which PSO is used to determine free parameters of support vector machine. The experimental data from several electric power companies in China is used to illustrate the performance of proposed PSO-SVM model. The experimental results indicate that the PSO-SVM method can achieve greater forecasting accuracy than grey model, artificial neural network under the circumstances of small sample. (author)

  15. Particle swarm optimization-based support vector machine for forecasting dissolved gases content in power transformer oil

    Energy Technology Data Exchange (ETDEWEB)

    Fei Shengwei [School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240 (China)], E-mail: feishengwei@sohu.com; Wang Mingjun; Miao Yubin; Tu Jun; Liu Chengliang [School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240 (China)

    2009-06-15

    Forecasting of dissolved gases content in power transformer oil is a complicated problem due to its nonlinearity and the small quantity of training data. Support vector machine (SVM) has been successfully employed to solve regression problem of nonlinearity and small sample. However, the practicability of SVM is effected due to the difficulty of selecting appropriate SVM parameters. Particle swarm optimization (PSO) is a new optimization method, which is motivated by social behaviour of organisms such as bird flocking and fish schooling. The method not only has strong global search capability, but also is very easy to implement. Thus, the proposed PSO-SVM model is applied to forecast dissolved gases content in power transformer oil in this paper, among which PSO is used to determine free parameters of support vector machine. The experimental data from several electric power companies in China is used to illustrate the performance of proposed PSO-SVM model. The experimental results indicate that the PSO-SVM method can achieve greater forecasting accuracy than grey model, artificial neural network under the circumstances of small sample.

  16. Response surface methodology (RSM) based multi-objective optimization of fusel oil -gasoline blends at different water content in SI engine

    International Nuclear Information System (INIS)

    Awad, Omar I.; Mamat, R.; Ali, Obed M.; Azmi, W.H.; Kadirgama, K.; Yusri, I.M.; Leman, A.M.; Yusaf, T.

    2017-01-01

    Highlights: • The optimal ratio ratio of fusel oil–gasoline blended fuels is proposed. • The water content of fusel oil was reduced from 13.5% to 6.5%. • The heating value of fusel oil was improved by 13%. • FAWE 20 fuels were found to be optimal values with a high desirability of 0.707. • RSM was applied to optimize the engine performance and exhaust emissions. - Abstract: The main objective of this study is to determine the optimal blend ratio of fusel oil–gasoline before and after water extraction (FBWE10, FBWE20, FAWE10, and FAWE20) regarding the performance and emissions of spark ignition engine using response surface methodology (RSM). The multi-objective optimization is applied to maximize the brake power, brake thermal efficiency and minimize the brake specific fuel consumption (BSFC), NOx emission, HC emission and CO emission. The water content of fusel oil has been extracted by employing rotary extractor method. The experimental of this study has been carried out with different fusel oil–gasoline blends, different throttle valve opening position (15%, 30%, 45% and 60%) and different engine speed (1500, 2500, 3500 and 4500 rpm). All the developed models for responses were determined to be statistically significant at 95% confidence level. The study results reveal an improvement in heating value of fusel oil after water extraction with FAWE20 (80 vol% gasoline fuel, 20 vol% fusel oil after water extracted) as the optimally blended fuel. The best condition of engine parameters with FAWE20 were 55.4% of WOT for load and 4499 rpm engine speed. In additional of the optimal values with a high desirability of 0.707 were 62.511 kW, 241.139 g/kW h, 36%, 1895.913 ppm140.829 ppm and % for brake power, BSFC, BTE, NO x , HC and CO emissions respectively. The reduction of water content in fusel oil has a statistical significance influence to increases BTE, NO x emission and decreases the BSFC, HC and CO emissions.

  17. Content Adaptive Lagrange Multiplier Selection for Rate-Distortion Optimization in 3-D Wavelet-Based Scalable Video Coding

    Directory of Open Access Journals (Sweden)

    Ying Chen

    2018-03-01

    Full Text Available Rate-distortion optimization (RDO plays an essential role in substantially enhancing the coding efficiency. Currently, rate-distortion optimized mode decision is widely used in scalable video coding (SVC. Among all the possible coding modes, it aims to select the one which has the best trade-off between bitrate and compression distortion. Specifically, this tradeoff is tuned through the choice of the Lagrange multiplier. Despite the prevalence of conventional method for Lagrange multiplier selection in hybrid video coding, the underlying formulation is not applicable to 3-D wavelet-based SVC where the explicit values of the quantization step are not available, with on consideration of the content features of input signal. In this paper, an efficient content adaptive Lagrange multiplier selection algorithm is proposed in the context of RDO for 3-D wavelet-based SVC targeting quality scalability. Our contributions are two-fold. First, we introduce a novel weighting method, which takes account of the mutual information, gradient per pixel, and texture homogeneity to measure the temporal subband characteristics after applying the motion-compensated temporal filtering (MCTF technique. Second, based on the proposed subband weighting factor model, we derive the optimal Lagrange multiplier. Experimental results demonstrate that the proposed algorithm enables more satisfactory video quality with negligible additional computational complexity.

  18. Using an optimal CC-PLSR-RBFNN model and NIR spectroscopy for the starch content determination in corn

    Science.gov (United States)

    Jiang, Hao; Lu, Jiangang

    2018-05-01

    Corn starch is an important material which has been traditionally used in the fields of food and chemical industry. In order to enhance the rapidness and reliability of the determination for starch content in corn, a methodology is proposed in this work, using an optimal CC-PLSR-RBFNN calibration model and near-infrared (NIR) spectroscopy. The proposed model was developed based on the optimal selection of crucial parameters and the combination of correlation coefficient method (CC), partial least squares regression (PLSR) and radial basis function neural network (RBFNN). To test the performance of the model, a standard NIR spectroscopy data set was introduced, containing spectral information and chemical reference measurements of 80 corn samples. For comparison, several other models based on the identical data set were also briefly discussed. In this process, the root mean square error of prediction (RMSEP) and coefficient of determination (Rp2) in the prediction set were used to make evaluations. As a result, the proposed model presented the best predictive performance with the smallest RMSEP (0.0497%) and the highest Rp2 (0.9968). Therefore, the proposed method combining NIR spectroscopy with the optimal CC-PLSR-RBFNN model can be helpful to determine starch content in corn.

  19. Team-Based Learning: Moderating Effects of Metacognitive Elaborative Rehearsal and Middle School History Content Recall

    Science.gov (United States)

    Roberts, Greg; Scammacca, Nancy; Osman, David J.; Hall, Colby; Mohammed, Sarojani S.; Vaughn, Sharon

    2014-01-01

    Promoting Acceleration of Comprehension and Content through Text (PACT) and similar team-based models directly engage and support students in learning situations that require cognitive elaboration as part of the processing of new information. Elaboration is subject to metacognitive control, as well (Karpicke, "Journal of Experimental…

  20. The Challenges in Developing E-Content

    Directory of Open Access Journals (Sweden)

    Jowati Juhary

    2010-11-01

    Full Text Available Malaysia is considered an active key player in information communication technologies (ICTs especially in education. In fact, in the National Higher Education Strategic Plan, one of the Critical Agenda Projects (CAPs of the Minister of Higher Education is e-learning. It goes without saying that all higher learning providers in Malaysia must be prepared to provide state-of-the-art facilities for the students. One critical aspect of e-learning is the quality and quantity of the content, or what will be referred by many scholars as e-content. This paper attempts to identify the challenges of content development for e-learning practice at the National Defence University of Malaysia (NDUM. It is crucial to investigate this issue since the university just purchased its Learning Management System (LMS. It is expected that resistance will be present as the academics at the defence university is a mixture of junior and senior lecturers, as well as civilian and military lecturers; and some of these academics have been teaching without the assistance of e-learning. In so doing, the methodology of this paper will mainly be content analysis of various reports, governmental documents, as well as semi-structured interviews with lecturers at the NDUM. As this paper acts as a preliminary investigation into the issue of e-content at the university, only seven lecturers were interviewed. Initial findings suggest that there are basically five challenges of developing e-content at the NDUM. These include the lack of ICT and e-learning policy that can provide guidelines to academics; the uncertainty of ownership for e-learning initiatives; the lack of understanding of the roles of e-learning; the lack of awareness on e-learning; and the difficulties to develop military based content due to confidentiality issues. Two possible solutions for these challenges are also examined which take into consideration the urgent need to set up an e-Learning Unit and to provide

  1. Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning.

    Science.gov (United States)

    Cole-Lewis, Heather; Varghese, Arun; Sanders, Amy; Schwarz, Mary; Pugatch, Jillian; Augustson, Erik

    2015-08-25

    Electronic cigarettes (e-cigarettes) continue to be a growing topic among social media users, especially on Twitter. The ability to analyze conversations about e-cigarettes in real-time can provide important insight into trends in the public's knowledge, attitudes, and beliefs surrounding e-cigarettes, and subsequently guide public health interventions. Our aim was to establish a supervised machine learning algorithm to build predictive classification models that assess Twitter data for a range of factors related to e-cigarettes. Manual content analysis was conducted for 17,098 tweets. These tweets were coded for five categories: e-cigarette relevance, sentiment, user description, genre, and theme. Machine learning classification models were then built for each of these five categories, and word groupings (n-grams) were used to define the feature space for each classifier. Predictive performance scores for classification models indicated that the models correctly labeled the tweets with the appropriate variables between 68.40% and 99.34% of the time, and the percentage of maximum possible improvement over a random baseline that was achieved by the classification models ranged from 41.59% to 80.62%. Classifiers with the highest performance scores that also achieved the highest percentage of the maximum possible improvement over a random baseline were Policy/Government (performance: 0.94; % improvement: 80.62%), Relevance (performance: 0.94; % improvement: 75.26%), Ad or Promotion (performance: 0.89; % improvement: 72.69%), and Marketing (performance: 0.91; % improvement: 72.56%). The most appropriate word-grouping unit (n-gram) was 1 for the majority of classifiers. Performance continued to marginally increase with the size of the training dataset of manually annotated data, but eventually leveled off. Even at low dataset sizes of 4000 observations, performance characteristics were fairly sound. Social media outlets like Twitter can uncover real-time snapshots of

  2. HSTLBO: A hybrid algorithm based on Harmony Search and Teaching-Learning-Based Optimization for complex high-dimensional optimization problems.

    Directory of Open Access Journals (Sweden)

    Shouheng Tuo

    Full Text Available Harmony Search (HS and Teaching-Learning-Based Optimization (TLBO as new swarm intelligent optimization algorithms have received much attention in recent years. Both of them have shown outstanding performance for solving NP-Hard optimization problems. However, they also suffer dramatic performance degradation for some complex high-dimensional optimization problems. Through a lot of experiments, we find that the HS and TLBO have strong complementarity each other. The HS has strong global exploration power but low convergence speed. Reversely, the TLBO has much fast convergence speed but it is easily trapped into local search. In this work, we propose a hybrid search algorithm named HSTLBO that merges the two algorithms together for synergistically solving complex optimization problems using a self-adaptive selection strategy. In the HSTLBO, both HS and TLBO are modified with the aim of balancing the global exploration and exploitation abilities, where the HS aims mainly to explore the unknown regions and the TLBO aims to rapidly exploit high-precision solutions in the known regions. Our experimental results demonstrate better performance and faster speed than five state-of-the-art HS variants and show better exploration power than five good TLBO variants with similar run time, which illustrates that our method is promising in solving complex high-dimensional optimization problems. The experiment on portfolio optimization problems also demonstrate that the HSTLBO is effective in solving complex read-world application.

  3. Deep learning architecture for iris recognition based on optimal Gabor filters and deep belief network

    Science.gov (United States)

    He, Fei; Han, Ye; Wang, Han; Ji, Jinchao; Liu, Yuanning; Ma, Zhiqiang

    2017-03-01

    Gabor filters are widely utilized to detect iris texture information in several state-of-the-art iris recognition systems. However, the proper Gabor kernels and the generative pattern of iris Gabor features need to be predetermined in application. The traditional empirical Gabor filters and shallow iris encoding ways are incapable of dealing with such complex variations in iris imaging including illumination, aging, deformation, and device variations. Thereby, an adaptive Gabor filter selection strategy and deep learning architecture are presented. We first employ particle swarm optimization approach and its binary version to define a set of data-driven Gabor kernels for fitting the most informative filtering bands, and then capture complex pattern from the optimal Gabor filtered coefficients by a trained deep belief network. A succession of comparative experiments validate that our optimal Gabor filters may produce more distinctive Gabor coefficients and our iris deep representations be more robust and stable than traditional iris Gabor codes. Furthermore, the depth and scales of the deep learning architecture are also discussed.

  4. USER-DEFINED CONTENT IN A MODERN LEARNING ENVIRONMENT FOR ENGINEERING GRAPHICS

    Directory of Open Access Journals (Sweden)

    DOLGA Lia

    2008-07-01

    Full Text Available New pedagogic methods are developed during the current “knowledge-based era”. They replace the “taught lesson” by collaboration, reflection and iteration; in this context, the internet should not remain only a convenient and cheep (if not free mechanism for delivering traditional materials online. As the amount of available information continues to enlarge and diversify, the skills needed to access and process this information become quickly outdated. The ability to use new technologies and a wide range of multimedia tools will define success. This paper outlines the important role played by the user-generated content in defining new pedagogical approaches to learning in the context of online communities. Graphical subjects, like “Computer Graphics” and “Computer Aided Design” require an active participation of the student. Students-led lessons and students generated content give consistency and aid value to the educational process. The term of “teaching” transforms in “studying”.

  5. A Spectral Reconstruction Algorithm of Miniature Spectrometer Based on Sparse Optimization and Dictionary Learning

    Science.gov (United States)

    Zhang, Shang; Fu, Hongyan; Huang, Shao-Lun; Zhang, Lin

    2018-01-01

    The miniaturization of spectrometer can broaden the application area of spectrometry, which has huge academic and industrial value. Among various miniaturization approaches, filter-based miniaturization is a promising implementation by utilizing broadband filters with distinct transmission functions. Mathematically, filter-based spectral reconstruction can be modeled as solving a system of linear equations. In this paper, we propose an algorithm of spectral reconstruction based on sparse optimization and dictionary learning. To verify the feasibility of the reconstruction algorithm, we design and implement a simple prototype of a filter-based miniature spectrometer. The experimental results demonstrate that sparse optimization is well applicable to spectral reconstruction whether the spectra are directly sparse or not. As for the non-directly sparse spectra, their sparsity can be enhanced by dictionary learning. In conclusion, the proposed approach has a bright application prospect in fabricating a practical miniature spectrometer. PMID:29470406

  6. Progressive sampling-based Bayesian optimization for efficient and automatic machine learning model selection.

    Science.gov (United States)

    Zeng, Xueqiang; Luo, Gang

    2017-12-01

    Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge and many labor-intensive manual iterations. To lower the bar to machine learning, miscellaneous automatic selection methods for algorithms and/or hyper-parameter values have been proposed. Existing automatic selection methods are inefficient on large data sets. This poses a challenge for using machine learning in the clinical big data era. To address the challenge, this paper presents progressive sampling-based Bayesian optimization, an efficient and automatic selection method for both algorithms and hyper-parameter values. We report an implementation of the method. We show that compared to a state of the art automatic selection method, our method can significantly reduce search time, classification error rate, and standard deviation of error rate due to randomization. This is major progress towards enabling fast turnaround in identifying high-quality solutions required by many machine learning-based clinical data analysis tasks.

  7. Developing biology teachers' pedagogical content knowledge through learning study: the case of teaching human evolution

    Science.gov (United States)

    Bravo, Paulina; Cofré, Hernán

    2016-11-01

    This work explores how pedagogical content knowledge (PCK) on evolution was modified by two biology teachers who participated in a professional development programme (PDP) that included a subsequent follow-up in the classroom. The PDP spanned a semester and included activities such as content updates, collaborative lesson planning, and the presentation of planned lessons. In the follow-up part, the lessons were videotaped and analysed, identifying strategies, activities, and conditions based on student learning about the theory of evolution. Data were collected in the first round with an interview before the training process, identifying these teachers' initial content representation (CoRe) for evolution. Then, a group interview was conducted after the lessons, and, finally, an interview of stimulated recall with each teacher was conducted regarding the subject taught to allow teachers to reflect on their practice (final CoRe). This information was analysed by the teachers and the researchers, reflecting on the components of the PCK, possible changes, and the rationale behind their actions. The results show that teachers changed their beliefs and knowledge about the best methods and strategies to teach evolution, and about students' learning obstacles and misconceptions on evolution. They realised how a review of their own practices promotes this transformation.

  8. An Improved Teaching-Learning-Based Optimization with the Social Character of PSO for Global Optimization

    Directory of Open Access Journals (Sweden)

    Feng Zou

    2016-01-01

    Full Text Available An improved teaching-learning-based optimization with combining of the social character of PSO (TLBO-PSO, which is considering the teacher’s behavior influence on the students and the mean grade of the class, is proposed in the paper to find the global solutions of function optimization problems. In this method, the teacher phase of TLBO is modified; the new position of the individual is determined by the old position, the mean position, and the best position of current generation. The method overcomes disadvantage that the evolution of the original TLBO might stop when the mean position of students equals the position of the teacher. To decrease the computation cost of the algorithm, the process of removing the duplicate individual in original TLBO is not adopted in the improved algorithm. Moreover, the probability of local convergence of the improved method is decreased by the mutation operator. The effectiveness of the proposed method is tested on some benchmark functions, and the results are competitive with respect to some other methods.

  9. High-performance membrane-electrode assembly with an optimal polytetrafluoroethylene content for high-temperature polymer electrolyte membrane fuel cells

    DEFF Research Database (Denmark)

    Jeong, Gisu; Kim, MinJoong; Han, Junyoung

    2016-01-01

    Although high-temperature polymer electrolyte membrane fuel cells (HT-PEMFCs) have a high carbon monoxide tolerance and allow for efficient water management, their practical applications are limited due to their lower performance than conventional low-temperature PEMFCs. Herein, we present a high......-performance membrane-electrode assembly (MEA) with an optimal polytetrafluoroethylene (PTFE) content for HT-PEMFCs. Low or excess PTFE content in the electrode leads to an inefficient electrolyte distribution or severe catalyst agglomeration, respectively, which hinder the formation of triple phase boundaries...

  10. Optimal medication dosing from suboptimal clinical examples: a deep reinforcement learning approach.

    Science.gov (United States)

    Nemati, Shamim; Ghassemi, Mohammad M; Clifford, Gari D

    2016-08-01

    Misdosing medications with sensitive therapeutic windows, such as heparin, can place patients at unnecessary risk, increase length of hospital stay, and lead to wasted hospital resources. In this work, we present a clinician-in-the-loop sequential decision making framework, which provides an individualized dosing policy adapted to each patient's evolving clinical phenotype. We employed retrospective data from the publicly available MIMIC II intensive care unit database, and developed a deep reinforcement learning algorithm that learns an optimal heparin dosing policy from sample dosing trails and their associated outcomes in large electronic medical records. Using separate training and testing datasets, our model was observed to be effective in proposing heparin doses that resulted in better expected outcomes than the clinical guidelines. Our results demonstrate that a sequential modeling approach, learned from retrospective data, could potentially be used at the bedside to derive individualized patient dosing policies.

  11. Effects of age and content of augmented feedback on learning an isometric force-production task

    NARCIS (Netherlands)

    van Dijk, Henk; Mulder, Theo; Hermens, Hermie J.

    2007-01-01

    This study addressed the interaction between age and the informational content of feedback on learning an isometric force-production task. Healthy men and women (30 young adults: 20 to 35 years; 30 older adults: 55 to 70 years) were randomly assigned to a certain type of feedback: knowledge of

  12. Digi Island: A Serious Game for Teaching and Learning Digital Circuit Optimization

    Science.gov (United States)

    Harper, Michael; Miller, Joseph; Shen, Yuzhong

    2011-01-01

    Karnaugh maps, also known as K-maps, are a tool used to optimize or simplify digital logic circuits. A K-map is a graphical display of a logic circuit. K-map optimization is essentially the process of finding a minimum number of maximal aggregations of K-map cells. with values of 1 according to a set of rules. The Digi Island is a serious game designed for aiding students to learn K-map optimization. The game takes place on an exotic island (called Digi Island) in the Pacific Ocean . The player is an adventurer to the Digi Island and will transform it into a tourist attraction by developing real estates, such as amusement parks.and hotels. The Digi Island game elegantly converts boring 1s and Os in digital circuits into usable and unusable spaces on a beautiful island and transforms K-map optimization into real estate development, an activity with which many students are familiar and also interested in. This paper discusses the design, development, and some preliminary results of the Digi Island game.

  13. Developing Pedagogical Content Knowledge: Lessons Learned from Intervention Studies

    Directory of Open Access Journals (Sweden)

    Marie Evens

    2015-01-01

    Full Text Available Pedagogical content knowledge (PCK is generally accepted as positively impacting teaching quality and student learning. Therefore, research on PCK development in (prospective teachers is highly relevant. Based on a search in three databases (ERIC, PsycInfo, and Web of Science, a systematic review is conducted on intervention studies aiming at PCK development. The research questions are threefold: (1 How are the studies designed? (2 How are the interventions designed? and (3 What elements of interventions contribute to PCK development? The results show that most intervention studies are conducted in math and science education and use a qualitative methodology. Reflection, PCK courses, contact with other teachers, and experiences in educational practice are typically part of effective interventions. The review enables the identification of clear guidelines that may strengthen future research on stimulating PCK.

  14. EFL oral skills behaviour when implementing blended learning in a content-subject teachers’ professional development course

    Directory of Open Access Journals (Sweden)

    Natalia Sanchez Narvaez

    2017-08-01

    Full Text Available The increasing use of technology in educational settings (Murray, 2014; Zandi, Thang, & Krish, 2014 encourages teachers to refocus their professional development by centering their efforts on becoming proficient in the use of information and communication technologies (ICTs in language lessons (Chen, Chen, & Tsai, 2009. As such, this qualitative action research project intended to describe content-subject teachers’ EFL oral behavior when blended learning was implemented in a professional development course and to determine the influence of blended learning in EFL oral skill behavior. The participants were seven content-subject teachers from a private school in Huila, Colombia. Data were gathered via in-depth interviews, class observations, video recording analysis, teachers’ reflection, students’ artifacts, and a survey. Data were collected during the implementation of an English blended course in which 12 lessons were divided into six face-to-face sessions and six online meetings. The findings suggest that EFL oral skill behavior is connected with use of vocabulary, use of body language, pronunciation and intonation patterns, production of chunks of language, monitoring oral production and, motivation and engagement. In addition, blended learning influenced participants’ oral production.

