Sample records for learning model dslm

  1. The Effect of Cooperative Learning with DSLM on Conceptual Understanding and Scientific Reasoning among Form Four Physics Students with Different Motivation Levels

    Directory of Open Access Journals (Sweden)

    M.S. Hamzah


    Full Text Available The purpose of this study was to investigate the effect of Cooperative Learning with a Dual Situated Learning Model (CLDSLM and a Dual Situated Learning Model (DSLM on (a conceptual understanding (CU and (b scientific reasoning (SR among Form Four students. The study further investigated the effect of the CLDSLM and DSLM methods on performance in conceptual understanding and scientific reasoning among students with different motivation levels. A quasi-experimental method with the 3 x 2 Factorial Design was applied in the study. The sample consisted of 240 stu¬dents in six (form four classes selected from three different schools, i.e. two classes from each school, with students randomly selected and assigned to the treatment groups. The results showed that students in the CLDSLM group outperformed their counterparts in the DSLM group—who, in turn, significantly outperformed other students in the traditional instructional method (T group in scientific reasoning and conceptual understanding. Also, high-motivation (HM students in the CLDSLM group significantly outperformed their counterparts in the T groups in conceptual understanding and scientific reasoning. Furthermore, HM students in the CLDSLM group significantly outperformed their counterparts in the DSLM group in scientific reasoning but did not significantly outperform their counterparts on conceptual understanding. Also, the DSLM instructional method has significant positive effects on highly motivated students’ (a conceptual understanding and (b scientific reason¬ing. The results also showed that LM students in the CLDSLM group significantly outperformed their counterparts in the DSLM group and (T method group in scientific reasoning and conceptual understanding. However, the low-motivation students taught via the DSLM instructional method significantly performed higher than the low-motivation students taught via the T method in scientific reasoning. Nevertheless, they did not

  2. Student Modeling and Machine Learning


    Sison , Raymund; Shimura , Masamichi


    After identifying essential student modeling issues and machine learning approaches, this paper examines how machine learning techniques have been used to automate the construction of student models as well as the background knowledge necessary for student modeling. In the process, the paper sheds light on the difficulty, suitability and potential of using machine learning for student modeling processes, and, to a lesser extent, the potential of using student modeling techniques in machine le...

  3. Visual Perceptual Learning and Models. (United States)

    Dosher, Barbara; Lu, Zhong-Lin


    Visual perceptual learning through practice or training can significantly improve performance on visual tasks. Originally seen as a manifestation of plasticity in the primary visual cortex, perceptual learning is more readily understood as improvements in the function of brain networks that integrate processes, including sensory representations, decision, attention, and reward, and balance plasticity with system stability. This review considers the primary phenomena of perceptual learning, theories of perceptual learning, and perceptual learning's effect on signal and noise in visual processing and decision. Models, especially computational models, play a key role in behavioral and physiological investigations of the mechanisms of perceptual learning and for understanding, predicting, and optimizing human perceptual processes, learning, and performance. Performance improvements resulting from reweighting or readout of sensory inputs to decision provide a strong theoretical framework for interpreting perceptual learning and transfer that may prove useful in optimizing learning in real-world applications.

  4. Active Learning with Statistical Models. (United States)


    Active Learning with Statistical Models ASC-9217041, NSF CDA-9309300 6. AUTHOR(S) David A. Cohn, Zoubin Ghahramani, and Michael I. Jordan 7. PERFORMING...TERMS 15. NUMBER OF PAGES Al, MIT, Artificial Intelligence, active learning , queries, locally weighted 6 regression, LOESS, mixtures of gaussians...COMPUTATIONAL LEARNING DEPARTMENT OF BRAIN AND COGNITIVE SCIENCES A.I. Memo No. 1522 January 9. 1995 C.B.C.L. Paper No. 110 Active Learning with

  5. The Game Enhanced Learning Model

    DEFF Research Database (Denmark)

    Reng, Lars; Schoenau-Fog, Henrik


    will describe the levels of the model, which is based on our experience in teaching professional game development at university level. Furthermore, we have been using the model to inspire numerous educators to improve their students’ motivation and skills. The model presents various game-based learning...... activities, and depicts their required planning and expected outcome through eight levels. At its lower levels, the model contains the possibilities of using stand-alone analogue and digital games as teachers, utilizing games as a facilitator of learning activities, exploiting gamification and motivating......In this paper, we will introduce the Game Enhanced learning Model (GEM), which describes a range of gameoriented learning activities. The model is intended to give an overview of the possibilities of game-based learning in general and all the way up to purposive game productions. In the paper, we...

  6. Model-based machine learning. (United States)

    Bishop, Christopher M


    Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications.

  7. Learning to Model in Engineering (United States)

    Gainsburg, Julie


    Policymakers and education scholars recommend incorporating mathematical modeling into mathematics education. Limited implementation of modeling instruction in schools, however, has constrained research on how students learn to model, leaving unresolved debates about whether modeling should be reified and explicitly taught as a competence, whether…

  8. Active Learning for Player Modeling

    DEFF Research Database (Denmark)

    Shaker, Noor; Abou-Zleikha, Mohamed; Shaker, Mohammad


    Learning models of player behavior has been the focus of several studies. This work is motivated by better understanding of player behavior, a knowledge that can ultimately be employed to provide player-adapted or personalized content. In this paper, we propose the use of active learning for player...... experience modeling. We use a dataset from hundreds of players playing Infinite Mario Bros. as a case study and we employ the random forest method to learn mod- els of player experience through the active learning approach. The results obtained suggest that only part of the dataset (up to half the size...... that the method can be used online during the content generation process where the mod- els can improve and better content can be presented as the game is being played....

  9. A Visual Detection Learning Model (United States)

    Beard, Bettina L.; Ahumada, Albert J., Jr.; Trejo, Leonard (Technical Monitor)


    Our learning model has memory templates representing the target-plus-noise and noise-alone stimulus sets. The best correlating template determines the response. The correlations and the feedback participate in the additive template updating rule. The model can predict the relative thresholds for detection in random, fixed and twin noise.

  10. Computational modeling of epiphany learning. (United States)

    Chen, Wei James; Krajbich, Ian


    Models of reinforcement learning (RL) are prevalent in the decision-making literature, but not all behavior seems to conform to the gradual convergence that is a central feature of RL. In some cases learning seems to happen all at once. Limited prior research on these "epiphanies" has shown evidence of sudden changes in behavior, but it remains unclear how such epiphanies occur. We propose a sequential-sampling model of epiphany learning (EL) and test it using an eye-tracking experiment. In the experiment, subjects repeatedly play a strategic game that has an optimal strategy. Subjects can learn over time from feedback but are also allowed to commit to a strategy at any time, eliminating all other options and opportunities to learn. We find that the EL model is consistent with the choices, eye movements, and pupillary responses of subjects who commit to the optimal strategy (correct epiphany) but not always of those who commit to a suboptimal strategy or who do not commit at all. Our findings suggest that EL is driven by a latent evidence accumulation process that can be revealed with eye-tracking data.

  11. Learning Analytics for Networked Learning Models (United States)

    Joksimovic, Srecko; Hatala, Marek; Gaševic, Dragan


    Teaching and learning in networked settings has attracted significant attention recently. The central topic of networked learning research is human-human and human-information interactions occurring within a networked learning environment. The nature of these interactions is highly complex and usually requires a multi-dimensional approach to…

  12. A New Mobile Learning Adaptation Model


    Mohamd Hassan Hassan; Jehad Al-Sadi


    This paper introduces a new model for m- Learning context adaptation due to the need of utilizing mobile technology in education. Mobile learning; m-Learning for short; in considered to be one of the hottest topics in the educational community, many researches had been done to conceptualize this new form of learning. We are presenting a promising design for a model to adapt the learning content in mobile learning applications in order to match the learner context, preferences and the educatio...

  13. Individual Learning Accounts and Other Models of Financing Lifelong Learning (United States)

    Schuetze, Hans G.


    To answer the question "Financing what?" this article distinguishes several models of lifelong learning as well as a variety of lifelong learning activities. Several financing methods are briefly reviewed, however the principal focus is on Individual Learning Accounts (ILAs) which were seen by some analysts as a promising model for…

  14. Urban Studies: A Learning Model. (United States)

    Cooper, Terry L.; Sundeen, Richard


    The urban studies learning model described in this article was found to increase students' self-esteem, imbue a more flexible and open perspective, contribute to the capacity for self-direction, produce increases on the feeling reactivity, spontaneity, and acceptance of aggression scales, and expand interpersonal competence. (Author/WI)

  15. Teaching Economics: A Cooperative Learning Model. (United States)

    Caropreso, Edward J.; Haggerty, Mark


    Describes an alternative approach to introductory economics based on a cooperative learning model, "Learning Together." Discussion of issues in economics education and cooperative learning in higher education leads to explanation of how to adapt the Learning Together Model to lesson planning in economics. A flow chart illustrates the process for a…

  16. Learning from erroneous models using SCYDynamics

    NARCIS (Netherlands)

    Mulder, Y.G.; Bollen, Lars; de Jong, Anthonius J.M.


    Dynamic phenomena are common in science education. Students can learn about such system dynamic processes through model based learning activities. This paper describes a study on the effects of a learning from erroneous models approach using the learning environment SCYDynamics. The study compared

  17. From Learning Object to Learning Cell: A Resource Organization Model for Ubiquitous Learning (United States)

    Yu, Shengquan; Yang, Xianmin; Cheng, Gang; Wang, Minjuan


    This paper presents a new model for organizing learning resources: Learning Cell. This model is open, evolving, cohesive, social, and context-aware. By introducing a time dimension into the organization of learning resources, Learning Cell supports the dynamic evolution of learning resources while they are being used. In addition, by introducing a…

  18. A Coterminous Collaborative Learning Model: Interconnectivity of Leadership and Learning

    Directory of Open Access Journals (Sweden)

    Ilana Margolin


    Full Text Available This qualitative ethnographic study examines a collaborative leadership model focused on learning and socially just practices within a change context of a wide educational partnership. The study analyzes a range of perspectives of novice teachers, mentor teachers, teacher educators and district superintendents on leadership and learning. The findings reveal the emergence of a coalition of leaders crossing borders at all levels of the educational system: local school level, district level and teacher education level who were involved in coterminous collaborative learning. Four categories of learning were identified as critical to leading a change in the educational system: learning in professional communities, learning from practice, learning through theory and research and learning from and with leaders. The implications of the study for policy makers as well as for practitioners are to adopt a holistic approach to the educational environment and plan a collaborative learning continuum from initial pre-service programs through professional development learning at all levels.

  19. Vicarious Learning from Human Models in Monkeys


    Falcone, Rossella; Brunamonti, Emiliano; Genovesio, Aldo


    We examined whether monkeys can learn by observing a human model, through vicarious learning. Two monkeys observed a human model demonstrating an object-reward association and consuming food found underneath an object. The monkeys observed human models as they solved more than 30 learning problems. For each problem, the human models made a choice between two objects, one of which concealed a piece of apple. In the test phase afterwards, the monkeys made a choice of their own. Learning was app...

  20. Towards a semantic learning model fostering learning object reusability


    Fernandes , Emmanuel; Madhour , Hend; Wentland Forte , Maia; Miniaoui , Sami


    We try in this paper to propose a domain model for both author's and learner's needs concerning learning objects reuse. First of all, we present four key criteria for an efficient authoring tool: adaptive level of granularity, flexibility, integration and interoperability. Secondly, we introduce and describe our six-level Semantic Learning Model (SLM) designed to facilitate multi-level reuse of learning materials and search by defining a multi-layer model for metadata. Finally, after mapping ...

  1. Model United Nations and Deep Learning: Theoretical and Professional Learning (United States)

    Engel, Susan; Pallas, Josh; Lambert, Sarah


    This article demonstrates that the purposeful subject design, incorporating a Model United Nations (MUN), facilitated deep learning and professional skills attainment in the field of International Relations. Deep learning was promoted in subject design by linking learning objectives to Anderson and Krathwohl's (2001) four levels of knowledge or…

  2. Learning Graphical Models With Hubs. (United States)

    Tan, Kean Ming; London, Palma; Mohan, Karthik; Lee, Su-In; Fazel, Maryam; Witten, Daniela


    We consider the problem of learning a high-dimensional graphical model in which there are a few hub nodes that are densely-connected to many other nodes. Many authors have studied the use of an ℓ 1 penalty in order to learn a sparse graph in the high-dimensional setting. However, the ℓ 1 penalty implicitly assumes that each edge is equally likely and independent of all other edges. We propose a general framework to accommodate more realistic networks with hub nodes, using a convex formulation that involves a row-column overlap norm penalty. We apply this general framework to three widely-used probabilistic graphical models: the Gaussian graphical model, the covariance graph model, and the binary Ising model. An alternating direction method of multipliers algorithm is used to solve the corresponding convex optimization problems. On synthetic data, we demonstrate that our proposed framework outperforms competitors that do not explicitly model hub nodes. We illustrate our proposal on a webpage data set and a gene expression data set.

  3. Vicarious learning from human models in monkeys. (United States)

    Falcone, Rossella; Brunamonti, Emiliano; Genovesio, Aldo


    We examined whether monkeys can learn by observing a human model, through vicarious learning. Two monkeys observed a human model demonstrating an object-reward association and consuming food found underneath an object. The monkeys observed human models as they solved more than 30 learning problems. For each problem, the human models made a choice between two objects, one of which concealed a piece of apple. In the test phase afterwards, the monkeys made a choice of their own. Learning was apparent from the first trial of the test phase, confirming the ability of monkeys to learn by vicarious observation of human models.

  4. Vicarious learning from human models in monkeys.

    Directory of Open Access Journals (Sweden)

    Rossella Falcone

    Full Text Available We examined whether monkeys can learn by observing a human model, through vicarious learning. Two monkeys observed a human model demonstrating an object-reward association and consuming food found underneath an object. The monkeys observed human models as they solved more than 30 learning problems. For each problem, the human models made a choice between two objects, one of which concealed a piece of apple. In the test phase afterwards, the monkeys made a choice of their own. Learning was apparent from the first trial of the test phase, confirming the ability of monkeys to learn by vicarious observation of human models.

  5. Learning to Act: Qualitative Learning of Deterministic Action Models

    DEFF Research Database (Denmark)

    Bolander, Thomas; Gierasimczuk, Nina


    In this article we study learnability of fully observable, universally applicable action models of dynamic epistemic logic. We introduce a framework for actions seen as sets of transitions between propositional states and we relate them to their dynamic epistemic logic representations as action...... in the limit (inconclusive convergence to the right action model). We show that deterministic actions are finitely identifiable, while arbitrary (non-deterministic) actions require more learning power—they are identifiable in the limit. We then move on to a particular learning method, i.e. learning via update......, which proceeds via restriction of a space of events within a learning-specific action model. We show how this method can be adapted to learn conditional and unconditional deterministic action models. We propose update learning mechanisms for the afore mentioned classes of actions and analyse...

  6. Learning with hierarchical-deep models. (United States)

    Salakhutdinov, Ruslan; Tenenbaum, Joshua B; Torralba, Antonio


    We introduce HD (or “Hierarchical-Deep”) models, a new compositional learning architecture that integrates deep learning models with structured hierarchical Bayesian (HB) models. Specifically, we show how we can learn a hierarchical Dirichlet process (HDP) prior over the activities of the top-level features in a deep Boltzmann machine (DBM). This compound HDP-DBM model learns to learn novel concepts from very few training example by learning low-level generic features, high-level features that capture correlations among low-level features, and a category hierarchy for sharing priors over the high-level features that are typical of different kinds of concepts. We present efficient learning and inference algorithms for the HDP-DBM model and show that it is able to learn new concepts from very few examples on CIFAR-100 object recognition, handwritten character recognition, and human motion capture datasets.

  7. Students’ mathematical learning in modelling activities

    DEFF Research Database (Denmark)

    Kjeldsen, Tinne Hoff; Blomhøj, Morten


    Ten years of experience with analyses of students’ learning in a modelling course for first year university students, led us to see modelling as a didactical activity with the dual goal of developing students’ modelling competency and enhancing their conceptual learning of mathematical concepts i...... create and help overcome hidden cognitive conflicts in students’ understanding; that reflections within modelling can play an important role for the students’ learning of mathematics. These findings are illustrated with a modelling project concerning the world population....

  8. Peer-to-Peer Learning and the Army Learning Model (United States)


    education will be delivered to the current and future force. This thesis examined the salient areas proposed by the ALM and its impact on P2P learning ...The Army Learning Model is the new educational model that develops adaptive leaders in an era of persistent conflict. Life-long, individual

  9. Inquiry based learning as didactic model in distant learning

    NARCIS (Netherlands)

    Rothkrantz, L.J.M.


    Recent years many universities are involved in development of Massive Open Online Courses (MOOCs). Unfortunately an appropriate didactic model for cooperated network learning is lacking. In this paper we introduce inquiry based learning as didactic model. Students are assumed to ask themselves

  10. A Model for Learning Development (United States)

    Kilfoil, W. R.


    This article looks at the way in which people perceive learning and the impact of these perceptions on teaching methods within the context of learning development in distance education. The context could, in fact, be any type of teaching and learning environment. The point is to balance approaches to teaching and learning depending on student…

  11. Hierarchical Bayesian Models of Subtask Learning (United States)

    Anglim, Jeromy; Wynton, Sarah K. A.


    The current study used Bayesian hierarchical methods to challenge and extend previous work on subtask learning consistency. A general model of individual-level subtask learning was proposed focusing on power and exponential functions with constraints to test for inconsistency. To study subtask learning, we developed a novel computer-based booking…

  12. Learning Markov Decision Processes for Model Checking

    DEFF Research Database (Denmark)

    Mao, Hua; Chen, Yingke; Jaeger, Manfred


    . The proposed learning algorithm is adapted from algorithms for learning deterministic probabilistic finite automata, and extended to include both probabilistic and nondeterministic transitions. The algorithm is empirically analyzed and evaluated by learning system models of slot machines. The evaluation......Constructing an accurate system model for formal model verification can be both resource demanding and time-consuming. To alleviate this shortcoming, algorithms have been proposed for automatically learning system models based on observed system behaviors. In this paper we extend the algorithm...... on learning probabilistic automata to reactive systems, where the observed system behavior is in the form of alternating sequences of inputs and outputs. We propose an algorithm for automatically learning a deterministic labeled Markov decision process model from the observed behavior of a reactive system...

  13. Caka E-Learning Model (United States)

    Gorsev, Gonca; Turkmen, Ugur; Askin, Cihat


    In today's world, in order to obtain the information in education, various approaches, methods and devices have been developed. Like many developing countries, e-learning and distance learning (internet based learning) are used today in many areas of education in Turkey. This research aims to contribute to education systems and develop a…

  14. Learning Bayesian Dependence Model for Student Modelling

    Directory of Open Access Journals (Sweden)

    Adina COCU


    Full Text Available Learning a Bayesian network from a numeric set of data is a challenging task because of dual nature of learning process: initial need to learn network structure, and then to find out the distribution probability tables. In this paper, we propose a machine-learning algorithm based on hill climbing search combined with Tabu list. The aim of learning process is to discover the best network that represents dependences between nodes. Another issue in machine learning procedure is handling numeric attributes. In order to do that, we must perform an attribute discretization pre-processes. This discretization operation can influence the results of learning network structure. Therefore, we make a comparative study to find out the most suitable combination between discretization method and learning algorithm, for a specific data set.

  15. Integrated Model for E-Learning Acceptance (United States)

    Ramadiani; Rodziah, A.; Hasan, S. M.; Rusli, A.; Noraini, C.


    E-learning is not going to work if the system is not used in accordance with user needs. User Interface is very important to encourage using the application. Many theories had discuss about user interface usability evaluation and technology acceptance separately, actually why we do not make it correlation between interface usability evaluation and user acceptance to enhance e-learning process. Therefore, the evaluation model for e-learning interface acceptance is considered important to investigate. The aim of this study is to propose the integrated e-learning user interface acceptance evaluation model. This model was combined some theories of e-learning interface measurement such as, user learning style, usability evaluation, and the user benefit. We formulated in constructive questionnaires which were shared at 125 English Language School (ELS) students. This research statistics used Structural Equation Model using LISREL v8.80 and MANOVA analysis.

  16. SUSTAIN: a network model of category learning. (United States)

    Love, Bradley C; Medin, Douglas L; Gureckis, Todd M


    SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a model of how humans learn categories from examples. SUSTAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g., it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the surprising event. Newly recruited clusters are available to explain future events and can themselves evolve into prototypes-attractors-rules. SUSTAIN's discovery of category substructure is affected not only by the structure of the world but by the nature of the learning task and the learner's goals. SUSTAIN successfully extends category learning models to studies of inference learning, unsupervised learning, category construction, and contexts in which identification learning is faster than classification learning.

  17. Digital Competence Model of Distance Learning Students (United States)

    da Silva, Ketia Kellen A.; Behar, Patricia A.


    This article presents the development of a digital competency model of Distance Learning (DL) students in Brazil called CompDigAl_EAD. The following topics were addressed in this study: Educational Competences, Digital Competences, and Distance Learning students. The model was developed between 2015 and 2016 and is being validated in 2017. It was…

  18. Team learning: building shared mental models

    NARCIS (Netherlands)

    Bossche, van den P.; Gijselaers, W.; Segers, M.; Woltjer, G.B.; Kirschner, P.


    To gain insight in the social processes that underlie knowledge sharing in teams, this article questions which team learning behaviors lead to the construction of a shared mental model. Additionally, it explores how the development of shared mental models mediates the relation between team learning

  19. Learning models of activities involving interacting objects

    DEFF Research Database (Denmark)

    Manfredotti, Cristina; Pedersen, Kim Steenstrup; Hamilton, Howard J.


    We propose the LEMAIO multi-layer framework, which makes use of hierarchical abstraction to learn models for activities involving multiple interacting objects from time sequences of data concerning the individual objects. Experiments in the sea navigation domain yielded learned models that were t...

  20. Modelling and Optimizing Mathematics Learning in Children (United States)

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


    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…



    Hayati .; Retno Dwi Suyanti


    The objective in this research: (1) Determine a better learning model to improve learning outcomes physics students among learning model Inquiry Training based multimedia and Inquiry Training learning model. (2) Determine the level of motivation to learn in affects physics student learning outcomes. (3) Knowing the interactions between the model of learning and motivation in influencing student learning outcomes. This research is a quasi experimental. The population in this research was all s...

  2. Model-Agnostic Interpretability of Machine Learning


    Ribeiro, Marco Tulio; Singh, Sameer; Guestrin, Carlos


    Understanding why machine learning models behave the way they do empowers both system designers and end-users in many ways: in model selection, feature engineering, in order to trust and act upon the predictions, and in more intuitive user interfaces. Thus, interpretability has become a vital concern in machine learning, and work in the area of interpretable models has found renewed interest. In some applications, such models are as accurate as non-interpretable ones, and thus are preferred f...

  3. Learning About Climate and Atmospheric Models Through Machine Learning (United States)

    Lucas, D. D.


    From the analysis of ensemble variability to improving simulation performance, machine learning algorithms can play a powerful role in understanding the behavior of atmospheric and climate models. To learn about model behavior, we create training and testing data sets through ensemble techniques that sample different model configurations and values of input parameters, and then use supervised machine learning to map the relationships between the inputs and outputs. Following this procedure, we have used support vector machines, random forests, gradient boosting and other methods to investigate a variety of atmospheric and climate model phenomena. We have used machine learning to predict simulation crashes, estimate the probability density function of climate sensitivity, optimize simulations of the Madden Julian oscillation, assess the impacts of weather and emissions uncertainty on atmospheric dispersion, and quantify the effects of model resolution changes on precipitation. This presentation highlights recent examples of our applications of machine learning to improve the understanding of climate and atmospheric models. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

  4. Enhanced democratic learning within the Aalborg Model

    DEFF Research Database (Denmark)

    Qvist, Palle


    The Aalborg PBL Model [Kjersdam & Enemark, 1997; Kolmos et al., 2004] is an example of a democratic learning system [Qvist, 2008]. Writing one project each semester in teams is an important element in the model. Medicine with Industrial Specialisation - a study at the Faculties of Engineering......, Science and Medicine at Aalborg University - has combined the Aalborg Model with solving cases as used by other models. A questionnaire survey related to democratic learning indicates that the democratic learning has been enhanced. This paper presents the results....

  5. Online Learning of Industrial Manipulators' Dynamics Models

    DEFF Research Database (Denmark)

    Polydoros, Athanasios


    , it was compared with multiple other state-of-the-art machine learning algorithms. Moreover, the thesis presents the application of the proposed learning method on robot control for achieving trajectory execution while learning the inverse dynamics models  on-the-fly . Also it is presented the application...... of the dynamics models. Those mainly derive from physics-based methods and thus they are based on physical properties which are hard to be calculated.  In this thesis, is presented, a novel online machine learning approach  which is able to model both inverse and forward dynamics models of industrial manipulators....... The proposed method belongs to the class of deep learning and exploits the concepts of self-organization, recurrent neural networks and iterative multivariate Bayesian regression. It has been evaluated on multiple datasets captured from industrial robots while they were performing various tasks. Also...

  6. Learning Markov models for stationary system behaviors

    DEFF Research Database (Denmark)

    Chen, Yingke; Mao, Hua; Jaeger, Manfred


    to a single long observation sequence, and in these situations existing automatic learning methods cannot be applied. In this paper, we adapt algorithms for learning variable order Markov chains from a single observation sequence of a target system, so that stationary system properties can be verified using......Establishing an accurate model for formal verification of an existing hardware or software system is often a manual process that is both time consuming and resource demanding. In order to ease the model construction phase, methods have recently been proposed for automatically learning accurate...... the learned model. Experiments demonstrate that system properties (formulated as stationary probabilities of LTL formulas) can be reliably identified using the learned model....

  7. A Hybrid Teaching and Learning Model (United States)

    Juhary, Jowati Binti

    This paper aims at analysing the needs for a specific teaching and learning model for the National Defence University of Malaysia (NDUM). The main argument is that whether there are differences between teaching and learning for academic component versus military component at the university. It is further argued that in order to achieve excellence, there should be one teaching and learning culture. Data were collected through interviews with military cadets. It is found that there are variations of teaching and learning strategies for academic courses, in comparison to a dominant teaching and learning style for military courses. Thus, in the interest of delivering quality education and training for students at the university, the paper argues that possibly a hybrid model for teaching and learning is fundamental in order to generate a one culture of academic and military excellence for the NDUM.

  8. Study on modeling of operator's learning mechanism

    International Nuclear Information System (INIS)

    Yoshimura, Seichi; Hasegawa, Naoko


    One effective method to analyze the causes of human errors is to model the behavior of human and to simulate it. The Central Research Institute of Electric Power Industry (CRIEPI) has developed an operator team behavior simulation system called SYBORG (Simulation System for the Behavior of an Operating Group) to analyze the human errors and to establish the countermeasures for them. As an operator behavior model which composes SYBORG has no learning mechanism and the knowledge of a plant is fixed, it cannot take suitable actions when unknown situations occur nor learn anything from the experience. However, considering actual operators, learning is an essential human factor to enhance their abilities to diagnose plant anomalies. In this paper, Q learning with 1/f fluctuation was proposed as a learning mechanism of an operator and simulation using the mechanism was conducted. The results showed the effectiveness of the learning mechanism. (author)

  9. The Effect of Cooperative Learning Model and Kolb Learning Styles on Learning Result of the Basics of Politics (United States)



    The aims of this research were to determine the effect of cooperative learning model and learning styles on learning result. This quasi-experimental study employed a 2x2 treatment by level, involved independent variables, i.e. cooperative learning model and learning styles, and learning result as the dependent variable. Findings signify that: (1)…

  10. Learning sparse generative models of audiovisual signals


    Monaci, Gianluca; Sommer, Friedrich T.; Vandergheynst, Pierre


    This paper presents a novel framework to learn sparse represen- tations for audiovisual signals. An audiovisual signal is modeled as a sparse sum of audiovisual kernels. The kernels are bimodal functions made of synchronous audio and video components that can be positioned independently and arbitrarily in space and time. We design an algorithm capable of learning sets of such audiovi- sual, synchronous, shift-invariant functions by alternatingly solving a coding and a learning pr...

  11. Mosaic model for sensorimotor learning and control. (United States)

    Haruno, M; Wolpert, D M; Kawato, M


    Humans demonstrate a remarkable ability to generate accurate and appropriate motor behavior under many different and often uncertain environmental conditions. We previously proposed a new modular architecture, the modular selection and identification for control (MOSAIC) model, for motor learning and control based on multiple pairs of forward (predictor) and inverse (controller) models. The architecture simultaneously learns the multiple inverse models necessary for control as well as how to select the set of inverse models appropriate for a given environment. It combines both feedforward and feedback sensorimotor information so that the controllers can be selected both prior to movement and subsequently during movement. This article extends and evaluates the MOSAIC architecture in the following respects. The learning in the architecture was implemented by both the original gradient-descent method and the expectation-maximization (EM) algorithm. Unlike gradient descent, the newly derived EM algorithm is robust to the initial starting conditions and learning parameters. Second, simulations of an object manipulation task prove that the architecture can learn to manipulate multiple objects and switch between them appropriately. Moreover, after learning, the model shows generalization to novel objects whose dynamics lie within the polyhedra of already learned dynamics. Finally, when each of the dynamics is associated with a particular object shape, the model is able to select the appropriate controller before movement execution. When presented with a novel shape-dynamic pairing, inappropriate activation of modules is observed followed by on-line correction.

  12. Inference in models with adaptive learning

    NARCIS (Netherlands)

    Chevillon, G.; Massmann, M.; Mavroeidis, S.


    Identification of structural parameters in models with adaptive learning can be weak, causing standard inference procedures to become unreliable. Learning also induces persistent dynamics, and this makes the distribution of estimators and test statistics non-standard. Valid inference can be

  13. Learning curves in energy planning models

    Energy Technology Data Exchange (ETDEWEB)

    Barreto, L; Kypreos, S [Paul Scherrer Inst. (PSI), Villigen (Switzerland)


    This study describes the endogenous representation of investment cost learning curves into the MARKAL energy planning model. A piece-wise representation of the learning curves is implemented using Mixed Integer Programming. The approach is briefly described and some results are presented. (author) 3 figs., 5 refs.

  14. Technology Learning Ratios in Global Energy Models

    International Nuclear Information System (INIS)

    Varela, M.


    The process of introduction of a new technology supposes that while its production and utilisation increases, also its operation improves and its investment costs and production decreases. The accumulation of experience and learning of a new technology increase in parallel with the increase of its market share. This process is represented by the technological learning curves and the energy sector is not detached from this process of substitution of old technologies by new ones. The present paper carries out a brief revision of the main energy models that include the technology dynamics (learning). The energy scenarios, developed by global energy models, assume that the characteristics of the technologies are variables with time. But this trend is incorporated in a exogenous way in these energy models, that is to say, it is only a time function. This practice is applied to the cost indicators of the technology such as the specific investment costs or to the efficiency of the energy technologies. In the last years, the new concept of endogenous technological learning has been integrated within these global energy models. This paper examines the concept of technological learning in global energy models. It also analyses the technological dynamics of the energy system including the endogenous modelling of the process of technological progress. Finally, it makes a comparison of several of the most used global energy models (MARKAL, MESSAGE and ERIS) and, more concretely, about the use these models make of the concept of technological learning. (Author) 17 refs

  15. Democratic learning in the Aalborg Model

    DEFF Research Database (Denmark)

    Qvist, Palle

    A democratic learning system can be defined as a system where decisions, processes and behaviour related to learning are established through argumentation (discussion) or negotiation (dialog), voting or consensus (alone or in combination) between those affected by the decision simultaneously...... reaching the learning outcomes, the technical and professional knowledge and insight. In principle the participants must be equal with equal rights and feel committed to the values of rationality and impartiality. The Aalborg Model is an example of a democratic learning system although not 100% democratic......, processes and behaviour related to learning can be established through argumentation (discussion) or negotiation (dialog), voting or consensus (alone or in combination) within the group simultaneously reaching the learning outcomes, the technical and professional knowledge and insight. This article...

  16. Kolb's Experiential Learning Model: Critique from a Modeling Perspective (United States)

    Bergsteiner, Harald; Avery, Gayle C.; Neumann, Ruth


    Kolb's experiential learning theory has been widely influential in adult learning. The theory and associated instruments continue to be criticized, but rarely is the graphical model itself examined. This is significant because models can aid scientific understanding and progress, as well as theory development and research. Applying accepted…

  17. Learning strategies: a synthesis and conceptual model (United States)

    Hattie, John A. C.; Donoghue, Gregory M.


    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.

  18. Problem Solving Model for Science Learning (United States)

    Alberida, H.; Lufri; Festiyed; Barlian, E.


    This research aims to develop problem solving model for science learning in junior high school. The learning model was developed using the ADDIE model. An analysis phase includes curriculum analysis, analysis of students of SMP Kota Padang, analysis of SMP science teachers, learning analysis, as well as the literature review. The design phase includes product planning a science-learning problem-solving model, which consists of syntax, reaction principle, social system, support system, instructional impact and support. Implementation of problem-solving model in science learning to improve students' science process skills. The development stage consists of three steps: a) designing a prototype, b) performing a formative evaluation and c) a prototype revision. Implementation stage is done through a limited trial. A limited trial was conducted on 24 and 26 August 2015 in Class VII 2 SMPN 12 Padang. The evaluation phase was conducted in the form of experiments at SMPN 1 Padang, SMPN 12 Padang and SMP National Padang. Based on the development research done, the syntax model problem solving for science learning at junior high school consists of the introduction, observation, initial problems, data collection, data organization, data analysis/generalization, and communicating.

  19. Learning Probabilistic Logic Models from Probabilistic Examples. (United States)

    Chen, Jianzhong; Muggleton, Stephen; Santos, José


    We revisit an application developed originally using abductive Inductive Logic Programming (ILP) for modeling inhibition in metabolic networks. The example data was derived from studies of the effects of toxins on rats using Nuclear Magnetic Resonance (NMR) time-trace analysis of their biofluids together with background knowledge representing a subset of the Kyoto Encyclopedia of Genes and Genomes (KEGG). We now apply two Probabilistic ILP (PILP) approaches - abductive Stochastic Logic Programs (SLPs) and PRogramming In Statistical modeling (PRISM) to the application. Both approaches support abductive learning and probability predictions. Abductive SLPs are a PILP framework that provides possible worlds semantics to SLPs through abduction. Instead of learning logic models from non-probabilistic examples as done in ILP, the PILP approach applied in this paper is based on a general technique for introducing probability labels within a standard scientific experimental setting involving control and treated data. Our results demonstrate that the PILP approach provides a way of learning probabilistic logic models from probabilistic examples, and the PILP models learned from probabilistic examples lead to a significant decrease in error accompanied by improved insight from the learned results compared with the PILP models learned from non-probabilistic examples.

  20. A Concept Transformation Learning Model for Architectural Design Learning Process (United States)

    Wu, Yun-Wu; Weng, Kuo-Hua; Young, Li-Ming


    Generally, in the foundation course of architectural design, much emphasis is placed on teaching of the basic design skills without focusing on teaching students to apply the basic design concepts in their architectural designs or promoting students' own creativity. Therefore, this study aims to propose a concept transformation learning model to…

  1. Learning, Learning Analytics, Activity Visualisation and Open learner Model

    DEFF Research Database (Denmark)

    Bull, Susan; Kickmeier-Rust, Michael; Vatrapu, Ravi


    This paper draws on visualisation approaches in learning analytics, considering how classroom visualisations can come together in practice. We suggest an open learner model in situations where many tools and activity visualisations produce more visual information than can be readily interpreted....

  2. Learning situation models in a smart home. (United States)

    Brdiczka, Oliver; Crowley, James L; Reignier, Patrick


    This paper addresses the problem of learning situation models for providing context-aware services. Context for modeling human behavior in a smart environment is represented by a situation model describing environment, users, and their activities. A framework for acquiring and evolving different layers of a situation model in a smart environment is proposed. Different learning methods are presented as part of this framework: role detection per entity, unsupervised extraction of situations from multimodal data, supervised learning of situation representations, and evolution of a predefined situation model with feedback. The situation model serves as frame and support for the different methods, permitting to stay in an intuitive declarative framework. The proposed methods have been integrated into a whole system for smart home environment. The implementation is detailed, and two evaluations are conducted in the smart home environment. The obtained results validate the proposed approach.

  3. Collaborative Inquiry Learning: Models, tools, and challenges (United States)

    Bell, Thorsten; Urhahne, Detlef; Schanze, Sascha; Ploetzner, Rolf


    Collaborative inquiry learning is one of the most challenging and exciting ventures for today's schools. It aims at bringing a new and promising culture of teaching and learning into the classroom where students in groups engage in self-regulated learning activities supported by the teacher. It is expected that this way of learning fosters students' motivation and interest in science, that they learn to perform steps of inquiry similar to scientists and that they gain knowledge on scientific processes. Starting from general pedagogical reflections and science standards, the article reviews some prominent models of inquiry learning. This comparison results in a set of inquiry processes being the basis for cooperation in the scientific network NetCoIL. Inquiry learning is conceived in several ways with emphasis on different processes. For an illustration of the spectrum, some main conceptions of inquiry and their focuses are described. In the next step, the article describes exemplary computer tools and environments from within and outside the NetCoIL network that were designed to support processes of collaborative inquiry learning. These tools are analysed by describing their functionalities as well as effects on student learning known from the literature. The article closes with challenges for further developments elaborated by the NetCoIL network.

  4. [Mathematical models of decision making and learning]. (United States)

    Ito, Makoto; Doya, Kenji


    Computational models of reinforcement learning have recently been applied to analysis of brain imaging and neural recording data to identity neural correlates of specific processes of decision making, such as valuation of action candidates and parameters of value learning. However, for such model-based analysis paradigms, selecting an appropriate model is crucial. In this study we analyze the process of choice learning in rats using stochastic rewards. We show that "Q-learning," which is a standard reinforcement learning algorithm, does not adequately reflect the features of choice behaviors. Thus, we propose a generalized reinforcement learning (GRL) algorithm that incorporates the negative reward effect of reward loss and forgetting of values of actions not chosen. Using the Bayesian estimation method for time-varying parameters, we demonstrated that the GRL algorithm can predict an animal's choice behaviors as efficiently as the best Markov model. The results suggest the usefulness of the GRL for the model-based analysis of neural processes involved in decision making.

  5. Multidimensional Learner Model In Intelligent Learning System (United States)

    Deliyska, B.; Rozeva, A.


    The learner model in an intelligent learning system (ILS) has to ensure the personalization (individualization) and the adaptability of e-learning in an online learner-centered environment. ILS is a distributed e-learning system whose modules can be independent and located in different nodes (servers) on the Web. This kind of e-learning is achieved through the resources of the Semantic Web and is designed and developed around a course, group of courses or specialty. An essential part of ILS is learner model database which contains structured data about learner profile and temporal status in the learning process of one or more courses. In the paper a learner model position in ILS is considered and a relational database is designed from learner's domain ontology. Multidimensional modeling agent for the source database is designed and resultant learner data cube is presented. Agent's modules are proposed with corresponding algorithms and procedures. Multidimensional (OLAP) analysis guidelines on the resultant learner module for designing dynamic learning strategy have been highlighted.

  6. Building a Model of Successful Collaborative Learning for Company Innovativeness

    Directory of Open Access Journals (Sweden)

    Agata Sudolska


    Full Text Available The aim of the paper is to develop a model of successful collaborative learning for company innovativeness. First of all, the paper explores the issue of inter-firm learning, focusing its attention on collaborative learning. Secondly, inter-firm learning relationships are considered. Thirdly, the ex ante conditions of collaborative learning and the intra-organizational enhancers of inter-firm learning processes are studied. Finally, a model of the critical success factors for collaborative learning is developed.

  7. Self Modeling: Expanding the Theories of Learning (United States)

    Dowrick, Peter W.


    Self modeling (SM) offers a unique expansion of learning theory. For several decades, a steady trickle of empirical studies has reported consistent evidence for the efficacy of SM as a procedure for positive behavior change across physical, social, educational, and diagnostic variations. SM became accepted as an extreme case of model similarity;…

  8. Modeling human learning involved in car driving

    NARCIS (Netherlands)

    Wewerinke, P.H.