  15. Analysis on the Metrics used in Optimizing Electronic Business based on Learning Techniques

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    Irina-Steliana STAN

    2014-09-01

    Full Text Available The present paper proposes a methodology of analyzing the metrics related to electronic business. The drafts of the optimizing models include KPIs that can highlight the business specific, if only they are integrated by using learning-based techniques. Having set the most important and high-impact elements of the business, the models should get in the end the link between them, by automating business flows. The human resource will be found in the situation of collaborating more and more with the optimizing models which will translate into high quality decisions followed by profitability increase.

  16. Developing the master learner: applying learning theory to the learner, the teacher, and the learning environment.

    Science.gov (United States)

    Schumacher, Daniel J; Englander, Robert; Carraccio, Carol

    2013-11-01

    As a result of the paradigm shift to a competency-based framework, both self-directed lifelong learning and learner-centeredness have become essential tenets of medical education. In the competency-based framework, learners drive their own educational process, and both learners and teachers share the responsibility for the path and content of learning. This learner-centered emphasis requires each physician to develop and maintain lifelong learning skills, which the authors propose culminate in becoming a "master leaner." To better understand the development of these skills and the attainment of that goal, the authors explore how learning theories inform the development of master learners and how to translate these theories into practical strategies for the learner, the teacher, and the learning environment so as to optimize this development.The authors begin by exploring self-determination theory, which lays the groundwork for understanding the motivation to learn. They next consider the theories of cognitive load and situated cognition, which inform the optimal context and environment for learning. Building from this foundation, the authors consider key educational theories that affect learners' abilities to serve as primary drivers of their learning, including self-directed learning (SDL); the self-assessment skills necessary for SDL; factors affecting self-assessment (self-concept, self-efficacy, illusory superiority, gap filling); and ways to mitigate the inaccuracies of self-assessment (reflection, self-monitoring, external information seeking, and self-directed assessment seeking).For each theory, they suggest practical action steps for the learner, the teacher, and the learning environment in an effort to provide a road map for developing master learners.

  17. Off-policy integral reinforcement learning optimal tracking control for continuous-time chaotic systems

    International Nuclear Information System (INIS)

    Wei Qing-Lai; Song Rui-Zhuo; Xiao Wen-Dong; Sun Qiu-Ye

    2015-01-01

    This paper estimates an off-policy integral reinforcement learning (IRL) algorithm to obtain the optimal tracking control of unknown chaotic systems. Off-policy IRL can learn the solution of the HJB equation from the system data generated by an arbitrary control. Moreover, off-policy IRL can be regarded as a direct learning method, which avoids the identification of system dynamics. In this paper, the performance index function is first given based on the system tracking error and control error. For solving the Hamilton–Jacobi–Bellman (HJB) equation, an off-policy IRL algorithm is proposed. It is proven that the iterative control makes the tracking error system asymptotically stable, and the iterative performance index function is convergent. Simulation study demonstrates the effectiveness of the developed tracking control method. (paper)

  18. Simultaneous learning and filtering without delusions: a Bayes-optimal combination of Predictive Inference and Adaptive Filtering.

    Science.gov (United States)

    Kneissler, Jan; Drugowitsch, Jan; Friston, Karl; Butz, Martin V

    2015-01-01

    Predictive coding appears to be one of the fundamental working principles of brain processing. Amongst other aspects, brains often predict the sensory consequences of their own actions. Predictive coding resembles Kalman filtering, where incoming sensory information is filtered to produce prediction errors for subsequent adaptation and learning. However, to generate prediction errors given motor commands, a suitable temporal forward model is required to generate predictions. While in engineering applications, it is usually assumed that this forward model is known, the brain has to learn it. When filtering sensory input and learning from the residual signal in parallel, a fundamental problem arises: the system can enter a delusional loop when filtering the sensory information using an overly trusted forward model. In this case, learning stalls before accurate convergence because uncertainty about the forward model is not properly accommodated. We present a Bayes-optimal solution to this generic and pernicious problem for the case of linear forward models, which we call Predictive Inference and Adaptive Filtering (PIAF). PIAF filters incoming sensory information and learns the forward model simultaneously. We show that PIAF is formally related to Kalman filtering and to the Recursive Least Squares linear approximation method, but combines these procedures in a Bayes optimal fashion. Numerical evaluations confirm that the delusional loop is precluded and that the learning of the forward model is more than 10-times faster when compared to a naive combination of Kalman filtering and Recursive Least Squares.

  19. Preparing Content-Rich Learning Environments with VPython and Excel, Controlled by Visual Basic for Applications

    Science.gov (United States)

    Prayaga, Chandra

    2008-01-01

    A simple interface between VPython and Microsoft (MS) Office products such as Word and Excel, controlled by Visual Basic for Applications, is described. The interface allows the preparation of content-rich, interactive learning environments by taking advantage of the three-dimensional (3D) visualization capabilities of VPython and the GUI…

  20. What's More Important--Literacy or Content? Confronting the Literacy-Content Dualism

    Science.gov (United States)

    Draper, Roni Jo; Smith, Leigh K.; Hall, Kendra M.; Siebert, Daniel

    2005-01-01

    The literacy-content dualism, which suggests that teachers must decide whether to provide literacy or content instruction, is a false dualism and adherence to it is detrimental to student participation in content-area reasoning, learning, and communicating. This article describes the experiences that prompted the teacher educators who authored…

  1. A comparison of numerical and machine-learning modeling of soil water content with limited input data

    Science.gov (United States)

    Karandish, Fatemeh; Šimůnek, Jiří

    2016-12-01

    Soil water content (SWC) is a key factor in optimizing the usage of water resources in agriculture since it provides information to make an accurate estimation of crop water demand. Methods for predicting SWC that have simple data requirements are needed to achieve an optimal irrigation schedule, especially for various water-saving irrigation strategies that are required to resolve both food and water security issues under conditions of water shortages. Thus, a two-year field investigation was carried out to provide a dataset to compare the effectiveness of HYDRUS-2D, a physically-based numerical model, with various machine-learning models, including Multiple Linear Regressions (MLR), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Support Vector Machines (SVM), for simulating time series of SWC data under water stress conditions. SWC was monitored using TDRs during the maize growing seasons of 2010 and 2011. Eight combinations of six, simple, independent parameters, including pan evaporation and average air temperature as atmospheric parameters, cumulative growth degree days (cGDD) and crop coefficient (Kc) as crop factors, and water deficit (WD) and irrigation depth (In) as crop stress factors, were adopted for the estimation of SWCs in the machine-learning models. Having Root Mean Square Errors (RMSE) in the range of 0.54-2.07 mm, HYDRUS-2D ranked first for the SWC estimation, while the ANFIS and SVM models with input datasets of cGDD, Kc, WD and In ranked next with RMSEs ranging from 1.27 to 1.9 mm and mean bias errors of -0.07 to 0.27 mm, respectively. However, the MLR models did not perform well for SWC forecasting, mainly due to non-linear changes of SWCs under the irrigation process. The results demonstrated that despite requiring only simple input data, the ANFIS and SVM models could be favorably used for SWC predictions under water stress conditions, especially when there is a lack of data. However, process-based numerical models are undoubtedly a

  2. [Conception and implementation of a novel E-learning module with EbM learning contents in operative dentistry].

    Science.gov (United States)

    Gerhardt-Szép, Susanne; Dreher, Stefanie; Rüttermann, Stefan; Weberschock, Tobias

    2017-11-01

    Computer-assisted learning (CAL) programs are becoming more widely used in medical and dental training. However, the combination of CAL programs and evidence-based education in dentistry has not been described previously. The aim was to determine the acceptance and user-friendliness of a CAL program combined with evidence-based training. The didactic concept of the module includes the case-oriented, problem-based embedding of a total of 32 EbM learning assignments, which can be completed interactively and self-determinedly in an interdisciplinary context using focus patients with different diseases. The present study was conducted at the Dental School of the Goethe University in Frankfurt/Main. Data on acceptance and user-friendliness were collected from three consecutive cohorts of 114 dental students attending their first clinical semester. They used the "Toothache Walk-in Clinic: FOCUS" CAL, which can be downloaded via the Internet. The instrument consisted of 64 statements. The first part addressed general information about the user. The second part contained 43 specific statements on the CAL program. These included factors A (handling and technical aspects), B (content and functional range), and C (didactics and suitability for education). Possible responses ranged from 0 to 3 (0 = strongly disagree, 3 = strongly agree). All of the 114 questionnaires distributed were returned (response rate 100%). Most users (90.1%) considered the topics of evidence-based dentistry important for their training. They rated the program by using German school grades, and the overall rating was 2.26 (SD = 0.64). Most students (88.6%) considered the program useful for their clinical training in the treatment of patients. The mean scores for the 43 specific items amounted to 1.90 (factor A, SD = 0.63), 1.55 (factor B, SD = 1.93), and 2.23 (factor C, SD = 0.79). The CAL program with dental medicine vignettes and learning elements for evidence-based medicine received a primarily

  3. Registered nurses' thoughts on blended learning in a postgraduate course in cancer care--content analyses of web surveys and a focus group interview.

    Science.gov (United States)

    Arving, Cecilia; Wadensten, Barbro; Johansson, Birgitta

    2014-06-01

    Purpose of the research was to describe registered nurses' (RNs) (n = 53) thoughts on the blended learning format in a 'specialist nursing programme in cancer care'. The study was conducted in autumn 2007 and 2008. A content analysis of answers to open-ended questions in a web-based questionnaire and a focus group interview were carried out. The analysis revealed that the RNs appreciated blended learning. The web lectures facilitated learning and gave RNs access to the education at any time. However, according to the RNs, knowledge is gained through interaction between RNs and teachers, and this aspect needed to be improved. The RNs also thought that the content of the seminars on campus should focus on evidence-based nursing knowledge and practical skills, not just taught as stable facts and procedures. The result from the present study could help to improve the design and content of advanced nursing courses using a blended learning format.

  4. Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I-V characteristics

    International Nuclear Information System (INIS)

    Chen, Zhicong; Wu, Lijun; Cheng, Shuying; Lin, Peijie; Wu, Yue; Lin, Wencheng

    2017-01-01

    Highlights: •An improved Simulink based modeling method is proposed for PV modules and arrays. •Key points of I-V curves and PV model parameters are used as the feature variables. •Kernel extreme learning machine (KELM) is explored for PV arrays fault diagnosis. •The parameters of KELM algorithm are optimized by the Nelder-Mead simplex method. •The optimized KELM fault diagnosis model achieves high accuracy and reliability. -- Abstract: Fault diagnosis of photovoltaic (PV) arrays is important for improving the reliability, efficiency and safety of PV power stations, because the PV arrays usually operate in harsh outdoor environment and tend to suffer various faults. Due to the nonlinear output characteristics and varying operating environment of PV arrays, many machine learning based fault diagnosis methods have been proposed. However, there still exist some issues: fault diagnosis performance is still limited due to insufficient monitored information; fault diagnosis models are not efficient to be trained and updated; labeled fault data samples are hard to obtain by field experiments. To address these issues, this paper makes contribution in the following three aspects: (1) based on the key points and model parameters extracted from monitored I-V characteristic curves and environment condition, an effective and efficient feature vector of seven dimensions is proposed as the input of the fault diagnosis model; (2) the emerging kernel based extreme learning machine (KELM), which features extremely fast learning speed and good generalization performance, is utilized to automatically establish the fault diagnosis model. Moreover, the Nelder-Mead Simplex (NMS) optimization method is employed to optimize the KELM parameters which affect the classification performance; (3) an improved accurate Simulink based PV modeling approach is proposed for a laboratory PV array to facilitate the fault simulation and data sample acquisition. Intensive fault experiments are

  5. Bayesian Network Constraint-Based Structure Learning Algorithms: Parallel and Optimized Implementations in the bnlearn R Package

    Directory of Open Access Journals (Sweden)

    Marco Scutari

    2017-03-01

    Full Text Available It is well known in the literature that the problem of learning the structure of Bayesian networks is very hard to tackle: Its computational complexity is super-exponential in the number of nodes in the worst case and polynomial in most real-world scenarios. Efficient implementations of score-based structure learning benefit from past and current research in optimization theory, which can be adapted to the task by using the network score as the objective function to maximize. This is not true for approaches based on conditional independence tests, called constraint-based learning algorithms. The only optimization in widespread use, backtracking, leverages the symmetries implied by the definitions of neighborhood and Markov blanket. In this paper we illustrate how backtracking is implemented in recent versions of the bnlearn R package, and how it degrades the stability of Bayesian network structure learning for little gain in terms of speed. As an alternative, we describe a software architecture and framework that can be used to parallelize constraint-based structure learning algorithms (also implemented in bnlearn and we demonstrate its performance using four reference networks and two real-world data sets from genetics and systems biology. We show that on modern multi-core or multiprocessor hardware parallel implementations are preferable over backtracking, which was developed when single-processor machines were the norm.

  6. Lights, camera, action research: The effects of didactic digital movie making on students' twenty-first century learning skills and science content in the middle school classroom

    Science.gov (United States)

    Ochsner, Karl

    Students are moving away from content consumption to content production. Short movies are uploaded onto video social networking sites and shared around the world. Unfortunately they usually contain little to no educational value, lack a narrative and are rarely created in the science classroom. According to new Arizona Technology standards and ISTE NET*S, along with the framework from the Partnership for 21st Century Learning Standards, our society demands students not only to learn curriculum, but to think critically, problem solve effectively, and become adept at communicating and collaborating. Didactic digital movie making in the science classroom may be one way that these twenty-first century learning skills may be implemented. An action research study using a mixed-methods approach to collect data was used to investigate if didactic moviemaking can help eighth grade students learn physical science content while incorporating 21st century learning skills of collaboration, communication, problem solving and critical thinking skills through their group production. Over a five week period, students researched lessons, wrote scripts, acted, video recorded and edited a didactic movie that contained a narrative plot to teach a science strand from the Arizona State Standards in physical science. A pretest/posttest science content test and KWL chart was given before and after the innovation to measure content learned by the students. Students then took a 21st Century Learning Skills Student Survey to measure how much they perceived that communication, collaboration, problem solving and critical thinking were taking place during the production. An open ended survey and a focus group of four students were used for qualitative analysis. Three science teachers used a project evaluation rubric to measure science content and production values from the movies. Triangulating the science content test, KWL chart, open ended questions and the project evaluation rubric, it

  7. METHODS OF CONTENTS CURATOR

    Directory of Open Access Journals (Sweden)

    V. Kukharenko

    2013-03-01

    Full Text Available Content curated - a new activity (started in 2008 qualified network users with process large amounts of information to represent her social network users. To prepare content curators developed 7 weeks distance course, which examines the functions, methods and tools curator. Courses showed a significant relationship success learning on the availability of advanced personal learning environment and the ability to process and analyze information.

  8. Optimizing Cellular Networks Enabled with Renewal Energy via Strategic Learning.

    Science.gov (United States)

    Sohn, Insoo; Liu, Huaping; Ansari, Nirwan

    2015-01-01

    An important issue in the cellular industry is the rising energy cost and carbon footprint due to the rapid expansion of the cellular infrastructure. Greening cellular networks has thus attracted attention. Among the promising green cellular network techniques, the renewable energy-powered cellular network has drawn increasing attention as a critical element towards reducing carbon emissions due to massive energy consumption in the base stations deployed in cellular networks. Game theory is a branch of mathematics that is used to evaluate and optimize systems with multiple players with conflicting objectives and has been successfully used to solve various problems in cellular networks. In this paper, we model the green energy utilization and power consumption optimization problem of a green cellular network as a pilot power selection strategic game and propose a novel distributed algorithm based on a strategic learning method. The simulation results indicate that the proposed algorithm achieves correlated equilibrium of the pilot power selection game, resulting in optimum green energy utilization and power consumption reduction.

  9. PEDAGOGICAL STRATEGIES AND CONTENT KNOWLEDGE IN 92 ENGLISH FOR MATHS LECTURE IN CONTENT-BASED INSTRUCTION TEACHING

    Directory of Open Access Journals (Sweden)

    Ayu Fitrianingsih

    2017-12-01

    Full Text Available This study was intended to find the pedagogical strategies applied by the teacher in the teaching learning process and to know teacher‘s content knowledge, how teacher need to understand the subject matter taught. This study was carried out in English for Math lecture of Mathematics education study program IKIP PGRI Bojonegoro which involved the teacher and the students as the respondent. This study is under qualitative case study. In collecting the data, questionnaire, observation and interview were conducted to get detail information of the issues. The result reveals: 1 the teacher combines some methods such as cooperative learning, problem-based learning and task-based learning to get the students enthusiasm; 2 based on teacher‘s educational background, although the teacher graduated from Bachelor Degree of Mathematics Education but she was able to combine English teaching through mathematics content very well. It can be concluded that Teacher‘s pedagogical strategy and content knowledge is very important in the application of content-based instruction teaching and learning.

  10. Scientific Approach and Inquiry Learning Model in the Topic of Buffer Solution: A Content Analysis

    Science.gov (United States)

    Kusumaningrum, I. A.; Ashadi, A.; Indriyanti, N. Y.

    2017-09-01

    Many concepts in buffer solution cause student’s misconception. Understanding science concepts should apply the scientific approach. One of learning models which is suitable with this approach is inquiry. Content analysis was used to determine textbook compatibility with scientific approach and inquiry learning model in the concept of buffer solution. By using scientific indicator tools (SIT) and Inquiry indicator tools (IIT), we analyzed three chemistry textbooks grade 11 of senior high school labeled as P, Q, and R. We described how textbook compatibility with scientific approach and inquiry learning model in the concept of buffer solution. The results show that textbook P and Q were very poor and book R was sufficient because the textbook still in procedural level. Chemistry textbooks used at school are needed to be improved in term of scientific approach and inquiry learning model. The result of these analyses might be of interest in order to write future potential textbooks.

  11. Supramodal processing optimizes visual perceptual learning and plasticity.

    Science.gov (United States)

    Zilber, Nicolas; Ciuciu, Philippe; Gramfort, Alexandre; Azizi, Leila; van Wassenhove, Virginie

    2014-06-01

    Multisensory interactions are ubiquitous in cortex and it has been suggested that sensory cortices may be supramodal i.e. capable of functional selectivity irrespective of the sensory modality of inputs (Pascual-Leone and Hamilton, 2001; Renier et al., 2013; Ricciardi and Pietrini, 2011; Voss and Zatorre, 2012). Here, we asked whether learning to discriminate visual coherence could benefit from supramodal processing. To this end, three groups of participants were briefly trained to discriminate which of a red or green intermixed population of random-dot-kinematograms (RDKs) was most coherent in a visual display while being recorded with magnetoencephalography (MEG). During training, participants heard no sound (V), congruent acoustic textures (AV) or auditory noise (AVn); importantly, congruent acoustic textures shared the temporal statistics - i.e. coherence - of visual RDKs. After training, the AV group significantly outperformed participants trained in V and AVn although they were not aware of their progress. In pre- and post-training blocks, all participants were tested without sound and with the same set of RDKs. When contrasting MEG data collected in these experimental blocks, selective differences were observed in the dynamic pattern and the cortical loci responsive to visual RDKs. First and common to all three groups, vlPFC showed selectivity to the learned coherence levels whereas selectivity in visual motion area hMT+ was only seen for the AV group. Second and solely for the AV group, activity in multisensory cortices (mSTS, pSTS) correlated with post-training performances; additionally, the latencies of these effects suggested feedback from vlPFC to hMT+ possibly mediated by temporal cortices in AV and AVn groups. Altogether, we interpret our results in the context of the Reverse Hierarchy Theory of learning (Ahissar and Hochstein, 2004) in which supramodal processing optimizes visual perceptual learning by capitalizing on sensory

  12. The Influence of Computer-Mediated Word-of-Mouth Communication on Student Perceptions of Instructors and Attitudes toward Learning Course Content

    Science.gov (United States)

    Edwards, Chad; Edwards, Autumn; Qing, Qingmei; Wahl, Shawn T.

    2007-01-01

    The purpose of this study was to experimentally test the influence of computer-mediated word-of-mouth communication (WOM) on student perceptions of instructors (attractiveness and credibility) and on student attitudes toward learning course content (affective learning and state motivation). It was hypothesized that students who receive positive…

  13. Developing a Conceptual Framework for Evaluation of E-Content of Virtual Courses: E-Learning Center of an Iranian University Case Study

    Science.gov (United States)

    Akhavan, Peyman; Arefi, Majid Feyz

    2014-01-01

    The purpose of this study is to obtain suitable quality criteria for evaluation of electronic content for virtual courses. We attempt to find the aspects which are important in developing e-content for virtual courses and to determine the criteria we need to judge for the quality and efficiency of learning objects and e-content. So we can classify…

  14. Supplier's optimal bidding strategy in electricity pay-as-bid auction: Comparison of the Q-learning and a model-based approach

    International Nuclear Information System (INIS)

    Rahimiyan, Morteza; Rajabi Mashhadi, Habib

    2008-01-01

    In this paper, the bidding decision making problem in electricity pay-as-bid auction is studied from a supplier's point of view. The bidding problem is a complicated task, because of suppliers' uncertain behaviors and demand fluctuation. In a specific case, in which, the market clearing price (MCP) is considered as a continuous random variable with a known probability distribution function (PDF), an analytic solution is proposed. The suggested solution is generalized to consider the effect of supplier market power due to transmission congestion. As a result, an algebraic equation is developed to compute optimal offering price. The basic assumption in this approach is to take the known probabilistic model for the MCP. The above-mentioned method, called model-based approach, is not more applicable in a realistic situation. In order to overcome the drawback of this method, which needs information about the MCP and its PDF, the supplier learns from past experiences using the Q-learning algorithm to find out the optimal bid price. The simulation results of the model-based and Q-learning methods are compared on a studied system. It is shown that a supplier using the Q-learning algorithm is able to find the optimal bidding strategy similar to one obtained by the model-based approach. Furthermore, to analyze a more realistic situation, the suppliers' behaviors are modeled using a multi-agent system. Simulation results illustrate that the studied supplier finds the optimal bidding strategy in power market using the Q-learning algorithm. (author)

  15. Parameters identification of photovoltaic models using self-adaptive teaching-learning-based optimization

    International Nuclear Information System (INIS)

    Yu, Kunjie; Chen, Xu; Wang, Xin; Wang, Zhenlei

    2017-01-01

    Highlights: • SATLBO is proposed to identify the PV model parameters efficiently. • In SATLBO, the learners self-adaptively select different learning phases. • An elite learning is developed in teacher phase to perform local searching. • A diversity learning is proposed in learner phase to maintain population diversity. • SATLBO achieves the first in ranking on overall performance among nine algorithms. - Abstract: Parameters identification of photovoltaic (PV) model based on measured current-voltage characteristic curves plays an important role in the simulation and evaluation of PV systems. To accurately and reliably identify the PV model parameters, a self-adaptive teaching-learning-based optimization (SATLBO) is proposed in this paper. In SATLBO, the learners can self-adaptively select different learning phases based on their knowledge level. The better learners are more likely to choose the learner phase for improving the population diversity, while the worse learners tend to choose the teacher phase to enhance the convergence rate. Thus, learners at different levels focus on different searching abilities to efficiently enhance the performance of algorithm. In addition, to improve the searching ability of different learning phases, an elite learning strategy and a diversity learning method are introduced into the teacher phase and learner phase, respectively. The performance of SATLBO is firstly evaluated on 34 benchmark functions, and experimental results show that SATLBO achieves the first in ranking on the overall performance among nine algorithms. Then, SATLBO is employed to identify parameters of different PV models, i.e., single diode, double diode, and PV module. Experimental results indicate that SATLBO exhibits high accuracy and reliability compared with other parameter extraction methods.