    In this paper, car driving is considered at the level of human tracking and maneuvering in the context of other traffic. A model analysis revealed the most salient features determining driving performance and safety. Learning car driving is modelled based on a system theoretical approach and based

  9. Learning in AN Oscillatory Cortical Model (United States)

    Scarpetta, Silvia; Li, Zhaoping; Hertz, John

    We study a model of generalized-Hebbian learning in asymmetric oscillatory neural networks modeling cortical areas such as hippocampus and olfactory cortex. The learning rule is based on the synaptic plasticity observed experimentally, in particular long-term potentiation and long-term depression of the synaptic efficacies depending on the relative timing of the pre- and postsynaptic activities during learning. The learned memory or representational states can be encoded by both the amplitude and the phase patterns of the oscillating neural populations, enabling more efficient and robust information coding than in conventional models of associative memory or input representation. Depending on the class of nonlinearity of the activation function, the model can function as an associative memory for oscillatory patterns (nonlinearity of class II) or can generalize from or interpolate between the learned states, appropriate for the function of input representation (nonlinearity of class I). In the former case, simulations of the model exhibits a first order transition between the "disordered state" and the "ordered" memory state.

  10. Learning Actions Models: Qualitative Approach

    DEFF Research Database (Denmark)

    Bolander, Thomas; Gierasimczuk, Nina


    In dynamic epistemic logic, actions are described using action models. In this paper we introduce a framework for studying learnability of action models from observations. We present first results concerning propositional action models. First we check two basic learnability criteria: finite ident...

  11. A multiplicative reinforcement learning model capturing learning dynamics and interindividual variability in mice


    Bathellier, Brice; Tee, Sui Poh; Hrovat, Christina; Rumpel, Simon


    Learning speed can strongly differ across individuals. This is seen in humans and animals. Here, we measured learning speed in mice performing a discrimination task and developed a theoretical model based on the reinforcement learning framework to account for differences between individual mice. We found that, when using a multiplicative learning rule, the starting connectivity values of the model strongly determine the shape of learning curves. This is in contrast to current learning models ...

  12. E-Model for Online Learning Communities. (United States)

    Rogo, Ellen J; Portillo, Karen M


    The purpose of this study was to explore the students' perspectives on the phenomenon of online learning communities while enrolled in a graduate dental hygiene program. A qualitative case study method was designed to investigate the learners' experiences with communities in an online environment. A cross-sectional purposive sampling method was used. Interviews were the data collection method. As the original data were being analyzed, the researchers noted a pattern evolved indicating the phenomenon developed in stages. The data were re-analyzed and validated by 2 member checks. The participants' experiences revealed an e-model consisting of 3 stages of formal learning community development as core courses in the curriculum were completed and 1 stage related to transmuting the community to an informal entity as students experienced the independent coursework in the program. The development of the formal learning communities followed 3 stages: Building a Foundation for the Learning Community, Building a Supportive Network within the Learning Community and Investing in the Community to Enhance Learning. The last stage, Transforming the Learning Community, signaled a transition to an informal network of learners. The e-model was represented by 3 key elements: metamorphosis of relationships, metamorphosis through the affective domain and metamorphosis through the cognitive domain, with the most influential element being the affective development. The e-model describes a 4 stage process through which learners experience a metamorphosis in their affective, relationship and cognitive development. Synergistic learning was possible based on the interaction between synergistic relationships and affective actions. Copyright © 2015 The American Dental Hygienists’ Association.

  13. Two Undergraduate Process Modeling Courses Taught Using Inductive Learning Methods (United States)

    Soroush, Masoud; Weinberger, Charles B.


    This manuscript presents a successful application of inductive learning in process modeling. It describes two process modeling courses that use inductive learning methods such as inquiry learning and problem-based learning, among others. The courses include a novel collection of multi-disciplinary complementary process modeling examples. They were…

  14. Development of a Model for Whole Brain Learning of Physiology (United States)

    Eagleton, Saramarie; Muller, Anton


    In this report, a model was developed for whole brain learning based on Curry's onion model. Curry described the effect of personality traits as the inner layer of learning, information-processing styles as the middle layer of learning, and environmental and instructional preferences as the outer layer of learning. The model that was developed…

  15. Prototype-based models in machine learning. (United States)

    Biehl, Michael; Hammer, Barbara; Villmann, Thomas


    An overview is given of prototype-based models in machine learning. In this framework, observations, i.e., data, are stored in terms of typical representatives. Together with a suitable measure of similarity, the systems can be employed in the context of unsupervised and supervised analysis of potentially high-dimensional, complex datasets. We discuss basic schemes of competitive vector quantization as well as the so-called neural gas approach and Kohonen's topology-preserving self-organizing map. Supervised learning in prototype systems is exemplified in terms of learning vector quantization. Most frequently, the familiar Euclidean distance serves as a dissimilarity measure. We present extensions of the framework to nonstandard measures and give an introduction to the use of adaptive distances in relevance learning. © 2016 Wiley Periodicals, Inc.

  16. Culture in Transition: A learning model

    DEFF Research Database (Denmark)

    Baca, Susan


    of organizational transition, and 3) demonstrating the efficacy of the model by using it to explain empirical research findings. It is argued that learning new cultural currency involves the use of active intelligence to locate and answer relevant questions, and further that this process requires the interplay......This paper addresses the problem of resistance to attempted changes in organizational culture, particularly those involving diversity, by 1) identifying precisely what is meant by organizational as opposed to societal culture, 2) developing a theoretical model of learning useful in contexts...... is useful for both management and labor in regulating transition processes, thus making a contribution to industrial relations....


    Directory of Open Access Journals (Sweden)

    Hayati .


    Full Text Available The objective in this research: (1 Determine a better learning model to improve learning outcomes physics students among learning model Inquiry Training based multimedia and Inquiry Training learning model. (2 Determine the level of motivation to learn in affects physics student learning outcomes. (3 Knowing the interactions between the model of learning and motivation in influencing student learning outcomes. This research is a quasi experimental. The population in this research was all students in class XI SMA Negeri 1 T.P Sunggal Semester I 2012/2013. The sample of this research was consisted of two classes with a sample of 70 peoples who are determined by purposive sampling, the IPA XI-2 as a class experiment using a model-based multimedia learning Training Inquiry as many as 35 peoples and XI IPA-3 as a control class using learning model Inquiry Training 35 peoples. Hypotheses were analyzed using the GLM at significant level of 0.05 using SPSS 17.0 for Windows. Based on data analysis and hypothesis testing conducted found that: (1 Training Inquiry-based multimedia learning model in improving student learning outcomes rather than learning model physics Inquiry Training. (2 The results of studying physics students who have high motivation to learn better than students who have a low learning motivation. (3 From this research there was an interaction between learning model inquiry-based multimedia training and motivation to study on learning outcomes of students.

  18. The 8 Learning Events Model: a Pedagogic Conceptual Tool Supporting Diversification of Learning Methods

    NARCIS (Netherlands)

    Verpoorten, Dominique; Poumay, M; Leclercq, D


    Please, cite this publication as: Verpoorten, D., Poumay, M., & Leclercq, D. (2006). The 8 Learning Events Model: a Pedagogic Conceptual Tool Supporting Diversification of Learning Methods. Proceedings of International Workshop in Learning Networks for Lifelong Competence Development, TENCompetence

  19. Learning Adversary Modeling from Games

    National Research Council Canada - National Science Library

    Avellino, Paul


    .... In the computer age, highly accurate models and simulations of the enemy can be created. However, including the effects of motivations, capabilities, and weaknesses of adversaries in current wars is still extremely difficult...

  20. A model of positive and negative learning : Learning demands and resources, learning engagement, critical thinking, and fake news detection

    NARCIS (Netherlands)

    Dormann, Christian; Demerouti, Eva; Bakker, Arnold; Zlatkin-Troitschanskaia, O.; Wittum, G.; Dengel, A.


    This chapter proposes a model of positive and negative learning (PNL model). We use the term negative learning when stress among students occurs, and when knowledge and abilities are not properly developed. We use the term positive learning if motivation is high and active learning occurs. The PNL

  1. Organizational Learning Supported by Reference Architecture Models

    DEFF Research Database (Denmark)

    Nardello, Marco; Møller, Charles; Gøtze, John


    of an emerging technical standard specific for the manufacturing industry. Global manufacturing experts consider the Reference Architecture Model Industry 4.0 (RAMI4.0) as one of the corner stones for the implementation of Industry 4.0. The instantiation contributed to organizational learning in the laboratory...

  2. Prototype-based models in machine learning

    NARCIS (Netherlands)

    Biehl, Michael; Hammer, Barbara; Villmann, Thomas


    An overview is given of prototype-based models in machine learning. In this framework, observations, i.e., data, are stored in terms of typical representatives. Together with a suitable measure of similarity, the systems can be employed in the context of unsupervised and supervised analysis of

  3. School Improvement Model to Foster Student Learning (United States)

    Rulloda, Rudolfo Barcena


    Many classroom teachers are still using the traditional teaching methods. The traditional teaching methods are one-way learning process, where teachers would introduce subject contents such as language arts, English, mathematics, science, and reading separately. However, the school improvement model takes into account that all students have…

  4. Constructing Pedagogical Models For E-learning


    Patricia Alejandra Behar


    This article brings forth an overview of the paradigmatic crisis and the introduction of new pedagogical practices. It also discusses the relationship between paradigm and pedagogical model, presenting a theoretical discussion on the concepts of pedagogical model for E-learning and its pedagogical architecture. To do so, the elements that are part of it such as organizational aspects, content, methodological and technological aspects are discussed. This theoretical discussion underlies the co...

  5. Correlation Between Blended Learning Model With The Perspective Of Learning Effectiveness For Nursing Student

    Directory of Open Access Journals (Sweden)

    Susila Sumartiningsih


    Full Text Available ABSTRACT The learning model is one of the enabling factors that influence the achievement of students. That students have a good learning outcomes the lecturer must choose appropriate learning models. But in fact not all lecturers choose the most appropriate learning model with the demands of learning outcomes and student characteristics.The study design was descriptive quantitative correlation. Total population of 785 the number of samples are 202 were taken by purposive sampling. Techniques of data collection is done by cross-sectional and then processed through the Spearman test. The results showed no significant relationship between classroom lecture method in the context of blended learning models to study the effectiveness perspective the p value of 0.001. There is a significant relationship between e-learning methods in the context of blended learning models with perspective of activities study of nursing students the p value of 0.028. There is a significant relationship between learning model of blended learning with the perspective of nursing students learning effectiveness p value 0.167. Researchers recommend to future researchers conduct more research on the comparison between the effectiveness of the learning model based on student learning centers with the e-learning models and its impact on student achievement of learning competencies as well as to the implications for other dimensions of learning outcomes and others.

  6. Effect Of Inquiry Learning Model And Motivation On Physics Outcomes Learning Students


    Pardede, Dahlia Megawati; Manurung, Sondang Rina


    The purposes of the research are: (a) to determine differences in learning outcomes of students with Inquiry Training models and conventional models, (b) to determine differences in physics learning outcomes of students who have high motivation and low motivation, (c) to determine the interaction between learning models with the level of motivation in improving student Physics learning outcomes. The results were found: (a) there are differences in physical students learning outcomes are taugh...

  7. Learning topic models by belief propagation. (United States)

    Zeng, Jia; Cheung, William K; Liu, Jiming


    Latent Dirichlet allocation (LDA) is an important hierarchical Bayesian model for probabilistic topic modeling, which attracts worldwide interest and touches on many important applications in text mining, computer vision and computational biology. This paper represents the collapsed LDA as a factor graph, which enables the classic loopy belief propagation (BP) algorithm for approximate inference and parameter estimation. Although two commonly used approximate inference methods, such as variational Bayes (VB) and collapsed Gibbs sampling (GS), have gained great success in learning LDA, the proposed BP is competitive in both speed and accuracy, as validated by encouraging experimental results on four large-scale document datasets. Furthermore, the BP algorithm has the potential to become a generic scheme for learning variants of LDA-based topic models in the collapsed space. To this end, we show how to learn two typical variants of LDA-based topic models, such as author-topic models (ATM) and relational topic models (RTM), using BP based on the factor graph representations.

  8. The Aalborg Model and participant directed learning

    DEFF Research Database (Denmark)

    Qvist, Palle


    Preparing students for a life as active citizens in a democratic society is one of the aims within the Bologna process. The Council of Europe has also stressed the importance of focus on democracy in Higher Education. Higher Education is seen as important to develop a democratic culture among...... students. Teaching democracy should be promoted in lessons and curricula. Creating democratic learning systems in institutions of higher education could be the answer to reaching the aim related to democracy. The Aalborg Model practised at Aalborg University is a learning system which has collaborative...

  9. A model of olfactory associative learning (United States)

    Tavoni, Gaia; Balasubramanian, Vijay

    We propose a mechanism, rooted in the known anatomy and physiology of the vertebrate olfactory system, by which presentations of rewarded and unrewarded odors lead to formation of odor-valence associations between piriform cortex (PC) and anterior olfactory nucleus (AON) which, in concert with neuromodulators release in the bulb, entrains a direct feedback from the AON representation of valence to a group of mitral cells (MCs). The model makes several predictions concerning MC activity during and after associative learning: (a) AON feedback produces synchronous divergent responses in a localized subset of MCs; (b) such divergence propagates to other MCs by lateral inhibition; (c) after learning, MC responses reconverge; (d) recall of the newly formed associations in the PC increases feedback inhibition in the MCs. These predictions have been confirmed in disparate experiments which we now explain in a unified framework. For cortex, our model further predicts that the response divergence developed during learning reshapes odor representations in the PC, with the effects of (a) decorrelating PC representations of odors with different valences, (b) increasing the size and reliability of those representations, and enabling recall correction and redundancy reduction after learning. Simons Foundation for Mathematical Modeling of Living Systems.


    Directory of Open Access Journals (Sweden)

    Dahlia Megawati Pardede


    Full Text Available The purposes of the research are: (a to determine differences in learning outcomes of students with Inquiry Training models and conventional models, (b to determine differences in physics learning outcomes of students who have high motivation and low motivation, (c to determine the interaction between learning models with the level of motivation in improving student Physics learning outcomes. The results were found: (a there are differences in physical students learning outcomes are taught by Inquiry Training models and conventional models. (b learning outcomes of students who are taught by Inquiry Learning Model Training better than student learning outcomes are taught with conventional model. (c there is a difference in student's learning outcomes that have high motivation and low motivation. (d Student learning outcomes that have a high motivation better than student learning outcomes than have a low motivation. (e there is interaction between learning and motivation to student learning outcomes. Learning outcomes of students who are taught by the model is influenced also by the motivation, while learning outcomes of students who are taught with conventional models are not affected by motivation.

  11. Temporal-pattern learning in neural models

    CERN Document Server

    Genís, Carme Torras


    While the ability of animals to learn rhythms is an unquestionable fact, the underlying neurophysiological mechanisms are still no more than conjectures. This monograph explores the requirements of such mechanisms, reviews those previously proposed and postulates a new one based on a direct electric coding of stimulation frequencies. Experi­ mental support for the option taken is provided both at the single neuron and neural network levels. More specifically, the material presented divides naturally into four parts: a description of the experimental and theoretical framework where this work becomes meaningful (Chapter 2), a detailed specifica­ tion of the pacemaker neuron model proposed together with its valida­ tion through simulation (Chapter 3), an analytic study of the behavior of this model when submitted to rhythmic stimulation (Chapter 4) and a description of the neural network model proposed for learning, together with an analysis of the simulation results obtained when varying seve­ ral factors r...

  12. Coaching Model + Clinical Playbook = Transformative Learning. (United States)

    Fletcher, Katherine A; Meyer, Mary


    Health care employers demand that workers be skilled in clinical reasoning, able to work within complex interprofessional teams to provide safe, quality patient-centered care in a complex evolving system. To this end, there have been calls for radical transformation of nursing education including the development of a baccalaureate generalist nurse. Based on recommendations from the American Association of Colleges of Nursing, faculty concluded that clinical education must change moving beyond direct patient care by applying the concepts associated with designer, manager, and coordinator of care and being a member of a profession. To accomplish this, the faculty utilized a system of focused learning assignments (FLAs) that present transformative learning opportunities that expose students to "disorienting dilemmas," alternative perspectives, and repeated opportunities to reflect and challenge their own beliefs. The FLAs collected in a "Playbook" were scaffolded to build the student's competencies over the course of the clinical experience. The FLAs were centered on the 6 Quality and Safety Education for Nurses competencies, with 2 additional concepts of professionalism and systems-based practice. The FLAs were competency-based exercises that students performed when not assigned to direct patient care or had free clinical time. Each FLA had a lesson plan that allowed the student and faculty member to see the competency addressed by the lesson, resources, time on task, student instructions, guide for reflection, grading rubric, and recommendations for clinical instructor. The major advantages of the model included (a) consistent implementation of structured learning experiences by a diverse teaching staff using a coaching model of instruction; (b) more systematic approach to present learning activities that build upon each other; (c) increased time for faculty to interact with students providing direct patient care; (d) guaranteed capture of selected transformative

  13. Learning versus correct models: influence of model type on the learning of a free-weight squat lift. (United States)

    McCullagh, P; Meyer, K N


    It has been assumed that demonstrating the correct movement is the best way to impart task-relevant information. However, empirical verification with simple laboratory skills has shown that using a learning model (showing an individual in the process of acquiring the skill to be learned) may accelerate skill acquisition and increase retention more than using a correct model. The purpose of the present study was to compare the effectiveness of viewing correct versus learning models on the acquisition of a sport skill (free-weight squat lift). Forty female participants were assigned to four learning conditions: physical practice receiving feedback, learning model with model feedback, correct model with model feedback, and learning model without model feedback. Results indicated that viewing either a correct or learning model was equally effective in learning correct form in the squat lift.


    Directory of Open Access Journals (Sweden)



    Full Text Available The present research aims at presenting a conceptual model for effective distance learning in higher education. Findings of this research shows that an understanding of the technological capabilities and learning theories especially constructive theory and independent learning theory and communicative and interaction theory in Distance learning is an efficient factor in the planning of effective Distance learning in higher education. Considering the theoretical foundations of the present research, in the effective distance learning model, the learner is situated at the center of learning environment. For this purpose, the learner needs to be ready for successful learning and the teacher has to be ready to design the teaching- learning activities when they initially enter the environment. In the present model, group and individual active teaching-learning approach, timely feedback, using IT and eight types of interactions have been designed with respect to theoretical foundations and current university missions. From among the issues emphasized in this model, one can refer to the Initial, Formative and Summative evaluations. In an effective distance learning environment, evaluation should be part of the learning process and the feedback resulting from it should be used to improve learning. For validating the specified features, the opinions of Distance learning experts in Payame Noor, Shiraz, Science and Technology and Amirkabir Universities have been used which verified a high percentage of the statistical sample of the above mentioned features.

  15. Modellus: Learning Physics with Mathematical Modelling (United States)

    Teodoro, Vitor

    Computers are now a major tool in research and development in almost all scientific and technological fields. Despite recent developments, this is far from true for learning environments in schools and most undergraduate studies. This thesis proposes a framework for designing curricula where computers, and computer modelling in particular, are a major tool for learning. The framework, based on research on learning science and mathematics and on computer user interface, assumes that: 1) learning is an active process of creating meaning from representations; 2) learning takes place in a community of practice where students learn both from their own effort and from external guidance; 3) learning is a process of becoming familiar with concepts, with links between concepts, and with representations; 4) direct manipulation user interfaces allow students to explore concrete-abstract objects such as those of physics and can be used by students with minimal computer knowledge. Physics is the science of constructing models and explanations about the physical world. And mathematical models are an important type of models that are difficult for many students. These difficulties can be rooted in the fact that most students do not have an environment where they can explore functions, differential equations and iterations as primary objects that model physical phenomena--as objects-to-think-with, reifying the formal objects of physics. The framework proposes that students should be introduced to modelling in a very early stage of learning physics and mathematics, two scientific areas that must be taught in very closely related way, as they were developed since Galileo and Newton until the beginning of our century, before the rise of overspecialisation in science. At an early stage, functions are the main type of objects used to model real phenomena, such as motions. At a later stage, rates of change and equations with rates of change play an important role. This type of equations

  16. Using IMS Learning Design to model collaborative learning activities

    NARCIS (Netherlands)

    Tattersall, Colin


    IMS Learning Design provides a counter to the trend towards designing for lone-learners reading from screens. It guides staff and educational developers to start not with content, but with learning activities and the achievement of learning objectives. It recognises that learning can happen without

  17. Service Learning In Physics: The Consultant Model (United States)

    Guerra, David


    Each year thousands of students across the country and across the academic disciplines participate in service learning. Unfortunately, with no clear model for integrating community service into the physics curriculum, there are very few physics students engaged in service learning. To overcome this shortfall, a consultant based service-learning program has been developed and successfully implemented at Saint Anselm College (SAC). As consultants, students in upper level physics courses apply their problem solving skills in the service of others. Most recently, SAC students provided technical and managerial support to a group from Girl's Inc., a national empowerment program for girls in high-risk, underserved areas, who were participating in the national FIRST Lego League Robotics competition. In their role as consultants the SAC students provided technical information through brainstorming sessions and helped the girls stay on task with project management techniques, like milestone charting. This consultant model of service-learning, provides technical support to groups that may not have a great deal of resources and gives physics students a way to improve their interpersonal skills, test their technical expertise, and better define the marketable skill set they are developing through the physics curriculum.

  18. Representing adaptive and adaptable Units of Learning. How to model personalized eLearning in IMS Learning Design


    Burgos, Daniel; Tattersall, Colin; Koper, Rob


    Burgos, D., Tattersall, C., & Koper, E. J. R. (2007). Representing adaptive and adaptable Units of Learning. How to model personalized eLearning in IMS Learning Design. In B. Fernández Manjon, J. M. Sanchez Perez, J. A. Gómez Pulido, M. A. Vega Rodriguez & J. Bravo (Eds.), Computers and Education: E-learning - from theory to practice. Germany: Kluwer.

  19. Online constrained model-based reinforcement learning

    CSIR Research Space (South Africa)

    Van Niekerk, B


    Full Text Available Constrained Model-based Reinforcement Learning Benjamin van Niekerk School of Computer Science University of the Witwatersrand South Africa Andreas Damianou∗ Cambridge, UK Benjamin Rosman Council for Scientific and Industrial Research, and School... MULTIPLE SHOOTING Using direct multiple shooting (Bock and Plitt, 1984), problem (1) can be transformed into a structured non- linear program (NLP). First, the time horizon [t0, t0 + T ] is partitioned into N equal subintervals [tk, tk+1] for k = 0...


    Directory of Open Access Journals (Sweden)

    Amalia Febri Aristi


    Full Text Available This study aimed to determine: (1 Is there a difference in student's learning outcomes with the application of learning models Investigation Group and Direct Instruction teaching model. (2 Is there a difference in students' motivation with the application of learning models Investigation Group and Direct Instruction teaching model, (3 Is there an interaction between learning models Investigation Group and Direct Instruction to improve students' motivation in learning outcomes Physics. This research is a quasi experimental. The study population was a student of class XII Tanjung Balai MAN. Random sample selection is done by randomizing the class. The instrument used consisted of: (1 achievement test (2 students' motivation questionnaire. The tests are used to obtain the data is shaped essay. The data in this study were analyzed using ANOVA analysis of two paths. The results showed that: (1 there were differences in learning outcomes between students who used the physics model of Group Investigation learning compared with students who used the Direct Instruction teaching model. (2 There was a difference in student's learning outcomes that had a low learning motivation and high motivation to learn both in the classroom and in the classroom Investigation Group Direct Instruction. (3 There was interaction between learning models Instruction Direct Group Investigation and motivation to learn in improving learning outcomes Physics.

  1. Bio-Inspired Neural Model for Learning Dynamic Models (United States)

    Duong, Tuan; Duong, Vu; Suri, Ronald


    A neural-network mathematical model that, relative to prior such models, places greater emphasis on some of the temporal aspects of real neural physical processes, has been proposed as a basis for massively parallel, distributed algorithms that learn dynamic models of possibly complex external processes by means of learning rules that are local in space and time. The algorithms could be made to perform such functions as recognition and prediction of words in speech and of objects depicted in video images. The approach embodied in this model is said to be "hardware-friendly" in the following sense: The algorithms would be amenable to execution by special-purpose computers implemented as very-large-scale integrated (VLSI) circuits that would operate at relatively high speeds and low power demands.

  2. A Multiobjective Sparse Feature Learning Model for Deep Neural Networks. (United States)

    Gong, Maoguo; Liu, Jia; Li, Hao; Cai, Qing; Su, Linzhi


    Hierarchical deep neural networks are currently popular learning models for imitating the hierarchical architecture of human brain. Single-layer feature extractors are the bricks to build deep networks. Sparse feature learning models are popular models that can learn useful representations. But most of those models need a user-defined constant to control the sparsity of representations. In this paper, we propose a multiobjective sparse feature learning model based on the autoencoder. The parameters of the model are learnt by optimizing two objectives, reconstruction error and the sparsity of hidden units simultaneously to find a reasonable compromise between them automatically. We design a multiobjective induced learning procedure for this model based on a multiobjective evolutionary algorithm. In the experiments, we demonstrate that the learning procedure is effective, and the proposed multiobjective model can learn useful sparse features.

  3. An entropy model for artificial grammar learning

    Directory of Open Access Journals (Sweden)

    Emmanuel Pothos


    Full Text Available A model is proposed to characterize the type of knowledge acquired in Artificial Grammar Learning (AGL. In particular, Shannon entropy is employed to compute the complexity of different test items in an AGL task, relative to the training items. According to this model, the more predictable a test item is from the training items, the more likely it is that this item should be selected as compatible with the training items. The predictions of the entropy model are explored in relation to the results from several previous AGL datasets and compared to other AGL measures. This particular approach in AGL resonates well with similar models in categorization and reasoning which also postulate that cognitive processing is geared towards the reduction of entropy.

  4. Organizational Learning Supported by Reference Architecture Models

    DEFF Research Database (Denmark)

    Nardello, Marco; Møller, Charles; Gøtze, John


    The wave of the fourth industrial revolution (Industry 4.0) is bringing a new vision of the manufacturing industry. In manufacturing, one of the buzzwords of the moment is “Smart production”. Smart production involves manufacturing equipment with many sensors that can generate and transmit large...... amounts of data. These data and information from manufacturing operations are however not shared in the organization. Therefore the organization is not using them to learn and improve their operations. To address this problem, the authors implemented in an Industry 4.0 laboratory an instance...... of an emerging technical standard specific for the manufacturing industry. Global manufacturing experts consider the Reference Architecture Model Industry 4.0 (RAMI4.0) as one of the corner stones for the implementation of Industry 4.0. The instantiation contributed to organizational learning in the laboratory...

  5. Cognitive components underpinning the development of model-based learning. (United States)

    Potter, Tracey C S; Bryce, Nessa V; Hartley, Catherine A


    Reinforcement learning theory distinguishes "model-free" learning, which fosters reflexive repetition of previously rewarded actions, from "model-based" learning, which recruits a mental model of the environment to flexibly select goal-directed actions. Whereas model-free learning is evident across development, recruitment of model-based learning appears to increase with age. However, the cognitive processes underlying the development of model-based learning remain poorly characterized. Here, we examined whether age-related differences in cognitive processes underlying the construction and flexible recruitment of mental models predict developmental increases in model-based choice. In a cohort of participants aged 9-25, we examined whether the abilities to infer sequential regularities in the environment ("statistical learning"), maintain information in an active state ("working memory") and integrate distant concepts to solve problems ("fluid reasoning") predicted age-related improvements in model-based choice. We found that age-related improvements in statistical learning performance did not mediate the relationship between age and model-based choice. Ceiling performance on our working memory assay prevented examination of its contribution to model-based learning. However, age-related improvements in fluid reasoning statistically mediated the developmental increase in the recruitment of a model-based strategy. These findings suggest that gradual development of fluid reasoning may be a critical component process underlying the emergence of model-based learning. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  6. A strategy learning model for autonomous agents based on classification

    Directory of Open Access Journals (Sweden)

    Śnieżyński Bartłomiej


    Full Text Available In this paper we propose a strategy learning model for autonomous agents based on classification. In the literature, the most commonly used learning method in agent-based systems is reinforcement learning. In our opinion, classification can be considered a good alternative. This type of supervised learning can be used to generate a classifier that allows the agent to choose an appropriate action for execution. Experimental results show that this model can be successfully applied for strategy generation even if rewards are delayed. We compare the efficiency of the proposed model and reinforcement learning using the farmer-pest domain and configurations of various complexity. In complex environments, supervised learning can improve the performance of agents much faster that reinforcement learning. If an appropriate knowledge representation is used, the learned knowledge may be analyzed by humans, which allows tracking the learning process

  7. Personal Coaching: Reflection on a Model for Effective Learning (United States)

    Griffiths, Kerryn


    The article "Personal Coaching: A Model for Effective Learning" (Griffiths, 2006) appeared in the "Journal of Learning Design" Volume 1, Issue 2 in 2006. Almost ten years on, Kerryn Griffiths reflects upon her original article. Specifically, Griffiths looks back at the combined coaching-learning model she suggested in her…

  8. Validating a Technology Enhanced Student-Centered Learning Model (United States)

    Kang, Myunghee; Hahn, Jungsun; Chung, Warren


    The Technology Enhanced Student Centered Learning (TESCL) Model in this study presents the core factors that ensure the quality of learning in a technology-supported environment. Although the model was conceptually constructed using a student-centered learning framework and drawing upon previous studies, it should be validated through real-world…

  9. A Judgement-Based Model of Workplace Learning (United States)

    Athanasou, James A.


    The purpose of this paper is to outline a judgement-based model of adult learning. This approach is set out as a Perceptual-Judgemental-Reinforcement approach to social learning under conditions of complexity and where there is no single, clearly identified correct response. The model builds upon the Hager-Halliday thesis of workplace learning and…

  10. Car-following Behavior Model Learning Using Timed Automata

    NARCIS (Netherlands)

    Zhang, Yihuan; Lin, Q.; Wang, Jun; Verwer, S.E.; Dochain, D.; Henrion, D.; Peaucelle, D.

    Learning driving behavior is fundamental for autonomous vehicles to “understand” traffic situations. This paper proposes a novel method for learning a behavioral model of car-following using automata learning algorithms. The model is interpretable for car-following behavior analysis. Frequent common

  11. Competition-Based Learning: A Model for the Integration of Competitions with Project-Based Learning Using Open Source LMS (United States)

    Issa, Ghassan; Hussain, Shakir M.; Al-Bahadili, Hussein


    In an effort to enhance the learning process in higher education, a new model for Competition-Based Learning (CBL) is presented. The new model utilizes two well-known learning models, namely, the Project-Based Learning (PBL) and competitions. The new model is also applied in a networked environment with emphasis on collective learning as well as…

  12. College English Students’ Autonomous Learning Motivation and Cultivation Model Research

    Institute of Scientific and Technical Information of China (English)

    王艳荣; 李娥


    Studying the autonomous learning motivation and excitation model can stimulate intrinsic motivation of foreign language learners,develop students self-management strategy evaluation are very necessary.The purpose of this paper is to give students the skills of listening and speaking for their autonomous learning.Then study the cultivation and motivation of college English students autonomous learning,hoping to make students to learn autonomous learning and stimulate their motivation fully.

  13. The layered learning practice model: Lessons learned from implementation. (United States)

    Pinelli, Nicole R; Eckel, Stephen F; Vu, Maihan B; Weinberger, Morris; Roth, Mary T


    Pharmacists' views about the implementation, benefits, and attributes of a layered learning practice model (LLPM) were examined. Eligible and willing attending pharmacists at the same institution that had implemented an LLPM completed an individual, 90-minute, face-to-face interview using a structured interview guide developed by the interdisciplinary study team. Interviews were digitally recorded and transcribed verbatim without personal identifiers. Three researchers independently reviewed preliminary findings to reach consensus on emerging themes. In cases where thematic coding diverged, the researchers discussed their analyses until consensus was reached. Of 25 eligible attending pharmacists, 24 (96%) agreed to participate. The sample was drawn from both acute and ambulatory care practice settings and all clinical specialty areas. Attending pharmacists described several experiences implementing the LLPM and perceived benefits of the model. Attending pharmacists identified seven key attributes for hospital and health-system pharmacy departments that are needed to design and implement effective LLPMs: shared leadership, a systematic approach, good communication, flexibility for attending pharmacists, adequate resources, commitment, and evaluation. Participants also highlighted several potential challenges and obstacles for organizations to consider before implementing an LLPM. According to attending pharmacists involved in an LLPM, successful implementation of an LLPM required shared leadership, a systematic approach, communication, flexibility, resources, commitment, and a process for evaluation. Copyright © 2016 by the American Society of Health-System Pharmacists, Inc. All rights reserved.

  14. The development of learning material using learning cycle 5E model based stem to improve students’ learning outcomes in Thermochemistry (United States)

    sugiarti, A. C.; suyatno, S.; Sanjaya, I. G. M.


    The objective of this study is describing the feasibility of Learning Cycle 5E STEM (Science, Technology, Engineering, and Mathematics) based learning material which is appropriate to improve students’ learning achievement in Thermochemistry. The study design used 4-D models and one group pretest-posttest design to obtain the information about the improvement of sudents’ learning outcomes. The subject was learning cycle 5E based STEM learning materials which the data were collected from 30 students of Science class at 11th Grade. The techniques used in this study were validation, observation, test, and questionnaire. Some result attain: (1) all the learning materials contents were valid, (2) the practicality and the effectiveness of all the learning materials contents were classified as good. The conclution of this study based on those three condition, the Learnig Cycle 5E based STEM learning materials is appropriate to improve students’ learning outcomes in studying Thermochemistry.

  15. Educational Modelling Language and Learning Design: new challenges for instructional re-usability and personalized learning

    NARCIS (Netherlands)

    Hummel, Hans; Manderveld, Jocelyn; Tattersall, Colin; Koper, Rob


    Published: Hummel, H. G. K., Manderveld, J. M., Tattersall, C.,& Koper, E. J. R. (2004). Educational Modelling Language: new challenges for instructional re-usability and personalized learning. International Journal of Learning Technology, 1, 1, 110-111.

  16. Game Based Learning (GBL) adoption model for universities: cesim ...

    African Journals Online (AJOL)

    Game Based Learning (GBL) adoption model for universities: cesim simulation. ... The global market has escalated the need of Game Based Learning (GBL) to offer a wide range of courses since there is a ... AJOL African Journals Online.

  17. Cognitive Models for Learning to Control Dynamic Systems

    National Research Council Canada - National Science Library

    Eberhart, Russ; Hu, Xiaohui; Chen, Yaobin


    Report developed under STTR contract for topic "Cognitive models for learning to control dynamic systems" demonstrated a swarm intelligence learning algorithm and its application in unmanned aerial vehicle (UAV) mission planning...

  18. The Game Object Model and expansive learning: Creation ...

    African Journals Online (AJOL)

    The Game Object Model and expansive learning: Creation, instantiation, ... The aim of the paper is to develop insights into the design, integration, evaluation and use of video games in learning and teaching. ... AJOL African Journals Online.

  19. Learning classification models with soft-label information. (United States)

    Nguyen, Quang; Valizadegan, Hamed; Hauskrecht, Milos


    Learning of classification models in medicine often relies on data labeled by a human expert. Since labeling of clinical data may be time-consuming, finding ways of alleviating the labeling costs is critical for our ability to automatically learn such models. In this paper we propose a new machine learning approach that is able to learn improved binary classification models more efficiently by refining the binary class information in the training phase with soft labels that reflect how strongly the human expert feels about the original class labels. Two types of methods that can learn improved binary classification models from soft labels are proposed. The first relies on probabilistic/numeric labels, the other on ordinal categorical labels. We study and demonstrate the benefits of these methods for learning an alerting model for heparin induced thrombocytopenia. The experiments are conducted on the data of 377 patient instances labeled by three different human experts. The methods are compared using the area under the receiver operating characteristic curve (AUC) score. Our AUC results show that the new approach is capable of learning classification models more efficiently compared to traditional learning methods. The improvement in AUC is most remarkable when the number of examples we learn from is small. A new classification learning framework that lets us learn from auxiliary soft-label information provided by a human expert is a promising new direction for learning classification models from expert labels, reducing the time and cost needed to label data.

  20. What’s about Peer Tutoring Learning Model? (United States)

    Muthma'innah, M.


    Mathematics learning outcomes in Indonesia in general is still far from satisfactory. One effort that could be expected to solve the problem is to apply the model of peer tutoring learning in mathematics. This study aims to determine whether the results of students’ mathematics learning can be enhanced through peer tutoring learning models. This type of research is the study of literature, so that the method used is to summarize and analyze the results of relevant research that has been done. Peer tutoring learning model is a model of learning in which students learn in small groups that are grouped with different ability levels, all group members to work together and help each other to understand the material. By paying attention to the syntax of the learning, then learning will be invaluable peer tutoring for students who served as teachers and students are taught. In mathematics, the implementation of this learning model can make students understand each other mathematical concepts and help students in solving mathematical problems that are poorly understood, due to the interaction between students in learning. Then it will be able to improve learning outcomes in mathematics. The impact, it can be applied in mathematics learning.

  1. The Answering Process for Multiple-Choice Questions in Collaborative Learning: A Mathematical Learning Model Analysis (United States)

    Nakamura, Yasuyuki; Nishi, Shinnosuke; Muramatsu, Yuta; Yasutake, Koichi; Yamakawa, Osamu; Tagawa, Takahiro


    In this paper, we introduce a mathematical model for collaborative learning and the answering process for multiple-choice questions. The collaborative learning model is inspired by the Ising spin model and the model for answering multiple-choice questions is based on their difficulty level. An intensive simulation study predicts the possibility of…

  2. Learning from video modeling examples: Does gender matter?

    NARCIS (Netherlands)

    Hoogerheide, V.; Loyens, S.M.M.; van Gog, T.


    Online learning from video modeling examples, in which a human model demonstrates and explains how to perform a learning task, is an effective instructional method that is increasingly used nowadays. However, model characteristics such as gender tend to differ across videos, and the model-observer

  3. Learning from Video Modeling Examples: Does Gender Matter? (United States)

    Hoogerheide, Vincent; Loyens, Sofie M. M.; van Gog, Tamara


    Online learning from video modeling examples, in which a human model demonstrates and explains how to perform a learning task, is an effective instructional method that is increasingly used nowadays. However, model characteristics such as gender tend to differ across videos, and the model-observer similarity hypothesis suggests that such…



    Rustan, Edhy


    The objectives of the study are to determine: (1) condition on learning creative writing at high school students in Makassar, (2) requirement of learning model in creative writing, (3) program planning and design model in ideal creative writing, (4) feasibility of model study based on creative writing in neurolinguistic programming, and (5) the effectiveness of the learning model based on creative writing in neurolinguisticprogramming.The method of this research uses research development of L...

  5. Electronic learning and constructivism: a model for nursing education. (United States)

    Kala, Sasikarn; Isaramalai, Sang-Arun; Pohthong, Amnart


    Nurse educators are challenged to teach nursing students to become competent professionals, who have both in-depth knowledge and decision-making skills. The use of electronic learning methods has been found to facilitate the teaching-learning process in nursing education. Although learning theories are acknowledged as useful guides to design strategies and activities of learning, integration of these theories into technology-based courses appears limited. Constructivism is a theoretical paradigm that could prove to be effective in guiding the design of electronic learning experiences for the purpose of providing positive outcomes, such as the acquisition of knowledge and decision-making skills. Therefore, the purposes of this paper are to: describe electronic learning, present a brief overview of what is known about the outcomes of electronic learning, discuss constructivism theory, present a model for electronic learning using constructivism, and describe educators' roles emphasizing the utilization of the model in developing electronic learning experiences in nursing education.

  6. Designing for Learning and Play - The Smiley Model as Framework

    DEFF Research Database (Denmark)

    Weitze, Charlotte Lærke


    digital games. The Smiley Model inspired and provided a scaffold or a heuristic for the overall gamified learning design –- as well as for the students’ learning game design processes when creating small games turning the learning situation into an engaging experience. The audience for the experiments......This paper presents a framework for designing engaging learning experiences in games – the Smiley Model. In this Design-Based Research project, student-game-designers were learning inside a gamified learning design - while designing and implementing learning goals from curriculum into the small...... was adult upper secondary general students as well as 7th grade primary school students. The intention with this article is to inspire future learning designers that would like to experiment with integrating learning and play....