  16. A Study on SVM Based on the Weighted Elitist Teaching-Learning-Based Optimization and Application in the Fault Diagnosis of Chemical Process

    Directory of Open Access Journals (Sweden)

    Cao Junxiang

    2015-01-01

    Full Text Available Teaching-Learning-Based Optimization (TLBO is a new swarm intelligence optimization algorithm that simulates the class learning process. According to such problems of the traditional TLBO as low optimizing efficiency and poor stability, this paper proposes an improved TLBO algorithm mainly by introducing the elite thought in TLBO and adopting different inertia weight decreasing strategies for elite and ordinary individuals of the teacher stage and the student stage. In this paper, the validity of the improved TLBO is verified by the optimizations of several typical test functions and the SVM optimized by the weighted elitist TLBO is used in the diagnosis and classification of common failure data of the TE chemical process. Compared with the SVM combining other traditional optimizing methods, the SVM optimized by the weighted elitist TLBO has a certain improvement in the accuracy of fault diagnosis and classification.

  17. Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning

    Science.gov (United States)

    Cole-Lewis, Heather; Varghese, Arun; Sanders, Amy; Schwarz, Mary; Pugatch, Jillian

    2015-01-01

    Background Electronic cigarettes (e-cigarettes) continue to be a growing topic among social media users, especially on Twitter. The ability to analyze conversations about e-cigarettes in real-time can provide important insight into trends in the public’s knowledge, attitudes, and beliefs surrounding e-cigarettes, and subsequently guide public health interventions. Objective Our aim was to establish a supervised machine learning algorithm to build predictive classification models that assess Twitter data for a range of factors related to e-cigarettes. Methods Manual content analysis was conducted for 17,098 tweets. These tweets were coded for five categories: e-cigarette relevance, sentiment, user description, genre, and theme. Machine learning classification models were then built for each of these five categories, and word groupings (n-grams) were used to define the feature space for each classifier. Results Predictive performance scores for classification models indicated that the models correctly labeled the tweets with the appropriate variables between 68.40% and 99.34% of the time, and the percentage of maximum possible improvement over a random baseline that was achieved by the classification models ranged from 41.59% to 80.62%. Classifiers with the highest performance scores that also achieved the highest percentage of the maximum possible improvement over a random baseline were Policy/Government (performance: 0.94; % improvement: 80.62%), Relevance (performance: 0.94; % improvement: 75.26%), Ad or Promotion (performance: 0.89; % improvement: 72.69%), and Marketing (performance: 0.91; % improvement: 72.56%). The most appropriate word-grouping unit (n-gram) was 1 for the majority of classifiers. Performance continued to marginally increase with the size of the training dataset of manually annotated data, but eventually leveled off. Even at low dataset sizes of 4000 observations, performance characteristics were fairly sound. Conclusions Social media outlets

  18. Conteúdos hipermodais para fins de aprendizagem: usos em contexto pelos alunos Hypermodal content to learning: student's contextualized use

    Directory of Open Access Journals (Sweden)

    Eduardo S Junqueira

    2010-12-01

    Full Text Available A análise dos usos de artefatos e conteúdos hipermodais pelos alunos, para fins de aprendizagem na modalidade do ensino a distância, indicou dois elementos-chave. A estrutura e a coesão dos conteúdos, identificados a partir de categorias estáveis de análi se semiótica de materiais hipermodais, não determinaram o uso. Norteados por elemen tos culturais e por suas intenções, os alunos formularam práticas comunicativas que extrapolaram a dicotomia impresso/digital. Imprimiram os conteúdos disponibilizados na tela do computador e instituíram a centralidade do impresso para a aprendizagem, sem, no entanto, ignorar os conteúdos hipermodais. A partir do impresso, utilizaram tais conteúdos em "rede" e construíram trilhas de navegação e leitura coerentes, voltadas a fins de aprendizagem específicos. Indica-se a necessidade de processos flexíveis e com a participação dos usuários-alunos para a produção de conteúdos mais interativos e democráticos.The analysis of the use of hypermodal artifacts and content by distance learning students indicated two key elements. Content structure and cohesion, estab lished by stable semiotic categories of hypermodal artifact's analysis, indicated that they have not determined the student's use. Guided by cultural elements and their own goals, students established communicative practices that extrapolated the print/digital dichoto my. The students printed the online content and established the centrality of the print ing material for their learning without disregarding hypermodal content. From the print ing material, they used content as "webs" and developed paths as they coherently navi gated and read the materials. The study indicates the need to establish flexible, open to participation processes of content design aimed at the student's participation to produce more interactive and democratic content for the production of learning.

  19. Machine Learning Approach to Optimizing Combined Stimulation and Medication Therapies for Parkinson's Disease.

    Science.gov (United States)

    Shamir, Reuben R; Dolber, Trygve; Noecker, Angela M; Walter, Benjamin L; McIntyre, Cameron C

    2015-01-01

    Deep brain stimulation (DBS) of the subthalamic region is an established therapy for advanced Parkinson's disease (PD). However, patients often require time-intensive post-operative management to balance their coupled stimulation and medication treatments. Given the large and complex parameter space associated with this task, we propose that clinical decision support systems (CDSS) based on machine learning algorithms could assist in treatment optimization. Develop a proof-of-concept implementation of a CDSS that incorporates patient-specific details on both stimulation and medication. Clinical data from 10 patients, and 89 post-DBS surgery visits, were used to create a prototype CDSS. The system was designed to provide three key functions: (1) information retrieval; (2) visualization of treatment, and; (3) recommendation on expected effective stimulation and drug dosages, based on three machine learning methods that included support vector machines, Naïve Bayes, and random forest. Measures of medication dosages, time factors, and symptom-specific pre-operative response to levodopa were significantly correlated with post-operative outcomes (P < 0.05) and their effect on outcomes was of similar magnitude to that of DBS. Using those results, the combined machine learning algorithms were able to accurately predict 86% (12/14) of the motor improvement scores at one year after surgery. Using patient-specific details, an appropriately parameterized CDSS could help select theoretically optimal DBS parameter settings and medication dosages that have potential to improve the clinical management of PD patients. Copyright © 2015 Elsevier Inc. All rights reserved.

  20. A Familiar(ity Problem: Assessing the Impact of Prerequisites and Content Familiarity on Student Learning.

    Directory of Open Access Journals (Sweden)

    Justin F Shaffer

    Full Text Available Prerequisites are embedded in most STEM curricula. However, the assumption that the content presented in these courses will improve learning in later courses has not been verified. Because a direct comparison of performance between students with and without required prerequisites is logistically difficult to arrange in a randomized fashion, we developed a novel familiarity scale, and used this to determine whether concepts introduced in a prerequisite course improved student learning in a later course (in two biology disciplines. Exam questions in the latter courses were classified into three categories, based on the degree to which the tested concept had been taught in the prerequisite course. If content familiarity mattered, it would be expected that exam scores on topics covered in the prerequisite would be higher than scores on novel topics. We found this to be partially true for "Very Familiar" questions (concepts covered in depth in the prerequisite. However, scores for concepts only briefly discussed in the prerequisite ("Familiar" were indistinguishable from performance on topics that were "Not Familiar" (concepts only taught in the later course. These results imply that merely "covering" topics in a prerequisite course does not result in improved future performance, and that some topics may be able to removed from a course thereby freeing up class time. Our results may therefore support the implementation of student-centered teaching methods such as active learning, as the time-intensive nature of active learning has been cited as a barrier to its adoption. In addition, we propose that our familiarity system could be broadly utilized to aid in the assessment of the effectiveness of prerequisites.

  1. A Familiar(ity) Problem: Assessing the Impact of Prerequisites and Content Familiarity on Student Learning.

    Science.gov (United States)

    Shaffer, Justin F; Dang, Jennifer V; Lee, Amanda K; Dacanay, Samantha J; Alam, Usman; Wong, Hollie Y; Richards, George J; Kadandale, Pavan; Sato, Brian K

    2016-01-01

    Prerequisites are embedded in most STEM curricula. However, the assumption that the content presented in these courses will improve learning in later courses has not been verified. Because a direct comparison of performance between students with and without required prerequisites is logistically difficult to arrange in a randomized fashion, we developed a novel familiarity scale, and used this to determine whether concepts introduced in a prerequisite course improved student learning in a later course (in two biology disciplines). Exam questions in the latter courses were classified into three categories, based on the degree to which the tested concept had been taught in the prerequisite course. If content familiarity mattered, it would be expected that exam scores on topics covered in the prerequisite would be higher than scores on novel topics. We found this to be partially true for "Very Familiar" questions (concepts covered in depth in the prerequisite). However, scores for concepts only briefly discussed in the prerequisite ("Familiar") were indistinguishable from performance on topics that were "Not Familiar" (concepts only taught in the later course). These results imply that merely "covering" topics in a prerequisite course does not result in improved future performance, and that some topics may be able to removed from a course thereby freeing up class time. Our results may therefore support the implementation of student-centered teaching methods such as active learning, as the time-intensive nature of active learning has been cited as a barrier to its adoption. In addition, we propose that our familiarity system could be broadly utilized to aid in the assessment of the effectiveness of prerequisites.

  2. Optimization of Aqueous Extraction from Kalanchoe pinnata Leaves to Obtain the Highest Content of an Anti-inflammatory Flavonoid using a Response Surface Model.

    Science.gov (United States)

    Dos Santos Nascimento, Luana Beatriz; de Aguiar, Paula Fernandes; Leal-Costa, Marcos Vinicius; Coutinho, Marcela Araújo Soares; Borsodi, Maria Paula Gonçalves; Rossi-Bergmann, Bartira; Tavares, Eliana Schwartz; Costa, Sônia Soares

    2018-05-01

    The medicinal plant Kalanchoe pinnata is a phenolic-rich species used worldwide. The reports on its pharmacological uses have increased by 70% in the last 10 years. The leaves of this plant are the main source of an unusual quercetin-diglycosyl flavonoid (QAR, quercetin arabinopyranosyl rhamnopyranoside), which can be easily extracted using water. QAR possess a strong in vivo anti-inflammatory activity. To optimize the aqueous extraction of QAR from K. pinnata leaves using a three-level full factorial design. After a previous screening design, time (x 1 ) and temperature (x 2 ) were chosen as the two independent variables for optimization. Freeze-dried leaves were extracted with water (20% w/v), at 30°C, 40°C or 50°C for 5, 18 or 30 min. QAR content (determined by HPLC-DAD) and yield of extracts were analyzed. The optimized extracts were also evaluated for cytotoxicity. The optimal heating times for extract yield and QAR content were similar in two-dimensional (2D) surface responses (between 12.8 and 30 min), but their optimal extraction temperatures were ranged between 40°C and 50°C for QAR content and 30°C and 38°C for extract yield. A compromise region for both parameters was at the mean points that were 40°C for the extraction temperature and 18 min for the total time. The optimized process is faster and spends less energy than the previous one (water; 30 min at 55°C); therefore is greener and more attractive for industrial purposes. This is the first report of extraction optimization of this bioactive flavonoid. Copyright © 2018 John Wiley & Sons, Ltd. Copyright © 2018 John Wiley & Sons, Ltd.

  3. A METHODOLOGICAL MODEL FOR INTEGRATING CHARACTER WITHIN CONTENT AND LANGUAGE INTEGRATED LEARNING IN SOCIOLOGY OF RELIGION

    Directory of Open Access Journals (Sweden)

    Moh Yasir Alimi

    2014-02-01

    Full Text Available AbstractIn this article, I describe a methodological model I used in a experimental study on how to integrate character within the practice of Content and Language Integrated Learning (CLIL at the higher education Indonesia.This research can be added to research about character education and CLIL in tertiary education, giving nuances to the practice of CLIL so far predominantly a practice in primary and secondary schools.The research was conducted in Semarang State University, in the Department of Sociology and Anthropology, in Sociology of Religion bilingual class. The research indicates that the integration of character within CLIL enrich the perspective of CLIL by strengthening the use of CLIL for intellectual growth and moral development. On the other side, the use of CLIL with character education gives methods and perspectives to the practice of character education which so far only emphasise contents reforms without learning methods reforms. The research also reveals that the weakness of CLIL in using text for classroom learning can be overcome by the use of specific reading and writing strategies. I develop a practical text strategy which can be effectively used in highly conceptual subject such as sociology of religion. AbstrakArtikel ini bertujuan untuk mendeskripsikan model metodologis yang saya pakai untuk mengintegrasikannya karakter dalam Content and Language Integrated Learning (CLIL pada pendidikan tinggi di Indonesia. Penelitian ini memperkaya penelitian mengenai pendidikan karakter dan penerapan CLIL di perguruan tinggi, selama ini penelitian semacam itu hanya biasa di level lebih rendah. Penelitian dilakukan di Universitas Negeri Semarang, pada kelas bilingual yang diikuti 25 mahasiswa, dan diujikan pada mata kuliah Sosiologi Agama. Pelajaran dari penelitian ini adalah integrasi karakter dalam CLIL dapat memperkaya CLIL. Sebaliknya penggunaan CLIL untuk mendidikkan karakter di kelas bilingual mampu menjawab berbagai tantangan

  4. Faster native vowel discrimination learning in musicians is mediated by an optimization of mnemonic functions.

    Science.gov (United States)

    Elmer, Stefan; Greber, Marielle; Pushparaj, Arethy; Kühnis, Jürg; Jäncke, Lutz

    2017-09-01

    The ability to discriminate phonemes varying in spectral and temporal attributes constitutes one of the most basic intrinsic elements underlying language learning mechanisms. Since previous work has consistently shown that professional musicians are characterized by perceptual and cognitive advantages in a variety of language-related tasks, and since vowels can be considered musical sounds within the domain of speech, here we investigated the behavioral and electrophysiological correlates of native vowel discrimination learning in a sample of professional musicians and non-musicians. We evaluated the contribution of both the neurophysiological underpinnings of perceptual (i.e., N1/P2 complex) and mnemonic functions (i.e., N400 and P600 responses) while the participants were instructed to judge whether pairs of native consonant-vowel (CV) syllables manipulated in the first formant transition of the vowel (i.e., from /tu/ to /to/) were identical or not. Results clearly demonstrated faster learning in musicians, compared to non-musicians, as reflected by shorter reaction times and higher accuracy. Most notably, in terms of morphology, time course, and voltage strength, this steeper learning curve was accompanied by distinctive N400 and P600 manifestations between the two groups. In contrast, we did not reveal any group differences during the early stages of auditory processing (i.e., N1/P2 complex), suggesting that faster learning was mediated by an optimization of mnemonic but not perceptual functions. Based on a clear taxonomy of the mnemonic functions involved in the task, results are interpreted as pointing to a relationship between faster learning mechanisms in musicians and an optimization of echoic (i.e., N400 component) and working memory (i.e., P600 component) functions. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. Creating Optimal Learning Environments through Invitational Education: An Alternative to Control Oriented School Reform

    Science.gov (United States)

    Fretz, Joan R.

    2015-01-01

    Understanding what motivates people to put forth effort, persevere in the face of obstacles, and choose their behaviors is key to creating an optimal learning environment--the type of school that policy makers desire, but are unknowingly sabotaging (Dweck, 2000). Many motivation and self-concept theories provide important insight with regard to…

  6. Simultaneous Learning and Filtering without Delusions: A Bayes-Optimal Derivation of Combining Predictive Inference and AdaptiveFiltering

    Directory of Open Access Journals (Sweden)

    Jan eKneissler

    2015-04-01

    Full Text Available Predictive coding appears to be one of the fundamental working principles of brain processing. Amongst other aspects, brains often predict the sensory consequences of their own actions. Predictive coding resembles Kalman filtering, where incoming sensory information is filtered to produce prediction errors for subsequent adaptation and learning. However, to generate prediction errors given motor commands, a suitable temporal forward model is required to generate predictions. While in engineering applications, it is usually assumed that this forward model is known, the brain has to learn it. When filtering sensory input and learning from the residual signal in parallel, a fundamental problem arises: the system can enter a delusional loop when filtering the sensory information using an overly trusted forward model. In this case, learning stalls before accurate convergence because uncertainty about the forward model is not properly accommodated. We present a Bayes-optimal solution to this generic and pernicious problem for the case of linear forward models, which we call Predictive Inference and Adaptive Filtering (PIAF. PIAF filters incoming sensory information and learns the forward model simultaneously. We show that PIAF is formally related to Kalman filtering and to the Recursive Least Squares linear approximation method, but combines these procedures in a Bayes optimal fashion. Numerical evaluations confirm that the delusional loop is precluded and that the learning of the forward model is more than ten-times faster when compared to a naive combination of Kalman filtering and Recursive Least Squares.

  7. Applied Gaussian Process in Optimizing Unburned Carbon Content in Fly Ash for Boiler Combustion

    Directory of Open Access Journals (Sweden)

    Chunlin Wang

    2017-01-01

    Full Text Available Recently, Gaussian Process (GP has attracted generous attention from industry. This article focuses on the application of coal fired boiler combustion and uses GP to design a strategy for reducing Unburned Carbon Content in Fly Ash (UCC-FA which is the most important indicator of boiler combustion efficiency. With getting rid of the complicated physical mechanisms, building a data-driven model as GP is an effective way for the proposed issue. Firstly, GP is used to model the relationship between the UCC-FA and boiler combustion operation parameters. The hyperparameters of GP model are optimized via Genetic Algorithm (GA. Then, served as the objective of another GA framework, the predicted UCC-FA from GP model is utilized in searching the optimal operation plan for the boiler combustion. Based on 670 sets of real data from a high capacity tangentially fired boiler, two GP models with 21 and 13 inputs, respectively, are developed. In the experimental results, the model with 21 inputs provides better prediction performance than that of the other. Choosing the results from 21-input model, the UCC-FA decreases from 2.7% to 1.7% via optimizing some of the operational parameters, which is a reasonable achievement for the boiler combustion.

  8. CONTENT AND LANGUAGE INTEGRATED LEARNING (CLIL: AN EXPERIMENTAL STUDY ON CLIL COMPATIBILITY WITH THE MODERN GREEK EDUCATIONAL SYSTEM

    Directory of Open Access Journals (Sweden)

    Catherine Georgopoulou Theodosiou

    2016-06-01

    Full Text Available This paper focuses on the Content and Language Integrated Learning (CLIL method for (foreign language teaching. The CLIL approach is rapidly gaining momentum across Europe and all over the world. It is the result of recent European Union efforts to develop and apply innovative educational practices of interdisciplinary character in order to bridge the gap between foreign language education and optimum learning outcomes. In order to investigate the compatibility of CLIL with the contemporary Greek educational reality, a small-scale experimental research study was set up, including the development of original e-learning material, a pilot class instruction based on this material and the evaluation of the results. The class instruction was based on Project-Based Learning whereas Collaborative Learning was supported by the Edmodo e-learning platform. Information on the progress of the pilot class instruction and the learning outcomes achieved was disseminated through a wiki set up for this task.

  9. Optimizing cationic and neutral lipids for efficient gene delivery at high serum content.

    Science.gov (United States)

    Chan, Chia-Ling; Ewert, Kai K; Majzoub, Ramsey N; Hwu, Yeu-Kuang; Liang, Keng S; Leal, Cecília; Safinya, Cyrus R

    2014-01-01

    Cationic liposome (CL)-DNA complexes are promising gene delivery vectors with potential application in gene therapy. A key challenge in creating CL-DNA complexes for application is that their transfection efficiency (TE) is adversely affected by serum. In particular, little is known about the effects of a high serum content on TE, even though this may provide design guidelines for application in vivo. We prepared CL-DNA complexes in which we varied the neutral lipid [1,2-dioleoyl-sn-glycerophosphatidylcholine, glycerol-monooleate (GMO), cholesterol], the headgroup charge and chemical structure of the cationic lipid, and the ratio of neutral to cationic lipid; we then measured the TE of these complexes as a function of serum content and assessed their cytotoxicity. We tested selected formulations in two human cancer cell lines (M21/melanoma and PC-3/prostate cancer). In the absence of serum, all CL-DNA complexes of custom-synthesized multivalent lipids show high TE. Certain combinations of multivalent lipids and neutral lipids, such as MVL5(5+)/GMO-DNA complexes or complexes based on the dendritic-headgroup lipid TMVLG3(8+) exhibited high TE both in the absence and presence of serum. Although their TE still dropped to a small extent in the presence of serum, it reached or surpassed that of benchmark commercial transfection reagents, particularly at a high serum content. Two-component vectors (one multivalent cationic lipid and one neutral lipid) can rival or surpass benchmark reagents at low and high serum contents (up to 50%, v/v). We propose guidelines for optimizing the serum resistance of CL-DNA complexes based on a given cationic lipid. Copyright © 2014 John Wiley & Sons, Ltd.