  7. Learner Open Modeling in Adaptive Mobile Learning System for Supporting Student to Learn English

    Directory of Open Access Journals (Sweden)

    Van Cong Pham


    Full Text Available This paper represents a personalized context-aware mobile learning architecture for supporting student to learn English as foreign language in order to prepare for TOEFL test. We consider how to apply open learner modeling techniques to adapt contents for different learners based on context, which includes location, amount of time to learn, the manner as well as learner's knowledge in learning progress. Through negotiation with system, the editable learner model will be updated to support adaptive engine to select adaptive contents meeting learner's demands. Empirical testing results for students who used application prototype indicate that interaction user modeling is helpful in supporting learner to learn adaptive materials.

  8. Development of a model for whole brain learning of physiology. (United States)

    Eagleton, Saramarie; Muller, Anton


    In this report, a model was developed for whole brain learning based on Curry's onion model. Curry described the effect of personality traits as the inner layer of learning, information-processing styles as the middle layer of learning, and environmental and instructional preferences as the outer layer of learning. The model that was developed elaborates on these layers by relating the personality traits central to learning to the different quadrants of brain preference, as described by Neethling's brain profile, as the inner layer of the onion. This layer is encircled by the learning styles that describe different information-processing preferences for each brain quadrant. For the middle layer, the different stages of Kolb's learning cycle are classified into the four brain quadrants associated with the different brain processing strategies within the information processing circle. Each of the stages of Kolb's learning cycle is also associated with a specific cognitive learning strategy. These two inner circles are enclosed by the circle representing the role of the environment and instruction on learning. It relates environmental factors that affect learning and distinguishes between face-to-face and technology-assisted learning. This model informs on the design of instructional interventions for physiology to encourage whole brain learning.

  9. Effect of quantum learning model in improving creativity and memory (United States)

    Sujatmika, S.; Hasanah, D.; Hakim, L. L.


    Quantum learning is a combination of many interactions that exist during learning. This model can be applied by current interesting topic, contextual, repetitive, and give opportunities to students to demonstrate their abilities. The basis of the quantum learning model are left brain theory, right brain theory, triune, visual, auditorial, kinesthetic, game, symbol, holistic, and experiential learning theory. Creativity plays an important role to be success in the working world. Creativity shows alternatives way to problem-solving or creates something. Good memory plays a role in the success of learning. Through quantum learning, students will use all of their abilities, interested in learning and create their own ways of memorizing concepts of the material being studied. From this idea, researchers assume that quantum learning models can improve creativity and memory of the students.

  10. Representing adaptive and adaptable Units of Learning. How to model personalized eLearning in IMS Learning Design

    NARCIS (Netherlands)

    Burgos, Daniel; Tattersall, Colin; Koper, Rob


    Burgos, D., Tattersall, C., & Koper, E. J. R. (2007). Representing adaptive and adaptable Units of Learning. How to model personalized eLearning in IMS Learning Design. In B. Fernández Manjon, J. M. Sanchez Perez, J. A. Gómez Pulido, M. A. Vega Rodriguez & J. Bravo (Eds.), Computers and Education:

  11. Group Modeling in Social Learning Environments (United States)

    Stankov, Slavomir; Glavinic, Vlado; Krpan, Divna


    Students' collaboration while learning could provide better learning environments. Collaboration assumes social interactions which occur in student groups. Social theories emphasize positive influence of such interactions on learning. In order to create an appropriate learning environment that enables social interactions, it is important to…

  12. Business Models for E-Learning


    Hoppe, Gabriela; Breitner, Michael H.


    E(Electronic)-learning becomes more and more important. Reasons are the paramount importance of knowledge, life-time learning, globalization and mobility. Not all providers of e-learning products succeed in closing the gap between production costs and revenues. Especially in the academic sector e-learning projects suffer more and more from decreasing funding. For many currently active research groups it is essential to market their research results, e. g. e-learning applications, in order to ...

  13. The Effect of Learning Based on Technology Model and Assessment Technique toward Thermodynamic Learning Achievement (United States)

    Makahinda, T.


    The purpose of this research is to find out the effect of learning model based on technology and assessment technique toward thermodynamic achievement by controlling students intelligence. This research is an experimental research. The sample is taken through cluster random sampling with the total respondent of 80 students. The result of the research shows that the result of learning of thermodynamics of students who taught the learning model of environmental utilization is higher than the learning result of student thermodynamics taught by simulation animation, after controlling student intelligence. There is influence of student interaction, and the subject between models of technology-based learning with assessment technique to student learning result of Thermodynamics, after controlling student intelligence. Based on the finding in the lecture then should be used a thermodynamic model of the learning environment with the use of project assessment technique.

  14. Efficient model learning methods for actor-critic control. (United States)

    Grondman, Ivo; Vaandrager, Maarten; Buşoniu, Lucian; Babuska, Robert; Schuitema, Erik


    We propose two new actor-critic algorithms for reinforcement learning. Both algorithms use local linear regression (LLR) to learn approximations of the functions involved. A crucial feature of the algorithms is that they also learn a process model, and this, in combination with LLR, provides an efficient policy update for faster learning. The first algorithm uses a novel model-based update rule for the actor parameters. The second algorithm does not use an explicit actor but learns a reference model which represents a desired behavior, from which desired control actions can be calculated using the inverse of the learned process model. The two novel methods and a standard actor-critic algorithm are applied to the pendulum swing-up problem, in which the novel methods achieve faster learning than the standard algorithm.

  15. Structural Equation Modeling towards Online Learning Readiness, Academic Motivations, and Perceived Learning (United States)

    Horzum, Mehmet Baris; Kaymak, Zeliha Demir; Gungoren, Ozlem Canan


    The relationship between online learning readiness, academic motivations, and perceived learning was investigated via structural equation modeling in the research. The population of the research consisted of 750 students who studied using the online learning programs of Sakarya University. 420 of the students who volunteered for the research and…

  16. The Effects of ePortfolio-Based Learning Model on Student Self-Regulated Learning (United States)

    Nguyen, Lap Trung; Ikeda, Mitsuru


    Self-regulated learners are aware of their knowledge and skills and proactive in learning. They view learning as a controllable process and accept more responsibility for the results of this process. The research described in this article proposes, implements, and evaluates an ePortfolio-based self-regulated learning model. An ePortfolio system…

  17. Engaging Students in Mathematical Modeling through Service-Learning (United States)

    Carducci, Olivia M.


    I have included a service-learning project in my mathematical modeling course for the last 6 years. This article describes my experience with service-learning in this course. The article includes a description of the course and the service-learning projects. There is a discussion of how to connect with community partners and identify…

  18. Stochastic Online Learning in Dynamic Networks under Unknown Models (United States)


    The key is to develop online learning strategies at each individual node. Specifically, through local information exchange with its neighbors, each...infinitely repeated game with incomplete information and developed a dynamic pricing strategy referred to as Competitive and Cooperative Demand Learning...Stochastic Online Learning in Dynamic Networks under Unknown Models This research aims to develop fundamental theories and practical algorithms for

  19. Research on Model of Student Engagement in Online Learning (United States)

    Peng, Wang


    In this study, online learning refers students under the guidance of teachers through the online learning platform for organized learning. Based on the analysis of related research results, considering the existing problems, the main contents of this paper include the following aspects: (1) Analyze and study the current student engagement model.…

  20. Stochastic collusion and the power law of learning: a general reinforcement learning model of cooperation

    NARCIS (Netherlands)

    Flache, A.


    Concerns about models of cultural adaptation as analogs of genetic selection have led cognitive game theorists to explore learning-theoretic specifications. Two prominent examples, the Bush-Mosteller stochastic learning model and the Roth-Erev payoff-matching model, are aligned and integrated as

  1. Knowledge transfer for learning robot models via local procrustes analysis

    CSIR Research Space (South Africa)

    Makondo, N


    Full Text Available Learning of robot kinematic and dynamic models from data has attracted much interest recently as an alternative to manually defined models. However, the amount of data required to learn these models becomes large when the number of degrees...

  2. A 3D Geometry Model Search Engine to Support Learning (United States)

    Tam, Gary K. L.; Lau, Rynson W. H.; Zhao, Jianmin


    Due to the popularity of 3D graphics in animation and games, usage of 3D geometry deformable models increases dramatically. Despite their growing importance, these models are difficult and time consuming to build. A distance learning system for the construction of these models could greatly facilitate students to learn and practice at different…

  3. An extended dual search space model of scientific discovery learning

    NARCIS (Netherlands)

    van Joolingen, Wouter; de Jong, Anthonius J.M.


    This article describes a theory of scientific discovery learning which is an extension of Klahr and Dunbar''s model of Scientific Discovery as Dual Search (SDDS) model. We present a model capable of describing and understanding scientific discovery learning in complex domains in terms of the SDDS

  4. Learning from video modeling examples: does gender matter?

    NARCIS (Netherlands)

    V. Hoogerheide (Vincent); S.M.M. Loyens (Sofie); T.A.J.M. van Gog (Tamara)


    textabstractOnline learning from video modeling examples, in which a human model demonstrates and explains how to perform a learning task, is an effective instructional method that is increasingly used nowadays. However, model characteristics such as gender tend to differ across videos, and the

  5. Exploring the Dimensions of E-learning Maturity Model

    Directory of Open Access Journals (Sweden)

    George Maher Iskander


    Full Text Available Despite the highlighting on e-learning, it was obvious that models for successful deployment have not yet been recognized. Even with the huge quantities of money being spent, it is not clear that any enhancement in student learning outcomes has been recognized. To address this issue, this qualitative research aimed to explore and understand dimensions of E-learning Maturity Model (ELMM. An inductive approach, using qualitative methods, was used in this research. Fifty interviewees suggested five dimensions: Students' Attitudes, University attitudes from students’ perspectives, E-learning features, E-learning implementation and Effects of E-learning on students. Students from different majors and levels participated in this study. Findings of this study show that, there are significant five factors which formulate ELMM. Moreover, the study demonstrates that e-learning features have significant effects on student. It also highlights the relevance of using qualitative research in exploring maturity concept in e- learning.

  6. Spectral Learning for Supervised Topic Models. (United States)

    Ren, Yong; Wang, Yining; Zhu, Jun


    Supervised topic models simultaneously model the latent topic structure of large collections of documents and a response variable associated with each document. Existing inference methods are based on variational approximation or Monte Carlo sampling, which often suffers from the local minimum defect. Spectral methods have been applied to learn unsupervised topic models, such as latent Dirichlet allocation (LDA), with provable guarantees. This paper investigates the possibility of applying spectral methods to recover the parameters of supervised LDA (sLDA). We first present a two-stage spectral method, which recovers the parameters of LDA followed by a power update method to recover the regression model parameters. Then, we further present a single-phase spectral algorithm to jointly recover the topic distribution matrix as well as the regression weights. Our spectral algorithms are provably correct and computationally efficient. We prove a sample complexity bound for each algorithm and subsequently derive a sufficient condition for the identifiability of sLDA. Thorough experiments on synthetic and real-world datasets verify the theory and demonstrate the practical effectiveness of the spectral algorithms. In fact, our results on a large-scale review rating dataset demonstrate that our single-phase spectral algorithm alone gets comparable or even better performance than state-of-the-art methods, while previous work on spectral methods has rarely reported such promising performance.

  7. Implications of Multimodal Learning Models for foreign language teaching and learning

    Directory of Open Access Journals (Sweden)

    Miguel Farías


    Full Text Available This literature review article approaches the topic of information and communications technologies from the perspective of their impact on the language learning process, with particular emphasis on the most appropriate designs of multimodal texts as informed by models of multimodal learning. The first part contextualizes multimodality within the fields of discourse studies, the psychology of learning and CALL; the second, deals with multimodal conceptions of reading and writing by discussing hypertextuality and literacy. A final section outlines the possible implications of multimodal learning models for foreign language teaching and learning.

  8. A model for hypermedia learning environments based on electronic books

    Directory of Open Access Journals (Sweden)

    Ignacio Aedo


    Full Text Available Current hypermedia learning environments do not have a common development basis. Their designers have often used ad-hoc solutions to solve the learning problems they have encountered. However, hypermedia technology can take advantage of employing a theoretical scheme - a model - which takes into account various kinds of learning activities, and solves some of the problems associated with its use in the learning process. The model can provide designers with the tools for creating a hypermedia learning system, by allowing the elements and functions involved in the definition of a specific application to be formally represented.

  9. The Implementation of Discovery Learning Model with Scientific Learning Approach to Improve Students’ Critical Thinking in Learning History

    Directory of Open Access Journals (Sweden)

    Edi Nurcahyo


    Full Text Available Historical learning has not reached optimal in the learning process. It is caused by the history teachers’ learning model has not used the innovative learning models. Furthermore, it supported by the perception of students to the history subject because it does not become final exam (UN subject so it makes less improvement and builds less critical thinking in students’ daily learning. This is due to the lack of awareness of historical events and the availability of history books for students and teachers in the library are still lacking. Discovery learning with scientific approach encourages students to solve problems actively and able to improve students' critical thinking skills with scientific approach so student can build scientific thinking include observing, asking, reasoning, trying, and networking   Keywords: discovery learning, scientific, critical thinking

  10. Statistical Learning Theory: Models, Concepts, and Results


    von Luxburg, Ulrike; Schoelkopf, Bernhard


    Statistical learning theory provides the theoretical basis for many of today's machine learning algorithms. In this article we attempt to give a gentle, non-technical overview over the key ideas and insights of statistical learning theory. We target at a broad audience, not necessarily machine learning researchers. This paper can serve as a starting point for people who want to get an overview on the field before diving into technical details.

  11. A Collaborative Model for Ubiquitous Learning Environments (United States)

    Barbosa, Jorge; Barbosa, Debora; Rabello, Solon


    Use of mobile devices and widespread adoption of wireless networks have enabled the emergence of Ubiquitous Computing. Application of this technology to improving education strategies gave rise to Ubiquitous e-Learning, also known as Ubiquitous Learning. There are several approaches to organizing ubiquitous learning environments, but most of them…

  12. Toward A Dual-Learning Systems Model of Speech Category Learning

    Directory of Open Access Journals (Sweden)

    Bharath eChandrasekaran


    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.



    Ana Ana; Lutfhiyah Nurlaela


    The study aims to find a model of patisserie project-based learning with production approach that can improve effectiveness of patisserie learning. Delphi Technique, Cohen's Kappa and percentages of agreements were used to assess model of patisserie project based learning. Data collection techniques employed in the study were questionnaire, check list worksheet, observation, and interview sheets. Subjects were 13 lectures of expertise food and nutrition and 91 students of Food and Nutrition ...


    Directory of Open Access Journals (Sweden)

    Jusep Saputra


    Full Text Available Self-regulated learning of learners can be achieved, if in the process of learning mathematics provides an open opportunity for students to learn independently. This research is a mixed method type embedded design, which aims to do studies focused on the use of the Problem Based Learning (PBL model assisted e-learning to student self-regulated learning. Sample selection is done on the purposive sampling and was taken 2 class contracting courses of school math III. Class A numbered 50 members, 24 the superior group and 26 the low group, given the treatment with PBL models assisted e-learning and class B numbered 50, 27 the superior group and 23 the low group, with expository. Instruments used in this research is self-regulated learning questionnaire with Likert scale. Based on data analysis we concluded that (1 Self-regulated learning of superior and low student who obtains aided PBL models assisted e-learning is better than self-regulated learning of superior and low superior students who obtain expository.

  15. A Constructionist Learning Environment for Teachers to Model Learning Designs (United States)

    Laurillard, D.; Charlton, P.; Craft, B.; Dimakopoulos, D.; Ljubojevic, D.; Magoulas, G.; Masterman, E.; Pujadas, R.; Whitley, E.A.; Whittlestone, K.


    The use of digital technologies is now widespread and increasing, but is not always optimized for effective learning. Teachers in higher education have little time or support to work on innovation and improvement of their teaching, which often means they simply replicate their current practice in a digital medium. This paper makes the case for a…

  16. A Model for Discussing the Quality of Technology-Enhanced Learning in Blended Learning Programmes (United States)

    Casanova, Diogo; Moreira, António


    This paper presents a comprehensive model for supporting informed and critical discussions concerning the quality of Technology-Enhanced Learning in Blended Learning programmes. The model aims to support discussions around domains such as how institutions are prepared, the participants' background and expectations, the course design, and the…

  17. Building entity models through observation and learning (United States)

    Garcia, Richard; Kania, Robert; Fields, MaryAnne; Barnes, Laura


    To support the missions and tasks of mixed robotic/human teams, future robotic systems will need to adapt to the dynamic behavior of both teammates and opponents. One of the basic elements of this adaptation is the ability to exploit both long and short-term temporal data. This adaptation allows robotic systems to predict/anticipate, as well as influence, future behavior for both opponents and teammates and will afford the system the ability to adjust its own behavior in order to optimize its ability to achieve the mission goals. This work is a preliminary step in the effort to develop online entity behavior models through a combination of learning techniques and observations. As knowledge is extracted from the system through sensor and temporal feedback, agents within the multi-agent system attempt to develop and exploit a basic movement model of an opponent. For the purpose of this work, extraction and exploitation is performed through the use of a discretized two-dimensional game. The game consists of a predetermined number of sentries attempting to keep an unknown intruder agent from penetrating their territory. The sentries utilize temporal data coupled with past opponent observations to hypothesize the probable locations of the opponent and thus optimize their guarding locations.

  18. Learning to Apply Models of Materials While Explaining Their Properties (United States)

    Karpin, Tiia; Juuti, Kalle; Lavonen, Jari


    Background: Applying structural models is important to chemistry education at the upper secondary level, but it is considered one of the most difficult topics to learn. Purpose: This study analyses to what extent in designed lessons students learned to apply structural models in explaining the properties and behaviours of various materials.…

  19. Integrating Collaborative and Decentralized Models to Support Ubiquitous Learning (United States)

    Barbosa, Jorge Luis Victória; Barbosa, Débora Nice Ferrari; Rigo, Sandro José; de Oliveira, Jezer Machado; Rabello, Solon Andrade, Jr.


    The application of ubiquitous technologies in the improvement of education strategies is called Ubiquitous Learning. This article proposes the integration between two models dedicated to support ubiquitous learning environments, called Global and CoolEdu. CoolEdu is a generic collaboration model for decentralized environments. Global is an…

  20. Refreshing Information Literacy: Learning from Recent British Information Literacy Models (United States)

    Martin, Justine


    Models play an important role in helping practitioners implement and promote information literacy. Over time models can lose relevance with the advances in technology, society, and learning theory. Practitioners and scholars often call for adaptations or transformations of these frameworks to articulate the learning needs in information literacy…

  1. Modelling unsupervised online-learning of artificial grammars: linking implicit and statistical learning. (United States)

    Rohrmeier, Martin A; Cross, Ian


    Humans rapidly learn complex structures in various domains. Findings of above-chance performance of some untrained control groups in artificial grammar learning studies raise questions about the extent to which learning can occur in an untrained, unsupervised testing situation with both correct and incorrect structures. The plausibility of unsupervised online-learning effects was modelled with n-gram, chunking and simple recurrent network models. A novel evaluation framework was applied, which alternates forced binary grammaticality judgments and subsequent learning of the same stimulus. Our results indicate a strong online learning effect for n-gram and chunking models and a weaker effect for simple recurrent network models. Such findings suggest that online learning is a plausible effect of statistical chunk learning that is possible when ungrammatical sequences contain a large proportion of grammatical chunks. Such common effects of continuous statistical learning may underlie statistical and implicit learning paradigms and raise implications for study design and testing methodologies. Copyright © 2014 Elsevier Inc. All rights reserved.


    Directory of Open Access Journals (Sweden)

    Oleksandra Bezverkha


    Full Text Available In the article, the acute problem of implementation of pedagogical innovations and online technologies into the educational process is analyzed. The article explores the advantages of blended learning as a latter-day educational program in comparison with traditional campus learning. Blended learning is regarded worldwide as the combination of classroom face-to-face sessions with interactive learning opportunities created online. The purpose of the article is to identify blended learning transformational potential impacting students and teachers by ensuring a more personalized learning experience. The concept of blended learning, as a means to enhance foreign language teaching and learning in the classroom during the traditional face-to-face interaction between a teacher and a student, combined with computer-mediated activities, is examined. In the article, the main classification of blended learning models is established. There are four main blended learning models which include both face-to-face instruction time and online learning: Rotation Model, Flex Model, A La Carte Model, and Enriched Virtual Model. Once implemented successfully, a blended model can take advantage of both brick-and-mortar and digital worlds, providing significant benefits for the educational establishments and learners. To integrate any of the blended learning models, a teacher can create online activities that enable learners to explore the topic online at home, and then develop face-to-face interactions to dig deeper into the subject matter at the lesson. The use of blended learning models in order to expand educational opportunities for students while the foreign language acquisition, by increasing the availability and flexibility of education, taking into account student individual learning needs, with some element of student control over time, place and pace, is explored. The realization of blended learning models in regards to age and physiological peculiarities of

  3. Intensitas Perilaku Pengguna E-learning System dengan Model Utaut


    Sari, Fatma; Purnamasari, Susan Dian


    This study aims to determine behavioral intention in the use of e-learning system using models UTAUT. The phenomenon underlying the research is: It is not yet optimal use of e-learning by students information systems in the learning process, not yet optimal socialization of the existence of e-learning, so that is not maximized and yet utilization measurability of the impact of using e-learning for lecturers.This study is limited in its scope: analysis of the influence of performance expectanc...

  4. A model of using social media for collaborative learning to enhance learners’ performance on learning

    Directory of Open Access Journals (Sweden)

    Waleed Mugahed Al-Rahmi


    Full Text Available Social media has been always described as the channel through which knowledge is transmitted between communities and learners. This social media has been utilized by colleges in a way to encourage collaborative learning and social interaction. This study explores the use of social media in the process of collaborative learning through learning Quran and Hadith. Through this investigation, different factors enhancing collaborative learning in learning Quran and Hadith in the context of using social media are going to be examined. 340 respondents participated in this study. The structural equation modeling (SEM was used to analyze the data obtained. Upon analysis and structural model validities, the study resulted in a model used for measuring the influences of the different variables. The study reported direct and indirect significant impacts of these variables on collaborative learning through the use of social media which might lead to a better performance by learners.

  5. Can model-free reinforcement learning explain deontological moral judgments? (United States)

    Ayars, Alisabeth


    Dual-systems frameworks propose that moral judgments are derived from both an immediate emotional response, and controlled/rational cognition. Recently Cushman (2013) proposed a new dual-system theory based on model-free and model-based reinforcement learning. Model-free learning attaches values to actions based on their history of reward and punishment, and explains some deontological, non-utilitarian judgments. Model-based learning involves the construction of a causal model of the world and allows for far-sighted planning; this form of learning fits well with utilitarian considerations that seek to maximize certain kinds of outcomes. I present three concerns regarding the use of model-free reinforcement learning to explain deontological moral judgment. First, many actions that humans find aversive from model-free learning are not judged to be morally wrong. Moral judgment must require something in addition to model-free learning. Second, there is a dearth of evidence for central predictions of the reinforcement account-e.g., that people with different reinforcement histories will, all else equal, make different moral judgments. Finally, to account for the effect of intention within the framework requires certain assumptions which lack support. These challenges are reasonable foci for future empirical/theoretical work on the model-free/model-based framework. Copyright © 2016 Elsevier B.V. All rights reserved.

  6. Learning general phonological rules from distributional information: a computational model. (United States)

    Calamaro, Shira; Jarosz, Gaja


    Phonological rules create alternations in the phonetic realizations of related words. These rules must be learned by infants in order to identify the phonological inventory, the morphological structure, and the lexicon of a language. Recent work proposes a computational model for the learning of one kind of phonological alternation, allophony (Peperkamp, Le Calvez, Nadal, & Dupoux, 2006). This paper extends the model to account for learning of a broader set of phonological alternations and the formalization of these alternations as general rules. In Experiment 1, we apply the original model to new data in Dutch and demonstrate its limitations in learning nonallophonic rules. In Experiment 2, we extend the model to allow it to learn general rules for alternations that apply to a class of segments. In Experiment 3, the model is further extended to allow for generalization by context; we argue that this generalization must be constrained by linguistic principles. Copyright © 2014 Cognitive Science Society, Inc.

  7. The Effect of Cooperative Learning Model of Teams Games Tournament (TGT) and Students' Motivation toward Physics Learning Outcome (United States)

    Nadrah; Tolla, Ismail; Ali, Muhammad Sidin; Muris


    This research aims at describing the effect of cooperative learning model of Teams Games Tournament (TGT) and motivation toward physics learning outcome. This research was a quasi-experimental research with a factorial design conducted at SMAN 2 Makassar. Independent variables were learning models. They were cooperative learning model of TGT and…

  8. Model brain based learning (BBL and whole brain teaching (WBT in learning

    Directory of Open Access Journals (Sweden)

    Baiq Sri Handayani


    Full Text Available The learning process is a process of change in behavior as a form of the result of learning. The learning model is a crucial component of the success of the learning process. The learning model is growing fastly, and each model has different characteristics. Teachers are required to be able to understand each model to teach the students optimally by matching the materials and the learning model. The best of the learning model is the model that based on the brain system in learning that are the model of Brain Based Learning (BBL and the model of Whole Brain Teaching (WBT. The purposes of this article are to obtain information related to (1 the brain’s natural learning system, (2 analyze the characteristics of the model BBL and WBT based on theory, brain sections that play a role associated with syntax, similarities, and differences, (3 explain the distinctive characteristics of both models in comparison to other models. The results of this study are: (1 the brain’s natural learning system are: (a the nerves in each hemisphere do not work independently, (b doing more activities can connect more brain nerves, (c the right hemisphere controls the left side motoric sensor of the body, and vice versa; (2 the characteristics of BBL and WBT are: (a BBL is based on the brain’s structure and function, while the model WBT is based on the instructional approach, neurolinguistic, and body language, (b the parts of the brain that work in BBL are: cerebellum, cerebral cortex, frontal lobe, limbic system, and prefrontal cortex; whereas the parts that work WBT are: prefrontal cortex, visual cortex, motor cortex, limbic system, and amygdala, (c the similarities between them are that they both rely on the brain’s system and they both promote gesture in learning, whereas the differences are on the view of the purposes of gestures and the learning theory that they rely on. BBL relies on cognitive theory while WBT relies on social theory; (3 the typical

  9. Use of the 5E learning cycle model combined with problem-based learning for a fundamentals of nursing course. (United States)

    Jun, Won Hee; Lee, Eun Ju; Park, Han Jong; Chang, Ae Kyung; Kim, Mi Ja


    The 5E learning cycle model has shown a positive effect on student learning in science education, particularly in courses with theory and practice components. Combining problem-based learning (PBL) with the 5E learning cycle was suggested as a better option for students' learning of theory and practice. The purpose of this study was to compare the effects of the traditional learning method with the 5E learning cycle model with PBL. The control group (n = 78) was subjected to a learning method that consisted of lecture and practice. The experimental group (n = 83) learned by using the 5E learning cycle model with PBL. The results showed that the experimental group had significantly improved self-efficacy, critical thinking, learning attitude, and learning satisfaction. Such an approach could be used in other countries to enhance students' learning of fundamental nursing. Copyright 2013, SLACK Incorporated.

  10. The "proactive" model of learning: Integrative framework for model-free and model-based reinforcement learning utilizing the associative learning-based proactive brain concept. (United States)

    Zsuga, Judit; Biro, Klara; Papp, Csaba; Tajti, Gabor; Gesztelyi, Rudolf


    Reinforcement learning (RL) is a powerful concept underlying forms of associative learning governed by the use of a scalar reward signal, with learning taking place if expectations are violated. RL may be assessed using model-based and model-free approaches. Model-based reinforcement learning involves the amygdala, the hippocampus, and the orbitofrontal cortex (OFC). The model-free system involves the pedunculopontine-tegmental nucleus (PPTgN), the ventral tegmental area (VTA) and the ventral striatum (VS). Based on the functional connectivity of VS, model-free and model based RL systems center on the VS that by integrating model-free signals (received as reward prediction error) and model-based reward related input computes value. Using the concept of reinforcement learning agent we propose that the VS serves as the value function component of the RL agent. Regarding the model utilized for model-based computations we turned to the proactive brain concept, which offers an ubiquitous function for the default network based on its great functional overlap with contextual associative areas. Hence, by means of the default network the brain continuously organizes its environment into context frames enabling the formulation of analogy-based association that are turned into predictions of what to expect. The OFC integrates reward-related information into context frames upon computing reward expectation by compiling stimulus-reward and context-reward information offered by the amygdala and hippocampus, respectively. Furthermore we suggest that the integration of model-based expectations regarding reward into the value signal is further supported by the efferent of the OFC that reach structures canonical for model-free learning (e.g., the PPTgN, VTA, and VS). (c) 2016 APA, all rights reserved).


    Directory of Open Access Journals (Sweden)

    Vasyl I. Kovalchuk


    Full Text Available The article presents three models of the use of elements of distance learning at school. All models partially or fully implement the training, interaction and collaboration of the participants in the educational process. The first model is determined by the use of open cloud services and Web 2.0 for the implementation of certain educational and managerial tasks of the school. The second model uses support for learning management and content creation. The introduction of the second model is possible with the development of the IT infrastructure of the school, the training of teachers for the use of distance learning technologies, the creation of electronic educational resources. The third model combines the use of Web 2.0 technologies and training and content management systems. Models of the use of elements of distance learning are presented of the results of regional research experimental work of schools.

  12. A Conceptual Model of eLearning Adoption

    Directory of Open Access Journals (Sweden)

    Muneer Abbad


    Full Text Available Internet-based learning systems are being used in many universities and firms but their adoption requires a solid understanding of the user acceptance processes. The technology acceptance model (TAM has been used to test the acceptance of various technologies and software within an e-learning context. This research aims to discuss the main factors of a successful e-learning adoption by students. A conceptual research framework of e-learning adoption is proposed based on the TAM model.

  13. Semantic Modelling for Learning Styles and Learning Material in an E-Learning Environment (United States)

    Alhasan, Khawla; Chen, Liming; Chen, Feng


    Various learners with various requirements have led to the raise of a crucial concern in the area of e-learning. A new technology for propagating learning to learners worldwide, has led to an evolution in the e-learning industry that takes into account all the requirements of the learning process. In spite of the wide growing, the e-learning…

  14. The effect of learning models and emotional intelligence toward students learning outcomes on reaction rate (United States)

    Sutiani, Ani; Silitonga, Mei Y.


    This research focused on the effect of learning models and emotional intelligence in students' chemistry learning outcomes on reaction rate teaching topic. In order to achieve the objectives of the research, with 2x2 factorial research design was used. There were two factors tested, namely: the learning models (factor A), and emotional intelligence (factor B) factors. Then, two learning models were used; problem-based learning/PBL (A1), and project-based learning/PjBL (A2). While, the emotional intelligence was divided into higher and lower types. The number of population was six classes containing 243 grade X students of SMAN 10 Medan, Indonesia. There were 15 students of each class were chosen as the sample of the research by applying purposive sampling technique. The data were analyzed by applying two-ways analysis of variance (2X2) at the level of significant α = 0.05. Based on hypothesis testing, there was the interaction between learning models and emotional intelligence in students' chemistry learning outcomes. Then, the finding of the research showed that students' learning outcomes in reaction rate taught by using PBL with higher emotional intelligence is higher than those who were taught by using PjBL. There was no significant effect between students with lower emotional intelligence taught by using both PBL and PjBL in reaction rate topic. Based on the finding, the students with lower emotional intelligence were quite hard to get in touch with other students in group discussion.

  15. Effectiveness of discovery learning model on mathematical problem solving (United States)

    Herdiana, Yunita; Wahyudin, Sispiyati, Ririn


    This research is aimed to describe the effectiveness of discovery learning model on mathematical problem solving. This research investigate the students' problem solving competency before and after learned by using discovery learning model. The population used in this research was student in grade VII in one of junior high school in West Bandung Regency. From nine classes, class VII B were randomly selected as the sample of experiment class, and class VII C as control class, which consist of 35 students every class. The method in this research was quasi experiment. The instrument in this research is pre-test, worksheet and post-test about problem solving of mathematics. Based on the research, it can be conclude that the qualification of problem solving competency of students who gets discovery learning model on level 80%, including in medium category and it show that discovery learning model effective to improve mathematical problem solving.

  16. Modeling learning technology systems as business systems

    NARCIS (Netherlands)

    Avgeriou, Paris; Retalis, Symeon; Papaspyrou, Nikolaos


    The design of Learning Technology Systems, and the Software Systems that support them, is largely conducted on an intuitive, ad hoc basis, thus resulting in inefficient systems that defectively support the learning process. There is now justifiable, increasing effort in formalizing the engineering


    Directory of Open Access Journals (Sweden)

    Mikhailova Elena Konstantinovna


    Full Text Available The paper is devoted to the problem of assessment of the school students’ learning success achievements. The problem is investigated from the viewpoint of assessing the students’ learning outcomes that is aimed to ensure the teachers and students with the means and conditions to improve the educational process and results.

  18. A pedagogical model for simulation-based learning in healthcare

    Directory of Open Access Journals (Sweden)

    Tuulikki Keskitalo


    Full Text Available The aim of this study was to design a pedagogical model for a simulation-based learning environment (SBLE in healthcare. Currently, simulation and virtual reality are a major focus in healthcare education. However, when and how these learning environments should be applied is not well-known. The present study tries to fill that gap. We pose the following research question: What kind of pedagogical model supports and facilitates students’ meaningful learning in SBLEs? The study used design-based research (DBR and case study approaches. We report the results from our second case study and how the pedagogical model was developed based on the lessons learned. The study involved nine facilitators and 25 students. Data were collected and analysed using mixed methods. The main result of this study is the refined pedagogical model. The model is based on the socio-cultural theory of learning and characteristics of meaningful learning as well as previous pedagogical models. The model will provide a more holistic and meaningful approach to teaching and learning in SBLEs. However, the model requires evidence and further development.


    Directory of Open Access Journals (Sweden)

    Felisa Irawani Hutabarat


    Full Text Available This research aims to know the effect of learning model of inquiry learning results students training material measurement. This type of research is quasi experiment. Sampling done by cluster random sampling by taking 2 classes from grade 9 i.e. class X SCIENCE experiments as a class-B that add up to 35 people and class X SCIENCE-C as control classes that add up to 35 people. The instruments used to find out the results of student learning is the learning outcomes tests have been validated in multiple choice form numbered 15 reserved and activity sheets students. The results of the value obtained 37.71 pretes and postest 70.11. The t-test analysis retrieved thitung greater than ttabel so that it can be concluded no difference due to the influence of the learning model of inquiry learning results students training material measurement.

  20. Group-Based Active Learning of Classification Models. (United States)

    Luo, Zhipeng; Hauskrecht, Milos


    Learning of classification models from real-world data often requires additional human expert effort to annotate the data. However, this process can be rather costly and finding ways of reducing the human annotation effort is critical for this task. The objective of this paper is to develop and study new ways of providing human feedback for efficient learning of classification models by labeling groups of examples. Briefly, unlike traditional active learning methods that seek feedback on individual examples, we develop a new group-based active learning framework that solicits label information on groups of multiple examples. In order to describe groups in a user-friendly way, conjunctive patterns are used to compactly represent groups. Our empirical study on 12 UCI data sets demonstrates the advantages and superiority of our approach over both classic instance-based active learning work, as well as existing group-based active-learning methods.

  1. General informatics teaching with B-Learning teaching model

    Directory of Open Access Journals (Sweden)

    Nguyen The Dung


    Full Text Available Blended learning (B-learning, a combination of face-to-face teaching and E-learning-supported-teaching in an online course, and Information and Communication Technology (ICT tools have been studied in recent years. In addition, the use of this teaching model is effective in teaching and learning conditions in which some certain subjects are appropriate for the specific teaching context. As it has been a matter of concern of the universities in Vietnam today, deep studies related to this topic is crucial to be conducted. In this article, the process of developing online courses and organizing teaching for the General Informatics subject for first-year students at the Hue University of Education with B-learning teaching model will be presented. The combination of 60% face-to-face and 40% online learning.

  2. Learning Behavior Models for Interpreting and Predicting Traffic Situations


    Gindele, Tobias


    In this thesis, we present Bayesian state estimation and machine learning methods for predicting traffic situations. The cognitive ability to assess situations and behaviors of traffic participants, and to anticipate possible developments is an essential requirement for several applications in the traffic domain, especially for self-driving cars. We present a method for learning behavior models from unlabeled traffic observations and develop improved learning methods for decision trees.

  3. Safe robot execution in model-based reinforcement learning


    Martínez Martínez, David; Alenyà Ribas, Guillem; Torras, Carme


    Task learning in robotics requires repeatedly executing the same actions in different states to learn the model of the task. However, in real-world domains, there are usually sequences of actions that, if executed, may produce unrecoverable errors (e.g. breaking an object). Robots should avoid repeating such errors when learning, and thus explore the state space in a more intelligent way. This requires identifying dangerous action effects to avoid including such actions in the generated plans...

  4. Hybrid Model for e-Learning Quality Evaluation

    Directory of Open Access Journals (Sweden)

    Suzana M. Savic


    Full Text Available E-learning is becoming increasingly important for the competitive advantage of economic organizations and higher education institutions. Therefore, it is becoming a significant aspect of quality which has to be integrated into the management system of every organization or institution. The paper examines e-learning quality characteristics, standards, criteria and indicators and presents a multi-criteria hybrid model for e-learning quality evaluation based on the method of Analytic Hierarchy Process, trend analysis, and data comparison.

  5. Distributional Language Learning: Mechanisms and Models of ategory Formation. (United States)

    Aslin, Richard N; Newport, Elissa L


    In the past 15 years, a substantial body of evidence has confirmed that a powerful distributional learning mechanism is present in infants, children, adults and (at least to some degree) in nonhuman animals as well. The present article briefly reviews this literature and then examines some of the fundamental questions that must be addressed for any distributional learning mechanism to operate effectively within the linguistic domain. In particular, how does a naive learner determine the number of categories that are present in a corpus of linguistic input and what distributional cues enable the learner to assign individual lexical items to those categories? Contrary to the hypothesis that distributional learning and category (or rule) learning are separate mechanisms, the present article argues that these two seemingly different processes---acquiring specific structure from linguistic input and generalizing beyond that input to novel exemplars---actually represent a single mechanism. Evidence in support of this single-mechanism hypothesis comes from a series of artificial grammar-learning studies that not only demonstrate that adults can learn grammatical categories from distributional information alone, but that the specific patterning of distributional information among attested utterances in the learning corpus enables adults to generalize to novel utterances or to restrict generalization when unattested utterances are consistently absent from the learning corpus. Finally, a computational model of distributional learning that accounts for the presence or absence of generalization is reviewed and the implications of this model for linguistic-category learning are summarized.

  6. Measuring organizational learning. Model testing in two Romanian universities


    Alexandra Luciana Guţă


    The scientific literature associates organizational learning with superior organization performance. If we refer to the academic environment, we appreciate that it can develop and reach better levels of performance through changes driven from the inside. Thus, through this paper we elaborate on a conceptual model of organizational learning and we test the model on a sample of employees (university teachers and researchers) from two Romanian universities. The model comprises the process of org...

  7. Statistical and Machine Learning Models to Predict Programming Performance


    Bergin, Susan


    This thesis details a longitudinal study on factors that influence introductory programming success and on the development of machine learning models to predict incoming student performance. Although numerous studies have developed models to predict programming success, the models struggled to achieve high accuracy in predicting the likely performance of incoming students. Our approach overcomes this by providing a machine learning technique, using a set of three significant...

  8. Personalised learning object based on multi-agent model and learners’ learning styles

    Directory of Open Access Journals (Sweden)

    Noppamas Pukkhem


    Full Text Available A multi-agent model is proposed in which learning styles and a word analysis technique to create a learning object recommendation system are used. On the basis of a learning style-based design, a concept map combination model is proposed to filter out unsuitable learning concepts from a given course. Our learner model classifies learners into eight styles and implements compatible computational methods consisting of three recommendations: i non-personalised, ii preferred feature-based, and iii neighbour-based collaborative filtering. The analysis of preference error (PE was performed by comparing the actual preferred learning object with the predicted one. In our experiments, the feature-based recommendation algorithm has the fewest PE.