  10. Simultaneous allocation of distributed resources using improved teaching learning based optimization

    International Nuclear Information System (INIS)

    Kanwar, Neeraj; Gupta, Nikhil; Niazi, K.R.; Swarnkar, Anil

    2015-01-01

    Highlights: • Simultaneous allocation of distributed energy resources in distribution networks. • Annual energy loss reduction is optimized using a multi-level load profile. • A new penalty factor approach is suggested to check node voltage deviations. • An improved TLBO is proposed by suggesting several modifications in standard TLBO. • An intelligent search is proposed to enhance the performance of solution technique. - Abstract: Active and reactive power flow in distribution networks can be effectively controlled by optimally placing distributed resources like shunt capacitors and distributed generators. This paper presents improved variant of Teaching Learning Based Optimization (TLBO) to efficiently and effectively deal with the problem of simultaneous allocation of these distributed resources in radial distribution networks while considering multi-level load scenario. Several algorithm specific modifications are suggested in the standard form of TLBO to cope against the intrinsic flaws of this technique. In addition, an intelligent search approach is proposed to restrict the problem search space without loss of diversity. This enhances the overall performance of the proposed method. The proposed method is investigated on IEEE 33-bus, 69-bus and 83-bus test distribution systems showing promising results

  11. Learning on the Trail: A Content Analysis of a University Arboretum's Exemplary Interpretive Science Signage System

    Science.gov (United States)

    Wandersee, James H.; Clary, Renee M.

    2007-01-01

    This is an in-depth content analysis of an exemplary outdoor science signage system. The authors offer useful criteria for assessing the quality of the "opportunity to learn" within science signage systems in informal educational sites. This research may be helpful in the design or improvement of trailside interpretive signage systems.

  12. Capturing the complexity: Content, type, and amount of instruction and quality of the classroom learning environment synergistically predict third graders' vocabulary and reading comprehension outcomes.

    Science.gov (United States)

    Connor, Carol McDonald; Spencer, Mercedes; Day, Stephanie L; Giuliani, Sarah; Ingebrand, Sarah W; McLean, Leigh; Morrison, Frederick J

    2014-08-01

    We examined classrooms as complex systems that affect students' literacy learning through interacting effects of content and amount of time individual students spent in literacy instruction along with the global quality of the classroom-learning environment. We observed 27 third grade classrooms serving 315 target students using two different observation systems. The first assessed instruction at a more micro-level; specifically, the amount of time individual students spent in literacy instruction defined by the type of instruction, role of the teacher, and content. The second assessed the quality of the classroom-learning environment at a more macro level focusing on classroom organization, teacher responsiveness, and support for vocabulary and language. Results revealed that both global quality of the classroom learning environment and time individual students spent in specific types of literacy instruction covering specific content interacted to predict students' comprehension and vocabulary gains whereas neither system alone did. These findings support a dynamic systems model of how individual children learn in the context of classroom literacy instruction and the classroom-learning environment, which can help to improve observations systems, advance research, elevate teacher evaluation and professional development, and enhance student achievement.

  13. Capturing the complexity: Content, type, and amount of instruction and quality of the classroom learning environment synergistically predict third graders’ vocabulary and reading comprehension outcomes

    Science.gov (United States)

    Connor, Carol McDonald; Spencer, Mercedes; Day, Stephanie L.; Giuliani, Sarah; Ingebrand, Sarah W.; McLean, Leigh; Morrison, Frederick J.

    2014-01-01

    We examined classrooms as complex systems that affect students’ literacy learning through interacting effects of content and amount of time individual students spent in literacy instruction along with the global quality of the classroom-learning environment. We observed 27 third grade classrooms serving 315 target students using two different observation systems. The first assessed instruction at a more micro-level; specifically, the amount of time individual students spent in literacy instruction defined by the type of instruction, role of the teacher, and content. The second assessed the quality of the classroom-learning environment at a more macro level focusing on classroom organization, teacher responsiveness, and support for vocabulary and language. Results revealed that both global quality of the classroom learning environment and time individual students spent in specific types of literacy instruction covering specific content interacted to predict students’ comprehension and vocabulary gains whereas neither system alone did. These findings support a dynamic systems model of how individual children learn in the context of classroom literacy instruction and the classroom-learning environment, which can help to improve observations systems, advance research, elevate teacher evaluation and professional development, and enhance student achievement. PMID:25400293

  14. Discrete-Time Stable Generalized Self-Learning Optimal Control With Approximation Errors.

    Science.gov (United States)

    Wei, Qinglai; Li, Benkai; Song, Ruizhuo

    2018-04-01

    In this paper, a generalized policy iteration (GPI) algorithm with approximation errors is developed for solving infinite horizon optimal control problems for nonlinear systems. The developed stable GPI algorithm provides a general structure of discrete-time iterative adaptive dynamic programming algorithms, by which most of the discrete-time reinforcement learning algorithms can be described using the GPI structure. It is for the first time that approximation errors are explicitly considered in the GPI algorithm. The properties of the stable GPI algorithm with approximation errors are analyzed. The admissibility of the approximate iterative control law can be guaranteed if the approximation errors satisfy the admissibility criteria. The convergence of the developed algorithm is established, which shows that the iterative value function is convergent to a finite neighborhood of the optimal performance index function, if the approximate errors satisfy the convergence criterion. Finally, numerical examples and comparisons are presented.

  15. EXTENSIVE LISTENING: LET STUDENTS EXPERIENCE LEARNING BY OPTIMIZING THE USE OF AUTHENTIC MATERIALS

    Directory of Open Access Journals (Sweden)

    Yulia Hapsari

    2014-10-01

    Full Text Available In a country like Indonesia, one of challenges in learning English as a foreign language is a lack of exposure of English in its authentic sense. The use of authentic materials seems to be an option to cope with this situation. One of the ways to optimize the use of the authentic materials to trigger students to experience learning and to enhance their active involvement in the learning process is by using it in extensive listening activities. Through extensive listening by using authentic materials, students are exposed to real native speech in meaningful language use. As the result, difficulties in listening gradually disappear.  In order to put the idea into practice, the first thing to do is to set objectives of each meeting based on core vocabulary and grammar that are suitable for the learners using comprehensible input principle as the basic consideration. Second, selecting authentic materials that suit the objectives and that give exposure to formulaic language and meaningful language use. Then, preparing activities in which the instruction is reasonable and lead to sufficient practice to develop fluency. Finally, synchronize teaching activities to increase students’ motivation to learn. As a follow up activities, students are informed and eventually involved in the whole process. Thus, students experience learning and actively involved in their learning process.

  16. Digital Content Strategies

    OpenAIRE

    Halbheer, Daniel; Stahl, Florian; Koenigsberg, Oded; Lehmann, Donald R

    2013-01-01

    This paper studies content strategies for online publishers of digital information goods. It examines sampling strategies and compares their performance to paid content and free content strategies. A sampling strategy, where some of the content is offered for free and consumers are charged for access to the rest, is known as a "metered model" in the newspaper industry. We analyze optimal decisions concerning the size of the sample and the price of the paid content when sampling serves the dua...

  17. Assessment of Student Performance in a PSI College Physics Course Using Ausubel's Learning Theory as a Theoretical Framework for Content Organization.

    Science.gov (United States)

    Moriera, M. A.

    1979-01-01

    David Ausubel's learning theory was used as a framework for the content organization of an experimental Personalized System of Instruction (PSI) course in physics. Evaluation suggests that the combination of PSI as a method of instruction and Ausubel's theory for organization might result in better learning outcomes. (Author/JMD)

  18. Morphing Wing Structural Optimization Using Opposite-Based Population-Based Incremental Learning and Multigrid Ground Elements

    Directory of Open Access Journals (Sweden)

    S. Sleesongsom

    2015-01-01

    Full Text Available This paper has twin aims. Firstly, a multigrid design approach for optimization of an unconventional morphing wing is proposed. The structural design problem is assigned to optimize wing mass, lift effectiveness, and buckling factor subject to structural safety requirements. Design variables consist of partial topology, nodal positions, and component sizes of a wing internal structure. Such a design process can be accomplished by using multiple resolutions of ground elements, which is called a multigrid approach. Secondly, an opposite-based multiobjective population-based incremental learning (OMPBIL is proposed for comparison with the original multiobjective population-based incremental learning (MPBIL. Multiobjective design problems with single-grid and multigrid design variables are then posed and tackled by OMPBIL and MPBIL. The results show that using OMPBIL in combination with a multigrid design approach is the best design strategy. OMPBIL is superior to MPBIL since the former provides better population diversity. Aeroelastic trim for an elastic morphing wing is also presented.

  19. Making Progress in Content and Language Integrated Learning (CLIL Lessons: An Indonesian Tertiary Context

    Directory of Open Access Journals (Sweden)

    Manafe Novriani Rabeka

    2018-01-01

    Full Text Available This paper outlines an attempt to discover students’ progress in both content and language skill in a content and language integrated learning (CLIL lessons at an Indonesia’s higher education context. This is a part of a research conducted at Faculty of Science and Technology of Nusa Cendana University in Kupang, East Nusa Tenggara Province. This study employs mixed method approach with 20 participants attending by taking pre-test and post-test as well as joining a focus group interview particularly for 6 students. The tests were aimed at measuring the participants’ comprehension of English as the language of CLIL lesson. They were also used as the tool to evaluate students’ mastery of Mathematics as the content subject. Based on the post-test results, the findings showed that more students made significant progress in content subject in comparison to their achievement in language proficiency. Regarding the interview, the students admitted that their failure to made progress in both subjects were mainly caused by their inadequate level of English. This, therefore, led to rising anxiety among the students to complete the tests.

  20. Optimization Extracting Technology of Cynomorium songaricum Rupr. Saponins by Ultrasonic and Determination of Saponins Content in Samples with Different Source

    OpenAIRE

    Xiaoli Wang; Qingwei Wei; Xinqiang Zhu; Chunmei Wang; Yonggang Wang; Peng Lin; Lin Yang

    2015-01-01

    Extraction process was optimized by single factor and orthogonal experiment (L9 (34)). Moreover, the content determination was studied in methodology. The optimum ultrasonic extraction conditions were: ethanol concentration of 75%, ultrasonic power of 420 w, the solid-liquid ratio of 1:15, extraction duration of 45 min, extraction temperature of 90°C and extraction for 2 times. Saponins content in Guazhou samples was significantly higher than those in Xinjiang and Inner Mongolia. Meanwhile, G...

  1. Cross learning synergies between Operation Management content and the use of generic analytic tools

    Directory of Open Access Journals (Sweden)

    Frederic Marimon

    2017-06-01

    By presenting both objectives simultaneously students are found to be more motivated towards working deeply in both objectives. Students know that the theoretical content will be put in practice through certain tools, strengthening the student's interest on the conceptual issues of the chapter. In turn, because students know that they will use a generic tool in a known context, their interests in these tools is reinforced. The result is a cross learning synergy.

  2. Combining different Technologies in a Funerary Archaeology content and language integrated Learning (CLIL) Course

    OpenAIRE

    Cignoni, Laura; Fornaciari, Gino

    2009-01-01

    The aim of this paper is to describe a project in which Italian undergraduate students at the Palaeopathology Division of Pisa University will attend a two-year Content and Language Integrated Learning (CLIL) course combining the study of funerary archaeology with English as vehicular language. At the presence of a subject and language teacher working together, the trainees will use different types of technology including devices such as electronic blackboards and Word applications with user-...

  3. "Wow! Look at That!": Discourse as a Means to Improve Teachers' Science Content Learning in Informal Science Institutions

    Science.gov (United States)

    Holliday, Gary M.; Lederman, Judith S.; Lederman, Norman G.

    2014-12-01

    Currently, it is not clear whether professional development staff at Informal Science Institutions (ISIs) are considering the way exhibits contribute to the social aspects of learning as described by the contextual model of learning (CML) (Falk & Dierking in The museum experience. Whalesback, Washington, 1992; Learning from museums: visitor experiences and the making of meaning. Altamira Press, New York, 2000) and recommended in the reform documents (see Cox-Peterson et al. in Journal of Research in Science Teaching 40:200-218, 2003). In order to move beyond only preparing science teachers for field trips, while necessary, it is also important to understand the role exhibits play in influencing teachers' content-related social interactions while engaged in ISI professional development. This study looked at a life science course that was offered at and taught by education staff of a large science and technology museum located in the Midwest, USA. The course was offered to three sections of teachers throughout the school year and met six times for a full day. The courses met approximately once a month from September through the beginning of June and provided 42 contact hours overall. Elementary and middle school teachers ( n = 94) were audio- and videotaped while participating in the content courses and interacting with the museum's exhibits. When considering the two factors within the sociocultural context of CML: within-group sociocultural mediation and facilitated mediation by others, the use of exhibits during both courses generally did not fully take into account these elements. In this study, it seemed that teachers' talk always had a purpose but it is argued that it did not always have a direction or connection to the desired content or exhibit. When freely exploring the museum, teachers often purely reacted to the display itself or the novelty of it. However, when PD staff made explicit connections between exhibits, content, and activities, participants were

  4. Cultural Challenges in Developing E-Learning Content

    Directory of Open Access Journals (Sweden)

    Marianne Amir Azer

    2011-03-01

    Full Text Available Education is an important component of any nation’s development process. Society has been credited with creating technology, but technology is simultaneously creating society. One of the key benefits of such technology creation includes learning and curriculum development, which is otherwise referred to as e-leaning, and more appropriately referred to as global e-learning. Global e-learning raises some implications, which include communication, culture, and technology, that must be addressed before successful implementation and outcome can occur. In this paper, we discuss cultural related issues such as culture influence on e-learning and the dimensions of cultural variability. In addition, we present the main challenges to provide e-learning opportunities. Finally, a case study for facing the cultural challenges is presented; this will be followed by concluding remarks at the end of this paper.

  5. A Study on the Instructor Role in Dealing with Mixed Contents: How It Affects Learner Satisfaction and Retention in e-Learning

    Directory of Open Access Journals (Sweden)

    Seung Jae Lee

    2018-03-01

    Full Text Available The information and communication technology has become an indispensable part of modern education. The paradigm shift in the educational environment makes the instructors recollect the traditional roles in classroom education and adjust their responsibilities to accommodate a transformed pedagogy and learner expectations. This paper aims at the instructor’s role in on-line education and studies how the instructor affects the learner satisfaction via the instructor involvement. Modifying the information system success model, the learning-environment qualities are rearranged into two-tiered formats—rigid and flexible contents—depending on the instructor’s manageability. A partial least square analysis was used to examine the structural relationship among rigid and flexible contents qualities (i.e., technology-assisted learning-environment qualities, learner satisfaction, and learner retention, and found that the instructor involvement had a moderating effect on flexible contents qualities (test and activity; further, the moderating effect of instructor is captured as high involvement in tests and low involvement in activities. Consequently, this paper confirms the relationship between learning-environment qualities, learner satisfaction, and instructor involvement. Empirically, the instructor role in on-line education and the degree of instructor involvement in higher education are substantiated; the result of this study will also contribute to e-learning design or content delivery system development in a practical way.

  6. Case-based ethics instruction: the influence of contextual and individual factors in case content on ethical decision-making.

    Science.gov (United States)

    Bagdasarov, Zhanna; Thiel, Chase E; Johnson, James F; Connelly, Shane; Harkrider, Lauren N; Devenport, Lynn D; Mumford, Michael D

    2013-09-01

    Cases have been employed across multiple disciplines, including ethics education, as effective pedagogical tools. However, the benefit of case-based learning in the ethics domain varies across cases, suggesting that not all cases are equal in terms of pedagogical value. Indeed, case content appears to influence the extent to which cases promote learning and transfer. Consistent with this argument, the current study explored the influences of contextual and personal factors embedded in case content on ethical decision-making. Cases were manipulated to include a clear description of the social context and the goals of the characters involved. Results indicated that social context, specifically the description of an autonomy-supportive environment, facilitated execution of sense making processes and resulted in greater decision ethicality. Implications for designing optimal cases and case-based training programs are discussed.

  7. Factor analysis in optimization of formulation of high content uniformity tablets containing low dose active substance.

    Science.gov (United States)

    Lukášová, Ivana; Muselík, Jan; Franc, Aleš; Goněc, Roman; Mika, Filip; Vetchý, David

    2017-11-15

    Warfarin is intensively discussed drug with narrow therapeutic range. There have been cases of bleeding attributed to varying content or altered quality of the active substance. Factor analysis is useful for finding suitable technological parameters leading to high content uniformity of tablets containing low amount of active substance. The composition of tabletting blend and technological procedure were set with respect to factor analysis of previously published results. The correctness of set parameters was checked by manufacturing and evaluation of tablets containing 1-10mg of warfarin sodium. The robustness of suggested technology was checked by using "worst case scenario" and statistical evaluation of European Pharmacopoeia (EP) content uniformity limits with respect to Bergum division and process capability index (Cpk). To evaluate the quality of active substance and tablets, dissolution method was developed (water; EP apparatus II; 25rpm), allowing for statistical comparison of dissolution profiles. Obtained results prove the suitability of factor analysis to optimize the composition with respect to batches manufactured previously and thus the use of metaanalysis under industrial conditions is feasible. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. classical optimization of bagasse ash content in cement-stabilized

    African Journals Online (AJOL)

    Optimization of construction materials with laboratory data is a very possible way of minimizing waste of resources (materials and cost). There had been several successful attempts of optimization of construction materials. However, optimization in soil stabilization for road-work has been very rare because of its complexities ...

  9. Strategies for active learning in online continuing education.

    Science.gov (United States)

    Phillips, Janet M

    2005-01-01

    Online continuing education and staff development is on the rise as the benefits of access, convenience, and quality learning are continuing to take shape. Strategies to enhance learning call for learner participation that is self-directed and independent, thus changing the educator's role from expert to coach and facilitator. Good planning of active learning strategies promotes optimal learning whether the learning content is presented in a course or a just-in-time short module. Active learning strategies can be used to enhance online learning during all phases of the teaching-learning process and can accommodate a variety of learning styles. Feedback from peers, educators, and technology greatly influences learner satisfaction and must be harnessed to provide effective learning experiences. Outcomes of active learning can be assessed online and implemented conveniently and successfully from the initiation of the course or module planning to the end of the evaluation process. Online learning has become accessible and convenient and allows the educator to track learner participation. The future of online education will continue to grow, and using active learning strategies will ensure that quality learning will occur, appealing to a wide variety of learning needs.

  10. The value of online learning and MRI: finding a niche for expensive technologies.

    Science.gov (United States)

    Cook, David A

    2014-11-01

    The benefits of online learning come at a price. How can we optimize the overall value? Critically appraise the value of online learning. Narrative review. Several prevalent myths overinflate the value of online learning. These include that online learning is cheap and easy (it is usually more expensive), that it is more efficient (efficiency depends on the instructional design, not the modality), that it will transform education (fundamental learning principles have not changed), and that the Net Generation expects it (there is no evidence of pent-up demand). However, online learning does add real value by enhancing flexibility, control and analytics. Costs may also go down if disruptive innovations (e.g. low-cost, low-tech, but instructionally sound "good enough" online learning) supplant technically superior but more expensive online learning products. Cost-lowering strategies include focusing on core principles of learning rather than technologies, using easy-to-learn authoring tools, repurposing content (organizing and sequencing existing resources rather than creating new content) and using course templates. Online learning represents just one tool in an educator's toolbox, as does the MRI for clinicians. We need to use the right tool(s) for the right learner at the right dose, time and route.

  11. Effective Educational Videos: Principles and Guidelines for Maximizing Student Learning from Video Content

    Science.gov (United States)

    Brame, Cynthia J.

    2016-01-01

    Educational videos have become an important part of higher education, providing an important content-delivery tool in many flipped, blended, and online classes. Effective use of video as an educational tool is enhanced when instructors consider three elements: how to manage cognitive load of the video; how to maximize student engagement with the video; and how to promote active learning from the video. This essay reviews literature relevant to each of these principles and suggests practical ways instructors can use these principles when using video as an educational tool. PMID:27789532

  12. Cultural Challenges in Developing E-Learning Content

    OpenAIRE

    Marianne Amir Azer; Ahmed Mostafa El-Sherbini

    2011-01-01

    Education is an important component of any nation’s development process. Society has been credited with creating technology, but technology is simultaneously creating society. One of the key benefits of such technology creation includes learning and curriculum development, which is otherwise referred to as e-leaning, and more appropriately referred to as global e-learning. Global e-learning raises some implications, which include communication, culture, and technology, that must be addressed ...

  13. An analysis of science content and representations in introductory college physics textbooks and multimodal learning resources

    Science.gov (United States)

    Donnelly, Suzanne M.

    This study features a comparative descriptive analysis of the physics content and representations surrounding the first law of thermodynamics as presented in four widely used introductory college physics textbooks representing each of four physics textbook categories (calculus-based, algebra/trigonometry-based, conceptual, and technical/applied). Introducing and employing a newly developed theoretical framework, multimodal generative learning theory (MGLT), an analysis of the multimodal characteristics of textbook and multimedia representations of physics principles was conducted. The modal affordances of textbook representations were identified, characterized, and compared across the four physics textbook categories in the context of their support of problem-solving. Keywords: college science, science textbooks, multimodal learning theory, thermodynamics, representations

  14. Learning strategies: a synthesis and conceptual model

    Science.gov (United States)

    Hattie, John A. C.; Donoghue, Gregory M.

    2016-08-01

    The purpose of this article is to explore a model of learning that proposes that various learning strategies are powerful at certain stages in the learning cycle. The model describes three inputs and outcomes (skill, will and thrill), success criteria, three phases of learning (surface, deep and transfer) and an acquiring and consolidation phase within each of the surface and deep phases. A synthesis of 228 meta-analyses led to the identification of the most effective strategies. The results indicate that there is a subset of strategies that are effective, but this effectiveness depends on the phase of the model in which they are implemented. Further, it is best not to run separate sessions on learning strategies but to embed the various strategies within the content of the subject, to be clearer about developing both surface and deep learning, and promoting their associated optimal strategies and to teach the skills of transfer of learning. The article concludes with a discussion of questions raised by the model that need further research.

  15. Blended Learning as Transformational Institutional Learning

    Science.gov (United States)

    VanDerLinden, Kim

    2014-01-01

    This chapter reviews institutional approaches to blended learning and the ways in which institutions support faculty in the intentional redesign of courses to produce optimal learning. The chapter positions blended learning as a strategic opportunity to engage in organizational learning.

  16. An Ensemble Approach in Converging Contents of LMS and KMS

    Science.gov (United States)

    Sabitha, A. Sai; Mehrotra, Deepti; Bansal, Abhay

    2017-01-01

    Currently the challenges in e-Learning are converging the learning content from various sources and managing them within e-learning practices. Data mining learning algorithms can be used and the contents can be converged based on the Metadata of the objects. Ensemble methods use multiple learning algorithms and it can be used to converge the…

  17. Content, Context & Connectivity Persuasive Interplay

    DEFF Research Database (Denmark)

    Sørensen, Christian Grund

    2013-01-01

    -supported research project under EACEA). In the development of this project several categories of content have been implemented in technology enhanced learning tools. These have been designed to support learning in different contexts and eventually the role of the connectivity of these learning objects and tools......The aim of this paper is to discuss the relationship between content, context and connectivity and suggesting a model of Dynamic Interplay. This is done in relation to a specific learning environment concerning cultural mediation, in casu the Kaj Munk Case of the EuroPLOT-project (an EU...... is discussed. Focus is here on The Kaj Munk Study Edition, The Conceptual Pond, Immersive Layers Design, and Generative Learning Objects (GLOs) which are applications affiliated with the Munk case. This paper explores the persuasive potential of the interplay between the different applications for the benefit...