    Directory of Open Access Journals (Sweden)

    Ratna Malawati


    Full Text Available This study aims to improve the physics Science Process Skills Students on cognitive and psychomotor aspects by using model based Project Based Learning training.The object of this study is the Project Based Learning model used in the learning process of Computationa Physics.The method used is classroom action research through two learning cycles, each cycle consisting of the stages of planning, implementation, observation and reflection. In the first cycle of treatment with their emphasis given training in the first phase up to third in the model Project Based Learning, while the second cycle is given additional treatment with emphasis discussion is collaboration in achieving the best results for each group of products. The results of data analysis showed increased ability to think Students on cognitive and Science Process Skills in the psychomotor.

  10. Learning to Learn: towards a Relational and Transformational Model of Learning for Improved Integrated Care Delivery

    Directory of Open Access Journals (Sweden)

    John Diamond


    Full Text Available Health and social care systems are implementing fundamental changes to organizational structures and work practices in an effort to achieve integrated care. While some integration initiatives have produced positive outcomes, many have not. We reframe the concept of integration as a learning process fueled by knowledge exchange across diverse professional and organizational communities. We thus focus on the cognitive and social dynamics of learning in complex adaptive systems, and on learning behaviours and conditions that foster collective learning and improved collaboration. We suggest that the capacity to learn how to learn shapes the extent to which diverse professional groups effectively exchange knowledge and self-organize for integrated care delivery.

  11. A Technology Enhanced Learning Model for Quality Education (United States)

    Sherly, Elizabeth; Uddin, Md. Meraj

    Technology Enhanced Learning and Teaching (TELT) Model provides learning through collaborations and interactions with a framework for content development and collaborative knowledge sharing system as a supplementary for learning to improve the quality of education system. TELT deals with a unique pedagogy model for Technology Enhanced Learning System which includes course management system, digital library, multimedia enriched contents and video lectures, open content management system and collaboration and knowledge sharing systems. Open sources like Moodle and Wiki for content development, video on demand solution with a low cost mid range system, an exhaustive digital library are provided in a portal system. The paper depicts a case study of e-learning initiatives with TELT model at IIITM-K and how effectively implemented.

  12. Learning Methods for Dynamic Topic Modeling in Automated Behavior Analysis. (United States)

    Isupova, Olga; Kuzin, Danil; Mihaylova, Lyudmila


    Semisupervised and unsupervised systems provide operators with invaluable support and can tremendously reduce the operators' load. In the light of the necessity to process large volumes of video data and provide autonomous decisions, this paper proposes new learning algorithms for activity analysis in video. The activities and behaviors are described by a dynamic topic model. Two novel learning algorithms based on the expectation maximization approach and variational Bayes inference are proposed. Theoretical derivations of the posterior estimates of model parameters are given. The designed learning algorithms are compared with the Gibbs sampling inference scheme introduced earlier in the literature. A detailed comparison of the learning algorithms is presented on real video data. We also propose an anomaly localization procedure, elegantly embedded in the topic modeling framework. It is shown that the developed learning algorithms can achieve 95% success rate. The proposed framework can be applied to a number of areas, including transportation systems, security, and surveillance.

  13. Learning and evolution in games and oligopoly models

    NARCIS (Netherlands)

    Possajennikov, A.


    Dynamic models of adjustment, as well as static models of equilibrium, are important to understand economic reality. This thesis considers such dynamic models applied to economic games. The models can broadly be divided into two categories: learning and evolution. This thesis analyzes reinforcement

  14. Learning of Chemical Equilibrium through Modelling-Based Teaching (United States)

    Maia, Poliana Flavia; Justi, Rosaria


    This paper presents and discusses students' learning process of chemical equilibrium from a modelling-based approach developed from the use of the "Model of Modelling" diagram. The investigation was conducted in a regular classroom (students 14-15 years old) and aimed at discussing how modelling-based teaching can contribute to students…

  15. Computational Modeling of Statistical Learning: Effects of Transitional Probability versus Frequency and Links to Word Learning (United States)

    Mirman, Daniel; Estes, Katharine Graf; Magnuson, James S.


    Statistical learning mechanisms play an important role in theories of language acquisition and processing. Recurrent neural network models have provided important insights into how these mechanisms might operate. We examined whether such networks capture two key findings in human statistical learning. In Simulation 1, a simple recurrent network…

  16. Grasping the Dynamic Complexity of Team Learning: An Integrative Model for Effective Team Learning in Organisations (United States)

    Decuyper, Stefan; Dochy, Filip; Van den Bossche, Piet


    In this article we present an integrative model of team learning. Literature shows that effective team learning requires the establishment of a dialogical space amongst team members, in which communicative behaviours such as "sharing", "co-construction" and "constructive conflict" are balanced. However, finding this balance is not enough.…

  17. Computer-Mediated Intersensory Learning Model for Students with Learning Disabilities (United States)

    Seok, Soonhwa; DaCosta, Boaventura; Kinsell, Carolyn; Poggio, John C.; Meyen, Edward L.


    This article proposes a computer-mediated intersensory learning model as an alternative to traditional instructional approaches for students with learning disabilities (LDs) in the inclusive classroom. Predominant practices of classroom inclusion today reflect the six principles of zero reject, nondiscriminatory evaluation, appropriate education,…

  18. Semi-supervised Learning with Deep Generative Models

    NARCIS (Netherlands)

    Kingma, D.P.; Rezende, D.J.; Mohamed, S.; Welling, M.


    The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant practical importance in modern data analysis. We revisit the approach to semi-supervised learning with generative models and

  19. A Rotational Blended Learning Model: Enhancement and Quality Assurance (United States)

    Ghoul, Said


    Research on blended learning theory and practice is growing nowadays with a focus on the development, evaluation, and quality assurance of case studies. However, the enhancement of blended learning existing models, the specification of their online parts, and the quality assurance related specifically to them have not received enough attention.…

  20. Towards a Social Networks Model for Online Learning & Performance (United States)

    Chung, Kon Shing Kenneth; Paredes, Walter Christian


    In this study, we develop a theoretical model to investigate the association between social network properties, "content richness" (CR) in academic learning discourse, and performance. CR is the extent to which one contributes content that is meaningful, insightful and constructive to aid learning and by social network properties we…

  1. A Computer Model of Simple Forms of Learning. (United States)

    Jones, Thomas L.

    A basic unsolved problem in science is that of understanding learning, the process by which people and machines use their experience in a situation to guide future action in similar situations. The ideas of Piaget, Pavlov, Hull, and other learning theorists, as well as previous heuristic programing models of human intelligence, stimulated this…

  2. Service learning in teacher education: an institutional model for an ...

    African Journals Online (AJOL)

    Interest in service learning is growing at a time of curriculum change in teacher education and institutional change in higher education in South Africa. This raises the question ";What models are available to guide institutions to develop service learning?"; This article outlines Pollack's typology of institutional responses to ...

  3. A Pedagogical Model for Science Education through Blended Learning

    NARCIS (Netherlands)

    Bidarra, José; Rusman, Ellen


    This paper proposes a framework to support science education through blended learning, based on a participatory and interactive approach supported by ICT-based tools, called Science Learning Activities Model (SLAM). The study constitutes a work in progress and started as a response to complex


    Directory of Open Access Journals (Sweden)

    Mar’atus Sholihah


    Full Text Available The purpose of this study was to determine the effect of Experiential Learning models developed by Kolb's theory of the critical thinking skills of high school students. This study uses a quasi experiment conducted in SMA Assa'adah Gresik. The population of students of class X IS second semester of academic year 2015/2016. Samples are 2 classes that are homogeneous. Methods of data collection using test questions and the ability to think critically using observation sheet. Data were analyzed by comparing the average acquisition value of critical thinking skills with experimental class control class. Average value of the critical thinking skills using model Experiential Learning higher at 80.9 while the control class is 71.2. Based on the average it can be concluded that the learning model of Experiential Learning can improve students' critical thinking skills. This study is expected to provide information on the application and benefits of the model Experiential Learning in teaching geography and make it more meaningful for students. Tujuan dari penelitian ini adalah mengetahui pengaruh model Experiential Learning yang dikembangkan oleh teori Kolb terhadap kemampuan berpikir kritis siswa SMA. Penelitian ini menggunakan metode quasi experimen yang dilakukan di SMA Assa’adah Gresik. Populasi siswa kelas X IS semester genap tahun pelajaran 2015/2016. Sampel yang digunakan sebanyak 2 kelas yang bersifat homogen. Metode pengumpulan data menggunakan soal tes kemampuan berpikir kritis serta menggunakan lembar observasi. Data yang diperoleh kemudian dianalisis dengan membandingkan rata-rata perolehan nilai kemampuan berpikir kritis kelas kontrol dengan kelas eksperimen. Nilai rata rata kemampuan berpikir kritis yang menggunakan model pembelajaran Experiential Learning lebih tinggi, yaitu sebesar 80,9, sedangkan kelas kontrol sebesar 71,2. Berdasarkan nilai rata-rata tersebut dapat disimpulkan bahwa model pembelajaran Experiential Learning dapat

  5. On the Conditioning of Machine-Learning-Assisted Turbulence Modeling (United States)

    Wu, Jinlong; Sun, Rui; Wang, Qiqi; Xiao, Heng


    Recently, several researchers have demonstrated that machine learning techniques can be used to improve the RANS modeled Reynolds stress by training on available database of high fidelity simulations. However, obtaining improved mean velocity field remains an unsolved challenge, restricting the predictive capability of current machine-learning-assisted turbulence modeling approaches. In this work we define a condition number to evaluate the model conditioning of data-driven turbulence modeling approaches, and propose a stability-oriented machine learning framework to model Reynolds stress. Two canonical flows, the flow in a square duct and the flow over periodic hills, are investigated to demonstrate the predictive capability of the proposed framework. The satisfactory prediction performance of mean velocity field for both flows demonstrates the predictive capability of the proposed framework for machine-learning-assisted turbulence modeling. With showing the capability of improving the prediction of mean flow field, the proposed stability-oriented machine learning framework bridges the gap between the existing machine-learning-assisted turbulence modeling approaches and the demand of predictive capability of turbulence models in real applications.

  6. Inclusion Community Model: Learning from Bali

    Directory of Open Access Journals (Sweden)

    David Samiyono


    Full Text Available AbstrakKonflik sering muncul ketika manusia bertindak secara ekslusif dengan hanya melihat diri sendiri dan kelompoknya. Beberapa tokoh pluralisme membuat konsep mengenai masyarakat inklusif dengan tujuan mengurangi terjadinya konflik. Nagara Indonesia memiliki potensi besar terjadinya konflik, hal ini disebabkan karena negara Indonesia terdiri dari berbagai suku, budaya dan agama. Apabila konflik tidak dikelola, maka potensi terjadinya dis-integrasi bangsa sangat besar. Meskipun hal ini dapat juga dilihat sebagai kekayaan bangsa, model masyarakat inklusif diperlukan bagi bangsa Indonesiasebagai alat pemersatu yang harus dipahami dan diajarkan dari generasi satu kepada generasi berikutnya.Dalam penelitian ini menggunakan pendekatan diskriptif-kualitatif yang sesuai dengan kondisi lokasi penelitian yaitu Bali dan Lampung. Analisis dilakukan melalui narasi dengan menggunakan informasi yang diperoleh dari informan atau partisipan. Hasil penelitian menunjukkan adanya nilai-nilai inklusif dalam budaya masyarakat Bali yang tinggal di Pulau Bali. Masyarakat Bali yang sudah bergaul dengan berbagaibudaya, agama, politik dan ekonomi. Oleh karena itu model masyarakat inklusif dari kasus masyarakat Bali perlu dilakukan dalam usaha untuk bisa diuji-cobakan pada masyarakat yang berbeda, terutama pada wailayah negara Indonesia yang majemuk.Kata kunci: Bali, Inclussion community, menyama braya. AbstractConflict often occurs when people behave closed and exclusive by looking at himself and his group. Some authors propose the concept of inclusion community to reduce the conflict and towards a harmonious society. Indonesia has a huge potential for conflict to happendue to the number of tribe, religion, race and class, but on the other hand it has had a noble wealth in society, which needs to be exposed and arranged to become a teaching material  for future generations. That is why this research is done. This research uses descriptive qualitative

  7. mLearning Scaffolding Model for Undergraduate English Language Learning: Bridging Formal and Informal Learning (United States)

    Abdullah, Muhammad Ridhuan Tony Lim; Hussin, Zaharah; Asra; Zakaria, Abd Razak


    Learning using mobile devices also known as mLearning is the current buzz word in the present debates over the use of technology in education. Although mLearning has a high prospect for future education, it is yet to be

  8. Social Learning Network Analysis Model to Identify Learning Patterns Using Ontology Clustering Techniques and Meaningful Learning (United States)

    Firdausiah Mansur, Andi Besse; Yusof, Norazah


    Clustering on Social Learning Network still not explored widely, especially when the network focuses on e-learning system. Any conventional methods are not really suitable for the e-learning data. SNA requires content analysis, which involves human intervention and need to be carried out manually. Some of the previous clustering techniques need…

  9. Modeling learning and memory using verbal learning tests: results from ACTIVE. (United States)

    Gross, Alden L; Rebok, George W; Brandt, Jason; Tommet, Doug; Marsiske, Michael; Jones, Richard N


    To investigate the influence of memory training on initial recall and learning. The Advanced Cognitive Training for Independent and Vital Elderly study of community-dwelling adults older than age 65 (n = 1,401). We decomposed trial-level recall in the Auditory Verbal Learning Test (AVLT) and Hopkins Verbal Learning Test (HVLT) into initial recall and learning across trials using latent growth models. Trial-level increases in words recalled in the AVLT and HVLT at each follow-up visit followed an approximately logarithmic shape. Over the 5-year study period, memory training was associated with slower decline in Trial 1 AVLT recall (Cohen's d = 0.35, p = .03) and steep pre- and posttraining acceleration in learning (d = 1.56, p learning, d = 3.10, p memory-trained group had a higher level of recall than the control group through the end of the 5-year study period despite faster decline in learning. This study contributes to the understanding of the mechanisms by which training benefits memory and expands current knowledge by reporting long-term changes in initial recall and learning, as measured from growth models and by characterization of the impact of memory training on these components. Results reveal that memory training delays the worsening of memory span and boosts learning.

  10. The structure of observed learning outcome (SOLO) taxonomy: a model to promote dental students' learning. (United States)

    Lucander, H; Bondemark, L; Brown, G; Knutsson, K


    Selective memorising of isolated facts or reproducing what is thought to be required - the surface approach to learning - is not the desired outcome for a dental student or a dentist in practice. The preferred outcome is a deep approach as defined by an intention to seek understanding, develop expertise and relate information and knowledge into a coherent whole. The aim of this study was to investigate whether the structure of observed learning outcome (SOLO) taxonomy could be used as a model to assist and promote the dental students to develop a deep approach to learning assessed as learning outcomes in a summative assessment. Thirty-two students, participating in course eight in 2007 at the Faculty of Odontology at Malmö University, were introduced to the SOLO taxonomy and constituted the test group. The control group consisted of 35 students participating in course eight in 2006. The effect of the introduction was measured by evaluating responses to a question in the summative assessment by using the SOLO taxonomy. The evaluators consisted of two teachers who performed the assessment of learning outcomes independently and separately on the coded material. The SOLO taxonomy as a model for learning was found to improve the quality of learning. Compared to the control group significantly more strings and structured relations between these strings were present in the test group after the SOLO taxonomy had been introduced (P SOLO taxonomy is recommended as a model for promoting and developing a deeper approach to learning in dentistry.

  11. Benchmarking Deep Learning Models on Large Healthcare Datasets. (United States)

    Purushotham, Sanjay; Meng, Chuizheng; Che, Zhengping; Liu, Yan


    Deep learning models (aka Deep Neural Networks) have revolutionized many fields including computer vision, natural language processing, speech recognition, and is being increasingly used in clinical healthcare applications. However, few works exist which have benchmarked the performance of the deep learning models with respect to the state-of-the-art machine learning models and prognostic scoring systems on publicly available healthcare datasets. In this paper, we present the benchmarking results for several clinical prediction tasks such as mortality prediction, length of stay prediction, and ICD-9 code group prediction using Deep Learning models, ensemble of machine learning models (Super Learner algorithm), SAPS II and SOFA scores. We used the Medical Information Mart for Intensive Care III (MIMIC-III) (v1.4) publicly available dataset, which includes all patients admitted to an ICU at the Beth Israel Deaconess Medical Center from 2001 to 2012, for the benchmarking tasks. Our results show that deep learning models consistently outperform all the other approaches especially when the 'raw' clinical time series data is used as input features to the models. Copyright © 2018 Elsevier Inc. All rights reserved.

  12. Predicting Market Impact Costs Using Nonparametric Machine Learning Models.

    Directory of Open Access Journals (Sweden)

    Saerom Park

    Full Text Available Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.

  13. Predicting Market Impact Costs Using Nonparametric Machine Learning Models. (United States)

    Park, Saerom; Lee, Jaewook; Son, Youngdoo


    Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.

  14. Flipped classroom model for learning evidence-based medicine. (United States)

    Rucker, Sydney Y; Ozdogan, Zulfukar; Al Achkar, Morhaf


    Journal club (JC), as a pedagogical strategy, has long been used in graduate medical education (GME). As evidence-based medicine (EBM) becomes a mainstay in GME, traditional models of JC present a number of insufficiencies and call for novel models of instruction. A flipped classroom model appears to be an ideal strategy to meet the demands to connect evidence to practice while creating engaged, culturally competent, and technologically literate physicians. In this article, we describe a novel model of flipped classroom in JC. We present the flow of learning activities during the online and face-to-face instruction, and then we highlight specific considerations for implementing a flipped classroom model. We show that implementing a flipped classroom model to teach EBM in a residency program not only is possible but also may constitute improved learning opportunity for residents. Follow-up work is needed to evaluate the effectiveness of this model on both learning and clinical practice.

  15. Knowledge Management through the Equilibrium Pattern Model for Learning (United States)

    Sarirete, Akila; Noble, Elizabeth; Chikh, Azeddine

    Contemporary students are characterized by having very applied learning styles and methods of acquiring knowledge. This behavior is consistent with the constructivist models where students are co-partners in the learning process. In the present work the authors developed a new model of learning based on the constructivist theory coupled with the cognitive development theory of Piaget. The model considers the level of learning based on several stages and the move from one stage to another requires learners' challenge. At each time a new concept is introduced creates a disequilibrium that needs to be worked out to return back to its equilibrium stage. This process of "disequilibrium/equilibrium" has been analyzed and validated using a course in computer networking as part of Cisco Networking Academy Program at Effat College, a women college in Saudi Arabia. The model provides a theoretical foundation for teaching especially in a complex knowledge domain such as engineering and can be used in a knowledge economy.

  16. Probabilistic models and machine learning in structural bioinformatics

    DEFF Research Database (Denmark)

    Hamelryck, Thomas


    . Recently, probabilistic models and machine learning methods based on Bayesian principles are providing efficient and rigorous solutions to challenging problems that were long regarded as intractable. In this review, I will highlight some important recent developments in the prediction, analysis...

  17. Ontology Update in the Cognitive Model of Ontology Learning

    Directory of Open Access Journals (Sweden)

    Zhang De-Hai


    Full Text Available Ontology has been used in many hot-spot fields, but most ontology construction methods are semiautomatic, and the construction process of ontology is still a tedious and painstaking task. In this paper, a kind of cognitive models is presented for ontology learning which can simulate human being’s learning from world. In this model, the cognitive strategies are applied with the constrained axioms. Ontology update is a key step when the new knowledge adds into the existing ontology and conflict with old knowledge in the process of ontology learning. This proposal designs and validates the method of ontology update based on the axiomatic cognitive model, which include the ontology update postulates, axioms and operations of the learning model. It is proved that these operators subject to the established axiom system.

  18. The behavioural motivation model in open distance learning

    DEFF Research Database (Denmark)

    Zaikin, Oleg; Malinowska, Magdalena; Kofoed, Lise B.


    The article contains the concept of developing a motivation model aimed at supporting activity of both students and teachers in the process of implementing and using an open and distance learning system. Proposed motivation model is focused on the task of filling the knowledge repository with high...... quality didactic material. Open and distance learning system assures a computer space for the teaching/learning process in open environment. The structure of the motivation model and formal assumptions are described. Additionally, there is presented a structure of the linguistic database, helping...... the teacher to assess the student's motivation and the basic simulation model to analysis the teaching/learning process constrains. The proposed approach is based on the games theory and simulation approach....

  19. Game Based Learning (GBL) Adoption Model for Universities ...

    African Journals Online (AJOL)



    Mar 5, 2018 ... faced while adopting Game Based Learning (GBL) model, its benefits and ... preferred traditional lectures styles, 7% online class and. 34% preferred .... students in developing problem-solving skills which in return may help ...

  20. Semantic modelling for learning styles and learning material in an e-learning environment


    Alhasan, K.; Chen, Liming; Chen, Feng


    The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the URI link. Various learners with various requirements have led to the raise of a crucial concern in the area of e-learning. A new technology for propagating learning to learners worldwide, has led to an evolution in the e-learning industry that takes into account all the requirements of the learning process. In spite of the wide growing, the e-learning te...

  1. A machine learning model with human cognitive biases capable of learning from small and biased datasets. (United States)

    Taniguchi, Hidetaka; Sato, Hiroshi; Shirakawa, Tomohiro


    Human learners can generalize a new concept from a small number of samples. In contrast, conventional machine learning methods require large amounts of data to address the same types of problems. Humans have cognitive biases that promote fast learning. Here, we developed a method to reduce the gap between human beings and machines in this type of inference by utilizing cognitive biases. We implemented a human cognitive model into machine learning algorithms and compared their performance with the currently most popular methods, naïve Bayes, support vector machine, neural networks, logistic regression and random forests. We focused on the task of spam classification, which has been studied for a long time in the field of machine learning and often requires a large amount of data to obtain high accuracy. Our models achieved superior performance with small and biased samples in comparison with other representative machine learning methods.

  2. The Influence Of Learning Model Guided Findings Of Student Learning Outcomes

    Directory of Open Access Journals (Sweden)

    A. SaefulBahri


    Full Text Available Abstract This study examines the influence of the learning model guided findings on student learning outcomes in subjects PAI eighth grade students of SMP Plus al Masoem. The research method used in this study is a quantitative method in the form of quasi-experiment Quasi-Experimental Design. The findings of the study are expected to demonstrate 1 the difference significant increase in learning outcomes between the experimental class using guided discovery method that uses the control class discussion of learning models 2 Constraints in the method of guided discovery activities and the limited ability of educators in the experimental class in implements the method of guided discovery and constraints faced by students while digging the information they need so we need special strategies to motivate students in the experimental class in order for them creatively find the right way to gather information that supports learning PAI.

  3. Active Learning of Classification Models with Likert-Scale Feedback. (United States)

    Xue, Yanbing; Hauskrecht, Milos


    Annotation of classification data by humans can be a time-consuming and tedious process. Finding ways of reducing the annotation effort is critical for building the classification models in practice and for applying them to a variety of classification tasks. In this paper, we develop a new active learning framework that combines two strategies to reduce the annotation effort. First, it relies on label uncertainty information obtained from the human in terms of the Likert-scale feedback. Second, it uses active learning to annotate examples with the greatest expected change. We propose a Bayesian approach to calculate the expectation and an incremental SVM solver to reduce the time complexity of the solvers. We show the combination of our active learning strategy and the Likert-scale feedback can learn classification models more rapidly and with a smaller number of labeled instances than methods that rely on either Likert-scale labels or active learning alone.

  4. Knowledge models as agents of meaninful learning and knowledge creation.


    Fermín María González García; Jorge Fernando Veloz Ortiz; Iovanna Alejandra Rodríguez Moreno; Luis Efrén Velos Ortiz; Beatriz Guardián Soto; Antoni Ballester Valori


    The educational change that pushes the current context requires a shift in the unfortunately predominant positivist-behaviourist model that favours mechanical      memoristic learning, ideal breeding ground for the existence and maintenance of conceptual errors, to another cognitive-constructivist that stimulates meaningful learning to allow students to build and master knowledge, therefore to be more creative and critical. We present here a model of knowledge where students construct new...

  5. Collaborative learning model inquiring based on digital game (United States)

    Yuan, Jiugen; Xing, Ruonan


    With the development of computer education software, digital educational game has become an important part in our life, entertainment and education. Therefore how to make full use of digital game's teaching functions and educate through entertainment has become the focus of current research. The thesis make a connection between educational game and collaborative learning, the current popular teaching model, and concludes digital game-based collaborative learning model combined with teaching practice.

  6. Continuous Online Sequence Learning with an Unsupervised Neural Network Model. (United States)

    Cui, Yuwei; Ahmad, Subutar; Hawkins, Jeff


    The ability to recognize and predict temporal sequences of sensory inputs is vital for survival in natural environments. Based on many known properties of cortical neurons, hierarchical temporal memory (HTM) sequence memory recently has been proposed as a theoretical framework for sequence learning in the cortex. In this letter, we analyze properties of HTM sequence memory and apply it to sequence learning and prediction problems with streaming data. We show the model is able to continuously learn a large number of variableorder temporal sequences using an unsupervised Hebbian-like learning rule. The sparse temporal codes formed by the model can robustly handle branching temporal sequences by maintaining multiple predictions until there is sufficient disambiguating evidence. We compare the HTM sequence memory with other sequence learning algorithms, including statistical methods: autoregressive integrated moving average; feedforward neural networks-time delay neural network and online sequential extreme learning machine; and recurrent neural networks-long short-term memory and echo-state networks on sequence prediction problems with both artificial and real-world data. The HTM model achieves comparable accuracy to other state-of-the-art algorithms. The model also exhibits properties that are critical for sequence learning, including continuous online learning, the ability to handle multiple predictions and branching sequences with high-order statistics, robustness to sensor noise and fault tolerance, and good performance without task-specific hyperparameter tuning. Therefore, the HTM sequence memory not only advances our understanding of how the brain may solve the sequence learning problem but is also applicable to real-world sequence learning problems from continuous data streams.

  7. Motivation to Improve Work through Learning: A Conceptual Model

    Directory of Open Access Journals (Sweden)

    Kueh Hua Ng


    Full Text Available This study aims to enhance our current understanding of the transfer of training by proposing a conceptual model that supports the mediating role of motivation to improve work through learning about the relationship between social support and the transfer of training. The examination of motivation to improve work through motivation to improve work through a learning construct offers a holistic view pertaining to a learner's profile in a workplace setting, which emphasizes learning for the improvement of work performance. The proposed conceptual model is expected to benefit human resource development theory building, as well as field practitioners by emphasizing the motivational aspects crucial for successful transfer of training.

  8. A theoretical design for learning model addressing the networked society

    DEFF Research Database (Denmark)

    Levinsen, Karin; Nielsen, Janni; Sørensen, Birgitte Holm


    The transition from the industrial to the networked society produces contradictions that challenges the educational system and force it to adapt to new conditions. In a Danish virtual Master in Information and Communication Technologies and Learning (MIL) these contradictions appear as a field of...... which enables students to develop Networked Society competencies and maintain progression in the learning process also during the online periods. Additionally we suggest that our model contributes to the innovation of a networked society's design for learning....... is continuously decreasing. We teach for deep learning but are confronted by students' cost-benefit strategies when they navigate through the study programme under time pressure. To meet these challenges a Design for Learning Model has been developed. The aim is to provide a scaffold that ensures students......' acquisition of the subject matter within a time limit and at a learning quality that support their deep learning process during a subsequent period of on-line study work. In the process of moving from theory to application the model passes through three stages: 1) Conceptual modelling; 2) Orchestration, and 3...

  9. Technological learning in energy-environment-economy modelling: A survey

    International Nuclear Information System (INIS)

    Kahouli-Brahmi, Sondes


    This paper aims at providing an overview and a critical analysis of the technological learning concept and its incorporation in energy-environment-economy models. A special emphasis is put on surveying and discussing, through the so-called learning curve, both studies estimating learning rates in the energy field and studies incorporating endogenous technological learning in bottom-up and top-down models. The survey of learning rate estimations gives special attention to interpreting and explaining the sources of variability of estimated rates, which is shown to be mainly inherent in R and D expenditures, the problem of omitted variable bias, the endogeneity relationship and the role of spillovers. Large-scale models survey show that, despite some methodological and computational complexity related to the non-linearity and the non-convexity associated with the learning curve incorporation, results of the numerous modelling experiments give several new insights with regard to the analysis of the prospects of specific technological options and their cost decrease potential (bottom-up models), and with regard to the analysis of strategic considerations, especially inherent in the innovation and energy diffusion process, in particular the energy sector's endogenous responses to environment policy instruments (top-down models)

  10. Effects of Problem-Based Learning Model versus Expository Model and Motivation to Achieve for Student's Physic Learning Result of Senior High School at Class XI (United States)



    "Problem-based learning" (PBL) is one of an innovative learning model which can provide an active learning to student, include the motivation to achieve showed by student when the learning is in progress. This research is aimed to know: (1) differences of physic learning result for student group which taught by PBL versus expository…

  11. Expert Systems as a Mindtool To Facilitate Mental Model Learning. (United States)

    Mason-Mason, Susan Dale; Tessmer, Martin A.


    This exploratory study investigated whether the process of constructing an expert system model promotes the formation of expert-like mental models. Discusses expert systems as mindtools, expert systems as learning tools, the assessment of mental models, results of pretests and posttests, and future research. (Contains 56 references.) (Author/LRW)

  12. Proof of Economic Viability of Blended Learning Business Models (United States)

    Druhmann, Carsten; Hohenberg, Gregor


    The discussion on economically sustainable business models with respect to information technology is lacking in many aspects of proven approaches. In the following contribution the economic viability is valued based on a procedural model for design and evaluation of e-learning business models in the form of a case study. As a case study object a…

  13. Toward a Generative Model of the Teaching-Learning Process. (United States)

    McMullen, David W.

    Until the rise of cognitive psychology, models of the teaching-learning process (TLP) stressed external rather than internal variables. Models remained general descriptions until control theory introduced explicit system analyses. Cybernetic models emphasize feedback and adaptivity but give little attention to creativity. Research on artificial…

  14. Probability Modeling and Thinking: What Can We Learn from Practice? (United States)

    Pfannkuch, Maxine; Budgett, Stephanie; Fewster, Rachel; Fitch, Marie; Pattenwise, Simeon; Wild, Chris; Ziedins, Ilze


    Because new learning technologies are enabling students to build and explore probability models, we believe that there is a need to determine the big enduring ideas that underpin probabilistic thinking and modeling. By uncovering the elements of the thinking modes of expert users of probability models we aim to provide a base for the setting of…

  15. Learning from video modeling examples : Effects of seeing the human model's face

    NARCIS (Netherlands)

    Van Gog, Tamara; Verveer, Ilse; Verveer, Lise


    Video modeling examples in which a human(-like) model shows learners how to perform a task are increasingly used in education, as they have become very easy to create and distribute in e-learning environments. However, little is known about design guidelines to optimize learning from video modeling

  16. Modeling Time Series Data for Supervised Learning (United States)

    Baydogan, Mustafa Gokce


    Temporal data are increasingly prevalent and important in analytics. Time series (TS) data are chronological sequences of observations and an important class of temporal data. Fields such as medicine, finance, learning science and multimedia naturally generate TS data. Each series provide a high-dimensional data vector that challenges the learning…

  17. The Learning Leader: Reflecting, Modeling, and Sharing (United States)

    Jacobs, Jacqueline E.; O'Gorman, Kevin L.


    With this book, principals, principals-in-training, and other school leaders get practical, easy-to-implement strategies for professional growth, strengthening relationships with faculty and staff, and making the necessary changes to improve K-12 learning environments. Grounded in specific, real-world examples and personal experiences, "The…

  18. The Model of Strategic e-Learning: Understanding and Evaluating Student e-Learning from Metacognitive Perspectives (United States)

    Tsai, Meng-Jung


    This paper presents the Model of Strategic e-Learning to explain and evaluate student e-learning from metacognitive perspectives. An in-depth interview, pilot study and main study are employed to construct the model and develop an instrument--the Online Learning Strategies Scale (OLSS). The model framework is constructed and illustrated by four…

  19. The Kinematic Learning Model using Video and Interfaces Analysis (United States)

    Firdaus, T.; Setiawan, W.; Hamidah, I.


    An educator currently in demand to apply the learning to not be separated from the development of technology. Educators often experience difficulties when explaining kinematics material, this is because kinematics is one of the lessons that often relate the concept to real life. Kinematics is one of the courses of physics that explains the cause of motion of an object, Therefore it takes the thinking skills and analytical skills in understanding these symptoms. Technology is one that can bridge between conceptual relationship with real life. A framework of technology-based learning models has been developed using video and interfaces analysis on kinematics concept. By using this learning model, learners will be better able to understand the concept that is taught by the teacher. This learning model is able to improve the ability of creative thinking, analytical skills, and problem-solving skills on the concept of kinematics.

  20. A developmental approach to learning causal models for cyber security (United States)

    Mugan, Jonathan


    To keep pace with our adversaries, we must expand the scope of machine learning and reasoning to address the breadth of possible attacks. One approach is to employ an algorithm to learn a set of causal models that describes the entire cyber network and each host end node. Such a learning algorithm would run continuously on the system and monitor activity in real time. With a set of causal models, the algorithm could anticipate novel attacks, take actions to thwart them, and predict the second-order effects flood of information, and the algorithm would have to determine which streams of that flood were relevant in which situations. This paper will present the results of efforts toward the application of a developmental learning algorithm to the problem of cyber security. The algorithm is modeled on the principles of human developmental learning and is designed to allow an agent to learn about the computer system in which it resides through active exploration. Children are flexible learners who acquire knowledge by actively exploring their environment and making predictions about what they will find,1, 2 and our algorithm is inspired by the work of the developmental psychologist Jean Piaget.3 Piaget described how children construct knowledge in stages and learn new concepts on top of those they already know. Developmental learning allows our algorithm to focus on subsets of the environment that are most helpful for learning given its current knowledge. In experiments, the algorithm was able to learn the conditions for file exfiltration and use that knowledge to protect sensitive files.

  1. Crash simulation: an immersive learning model. (United States)

    Wenham, John; Bennett, Paul; Gleeson, Wendy


    Far West New South Wales Local Emergency Management Committee runs an annual crash simulation exercise to assess the operational readiness of all local emergency services to coordinate and manage a multi-casualty exercise. Since 2009, the Broken Hill University Department of Rural Health (BHUDRH) has collaborated with the committee, enabling the inclusion of health students in this exercise. It is an immersive interprofessional learning experience that evaluates teamwork, communication and safe effective clinical trauma management outside the hospital setting. After 7 years of modifying and developing the exercise, we set out to evaluate its impact on the students' learning, and sought ethics approval from the University of Sydney for this study. At the start of this year's crash simulation, students were given information sheets and consent forms with regards to the research. Once formal debriefing had finished, the researchers conducted a semi-structured focus-group interview with the health students to gain insight into their experience and their perceived value of the training. Students also completed short-answer questionnaires, and the anonymised responses were analysed. Crash simulation … evaluates teamwork, communication and safe effective clinical trauma management IMPLICATIONS: Participants identified that this multidisciplinary learning opportunity in a pre-hospital mass casualty situation was of value to them. It has taken them outside of their usually protected hospital or primary care setting and tested their critical thinking and communication skills. We recommend this learning concept to other educational institutions. Further research will assess the learning value of the simulated event to the other agencies involved. © 2017 John Wiley & Sons Ltd and The Association for the Study of Medical Education.

  2. Modeling Meaningful Learning in Chemistry Using Structural Equation Modeling (United States)

    Brandriet, Alexandra R.; Ward, Rose Marie; Bretz, Stacey Lowery


    Ausubel and Novak's construct of "meaningful learning" stipulates that substantive connections between new knowledge and what is already known requires the integration of thinking, feeling, and performance (Novak J. D., (2010), "Learning, creating, and using knowledge: concept maps as facilitative tools in schools and…

  3. The Effect of Group Investigation Learning Model with Brainstroming Technique on Students Learning Outcomes

    Directory of Open Access Journals (Sweden)

    Astiti Kade kAyu


    Full Text Available This study aims to determine the effect of group investigation (GI learning model with brainstorming technique on student physics learning outcomes (PLO compared to jigsaw learning model with brainstroming technique. The learning outcome in this research are the results of learning in the cognitive domain. The method used in this research is experiment with Randomised Postest Only Control Group Design. Population in this research is all students of class XI IPA SMA Negeri 9 Kupang year lesson 2015/2016. The selected sample are 40 students of class XI IPA 1 as the experimental class and 38 students of class XI IPA 2 as the control class using simple random sampling technique. The instrument used is 13 items description test. The first hypothesis was tested by using two tailed t-test. From that, it is obtained that H0 rejected which means there are differences of students physics learning outcome. The second hypothesis was tested using one tailed t-test. It is obtained that H0 rejected which means the students PLO in experiment class were higher than control class. Based on the results of this study, researchers recommend the use of GI learning models with brainstorming techniques to improve PLO, especially in the cognitive domain.

  4. Student Modelling in Adaptive E-Learning Systems

    Directory of Open Access Journals (Sweden)

    Clemens Bechter


    Full Text Available Most e-Learning systems provide web-based learning so that students can access the same online courses via the Internet without adaptation, based on each student's profile and behavior. In an e-Learning system, one size does not fit all. Therefore, it is a challenge to make e-Learning systems that are suitably “adaptive”. The aim of adaptive e-Learning is to provide the students the appropriate content at the right time, means that the system is able to determine the knowledge level, keep track of usage, and arrange content automatically for each student for the best learning result. This study presents a proposed system which includes major adaptive features based on a student model. The proposed system is able to initialize the student model for determining the knowledge level of a student when the student registers for the course. After a student starts learning the lessons and doing many activities, the system can track information of the student until he/she takes a test. The student’s knowledge level, based on the test scores, is updated into the system for use in the adaptation process, which combines the student model with the domain model in order to deliver suitable course contents to the students. In this study, the proposed adaptive e-Learning system is implemented on an “Introduction to Java Programming Language” course, using LearnSquare software. After the system was tested, the results showed positive feedback towards the proposed system, especially in its adaptive capability.

  5. Gender-related model for mobile-based learning (United States)

    Simanjuntak, R. R.; Dewi, U. P.; Rifai, I.


    The study investigates gender influence on mobile-based learning. This case study of university students in Jakarta involved 235 students (128 male, 97 female). Results of this qualitative study showed 96% preference for mobile-based learning. A further 94% showed the needs for collaboration and authenticity for 92%. Hofstede’s cultural dimensions were used to identify the gender aspects of MALL. Preference for Masculinity (65%) was showed rather than Femininity (35%), even among the female respondents (70% of the population). Professions and professionalism received strongest preference (70%) while Individuality and Collectivism had equal preferences among students. Both female and male respondents requested Indulgence (84%) for mobile-based learning with more male respondents opted for Indulgence. The study provided a model for this gender sensitive mobile-based learning. Implications of implementing mobile-based learning as an ideal alternative for well-accommodated education are is also discussed.

  6. Using Active Learning for Speeding up Calibration in Simulation Models. (United States)

    Cevik, Mucahit; Ergun, Mehmet Ali; Stout, Natasha K; Trentham-Dietz, Amy; Craven, Mark; Alagoz, Oguzhan


    Most cancer simulation models include unobservable parameters that determine disease onset and tumor growth. These parameters play an important role in matching key outcomes such as cancer incidence and mortality, and their values are typically estimated via a lengthy calibration procedure, which involves evaluating a large number of combinations of parameter values via simulation. The objective of this study is to demonstrate how machine learning approaches can be used to accelerate the calibration process by reducing the number of parameter combinations that are actually evaluated. Active learning is a popular machine learning method that enables a learning algorithm such as artificial neural networks to interactively choose which parameter combinations to evaluate. We developed an active learning algorithm to expedite the calibration process. Our algorithm determines the parameter combinations that are more likely to produce desired outputs and therefore reduces the number of simulation runs performed during calibration. We demonstrate our method using the previously developed University of Wisconsin breast cancer simulation model (UWBCS). In a recent study, calibration of the UWBCS required the evaluation of 378 000 input parameter combinations to build a race-specific model, and only 69 of these combinations produced results that closely matched observed data. By using the active learning algorithm in conjunction with standard calibration methods, we identify all 69 parameter combinations by evaluating only 5620 of the 378 000 combinations. Machine learning methods hold potential in guiding model developers in the selection of more promising parameter combinations and hence speeding up the calibration process. Applying our machine learning algorithm to one model shows that evaluating only 1.49% of all parameter combinations would be sufficient for the calibration. © The Author(s) 2015.

  7. Toxin-Induced Experimental Models of Learning and Memory Impairment. (United States)

    More, Sandeep Vasant; Kumar, Hemant; Cho, Duk-Yeon; Yun, Yo-Sep; Choi, Dong-Kug


    Animal models for learning and memory have significantly contributed to novel strategies for drug development and hence are an imperative part in the assessment of therapeutics. Learning and memory involve different stages including acquisition, consolidation, and retrieval and each stage can be characterized using specific toxin. Recent studies have postulated the molecular basis of these processes and have also demonstrated many signaling molecules that are involved in several stages of memory. Most insights into learning and memory impairment and to develop a novel compound stems from the investigations performed in experimental models, especially those produced by neurotoxins models. Several toxins have been utilized based on their mechanism of action for learning and memory impairment such as scopolamine, streptozotocin, quinolinic acid, and domoic acid. Further, some toxins like 6-hydroxy dopamine (6-OHDA), 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and amyloid-β are known to cause specific learning and memory impairment which imitate the disease pathology of Parkinson's disease dementia and Alzheimer's disease dementia. Apart from these toxins, several other toxins come under a miscellaneous category like an environmental pollutant, snake venoms, botulinum, and lipopolysaccharide. This review will focus on the various classes of neurotoxin models for learning and memory impairment with their specific mechanism of action that could assist the process of drug discovery and development for dementia and cognitive disorders.

  8. What's statistical about learning? Insights from modelling statistical learning as a set of memory processes. (United States)

    Thiessen, Erik D


    Statistical learning has been studied in a variety of different tasks, including word segmentation, object identification, category learning, artificial grammar learning and serial reaction time tasks (e.g. Saffran et al. 1996 Science 274: , 1926-1928; Orban et al. 2008 Proceedings of the National Academy of Sciences 105: , 2745-2750; Thiessen & Yee 2010 Child Development 81: , 1287-1303; Saffran 2002 Journal of Memory and Language 47: , 172-196; Misyak & Christiansen 2012 Language Learning 62: , 302-331). The difference among these tasks raises questions about whether they all depend on the same kinds of underlying processes and computations, or whether they are tapping into different underlying mechanisms. Prior theoretical approaches to statistical learning have often tried to explain or model learning in a single task. However, in many cases these approaches appear inadequate to explain performance in multiple tasks. For example, explaining word segmentation via the computation of sequential statistics (such as transitional probability) provides little insight into the nature of sensitivity to regularities among simultaneously presented features. In this article, we will present a formal computational approach that we believe is a good candidate to provide a unifying framework to explore and explain learning in a wide variety of statistical learning tasks. This framework suggests that statistical learning arises from a set of processes that are inherent in memory systems, including activation, interference, integration of information and forgetting (e.g. Perruchet & Vinter 1998 Journal of Memory and Language 39: , 246-263; Thiessen et al. 2013 Psychological Bulletin 139: , 792-814). From this perspective, statistical learning does not involve explicit computation of statistics, but rather the extraction of elements of the input into memory traces, and subsequent integration across those memory traces that emphasize consistent information (Thiessen and Pavlik

  9. Perbandingan antara Keefektifan Model Guided Discovery Learning dan Project-Based Learning pada Matakuliah Geometri

    Directory of Open Access Journals (Sweden)

    Okky Riswandha Imawan


    Abstract This research aims to describe the effectiveness and effectiveness differences of the Guided Discovery Learning (GDL Model and the Project Based Learning (PjBL Model in terms of achievement, self-confidence, and critical thinking skills of students on the Solid Geometry subjects. This research was quasi experimental. The research subjects were two undergraduate classes of Mathematics Education Program, Ahmad Dahlan University, in their second semester, established at random. The data analysis to test the effectiveness of the GDL and PjBL Models in terms of each of the dependent variables used the t-test. The data analysis to test differences between effectiveness of the GDL and that of the PjBL Model used the MANOVA test. The results of this research show that viewed from achievement, self confidence, and critical thinking skills of the students are the application of the GDL Model on Solid Geometry subject is effective, the application of the PjBL Model on Solid Geometry subject is effective, and there is no difference in the effectiveness of GDL and PjBL Models on Solid Geometry subject in terms of achievement, self confidence, and critical thinking skills of the students. Keywords: guided discovery learning model, project-based learning model, achievement, self-confidence, critical thinking skills

  10. Concept Model For Designing Engaging And Motivating Games For Learning - The Smiley-Model

    DEFF Research Database (Denmark)

    Weitze, Charlotte Lærke; Ørngreen, Rikke


    The desire to use learning games in education is increasing, but the development of games for learning is still a growing field. Research shows that it remains difficult to develop learning games that are both instructive and engaging, although it is precisely the presence of these two elements...... that is believed to be an advantage when using learning games in education. In this paper the Smiley-model is presented (figure 1). The model describes which parameters and elements are important when designing a learning game. The present research is a result of a case-based action research study for designing...... a music learning game that teaches children to play piano using sheet music, and at the same time is fun and engaging. Although the model was originally developed for and through music, it has a more generic nature, and may be relevant for other fields as well. The Smiley-model is a condensed version...

  11. The Self-Regulated Learning Model and Music Education

    Directory of Open Access Journals (Sweden)

    Maja Marijan


    Full Text Available Self-regulation and self-regulated learning (SRL are important features in music education. In this research self-regulated learning model is presented as a complex, multidimensional structure. SRL starts with the self-regulation. Self-regulation is formed through interaction with the environment, thus self-learning, self-analysis, self-judgment, self-instruction, and self-monitoring are the main functions in self-regulatory structure. Co-regulation is needed, and helps self-regulation to be activated and monitored. In music education, co-regulation refers to the instructions that teacher introduces in the lessons. These instructions have to enhance learning and develop regulation over emotions, cognitive, auditor, and motor skills in students. Learning techniques and learning strategies are core components in music education. Adapting those, students become aware of their learning processes, actions, thoughts, feelings and behaviors that are involved in learning. It is suggested that every teaching methodology has to develop learning techniques, as well as metamemory and metacognition in students, in order to gain expertise. The author has emphasized her attention to every aspect that is believed to belong to SRL. There are not many articles on the SRL in music education, written by musicians, in compare with those written by psychologists and neurologists,. Therefore, the author has suggested that this paper would encourage music teachers and performers to take an advantage in the research of SRL. These researches would help music educational systems and teachers to develop and promote learning techniques and strategies. The results would show improvement in student’s learning and self-regulation.

  12. A Bayesian Approach for Structural Learning with Hidden Markov Models

    Directory of Open Access Journals (Sweden)

    Cen Li


    Full Text Available Hidden Markov Models(HMM have proved to be a successful modeling paradigm for dynamic and spatial processes in many domains, such as speech recognition, genomics, and general sequence alignment. Typically, in these applications, the model structures are predefined by domain experts. Therefore, the HMM learning problem focuses on the learning of the parameter values of the model to fit the given data sequences. However, when one considers other domains, such as, economics and physiology, model structure capturing the system dynamic behavior is not available. In order to successfully apply the HMM methodology in these domains, it is important that a mechanism is available for automatically deriving the model structure from the data. This paper presents a HMM learning procedure that simultaneously learns the model structure and the maximum likelihood parameter values of a HMM from data. The HMM model structures are derived based on the Bayesian model selection methodology. In addition, we introduce a new initialization procedure for HMM parameter value estimation based on the K-means clustering method. Experimental results with artificially generated data show the effectiveness of the approach.

  13. Representative Model of the Learning Process in Virtual Spaces Supported by ICT (United States)

    Capacho, José


    This paper shows the results of research activities for building the representative model of the learning process in virtual spaces (e-Learning). The formal basis of the model are supported in the analysis of models of learning assessment in virtual spaces and specifically in Dembo´s teaching learning model, the systemic approach to evaluating…

  14. Cross-Situational Learning with Bayesian Generative Models for Multimodal Category and Word Learning in Robots

    Directory of Open Access Journals (Sweden)

    Akira Taniguchi


    Full Text Available In this paper, we propose a Bayesian generative model that can form multiple categories based on each sensory-channel and can associate words with any of the four sensory-channels (action, position, object, and color. This paper focuses on cross-situational learning using the co-occurrence between words and information of sensory-channels in complex situations rather than conventional situations of cross-situational learning. We conducted a learning scenario using a simulator and a real humanoid iCub robot. In the scenario, a human tutor provided a sentence that describes an object of visual attention and an accompanying action to the robot. The scenario was set as follows: the number of words per sensory-channel was three or four, and the number of trials for learning was 20 and 40 for the simulator and 25 and 40 for the real robot. The experimental results showed that the proposed method was able to estimate the multiple categorizations and to learn the relationships between multiple sensory-channels and words accurately. In addition, we conducted an action generation task and an action description task based on word meanings learned in the cross-situational learning scenario. The experimental results showed that the robot could successfully use the word meanings learned by using the proposed method.


    Directory of Open Access Journals (Sweden)

    Miguel Ángel Montero


    Full Text Available The experience which we count with in the university education, the development of the ICT (Information and Communications Technology, the integration in the ESSE, the new qualifications (or Grades and mainly the desire to improve push us to innovate and to put into practice new methodologies in the teaching and learning of the subjects of Mathematics and Statistic assigned to our department. These methods totally renovate the lecturer’s roll and the traditional teaching, introducing multimedia tools, support platforms and new resources that provide students an autonomy which before they did not have, modifying the organization of time and space, increasing modalities and strategies of teaching-learning-tutorization and therefore developing more flexible models. It is tried to facilitate the learning of these subjects, providing a model b-learning, a comple- ment or alternative to the attendance classes, reinforcing the student’s active self-training.

  16. Blended learning in anesthesia education: current state and future model. (United States)

    Kannan, Jaya; Kurup, Viji


    Educators in anesthesia residency programs across the country are facing a number of challenges as they attempt to integrate blended learning techniques in their curriculum. Compared with the rest of higher education, which has made advances to varying degrees in the adoption of online learning anesthesiology education has been sporadic in the active integration of blended learning. The purpose of this review is to discuss the challenges in anesthesiology education and relevance of the Universal Design for Learning framework in addressing them. There is a wide chasm between student demand for online education and the availability of trained faculty to teach. The design of the learning interface is important and will significantly affect the learning experience for the student. This review examines recent literature pertaining to this field, both in the realm of higher education in general and medical education in particular, and proposes the application of a comprehensive learning model that is new to anesthesiology education and relevant to its goals of promoting self-directed learning.

  17. Bidirectional Nonnegative Deep Model and Its Optimization in Learning

    Directory of Open Access Journals (Sweden)

    Xianhua Zeng


    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.

  18. Polarimetric SAR image classification based on discriminative dictionary learning model (United States)

    Sang, Cheng Wei; Sun, Hong


    Polarimetric SAR (PolSAR) image classification is one of the important applications of PolSAR remote sensing. It is a difficult high-dimension nonlinear mapping problem, the sparse representations based on learning overcomplete dictionary have shown great potential to solve such problem. The overcomplete dictionary plays an important role in PolSAR image classification, however for PolSAR image complex scenes, features shared by different classes will weaken the discrimination of learned dictionary, so as to degrade classification performance. In this paper, we propose a novel overcomplete dictionary learning model to enhance the discrimination of dictionary. The learned overcomplete dictionary by the proposed model is more discriminative and very suitable for PolSAR classification.

  19. Model Transport: Towards Scalable Transfer Learning on Manifolds

    DEFF Research Database (Denmark)

    Freifeld, Oren; Hauberg, Søren; Black, Michael J.


    We consider the intersection of two research fields: transfer learning and statistics on manifolds. In particular, we consider, for manifold-valued data, transfer learning of tangent-space models such as Gaussians distributions, PCA, regression, or classifiers. Though one would hope to simply use...... ordinary Rn-transfer learning ideas, the manifold structure prevents it. We overcome this by basing our method on inner-product-preserving parallel transport, a well-known tool widely used in other problems of statistics on manifolds in computer vision. At first, this straightforward idea seems to suffer...... “commutes” with learning. Consequently, our compact framework, applicable to a large class of manifolds, is not restricted by the size of either the training or test sets. We demonstrate the approach by transferring PCA and logistic-regression models of real-world data involving 3D shapes and image...

  20. Improving Student Learning Outcomes Marketing Strategy Lesson By Applying SFAE Learning Model

    Directory of Open Access Journals (Sweden)

    Winda Nur Rohmawati


    Full Text Available Research objectives for improving student learning outcomes on the subjects of marketing strategy through the implementation of model learning SFAE. This type of research this is a class action research using a qualitative approach which consists of two cycles with the subject Marketing X grade SMK YPI Darussalam 2 Cerme Gresik Regency. This research consists of four stages: (1 the Planning Act, (2 the implementation of the action, (3 observations (observation, and (4 Reflection. The result of the research shows that cognitive and affective learning outcomes of students have increased significantly.


    Directory of Open Access Journals (Sweden)

    Dmytro S. Morozov


    Full Text Available The research paper outlines the problem of organization collaboration of users group on creation distance learning courses. The article contains analysis of the courses data structure. According to proposed structure the model of developer’s collaboration on creating distance learning courses based on basic principles of source code management was proposed. The article also provides result of research on necessary tools for collaborative development of courses in distance learning platforms. According to the requirements of flexibility and simplicity of access to system for any level educational institutions, technological decisions on granting permissions on performing basic operations on course elements and providing to user moderation’s privileges were proposed.

  2. The Self-Regulated Learning Model and Music Education


    Maja Marijan


    Self-regulation and self-regulated learning (SRL) are important features in music education. In this research self-regulated learning model is presented as a complex, multidimensional structure. SRL starts with the self-regulation. Self-regulation is formed through interaction with the environment, thus self-learning, self-analysis, self-judgment, self-instruction, and self-monitoring are the main functions in self-regulatory structure. Co-regulation is needed, and helps self-regulation to be...

  3. Theory-based Bayesian models of inductive learning and reasoning. (United States)

    Tenenbaum, Joshua B; Griffiths, Thomas L; Kemp, Charles


    Inductive inference allows humans to make powerful generalizations from sparse data when learning about word meanings, unobserved properties, causal relationships, and many other aspects of the world. Traditional accounts of induction emphasize either the power of statistical learning, or the importance of strong constraints from structured domain knowledge, intuitive theories or schemas. We argue that both components are necessary to explain the nature, use and acquisition of human knowledge, and we introduce a theory-based Bayesian framework for modeling inductive learning and reasoning as statistical inferences over structured knowledge representations.

  4. Models in Science Education: Applications of Models in Learning and Teaching Science (United States)

    Ornek, Funda


    In this paper, I discuss different types of models in science education and applications of them in learning and teaching science, in particular physics. Based on the literature, I categorize models as conceptual and mental models according to their characteristics. In addition to these models, there is another model called "physics model" by the…

  5. Learning of couplings for random asymmetric kinetic Ising models revisited: random correlation matrices and learning curves

    International Nuclear Information System (INIS)

    Bachschmid-Romano, Ludovica; Opper, Manfred


    We study analytically the performance of a recently proposed algorithm for learning the couplings of a random asymmetric kinetic Ising model from finite length trajectories of the spin dynamics. Our analysis shows the importance of the nontrivial equal time correlations between spins induced by the dynamics for the speed of learning. These correlations become more important as the spin’s stochasticity is decreased. We also analyse the deviation of the estimation error (paper)

  6. Time representation in reinforcement learning models of the basal ganglia

    Directory of Open Access Journals (Sweden)

    Samuel Joseph Gershman


    Full Text Available Reinforcement learning models have been influential in understanding many aspects of basal ganglia function, from reward prediction to action selection. Time plays an important role in these models, but there is still no theoretical consensus about what kind of time representation is used by the basal ganglia. We review several theoretical accounts and their supporting evidence. We then discuss the relationship between reinforcement learning models and the timing mechanisms that have been attributed to the basal ganglia. We hypothesize that a single computational system may underlie both reinforcement learning and interval timing—the perception of duration in the range of seconds to hours. This hypothesis, which extends earlier models by incorporating a time-sensitive action selection mechanism, may have important implications for understanding disorders like Parkinson's disease in which both decision making and timing are impaired.

  7. Occam factors and model independent Bayesian learning of continuous distributions

    International Nuclear Information System (INIS)

    Nemenman, Ilya; Bialek, William


    Learning of a smooth but nonparametric probability density can be regularized using methods of quantum field theory. We implement a field theoretic prior numerically, test its efficacy, and show that the data and the phase space factors arising from the integration over the model space determine the free parameter of the theory ('smoothness scale') self-consistently. This persists even for distributions that are atypical in the prior and is a step towards a model independent theory for learning continuous distributions. Finally, we point out that a wrong parametrization of a model family may sometimes be advantageous for small data sets

  8. Model-observer similarity, error modeling and social learning in rhesus macaques.

    Directory of Open Access Journals (Sweden)

    Elisabetta Monfardini

    Full Text Available Monkeys readily learn to discriminate between rewarded and unrewarded items or actions by observing their conspecifics. However, they do not systematically learn from humans. Understanding what makes human-to-monkey transmission of knowledge work or fail could help identify mediators and moderators of social learning that operate regardless of language or culture, and transcend inter-species differences. Do monkeys fail to learn when human models show a behavior too dissimilar from the animals' own, or when they show a faultless performance devoid of error? To address this question, six rhesus macaques trained to find which object within a pair concealed a food reward were successively tested with three models: a familiar conspecific, a 'stimulus-enhancing' human actively drawing the animal's attention to one object of the pair without actually performing the task, and a 'monkey-like' human performing the task in the same way as the monkey model did. Reward was manipulated to ensure that all models showed equal proportions of errors and successes. The 'monkey-like' human model improved the animals' subsequent object discrimination learning as much as a conspecific did, whereas the 'stimulus-enhancing' human model tended on the contrary to retard learning. Modeling errors rather than successes optimized learning from the monkey and 'monkey-like' models, while exacerbating the adverse effect of the 'stimulus-enhancing' model. These findings identify error modeling as a moderator of social learning in monkeys that amplifies the models' influence, whether beneficial or detrimental. By contrast, model-observer similarity in behavior emerged as a mediator of social learning, that is, a prerequisite for a model to work in the first place. The latter finding suggests that, as preverbal infants, macaques need to perceive the model as 'like-me' and that, once this condition is fulfilled, any agent can become an effective model.

  9. Identification of Chemistry Learning Problems Viewed From Conceptual Change Model


    Redhana, I. W; Sudria, I. B. N; Hidayat, I; Merta, L. M


    This study aimed at describing and explaining chemistry learning problems viewed from conceptual change model and misconceptions of students. The study was qualitative research of case study type conducted in one class of SMAN 1 Singaraja. Subjects of the study were a chemistry teacher and students. Data were obtained through classroom observation, interviews, and conception tests. The chemistry learning problems were grouped based on aspects of necessity, intelligibility, plausibility, and f...

  10. Modeling the learning of the English past tense with memory-based learning

    NARCIS (Netherlands)

    van Noord, Rik; Spenader, Jennifer K.


    Modeling the acquisition and final state of English past tense inflection has been an ongoing challenge since the mid-eighties. A number of rule-based and connectionist models have been proposed over the years, but the former usually have no explanation of how the rules are learned and the latter

  11. The Use of Problem-Based Learning Model to Improve Quality Learning Students Morals (United States)



    Model of moral cultivation in MTsN Bangunharja done using three methods, classical cultivation methods, extra-curricular activities in the form of religious activities, scouting, sports, and Islamic art, and habituation of morals. Problem base learning models in MTsN Bangunharja applied using the following steps: find the problem, define the…

  12. Computational neurorehabilitation: modeling plasticity and learning to predict recovery. (United States)

    Reinkensmeyer, David J; Burdet, Etienne; Casadio, Maura; Krakauer, John W; Kwakkel, Gert; Lang, Catherine E; Swinnen, Stephan P; Ward, Nick S; Schweighofer, Nicolas


    Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling - regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity.

  13. Runtime Optimizations for Tree-Based Machine Learning Models

    NARCIS (Netherlands)

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


    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

  14. Learning of Cross-Sectional Anatomy Using Clay Models (United States)

    Oh, Chang-Seok; Kim, Ji-Young; Choe, Yeon Hyeon


    We incorporated clay modeling into gross anatomy and neuro-anatomy courses to help students understand cross-sectional anatomy. By making clay models, cutting them and comparing cut surfaces to CT and MR images, students learned how cross-sectional two-dimensional images were created from three-dimensional structure of human organs. Most students…

  15. Modeling individuals’ cognitive and affective responses in spatial learning behavior

    NARCIS (Netherlands)

    Han, Q.; Arentze, T.A.; Timmermans, H.J.P.; Janssens, D.; Wets, G.; Lo, H.P.; Leung, Stephen C.H.; Tan, Susanna M.L.


    Activity-based analysis has slowly shifted gear from analysis of daily activity patterns to analysis and modeling of dynamic activity-travel patterns. In this paper, we describe a dynamic model that is concerned with simulating cognitive and affective responses in spatial learning behavior for a

  16. Scalable learning of probabilistic latent models for collaborative filtering

    DEFF Research Database (Denmark)

    Langseth, Helge; Nielsen, Thomas Dyhre


    variational Bayes learning and inference algorithm for these types of models. Empirical results show that the proposed algorithm achieves significantly better accuracy results than other straw-men models evaluated on a collection of well-known data sets. We also demonstrate that the algorithm has a highly...

  17. An Active Learning Exercise for Introducing Agent-Based Modeling (United States)

    Pinder, Jonathan P.


    Recent developments in agent-based modeling as a method of systems analysis and optimization indicate that students in business analytics need an introduction to the terminology, concepts, and framework of agent-based modeling. This article presents an active learning exercise for MBA students in business analytics that demonstrates agent-based…

  18. VISIONS2 Learning for Life Initiative. Workplace Literacy Implementation Model. (United States)

    Walsh, Chris L.; Ferguson, Susan E.; Taylor, Mary Lou

    This document presents a model for implementing workplace literacy education that focuses on giving front-line workers or first-line workers basic skills instruction and an appreciation for lifelong learning. The introduction presents background information on the model, which was developed during a partnership between a technical college and an…

  19. Mathematical Models of Elementary Mathematics Learning and Performance. Final Report. (United States)

    Suppes, Patrick

    This project was concerned with the development of mathematical models of elementary mathematics learning and performance. Probabilistic finite automata and register machines with a finite number of registers were developed as models and extensively tested with data arising from the elementary-mathematics strand curriculum developed by the…

  20. The Gain-Loss Model: A Probabilistic Skill Multimap Model for Assessing Learning Processes (United States)

    Robusto, Egidio; Stefanutti, Luca; Anselmi, Pasquale


    Within the theoretical framework of knowledge space theory, a probabilistic skill multimap model for assessing learning processes is proposed. The learning process of a student is modeled as a function of the student's knowledge and of an educational intervention on the attainment of specific skills required to solve problems in a knowledge…

  1. Models for Validation of Prior Learning (VPL)

    DEFF Research Database (Denmark)

    Ehlers, Søren

    The national policies for the education/training of adults are in the 21st century highly influenced by proposals which are formulated and promoted by The European Union (EU) as well as other transnational players and this shift in policy making has consequences. One is that ideas which in the past...... would have been categorized as utopian can become realpolitik. Validation of Prior Learning (VPL) was in Europe mainly regarded as utopian while universities in the United States of America (USA) were developing ways to obtain credits to those students which was coming with experiences from working life....

  2. Credit Risk Analysis Using Machine and Deep Learning Models

    Directory of Open Access Journals (Sweden)

    Peter Martey Addo


    Full Text Available Due to the advanced technology associated with Big Data, data availability and computing power, most banks or lending institutions are renewing their business models. Credit risk predictions, monitoring, model reliability and effective loan processing are key to decision-making and transparency. In this work, we build binary classifiers based on machine and deep learning models on real data in predicting loan default probability. The top 10 important features from these models are selected and then used in the modeling process to test the stability of binary classifiers by comparing their performance on separate data. We observe that the tree-based models are more stable than the models based on multilayer artificial neural networks. This opens several questions relative to the intensive use of deep learning systems in enterprises.

  3. A Model for Learning Over Time: The Big Picture (United States)

    Amato, Herbert K.; Konin, Jeff G.; Brader, Holly


    Objective: To present a method of describing the concept of “learning over time” with respect to its implementation into an athletic training education program curriculum. Background: The formal process of learning over time has recently been introduced as a required way for athletic training educational competencies and clinical proficiencies to be delivered and mastered. Learning over time incorporates the documented cognitive, psychomotor, and affective skills associated with the acquisition, progression, and reflection of information. This method of academic preparation represents a move away from a quantitative-based learning module toward a proficiency-based mastery of learning. Little research or documentation can be found demonstrating either the specificity of this concept or suggestions for its application. Description: We present a model for learning over time that encompasses multiple indicators for assessment in a successive format. Based on a continuum approach, cognitive, psychomotor, and affective characteristics are assessed at different levels in classroom and clinical environments. Clinical proficiencies are a common set of entry-level skills that need to be integrated into the athletic training educational domains. Objective documentation is presented, including the skill breakdown of a task and a matrix to identify a timeline of competency and proficiency delivery. Clinical Advantages: The advantages of learning over time pertain to the integration of cognitive knowledge into clinical skill acquisition. Given the fact that learning over time has been implemented as a required concept for athletic training education programs, this model may serve to assist those program faculty who have not yet developed, or are in the process of developing, a method of administering this approach to learning. PMID:12937551

  4. Implementation of Reseptive Esteemy Approach Model in Learning Reading Literature

    Directory of Open Access Journals (Sweden)

    Titin Nurhayatin


    Full Text Available Research on the implementation of aesthetic model of receptive aesthetic approach in learning to read the literature on the background of the low quality of results and learning process of Indonesian language, especially the study of literature. Students as prospective teachers of Indonesian language are expected to have the ability to speak, have literature, and their learning in a balanced manner in accordance with the curriculum demands. This study examines the effectiveness, quality, acceptability, and sustainability of the aesthetic approach of receptions in improving students' literary skills. Based on these problems, this study is expected to produce a learning model that contributes high in improving the quality of results and the process of learning literature. This research was conducted on the students of Language Education Program, Indonesian Literature and Regional FKIP Pasundan University. The research method used is experiment with randomized type pretest-posttest control group design. Based on preliminary and final test data obtained in the experimental class the average preliminary test was 55.86 and the average final test was 76.75. From the preliminary test data in the control class the average score was 55.07 and the average final test was 68.76. These data suggest that there is a greater increase in grades in the experimental class using the aesthetic approach of the reception compared with the increase in values in the control class using a conventional approach. The results show that the aesthetic approach of receptions is more effective than the conventional approach in literary reading. Based on observations, acceptance, and views of sustainability, the aesthetic approach of receptions in literary learning is expected to be an alternative and solution in overcoming the problems of literary learning and improving the quality of Indonesian learning outcomes and learning process.

  5. Organizational Learning, Strategic Flexibility and Business Model Innovation: An Empirical Research Based on Logistics Enterprises (United States)

    Bao, Yaodong; Cheng, Lin; Zhang, Jian

    Using the data of 237 Jiangsu logistics firms, this paper empirically studies the relationship among organizational learning capability, business model innovation, strategic flexibility. The results show as follows; organizational learning capability has positive impacts on business model innovation performance; strategic flexibility plays mediating roles on the relationship between organizational learning capability and business model innovation; interaction among strategic flexibility, explorative learning and exploitative learning play significant roles in radical business model innovation and incremental business model innovation.

  6. Blended learning models for directing the self-learning activity of “Software Engineering” specialty students

    Directory of Open Access Journals (Sweden)

    Vera V. Lyubchenko


    Full Text Available The adoption of Law of Ukraine “On Higher Education” (2014 involves the increase in students’ self-learning activity part in the curriculum. Therefore the self-learning activities’ arrangement in a way augmenting the result quality becomes a top priority task. This research objective consists in elaborating the scenario for organization of the students’ qualitative self-study, based on blended learning models. The author analyzes four blended learning models: the rotation model, flex-model, self-blend model and online driver model, and gives examples of their use. It is shown that first two models are the most suitable for full-time students. A general scenario for the use of blended learning models is described. Although the use of blended learning models causes several difficulties, it also essentially contributes into students’ self-study monitoring and control support.

  7. The Emergence of the Open Networked ``i-Learning'' Model (United States)

    Elia, Gianluca

    The most significant forces that are changing the business world and the society behaviors in this beginning of the twenty-first century can be identified into the globalization of the economy, technological evolution and convergence, change of the workers' expectations, workplace diversity and mobility, and mostly, knowledge and learning as major organizational assets. But which type of ­learning dynamics must be nurtured and pursued within the organizations, today, in order to generate valuable knowledge and its effective applications? After a brief discussion on the main changes observable in management, ICT and society/workplace in the last years, this chapter aims to answer to this question, through the proposition of the “Π-shaped” profile (a new professional archetype for leading change), and through the discussion of the open networked “i-Learning” model (a new framework to “incubate” innovation in learning processes). Actually, the “i” stands for “innovation” (to highlight the nature of the impact on traditional ­learning model), but also it stands for “incubation” (to underline the urgency to have new environments in which incubating new professional profiles). Specifically, the main key characteristics at the basis of the innovation of the learning processes will be ­presented and described, by highlighting the managerial, technological and societal aspects of their nature. A set of operational guidelines will be also ­provided to ­activate and sustain the innovation process, so implementing changes in the strategic dimensions of the model. Finally, the “i-Learning Radar” is presented as an operational tool to design, communicate and control an “i-Learning experience”. This tool is represented by a radar diagram with six strategic dimensions of a ­learning initiative.

  8. A workflow learning model to improve geovisual analytics utility. (United States)

    Roth, Robert E; Maceachren, Alan M; McCabe, Craig A


    INTRODUCTION: This paper describes the design and implementation of the G-EX Portal Learn Module, a web-based, geocollaborative application for organizing and distributing digital learning artifacts. G-EX falls into the broader context of geovisual analytics, a new research area with the goal of supporting visually-mediated reasoning about large, multivariate, spatiotemporal information. Because this information is unprecedented in amount and complexity, GIScientists are tasked with the development of new tools and techniques to make sense of it. Our research addresses the challenge of implementing these geovisual analytics tools and techniques in a useful manner. OBJECTIVES: The objective of this paper is to develop and implement a method for improving the utility of geovisual analytics software. The success of software is measured by its usability (i.e., how easy the software is to use?) and utility (i.e., how useful the software is). The usability and utility of software can be improved by refining the software, increasing user knowledge about the software, or both. It is difficult to achieve transparent usability (i.e., software that is immediately usable without training) of geovisual analytics software because of the inherent complexity of the included tools and techniques. In these situations, improving user knowledge about the software through the provision of learning artifacts is as important, if not more so, than iterative refinement of the software itself. Therefore, our approach to improving utility is focused on educating the user. METHODOLOGY: The research reported here was completed in two steps. First, we developed a model for learning about geovisual analytics software. Many existing digital learning models assist only with use of the software to complete a specific task and provide limited assistance with its actual application. To move beyond task-oriented learning about software use, we propose a process-oriented approach to learning based on

  9. A New Profile Learning Model for Recommendation System based on Machine Learning Technique

    Directory of Open Access Journals (Sweden)

    Shereen H. Ali


    Full Text Available Recommender systems (RSs have been used to successfully address the information overload problem by providing personalized and targeted recommendations to the end users. RSs are software tools and techniques providing suggestions for items to be of use to a user, hence, they typically apply techniques and methodologies from Data Mining. The main contribution of this paper is to introduce a new user profile learning model to promote the recommendation accuracy of vertical recommendation systems. The proposed profile learning model employs the vertical classifier that has been used in multi classification module of the Intelligent Adaptive Vertical Recommendation (IAVR system to discover the user’s area of interest, and then build the user’s profile accordingly. Experimental results have proven the effectiveness of the proposed profile learning model, which accordingly will promote the recommendation accuracy.

  10. Students’ Mathematical Problem-Solving Abilities Through The Application of Learning Models Problem Based Learning (United States)

    Nasution, M. L.; Yerizon, Y.; Gusmiyanti, R.


    One of the purpose mathematic learning is to develop problem solving abilities. Problem solving is obtained through experience in questioning non-routine. Improving students’ mathematical problem-solving abilities required an appropriate strategy in learning activities one of them is models problem based learning (PBL). Thus, the purpose of this research is to determine whether the problem solving abilities of mathematical students’ who learn to use PBL better than on the ability of students’ mathematical problem solving by applying conventional learning. This research included quasi experiment with static group design and population is students class XI MIA SMAN 1 Lubuk Alung. Class experiment in the class XI MIA 5 and class control in the class XI MIA 6. The instrument of final test students’ mathematical problem solving used essay form. The result of data final test in analyzed with t-test. The result is students’ mathematical problem solving abilities with PBL better then on the ability of students’ mathematical problem solving by applying conventional learning. It’s seen from the high percentage achieved by the group of students who learn to use PBL for each indicator of students’ mathematical problem solving.

  11. Integrating Learning Styles and Personality Traits into an Affective Model to Support Learner's Learning (United States)

    Leontidis, Makis; Halatsis, Constantin

    The aim of this paper is to present a model in order to integrate the learning style and the personality traits of a learner into an enhanced Affective Style which is stored in the learner’s model. This model which can deal with the cognitive abilities as well as the affective preferences of the learner is called Learner Affective Model (LAM). The LAM is used to retain learner’s knowledge and activities during his interaction with a Web-based learning environment and also to provide him with the appropriate pedagogical guidance. The proposed model makes use of an ontological approach in combination with the Bayesian Network model and contributes to the efficient management of the LAM in an Affective Module.

  12. Screening for Prediabetes Using Machine Learning Models

    Directory of Open Access Journals (Sweden)

    Soo Beom Choi


    Full Text Available The global prevalence of diabetes is rapidly increasing. Studies support the necessity of screening and interventions for prediabetes, which could result in serious complications and diabetes. This study aimed at developing an intelligence-based screening model for prediabetes. Data from the Korean National Health and Nutrition Examination Survey (KNHANES were used, excluding subjects with diabetes. The KNHANES 2010 data (n=4685 were used for training and internal validation, while data from KNHANES 2011 (n=4566 were used for external validation. We developed two models to screen for prediabetes using an artificial neural network (ANN and support vector machine (SVM and performed a systematic evaluation of the models using internal and external validation. We compared the performance of our models with that of a screening score model based on logistic regression analysis for prediabetes that had been developed previously. The SVM model showed the areas under the curve of 0.731 in the external datasets, which is higher than those of the ANN model (0.729 and the screening score model (0.712, respectively. The prescreening methods developed in this study performed better than the screening score model that had been developed previously and may be more effective method for prediabetes screening.

  13. Sensorimotor learning biases choice behavior: a learning neural field model for decision making.

    Directory of Open Access Journals (Sweden)

    Christian Klaes

    Full Text Available According to a prominent view of sensorimotor processing in primates, selection and specification of possible actions are not sequential operations. Rather, a decision for an action emerges from competition between different movement plans, which are specified and selected in parallel. For action choices which are based on ambiguous sensory input, the frontoparietal sensorimotor areas are considered part of the common underlying neural substrate for selection and specification of action. These areas have been shown capable of encoding alternative spatial motor goals in parallel during movement planning, and show signatures of competitive value-based selection among these goals. Since the same network is also involved in learning sensorimotor associations, competitive action selection (decision making should not only be driven by the sensory evidence and expected reward in favor of either action, but also by the subject's learning history of different sensorimotor associations. Previous computational models of competitive neural decision making used predefined associations between sensory input and corresponding motor output. Such hard-wiring does not allow modeling of how decisions are influenced by sensorimotor learning or by changing reward contingencies. We present a dynamic neural field model which learns arbitrary sensorimotor associations with a reward-driven Hebbian learning algorithm. We show that the model accurately simulates the dynamics of action selection with different reward contingencies, as observed in monkey cortical recordings, and that it correctly predicted the pattern of choice errors in a control experiment. With our adaptive model we demonstrate how network plasticity, which is required for association learning and adaptation to new reward contingencies, can influence choice behavior. The field model provides an integrated and dynamic account for the operations of sensorimotor integration, working memory and action

  14. Modeling the behavioral substrates of associate learning and memory - Adaptive neural models (United States)

    Lee, Chuen-Chien


    Three adaptive single-neuron models based on neural analogies of behavior modification episodes are proposed, which attempt to bridge the gap between psychology and neurophysiology. The proposed models capture the predictive nature of Pavlovian conditioning, which is essential to the theory of adaptive/learning systems. The models learn to anticipate the occurrence of a conditioned response before the presence of a reinforcing stimulus when training is complete. Furthermore, each model can find the most nonredundant and earliest predictor of reinforcement. The behavior of the models accounts for several aspects of basic animal learning phenomena in Pavlovian conditioning beyond previous related models. Computer simulations show how well the models fit empirical data from various animal learning paradigms.

  15. A Technology-based Model for Learning

    Directory of Open Access Journals (Sweden)

    Michael Williams


    Full Text Available The Math Emporium, opened in 1997, is an open 7000-squaremeter facility with 550+ workstations arranged in an array of widely spaced hexagonal "pods", designed to support group work at the same time maintaining an academic air. We operate it 24/7 with math support personnel in attendance 12 hours per day. Students have access to online course resources at all times, from anywhere. We have used this unique asset to transform traditional classroom-based courses into technology based learning programs that have no class meetings at all. The structure of the program is very different from the conventional one, having a new set of expectations and motivations. The results include: more effective students, substantial cost savings, economies of scale and scope and a stream-lined process for creating new on-line courses.

  16. A Convergent Participation Model for Evaluation of Learning Objects

    Directory of Open Access Journals (Sweden)

    John Nesbit


    Full Text Available The properties that distinguish learning objects from other forms of educational software - global accessibility, metadata standards, finer granularity and reusability - have implications for evaluation. This article proposes a convergent participation model for learning object evaluation in which representatives from stakeholder groups (e.g., students, instructors, subject matter experts, instructional designers, and media developers converge toward more similar descriptions and ratings through a two-stage process supported by online collaboration tools. The article reviews evaluation models that have been applied to educational software and media, considers models for gathering and meta-evaluating individual user reviews that have recently emerged on the Web, and describes the peer review model adopted for the MERLOT repository. The convergent participation model is assessed in relation to other models and with respect to its support for eight goals of learning object evaluation: (1 aid for searching and selecting, (2 guidance for use, (3 formative evaluation, (4 influence on design practices, (5 professional development and student learning, (6 community building, (7 social recognition, and (8 economic exchange.

  17. Application of Learning Curves for Didactic Model Evaluation: Case Studies

    Directory of Open Access Journals (Sweden)

    Felix Mödritscher


    Full Text Available The success of (online courses depends, among other factors, on the underlying didactical models which have always been evaluated with qualitative and quantitative research methods. Several new evaluation techniques have been developed and established in the last years. One of them is ‘learning curves’, which aim at measuring error rates of users when they interact with adaptive educational systems, thereby enabling the underlying models to be evaluated and improved. In this paper, we report how we have applied this new method to two case studies to show that learning curves are useful to evaluate didactical models and their implementation in educational platforms. Results show that the error rates follow a power law distribution with each additional attempt if the didactical model of an instructional unit is valid. Furthermore, the initial error rate, the slope of the curve and the goodness of fit of the curve are valid indicators for the difficulty level of a course and the quality of its didactical model. As a conclusion, the idea of applying learning curves for evaluating didactical model on the basis of usage data is considered to be valuable for supporting teachers and learning content providers in improving their online courses.