  18. Neuroevolutionary Constrained Optimization for Content Creation

    DEFF Research Database (Denmark)

    Liapis, Antonios; Yannakakis, Georgios N.; Togelius, Julian

    2011-01-01

    and thruster types and topologies) independently of game physics and steering strategies. According to the proposed framework, the designer picks a set of requirements for the spaceship that a constrained optimizer attempts to satisfy. The constraint satisfaction approach followed is based on neuroevolution...... and survival tasks and are also visually appealing....

  19. Provincial carbon intensity abatement potential estimation in China: A PSO–GA-optimized multi-factor environmental learning curve method

    International Nuclear Information System (INIS)

    Yu, Shiwei; Zhang, Junjie; Zheng, Shuhong; Sun, Han

    2015-01-01

    This study aims to estimate carbon intensity abatement potential in China at the regional level by proposing a particle swarm optimization–genetic algorithm (PSO–GA) multivariate environmental learning curve estimation method. The model uses two independent variables, namely, per capita gross domestic product (GDP) and the proportion of the tertiary industry in GDP, to construct carbon intensity learning curves (CILCs), i.e., CO 2 emissions per unit of GDP, of 30 provinces in China. Instead of the traditional ordinary least squares (OLS) method, a PSO–GA intelligent optimization algorithm is used to optimize the coefficients of a learning curve. The carbon intensity abatement potentials of the 30 Chinese provinces are estimated via PSO–GA under the business-as-usual scenario. The estimation reveals the following results. (1) For most provinces, the abatement potentials from improving a unit of the proportion of the tertiary industry in GDP are higher than the potentials from raising a unit of per capita GDP. (2) The average potential of the 30 provinces in 2020 will be 37.6% based on the emission's level of 2005. The potentials of Jiangsu, Tianjin, Shandong, Beijing, and Heilongjiang are over 60%. Ningxia is the only province without intensity abatement potential. (3) The total carbon intensity in China weighted by the GDP shares of the 30 provinces will decline by 39.4% in 2020 compared with that in 2005. This intensity cannot achieve the 40%–45% carbon intensity reduction target set by the Chinese government. Additional mitigation policies should be developed to uncover the potentials of Ningxia and Inner Mongolia. In addition, the simulation accuracy of the CILCs optimized by PSO–GA is higher than that of the CILCs optimized by the traditional OLS method. - Highlights: • A PSO–GA-optimized multi-factor environmental learning curve method is proposed. • The carbon intensity abatement potentials of the 30 Chinese provinces are estimated by

  20. The effect of content delivery style on student performance in anatomy.

    Science.gov (United States)

    White, Lloyd J; McGowan, Heath W; McDonald, Aaron C

    2018-04-12

    The development of new technologies and ensuing pedagogical research has led many tertiary institutions to integrate and adopt online learning strategies. The authors of this study have incorporated online learning strategies into existing educational practices of a second year anatomy course, resulting in half of the course content delivered via face-to-face lectures, and half delivered online via tailored video vignettes, with accompanying worksheets and activities. The effect of the content delivery mode on student learning was analyzed by tailoring questions to content presented either face-to-face or online. Four practical tests were conducted across the semester with each consisting of four questions. Within each test, two questions were based on content delivered face-to-face, and two questions were based on content delivered online. Examination multiple choice questions were similarly divided and assessed. Findings indicate that student learning is consistent regardless of the mode of content delivery. However, student viewing habits had a significant impact on learning, with students who viewed videos multiple times achieving higher marks than those less engaged with the online content. Student comments also indicated that content delivery mode was not an influence on learning. Therefore student engagement, rather than the mode of content delivery, is a determinant of student learning and performance in human anatomy. Anat Sci Educ. © 2018 American Association of Anatomists. © 2018 American Association of Anatomists.

  1. Optimizing Distributed Machine Learning for Large Scale EEG Data Set

    Directory of Open Access Journals (Sweden)

    M Bilal Shaikh

    2017-06-01

    Full Text Available Distributed Machine Learning (DML has gained its importance more than ever in this era of Big Data. There are a lot of challenges to scale machine learning techniques on distributed platforms. When it comes to scalability, improving the processor technology for high level computation of data is at its limit, however increasing machine nodes and distributing data along with computation looks as a viable solution. Different frameworks   and platforms are available to solve DML problems. These platforms provide automated random data distribution of datasets which miss the power of user defined intelligent data partitioning based on domain knowledge. We have conducted an empirical study which uses an EEG Data Set collected through P300 Speller component of an ERP (Event Related Potential which is widely used in BCI problems; it helps in translating the intention of subject w h i l e performing any cognitive task. EEG data contains noise due to waves generated by other activities in the brain which contaminates true P300Speller. Use of Machine Learning techniques could help in detecting errors made by P300 Speller. We are solving this classification problem by partitioning data into different chunks and preparing distributed models using Elastic CV Classifier. To present a case of optimizing distributed machine learning, we propose an intelligent user defined data partitioning approach that could impact on the accuracy of distributed machine learners on average. Our results show better average AUC as compared to average AUC obtained after applying random data partitioning which gives no control to user over data partitioning. It improves the average accuracy of distributed learner due to the domain specific intelligent partitioning by the user. Our customized approach achieves 0.66 AUC on individual sessions and 0.75 AUC on mixed sessions, whereas random / uncontrolled data distribution records 0.63 AUC.

  2. Content validation of an interprofessional learning video peer assessment tool.

    Science.gov (United States)

    Nisbet, Gillian; Jorm, Christine; Roberts, Chris; Gordon, Christopher J; Chen, Timothy F

    2017-12-16

    Large scale models of interprofessional learning (IPL) where outcomes are assessed are rare within health professional curricula. To date, there is sparse research describing robust assessment strategies to support such activities. We describe the development of an IPL assessment task based on peer rating of a student generated video evidencing collaborative interprofessional practice. We provide content validation evidence of an assessment rubric in the context of large scale IPL. Two established approaches to scale development in an educational setting were combined. A literature review was undertaken to develop a conceptual model of the relevant domains and issues pertaining to assessment of student generated videos within IPL. Starting with a prototype rubric developed from the literature, a series of staff and student workshops were undertaken to integrate expert opinion and user perspectives. Participants assessed five-minute videos produced in a prior pilot IPL activity. Outcomes from each workshop informed the next version of the rubric until agreement was reached on anchoring statements and criteria. At this point the rubric was declared fit to be used in the upcoming mandatory large scale IPL activity. The assessment rubric consisted of four domains: patient issues, interprofessional negotiation; interprofessional management plan in action; and effective use of video medium to engage audience. The first three domains reflected topic content relevant to the underlying construct of interprofessional collaborative practice. The fourth domain was consistent with the broader video assessment literature calling for greater emphasis on creativity in education. We have provided evidence for the content validity of a video-based peer assessment task portraying interprofessional collaborative practice in the context of large-scale IPL activities for healthcare professional students. Further research is needed to establish the reliability of such a scale.

  3. Managerial instrument for didactic staff structure optimization for Distance Learning

    Directory of Open Access Journals (Sweden)

    Gavrus Cristina

    2017-01-01

    Full Text Available Distance learning is a modern system for providing educational services and is relatively new in Romania, if related to the date of its emergence in Europe. More and more active working people are interested in this form of education, paying of course a special attention to its quality. It is quite difficult to appraise the quality of educational programs but several instruments and criteria have been developed over time. The present paper proposes an original mathematical instrument that is aiming at human resources, this type of resources being considered extremely important in case of providing educational service. The number of teachers is crucial for a distance learning program study, because the didactic staff must cover a number of didactic classes that take place on weekends. Concretely, this paper is focused on finding an algorithm that allows the didactic staff structure optimization. For accomplishing this objective, two managerial instruments were use. One of them is mathematical linear programing technique, that develops a mathematical model for didactic staff structure and the other one is WinQSB software package that tests the mathematical model.

  4. EDUCATING TEACHERS FOR CONTENT AND LANGUAGE INTEGRATED LEARNING IN KAZAKHSTAN: DEVELOPING POSITIVE ATTITUDES

    Directory of Open Access Journals (Sweden)

    Artyom Sergeyevich Dontsov

    2018-04-01

    Full Text Available The aim of the present research is to identify whether teachers' attitudes towards the use of Content and Language Integrated Learning (CLIL in the Republic of Kazakhstan can undergo significant changes if they study a course introducing them to the fundamentals of CLIL. Despite the country's plans to adopt English as one of the languages of education, stakeholders’ attitudes towards teaching through the medium of this language remain rather skeptical. A survey was held among Master’s degree students majoring in Education (n = 59 at Pavlodar State University before the course and after its completion. Since it is the affective component that largely determines the quality of attitudes, the levels of participants' anxiety, self-esteem and motivation were used as the indicators. The tools for measuring these variables were the State-Trait Anxiety Inventory, Dembo-Rubinstein's Method of Self-esteem Measurement and Dubovitskaya's Diagnostics of Learning Motivation Orientation. The end-of-course results show a marked reduction in the level of participants' state anxiety, a growth in self-esteem in terms of the readiness to use CLIL, and a shift towards intrinsic motivation. It is argued that for attitudes shift to take place, it is necessary to adopt a constructivist approach to teaching and learning.

  5. Effective Modification of a Nonprescription Medicines Course to Optimize Learning of Millennial Generation Students

    Directory of Open Access Journals (Sweden)

    Bella H Mehta

    2013-01-01

    Full Text Available Objective: To describe examples of effective teaching strategies utilized within a required nonprescription therapeutics course, in order to accommodate learning characteristics of Millennials. Case Study: Instructors identified unique characteristics of Millennial generation students through literature review and focused educational workshops. These characteristics include the desire for active learning where didactic lectures make a connection to life, the incorporation of technology, and assignments that focus on team work. Course modifications were then made based on these characteristics including redesign of large group course lectures with incorporation of patient cases, inclusion of a variety of online components including the opportunity to provide course feedback, and active learning small group projects within workshop sections. Evaluation:Student evaluation of the course and instructors significantly improved after introducing changes to the course compared to previous years. Each component of the student evaluation resulted in a statistically significant change in mean score. Verbal and written evaluations indicated a very positive learning experience for students. Grade mean (3.3 vs. 3.8, p Conclusions: By identifying characteristics of Millennial generation student learners, traditional teaching methods can be modified in order to enhance retention of material and optimize their learning process. Course changes improved the learning experience for students and instructors. Instructors' willingness to evaluate generational differences and adapt teaching enhances the learning experiences in the classroom for both students and instructors.   Type: Case Study

  6. Using Differential Evolution to Optimize Learning from Signals and Enhance Network Security

    Energy Technology Data Exchange (ETDEWEB)

    Harmer, Paul K [Air Force Institute of Technology; Temple, Michael A [Air Force Institute of Technology; Buckner, Mark A [ORNL; Farquhar, Ethan [ORNL

    2011-01-01

    Computer and communication network attacks are commonly orchestrated through Wireless Access Points (WAPs). This paper summarizes proof-of-concept research activity aimed at developing a physical layer Radio Frequency (RF) air monitoring capability to limit unauthorizedWAP access and mprove network security. This is done using Differential Evolution (DE) to optimize the performance of a Learning from Signals (LFS) classifier implemented with RF Distinct Native Attribute (RF-DNA) fingerprints. Performance of the resultant DE-optimized LFS classifier is demonstrated using 802.11a WiFi devices under the most challenging conditions of intra-manufacturer classification, i.e., using emissions of like-model devices that only differ in serial number. Using identical classifier input features, performance of the DE-optimized LFS classifier is assessed relative to a Multiple Discriminant Analysis / Maximum Likelihood (MDA/ML) classifier that has been used for previous demonstrations. The comparative assessment is made using both Time Domain (TD) and Spectral Domain (SD) fingerprint features. For all combinations of classifier type, feature type, and signal-to-noise ratio considered, results show that the DEoptimized LFS classifier with TD features is uperior and provides up to 20% improvement in classification accuracy with proper selection of DE parameters.

  7. Research Trends in Technology-Based Learning from 2000 to 2009: A Content Analysis of Publications in Selected Journals

    Science.gov (United States)

    Hsu, Yu-Chen; Ho, Hsin Ning Jessie; Tsai, Chin-Chung; Hwang, Gwo-Jen; Chu, Hui-Chun; Wang, Chin-Yeh; Chen, Nian-Shing

    2012-01-01

    This paper provides a content analysis of studies in technology-based learning (TBL) that were published in five Social Sciences Citation Index (SSCI) journals (i.e. "the British Journal of Educational Technology, Computers & Education, Educational Technology Research & Development, Educational Technology & Society, the Journal of Computer…

  8. Optimizing physicians' instruction of PACS through e-learning: cognitive load theory applied.

    Science.gov (United States)

    Devolder, P; Pynoo, B; Voet, T; Adang, L; Vercruysse, J; Duyck, P

    2009-03-01

    This article outlines the strategy used by our hospital to maximize the knowledge transfer to referring physicians on using a picture archiving and communication system (PACS). We developed an e-learning platform underpinned by the cognitive load theory (CLT) so that in depth knowledge of PACS' abilities becomes attainable regardless of the user's prior experience with computers. The application of the techniques proposed by CLT optimizes the learning of the new actions necessary to obtain and manipulate radiological images. The application of cognitive load reducing techniques is explained with several examples. We discuss the need to safeguard the physicians' main mental processes to keep the patient's interests in focus. A holistic adoption of CLT techniques both in teaching and in configuration of information systems could be adopted to attain this goal. An overview of the advantages of this instruction method is given both on the individual and organizational level.

  9. Developing and Deploying an XML-based Learning Content Management System at the FernUniversität Hagen

    Directory of Open Access Journals (Sweden)

    Gerd Steinkamp

    2005-02-01

    Full Text Available This paper is a report about the FuXML project carried out at the FernUniversität Hagen. FuXML is a Learning Content Management System (LCMS aimed at providing a practical and efficient solution for the issues attributed to authoring, maintenance, production and distribution of online and offline distance learning material. The paper presents the environment for which the system was conceived and describes the technical realisation. We discuss the reasons for specific implementation decisions and also address the integration of the system within the organisational and technical infrastructure of the university.

  10. Mobile Phones in the Classroom: Examining the Effects of Texting, Twitter, and Message Content on Student Learning

    Science.gov (United States)

    Kuznekoff, Jeffrey H.; Munz, Stevie; Titsworth, Scott

    2015-01-01

    This study examined mobile phone use in the classroom by using an experimental design to study how message content (related or unrelated to class lecture) and message creation (responding to or creating a message) impact student learning. Participants in eight experimental groups and a control group watched a video lecture, took notes, and…

  11. Technology Education Using a Novel Approach in e-Learning-Towards Optimizing the Quality of Learning Outcomes

    Science.gov (United States)

    Malkawi, M. I.; Hawarey, M. M.

    2012-04-01

    Ever since the advent of the new era in presenting taught material in Electronic Form, international bodies, academic institutions, public sectors, as well as specialized entities in the private sector, globally, have all persevered to exploit the power of Distance Learning and e-Learning to disseminate the knowledge in Science and Art using the ubiquitous World Wide Web and its supporting Internet and Internetworking. Many Science & Education-sponsoring bodies, like UNESCO, the European Community, and the World Bank have been keen at funding multinational Distance Learning projects, many of which were directed at an educated audience in certain technical areas. Many countries around the Middle East have found a number of interested European partners to launch funding requests, and were generally successful in their solicitation efforts for the needed funds from these funding bodies. Albeit their intricacies in generating a wealth of knowledge in electronic form, many of the e-Learning schemas developed thus far, have only pursued their goals in the most conventional of ways; In essence, there had been little innovation introduced to gain anything, if any, above traditional classroom lecturing, other than, of course, the gained advantage of the simultaneous online testing and evaluation of the learned material by the examinees. In a sincere effort to change the way in which people look at the merits of e-Learning, and seek the most out of it, we shall propose a novel approach aimed at optimizing the learning outcomes of presented materials. In this paper we propose what shall henceforth be called as Iterative e-Learning. In Iterative e-Learning, as the name implies, a student uses some form of electronic media to access course material in a specific subject. At the end of each phase (Section, Chapter, Session, etc.) on a specific topic, the student is assessed online of how much he/she would have achieved before he/she would move on. If the student fails, due to

  12. Optimizing Virtual Network Functions Placement in Virtual Data Center Infrastructure Using Machine Learning

    Science.gov (United States)

    Bolodurina, I. P.; Parfenov, D. I.

    2018-01-01

    We have elaborated a neural network model of virtual network flow identification based on the statistical properties of flows circulating in the network of the data center and characteristics that describe the content of packets transmitted through network objects. This enabled us to establish the optimal set of attributes to identify virtual network functions. We have established an algorithm for optimizing the placement of virtual data functions using the data obtained in our research. Our approach uses a hybrid method of visualization using virtual machines and containers, which enables to reduce the infrastructure load and the response time in the network of the virtual data center. The algorithmic solution is based on neural networks, which enables to scale it at any number of the network function copies.

  13. RSMDP-based Robust Q-learning for Optimal Path Planning in a Dynamic Environment

    Directory of Open Access Journals (Sweden)

    Yunfei Zhang

    2014-07-01

    Full Text Available This paper presents arobust Q-learning method for path planningin a dynamic environment. The method consists of three steps: first, a regime-switching Markov decision process (RSMDP is formed to present the dynamic environment; second a probabilistic roadmap (PRM is constructed, integrated with the RSMDP and stored as a graph whose nodes correspond to a collision-free world state for the robot; and third, an onlineQ-learning method with dynamic stepsize, which facilitates robust convergence of the Q-value iteration, is integrated with the PRM to determine an optimal path for reaching the goal. In this manner, the robot is able to use past experience for improving its performance in avoiding not only static obstacles but also moving obstacles, without knowing the nature of the obstacle motion. The use ofregime switching in the avoidance of obstacles with unknown motion is particularly innovative.  The developed approach is applied to a homecare robot in computer simulation. The results show that the online path planner with Q-learning is able torapidly and successfully converge to the correct path.

  14. Authoring of Learning Objects in Context

    Science.gov (United States)

    Specht, Marcus; Kravcik, Milos

    2006-01-01

    Learning objects and content interchange standards provide new possibilities for e-learning. Nevertheless the content often lacks context data to find appropriate use for adaptive learning on demand and personalized learning experiences. In the Remotely Accessible Field Trips (RAFT) project mobile authoring of learning content in context has shown…

  15. E-learning optimization: the relative and combined effects of mental practice and modeling on enhanced podcast-based learning-a randomized controlled trial.

    Science.gov (United States)

    Alam, Fahad; Boet, Sylvain; Piquette, Dominique; Lai, Anita; Perkes, Christopher P; LeBlanc, Vicki R

    2016-10-01

    Enhanced podcasts increase learning, but evidence is lacking on how they should be designed to optimize their effectiveness. This study assessed the impact two learning instructional design methods (mental practice and modeling), either on their own or in combination, for teaching complex cognitive medical content when incorporated into enhanced podcasts. Sixty-three medical students were randomised to one of four versions of an airway management enhanced podcast: (1) control: narrated presentation; (2) modeling: narration with video demonstration of skills; (3) mental practice: narrated presentation with guided mental practice; (4) combined: modeling and mental practice. One week later, students managed a manikin-based simulated airway crisis. Knowledge acquisition was assessed by baseline and retention multiple-choice quizzes. Two blinded raters assessed all videos obtained from simulated crises to measure the students' skills using a key-elements scale, critical error checklist, and the Ottawa global rating scale (GRS). Baseline knowledge was not different between all four groups (p = 0.65). One week later, knowledge retention was significantly higher for (1) both the mental practice and modeling group than the control group (p = 0.01; p = 0.01, respectively) and (2) the combined mental practice and modeling group compared to all other groups (all ps = 0.01). Regarding skills acquisition, the control group significantly under-performed in comparison to all other groups on the key-events scale (all ps ≤ 0.05), the critical error checklist (all ps ≤ 0.05), and the Ottawa GRS (all ps ≤ 0.05). The combination of mental practice and modeling led to greater improvement on the key events checklist (p = 0.01) compared to either strategy alone. However, the combination of the two strategies did not result in any further learning gains on the two other measures of clinical performance (all ps > 0.05). The effectiveness of enhanced podcasts for

  16. But Science Is International! Finding Time and Space to Encourage Intercultural Learning in a Content-Driven Physiology Unit

    Science.gov (United States)

    Etherington, Sarah J.

    2014-01-01

    Internationalization of the curriculum is central to the strategic direction of many modern universities and has widespread benefits for student learning. However, these clear aspirations for internationalization of the curriculum have not been widely translated into more internationalized course content and teaching methods in the classroom,…

  17. Mit Blended Learning zur effizienten Literatursuche / Blended Learning: a way to efficient literature search

    Directory of Open Access Journals (Sweden)

    Schubnell, Brigitte

    2007-12-01

    Full Text Available Since 2004 Information Literacy has been part of medical studies at the University of Zurich. The practical course “transfer of knowledge” takes place in the 2nd semester and is mandatory. In 2004 and 2005 the Main Library University of Zurich introduced the medical students, roughly 300, into the topic using a classic approach, i.e. by presentations and exercises. 32 double lessons were given by five employees of the Main Library. Evaluations showed that the students had only little interest in the topic and rated the course poorly. Among others, the following reasons were considered to explain the students’ weak motivation:lack of stimulation such as relevance for exams or other controls of performance limited possibilities to apply the gained knowledge in the 1st year of medical studies; thus students considered some contents irrelevant some exercises were not designed optimally, thereby challenging the students too little In the summer semester 2006 the course was given for the first time using blended learning. The goal was to increase the activity of the students and to make the contents available in the long term. The new form of teaching and learning has been a success and in 2007 the second round took place in the new mode. In the new course, students acquire the theory by self-study using an e-learning module. The period of self-study, which is finished by an online-test, is followed by a double lesson. This double lesson has the character of an exercise and is used to discuss problems and to consolidate the learnt contents using a given question. The e-learning module is freely accessible on the Virtual Education Platform Medicine (VAM.