  18. The Integration of Environmental Education in Science Materials by Using "MOTORIC" Learning Model (United States)

    Sukarjita, I. Wayan; Ardi, Muhammad; Rachman, Abdul; Supu, Amiruddin; Dirawan, Gufran Darma


    The research of the integration of Environmental Education in science subject matter by application of "MOTORIC" Learning models has carried out on Junior High School Kupang Nusa Tenggara Timur Indonesia. "MOTORIC" learning model is an Environmental Education (EE) learning model that collaborate three learning approach i.e.…

  19. Startle reduces recall of a recently learned internal model. (United States)

    Wright, Zachary; Patton, James L; Ravichandran, Venn


    Recent work has shown that preplanned motor programs are released early from subcortical areas by the using a startling acoustic stimulus (SAS). Our question is whether this response might also contain a recently learned internal model, which draws on experience to predict and compensate for expected perturbations in a feedforward manner. Studies of adaptation to robotic forces have shown some evidence of this, but were potentially confounded by cocontraction caused by startle. We performed a new adaptation experiment using a visually distorted field that could not be confounded by cocontraction. We found that in all subjects that exhibited startle, the startle stimulus (1) reduced performance of the recently learned task (2) reduced after-effect magnitudes. Because startle reduced but did not eliminate the recall of learned control, we suggest that multiple neural centers (cortical and subcortical) are involved in such learning and adaptation, which can impact training areas such as piloting, teleoperation, sports, and rehabilitation. © 2011 IEEE

  20. Modeling Geomagnetic Variations using a Machine Learning Framework (United States)

    Cheung, C. M. M.; Handmer, C.; Kosar, B.; Gerules, G.; Poduval, B.; Mackintosh, G.; Munoz-Jaramillo, A.; Bobra, M.; Hernandez, T.; McGranaghan, R. M.


    We present a framework for data-driven modeling of Heliophysics time series data. The Solar Terrestrial Interaction Neural net Generator (STING) is an open source python module built on top of state-of-the-art statistical learning frameworks (traditional machine learning methods as well as deep learning). To showcase the capability of STING, we deploy it for the problem of predicting the temporal variation of geomagnetic fields. The data used includes solar wind measurements from the OMNI database and geomagnetic field data taken by magnetometers at US Geological Survey observatories. We examine the predictive capability of different machine learning techniques (recurrent neural networks, support vector machines) for a range of forecasting times (minutes to 12 hours). STING is designed to be extensible to other types of data. We show how STING can be used on large sets of data from different sensors/observatories and adapted to tackle other problems in Heliophysics.

  1. Revising process models through inductive learning

    NARCIS (Netherlands)

    Maggi, F.M.; Corapi, D.; Russo, A.; Lupu, E.; Visaggio, G.; Muehlen, zur M.; Su, J.


    Discovering the Business Process (BP) model underpinning existing practices through analysis of event logs, allows users to understand, analyse and modify the process. But, to be useful, the BP model must be kept in line with practice throughout its lifetime, as changes occur to the business

  2. Trustless Machine Learning Contracts; Evaluating and Exchanging Machine Learning Models on the Ethereum Blockchain


    Kurtulmus, A. Besir; Daniel, Kenny


    Using blockchain technology, it is possible to create contracts that offer a reward in exchange for a trained machine learning model for a particular data set. This would allow users to train machine learning models for a reward in a trustless manner. The smart contract will use the blockchain to automatically validate the solution, so there would be no debate about whether the solution was correct or not. Users who submit the solutions won't have counterparty risk that they won't get paid fo...

  3. A phenomenological memristor model for synaptic memory and learning behaviors

    Institute of Scientific and Technical Information of China (English)

    Nan Shao; Sheng-Bing Zhang; Shu-Yuan Shao


    Properties that are similar to the memory and learning functions in biological systems have been observed and reported in the experimental studies of memristors fabricated by different materials.These properties include the forgetting effect,the transition from short-term memory (STM) to long-term memory (LTM),learning-experience behavior,etc.The mathematical model of this kind of memristor would be very important for its theoretical analysis and application design.In our analysis of the existing memristor model with these properties,we find that some behaviors of the model are inconsistent with the reported experimental observations.A phenomenological memristor model is proposed for this kind of memristor.The model design is based on the forgetting effect and STM-to-LTM transition since these behaviors are two typical properties of these memristors.Further analyses of this model show that this model can also be used directly or modified to describe other experimentally observed behaviors.Simulations show that the proposed model can give a better description of the reported memory and learning behaviors of this kind of memristor than the existing model.

  4. Development of Learning Models Based on Problem Solving and Meaningful Learning Standards by Expert Validity for Animal Development Course (United States)

    Lufri, L.; Fitri, R.; Yogica, R.


    The purpose of this study is to produce a learning model based on problem solving and meaningful learning standards by expert assessment or validation for the course of Animal Development. This research is a development research that produce the product in the form of learning model, which consist of sub product, namely: the syntax of learning model and student worksheets. All of these products are standardized through expert validation. The research data is the level of validity of all sub products obtained using questionnaire, filled by validators from various field of expertise (field of study, learning strategy, Bahasa). Data were analysed using descriptive statistics. The result of the research shows that the problem solving and meaningful learning model has been produced. Sub products declared appropriate by expert include the syntax of learning model and student worksheet.

  5. Gamification in online education: proposal for a participatory learning model

    Directory of Open Access Journals (Sweden)

    Fabiana Bigão Silva


    Full Text Available Empirical studies have suggested limitations on the form of application of gamification mechanics in the context of online education. These mechanics have been applied without reference to a theoretical model dedicated to this type of education. The objective of the paper is to propose a model for a gamified platform for online education that contributes to a more participatory learning, taking into account the different student profiles. Based on literature review about approaches to gamification systems design, a set of steps was followed in order to develop a generic model for a framework dedicated to online education. The model proposed is based on the Educational Gamification Design Principles proposed by Dicheva et al. (2015. The model may contribute to the promotion of participatory learning, taking into account the different student profiles. The results of such evaluation will be published in the future.

  6. Biology learning evaluation model in Senior High Schools

    Directory of Open Access Journals (Sweden)

    Sri Utari


    Full Text Available The study was to develop a Biology learning evaluation model in senior high schools that referred to the research and development model by Borg & Gall and the logic model. The evaluation model included the components of input, activities, output and outcomes. The developing procedures involved a preliminary study in the form of observation and theoretical review regarding the Biology learning evaluation in senior high schools. The product development was carried out by designing an evaluation model, designing an instrument, performing instrument experiment and performing implementation. The instrument experiment involved teachers and Students from Grade XII in senior high schools located in the City of Yogyakarta. For the data gathering technique and instrument, the researchers implemented observation sheet, questionnaire and test. The questionnaire was applied in order to attain information regarding teacher performance, learning performance, classroom atmosphere and scientific attitude; on the other hand, test was applied in order to attain information regarding Biology concept mastery. Then, for the analysis of instrument construct, the researchers performed confirmatory factor analysis by means of Lisrel 0.80 software and the results of this analysis showed that the evaluation instrument valid and reliable. The construct validity was between 0.43-0.79 while the reliability of measurement model was between 0.88-0.94. Last but not the least, the model feasibility test showed that the theoretical model had been supported by the empirical data.

  7. Using Deep Learning Model for Meteorological Satellite Cloud Image Prediction (United States)

    Su, X.


    A satellite cloud image contains much weather information such as precipitation information. Short-time cloud movement forecast is important for precipitation forecast and is the primary means for typhoon monitoring. The traditional methods are mostly using the cloud feature matching and linear extrapolation to predict the cloud movement, which makes that the nonstationary process such as inversion and deformation during the movement of the cloud is basically not considered. It is still a hard task to predict cloud movement timely and correctly. As deep learning model could perform well in learning spatiotemporal features, to meet this challenge, we could regard cloud image prediction as a spatiotemporal sequence forecasting problem and introduce deep learning model to solve this problem. In this research, we use a variant of Gated-Recurrent-Unit(GRU) that has convolutional structures to deal with spatiotemporal features and build an end-to-end model to solve this forecast problem. In this model, both the input and output are spatiotemporal sequences. Compared to Convolutional LSTM(ConvLSTM) model, this model has lower amount of parameters. We imply this model on GOES satellite data and the model perform well.

  8. Simulation modelling: educational development roles for learning technologists

    Directory of Open Access Journals (Sweden)

    David Riley


    Full Text Available Simulation modelling was in the mainstream of CAL development in the 1980s when the late David Squires introduced this author to the Dynamic Modelling System. Since those early days, it seems that simulation modelling has drifted into a learning technology backwater to become a member of Laurillard's underutilized, 'adaptive and productive' media. Referring to her Conversational Framework, Laurillard constructs a pedagogic case for modelling as a productive student activity but provides few references to current practice and available resources. This paper seeks to complement her account by highlighting the pioneering initiatives of the Computers in the Curriculum Project and more recent developments in systems modelling within geographic and business education. The latter include improvements to system dynamics modelling programs such as STELLA®, the publication of introductory textbooks, and the emergence of online resources. The paper indicates several ways in which modelling activities may be approached and identifies some educational development roles for learning technologists. The paper concludes by advocating simulation modelling as an exemplary use of learning technologies - one that realizes their creative-transformative potential.

  9. A Theoretical Model for Meaning Construction through Constructivist Concept Learning

    DEFF Research Database (Denmark)

    Badie, Farshad

    The central focus of this Ph.D. research is on ‘Logic and Cognition’ and, more specifically, this research covers the quintuple (Logic and Logical Philosophy, Philosophy of Education, Educational Psychology, Cognitive Science, Computer Science). The most significant contributions of this Ph.D. di...... of ‘learning’, ‘mentoring’, and ‘knowledge’ within learning and knowledge acquisition systems. Constructivism as an epistemology and as a model of knowing and, respectively as a theoretical model of learning builds up the central framework of this research........D. dissertation are conceptual, logical, terminological, and semantic analysis of Constructivist Concept Learning (specifically, in the context of humans’ interactions with their environment and with other agents). This dissertation is concerned with the specification of the conceptualisation of the phenomena...

  10. Intelligent Cloud Learning Model for Online Overseas Chinese Education

    Directory of Open Access Journals (Sweden)

    Yidong Chen


    Full Text Available With the development of Chinese economy, oversea Chinese education has been paid more and more attention. However, the overseas Chinese education resource is relatively lack because of historical reasons, which hindered further development . How to better share the Chinese education resources and provide intelligent personalized information service for overseas student is a key problem to be solved. In recent years, the rise of cloud computing provides us an opportunity to realize intelligent learning mode. Cloud computing offers some advantages by allowing users to use infrastructure, platforms and software . In this paper we proposed an intelligent cloud learning model based on cloud computing. The learning model can utilize network resources sufficiently to implement resource sharing according to the personal needs of students, and provide a good practicability for online overseas Chinese education.

  11. Advancing Affect Modeling via Preference Learning and Unsupervised Feature Extraction

    DEFF Research Database (Denmark)

    Martínez, Héctor Pérez

    strategies (error functions and training algorithms) for artificial neural networks are examined across synthetic and psycho-physiological datasets, and compared against support vector machines and Cohen’s method. Results reveal the best training strategies for neural networks and suggest their superiority...... difficulties, ordinal reports such as rankings and ratings can yield more reliable affect annotations than alternative tools. This thesis explores preference learning methods to automatically learn computational models from ordinal annotations of affect. In particular, an extensive collection of training...... over the other examined methods. The second challenge addressed in this thesis refers to the extraction of relevant information from physiological modalities. Deep learning is proposed as an automatic approach to extract input features for models of affect from physiological signals. Experiments...

  12. Quality Assurance in E-Learning: PDPP Evaluation Model and Its Application (United States)

    Zhang, Weiyuan; Cheng, Y. L.


    E-learning has become an increasingly important teaching and learning mode in educational institutions and corporate training. The evaluation of e-learning, however, is essential for the quality assurance of e-learning courses. This paper constructs a four-phase evaluation model for e-learning courses, which includes planning, development,…

  13. Acceptance on Mobile Learning via SMS: A Rasch Model Analysis

    Directory of Open Access Journals (Sweden)

    Issham Ismail


    Full Text Available This study investigated whether mobile learning via Short Message Service (SMS-learning is accepted by the students enrolled in the distance learning academic programme in the Universiti Sains Malaysia. This study explored the impact of perceived usefulness, perceived ease of use and usability of the system to their acceptability. The survey was constructed using a questionnaire consisting of statements regarding the participants’ demographics, experiences in and perception of using mobile learning via SMS, involving 105 students from management and sciences disciplines. The Rasch Model Analysis was used for measurement correspond to a 5 point Likert. Results indicated that the usability of the system contributed to be effectiveness in assisting the students with their study. Respondents agree that SMS-learning is easy, effective and useful to help them study. However, the results found that there has been a problem in mobile learning that less interaction with lecturers. It implies that the acceptability of students to this mode on communication and interaction is highly endorsed.

  14. Enhanced HMAX model with feedforward feature learning for multiclass categorization

    Directory of Open Access Journals (Sweden)

    Yinlin eLi


    Full Text Available In recent years, the interdisciplinary research between neuroscience and computer vision has promoted the development in both fields. Many biologically inspired visual models are proposed, and among them, the Hierarchical Max-pooling model (HMAX is a feedforward model mimicking the structures and functions of V1 to posterior inferotemporal (PIT layer of the primate visual cortex, which could generate a series of position- and scale- invariant features. However, it could be improved with attention modulation and memory processing, which are two important properties of the primate visual cortex. Thus, in this paper, based on recent biological research on the primate visual cortex, we still mimic the first 100-150 milliseconds of visual cognition to enhance the HMAX model, which mainly focuses on the unsupervised feedforward feature learning process. The main modifications are as follows: 1 To mimic the attention modulation mechanism of V1 layer, a bottom-up saliency map is computed in the S1 layer of the HMAX model, which can support the initial feature extraction for memory processing; 2 To mimic the learning, clustering and short-term memory to long-term memory conversion abilities of V2 and IT, an unsupervised iterative clustering method is used to learn clusters with multiscale middle level patches, which are taken as long-term memory; 3 Inspired by the multiple feature encoding mode of the primate visual cortex, information including color, orientation, and spatial position are encoded in different layers of the HMAX model progressively. By adding a softmax layer at the top of the model, multiclass categorization experiments can be conducted, and the results on Caltech101 show that the enhanced model with a smaller memory size exhibits higher accuracy than the original HMAX model, and could also achieve better accuracy than other unsupervised feature learning methods in multiclass categorization task.

  15. Enhanced HMAX model with feedforward feature learning for multiclass categorization. (United States)

    Li, Yinlin; Wu, Wei; Zhang, Bo; Li, Fengfu


    In recent years, the interdisciplinary research between neuroscience and computer vision has promoted the development in both fields. Many biologically inspired visual models are proposed, and among them, the Hierarchical Max-pooling model (HMAX) is a feedforward model mimicking the structures and functions of V1 to posterior inferotemporal (PIT) layer of the primate visual cortex, which could generate a series of position- and scale- invariant features. However, it could be improved with attention modulation and memory processing, which are two important properties of the primate visual cortex. Thus, in this paper, based on recent biological research on the primate visual cortex, we still mimic the first 100-150 ms of visual cognition to enhance the HMAX model, which mainly focuses on the unsupervised feedforward feature learning process. The main modifications are as follows: (1) To mimic the attention modulation mechanism of V1 layer, a bottom-up saliency map is computed in the S1 layer of the HMAX model, which can support the initial feature extraction for memory processing; (2) To mimic the learning, clustering and short-term memory to long-term memory conversion abilities of V2 and IT, an unsupervised iterative clustering method is used to learn clusters with multiscale middle level patches, which are taken as long-term memory; (3) Inspired by the multiple feature encoding mode of the primate visual cortex, information including color, orientation, and spatial position are encoded in different layers of the HMAX model progressively. By adding a softmax layer at the top of the model, multiclass categorization experiments can be conducted, and the results on Caltech101 show that the enhanced model with a smaller memory size exhibits higher accuracy than the original HMAX model, and could also achieve better accuracy than other unsupervised feature learning methods in multiclass categorization task.

  16. Technology learning in a small open economy-The systems, modelling and exploiting the learning effect

    International Nuclear Information System (INIS)

    Martinsen, Thomas


    This paper reviews the characteristics of technology learning and discusses its application in energy system modelling in a global-local perspective. Its influence on the national energy system, exemplified by Norway, is investigated using a global and national Markal model. The dynamic nature of the learning system boundary and coupling between the national energy system and the global development and manufacturing system is elaborated. Some criteria important for modelling of spillover are suggested. Particularly, to ensure balance in global energy demand and supply and accurately reflect alternative global pathways spillover for all technologies as well as energy carrier cost/prices should be estimated under the same global scenario. The technology composition, CO 2 emissions and system cost in Norway up to 2050 exhibit sensitivity to spillover. Moreover, spillover may reduce both CO 2 emissions and total system cost. National energy system analysis of low carbon society should therefore consider technology development paths in global policy scenarios. Without the spillover from international deployment a domestic technology relies only on endogenous national learning. However, with high but realistic learning rates offshore floating wind may become cost-efficient even if initially deployed only in Norwegian niche markets. - Research highlights: → Spillover for all technologies should emanate from the same global scenario. → A global model is called for to estimate spillover.→ Spillover may reduce CO 2 emissions and the total system cost in a small open economy. → Off-shore floating wind may become cost-efficient in a national niche market.

  17. Learning about Ecological Systems by Constructing Qualitative Models with DynaLearn (United States)

    Leiba, Moshe; Zuzovsky, Ruth; Mioduser, David; Benayahu, Yehuda; Nachmias, Rafi


    A qualitative model of a system is an abstraction that captures ordinal knowledge and predicts the set of qualitatively possible behaviours of the system, given a qualitative description of its structure and initial state. This paper examines an innovative approach to science education using an interactive learning environment that supports…

  18. [Connectionist models of social learning: a case of learning by observing a simple task]. (United States)

    Paignon, A; Desrichard, O; Bollon, T


    This article proposes a connectionist model of the social learning theory developed by Bandura (1977). The theory posits that an individual in an interactive situation is capable of learning new behaviours merely by observing them in others. Such learning is acquired through an initial phase in which the individual memorizes what he has observed (observation phase), followed by a second phase where he puts the recorded observations to use as a guide for adjusting his own behaviour (reproduction phase). We shall refer to the two above-mentioned phases to demonstrate that it is conceivable to simulate learning by observation otherwise than through the recording of perceived information using symbolic representation. To this end we shall rely on the formalism of ecological neuron networks (Parisi, Cecconi, & Nolfi, 1990) to implement an agent provided with the major processes identified as essential to learning through observation. The connectionist model so designed shall implement an agent capable of recording perceptive information and producing motor behaviours. The learning situation we selected associates an agent demonstrating goal-achievement behaviour and an observer agent learning the same behaviour by observation. Throughout the acquisition phase, the demonstrator supervises the observer's learning process based on association between spatial information (input) and behavioural information (output). Representation thus constructed then serves as an adjustment guide during the production phase, involving production by the observer of a sequence of actions which he compares to the representation stored in distributed form as constructed through observation. An initial simulation validates model architecture by confirming the requirement for both phases identified in the literature (Bandura, 1977) to simulate learning through observation. The representation constructed over the observation phase evidences acquisition of observed behaviours, although this phase

  19. Knowledge Management System Model for Learning Organisations (United States)

    Amin, Yousif; Monamad, Roshayu


    Based on the literature of knowledge management (KM), this paper reports on the progress of developing a new knowledge management system (KMS) model with components architecture that are distributed over the widely-recognised socio-technical system (STS) aspects to guide developers for selecting the most applicable components to support their KM…

  20. Flipped classroom model for learning evidence-based medicine

    Directory of Open Access Journals (Sweden)

    Rucker SY


    Full Text Available Sydney Y Rucker,1 Zulfukar Ozdogan,1 Morhaf Al Achkar2 1School of Education, Indiana University, Bloomington, IN, 2Department of Family Medicine, School of Medicine, University of Washington, Seattle, WA, USA Abstract: Journal club (JC, as a pedagogical strategy, has long been used in graduate medical education (GME. As evidence-based medicine (EBM becomes a mainstay in GME, traditional models of JC present a number of insufficiencies and call for novel models of instruction. A flipped classroom model appears to be an ideal strategy to meet the demands to connect evidence to practice while creating engaged, culturally competent, and technologically literate physicians. In this article, we describe a novel model of flipped classroom in JC. We present the flow of learning activities during the online and face-to-face instruction, and then we highlight specific considerations for implementing a flipped classroom model. We show that implementing a flipped classroom model to teach EBM in a residency program not only is possible but also may constitute improved learning opportunity for residents. Follow-up work is needed to evaluate the effectiveness of this model on both learning and clinical practice. Keywords: evidence-based medicine, flipped classroom, residency education

  1. Learning and Control Model of the Arm for Loading (United States)

    Kim, Kyoungsik; Kambara, Hiroyuki; Shin, Duk; Koike, Yasuharu

    We propose a learning and control model of the arm for a loading task in which an object is loaded onto one hand with the other hand, in the sagittal plane. Postural control during object interactions provides important points to motor control theories in terms of how humans handle dynamics changes and use the information of prediction and sensory feedback. For the learning and control model, we coupled a feedback-error-learning scheme with an Actor-Critic method used as a feedback controller. To overcome sensory delays, a feedforward dynamics model (FDM) was used in the sensory feedback path. We tested the proposed model in simulation using a two-joint arm with six muscles, each with time delays in muscle force generation. By applying the proposed model to the loading task, we showed that motor commands started increasing, before an object was loaded on, to stabilize arm posture. We also found that the FDM contributes to the stabilization by predicting how the hand changes based on contexts of the object and efferent signals. For comparison with other computational models, we present the simulation results of a minimum-variance model.

  2. Motivation within the Information Processing Model of Foreign Language Learning (United States)

    Manolopoulou-Sergi, Eleni


    The present article highlights the importance of the motivational construct for the foreign language learning (FLL) process. More specifically, in the present article it is argued that motivation is likely to play a significant role at all three stages of the FLL process as they are discussed within the information processing model of FLL, namely,…

  3. Reformulating the Depression Model of Learned Hopelessness for Academic Outcomes (United States)

    Au, Raymond C. P.; Watkins, David; Hattie, John; Alexander, Patricia


    This review explores developments in the construct of learned hopelessness, which originated in the clinical literature dealing with depression. In that context, the model developed by Abramson, Metalsky, and Alloy [Abramson, L. Y., Metalsky, G. I., & Alloy, L. B. (1989). "Hopelessness depression: A theory-based subtype of depression."…

  4. A Professionalism Curricular Model to Promote Transformative Learning Among Residents. (United States)

    Foshee, Cecile M; Mehdi, Ali; Bierer, S Beth; Traboulsi, Elias I; Isaacson, J Harry; Spencer, Abby; Calabrese, Cassandra; Burkey, Brian B


    Using the frameworks of transformational learning and situated learning theory, we developed a technology-enhanced professionalism curricular model to build a learning community aimed at promoting residents' self-reflection and self-awareness. The RAPR model had 4 components: (1) R ecognize : elicit awareness; (2) A ppreciate : question assumptions and take multiple perspectives; (3) P ractice : try new/changed perspectives; and (4) R eflect : articulate implications of transformed views on future actions. The authors explored the acceptability and practicality of the RAPR model in teaching professionalism in a residency setting, including how residents and faculty perceive the model, how well residents carry out the curricular activities, and whether these activities support transformational learning. A convenience sample of 52 postgraduate years 1 through 3 internal medicine residents participated in the 10-hour curriculum over 4 weeks. A constructivist approach guided the thematic analysis of residents' written reflections, which were a required curricular task. A total of 94% (49 of 52) of residents participated in 2 implementation periods (January and March 2015). Findings suggested that RAPR has the potential to foster professionalism transformation in 3 domains: (1) attitudinal, with participants reporting they viewed professionalism in a more positive light and felt more empathetic toward patients; (2) behavioral, with residents indicating their ability to listen to patients increased; and (3) cognitive, with residents indicating the discussions improved their ability to reflect, and this helped them create meaning from experiences. Our findings suggest that RAPR offers an acceptable and practical strategy to teach professionalism to residents.

  5. Competence Models in Technology-enhanced Competence-based Learning

    NARCIS (Netherlands)

    Sampson, Demetrios; Fytros, Demetrios


    Please cite as: Sampson, D., & Fytros, D. (2008). Competence Models in Technology-enhanced Competence-based Learning. In H. H. Adelsberger, Kinshuk, J. M. Pawlowski & D. Sampson (Eds.), International Handbook on Information Technologies for Education and Training, 2nd Edition, Springer, June 2008


    Directory of Open Access Journals (Sweden)

    Aryani Novianti


    Full Text Available Tujuan dari penelitian ini adalah untuk mengetahui pengaruh model pembelajaran Learning Cycle pada konsep Sistem Pencernaan pada Manusia terhadap keterampilan berpikir kritis siswa. Adapun model pembelajaran Learning Cycle yang diterapkan adalah jenis 5E (Engangement, Exploration, Explanation, Elaboration dan Evaluation. Populasi dari penelitian ini adalah seluruh siswa kelas VIII SMP N 9 Kota Tangerang Selatan sedangkan sampelnya adalah seluruh siswa di kelas VIII 7 (38 orang dan VIII 8 (38 orang SMP N 9 Kota Tangsel. Teknik pengambilan sampel dalam penelitian ini dilakukan dengan teknik Sampling Purposive. Metode penelitian yang digunakan dalam penelitian ini adalah metode penelitian Quasi-eksperimental design dengan desain penelitian berupa nonequivalent control group design. Instrumen yang digunakan berupa tes tertulis berupa pilihan ganda dan esai yang ditujukan untuk mengukur keterampilan berpikir kritis. Sedangkan lembar observasi digunakan untuk mengamati keterlaksanaan model pembelajaran Learning Cycle oleh guru dan keterampilan berpikir kritis yang tergali oleh siswa. Analisis data menggunakan uji-t diperoleh hasil thitung 3,703 dan ttabel pada taraf signifikansi 5 % sebesar 2, maka thitung > ttabel. Hal ini dapat disimpulkan bahwa penerapan model pembelajaran Learning Cycle pada konsep Sistem Pencernaan pada Manusia berpengaruh terhadap keterampilan berpikir kritis siswa.

  7. Satlc model lesson for teaching and learning complex ...

    African Journals Online (AJOL)

    Environmental chemistry is one of the disciplines of Science. For the goal of the deep learning of the subject, it is indispensable to present perception and models of chemical behaviour explicitly. This can be accomplished by giving careful consideration to the development of concepts such that newer approaches are given ...

  8. An Online Interactive Competition Model for E-Learning System ...

    African Journals Online (AJOL)

    An Online Interactive Competition Model for E-Learning System. ... A working prototype of the system was developed using MySQL Database Management System (DBMS), PHP as the scripting language and Apache as the web server. The system was tested and the results were presented graphically in this paper.

  9. Assessing Implicit Knowledge in BIM Models with Machine Learning

    DEFF Research Database (Denmark)

    Krijnen, Thomas; Tamke, Martin


    architects and engineers are able to deduce non-explicitly explicitly stated information, which is often the core of the transported architectural information. This paper investigates how machine learning approaches allow a computational system to deduce implicit knowledge from a set of BIM models....

  10. Business Models Associated with Distance Learning in Higher Education (United States)

    Wang, Shouhong; Wang, Hai


    Textbook prices are continuously rising in higher education. This paper analyzes a business model which makes commercial textbooks more expensive, and explains why this issue tends to be more severe in the field of distance learning in higher education. It reports a case of adoption of open educational resources (OER) textbook for an online course…

  11. Dynamic Textures Modeling via Joint Video Dictionary Learning. (United States)

    Wei, Xian; Li, Yuanxiang; Shen, Hao; Chen, Fang; Kleinsteuber, Martin; Wang, Zhongfeng


    Video representation is an important and challenging task in the computer vision community. In this paper, we consider the problem of modeling and classifying video sequences of dynamic scenes which could be modeled in a dynamic textures (DT) framework. At first, we assume that image frames of a moving scene can be modeled as a Markov random process. We propose a sparse coding framework, named joint video dictionary learning (JVDL), to model a video adaptively. By treating the sparse coefficients of image frames over a learned dictionary as the underlying "states", we learn an efficient and robust linear transition matrix between two adjacent frames of sparse events in time series. Hence, a dynamic scene sequence is represented by an appropriate transition matrix associated with a dictionary. In order to ensure the stability of JVDL, we impose several constraints on such transition matrix and dictionary. The developed framework is able to capture the dynamics of a moving scene by exploring both sparse properties and the temporal correlations of consecutive video frames. Moreover, such learned JVDL parameters can be used for various DT applications, such as DT synthesis and recognition. Experimental results demonstrate the strong competitiveness of the proposed JVDL approach in comparison with state-of-the-art video representation methods. Especially, it performs significantly better in dealing with DT synthesis and recognition on heavily corrupted data.

  12. An Intervention Model of Constructive Conflict Resolution and Cooperative Learning. (United States)

    Zhang, Quanwu


    Tests an intervention model of constructive conflict resolution (CCR) and cooperative learning in three urban high schools. Findings show that improvements in CCR increased social support and decreased victimization for the students. These changes improved student's attitudes, self-esteem, interpersonal relations, and academic achievement. (GLR)

  13. Organizational Learning and Product Design Management: Towards a Theoretical Model. (United States)

    Chiva-Gomez, Ricardo; Camison-Zornoza, Cesar; Lapiedra-Alcami, Rafael


    Case studies of four Spanish ceramics companies were used to construct a theoretical model of 14 factors essential to organizational learning. One set of factors is related to the conceptual-analytical phase of the product design process and the other to the creative-technical phase. All factors contributed to efficient product design management…

  14. Learning in an estimated medium-scale DSGE model

    Czech Academy of Sciences Publication Activity Database

    Slobodyan, Sergey; Wouters, R.


    Roč. 36, č. 1 (2012), s. 26-46 ISSN 0165-1889 R&D Projects: GA ČR(CZ) GCP402/11/J018 Institutional support: PRVOUK-P23 Keywords : constant-gain adaptive learning * medium-scale DSGE model * DSGE- VAR Subject RIV: AH - Economics Impact factor: 0.807, year: 2012

  15. Exploration Of Deep Learning Algorithms Using Openacc Parallel Programming Model

    KAUST Repository

    Hamam, Alwaleed A.


    Deep learning is based on a set of algorithms that attempt to model high level abstractions in data. Specifically, RBM is a deep learning algorithm that used in the project to increase it\\'s time performance using some efficient parallel implementation by OpenACC tool with best possible optimizations on RBM to harness the massively parallel power of NVIDIA GPUs. GPUs development in the last few years has contributed to growing the concept of deep learning. OpenACC is a directive based ap-proach for computing where directives provide compiler hints to accelerate code. The traditional Restricted Boltzmann Ma-chine is a stochastic neural network that essentially perform a binary version of factor analysis. RBM is a useful neural net-work basis for larger modern deep learning model, such as Deep Belief Network. RBM parameters are estimated using an efficient training method that called Contrastive Divergence. Parallel implementation of RBM is available using different models such as OpenMP, and CUDA. But this project has been the first attempt to apply OpenACC model on RBM.

  16. Exploration Of Deep Learning Algorithms Using Openacc Parallel Programming Model

    KAUST Repository

    Hamam, Alwaleed A.; Khan, Ayaz H.


    Deep learning is based on a set of algorithms that attempt to model high level abstractions in data. Specifically, RBM is a deep learning algorithm that used in the project to increase it's time performance using some efficient parallel implementation by OpenACC tool with best possible optimizations on RBM to harness the massively parallel power of NVIDIA GPUs. GPUs development in the last few years has contributed to growing the concept of deep learning. OpenACC is a directive based ap-proach for computing where directives provide compiler hints to accelerate code. The traditional Restricted Boltzmann Ma-chine is a stochastic neural network that essentially perform a binary version of factor analysis. RBM is a useful neural net-work basis for larger modern deep learning model, such as Deep Belief Network. RBM parameters are estimated using an efficient training method that called Contrastive Divergence. Parallel implementation of RBM is available using different models such as OpenMP, and CUDA. But this project has been the first attempt to apply OpenACC model on RBM.

  17. International workshop on learning by modelling in science education

    NARCIS (Netherlands)

    Bredeweg, B.; Salles, P.; Biswas, G.; Bull, S.; Kay, J.; Mitrovic, A.


    Modelling is nowadays a well-established methodology in the sciences, supporting the inquiry and understanding of complex phenomena and systems in the natural, social and artificial worlds. Hence its strong potential as pedagogical approach fostering students' learning of scientific concepts and

  18. Hebbian errors in learning: an analysis using the Oja model. (United States)

    Rădulescu, Anca; Cox, Kingsley; Adams, Paul


    Recent work on long term potentiation in brain slices shows that Hebb's rule is not completely synapse-specific, probably due to intersynapse diffusion of calcium or other factors. We previously suggested that such errors in Hebbian learning might be analogous to mutations in evolution. We examine this proposal quantitatively, extending the classical Oja unsupervised model of learning by a single linear neuron to include Hebbian inspecificity. We introduce an error matrix E, which expresses possible crosstalk between updating at different connections. When there is no inspecificity, this gives the classical result of convergence to the first principal component of the input distribution (PC1). We show the modified algorithm converges to the leading eigenvector of the matrix EC, where C is the input covariance matrix. In the most biologically plausible case when there are no intrinsically privileged connections, E has diagonal elements Q and off-diagonal elements (1-Q)/(n-1), where Q, the quality, is expected to decrease with the number of inputs n and with a synaptic parameter b that reflects synapse density, calcium diffusion, etc. We study the dependence of the learning accuracy on b, n and the amount of input activity or correlation (analytically and computationally). We find that accuracy increases (learning becomes gradually less useful) with increases in b, particularly for intermediate (i.e., biologically realistic) correlation strength, although some useful learning always occurs up to the trivial limit Q=1/n. We discuss the relation of our results to Hebbian unsupervised learning in the brain. When the mechanism lacks specificity, the network fails to learn the expected, and typically most useful, result, especially when the input correlation is weak. Hebbian crosstalk would reflect the very high density of synapses along dendrites, and inevitably degrades learning.

  19. Prenatal treatment prevents learning deficit in Down syndrome model. (United States)

    Incerti, Maddalena; Horowitz, Kari; Roberson, Robin; Abebe, Daniel; Toso, Laura; Caballero, Madeline; Spong, Catherine Y


    Down syndrome is the most common genetic cause of mental retardation. Active fragments of neurotrophic factors release by astrocyte under the stimulation of vasoactive intestinal peptide, NAPVSIPQ (NAP) and SALLRSIPA (SAL) respectively, have shown therapeutic potential for developmental delay and learning deficits. Previous work demonstrated that NAP+SAL prevent developmental delay and glial deficit in Ts65Dn that is a well-characterized mouse model for Down syndrome. The objective of this study is to evaluate if prenatal treatment with these peptides prevents the learning deficit in the Ts65Dn mice. Pregnant Ts65Dn female and control pregnant females were randomly treated (intraperitoneal injection) on pregnancy days 8 through 12 with saline (placebo) or peptides (NAP 20 µg +SAL 20 µg) daily. Learning was assessed in the offspring (8-10 months) using the Morris Watermaze, which measures the latency to find the hidden platform (decrease in latency denotes learning). The investigators were blinded to the prenatal treatment and genotype. Pups were genotyped as trisomic (Down syndrome) or euploid (control) after completion of all tests. two-way ANOVA followed by Neuman-Keuls test for multiple comparisons, PDown syndrome-placebo; n = 11) did not demonstrate learning over the five day period. DS mice that were prenatally exposed to peptides (Down syndrome-peptides; n = 10) learned significantly better than Down syndrome-placebo (ptreatment with the neuroprotective peptides (NAP+SAL) prevented learning deficits in a Down syndrome model. These findings highlight a possibility for the prevention of sequelae in Down syndrome and suggest a potential pregnancy intervention that may improve outcome.

  20. Online Library of Scientific Models, A New Way to Teach, Learn, and Share Learning Experience

    Directory of Open Access Journals (Sweden)

    Hatem H. Elrefaei


    Full Text Available While scientific models are usually communicated in paper format, the need to reprogram every model by every user results in a huge loss of efforts, time and money, hence lengthening the educational and research developing cycle and loosing the learning experience and expertise gained by every user. We demonstrate a new portal that hosts a library of scientific models for electrical engineers in the form of java applets. They are all conformal, informative, with strong input and output filing system. The software design allows a fast developing cycle and it represents a strong infrastructure that can be shared by researchers to develop their own applets to be posted on the library. We aim for a community based library of scientific models that enhances the e-learning process for engineering students.

  1. A cognitive learning model of clinical nursing leadership. (United States)

    Pepin, Jacinthe; Dubois, Sylvie; Girard, Francine; Tardif, Jacques; Ha, Laurence


    Cognitive modeling of competencies is important to facilitate learning and evaluation. Clinical nursing leadership is considered a competency, as it is a "complex know-act" that students and nurses develop for the quality of care of patients and their families. Previous research on clinical leadership describes the attributes and characteristics of leaders and leadership, but, to our knowledge, a cognitive learning model (CLM) has yet to be developed. The purpose of our research was to develop a CLM of the clinical nursing leadership competency, from the beginning of a nursing program to expertise. An interpretative phenomenological study design was used 1) to document the experience of learning and practicing clinical leadership, and 2) to identify critical-learning turning points. Data was gathered from interviews with 32 baccalaureate students and 21 nurses from two clinical settings. An inductive analysis of data was conducted to determine the learning stages experienced: awareness of clinical leadership in nursing; integration of clinical leadership in actions; active leadership with patient/family; active leadership with the team; and, embedded clinical leadership extended to organizational level and beyond. The resulting CLM could have significant impact on both basic and continuing nursing education. Copyright © 2010 Elsevier Ltd. All rights reserved.

  2. Hidden Dangers of Computer Modelling: Remarks on Sokolik and Smith's Connectionist Learning Model of French Gender. (United States)

    Carroll, Susanne E.


    Criticizes the computer modelling experiments conducted by Sokolik and Smith (1992), which involved the learning of French gender attribution using connectionist architecture. The article argues that the experiments greatly oversimplified the complexity of gender learning, in that they were designed in such a way that knowledge that must be…

  3. Learning Supervised Topic Models for Classification and Regression from Crowds. (United States)

    Rodrigues, Filipe; Lourenco, Mariana; Ribeiro, Bernardete; Pereira, Francisco C


    The growing need to analyze large collections of documents has led to great developments in topic modeling. Since documents are frequently associated with other related variables, such as labels or ratings, much interest has been placed on supervised topic models. However, the nature of most annotation tasks, prone to ambiguity and noise, often with high volumes of documents, deem learning under a single-annotator assumption unrealistic or unpractical for most real-world applications. In this article, we propose two supervised topic models, one for classification and another for regression problems, which account for the heterogeneity and biases among different annotators that are encountered in practice when learning from crowds. We develop an efficient stochastic variational inference algorithm that is able to scale to very large datasets, and we empirically demonstrate the advantages of the proposed model over state-of-the-art approaches.

  4. Learning about physical parameters: the importance of model discrepancy

    International Nuclear Information System (INIS)

    Brynjarsdóttir, Jenný; O'Hagan, Anthony


    Science-based simulation models are widely used to predict the behavior of complex physical systems. It is also common to use observations of the physical system to solve the inverse problem, that is, to learn about the values of parameters within the model, a process which is often called calibration. The main goal of calibration is usually to improve the predictive performance of the simulator but the values of the parameters in the model may also be of intrinsic scientific interest in their own right. In order to make appropriate use of observations of the physical system it is important to recognize model discrepancy, the difference between reality and the simulator output. We illustrate through a simple example that an analysis that does not account for model discrepancy may lead to biased and over-confident parameter estimates and predictions. The challenge with incorporating model discrepancy in statistical inverse problems is being confounded with calibration parameters, which will only be resolved with meaningful priors. For our simple example, we model the model-discrepancy via a Gaussian process and demonstrate that through accounting for model discrepancy our prediction within the range of data is correct. However, only with realistic priors on the model discrepancy do we uncover the true parameter values. Through theoretical arguments we show that these findings are typical of the general problem of learning about physical parameters and the underlying physical system using science-based mechanistic models. (paper)

  5. Variability in Dopamine Genes Dissociates Model-Based and Model-Free Reinforcement Learning. (United States)

    Doll, Bradley B; Bath, Kevin G; Daw, Nathaniel D; Frank, Michael J


    Considerable evidence suggests that multiple learning systems can drive behavior. Choice can proceed reflexively from previous actions and their associated outcomes, as captured by "model-free" learning algorithms, or flexibly from prospective consideration of outcomes that might occur, as captured by "model-based" learning algorithms. However, differential contributions of dopamine to these systems are poorly understood. Dopamine is widely thought to support model-free learning by modulating plasticity in striatum. Model-based learning may also be affected by these striatal effects, or by other dopaminergic effects elsewhere, notably on prefrontal working memory function. Indeed, prominent demonstrations linking striatal dopamine to putatively model-free learning did not rule out model-based effects, whereas other studies have reported dopaminergic modulation of verifiably model-based learning, but without distinguishing a prefrontal versus striatal locus. To clarify the relationships between dopamine, neural systems, and learning strategies, we combine a genetic association approach in humans with two well-studied reinforcement learning tasks: one isolating model-based from model-free behavior and the other sensitive to key aspects of striatal plasticity. Prefrontal function was indexed by a polymorphism in the COMT gene, differences of which reflect dopamine levels in the prefrontal cortex. This polymorphism has been associated with differences in prefrontal activity and working memory. Striatal function was indexed by a gene coding for DARPP-32, which is densely expressed in the striatum where it is necessary for synaptic plasticity. We found evidence for our hypothesis that variations in prefrontal dopamine relate to model-based learning, whereas variations in striatal dopamine function relate to model-free learning. Decisions can stem reflexively from their previously associated outcomes or flexibly from deliberative consideration of potential choice outcomes

  6. Learning models for multi-source integration

    Energy Technology Data Exchange (ETDEWEB)

    Tejada, S.; Knoblock, C.A.; Minton, S. [Univ. of Southern California/ISI, Marina del Rey, CA (United States)


    Because of the growing number of information sources available through the internet there are many cases in which information needed to solve a problem or answer a question is spread across several information sources. For example, when given two sources, one about comic books and the other about super heroes, you might want to ask the question {open_quotes}Is Spiderman a Marvel Super Hero?{close_quotes} This query accesses both sources; therefore, it is necessary to have information about the relationships of the data within each source and between sources to properly access and integrate the data retrieved. The SIMS information broker captures this type of information in the form of a model. All the information sources map into the model providing the user a single interface to multiple sources.

  7. Efficient Matrix Models for Relational Learning (United States)


    base learners and h1:r is the ensemble learner. For example, consider the case where h1, . . . , hr are linear discriminants. The weighted vote of...a multilinear form naturally leads one to consider tensor factorization: e.g., UAV T is a special case of Tucker decomposition [129] on a 2D- tensor , a...matrix. Our five modeling choices can also be used to differentiate tensor factorizations, but the choices may be subtler for tensors than for

  8. Use of machine learning techniques for modeling of snow depth

    Directory of Open Access Journals (Sweden)

    G. V. Ayzel


    Full Text Available Snow exerts significant regulating effect on the land hydrological cycle since it controls intensity of heat and water exchange between the soil-vegetative cover and the atmosphere. Estimating of a spring flood runoff or a rain-flood on mountainous rivers requires understanding of the snow cover dynamics on a watershed. In our work, solving a problem of the snow cover depth modeling is based on both available databases of hydro-meteorological observations and easily accessible scientific software that allows complete reproduction of investigation results and further development of this theme by scientific community. In this research we used the daily observational data on the snow cover and surface meteorological parameters, obtained at three stations situated in different geographical regions: Col de Porte (France, Sodankyla (Finland, and Snoquamie Pass (USA.Statistical modeling of the snow cover depth is based on a complex of freely distributed the present-day machine learning models: Decision Trees, Adaptive Boosting, Gradient Boosting. It is demonstrated that use of combination of modern machine learning methods with available meteorological data provides the good accuracy of the snow cover modeling. The best results of snow cover depth modeling for every investigated site were obtained by the ensemble method of gradient boosting above decision trees – this model reproduces well both, the periods of snow cover accumulation and its melting. The purposeful character of learning process for models of the gradient boosting type, their ensemble character, and use of combined redundancy of a test sample in learning procedure makes this type of models a good and sustainable research tool. The results obtained can be used for estimating the snow cover characteristics for river basins where hydro-meteorological information is absent or insufficient.

  9. Project-Based Learning Using Discussion and Lesson-Learned Methods via Social Media Model for Enhancing Problem Solving Skills (United States)

    Jewpanich, Chaiwat; Piriyasurawong, Pallop


    This research aims to 1) develop the project-based learning using discussion and lesson-learned methods via social media model (PBL-DLL SoMe Model) used for enhancing problem solving skills of undergraduate in education student, and 2) evaluate the PBL-DLL SoMe Model used for enhancing problem solving skills of undergraduate in education student.…

  10. Improving a Deep Learning based RGB-D Object Recognition Model by Ensemble Learning

    DEFF Research Database (Denmark)

    Aakerberg, Andreas; Nasrollahi, Kamal; Heder, Thomas


    Augmenting RGB images with depth information is a well-known method to significantly improve the recognition accuracy of object recognition models. Another method to im- prove the performance of visual recognition models is ensemble learning. However, this method has not been widely explored...... in combination with deep convolutional neural network based RGB-D object recognition models. Hence, in this paper, we form different ensembles of complementary deep convolutional neural network models, and show that this can be used to increase the recognition performance beyond existing limits. Experiments...

  11. Table-sized matrix model in fractional learning (United States)

    Soebagyo, J.; Wahyudin; Mulyaning, E. C.


    This article provides an explanation of the fractional learning model i.e. a Table-Sized Matrix model in which fractional representation and its operations are symbolized by the matrix. The Table-Sized Matrix are employed to develop problem solving capabilities as well as the area model. The Table-Sized Matrix model referred to in this article is used to develop an understanding of the fractional concept to elementary school students which can then be generalized into procedural fluency (algorithm) in solving the fractional problem and its operation.

  12. Design ensemble machine learning model for breast cancer diagnosis. (United States)

    Hsieh, Sheau-Ling; Hsieh, Sung-Huai; Cheng, Po-Hsun; Chen, Chi-Huang; Hsu, Kai-Ping; Lee, I-Shun; Wang, Zhenyu; Lai, Feipei


    In this paper, we classify the breast cancer of medical diagnostic data. Information gain has been adapted for feature selections. Neural fuzzy (NF), k-nearest neighbor (KNN), quadratic classifier (QC), each single model scheme as well as their associated, ensemble ones have been developed for classifications. In addition, a combined ensemble model with these three schemes has been constructed for further validations. The experimental results indicate that the ensemble learning performs better than individual single ones. Moreover, the combined ensemble model illustrates the highest accuracy of classifications for the breast cancer among all models.

  13. Evaluation of Deep Learning Models for Predicting CO2 Flux (United States)

    Halem, M.; Nguyen, P.; Frankel, D.


    Artificial neural networks have been employed to calculate surface flux measurements from station data because they are able to fit highly nonlinear relations between input and output variables without knowing the detail relationships between the variables. However, the accuracy in performing neural net estimates of CO2 flux from observations of CO2 and other atmospheric variables is influenced by the architecture of the neural model, the availability, and complexity of interactions between physical variables such as wind, temperature, and indirect variables like latent heat, and sensible heat, etc. We evaluate two deep learning models, feed forward and recurrent neural network models to learn how they each respond to the physical measurements, time dependency of the measurements of CO2 concentration, humidity, pressure, temperature, wind speed etc. for predicting the CO2 flux. In this paper, we focus on a) building neural network models for estimating CO2 flux based on DOE data from tower Atmospheric Radiation Measurement data; b) evaluating the impact of choosing the surface variables and model hyper-parameters on the accuracy and predictions of surface flux; c) assessing the applicability of the neural network models on estimate CO2 flux by using OCO-2 satellite data; d) studying the efficiency of using GPU-acceleration for neural network performance using IBM Power AI deep learning software and packages on IBM Minsky system.

  14. Keefektifan Model Children Learning in Science (Clis) Untuk Meningkatkan Keterampilan Berpikir Rasional Siswa


    Marlina, Marlina; Zainuddin, Zainuddin; An'nur, Syubhan


    The low of rational thinking skills of students in learning physics models encourage researchers to carry out Children's Learning in Science (CLIS). This study aims to determine how the effectiveness of learning by using the model CLIS. The specific aims of research describing: (1) the ability of the teacher to manage the model CLIS, (2) rational thinking skills of students, (3) classical completeness student learning outcomes after participating in learning, (4) students' response to the mod...

  15. Using Learning Analytics to Understand Scientific Modeling in the Classroom

    Directory of Open Access Journals (Sweden)

    David Quigley


    Full Text Available Scientific models represent ideas, processes, and phenomena by describing important components, characteristics, and interactions. Models are constructed across various scientific disciplines, such as the food web in biology, the water cycle in Earth science, or the structure of the solar system in astronomy. Models are central for scientists to understand phenomena, construct explanations, and communicate theories. Constructing and using models to explain scientific phenomena is also an essential practice in contemporary science classrooms. Our research explores new techniques for understanding scientific modeling and engagement with modeling practices. We work with students in secondary biology classrooms as they use a web-based software tool—EcoSurvey—to characterize organisms and their interrelationships found in their local ecosystem. We use learning analytics and machine learning techniques to answer the following questions: (1 How can we automatically measure the extent to which students’ scientific models support complete explanations of phenomena? (2 How does the design of student modeling tools influence the complexity and completeness of students’ models? (3 How do clickstreams reflect and differentiate student engagement with modeling practices? We analyzed EcoSurvey usage data collected from two different deployments with over 1,000 secondary students across a large urban school district. We observe large variations in the completeness and complexity of student models, and large variations in their iterative refinement processes. These differences reveal that certain key model features are highly predictive of other aspects of the model. We also observe large differences in student modeling practices across different classrooms and teachers. We can predict a student’s teacher based on the observed modeling practices with a high degree of accuracy without significant tuning of the predictive model. These results highlight

  16. Machine Learning and Deep Learning Models to Predict Runoff Water Quantity and Quality (United States)

    Bradford, S. A.; Liang, J.; Li, W.; Murata, T.; Simunek, J.


    Contaminants can be rapidly transported at the soil surface by runoff to surface water bodies. Physically-based models, which are based on the mathematical description of main hydrological processes, are key tools for predicting surface water impairment. Along with physically-based models, data-driven models are becoming increasingly popular for describing the behavior of hydrological and water resources systems since these models can be used to complement or even replace physically based-models. In this presentation we propose a new data-driven model as an alternative to a physically-based overland flow and transport model. First, we have developed a physically-based numerical model to simulate overland flow and contaminant transport (the HYDRUS-1D overland flow module). A large number of numerical simulations were carried out to develop a database containing information about the impact of various input parameters (weather patterns, surface topography, vegetation, soil conditions, contaminants, and best management practices) on runoff water quantity and quality outputs. This database was used to train data-driven models. Three different methods (Neural Networks, Support Vector Machines, and Recurrence Neural Networks) were explored to prepare input- output functional relations. Results demonstrate the ability and limitations of machine learning and deep learning models to predict runoff water quantity and quality.

  17. Improving Semi-Supervised Learning with Auxiliary Deep Generative Models

    DEFF Research Database (Denmark)

    Maaløe, Lars; Sønderby, Casper Kaae; Sønderby, Søren Kaae

    Deep generative models based upon continuous variational distributions parameterized by deep networks give state-of-the-art performance. In this paper we propose a framework for extending the latent representation with extra auxiliary variables in order to make the variational distribution more...... expressive for semi-supervised learning. By utilizing the stochasticity of the auxiliary variable we demonstrate how to train discriminative classifiers resulting in state-of-the-art performance within semi-supervised learning exemplified by an 0.96% error on MNIST using 100 labeled data points. Furthermore...

  18. Learning Supervised Topic Models for Classification and Regression from Crowds

    DEFF Research Database (Denmark)

    Rodrigues, Filipe; Lourenco, Mariana; Ribeiro, Bernardete


    problems, which account for the heterogeneity and biases among different annotators that are encountered in practice when learning from crowds. We develop an efficient stochastic variational inference algorithm that is able to scale to very large datasets, and we empirically demonstrate the advantages...... annotation tasks, prone to ambiguity and noise, often with high volumes of documents, deem learning under a single-annotator assumption unrealistic or unpractical for most real-world applications. In this article, we propose two supervised topic models, one for classification and another for regression...

  19. Impact of censoring on learning Bayesian networks in survival modelling. (United States)

    Stajduhar, Ivan; Dalbelo-Basić, Bojana; Bogunović, Nikola


    Bayesian networks are commonly used for presenting uncertainty and covariate interactions in an easily interpretable way. Because of their efficient inference and ability to represent causal relationships, they are an excellent choice for medical decision support systems in diagnosis, treatment, and prognosis. Although good procedures for learning Bayesian networks from data have been defined, their performance in learning from censored survival data has not been widely studied. In this paper, we explore how to use these procedures to learn about possible interactions between prognostic factors and their influence on the variate of interest. We study how censoring affects the probability of learning correct Bayesian network structures. Additionally, we analyse the potential usefulness of the learnt models for predicting the time-independent probability of an event of interest. We analysed the influence of censoring with a simulation on synthetic data sampled from randomly generated Bayesian networks. We used two well-known methods for learning Bayesian networks from data: a constraint-based method and a score-based method. We compared the performance of each method under different levels of censoring to those of the naive Bayes classifier and the proportional hazards model. We did additional experiments on several datasets from real-world medical domains. The machine-learning methods treated censored cases in the data as event-free. We report and compare results for several commonly used model evaluation metrics. On average, the proportional hazards method outperformed other methods in most censoring setups. As part of the simulation study, we also analysed structural similarities of the learnt networks. Heavy censoring, as opposed to no censoring, produces up to a 5% surplus and up to 10% missing total arcs. It also produces up to 50% missing arcs that should originally be connected to the variate of interest. Presented methods for learning Bayesian networks from

  20. Preference learning with evolutionary Multivariate Adaptive Regression Spline model

    DEFF Research Database (Denmark)

    Abou-Zleikha, Mohamed; Shaker, Noor; Christensen, Mads Græsbøll


    This paper introduces a novel approach for pairwise preference learning through combining an evolutionary method with Multivariate Adaptive Regression Spline (MARS). Collecting users' feedback through pairwise preferences is recommended over other ranking approaches as this method is more appealing...... for function approximation as well as being relatively easy to interpret. MARS models are evolved based on their efficiency in learning pairwise data. The method is tested on two datasets that collectively provide pairwise preference data of five cognitive states expressed by users. The method is analysed...

  1. Learning Quantum Chemical Model with Learning Media Concept Map and Power Point Viewed from Memory and Creativity Skills Students

    Directory of Open Access Journals (Sweden)

    Agus Wahidi


    Full Text Available This research is experimental, using first class learning a quantum model of learning with concept maps media and the second media using real environments by power point presentation. The population is all class XI Science, number 2 grade. The sampling technique is done by purposive random sampling. Data collection techniques to test for cognitive performance and memory capabilities, with a questionnaire for creativity. Hypothesis testing using three-way ANOVA different cells with the help of software Minitab 15.Based on the results of data processing, concluded: (1 there is no influence of the quantum model of learning with media learning concept maps and real environments for learning achievement chemistry, (2 there is a high impact memory ability and low on student achievement, (3 there is no the effect of high and low creativity in student performance, (4 there is no interaction learning model quantum media learning concept maps and real environments with memory ability on student achievement, (5 there is no interaction learning model quantum media learning concept maps and real environments with creativity of student achievement, (6 there is no interaction memory skills and creativity of student achievement, (7 there is no interaction learning model quantum media learning concept maps and real environments, memory skills, and creativity on student achievement.

  2. Glutamatergic model psychoses: prediction error, learning, and inference. (United States)

    Corlett, Philip R; Honey, Garry D; Krystal, John H; Fletcher, Paul C


    Modulating glutamatergic neurotransmission induces alterations in conscious experience that mimic the symptoms of early psychotic illness. We review studies that use intravenous administration of ketamine, focusing on interindividual variability in the profundity of the ketamine experience. We will consider this individual variability within a hypothetical model of brain and cognitive function centered upon learning and inference. Within this model, the brains, neural systems, and even single neurons specify expectations about their inputs and responding to violations of those expectations with new learning that renders future inputs more predictable. We argue that ketamine temporarily deranges this ability by perturbing both the ways in which prior expectations are specified and the ways in which expectancy violations are signaled. We suggest that the former effect is predominantly mediated by NMDA blockade and the latter by augmented and inappropriate feedforward glutamatergic signaling. We suggest that the observed interindividual variability emerges from individual differences in neural circuits that normally underpin the learning and inference processes described. The exact source for that variability is uncertain, although it is likely to arise not only from genetic variation but also from subjects' previous experiences and prior learning. Furthermore, we argue that chronic, unlike acute, NMDA blockade alters the specification of expectancies more profoundly and permanently. Scrutinizing individual differences in the effects of acute and chronic ketamine administration in the context of the Bayesian brain model may generate new insights about the symptoms of psychosis; their underlying cognitive processes and neurocircuitry.

  3. Machine learning modelling for predicting soil liquefaction susceptibility

    Directory of Open Access Journals (Sweden)

    P. Samui


    Full Text Available This study describes two machine learning techniques applied to predict liquefaction susceptibility of soil based on the standard penetration test (SPT data from the 1999 Chi-Chi, Taiwan earthquake. The first machine learning technique which uses Artificial Neural Network (ANN based on multi-layer perceptions (MLP that are trained with Levenberg-Marquardt backpropagation algorithm. The second machine learning technique uses the Support Vector machine (SVM that is firmly based on the theory of statistical learning theory, uses classification technique. ANN and SVM have been developed to predict liquefaction susceptibility using corrected SPT [(N160] and cyclic stress ratio (CSR. Further, an attempt has been made to simplify the models, requiring only the two parameters [(N160 and peck ground acceleration (amax/g], for the prediction of liquefaction susceptibility. The developed ANN and SVM models have also been applied to different case histories available globally. The paper also highlights the capability of the SVM over the ANN models.

  4. Learning-based stochastic object models for characterizing anatomical variations (United States)

    Dolly, Steven R.; Lou, Yang; Anastasio, Mark A.; Li, Hua


    It is widely known that the optimization of imaging systems based on objective, task-based measures of image quality via computer-simulation requires the use of a stochastic object model (SOM). However, the development of computationally tractable SOMs that can accurately model the statistical variations in human anatomy within a specified ensemble of patients remains a challenging task. Previously reported numerical anatomic models lack the ability to accurately model inter-patient and inter-organ variations in human anatomy among a broad patient population, mainly because they are established on image data corresponding to a few of patients and individual anatomic organs. This may introduce phantom-specific bias into computer-simulation studies, where the study result is heavily dependent on which phantom is used. In certain applications, however, databases of high-quality volumetric images and organ contours are available that can facilitate this SOM development. In this work, a novel and tractable methodology for learning a SOM and generating numerical phantoms from a set of volumetric training images is developed. The proposed methodology learns geometric attribute distributions (GAD) of human anatomic organs from a broad patient population, which characterize both centroid relationships between neighboring organs and anatomic shape similarity of individual organs among patients. By randomly sampling the learned centroid and shape GADs with the constraints of the respective principal attribute variations learned from the training data, an ensemble of stochastic objects can be created. The randomness in organ shape and position reflects the learned variability of human anatomy. To demonstrate the methodology, a SOM of an adult male pelvis is computed and examples of corresponding numerical phantoms are created.

  5. Examining Asymmetrical Relationships of Organizational Learning Antecedents: A Theoretical Model

    Directory of Open Access Journals (Sweden)

    Ery Tri Djatmika


    Full Text Available Global era is characterized by highly competitive advantage market demand. Responding to the challenge of rapid environmental changes, organizational learning is becoming a strategic way and solution to empower people themselves within the organization in order to create a novelty as valuable positioning source. For research purposes, determining the influential antecedents that affect organizational learning is vital to understand research-based solutions given for practical implications. Accordingly, identification of variables examined by asymmetrical relationships is critical to establish. Possible antecedent variables come from organizational and personal point of views. It is also possible to include a moderating one. A proposed theoretical model of asymmetrical effects of organizational learning and its antecedents is discussed in this article.

  6. Improving wave forecasting by integrating ensemble modelling and machine learning (United States)

    O'Donncha, F.; Zhang, Y.; James, S. C.


    Modern smart-grid networks use technologies to instantly relay information on supply and demand to support effective decision making. Integration of renewable-energy resources with these systems demands accurate forecasting of energy production (and demand) capacities. For wave-energy converters, this requires wave-condition forecasting to enable estimates of energy production. Current operational wave forecasting systems exhibit substantial errors with wave-height RMSEs of 40 to 60 cm being typical, which limits the reliability of energy-generation predictions thereby impeding integration with the distribution grid. In this study, we integrate physics-based models with statistical learning aggregation techniques that combine forecasts from multiple, independent models into a single "best-estimate" prediction of the true state. The Simulating Waves Nearshore physics-based model is used to compute wind- and currents-augmented waves in the Monterey Bay area. Ensembles are developed based on multiple simulations perturbing input data (wave characteristics supplied at the model boundaries and winds) to the model. A learning-aggregation technique uses past observations and past model forecasts to calculate a weight for each model. The aggregated forecasts are compared to observation data to quantify the performance of the model ensemble and aggregation techniques. The appropriately weighted ensemble model outperforms an individual ensemble member with regard to forecasting wave conditions.

  7. Machine learning models in breast cancer survival prediction. (United States)

    Montazeri, Mitra; Montazeri, Mohadeseh; Montazeri, Mahdieh; Beigzadeh, Amin


    Breast cancer is one of the most common cancers with a high mortality rate among women. With the early diagnosis of breast cancer survival will increase from 56% to more than 86%. Therefore, an accurate and reliable system is necessary for the early diagnosis of this cancer. The proposed model is the combination of rules and different machine learning techniques. Machine learning models can help physicians to reduce the number of false decisions. They try to exploit patterns and relationships among a large number of cases and predict the outcome of a disease using historical cases stored in datasets. The objective of this study is to propose a rule-based classification method with machine learning techniques for the prediction of different types of Breast cancer survival. We use a dataset with eight attributes that include the records of 900 patients in which 876 patients (97.3%) and 24 (2.7%) patients were females and males respectively. Naive Bayes (NB), Trees Random Forest (TRF), 1-Nearest Neighbor (1NN), AdaBoost (AD), Support Vector Machine (SVM), RBF Network (RBFN), and Multilayer Perceptron (MLP) machine learning techniques with 10-cross fold technique were used with the proposed model for the prediction of breast cancer survival. The performance of machine learning techniques were evaluated with accuracy, precision, sensitivity, specificity, and area under ROC curve. Out of 900 patients, 803 patients and 97 patients were alive and dead, respectively. In this study, Trees Random Forest (TRF) technique showed better results in comparison to other techniques (NB, 1NN, AD, SVM and RBFN, MLP). The accuracy, sensitivity and the area under ROC curve of TRF are 96%, 96%, 93%, respectively. However, 1NN machine learning technique provided poor performance (accuracy 91%, sensitivity 91% and area under ROC curve 78%). This study demonstrates that Trees Random Forest model (TRF) which is a rule-based classification model was the best model with the highest level of

  8. Problem solving based learning model with multiple representations to improve student's mental modelling ability on physics (United States)

    Haili, Hasnawati; Maknun, Johar; Siahaan, Parsaoran


    Physics is a lessons that related to students' daily experience. Therefore, before the students studying in class formally, actually they have already have a visualization and prior knowledge about natural phenomenon and could wide it themselves. The learning process in class should be aimed to detect, process, construct, and use students' mental model. So, students' mental model agree with and builds in the right concept. The previous study held in MAN 1 Muna informs that in learning process the teacher did not pay attention students' mental model. As a consequence, the learning process has not tried to build students' mental modelling ability (MMA). The purpose of this study is to describe the improvement of students' MMA as a effect of problem solving based learning model with multiple representations approach. This study is pre experimental design with one group pre post. It is conducted in XI IPA MAN 1 Muna 2016/2017. Data collection uses problem solving test concept the kinetic theory of gasses and interview to get students' MMA. The result of this study is clarification students' MMA which is categorized in 3 category; High Mental Modelling Ability (H-MMA) for 7Mental Modelling Ability (M-MMA) for 3Mental Modelling Ability (L-MMA) for 0 ≤ x ≤ 3 score. The result shows that problem solving based learning model with multiple representations approach can be an alternative to be applied in improving students' MMA.

  9. Semantic modeling of portfolio assessment in e-learning environment

    Directory of Open Access Journals (Sweden)

    Lucila Romero


    Full Text Available In learning environment, portfolio is used as a tool to keep track of learner’s progress. Particularly, when it comes to e-learning, continuous assessment allows greater customization and efficiency in learning process and prevents students lost interest in their study. Also, each student has his own characteristics and learning skills that must be taken into account in order to keep learner`s interest. So, personalized monitoring is the key to guarantee the success of technology-based education. In this context, portfolio assessment emerge as the solution because is an easy way to allow teacher organize and personalize assessment according to students characteristic and need. A portfolio assessment can contain various types of assessment like formative assessment, summative assessment, hetero or self-assessment and use different instruments like multiple choice questions, conceptual maps, and essay among others. So, a portfolio assessment represents a compilation of all assessments must be solved by a student in a course, it documents progress and set targets. In previous work, it has been proposed a conceptual framework that consist of an ontology network named AOnet which is a semantic tool conceptualizing different types of assessments. Continuing that work, this paper presents a proposal to implement portfolios assessment in e-learning environments. The proposal consists of a semantic model that describes key components and relations of this domain to set the bases to develop a tool to generate, manage and perform portfolios assessment.

  10. Statistical mechanics of learning orthogonal signals for general covariance models

    International Nuclear Information System (INIS)

    Hoyle, David C


    Statistical mechanics techniques have proved to be useful tools in quantifying the accuracy with which signal vectors are extracted from experimental data. However, analysis has previously been limited to specific model forms for the population covariance C, which may be inappropriate for real world data sets. In this paper we obtain new statistical mechanical results for a general population covariance matrix C. For data sets consisting of p sample points in R N we use the replica method to study the accuracy of orthogonal signal vectors estimated from the sample data. In the asymptotic limit of N,p→∞ at fixed α = p/N, we derive analytical results for the signal direction learning curves. In the asymptotic limit the learning curves follow a single universal form, each displaying a retarded learning transition. An explicit formula for the location of the retarded learning transition is obtained and we find marked variation in the location of the retarded learning transition dependent on the distribution of population covariance eigenvalues. The results of the replica analysis are confirmed against simulation

  11. Quasilinear Extreme Learning Machine Model Based Internal Model Control for Nonlinear Process

    Directory of Open Access Journals (Sweden)

    Dazi Li


    Full Text Available A new strategy for internal model control (IMC is proposed using a regression algorithm of quasilinear model with extreme learning machine (QL-ELM. Aimed at the chemical process with nonlinearity, the learning process of the internal model and inverse model is derived. The proposed QL-ELM is constructed as a linear ARX model with a complicated nonlinear coefficient. It shows some good approximation ability and fast convergence. The complicated coefficients are separated into two parts. The linear part is determined by recursive least square (RLS, while the nonlinear part is identified through extreme learning machine. The parameters of linear part and the output weights of ELM are estimated iteratively. The proposed internal model control is applied to CSTR process. The effectiveness and accuracy of the proposed method are extensively verified through numerical results.

  12. Advances in Bayesian Model Based Clustering Using Particle Learning

    Energy Technology Data Exchange (ETDEWEB)

    Merl, D M


    Recent work by Carvalho, Johannes, Lopes and Polson and Carvalho, Lopes, Polson and Taddy introduced a sequential Monte Carlo (SMC) alternative to traditional iterative Monte Carlo strategies (e.g. MCMC and EM) for Bayesian inference for a large class of dynamic models. The basis of SMC techniques involves representing the underlying inference problem as one of state space estimation, thus giving way to inference via particle filtering. The key insight of Carvalho et al was to construct the sequence of filtering distributions so as to make use of the posterior predictive distribution of the observable, a distribution usually only accessible in certain Bayesian settings. Access to this distribution allows a reversal of the usual propagate and resample steps characteristic of many SMC methods, thereby alleviating to a large extent many problems associated with particle degeneration. Furthermore, Carvalho et al point out that for many conjugate models the posterior distribution of the static variables can be parametrized in terms of [recursively defined] sufficient statistics of the previously observed data. For models where such sufficient statistics exist, particle learning as it is being called, is especially well suited for the analysis of streaming data do to the relative invariance of its algorithmic complexity with the number of data observations. Through a particle learning approach, a statistical model can be fit to data as the data is arriving, allowing at any instant during the observation process direct quantification of uncertainty surrounding underlying model parameters. Here we describe the use of a particle learning approach for fitting a standard Bayesian semiparametric mixture model as described in Carvalho, Lopes, Polson and Taddy. In Section 2 we briefly review the previously presented particle learning algorithm for the case of a Dirichlet process mixture of multivariate normals. In Section 3 we describe several novel extensions to the original

  13. Keeping learning central: a model for implementing emerging technologies (United States)

    Willcockson, Irmgard U.; Phelps, Cynthia L.


    Felt problem Technology integration continues to be a challenge for health science faculty. While students expect emerging technologies to be used in the classroom, faculty members desire a strategic process to incorporate technology for the students' benefit. Our solution We have developed a model that provides faculty a strategy for integrating emerging technologies into the classroom. The model is grounded in student learning and may be applied to any technology. We present the model alongside examples from faculty who have used it to incorporate technology into their health sciences classrooms. PMID:20165698

  14. Learning reliable manipulation strategies without initial physical models (United States)

    Christiansen, Alan D.; Mason, Matthew T.; Mitchell, Tom M.


    A description is given of a robot, possessing limited sensory and effectory capabilities but no initial model of the effects of its actions on the world, that acquires such a model through exploration, practice, and observation. By acquiring an increasingly correct model of its actions, it generates increasingly successful plans to achieve its goals. In an apparently nondeterministic world, achieving reliability requires the identification of reliable actions and a preference for using such actions. Furthermore, by selecting its training actions carefully, the robot can significantly improve its learning rate.

  15. Hidden physics models: Machine learning of nonlinear partial differential equations (United States)

    Raissi, Maziar; Karniadakis, George Em


    While there is currently a lot of enthusiasm about "big data", useful data is usually "small" and expensive to acquire. In this paper, we present a new paradigm of learning partial differential equations from small data. In particular, we introduce hidden physics models, which are essentially data-efficient learning machines capable of leveraging the underlying laws of physics, expressed by time dependent and nonlinear partial differential equations, to extract patterns from high-dimensional data generated from experiments. The proposed methodology may be applied to the problem of learning, system identification, or data-driven discovery of partial differential equations. Our framework relies on Gaussian processes, a powerful tool for probabilistic inference over functions, that enables us to strike a balance between model complexity and data fitting. The effectiveness of the proposed approach is demonstrated through a variety of canonical problems, spanning a number of scientific domains, including the Navier-Stokes, Schrödinger, Kuramoto-Sivashinsky, and time dependent linear fractional equations. The methodology provides a promising new direction for harnessing the long-standing developments of classical methods in applied mathematics and mathematical physics to design learning machines with the ability to operate in complex domains without requiring large quantities of data.

  16. Interpersonal social responsibility model of service learning: A longitudinal study. (United States)

    Lahav, Orit; Daniely, Noa; Yalon-Chamovitz, Shira


    Service-learning (SL) is commonly used in Occupational Therapy (OT) programs worldwide as a community placement educational strategy. However, most SL models are not clearly defined in terms of both methodology and learning outcomes. This longitudinal study explores a structured model of Service-Learning (Interpersonal Social Responsibility-Service Learning: ISR-SL) aimed towards the development of professional identity among OT students. Based on OT students experiences from the end of the course through later stages as mature students and professionals. A qualitative research design was used to explore the perceptions and experiences of 150 first, second, and third-year OT students and graduates who have participated in ISR-SL during their first academic year. Our findings suggest that the structured, long-term relationship with a person with a disability in the natural environment, which is the core of the ISR-SL, allowed students to develop a professional identity based on seeing the person as a whole and recognizing his/her centrality in the therapeutic relationship. This study suggests ISR-SL as future direction or next step for implementing SL in OT and other healthcare disciplines programs.

  17. Designing An Effective Mobile-learning Model By Integrating Student Culture


    Ibrahim Mohamad; Abdalla AlAmeen


    Mobile learning is a good technology because it allows communication, collaboration, and sharing information or resources among all of learning members. Mobile learning can be used as perfect solutions to support the learning process. Thither are many concepts and factors influencing effective learning results through creativity, collaboration, and communication. However, culture is an unaccounted factor which should be appended to the existing M-learning model. Culture may improve the learni...

  18. Modeling conflict : research methods, quantitative modeling, and lessons learned.

    Energy Technology Data Exchange (ETDEWEB)

    Rexroth, Paul E.; Malczynski, Leonard A.; Hendrickson, Gerald A.; Kobos, Peter Holmes; McNamara, Laura A.


    This study investigates the factors that lead countries into conflict. Specifically, political, social and economic factors may offer insight as to how prone a country (or set of countries) may be for inter-country or intra-country conflict. Largely methodological in scope, this study examines the literature for quantitative models that address or attempt to model conflict both in the past, and for future insight. The analysis concentrates specifically on the system dynamics paradigm, not the political science mainstream approaches of econometrics and game theory. The application of this paradigm builds upon the most sophisticated attempt at modeling conflict as a result of system level interactions. This study presents the modeling efforts built on limited data and working literature paradigms, and recommendations for future attempts at modeling conflict.

  19. Using an LMS for Foreign Language Teaching/Learning: An Attempt Based on the "Cyclic Model of Learning"


    SUMI, Seijiro; TAKEUCHI, Osamu


    The purposes of the study are (a) to put the “cyclic model of learning” into practice by means of an LMS (Learning Management System) for foreign language teaching /learning, and (b) to examine how the “cyclic model of learning” influences improvement of students' English ability in both proficiency and achievement. Current major concerns of CALL (Computer Assisted Language learning) research have shifted from piecemeal and experimental tests of the use of technology in a single computer lab ...


    Energy Technology Data Exchange (ETDEWEB)

    VERSPOOR, KARIN [Los Alamos National Laboratory; LIN, SHOU-DE [Los Alamos National Laboratory


    An N-gram language model aims at capturing statistical syntactic word order information from corpora. Although the concept of language models has been applied extensively to handle a variety of NLP problems with reasonable success, the standard model does not incorporate semantic information, and consequently limits its applicability to semantic problems such as word sense disambiguation. We propose a framework that integrates semantic information into the language model schema, allowing a system to exploit both syntactic and semantic information to address NLP problems. Furthermore, acknowledging the limited availability of semantically annotated data, we discuss how the proposed model can be learned without annotated training examples. Finally, we report on a case study showing how the semantics-enhanced language model can be applied to unsupervised word sense disambiguation with promising results.

  1. Perspectives of Students’ Behavior Towards Mobile Learning (M-learning in Egypt: an Extension of the UTAUT Model

    Directory of Open Access Journals (Sweden)

    R. A. Ali


    Full Text Available The rapid development of third-generation (3G mobile technologies has led to the emergence of a new kind of learning called mobile learning (m-learning. M-learning means the use of mobile devices to access learning materials at anytime and anywhere with the aid of mobile terminals and networks. This paper explores the possibility of applying m-learning for schools in Egypt through a proposed model of acceptance factors that may affect the students’ intentions to adopt m-learning. We use the original model of Unified Theory of Acceptance and Use of Technology (UTAUT and extended it with three new factors mobility, interactivity, and enjoyment.

  2. Stakeholder Perceptions, Learning Opportunities, and Student Outcomes in Three Clinical Learning Models. (United States)

    Hendricks, Susan; DeMeester, Deborah; Stephenson, Evelyn; Welch, Janet


    Understanding the strengths and challenges of various clinical models is important for nursing education. Three long-standing clinical models (preceptored, hybrid, and traditional) were compared on several outcome measures related to satisfaction, learning opportunities, and student outcomes. Students, faculty, and preceptors participated in this study. Although no differences were noted in satisfaction or standardized examination scores, students in the preceptored clinical model were able to practice more psychomotor skills. Although participants in the preceptored model reported spending more time communicating with staff nurses than did those in the other models, students in the traditional model spent more time with faculty. No differences were noted among groups in student clinical observation time. All clinical learning models were focused on how clinical time was structured, without an emphasis on how faculty and preceptors work with students to develop nursing clinical reasoning skills. Identifying methodology to impact thinking in the clinical environment is a key next step. [J Nurs Educ. 2016;55(5):271-277.]. Copyright 2016, SLACK Incorporated.


    Directory of Open Access Journals (Sweden)

    A. Cahyarini


    Full Text Available The aim of this study was to investigate the effect of 5E learning cycle instructional model using socioscientific issues (SSI learning context on students’ critical thinking skills of acid-base. This study used quasi-experimental posttest only control group design. The sample consisted of three classes, which were XI MIA-4class (n = 32 that learned using 5E LC model, XI MIA-5 class (n = 33 that learned using 5E LC+SSI, and XI MIA-6 class (n = 32 that learned using conventional method. The samples were choosen by convenience sampling technique. The test instrument consisted of 15 multiple choice items which were valid and reliable (r = 0.806. The data were analyzed using one way ANOVA test and LSD posthoc test. The results of this study indicated that the students who learned using 5E LC+SSI model showed greater levels of critical thinking skills (  = 74,95 than both the student who learned using 5E LC model (  = 74,17 and  the student who learned using conventional method (  = 68,96. Based on statistics analysis, there was significant differences on students’ critical thinkings between students taught using conventional method and students taught either using 5E LC+SSI model and 5E LC model. However,  there was no significant differences on students’ critical thinking skills between students taught using 5E LC+SSI model and the students taught using 5E LC model.

  4. The Experimental Research on E-Learning Instructional Design Model Based on Cognitive Flexibility Theory (United States)

    Cao, Xianzhong; Wang, Feng; Zheng, Zhongmei

    The paper reports an educational experiment on the e-Learning instructional design model based on Cognitive Flexibility Theory, the experiment were made to explore the feasibility and effectiveness of the model in promoting the learning quality in ill-structured domain. The study performed the experiment on two groups of students: one group learned through the system designed by the model and the other learned by the traditional method. The results of the experiment indicate that the e-Learning designed through the model is helpful to promote the intrinsic motivation, learning quality in ill-structured domains, ability to resolve ill-structured problem and creative thinking ability of the students.

  5. Learning and Model-checking Networks of I/O Automata

    DEFF Research Database (Denmark)

    Mao, Hua; Jaeger, Manfred


    We introduce a new statistical relational learning (SRL) approach in which models for structured data, especially network data, are constructed as networks of communicating nite probabilistic automata. Leveraging existing automata learning methods from the area of grammatical inference, we can...... learn generic models for network entities in the form of automata templates. As is characteristic for SRL techniques, the abstraction level aorded by learning generic templates enables one to apply the learned model to new domains. A main benet of learning models based on nite automata lies in the fact...

  6. Dopamine selectively remediates 'model-based' reward learning: a computational approach. (United States)

    Sharp, Madeleine E; Foerde, Karin; Daw, Nathaniel D; Shohamy, Daphna


    Patients with loss of dopamine due to Parkinson's disease are impaired at learning from reward. However, it remains unknown precisely which aspect of learning is impaired. In particular, learning from reward, or reinforcement learning, can be driven by two distinct computational processes. One involves habitual stamping-in of stimulus-response associations, hypothesized to arise computationally from 'model-free' learning. The other, 'model-based' learning, involves learning a model of the world that is believed to support goal-directed behaviour. Much work has pointed to a role for dopamine in model-free learning. But recent work suggests model-based learning may also involve dopamine modulation, raising the possibility that model-based learning may contribute to the learning impairment in Parkinson's disease. To directly test this, we used a two-step reward-learning task which dissociates model-free versus model-based learning. We evaluated learning in patients with Parkinson's disease tested ON versus OFF their dopamine replacement medication and in healthy controls. Surprisingly, we found no effect of disease or medication on model-free learning. Instead, we found that patients tested OFF medication showed a marked impairment in model-based learning, and that this impairment was remediated by dopaminergic medication. Moreover, model-based learning was positively correlated with a separate measure of working memory performance, raising the possibility of common neural substrates. Our results suggest that some learning deficits in Parkinson's disease may be related to an inability to pursue reward based on complete representations of the environment. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email:

  7. Fostering the development of effective person-centered healthcare communication skills: an interprofessional shared learning model. (United States)

    Cavanaugh, James T; Konrad, Shelley Cohen


    To describe the implementation of an interprofessional shared learning model designed to promote the development of person-centered healthcare communication skills. Master of social work (MSW) and doctor of physical therapy (DPT) degree students. The model used evidence-based principles of effective healthcare communication and shared learning methods; it was aligned with student learning outcomes contained in MSW and DPT curricula. Students engaged in 3 learning sessions over 2 days. Sessions involved interactive reflective learning, simulated role-modeling with peer assessment, and context-specific practice of communication skills. The perspective of patients/clients was included in each learning activity. Activities were evaluated through narrative feedback. Students valued opportunities to learn directly from each other and from healthcare consumers. Important insights and directions for future interprofessional learning experiences were gleaned from model implementation. The interprofessional shared learning model shows promise as an effective method for developing person-centered communication skills.