  18. Curriculum structure, content, learning and assessment in European undergraduate dental education - update 2010.

    LENUS (Irish Health Repository)

    Manogue, M

    2011-08-01

    This paper presents an updated statement on behalf of the Association for Dental Education in Europe (ADEE) in relation to proposals for undergraduate Curriculum Structure, Content, Learning, Assessment and Student \\/ Staff Exchange for dental education in Europe. A task force was constituted to consider these issues and the two previous, related publications produced by the Association (Plasschaert et al 2006 and 2007) were revised. The broad European dental community was circulated and contributed to the revisions. The paper was approved at the General Assembly of ADEE, held in Amsterdam in August 2010 and will be updated again in 2015.

  19. Elementary student teachers' science content representations

    Science.gov (United States)

    Zembal-Saul, Carla; Krajcik, Joseph; Blumenfeld, Phyllis

    2002-08-01

    This purpose of this study was to examine the ways in which three prospective teachers who had early opportunities to teach science would approach representing science content within the context of their student teaching experiences. The study is framed in the literature on pedagogical content knowledge and learning to teach. A situated perspective on cognition is applied to better understand the influence of context and the role of the cooperating teacher. The three participants were enrolled in an experimental teacher preparation program designed to enhance the teaching of science at the elementary level. Qualitative case study design guided the collection, organization, and analysis of data. Multiple forms of data associated with student teachers' content representations were collected, including audiotaped planning and reflection interviews, written lesson plans and reflections, and videotaped teaching experiences. Broad analysis categories were developed and refined around the subconstructs of content representation (i.e., knowledge of instructional strategies that promote learning and knowledge of students and their requirements for meaningful science learning). Findings suggest that when prospective teachers are provided with opportunities to apply and reflect substantively on their developing considerations for supporting children's science learning, they are able to maintain a subject matter emphasis. However, in the absence of such opportunities, student teachers abandon their subject matter emphasis, even when they have had extensive background and experiences addressing subject-specific considerations for teaching and learning.

  20. Quantum learning algorithms for quantum measurements

    Energy Technology Data Exchange (ETDEWEB)

    Bisio, Alessandro, E-mail: alessandro.bisio@unipv.it [QUIT Group, Dipartimento di Fisica ' A. Volta' and INFN, via Bassi 6, 27100 Pavia (Italy); D' Ariano, Giacomo Mauro, E-mail: dariano@unipv.it [QUIT Group, Dipartimento di Fisica ' A. Volta' and INFN, via Bassi 6, 27100 Pavia (Italy); Perinotti, Paolo, E-mail: paolo.perinotti@unipv.it [QUIT Group, Dipartimento di Fisica ' A. Volta' and INFN, via Bassi 6, 27100 Pavia (Italy); Sedlak, Michal, E-mail: michal.sedlak@unipv.it [QUIT Group, Dipartimento di Fisica ' A. Volta' and INFN, via Bassi 6, 27100 Pavia (Italy); Institute of Physics, Slovak Academy of Sciences, Dubravska cesta 9, 845 11 Bratislava (Slovakia)

    2011-09-12

    We study quantum learning algorithms for quantum measurements. The optimal learning algorithm is derived for arbitrary von Neumann measurements in the case of training with one or two examples. The analysis of the case of three examples reveals that, differently from the learning of unitary gates, the optimal algorithm for learning of quantum measurements cannot be parallelized, and requires quantum memories for the storage of information. -- Highlights: → Optimal learning algorithm for von Neumann measurements. → From 2 copies to 1 copy: the optimal strategy is parallel. → From 3 copies to 1 copy: the optimal strategy must be non-parallel.

  1. Quantum learning algorithms for quantum measurements

    International Nuclear Information System (INIS)

    Bisio, Alessandro; D'Ariano, Giacomo Mauro; Perinotti, Paolo; Sedlak, Michal

    2011-01-01

    We study quantum learning algorithms for quantum measurements. The optimal learning algorithm is derived for arbitrary von Neumann measurements in the case of training with one or two examples. The analysis of the case of three examples reveals that, differently from the learning of unitary gates, the optimal algorithm for learning of quantum measurements cannot be parallelized, and requires quantum memories for the storage of information. -- Highlights: → Optimal learning algorithm for von Neumann measurements. → From 2 copies to 1 copy: the optimal strategy is parallel. → From 3 copies to 1 copy: the optimal strategy must be non-parallel.

  2. Semi-Supervised Learning of Lift Optimization of Multi-Element Three-Segment Variable Camber Airfoil

    Science.gov (United States)

    Kaul, Upender K.; Nguyen, Nhan T.

    2017-01-01

    This chapter describes a new intelligent platform for learning optimal designs of morphing wings based on Variable Camber Continuous Trailing Edge Flaps (VCCTEF) in conjunction with a leading edge flap called the Variable Camber Krueger (VCK). The new platform consists of a Computational Fluid Dynamics (CFD) methodology coupled with a semi-supervised learning methodology. The CFD component of the intelligent platform comprises of a full Navier-Stokes solution capability (NASA OVERFLOW solver with Spalart-Allmaras turbulence model) that computes flow over a tri-element inboard NASA Generic Transport Model (GTM) wing section. Various VCCTEF/VCK settings and configurations were considered to explore optimal design for high-lift flight during take-off and landing. To determine globally optimal design of such a system, an extremely large set of CFD simulations is needed. This is not feasible to achieve in practice. To alleviate this problem, a recourse was taken to a semi-supervised learning (SSL) methodology, which is based on manifold regularization techniques. A reasonable space of CFD solutions was populated and then the SSL methodology was used to fit this manifold in its entirety, including the gaps in the manifold where there were no CFD solutions available. The SSL methodology in conjunction with an elastodynamic solver (FiDDLE) was demonstrated in an earlier study involving structural health monitoring. These CFD-SSL methodologies define the new intelligent platform that forms the basis for our search for optimal design of wings. Although the present platform can be used in various other design and operational problems in engineering, this chapter focuses on the high-lift study of the VCK-VCCTEF system. Top few candidate design configurations were identified by solving the CFD problem in a small subset of the design space. The SSL component was trained on the design space, and was then used in a predictive mode to populate a selected set of test points outside

  3. Quantification of Cannabinoid Content in Cannabis

    Science.gov (United States)

    Tian, Y.; Zhang, F.; Jia, K.; Wen, M.; Yuan, Ch.

    2015-09-01

    Cannabis is an economically important plant that is used in many fields, in addition to being the most commonly consumed illicit drug worldwide. Monitoring the spatial distribution of cannabis cultivation and judging whether it is drug- or fiber-type cannabis is critical for governments and international communities to understand the scale of the illegal drug trade. The aim of this study was to investigate whether the cannabinoids content in cannabis could be spectrally quantified using a spectrometer and to identify the optimal wavebands for quantifying the cannabinoid content. Spectral reflectance data of dried cannabis leaf samples and the cannabis canopy were measured in the laboratory and in the field, respectively. Correlation analysis and the stepwise multivariate regression method were used to select the optimal wavebands for cannabinoid content quantification based on the laboratory-measured spectral data. The results indicated that the delta-9-tetrahydrocannabinol (THC) content in cannabis leaves could be quantified using laboratory-measured spectral reflectance data and that the 695 nm band is the optimal band for THC content quantification. This study provides prerequisite information for designing spectral equipment to enable immediate quantification of THC content in cannabis and to discriminate drug- from fiber-type cannabis based on THC content quantification in the field.

  4. A Framework for the Flexible Content Packaging of Learning Objects and Learning Designs

    Science.gov (United States)

    Lukasiak, Jason; Agostinho, Shirley; Burnett, Ian; Drury, Gerrard; Goodes, Jason; Bennett, Sue; Lockyer, Lori; Harper, Barry

    2004-01-01

    This paper presents a platform-independent method for packaging learning objects and learning designs. The method, entitled a Smart Learning Design Framework, is based on the MPEG-21 standard, and uses IEEE Learning Object Metadata (LOM) to provide bibliographic, technical, and pedagogical descriptors for the retrieval and description of learning…

  5. Internet-Based Assessment of Oncology Health Care Professional Learning Style and Optimization of Materials for Web-Based Learning: Controlled Trial With Concealed Allocation.

    Science.gov (United States)

    Micheel, Christine M; Anderson, Ingrid A; Lee, Patricia; Chen, Sheau-Chiann; Justiss, Katy; Giuse, Nunzia B; Ye, Fei; Kusnoor, Sheila V; Levy, Mia A

    2017-07-25

    or 4.0% more improvement on average; P=.004) and a higher follow-up test score than the control group (0.3 points or 3.3% more improvement on average; P=.02). Although the study demonstrated more learning with learning style-tailored educational materials, the magnitude of increased learning and the largely multimodal learning styles preferred by the study participants lead us to conclude that future content-creation efforts should focus on multimodal educational materials rather than learning style-tailored content. ©Christine M Micheel, Ingrid A Anderson, Patricia Lee, Sheau-Chiann Chen, Katy Justiss, Nunzia B Giuse, Fei Ye, Sheila V Kusnoor, Mia A Levy. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 25.07.2017.

  6. The Influence of Student Teacher Self-Regulation of Learning on Their Curricular Content-Knowledge and Course-Design Skills

    Science.gov (United States)

    Shawer, Saad

    2010-01-01

    This investigation examined the influence of EFL student teacher self-regulation of learning (SRL) on their curricular content-knowledge and course-design skills. Positivism guided this study at the levels of: ontology (one form of reality); epistemology (detachment from the subjects); and methodology, using nomothetic research strategy (causal…

  7. Repurposeable Learning Objects Linked to Teaching and Learning Styles

    Directory of Open Access Journals (Sweden)

    Jeremy Dunning

    2004-02-01

    Full Text Available Multimedia learning objects are an essential component of high quality, technology-mediated instruction. Learning objects allow the student to use the content learned in a particular part of a course and; 1. demonstrate mastery of the content, 2. apply that knowledge to solving a problem, and 3. use the content in a critical thinking exercise that both demonstrates mastery and allows the student to place the content within the context of the larger topic of the course. The difficulty associated with the use of learning objects on a broad scale is that they require programming skills most professors and instructors do not possess. Learning objects also tend to be custom productions and are defined in terms of the programming and code terminology, further limiting the professor's ability to understand how they are created. Learning objects defined in terms of styles of learning and teaching allow professors and instructors to develop a deeper understanding of the learning objects and the design process. A set of learning objects has been created that are designed for some of the important styles of learning and teaching. They include; visual learning, writing skills, critical thinking, time-revealed scenarios, case studies and empirical observation. The learning objects are designed and described in terms that the average instructor can readily understand , redesign and incorporate into their own courses. They are also designed in such a way that they can readily be repurposed for new applications in other courses and subject areas, with little or no additional programming.

  8. An effective secondary decomposition approach for wind power forecasting using extreme learning machine trained by crisscross optimization

    International Nuclear Information System (INIS)

    Yin, Hao; Dong, Zhen; Chen, Yunlong; Ge, Jiafei; Lai, Loi Lei; Vaccaro, Alfredo; Meng, Anbo

    2017-01-01

    Highlights: • A secondary decomposition approach is applied in the data pre-processing. • The empirical mode decomposition is used to decompose the original time series. • IMF1 continues to be decomposed by applying wavelet packet decomposition. • Crisscross optimization algorithm is applied to train extreme learning machine. • The proposed SHD-CSO-ELM outperforms other pervious methods in the literature. - Abstract: Large-scale integration of wind energy into electric grid is restricted by its inherent intermittence and volatility. So the increased utilization of wind power necessitates its accurate prediction. The contribution of this study is to develop a new hybrid forecasting model for the short-term wind power prediction by using a secondary hybrid decomposition approach. In the data pre-processing phase, the empirical mode decomposition is used to decompose the original time series into several intrinsic mode functions (IMFs). A unique feature is that the generated IMF1 continues to be decomposed into appropriate and detailed components by applying wavelet packet decomposition. In the training phase, all the transformed sub-series are forecasted with extreme learning machine trained by our recently developed crisscross optimization algorithm (CSO). The final predicted values are obtained from aggregation. The results show that: (a) The performance of empirical mode decomposition can be significantly improved with its IMF1 decomposed by wavelet packet decomposition. (b) The CSO algorithm has satisfactory performance in addressing the premature convergence problem when applied to optimize extreme learning machine. (c) The proposed approach has great advantage over other previous hybrid models in terms of prediction accuracy.

  9. A multi-instructor, team-based, active-learning exercise to integrate basic and clinical sciences content.

    Science.gov (United States)

    Kolluru, Srikanth; Roesch, Darren M; Akhtar de la Fuente, Ayesha

    2012-03-12

    To introduce a multiple-instructor, team-based, active-learning exercise to promote the integration of basic sciences (pathophysiology, pharmacology, and medicinal chemistry) and clinical sciences in a doctor of pharmacy curriculum. A team-based learning activity that involved pre-class reading assignments, individual-and team-answered multiple-choice questions, and evaluation and discussion of a clinical case, was designed, implemented, and moderated by 3 faculty members from the pharmaceutical sciences and pharmacy practice departments. Student performance was assessed using a multiple-choice examination, an individual readiness assurance test (IRAT), a team readiness assurance test (TRAT), and a subjective, objective, assessment, and plan (SOAP) note. Student attitudes were assessed using a pre- and post-exercise survey instrument. Students' understanding of possible correct treatment strategies for depression improved. Students were appreciative of this true integration of basic sciences knowledge in a pharmacotherapy course and to have faculty members from both disciplines present to answer questions. Mean student score on the on depression module for the examination was 80.4%, indicating mastery of the content. An exercise led by multiple instructors improved student perceptions of the importance of team-based teaching. Integrated teaching and learning may be achieved when instructors from multiple disciplines work together in the classroom using proven team-based, active-learning exercises.

  10. Solving a Higgs optimization problem with quantum annealing for machine learning.

    Science.gov (United States)

    Mott, Alex; Job, Joshua; Vlimant, Jean-Roch; Lidar, Daniel; Spiropulu, Maria

    2017-10-18

    The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect simulations of the physical processes involved, often resulting in incorrect labelling of background processes or signals (label noise) and systematic errors. Here we use quantum and classical annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machine learning optimization problem, mapped to a problem of finding the ground state of a corresponding Ising spin model. We build a set of weak classifiers based on the kinematic observables of the Higgs decay photons, which we then use to construct a strong classifier. This strong classifier is highly resilient against overtraining and against errors in the correlations of the physical observables in the training data. We show that the resulting quantum and classical annealing-based classifier systems perform comparably to the state-of-the-art machine learning methods that are currently used in particle physics. However, in contrast to these methods, the annealing-based classifiers are simple functions of directly interpretable experimental parameters with clear physical meaning. The annealer-trained classifiers use the excited states in the vicinity of the ground state and demonstrate some advantage over traditional machine learning methods for small training datasets. Given the relative simplicity of the algorithm and its robustness to error, this technique may find application in other areas of experimental particle physics, such as real-time decision making in event-selection problems and classification in neutrino physics.

  11. Varying plant density and harvest time to optimize cowpea leaf yield and nutrient content

    Science.gov (United States)

    Ohler, T. A.; Nielsen, S. S.; Mitchell, C. A.

    1996-01-01

    Plant density and harvest time were manipulated to optimize vegetative (foliar) productivity of cowpea [Vigna unguiculata (L.) Walp.] canopies for future dietary use in controlled ecological life-support systems as vegetables or salad greens. Productivity was measured as total shoot and edible dry weights (DW), edible yield rate [(EYR) grams DW per square meter per day], shoot harvest index [(SHI) grams DW per edible gram DW total shoot], and yield-efficiency rate [(YER) grams DW edible per square meter per day per grams DW nonedible]. Cowpeas were grown in a greenhouse for leaf-only harvest at 14, 28, 42, 56, 84, or 99 plants/m2 and were harvested 20, 30, 40, or 50 days after planting (DAP). Shoot and edible dry weights increased as plant density and time to harvest increased. A maximum of 1189 g shoot DW/m2 and 594 g edible DW/m2 were achieved at an estimated plant density of 85 plants/m2 and harvest 50 DAP. EYR also increased as plant density and time to harvest increased. An EYR of 11 g m-2 day-1 was predicted to occur at 86 plants/m2 and harvest 50 DAP. SHI and YER were not affected by plant density. However, the highest values of SHI (64%) and YER (1.3 g m-2 day-1 g-1) were attained when cowpeas were harvested 20 DAP. The average fat and ash contents [dry-weight basis (dwb)] of harvested leaves remained constant regardless of harvest time. Average protein content increased from 25% DW at 30 DAP to 45% DW at 50 DAP. Carbohydrate content declined from 50% DW at 30 DAP to 45% DW at 50 DAP. Total dietary fiber content (dwb) of the leaves increased from 19% to 26% as time to harvest increased from 20 to 50 days.

  12. Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening

    Directory of Open Access Journals (Sweden)

    Hao Li

    2017-01-01

    Full Text Available Predicting the performance of solar water heater (SWH is challenging due to the complexity of the system. Fortunately, knowledge-based machine learning can provide a fast and precise prediction method for SWH performance. With the predictive power of machine learning models, we can further solve a more challenging question: how to cost-effectively design a high-performance SWH? Here, we summarize our recent studies and propose a general framework of SWH design using a machine learning-based high-throughput screening (HTS method. Design of water-in-glass evacuated tube solar water heater (WGET-SWH is selected as a case study to show the potential application of machine learning-based HTS to the design and optimization of solar energy systems.

  13. Toward A Dual-Learning Systems Model of Speech Category Learning

    Directory of Open Access Journals (Sweden)

    Bharath eChandrasekaran

    2014-07-01

    Full Text Available More than two decades of work in vision posits the existence of dual-learning systems of category learning. The reflective system uses working memory to develop and test rules for classifying in an explicit fashion, while the reflexive system operates by implicitly associating perception with actions that lead to reinforcement. Dual-learning systems models hypothesize that in learning natural categories, learners initially use the reflective system and, with practice, transfer control to the reflexive system. The role of reflective and reflexive systems in auditory category learning and more specifically in speech category learning has not been systematically examined. In this article we describe a neurobiologically-constrained dual-learning systems theoretical framework that is currently being developed in speech category learning and review recent applications of this framework. Using behavioral and computational modeling approaches, we provide evidence that speech category learning is predominantly mediated by the reflexive learning system. In one application, we explore the effects of normal aging on non-speech and speech category learning. We find an age related deficit in reflective-optimal but not reflexive-optimal auditory category learning. Prominently, we find a large age-related deficit in speech learning. The computational modeling suggests that older adults are less likely to transition from simple, reflective, uni-dimensional rules to more complex, reflexive, multi-dimensional rules. In a second application we summarize a recent study examining auditory category learning in individuals with elevated depressive symptoms. We find a deficit in reflective-optimal and an enhancement in reflexive-optimal auditory category learning. Interestingly, individuals with elevated depressive symptoms also show an advantage in learning speech categories. We end with a brief summary and description of a number of future directions.

  14. An Instructional and Collaborative Learning System with Content Recommendation

    Science.gov (United States)

    Zheng, Xiang-wei; Ma, Hong-wei; Li, Yan

    2013-01-01

    With the rapid development of Internet, e-learning has become a new teaching and learning mode. However, lots of e-learning systems deployed on Internet are just electronic learning materials with very limited interactivity and diagnostic capability. This paper presents an integrated e-learning environment named iCLSR. Firstly, iCLSR provides an…

  15. Principles of formation of the content of an educational electronic resource on the basis of general and didactic patterns of learning

    Directory of Open Access Journals (Sweden)

    Ольга Юрьевна Заславская

    2018-12-01

    Full Text Available The article considers the influence of the development of technical means of teaching on the effectiveness of educational and methodical resources. Modern opportunities of information and communication technologies allow creating electronic educational resources that represent educational information that automates the learning process, provide information assistance, if necessary, collect and process statistical information on the degree of development of the content of the school material by schoolchildren, set an individual trajectory of learning, and so on. The main principle of data organization is the division of the training course into separate sections on the thematic elements and components of the learning process. General regularities include laws that encompass the entire didactic system, and in specific (particular cases, those whose actions extend to a separate component (aspect of the system. From the standpoint of the existence of three types of electronic training modules in the aggregate content of the electronic learning resource - information, control and module of practical classes - the principles of the formation of the electronic learning resource, in our opinion, should regulate all these components. Each of the certain principles is considered in the groups: scientific orientation, methodological orientation, systemic nature, accounting of interdisciplinary connections, fundamentalization, systematic and dosage sequence, rational use of study time, accessibility, minimization, operationalization of goals, unified identification diagnosis.

  16. Unsupervised learning of mixture models based on swarm intelligence and neural networks with optimal completion using incomplete data

    Directory of Open Access Journals (Sweden)

    Ahmed R. Abas

    2012-07-01

    Full Text Available In this paper, a new algorithm is presented for unsupervised learning of finite mixture models (FMMs using data set with missing values. This algorithm overcomes the local optima problem of the Expectation-Maximization (EM algorithm via integrating the EM algorithm with Particle Swarm Optimization (PSO. In addition, the proposed algorithm overcomes the problem of biased estimation due to overlapping clusters in estimating missing values in the input data set by integrating locally-tuned general regression neural networks with Optimal Completion Strategy (OCS. A comparison study shows the superiority of the proposed algorithm over other algorithms commonly used in the literature in unsupervised learning of FMM parameters that result in minimum mis-classification errors when used in clustering incomplete data set that is generated from overlapping clusters and these clusters are largely different in their sizes.

  17. Learning from Experiments in Optimization

    DEFF Research Database (Denmark)

    Winthereik, Brit Ross; Jensen, Casper Bruun

    2017-01-01

    This article examines attempts by professionals in the Danish branch of the environmental NGO NatureAid to optimize their practice by developing a local standard. Describing these efforts as an experiment in optimization, we outline a post-critical alternative to critiques that centre on the redu...... of management as ‘broken up;’ as a distributed, ambient activity, variably performed by different actors using different standards....

  18. An emerging role: the nurse content curator.

    Science.gov (United States)

    Brooks, Beth A

    2015-01-01

    A new phenomenon, the inverted or "flipped" classroom, assumes that students are no longer acquiring knowledge exclusively through textbooks or lectures. Instead, they are seeking out the vast amount of free information available to them online (the very essence of open source) to supplement learning gleaned in textbooks and lectures. With so much open-source content available to nursing faculty, it benefits the faculty to use readily available, technologically advanced content. The nurse content curator supports nursing faculty in its use of such content. Even more importantly, the highly paid, time-strapped faculty is not spending an inordinate amount of effort surfing for and evaluating content. The nurse content curator does that work, while the faculty uses its time more effectively to help students vet the truth, make meaning of the content, and learn to problem-solve. Brooks. © 2014 Wiley Periodicals, Inc.

  19. Content, Affective, and Behavioral Challenges to Learning: Students' Experiences Learning Statistics

    Science.gov (United States)

    McGrath, April L.

    2014-01-01

    This study examined the experiences of and challenges faced by students when completing a statistics course. As part of the requirement for this course, students completed a learning check-in, which consisted of an individual meeting with the instructor to discuss questions and the completion of a learning reflection and study plan. Forty…

  20. Optimization of conditions to achieve high content of gamma amino butyric acid in germinated black rice, and changes in bioactivities

    Directory of Open Access Journals (Sweden)

    Chaiyavat CHAIYASUT

    Full Text Available Abstract The present study estimated the optimum germination conditions to achieve high content of Gamma-amino butyric acid (GABA and other phytochemicals in Thai black rice cultivar Kum Payao (BR. The Box–Behnken design of response surface methodology was employed to optimize the germination conditions. The changes in the GABA, phytochemical content, impact of salt, and temperature stress variation on phytochemical content, and stability of GABA were studied. The results showed that 12 h of soaking at pH 7, followed by 36 h of germination was the optimum condition to achieve maximum GABA content (0.2029 mg/g of germinated BR (GBR. The temperature (8 and 30 °C, and salt (50-200 mM NaCl content affected the phytochemicals of GBR, especially GABA, and anthocyanins. Obviously, the antioxidant capability, and enzyme (α-amylase and α-glucosidase inhibiting nature of BR was significantly (P < 0.001 increased after germination. The storage of GBR at 4 °C significantly, preserved the GABA content (∼80% for 45 days. Primarily, the current study revealed the changes in phytochemical content, and bioactivity of Thai black rice cr. Kum Payao during germination. More studies should be carried out on pharmacological benefits of GABA-rich GBR.

  1. From Content to Practice: Sharing Educational Practice in Edu-Sharing

    Science.gov (United States)

    Klebl, Michael; Kramer, Bernd J.; Zobel, Annett

    2010-01-01

    For technology-enhanced learning, the idea of "learning objects" transfers the technologies of content management, methods of software engineering and principles of open access to educational resources. This paper reports on CampusContent, a research project and competence centre for e-learning at FernUniversitat in Hagen that designed…

  2. Reinforcement learning for adaptive optimal control of unknown continuous-time nonlinear systems with input constraints

    Science.gov (United States)

    Yang, Xiong; Liu, Derong; Wang, Ding

    2014-03-01

    In this paper, an adaptive reinforcement learning-based solution is developed for the infinite-horizon optimal control problem of constrained-input continuous-time nonlinear systems in the presence of nonlinearities with unknown structures. Two different types of neural networks (NNs) are employed to approximate the Hamilton-Jacobi-Bellman equation. That is, an recurrent NN is constructed to identify the unknown dynamical system, and two feedforward NNs are used as the actor and the critic to approximate the optimal control and the optimal cost, respectively. Based on this framework, the action NN and the critic NN are tuned simultaneously, without the requirement for the knowledge of system drift dynamics. Moreover, by using Lyapunov's direct method, the weights of the action NN and the critic NN are guaranteed to be uniformly ultimately bounded, while keeping the closed-loop system stable. To demonstrate the effectiveness of the present approach, simulation results are illustrated.

  3. An intelligent service-based layered architecture for e learning and assessment

    International Nuclear Information System (INIS)

    Javaid, Q.; Arif, F.

    2017-01-01

    The rapid advancement in ICT (Information and Communication Technology) is causing a paradigm shift in eLearning domain. Traditional eLearning systems suffer from certain shortcomings like tight coupling of system components, lack of personalization, flexibility, and scalability and performance issues. This study aims at addressing these challenges through an MAS (Multi Agent System) based multi-layer architecture supported by web services. The foremost objective of this study is to enhance learning process efficiency by provision of flexibility features for learning and assessment processes. Proposed architecture consists of two sub-system namely eLearning and eAssesssment. This architecture comprises of five distinct layers for each sub-system, with active agents responsible for miscellaneous tasks including content handling, updating, resource optimization, load handling and provision of customized environments for learners and instructors. Our proposed architecture aims at establishment of a facilitation level to learners as well as instructors for convenient acquisition and dissemination of knowledge. Personalization features like customized environments, personalized content retrieval and recommendations, adaptive assessment and reduced response time, are believed to significantly enhance learning and tutoring experience. In essence characteristics like intelligence, personalization, interactivity, usability, laidback accessibility and security, signify aptness of proposed architecture for improving conventional learning and assessment processes. Finally we have evaluated our proposed architecture by means of analytical comparison and survey considering certain quality attributes. (author)

  4. Concern for Others Leads to Vicarious Optimism.

    Science.gov (United States)

    Kappes, Andreas; Faber, Nadira S; Kahane, Guy; Savulescu, Julian; Crockett, Molly J

    2018-03-01

    An optimistic learning bias leads people to update their beliefs in response to better-than-expected good news but neglect worse-than-expected bad news. Because evidence suggests that this bias arises from self-concern, we hypothesized that a similar bias may affect beliefs about other people's futures, to the extent that people care about others. Here, we demonstrated the phenomenon of vicarious optimism and showed that it arises from concern for others. Participants predicted the likelihood of unpleasant future events that could happen to either themselves or others. In addition to showing an optimistic learning bias for events affecting themselves, people showed vicarious optimism when learning about events affecting friends and strangers. Vicarious optimism for strangers correlated with generosity toward strangers, and experimentally increasing concern for strangers amplified vicarious optimism for them. These findings suggest that concern for others can bias beliefs about their future welfare and that optimism in learning is not restricted to oneself.

  5. Service learning in Guatemala: using qualitative content analysis to explore an interdisciplinary learning experience among students in health care professional programs.

    Science.gov (United States)

    Fries, Kathleen S; Bowers, Donna M; Gross, Margo; Frost, Lenore

    2013-01-01

    Interprofessional collaboration among health care professionals yields improved patient outcomes, yet many students in health care programs have limited exposure to interprofessional collaboration in the classroom and in clinical and service-learning experiences. This practice gap implies that students enter their professions without valuing interprofessional collaboration and the impact it has on promoting positive patient outcomes. The aim of this study was to describe the interprofessional experiences of students in health care professional programs as they collaborated to provide health care to Guatemalan citizens over a 7-day period. In light of the identified practice gap and a commitment by college administration to fund interprofessional initiatives, faculty educators from nursing, occupational therapy, and physical therapy conducted a qualitative study to explore a service-learning initiative focused on promoting interprofessional collaboration. Students collaborated in triads (one student from each of the three disciplines) to provide supervised health care to underserved Guatemalan men, women, children, and infants across a variety of community and health care settings. Eighteen students participated in a qualitative research project by describing their experience of interprofessional collaboration in a service-learning environment. Twice before arriving in Guatemala, and on three occasions during the trip, participants reflected on their experiences and provided narrative responses to open-ended questions. Qualitative content analysis methodology was used to describe their experiences of interprofessional collaboration. An interprofessional service-learning experience positively affected students' learning, their growth in interprofessional collaboration, and their understanding and appreciation of health care professions besides their own. The experience also generated feelings of gratitude for the opportunity to be a member of an interprofessional

  6. Defining Levels of Learning for Strengths Development Programs in Pharmacy

    Directory of Open Access Journals (Sweden)

    Kristin K. Janke, Ph.D.

    2010-01-01

    Full Text Available The Clifton StrengthsFinder® is an online measure of personal talent that identifies where an individual’s greatest potential for building strengths exists. This paper describes a framework for strengths education in pharmacy which includes introductory, intermediate and advanced levels of learning. The use of the StrengthsFinder® assessment and supporting workshops aids student pharmacists, pharmacy residents and practitioners in identifying and refining their talents and connecting talents to roles in the profession. Additional learning strategies support a learner’s progression to intermediate and advanced levels of learning, which focus on the application of strengths in teams, leadership, and organizational development. By articulating and recognizing levels of learning around strengths-related content and skills, strong instructional design is fostered. Optimal design includes development of a sequence of learning opportunities delivered over time, a roll-out plan and consideration of the instructional resources required.

  7. Defining Levels of Learning for Strengths Development Programs in Pharmacy

    Directory of Open Access Journals (Sweden)

    Kristin Janke

    2010-01-01

    Full Text Available The Clifton StrengthsFinder™ is an online measure of personal talent that identifies where an individual's greatest potential for building strengths exists. This paper describes a framework for strengths education in pharmacy which includes introductory, intermediate and advanced levels of learning. The use of the StrengthsFinder™ assessment and supporting workshops aids student pharmacists, pharmacy residents and practitioners in identifying and refining their talents and connecting talents to roles in the profession. Additional learning strategies support a learner's progression to intermediate and advanced levels of learning, which focus on the application of strengths in teams, leadership, and organizational development. By articulating and recognizing levels of learning around strengths-related content and skills, strong instructional design is fostered. Optimal design includes development of a sequence of learning opportunities delivered over time, a roll-out plan and consideration of the instructional resources required. Type: Idea Paper

  8. Intelligent and Adaptive Educational-Learning Systems Achievements and Trends

    CERN Document Server

    2013-01-01

    The Smart Innovation, Systems and Technologies book series encompasses the topics of knowledge, intelligence, innovation and sustainability. The aim of the series is to make available a platform for the publication of books on all aspects of single and multi-disciplinary research on these themes in order to make the latest results available in a readily-accessible form.  This book is devoted to the “Intelligent and Adaptive Educational-Learning Systems”. It privileges works that highlight key achievements and outline trends to inspire future research.  After a rigorous revision process twenty manuscripts were accepted and organized into four parts as follows: ·     Modeling: The first part embraces five chapters oriented to: 1) shape the affective behavior; 2) depict the adaptive learning curriculum; 3) predict learning achievements; 4) mine learner models to outcome optimized and adaptive e-learning objects; 5) classify learning preferences of learners. ·     Content: The second part encompas...

  9. Does individual learning styles influence the choice to use a web-based ECG learning programme in a blended learning setting?

    Science.gov (United States)

    Nilsson, Mikael; Östergren, Jan; Fors, Uno; Rickenlund, Anette; Jorfeldt, Lennart; Caidahl, Kenneth; Bolinder, Gunilla

    2012-01-16

    The compressed curriculum in modern knowledge-intensive medicine demands useful tools to achieve approved learning aims in a limited space of time. Web-based learning can be used in different ways to enhance learning. Little is however known regarding its optimal utilisation. Our aim was to investigate if the individual learning styles of medical students influence the choice to use a web-based ECG learning programme in a blended learning setting. The programme, with three types of modules (learning content, self-assessment questions and interactive ECG interpretation training), was offered on a voluntary basis during a face to face ECG learning course for undergraduate medical students. The Index of Learning Styles (ILS) and a general questionnaire including questions about computer and Internet usage, preferred future speciality and prior experience of E-learning were used to explore different factors related to the choice of using the programme or not. 93 (76%) out of 123 students answered the ILS instrument and 91 the general questionnaire. 55 students (59%) were defined as users of the web-based ECG-interpretation programme. Cronbach's alpha was analysed with coefficients above 0.7 in all of the four dimensions of ILS. There were no significant differences with regard to learning styles, as assessed by ILS, between the user and non-user groups; Active/Reflective; Visual/Verbal; Sensing/Intuitive; and Sequential/Global (p = 0.56-0.96). Neither did gender, prior experience of E-learning or preference for future speciality differ between groups. Among medical students, neither learning styles according to ILS, nor a number of other characteristics seem to influence the choice to use a web-based ECG programme. This finding was consistent also when the usage of the different modules in the programme were considered. Thus, the findings suggest that web-based learning may attract a broad variety of medical students.

  10. Auto-SEIA: simultaneous optimization of image processing and machine learning algorithms

    Science.gov (United States)

    Negro Maggio, Valentina; Iocchi, Luca

    2015-02-01

    Object classification from images is an important task for machine vision and it is a crucial ingredient for many computer vision applications, ranging from security and surveillance to marketing. Image based object classification techniques properly integrate image processing and machine learning (i.e., classification) procedures. In this paper we present a system for automatic simultaneous optimization of algorithms and parameters for object classification from images. More specifically, the proposed system is able to process a dataset of labelled images and to return a best configuration of image processing and classification algorithms and of their parameters with respect to the accuracy of classification. Experiments with real public datasets are used to demonstrate the effectiveness of the developed system.

  11. Authoring Systems Delivering Reusable Learning Objects

    Directory of Open Access Journals (Sweden)

    George Nicola Sammour

    2009-10-01

    Full Text Available A three layer e-learning course development model has been defined based on a conceptual model of learning content object. It starts by decomposing the learning content into small chunks which are initially placed in a hierarchic structure of units and blocks. The raw content components, being the atomic learning objects (ALO, were linked to the blocks and are structured in the database. We set forward a dynamic generation of LO's using re-usable e-learning raw materials or ALO’s In that view we need a LO authoring/ assembling system fitting the requirements of interoperability and reusability and starting from selecting the raw learning content from the learning materials content database. In practice authoring systems are used to develop e-learning courses. The company EDUWEST has developed an authoring system that is database based and will be SCORM compliant in the near future.

  12. Impact of an engineering design-based curriculum compared to an inquiry-based curriculum on fifth graders' content learning of simple machines

    Science.gov (United States)

    Marulcu, Ismail; Barnett, Michael

    2016-01-01

    Background: Elementary Science Education is struggling with multiple challenges. National and State test results confirm the need for deeper understanding in elementary science education. Moreover, national policy statements and researchers call for increased exposure to engineering and technology in elementary science education. The basic motivation of this study is to suggest a solution to both improving elementary science education and increasing exposure to engineering and technology in it. Purpose/Hypothesis: This mixed-method study examined the impact of an engineering design-based curriculum compared to an inquiry-based curriculum on fifth graders' content learning of simple machines. We hypothesize that the LEGO-engineering design unit is as successful as the inquiry-based unit in terms of students' science content learning of simple machines. Design/Method: We used a mixed-methods approach to investigate our research questions; we compared the control and the experimental groups' scores from the tests and interviews by using Analysis of Covariance (ANCOVA) and compared each group's pre- and post-scores by using paired t-tests. Results: Our findings from the paired t-tests show that both the experimental and comparison groups significantly improved their scores from the pre-test to post-test on the multiple-choice, open-ended, and interview items. Moreover, ANCOVA results show that students in the experimental group, who learned simple machines with the design-based unit, performed significantly better on the interview questions. Conclusions: Our analyses revealed that the design-based Design a people mover: Simple machines unit was, if not better, as successful as the inquiry-based FOSS Levers and pulleys unit in terms of students' science content learning.

  13. Open source machine-learning algorithms for the prediction of optimal cancer drug therapies.

    Science.gov (United States)

    Huang, Cai; Mezencev, Roman; McDonald, John F; Vannberg, Fredrik

    2017-01-01

    Precision medicine is a rapidly growing area of modern medical science and open source machine-learning codes promise to be a critical component for the successful development of standardized and automated analysis of patient data. One important goal of precision cancer medicine is the accurate prediction of optimal drug therapies from the genomic profiles of individual patient tumors. We introduce here an open source software platform that employs a highly versatile support vector machine (SVM) algorithm combined with a standard recursive feature elimination (RFE) approach to predict personalized drug responses from gene expression profiles. Drug specific models were built using gene expression and drug response data from the National Cancer Institute panel of 60 human cancer cell lines (NCI-60). The models are highly accurate in predicting the drug responsiveness of a variety of cancer cell lines including those comprising the recent NCI-DREAM Challenge. We demonstrate that predictive accuracy is optimized when the learning dataset utilizes all probe-set expression values from a diversity of cancer cell types without pre-filtering for genes generally considered to be "drivers" of cancer onset/progression. Application of our models to publically available ovarian cancer (OC) patient gene expression datasets generated predictions consistent with observed responses previously reported in the literature. By making our algorithm "open source", we hope to facilitate its testing in a variety of cancer types and contexts leading to community-driven improvements and refinements in subsequent applications.

  14. Open source machine-learning algorithms for the prediction of optimal cancer drug therapies.

    Directory of Open Access Journals (Sweden)

    Cai Huang

    Full Text Available Precision medicine is a rapidly growing area of modern medical science and open source machine-learning codes promise to be a critical component for the successful development of standardized and automated analysis of patient data. One important goal of precision cancer medicine is the accurate prediction of optimal drug therapies from the genomic profiles of individual patient tumors. We introduce here an open source software platform that employs a highly versatile support vector machine (SVM algorithm combined with a standard recursive feature elimination (RFE approach to predict personalized drug responses from gene expression profiles. Drug specific models were built using gene expression and drug response data from the National Cancer Institute panel of 60 human cancer cell lines (NCI-60. The models are highly accurate in predicting the drug responsiveness of a variety of cancer cell lines including those comprising the recent NCI-DREAM Challenge. We demonstrate that predictive accuracy is optimized when the learning dataset utilizes all probe-set expression values from a diversity of cancer cell types without pre-filtering for genes generally considered to be "drivers" of cancer onset/progression. Application of our models to publically available ovarian cancer (OC patient gene expression datasets generated predictions consistent with observed responses previously reported in the literature. By making our algorithm "open source", we hope to facilitate its testing in a variety of cancer types and contexts leading to community-driven improvements and refinements in subsequent applications.

  15. BlueSky Cloud Framework: An E-Learning Framework Embracing Cloud Computing

    Science.gov (United States)

    Dong, Bo; Zheng, Qinghua; Qiao, Mu; Shu, Jian; Yang, Jie

    Currently, E-Learning has grown into a widely accepted way of learning. With the huge growth of users, services, education contents and resources, E-Learning systems are facing challenges of optimizing resource allocations, dealing with dynamic concurrency demands, handling rapid storage growth requirements and cost controlling. In this paper, an E-Learning framework based on cloud computing is presented, namely BlueSky cloud framework. Particularly, the architecture and core components of BlueSky cloud framework are introduced. In BlueSky cloud framework, physical machines are virtualized, and allocated on demand for E-Learning systems. Moreover, BlueSky cloud framework combines with traditional middleware functions (such as load balancing and data caching) to serve for E-Learning systems as a general architecture. It delivers reliable, scalable and cost-efficient services to E-Learning systems, and E-Learning organizations can establish systems through these services in a simple way. BlueSky cloud framework solves the challenges faced by E-Learning, and improves the performance, availability and scalability of E-Learning systems.

  16. Optimization of the contents of hollow glass microsphere and sodium hexametaphosphate for glass fiber vacuum insulation panel

    Science.gov (United States)

    Li, C. D.; Chen, Z. F.; Zhou, J. M.

    2016-07-01

    In this paper, various additive amounts of hollow glass microspheres (HGMs) and sodium hexametaphosphate (SHMP) powders were blended with flame attenuated glass wool (FAGW) to form hybrid core materials (HCMs) through the wet method. Among them, the SHMP was dissolved in the glass fiber suspension and coated on the surface of glass fibers while the HGMs were insoluble in the glass fiber suspension and filled in the fiber-fiber pores. The average pore diameter of the FAGW/HGM HCMs was 8-11 μm which was near the same as that of flame attenuated glass fiber mats (FAGMs, i.e., 10.5 µm). The tensile strength of the SHMP coated FAGMs was enhanced from 160 N/m to 370 N/m when SHMP content increased from 0 wt.% to 0.2 wt.%. By contrast, the tensile strength of the FAGW/HGM HCMs decreased from 160 N/m to 40 N/m when HGM content increased from 0 wt.% to 50 wt.%. Both the FAGW/HGM HCMs and SHMP coated FAGMs were vacuumed completely to form vacuum insulation panels (VIPs). The results showed that both the addition of SHMP and HGM led a slight increase in the thermal conductivity of the corresponding VIPs. To obtain a high-quality VIP, the optimal SHMP content and HGM content in glass fiber suspension was 0.12-0.2 wt.% and 0 wt.%.

  17. Pemanfaatan Aplikasi Facebook Dalam Membangun E-Learning Dengan Metode Asynchronous Collaborative Learning di Politeknik Cilacap

    Directory of Open Access Journals (Sweden)

    Gusti Darma Yudha

    2016-02-01

    Full Text Available The development of information systems in human life along with the of human civilization itself until finally getting to know the term information technology. Pay attention to the development of such information, we will study briefly the history of information technology in an effort to get the integrity of science and knowledge about information technology. History of technology we can divide into pre-history, past history, and modern times. The utilization of ICT in learning in Indonesia has had a long enough history. Initiative hosted the radio broadcast education and educational television is an attempt do the dissemination of information to the education units are scattered throughout the archipelago. This is a manifestation of consciousness to optimize the domestication of technology in helping the learning process of the community. Along with the need for methods and concept learning is more effective and efficient, the utilization of information technology for education was not inevitable. The concept would later be known as e-learning brings the influence of conventional education, to the process of tranformasi in digital form, either generally contents (contents and his system. This research aims to apply and implement the concept of e-learning is a teaching and learning that enables tersampaikannya learning materials to students using internet media. With the e-learning is expected to facilitate the teaching and learning process is conducted without limited location, time and costs. The method is done by charging a questionnaire to find out the needs of the user that there is a facebook application and utilization in building E-learning applications. The programming is done with the PHP language and customize with FBML is a framework from Facebook itself. Research results is a system application that will be used in assisting the process of teaching and learning online. This application can support distance learning

  18. Building Bridges between Technology and Content Literacy in Special Education: Lessons Learned from Special Educators' Use of Integrated Technology and Perceived Benefits for Students

    Science.gov (United States)

    Ciampa, Katia

    2017-01-01

    This single-site case study describes the outcomes and lessons learned from the implementation of a technology professional development initiative aimed at helping three special education teachers from an urban elementary school learn how to infuse technology in their content literacy instruction. Three types of qualitative data were collected:…

  19. Research Trends in the Field of E-Learning from 2003 to 2008: A Scientometric and Content Analysis for Selected Journals and Conferences Using Visualization

    Science.gov (United States)

    Maurer, Hermann; Khan, Muhammad Salman

    2010-01-01

    Purpose: The purpose of this paper is to provide a scientometric and content analysis of the studies in the field of e-learning that were published in five Social Science Citation Index (SSCI) journals ("Journal of Computer Assisted Learning, Computers & Education, British Journal of Educational Technology, Innovations in Education and Teaching…

  20. A Machine-Learning and Filtering Based Data Assimilation Framework for Geologic Carbon Sequestration Monitoring Optimization

    Science.gov (United States)

    Chen, B.; Harp, D. R.; Lin, Y.; Keating, E. H.; Pawar, R.

    2017-12-01

    Monitoring is a crucial aspect of geologic carbon sequestration (GCS) risk management. It has gained importance as a means to ensure CO2 is safely and permanently stored underground throughout the lifecycle of a GCS project. Three issues are often involved in a monitoring project: (i) where is the optimal location to place the monitoring well(s), (ii) what type of data (pressure, rate and/or CO2 concentration) should be measured, and (iii) What is the optimal frequency to collect the data. In order to address these important issues, a filtering-based data assimilation procedure is developed to perform the monitoring optimization. The optimal monitoring strategy is selected based on the uncertainty reduction of the objective of interest (e.g., cumulative CO2 leak) for all potential monitoring strategies. To reduce the computational cost of the filtering-based data assimilation process, two machine-learning algorithms: Support Vector Regression (SVR) and Multivariate Adaptive Regression Splines (MARS) are used to develop the computationally efficient reduced-order-models (ROMs) from full numerical simulations of CO2 and brine flow. The proposed framework for GCS monitoring optimization is demonstrated with two examples: a simple 3D synthetic case and a real field case named Rock Spring Uplift carbon storage site in Southwestern Wyoming.

  1. Improving Web Learning through model Optimization using Bootstrap for a Tour-Guide Robot

    Directory of Open Access Journals (Sweden)

    Rafael León

    2012-09-01

    Full Text Available We perform a review of Web Mining techniques and we describe a Bootstrap Statistics methodology applied to pattern model classifier optimization and verification for Supervised Learning for Tour-Guide Robot knowledge repository management. It is virtually impossible to test thoroughly Web Page Classifiers and many other Internet Applications with pure empirical data, due to the need for human intervention to generate training sets and test sets. We propose using the computer-based Bootstrap paradigm to design a test environment where they are checked with better reliability

  2. How can students contribute? A qualitative study of active student involvement in development of technological learning material for clinical skills training.

    Science.gov (United States)

    Haraldseid, Cecilie; Friberg, Febe; Aase, Karina

    2016-01-01

    Policy initiatives and an increasing amount of the literature within higher education both call for students to become more involved in creating their own learning. However, there is a lack of studies in undergraduate nursing education that actively involve students in developing such learning material with descriptions of the students' roles in these interactive processes. Explorative qualitative study, using data from focus group interviews, field notes and student notes. The data has been subjected to qualitative content analysis. Active student involvement through an iterative process identified five different learning needs that are especially important to the students: clarification of learning expectations, help to recognize the bigger picture, stimulation of interaction, creation of structure, and receiving context- specific content. The iterative process involvement of students during the development of new technological learning material will enhance the identification of important learning needs for students. The use of student and teacher knowledge through an adapted co-design process is the most optimal level of that involvement.

  3. Analysis of pedagogical content knowledge (PCK) ability of science teachers in planning and reflecting on environmental pollution content

    Science.gov (United States)

    Purwianingsih, W.; Mardiyah, A.

    2018-05-01

    Pedagogical Content Knowledge (PCK) is a blend of content knowledge and pedagogy knowledge, which can illustrate the ability of teachers to design and to teach a content by accessing what they knows about the material, students, curriculum and how best to teach the content. Description of PCK ability of science teachers can be accessed through an analysis of their ability to plan and reflect on learning. This study aims to provide an overview of teachers’ PCK skills on environmental pollution materials through use of Content Representation (CoRe) and Pedagogical and Professional-experience Repertoires (PaP-eRs). Descriptive method used in this study with six of science teachers on 7th class from three different schools as subject. The results show that teachers’ PCK skills in planning through CoRe and reflecting through PaP-eRs are in fairly good category. The teacher’s ability in implementing environmental pollution learning materials is in good category. However, there is still a discrepancy between planning through CoRe and the implementation of classroom learning. The teacher’s PCK is influenced by teaching experience and educational background.

  4. Pedagogical content knowledge: Knowledge of pedagogy novice teachers in mathematics learning on limit algebraic function

    Science.gov (United States)

    Ma'rufi, Budayasa, I. Ketut; Juniati, Dwi

    2017-02-01

    Teacher is one of the key aspects of student's achievement. Teachers should master content material taught, how to teach it, and can interpret the students' thinking so that students easily understand the subject matter. This research was a qualitative research that aimed at describing profile of PCK's teachers in mathematics on limit algebraic functions in terms of the differences of teaching experience. Pedagogical Content Knowledge (PCK) and understanding of teachers is defined as involving the relationship between knowledge of teaching materials, how to transfer the subject matter, and the knowledge of students in mathematics on limit algebraic functions that the subject matter may be understood by students. The PCK components in this research were knowledge of subject matter, knowledge of pedagogy, and knowledge of students. Knowledge of pedagogy defines as knowledge and understanding of teachers about the planning and organization of the learning and teaching strategy of limit algebraic function. The subjects were two mathematics high school teachers who teach in class XI IPS. Data were collected through observation of learning during five meetings and interviews before and after the lesson continued with qualitative data analysis. Focus of this article was to describe novice teacher's knowledge of student in mathematics learning on limit algebraic function. Based on the results of the analysis of qualitative data the data concluded that novice teacher's knowledge of pedagogy in mathematics on limit algebraic function showed: 1) in teaching the definitions tend to identify prior knowledge of the student experience with the material to be studied, but not in the form of a problem, 2) in posing the questions tend to be monotonous non lead and dig, 3) in response to student questions preservice teachers do not take advantage of the characteristics or the potential of other students, 4) in addressing the problem of students, tend to use the drill approach and did

  5. Active learning methods for interactive image retrieval.

    Science.gov (United States)

    Gosselin, Philippe Henri; Cord, Matthieu

    2008-07-01

    Active learning methods have been considered with increased interest in the statistical learning community. Initially developed within a classification framework, a lot of extensions are now being proposed to handle multimedia applications. This paper provides algorithms within a statistical framework to extend active learning for online content-based image retrieval (CBIR). The classification framework is presented with experiments to compare several powerful classification techniques in this information retrieval context. Focusing on interactive methods, active learning strategy is then described. The limitations of this approach for CBIR are emphasized before presenting our new active selection process RETIN. First, as any active method is sensitive to the boundary estimation between classes, the RETIN strategy carries out a boundary correction to make the retrieval process more robust. Second, the criterion of generalization error to optimize the active learning selection is modified to better represent the CBIR objective of database ranking. Third, a batch processing of images is proposed. Our strategy leads to a fast and efficient active learning scheme to retrieve sets of online images (query concept). Experiments on large databases show that the RETIN method performs well in comparison to several other active strategies.

  6. Analysis of chemistry textbook content and national science education standards in terms of air quality-related learning goals

    Science.gov (United States)

    Naughton, Wendy

    In this study's Phase One, representatives of nine municipal agencies involved in air quality education were interviewed and interview transcripts were analyzed for themes related to what citizens need to know or be able to do regarding air quality concerns. Based on these themes, eight air quality Learning Goal Sets were generated and validated via peer and member checks. In Phase Two, six college-level, liberal-arts chemistry textbooks and the National Science Education Standards (NSES) were analyzed for congruence with Phase One learning goals. Major categories of desired citizen understandings highlighted in agency interviews concerned air pollution sources, impact, detection, and transport. Identified cognitive skills focused on information-gathering and -evaluating skills, enabling informed decision-making. A content match was found between textbooks and air quality learning goals, but most textbooks fail to address learning goals that remediate citizen misconceptions and inabilities---particularly those with a "personal experience" focus. A partial match between NSES and air quality learning goals was attributed to differing foci: Researcher-derived learning goals deal specifically with air quality, while NSES focus is on "fundamental science concepts," not "many science topics." Analysis of findings within a situated cognition framework suggests implications for instruction and NSES revision.

  7. Content Development, Presentation and Delivery for eLearning in Nuclear Science and Engineering: Experiences with Emerging Authoring Tools

    International Nuclear Information System (INIS)

    Bamford, S.; Afriyie, P.; Comlan, E.

    2016-01-01

    Full text: Transference of explicit knowledge starts from content development, and proceeds with packaging and delivery. A comparative study of some selected authoring tools for knowledge creation in Nuclear Sciences and Engineering education is being carried out at the School of Nuclear and Allied Sciences in Accra, Ghana. These authoring tools include commercial software (Macromedia Suite CS6, Learning 6.0) as well as freeware software (Xerte, eXe). A course, X-ray Fluorescence Spectrometry (NSAP 603), at the postgraduate School of Nuclear and Allied Sciences (SNAS), has been selected for migration onto an eLearning platform. Different authoring tools have been employed to create some ICT-based modules for teaching and learning. This paper therefore shares the experiences realized in moving from course syllabus to digitized modules, integrating pedagogical considerations, the strengths and weakness of the selected authoring tools, user-interactivity and usability of the modules produced. The need and the basis for the adoption of an appropriate authoring tool for creation of scientific, mathematical, and engineering documents and learning materials has also been discussed. Leveraging on ICT to produce pedagogically sound learning materials for eLearning platforms promotes interests of students in nuclear sciences, and ensures continuity in producing qualified professionals. (author

  8. Optimization of calcium chloride content on bioactivity and mechanical properties of white Portland cement

    International Nuclear Information System (INIS)

    Torkittikul, Pincha; Chaipanich, Arnon

    2012-01-01

    This research investigates the optimization of calcium chloride content on the bioactivity and mechanical properties of white Portland cement. Calcium chloride was used as an addition of White Portland cement at 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 and 10% by weight. Calcium chloride was dissolved in sterile distilled water and blended with White Portland cement using a water to cement ratio of 0.5. Analysis of the bioactivity and pH of white Portland cement pastes with calcium chloride added at various amounts was carried out in simulated body fluid. Setting time, density, compressive strength and volume of permeable voids were also investigated. The characteristics of cement pastes were examined by X-ray diffractometer and scanning electron microscope linked to an energy-dispersive X-ray analyzer. The result indicated that the addition of calcium chloride could accelerate the hydration of white Portland cement, resulting in a decrease in setting time and an increase in early strength of the pastes. The compressive strength of all cement pastes with added calcium chloride was higher than that of the pure cement paste, and the addition of calcium chloride at 8 wt.% led to achieving the highest strength. Furthermore, white Portland cement pastes both with and without calcium chloride showed well-established bioactivity with respect to the formation of a hydroxyapatite layer on the material within 7 days following immersion in simulated body fluid; white Portland cement paste with added 3%CaCl 2 exhibited the best bioactivity. - Highlights: ► Optimization CaCl 2 content on the bioactivity and mechanical properties. ► CaCl 2 was used as an addition at 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 and 10% by weight. ► CaCl 2 resulted in a decrease in setting time and an increase in early strength. ► Addition of 3%CaCl 2 exhibited the optimum formation of hydroxyapatite.

  9. Content analysis of science material in junior school-based inquiry and science process skills

    Science.gov (United States)

    Patonah, S.; Nuvitalia, D.; Saptaningrum, E.

    2018-03-01

    The purpose of this research is to obtain the characteristic map of science material content in Junior School which can be optimized using inquiry learning model to tone the science process skill. The research method used in the form of qualitative research on SMP science curriculum document in Indonesia. Documents are reviewed on the basis of the basic competencies of each level as well as their potential to trace the skills of the science process using inquiry learning models. The review was conducted by the research team. The results obtained, science process skills in grade 7 have the potential to be trained using the model of inquiry learning by 74%, 8th grade by 83%, and grade 9 by 75%. For the dominant process skills in each chapter and each level is the observing skill. Follow-up research is used to develop instructional inquiry tools to trace the skills of the science process.

  10. Optimization and approximation

    CERN Document Server

    Pedregal, Pablo

    2017-01-01

    This book provides a basic, initial resource, introducing science and engineering students to the field of optimization. It covers three main areas: mathematical programming, calculus of variations and optimal control, highlighting the ideas and concepts and offering insights into the importance of optimality conditions in each area. It also systematically presents affordable approximation methods. Exercises at various levels have been included to support the learning process.

  11. High content analysis platform for optimization of lipid mediated CRISPR-Cas9 delivery strategies in human cells.

    Science.gov (United States)

    Steyer, Benjamin; Carlson-Stevermer, Jared; Angenent-Mari, Nicolas; Khalil, Andrew; Harkness, Ty; Saha, Krishanu

    2016-04-01

    Non-viral gene-editing of human cells using the CRISPR-Cas9 system requires optimized delivery of multiple components. Both the Cas9 endonuclease and a single guide RNA, that defines the genomic target, need to be present and co-localized within the nucleus for efficient gene-editing to occur. This work describes a new high-throughput screening platform for the optimization of CRISPR-Cas9 delivery strategies. By exploiting high content image analysis and microcontact printed plates, multi-parametric gene-editing outcome data from hundreds to thousands of isolated cell populations can be screened simultaneously. Employing this platform, we systematically screened four commercially available cationic lipid transfection materials with a range of RNAs encoding the CRISPR-Cas9 system. Analysis of Cas9 expression and editing of a fluorescent mCherry reporter transgene within human embryonic kidney cells was monitored over several days after transfection. Design of experiments analysis enabled rigorous evaluation of delivery materials and RNA concentration conditions. The results of this analysis indicated that the concentration and identity of transfection material have significantly greater effect on gene-editing than ratio or total amount of RNA. Cell subpopulation analysis on microcontact printed plates, further revealed that low cell number and high Cas9 expression, 24h after CRISPR-Cas9 delivery, were strong predictors of gene-editing outcomes. These results suggest design principles for the development of materials and transfection strategies with lipid-based materials. This platform could be applied to rapidly optimize materials for gene-editing in a variety of cell/tissue types in order to advance genomic medicine, regenerative biology and drug discovery. CRISPR-Cas9 is a new gene-editing technology for "genome surgery" that is anticipated to treat genetic diseases. This technology uses multiple components of the Cas9 system to cut out disease-causing mutations

  12. Effects of learning content in context on knowledge acquisition and recall: a pretest-posttest control group design.

    Science.gov (United States)

    Bergman, Esther M; de Bruin, Anique B H; Vorstenbosch, Marc A T M; Kooloos, Jan G M; Puts, Ghita C W M; Leppink, Jimmie; Scherpbier, Albert J J A; van der Vleuten, Cees P M

    2015-08-15

    It is generally assumed that learning in context increases performance. This study investigates the relationship between the characteristics of a paper-patient context (relevance and familiarity), the mechanisms through which the cognitive dimension of context could improve learning (activation of prior knowledge, elaboration and increasing retrieval cues), and test performance. A total of 145 medical students completed a pretest of 40 questions, of which half were with a patient vignette. One week later, they studied musculoskeletal anatomy in the dissection room without a paper-patient context (control group) or with (ir)relevant-(un)familiar context (experimental groups), and completed a cognitive load scale. Following a short delay, the students completed a posttest. Surprisingly, our results show that students who studied in context did not perform better than students who studied without context. This finding may be explained by an interaction of the participants' expertise level, the nature of anatomical knowledge and students' approaches to learning. A relevant-familiar context only reduced the negative effect of learning the content in context. Our results suggest discouraging the introduction of an uncommon disease to illustrate a basic science concept. Higher self-perceived learning scores predict higher performance. Interestingly, students performed significantly better on the questions with context in both tests, possibly due to a 'framing effect'. Since studies focusing on the physical and affective dimensions of context have also failed to find a positive influence of learning in a clinically relevant context, further research seems necessary to refine our theories around the role of context in learning.

  13. Editorial: Advanced Learning Technologies, Performance Technologies, Open Contents, and Standards - Some Papers from the Best Papers of the Conference ICCE C3 2009

    Directory of Open Access Journals (Sweden)

    Fanny Klett (IEEE Fellow

    2010-09-01

    Full Text Available This special issue deals with several cutting edge research outcomes from recent advancement of learning technologies. Advanced learning technologies are the composition of various related technologies and concepts such as i internet technologies and mobile technologies, ii human and organizational performance/knowledge management, and iii underlying trends toward open technology, open content and open education. This editorial note describes the overview of these topics related to the advanced learning technologies to provide the common framework for the accepted papers in this special issue.

  14. Encoder-decoder optimization for brain-computer interfaces.

    Science.gov (United States)

    Merel, Josh; Pianto, Donald M; Cunningham, John P; Paninski, Liam

    2015-06-01

    Neuroprosthetic brain-computer interfaces are systems that decode neural activity into useful control signals for effectors, such as a cursor on a computer screen. It has long been recognized that both the user and decoding system can adapt to increase the accuracy of the end effector. Co-adaptation is the process whereby a user learns to control the system in conjunction with the decoder adapting to learn the user's neural patterns. We provide a mathematical framework for co-adaptation and relate co-adaptation to the joint optimization of the user's control scheme ("encoding model") and the decoding algorithm's parameters. When the assumptions of that framework are respected, co-adaptation cannot yield better performance than that obtainable by an optimal initial choice of fixed decoder, coupled with optimal user learning. For a specific case, we provide numerical methods to obtain such an optimized decoder. We demonstrate our approach in a model brain-computer interface system using an online prosthesis simulator, a simple human-in-the-loop pyschophysics setup which provides a non-invasive simulation of the BCI setting. These experiments support two claims: that users can learn encoders matched to fixed, optimal decoders and that, once learned, our approach yields expected performance advantages.

  15. Encoder-decoder optimization for brain-computer interfaces.

    Directory of Open Access Journals (Sweden)

    Josh Merel

    2015-06-01

    Full Text Available Neuroprosthetic brain-computer interfaces are systems that decode neural activity into useful control signals for effectors, such as a cursor on a computer screen. It has long been recognized that both the user and decoding system can adapt to increase the accuracy of the end effector. Co-adaptation is the process whereby a user learns to control the system in conjunction with the decoder adapting to learn the user's neural patterns. We provide a mathematical framework for co-adaptation and relate co-adaptation to the joint optimization of the user's control scheme ("encoding model" and the decoding algorithm's parameters. When the assumptions of that framework are respected, co-adaptation cannot yield better performance than that obtainable by an optimal initial choice of fixed decoder, coupled with optimal user learning. For a specific case, we provide numerical methods to obtain such an optimized decoder. We demonstrate our approach in a model brain-computer interface system using an online prosthesis simulator, a simple human-in-the-loop pyschophysics setup which provides a non-invasive simulation of the BCI setting. These experiments support two claims: that users can learn encoders matched to fixed, optimal decoders and that, once learned, our approach yields expected performance advantages.

  16. Digital Content: Making Learning Relevant

    Science.gov (United States)

    Levin, Douglas A.

    2011-01-01

    Approximately 15 states are changing their policies to give school districts more flexibility in acquiring content. They have changed laws or policies or have bills pending in state legislatures to redefine "textbooks". Most of those changes are similar to the approach Indiana took in its new law: a "textbook" is not only a…

  17. Improving Elementary School Students’ English Vocabulary Through Local Cultural Content Materials

    Directory of Open Access Journals (Sweden)

    Frans Manurung

    2015-06-01

    Full Text Available Abstract Elementary students of a certain public school in Indonesia had difficulties in learning English. One of the crucial problems was learning English vocabulary. In an attempt to help the students learn and improve English vocabulary, the researchers decided to use CAR to teach English vocabulary with local cultural content materials. The aim of this study was to investigate how the teaching of English vocabulary with local cultural content materials contributed to the improvement of the students’ English vocabulary mastery. The topics covered in the materials were selected based on schemata theory. Vocabulary learning process was done through several activities provided in the materials: classroom and outside vocabulary learning. The results showed that the teaching of local cultural content materials have contributed to the improvement of the Elementary students’ vocabulary mastery. The schematic knowledge found in the familiar topics has aroused the students’ interest and motivation in learning English vocabulary. Students who were more familiar with the topics could respond to the vocabulary learning better than those who were not familiar with. The vocabulary mastery was more successful only if the students participated in both classroom and outside vocabulary learning process. Keywords: Vocabulary Mastery, Vocabulary Improvement, Local Cultural Content Materials, Vocabulary Learning, Schemata

  18. Curriculum renewal: Alignment of introductory pharmacy practice experiences with didactic course content.

    Science.gov (United States)

    Nuffer, Wesley; Botts, Sheila; Franson, Kari; Gilliam, Eric; Knutsen, Randy; Nuffer, Monika; O'Brien, Elizabeth; Saseen, Joseph; Thompson, Megan; Vande Griend, Joseph; Willis, Robert

    2017-11-01

    The University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences (SSPPS) used the opportunity of curriculum renewal to integrate knowledge and skills learned from didactic courses into the introductory pharmacy practice experiences (IPPEs) occurring simultaneously. This paper describes and evaluates the meaningful application of course content into IPPEs, and evaluates the success using qualitative feedback. Students entering the renewed curriculum starting in fall 2012 were provided a list of pharmacy skills and activities from didactic course directors that reinforced course content for that semester. The skills and activities were to be completed during the students' IPPE visits in the community or health systems settings, depending on the program year and semester. Students successfully completed course assignments during their IPPE course program. Not all activities could be completed as designed, and many required modification, including simulated experiences. Feedback from faculty and preceptor members of the school's experiential education committee demonstrated that these activities were valuable and improved learning of course material, but were challenging to implement. A renewed curriculum that mapped course assignments for completion in experiential settings was successfully established, after some modifications. The program was modified at regular intervals to improve the ability of preceptors to complete these activities in their individual practice environment. A balance between the school providing guidance on what activities students should perform and allowing unstructured independent learning with the preceptor is needed for an optimal experience. Copyright © 2017 Elsevier Inc. All rights reserved.

  19. Asymptotically Optimal Agents

    OpenAIRE

    Lattimore, Tor; Hutter, Marcus

    2011-01-01

    Artificial general intelligence aims to create agents capable of learning to solve arbitrary interesting problems. We define two versions of asymptotic optimality and prove that no agent can satisfy the strong version while in some cases, depending on discounting, there does exist a non-computable weak asymptotically optimal agent.

  20. Privacy Preservation in Distributed Subgradient Optimization Algorithms

    OpenAIRE

    Lou, Youcheng; Yu, Lean; Wang, Shouyang

    2015-01-01

    Privacy preservation is becoming an increasingly important issue in data mining and machine learning. In this paper, we consider the privacy preserving features of distributed subgradient optimization algorithms. We first show that a well-known distributed subgradient synchronous optimization algorithm, in which all agents make their optimization updates simultaneously at all times, is not privacy preserving in the sense that the malicious agent can learn other agents' subgradients asymptotic...