  8. The Use of Engineering Design Concept for Computer Programming Course: A Model of Blended Learning Environment (United States)

    Tritrakan, Kasame; Kidrakarn, Pachoen; Asanok, Manit


    The aim of this research is to develop a learning model which blends factors from learning environment and engineering design concept for learning in computer programming course. The usage of the model was also analyzed. This study presents the design, implementation, and evaluation of the model. The research methodology is divided into three…

  9. Hyperparameterization of soil moisture statistical models for North America with Ensemble Learning Models (Elm) (United States)

    Steinberg, P. D.; Brener, G.; Duffy, D.; Nearing, G. S.; Pelissier, C.


    Hyperparameterization, of statistical models, i.e. automated model scoring and selection, such as evolutionary algorithms, grid searches, and randomized searches, can improve forecast model skill by reducing errors associated with model parameterization, model structure, and statistical properties of training data. Ensemble Learning Models (Elm), and the related Earthio package, provide a flexible interface for automating the selection of parameters and model structure for machine learning models common in climate science and land cover classification, offering convenient tools for loading NetCDF, HDF, Grib, or GeoTiff files, decomposition methods like PCA and manifold learning, and parallel training and prediction with unsupervised and supervised classification, clustering, and regression estimators. Continuum Analytics is using Elm to experiment with statistical soil moisture forecasting based on meteorological forcing data from NASA's North American Land Data Assimilation System (NLDAS). There Elm is using the NSGA-2 multiobjective optimization algorithm for optimizing statistical preprocessing of forcing data to improve goodness-of-fit for statistical models (i.e. feature engineering). This presentation will discuss Elm and its components, including dask (distributed task scheduling), xarray (data structures for n-dimensional arrays), and scikit-learn (statistical preprocessing, clustering, classification, regression), and it will show how NSGA-2 is being used for automate selection of soil moisture forecast statistical models for North America.

  10. The Clinical Learning Dyad Model: An Innovation in Midwifery Education. (United States)

    Cohen, Susanna R; Thomas, Celeste R; Gerard, Claudia


    There is a national shortage of women's health and primary care providers in the United States, including certified nurse-midwives and certified midwives. This shortage is directly related to how many students can be trained within the existing system. The current model of midwifery clinical training is based on apprenticeship, with one-on-one interaction between a student and preceptor. Thus, the number of newly trained midwifery providers is limited by the number of available and willing preceptors. The clinical learning dyad model (CLDM), which pairs 2 beginning midwifery students with one preceptor in a busy practice, addresses this problem. In addition, this model brings in a senior midwife student as a near-peer mentor when the students are first oriented into outpatient clinical practice. The model began as a pilot project to improve the quality of training and increase available student spots in clinical education. This article discusses the origins of the model, the specifics of its design, and the results of a midterm and one-year postintervention survey. Students and preceptors involved in this model identified several advantages to the program, including increased student accountability, enhanced socialization into the profession, improved learning, and reduced teaching burden on preceptors. An additional benefit of the CLDM is that students form a learning community and collaborate with preceptors to care for women in busy clinical settings. Challenges of the model will also be discussed. Further research is needed to evaluate the effectiveness of the CLDM. This article is part of a special series of articles that address midwifery innovations in clinical practice, education, interprofessional collaboration, health policy, and global health. © 2015 by the American College of Nurse-Midwives.

  11. Applying CIPP Model for Learning-Object Management (United States)

    Morgado, Erla M. Morales; Peñalvo, Francisco J. García; Martín, Carlos Muñoz; Gonzalez, Miguel Ángel Conde

    Although knowledge management process needs to receive some evaluation in order to determine their suitable functionality. There is not a clear definition about the stages where LOs need to be evaluated and the specific metrics to continuously promote their quality. This paper presents a proposal for LOs evaluation during their management for e-learning systems. To achieve this, we suggest specific steps for LOs design, implementation and evaluation into the four stages proposed by CIPP model (Context, Input, Process, Product).

  12. Delta Learning Rule for the Active Sites Model


    Lingashetty, Krishna Chaithanya


    This paper reports the results on methods of comparing the memory retrieval capacity of the Hebbian neural network which implements the B-Matrix approach, by using the Widrow-Hoff rule of learning. We then, extend the recently proposed Active Sites model by developing a delta rule to increase memory capacity. Also, this paper extends the binary neural network to a multi-level (non-binary) neural network.

  13. The Self-Perception Theory vs. a Dynamic Learning Model


    Swank, Otto H.


    Several economists have directed our attention to a finding in the social psychological literature that extrinsic motivation may undermine intrinsic motivation. The self-perception (SP) theory developed by Bem (1972) explains this finding. The crux of this theory is that people remember their past decisions and the extrinsic rewards they received, but they do not recall their intrinsic motives. In this paper I show that the SP theory can be modeled as a variant of a conventional dynamic learn...

  14. Model Appreciative Learning Untuk Perancangan Reward Pada Game Pendidikan


    Haryanto, Hanny; Kardianawati, Acun; Rosyidah, Umi


    Reward adalah elemen dari game yang sangat penting untuk membentuk pengalaman positif dan motivasi bagi pemain. Reward dalam suatu game pendidikan memegang peranan penting dalam menjaga motivasi pembelajar dan memberikan evaluasi dari apa yang dikerjakan. Namun perancangan reward seringkali masih tidak terkonsep dengan baik, acak dan bersifat subjektif. Penelitian ini menggunakan model Appreciative Learning, yang terdiri dari tahapan Discovery, Dream, Design dan Destiny, untuk mel...

  15. Modeling in the Classroom: An Evolving Learning Tool (United States)

    Few, A. A.; Marlino, M. R.; Low, R.


    Among the early programs (early 1990s) focused on teaching Earth System Science were the Global Change Instruction Program (GCIP) funded by NSF through UCAR and the Earth System Science Education Program (ESSE) funded by NASA through USRA. These two programs introduced modeling as a learning tool from the beginning, and they provided workshops, demonstrations and lectures for their participating universities. These programs were aimed at university-level education. Recently, classroom modeling is experiencing a revival of interest. Drs John Snow and Arthur Few conducted two workshops on modeling at the ESSE21 meeting in Fairbanks, Alaska, in August 2005. The Digital Library for Earth System Education (DLESE) at provides web access to STELLA models and tutorials, and UCAR's Education and Outreach (EO) program holds workshops that include training in modeling. An important innovation to the STELLA modeling software by isee systems,, called "isee Player" is available as a free download. The Player allows users to view and run STELLA models, change model parameters, share models with colleagues and students, and make working models available on the web. This is important because the expert can create models, and the user can learn how the modeled system works. Another aspect of this innovation is that the educational benefits of modeling concepts can be extended throughout most of the curriculum. The procedure for building a working computer model of an Earth Science System follows this general format: (1) carefully define the question(s) for which you seek the answer(s); (2) identify the interacting system components and inputs contributing to the system's behavior; (3) collect the information and data that will be required to complete the conceptual model; (4) construct a system diagram (graphic) of the system that displays all of system's central questions, components, relationships and required inputs. At this stage

  16. A Model for Collaborative Learning in Undergraduate Climate Change Courses (United States)

    Teranes, J. L.


    Like several colleges and universities across the nation, the University of California, San Diego, has introduced climate change topics into many existing and new undergraduate courses. I have administered a program in this area at UCSD and have also developed and taught a new lower-division UCSD course entitled "Climate Change and Society", a general education course for non-majors. This class covers the basics of climate change, such as the science that explains it, the causes of climate change, climate change impacts, and mitigation strategies. The teaching methods for this course stress interdisciplinary approaches. I find that inquiry-based and collaborative modes of learning are particularly effective when applied to science-based climate, environmental and sustainability topics. Undergraduate education is often dominated by a competitive and individualistic approach to learning. In this approach, individual success is frequently perceived as contingent on others being less successful. Such a model is at odds with commonly stated goals of teaching climate change and sustainability, which are to equip students to contribute to the debate on global environmental change and societal adaptation strategies; and to help students become better informed citizens and decision makers. I present classroom-tested strategies for developing collaborative forms of learning in climate change and environmental courses, including team projects, group presentations and group assessment exercises. I show how critical thinking skills and long-term retention of information can benefit in the collaborative mode of learning. I find that a collaborative learning model is especially appropriate to general education courses in which the enrolled student body represents a wide diversity of majors, class level and expertise. I also connect collaborative coursework in interdisciplinary environmental topics directly to applications in the field, where so much "real-world" achievement in

  17. Modeling Avoidance in Mood and Anxiety Disorders Using Reinforcement Learning. (United States)

    Mkrtchian, Anahit; Aylward, Jessica; Dayan, Peter; Roiser, Jonathan P; Robinson, Oliver J


    Serious and debilitating symptoms of anxiety are the most common mental health problem worldwide, accounting for around 5% of all adult years lived with disability in the developed world. Avoidance behavior-avoiding social situations for fear of embarrassment, for instance-is a core feature of such anxiety. However, as for many other psychiatric symptoms the biological mechanisms underlying avoidance remain unclear. Reinforcement learning models provide formal and testable characterizations of the mechanisms of decision making; here, we examine avoidance in these terms. A total of 101 healthy participants and individuals with mood and anxiety disorders completed an approach-avoidance go/no-go task under stress induced by threat of unpredictable shock. We show an increased reliance in the mood and anxiety group on a parameter of our reinforcement learning model that characterizes a prepotent (pavlovian) bias to withhold responding in the face of negative outcomes. This was particularly the case when the mood and anxiety group was under stress. This formal description of avoidance within the reinforcement learning framework provides a new means of linking clinical symptoms with biophysically plausible models of neural circuitry and, as such, takes us closer to a mechanistic understanding of mood and anxiety disorders. Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

  18. A System Computational Model of Implicit Emotional Learning. (United States)

    Puviani, Luca; Rama, Sidita


    Nowadays, the experimental study of emotional learning is commonly based on classical conditioning paradigms and models, which have been thoroughly investigated in the last century. Unluckily, models based on classical conditioning are unable to explain or predict important psychophysiological phenomena, such as the failure of the extinction of emotional responses in certain circumstances (for instance, those observed in evaluative conditioning, in post-traumatic stress disorders and in panic attacks). In this manuscript, starting from the experimental results available from the literature, a computational model of implicit emotional learning based both on prediction errors computation and on statistical inference is developed. The model quantitatively predicts (a) the occurrence of evaluative conditioning, (b) the dynamics and the resistance-to-extinction of the traumatic emotional responses, (c) the mathematical relation between classical conditioning and unconditioned stimulus revaluation. Moreover, we discuss how the derived computational model can lead to the development of new animal models for resistant-to-extinction emotional reactions and novel methodologies of emotions modulation.

  19. Memory and learning in a class of neural network models

    International Nuclear Information System (INIS)

    Wallace, D.J.


    The author discusses memory and learning properties of the neural network model now identified with Hopfield's work. The model, how it attempts to abstract some key features of the nervous system, and the sense in which learning and memory are identified in the model are described. A brief report is presented on the important role of phase transitions in the model and their implications for memory capacity. The results of numerical simulations obtained using the ICL Distributed Array Processors at Edinburgh are presented. A summary is presented on how the fraction of images which are perfectly stored, depends on the number of nodes and the number of nominal images which one attempts to store using the prescription in Hopfield's paper. Results are presented on the second phase transition in the model, which corresponds to almost total loss of storage capacity as the number of nominal images is increased. Results are given on the performance of a new iterative algorithm for exact storage of up to N images in an N node model

  20. The Interaction Model in iLearning Environments and its Use in the Smart Lab Concept

    Directory of Open Access Journals (Sweden)

    Yuliya Lyalina


    Full Text Available This paper identifies and discusses current trends and challenges, offers an overview of state-of-the-art technologies in the development of remote and smart laboratories, and introduces the iLearning interaction model. The use of the model allows reconstructing already- existing iLearning environments. The smart lab model is described for face-to-face, Mobile and Blended Learning. As a result, this allows offering new information technology that organizes the educational process according to learning type (face-to-face, hands-on learning, Life Long Learning, E-Learning, M-Learning, Blended learning, Game-based learning, etc.. The remote access Architecture and Interface for the multifunctional Smart Lab will be developed.

  1. A Learning State-Space Model for Image Retrieval

    Directory of Open Access Journals (Sweden)

    Lee Greg C


    Full Text Available This paper proposes an approach based on a state-space model for learning the user concepts in image retrieval. We first design a scheme of region-based image representation based on concept units, which are integrated with different types of feature spaces and with different region scales of image segmentation. The design of the concept units aims at describing similar characteristics at a certain perspective among relevant images. We present the details of our proposed approach based on a state-space model for interactive image retrieval, including likelihood and transition models, and we also describe some experiments that show the efficacy of our proposed model. This work demonstrates the feasibility of using a state-space model to estimate the user intuition in image retrieval.

  2. Technological Learning in Energy Models: Experience and Scenario Analysis with MARKAL and the ERIS Model Prototype

    Energy Technology Data Exchange (ETDEWEB)

    Barreto, L.; Kypreos, S.


    Understanding technology dynamics, a fundamental driving factor of the evolution of energy systems, is essential for sound policy formulation and decision making. Technological change is not an autonomous process, but evolves from a number of endogenous interactions within the social system. Technologies evolve and improve only if experience with them is possible. Efforts must be devoted to improve our analytical tools concerning the treatment given to the technological variable, recognising the cumulative and gradual nature of technological change and the important role played by learning processes. This report presents a collection of works developed by the authors concerning the endogenisation of technological change in energy optimisation models, as a contribution to the Energy Technology Dynamics andAdvanced Energy System Modelling Project (TEEM), developed in the framework of the Non Nuclear Energy Programme JOULE III of the European Union (DGXII). Here, learning curves, an empirically observed manifestation of the cumulative technological learning processes, are endogenised in two energy optimisation models. MARKAL, a widely used bottom-up model developed by the ETSAP programme of the IEA and ERIS, a model prototype, developed within the TEEM project for assessing different concepts and approaches. The methodological approach is described and some results and insights derived from the model analyses are presented. The incorporation of learning curves results in significantly different model outcomes than those obtained with traditional approaches. New, innovative technologies, hardly considered by the standard models, are introduced to the solution when endogenous learning is present. Up-front investments in initially expensive, but promising, technologies allow the necessary accumulation of experience to render them cost-effective. When uncertainty in emission reduction commitments is considered, the results point also in the direction of undertaking early

  3. Technological Learning in Energy Models: Experience and Scenario Analysis with MARKAL and the ERIS Model Prototype

    International Nuclear Information System (INIS)

    Barreto, L.; Kypreos, S.


    Understanding technology dynamics, a fundamental driving factor of the evolution of energy systems, is essential for sound policy formulation and decision making. Technological change is not an autonomous process, but evolves from a number of endogenous interactions within the social system. Technologies evolve and improve only if experience with them is possible. Efforts must be devoted to improve our analytical tools concerning the treatment given to the technological variable, recognising the cumulative and gradual nature of technological change and the important role played by learning processes. This report presents a collection of works developed by the authors concerning the endogenisation of technological change in energy optimisation models, as a contribution to the Energy Technology Dynamics and Advanced Energy System Modelling Project (TEEM), developed in the framework of the Non Nuclear Energy Programme JOULE III of the European Union (DGXII). Here, learning curves, an empirically observed manifestation of the cumulative technological learning processes, are endogenised in two energy optimisation models. MARKAL, a widely used bottom-up model developed by the ETSAP programme of the IEA and ERIS, a model prototype, developed within the TEEM project for assessing different concepts and approaches. The methodological approach is described and some results and insights derived from the model analyses are presented. The incorporation of learning curves results in significantly different model outcomes than those obtained with traditional approaches. New, innovative technologies, hardly considered by the standard models, are introduced to the solution when endogenous learning is present. Up-front investments in initially expensive, but promising, technologies allow the necessary accumulation of experience to render them cost-effective. When uncertainty in emission reduction commitments is considered, the results point also in the direction of undertaking early


    Directory of Open Access Journals (Sweden)

    Aline Germain-RUTHERFORD


    Full Text Available The impact of ever-increasing numbers of online courses on the demographic composition of classes has meant that the notions of diversity, multiculturality and globalization are now key aspects of curriculum planning. With the internationalization and globalization of education, and faced with rising needs for an increasingly educated and more adequately trained workforce, universities are offering more flexible programs, assisted by new educational and communications technologies. Faced with this diversity of populations and needs, many instructors are becoming aware of the importance of addressing the notions of multiculturality and interculturality in the design of online however this raises many questions. For example, how do we integrate and address this multicultural dimension in a distance education course aimed at students who live in diverse cultural environments? How do the challenges of intercultural communication in an online environment affect online teaching and learning? What are the characteristics of an online course that is inclusive of all types of diversity, and what are the guiding principles for designing such courses? We will attempt to answer some of these questions by first exploring the concepts of culture and learning cultures. This will help us to characterize the impact on online learning of particular cultural dimensions. We will then present and discuss different online instructional design models that are culturally inclusive, and conclude with the description of a mediated instructional training module on the management of the cultural dimension of online teaching and learning. This module is mainly addressed to teachers and designers of online courses.

  5. A distance learning model in a physical therapy curriculum. (United States)

    English, T; Harrison, A L; Hart, A L


    In response to the rural health initiative established in 1991, the University of Kentucky has developed an innovative distance learning program of physical therapy instruction that combines classroom lecture and discussion via compressed video technology with laboratory experiences. The authors describe the process of planning, implementing, and evaluating a specific distance learning course in pathomechanics for the professional-level master's-degree physical therapy students at the University of Kentucky. This presentation may serve as a model for teaching distance learning. Descriptions of optimal approaches to preclass preparation, scheduling, course delivery, use of audiovisual aids, use of handout material, and video production are given. Special activities that may enhance or deter the achievement of the learning objectives are outlined, and a problem-solving approach to common problems encountered is presented. An approach to evaluating and comparing course outcomes for the distance learnere is presented. For this particular course, there was no statistically significant difference in the outcome measures utilized to compare the distance learners with the on-site learners.

  6. Innovations in Language Learning: The Oregon Chinese Flagship Model

    Directory of Open Access Journals (Sweden)

    Carl Falsgraf


    Full Text Available Language learning in the United States suffers from a culture of low expectations. Lacking bilingual role models around them, students often view language class as, at best, a way to become a tourist in a country with a language different from their own. Monolingual policymakers assume that learning another language fluently is impossible and inconsequential, since they themselves are capable professionals with one language. Educators, discouraged by years of inadequate funding and support, have come to hope for nothing more than incremental improvements. The National Flagship Language Program (NFLP aims to break this cycle of low expectations and low results by providing funding to institutions willing to accept the challenge of producing Superior (Level 3 language users through a radical re-engineering of the language learning enterprise. The need for fundamental change in language education is longstanding, but the events of September 11 brought the importance of this need to the awareness of national policymakers. Due to the emphasis of critical languages, responsibility for carrying out this fundamental re-examination of language learning has fallen to those engaged in the less commonly taught languages. 1

  7. Machine learning, computer vision, and probabilistic models in jet physics

    CERN Multimedia

    CERN. Geneva; NACHMAN, Ben


    In this talk we present recent developments in the application of machine learning, computer vision, and probabilistic models to the analysis and interpretation of LHC events. First, we will introduce the concept of jet-images and computer vision techniques for jet tagging. Jet images enabled the connection between jet substructure and tagging with the fields of computer vision and image processing for the first time, improving the performance to identify highly boosted W bosons with respect to state-of-the-art methods, and providing a new way to visualize the discriminant features of different classes of jets, adding a new capability to understand the physics within jets and to design more powerful jet tagging methods. Second, we will present Fuzzy jets: a new paradigm for jet clustering using machine learning methods. Fuzzy jets view jet clustering as an unsupervised learning task and incorporate a probabilistic assignment of particles to jets to learn new features of the jet structure. In particular, we wi...

  8. A Fuzzy Logic Framework for Integrating Multiple Learned Models

    Energy Technology Data Exchange (ETDEWEB)

    Hartog, Bobi Kai Den [Univ. of Nebraska, Lincoln, NE (United States)


    The Artificial Intelligence field of Integrating Multiple Learned Models (IMLM) explores ways to combine results from sets of trained programs. Aroclor Interpretation is an ill-conditioned problem in which trained programs must operate in scenarios outside their training ranges because it is intractable to train them completely. Consequently, they fail in ways related to the scenarios. We developed a general-purpose IMLM solution, the Combiner, and applied it to Aroclor Interpretation. The Combiner's first step, Scenario Identification (M), learns rules from very sparse, synthetic training data consisting of results from a suite of trained programs called Methods. S1 produces fuzzy belief weights for each scenario by approximately matching the rules. The Combiner's second step, Aroclor Presence Detection (AP), classifies each of three Aroclors as present or absent in a sample. The third step, Aroclor Quantification (AQ), produces quantitative values for the concentration of each Aroclor in a sample. AP and AQ use automatically learned empirical biases for each of the Methods in each scenario. Through fuzzy logic, AP and AQ combine scenario weights, automatically learned biases for each of the Methods in each scenario, and Methods' results to determine results for a sample.

  9. Recommender System and Web 2.0 Tools to Enhance a Blended Learning Model (United States)

    Hoic-Bozic, Natasa; Dlab, Martina Holenko; Mornar, Vedran


    Blended learning models that combine face-to-face and online learning are of great importance in modern higher education. However, their development should be in line with the recent changes in e-learning that emphasize a student-centered approach and use tools available on the Web to support the learning process. This paper presents research on…

  10. Developing of Indicators of an E-Learning Benchmarking Model for Higher Education Institutions (United States)

    Sae-Khow, Jirasak


    This study was the development of e-learning indicators used as an e-learning benchmarking model for higher education institutes. Specifically, it aimed to: 1) synthesize the e-learning indicators; 2) examine content validity by specialists; and 3) explore appropriateness of the e-learning indicators. Review of related literature included…

  11. Learning from and with Customers with Social Media: A Model for Social Customer Learning

    Directory of Open Access Journals (Sweden)

    Hannu Kärkkäinen


    Full Text Available Social media can enable and significantly increase the collaboration andlearning from customers in various ways, for instance by novel social waysof providing and receiving feedback from new products and concepts. Wehave created a model that can support managers and researchers to betteranalyse and understand the possibilities of social media approaches especiallyfrom the business-to-business (B2B customer interface standpoint. Weused the model to analyse found various types of business-to-business relatedsocial media approaches to create new understanding of the scarcelyresearched field of social media in the customer learning and the customerinterface of B2B innovation.

  12. Critical-Inquiry-Based-Learning: Model of Learning to Promote Critical Thinking Ability of Pre-service Teachers (United States)

    Prayogi, S.; Yuanita, L.; Wasis


    This study aimed to develop Critical-Inquiry-Based-Learning (CIBL) learning model to promote critical thinking (CT) ability of preservice teachers. The CIBL learning model was developed by meeting the criteria of validity, practicality, and effectiveness. Validation of the model involves 4 expert validators through the mechanism of the focus group discussion (FGD). CIBL learning model declared valid to promote CT ability, with the validity level (Va) of 4.20 and reliability (r) of 90,1% (very reliable). The practicality of the model was evaluated when it was implemented that involving 17 of preservice teachers. The CIBL learning model had been declared practice, its measuring from learning feasibility (LF) with very good criteria (LF-score = 4.75). The effectiveness of the model was evaluated from the improvement CT ability after the implementation of the model. CT ability were evaluated using the scoring technique adapted from Ennis-Weir Critical Thinking Essay Test. The average score of CT ability on pretest is - 1.53 (uncritical criteria), whereas on posttest is 8.76 (critical criteria), with N-gain score of 0.76 (high criteria). Based on the results of this study, it can be concluded that developed CIBL learning model is feasible to promote CT ability of preservice teachers.

  13. Ready to learn physics: a team-based learning model for first year university (United States)

    Parappilly, Maria; Schmidt, Lisa; De Ritter, Samantha


    Team-based learning (TBL) is an established model of group work which aims to improve students' ability to apply discipline-related content. TBL consists of a readiness assurance process (RAP), student groups and application activities. While TBL has not been implemented widely in science, technology, engineering and mathematics disciplines, it has been effective in improving student learning in other disciplines. This paper describes the incorporation of TBL activities into a non-calculus based introductory level physics topic—Physics for the Modern World. Students were given pre-class preparation materials and an individual RAP online test before the workshops. The pre-workshop individual RAP test ensured that all students were exposed to concept-based questions before their workshops and motivated them to use the preparatory materials in readiness for the workshop. The students were placed into random teams and during the first part of the workshop, the teams went through a subset of the quiz questions (team RAP test) and in the remaining time, teams completed an in-class assignment. After the workshop students were allowed another attempt at the individual RAP test to see if their knowledge had improved. The ability of TBL to promote student learning of key concepts was evaluated by experiment using pre- and post- testing. The students’ perception of TBL was monitored by discussion posts and survey responses. Finally, the ability of TBL to support peer-peer interaction was evaluated by video analysis of the class. We found that the TBL process improved student learning; students did interact with each other in class; and the students had a positive view of TBL. To assess the transferability of this model to other topics, we conducted a comparison study with an environmental science topic which produced similar results. Our study supports the use of this TBL model in science topics.

  14. Towards using the chordal graph polytope in learning decomposable models

    Czech Academy of Sciences Publication Activity Database

    Studený, Milan; Cussens, J.


    Roč. 88, č. 1 (2017), s. 259-281 ISSN 0888-613X. [8th International Conference of Probabilistic Graphical Models. Lugano, 06.09.2016-09.09.2016] R&D Projects: GA ČR(CZ) GA16-12010S Institutional support: RVO:67985556 Keywords : learning decomposable models * integer linear programming * characteristic imset * chordal graph polytope * clutter inequalities * separation problem Subject RIV: BA - General Mathematics OBOR OECD: Statistics and probability Impact factor: 2.845, year: 2016

  15. Implementation of ICARE learning model using visualization animation on biotechnology course (United States)

    Hidayat, Habibi


    ICARE is a learning model that directly ensure the students to actively participate in the learning process using animation media visualization. ICARE have five key elements of learning experience from children and adult that is introduction, connection, application, reflection and extension. The use of Icare system to ensure that participants have opportunity to apply what have been they learned. So that, the message delivered by lecture to students can be understood and recorded by students in a long time. Learning model that was deemed capable of improving learning outcomes and interest to learn in following learning process Biotechnology with applying the ICARE learning model using visualization animation. This learning model have been giving motivation to participate in the learning process and learning outcomes obtained becomes more increased than before. From the results of student learning in subjects Biotechnology by applying the ICARE learning model using Visualization Animation can improving study results of student from the average value of middle test amounted to 70.98 with the percentage of 75% increased value of final test to be 71.57 with the percentage of 68.63%. The interest to learn from students more increasing visits of student activities at each cycle, namely the first cycle obtained average value by 33.5 with enough category. The second cycle is obtained an average value of 36.5 to good category and third cycle the average value of 36.5 with a student activity to good category.

  16. Developmental song learning as a model to understand neural mechanisms that limit and promote the ability to learn. (United States)

    London, Sarah E


    Songbirds famously learn their vocalizations. Some species can learn continuously, others seasonally, and still others just once. The zebra finch (Taeniopygia guttata) learns to sing during a single developmental "Critical Period," a restricted phase during which a specific experience has profound and permanent effects on brain function and behavioral patterns. The zebra finch can therefore provide fundamental insight into features that promote and limit the ability to acquire complex learned behaviors. For example, what properties permit the brain to come "on-line" for learning? How does experience become encoded to prevent future learning? What features define the brain in receptive compared to closed learning states? This piece will focus on epigenomic, genomic, and molecular levels of analysis that operate on the timescales of development and complex behavioral learning. Existing data will be discussed as they relate to Critical Period learning, and strategies for future studies to more directly address these questions will be considered. Birdsong learning is a powerful model for advancing knowledge of the biological intersections of maturation and experience. Lessons from its study not only have implications for understanding developmental song learning, but also broader questions of learning potential and the enduring effects of early life experience on neural systems and behavior. Copyright © 2017. Published by Elsevier B.V.

  17. A Survey: Time Travel in Deep Learning Space: An Introduction to Deep Learning Models and How Deep Learning Models Evolved from the Initial Ideas


    Wang, Haohan; Raj, Bhiksha


    This report will show the history of deep learning evolves. It will trace back as far as the initial belief of connectionism modelling of brain, and come back to look at its early stage realization: neural networks. With the background of neural network, we will gradually introduce how convolutional neural network, as a representative of deep discriminative models, is developed from neural networks, together with many practical techniques that can help in optimization of neural networks. On t...

  18. Error modeling for surrogates of dynamical systems using machine learning (United States)

    Trehan, Sumeet; Carlberg, Kevin T.; Durlofsky, Louis J.


    A machine-learning-based framework for modeling the error introduced by surrogate models of parameterized dynamical systems is proposed. The framework entails the use of high-dimensional regression techniques (e.g., random forests, LASSO) to map a large set of inexpensively computed `error indicators' (i.e., features) produced by the surrogate model at a given time instance to a prediction of the surrogate-model error in a quantity of interest (QoI). This eliminates the need for the user to hand-select a small number of informative features. The methodology requires a training set of parameter instances at which the time-dependent surrogate-model error is computed by simulating both the high-fidelity and surrogate models. Using these training data, the method first determines regression-model locality (via classification or clustering), and subsequently constructs a `local' regression model to predict the time-instantaneous error within each identified region of feature space. We consider two uses for the resulting error model: (1) as a correction to the surrogate-model QoI prediction at each time instance, and (2) as a way to statistically model arbitrary functions of the time-dependent surrogate-model error (e.g., time-integrated errors). We apply the proposed framework to model errors in reduced-order models of nonlinear oil--water subsurface flow simulations. The reduced-order models used in this work entail application of trajectory piecewise linearization with proper orthogonal decomposition. When the first use of the method is considered, numerical experiments demonstrate consistent improvement in accuracy in the time-instantaneous QoI prediction relative to the original surrogate model, across a large number of test cases. When the second use is considered, results show that the proposed method provides accurate statistical predictions of the time- and well-averaged errors.

  19. Development Of Entrepreneur Learning Model Based On Problem Based Learning To Increase Competency Independence And Creativity Students Of Industrial Engineering

    Directory of Open Access Journals (Sweden)

    Leola Dewiyani


    Full Text Available Currently it is undeniable that the competition to get a job is very tight and of course universities have an important role in printing human resources that can compete globally not least with the Department of Industrial Engineering Faculty of Engineering Muhammadiyah University of Jakarta FT UMJ. Problems that occur is based on the analysis obtained from the track record of graduates researchers found that 60 percent of students of Industrial Engineering FT UMJ work not in accordance with the level of education owned so financially their income is still below the standard. This study aims to improve the competence of students of Industrial Engineering Department FT UMJ in entrepreneurship courses especially through the development of Problem Based Learning based learning model. Specific targets of this research were conducted with the aim to identify and analyze the need to implement learning model based on Problem Based Learning Entrepreneurship and to design and develop the model of entrepreneurship based on Problem Based Learning to improve the competence independence and creativity of Industrial Engineering students of FT UMJ in Entrepreneurship course. To achieve the above objectives this research uses research and development R amp D method. The product produced in this research is the detail of learning model of entrepreneurial model based on Problem Based Learning entrepreneurship model based on Problem Based Learning and international journals

  20. ModelHub: Towards Unified Data and Lifecycle Management for Deep Learning


    Miao, Hui; Li, Ang; Davis, Larry S.; Deshpande, Amol


    Deep learning has improved state-of-the-art results in many important fields, and has been the subject of much research in recent years, leading to the development of several systems for facilitating deep learning. Current systems, however, mainly focus on model building and training phases, while the issues of data management, model sharing, and lifecycle management are largely ignored. Deep learning modeling lifecycle generates a rich set of data artifacts, such as learned parameters and tr...

  1. Daily Discharge Estimation in Talar River Using Lazy Learning Model

    Directory of Open Access Journals (Sweden)

    Zahra Abdollahi


    Full Text Available Introduction: River discharge as one of the most important hydrology factors has a vital role in physical, ecological, social and economic processes. So, accurate and reliable prediction and estimation of river discharge have been widely considered by many researchers in different fields such as surface water management, design of hydraulic structures, flood control and ecological studies in spetialand temporal scale. Therefore, in last decades different techniques for short-term and long-term estimation of hourly, daily, monthly and annual discharge have been developed for many years. However, short-term estimation models are less sophisticated and more accurate.Various global and local algorithms have been widely used to estimate hydrologic variables. The current study effort to use Lazy Learning approach to evaluate the adequacy of input data in order to follow the variation of discharge and also simulate next-day discharge in Talar River in KasilianBasinwhere is located in north of Iran with an area of 66.75 km2. Lazy learning is a local linear modelling approach in which generalization beyond the training data is delayed until a query is made to the system, as opposed to in eager learning, where the system tries to generalize the training data before receiving queries Materials and Methods: The current study was conducted in Kasilian Basin, where is located in north of Iran with an area of 66.75 km2. The main river of this basin joins to Talar River near Valicbon village and then exit from the watershed. Hydrometric station located near Valicbon village is equipped with Parshall flume and Limnogragh which can record river discharge of about 20 cubic meters per second.In this study, daily data of discharge recorded in Valicbon station related to 2002 to 2012 was used to estimate the discharge of 19 September 2012. The mean annual discharge of considered river was also calculated by using available data about 0.441 cubic meters per second. To

  2. Diagnostic Machine Learning Models for Acute Abdominal Pain: Towards an e-Learning Tool for Medical Students. (United States)

    Khumrin, Piyapong; Ryan, Anna; Judd, Terry; Verspoor, Karin


    Computer-aided learning systems (e-learning systems) can help medical students gain more experience with diagnostic reasoning and decision making. Within this context, providing feedback that matches students' needs (i.e. personalised feedback) is both critical and challenging. In this paper, we describe the development of a machine learning model to support medical students' diagnostic decisions. Machine learning models were trained on 208 clinical cases presenting with abdominal pain, to predict five diagnoses. We assessed which of these models are likely to be most effective for use in an e-learning tool that allows students to interact with a virtual patient. The broader goal is to utilise these models to generate personalised feedback based on the specific patient information requested by students and their active diagnostic hypotheses.

  3. Bifurcation and category learning in network models of oscillating cortex (United States)

    Baird, Bill


    A genetic model of oscillating cortex, which assumes “minimal” coupling justified by known anatomy, is shown to function as an associative memory, using previously developed theory. The network has explicit excitatory neurons with local inhibitory interneuron feedback that forms a set of nonlinear oscillators coupled only by long-range excitatory connections. Using a local Hebb-like learning rule for primary and higher-order synapses at the ends of the long-range connections, the system learns to store the kinds of oscillation amplitude patterns observed in olfactory and visual cortex. In olfaction, these patterns “emerge” during respiration by a pattern forming phase transition which we characterize in the model as a multiple Hopf bifurcation. We argue that these bifurcations play an important role in the operation of real digital computers and neural networks, and we use bifurcation theory to derive learning rules which analytically guarantee CAM storage of continuous periodic sequences-capacity: N/2 Fourier components for an N-node network-no “spurious” attractors.

  4. Quality Assurance in E-Learning: PDPP Evaluation Model and its Application

    Directory of Open Access Journals (Sweden)

    Weiyuan Zhang


    Full Text Available E-learning has become an increasingly important teaching and learning mode in educational institutions and corporate training. The evaluation of e-learning, however, is essential for the quality assurance of e-learning courses. This paper constructs a four-phase evaluation model for e-learning courses, which includes planning, development, process, and product evaluation, called the PDPP evaluation model. Planning evaluation includes market demand, feasibility, target student group, course objectives, and finance. Development evaluation includes instructional design, course material design, course Web site design, flexibility, student-student interaction, teacher/tutor support, technical support, and assessment. Process evaluation includes technical support, Web site utilization, learning interaction, learning evaluation, learning support, and flexibility. Product evaluation includes student satisfaction, teaching effectiveness, learning effectiveness, and sustainability. Using the PDPP model as a research framework, a purely e-learning course on Research Methods in Distance Education, developed by the School of Professional and Continuing Education at the University of Hong Kong (HKU SPACE and jointly offered with the School of Distance Learning for Medical Education of Peking University (SDLME, PKU, was used as a case study. Sixty students from mainland China, Hong Kong, Macau, and Malaysia were recruited for this course. According to summative evaluation through a student e-learning experience survey, the majority of students were very satisfied/satisfied on all e-learning dimensions of this course. The majority of students thought that the learning effectiveness of this course was equivalent, even better, than face-to-face learning because of cross-border collaborative learning, student-centred learning, sufficient learning support, and learning flexibility. This study shows that a high quality of teaching and learning might be assured by

  5. Model Manajemen E-learning di Perguruan Tinggi


    Prasojo, Lantip Diat


    Information and Communication Technology (ICT) is growing fast included in the case of learning management based on ICT. Learning management based on ICT which is being trend namely e-learning management. E-learning management has implemented to educational institutions. Based on the background, thus emerged general problem ”how e-learning management at university can increase quality college students' learning outcome quality”.The effectiveness and efficiency of E-learning management at univ...

  6. Statistical learning modeling method for space debris photometric measurement (United States)

    Sun, Wenjing; Sun, Jinqiu; Zhang, Yanning; Li, Haisen


    Photometric measurement is an important way to identify the space debris, but the present methods of photometric measurement have many constraints on star image and need complex image processing. Aiming at the problems, a statistical learning modeling method for space debris photometric measurement is proposed based on the global consistency of the star image, and the statistical information of star images is used to eliminate the measurement noises. First, the known stars on the star image are divided into training stars and testing stars. Then, the training stars are selected as the least squares fitting parameters to construct the photometric measurement model, and the testing stars are used to calculate the measurement accuracy of the photometric measurement model. Experimental results show that, the accuracy of the proposed photometric measurement model is about 0.1 magnitudes.

  7. Learning the Task Management Space of an Aircraft Approach Model (United States)

    Krall, Joseph; Menzies, Tim; Davies, Misty


    Validating models of airspace operations is a particular challenge. These models are often aimed at finding and exploring safety violations, and aim to be accurate representations of real-world behavior. However, the rules governing the behavior are quite complex: nonlinear physics, operational modes, human behavior, and stochastic environmental concerns all determine the responses of the system. In this paper, we present a study on aircraft runway approaches as modeled in Georgia Tech's Work Models that Compute (WMC) simulation. We use a new learner, Genetic-Active Learning for Search-Based Software Engineering (GALE) to discover the Pareto frontiers defined by cognitive structures. These cognitive structures organize the prioritization and assignment of tasks of each pilot during approaches. We discuss the benefits of our approach, and also discuss future work necessary to enable uncertainty quantification.

  8. Experiential Learning Model in Enhancing Prospective English Teachers` Teaching Competence

    Directory of Open Access Journals (Sweden)

    Heri Mudra


    Full Text Available This study aimed to describe the effectiveness of Experiential Learning (EL model in improving prospective EFL teachers’ (PETs teaching competence. The method of this study was Classroom Action Research (CAR consisting of planning, observing, acting, and reflecting phases. There were two cycles needed in implementing EL to the twenty one EFL learners as the participants. The results revealed that each subcompetence in cycle I and cycle II was achieved in the following score: planning & preparation for learning (Mean in cycle I=2,8; Mean in cycle II=3,38, classroom management (Mean in cycle I=2,5; Mean in cycle II=2,95, delivery of instruction (Mean in cycle I=2,6; Mean in cycle II=2,90, and monitoring, assessment, and follow-up (Mean in cycle I=2,4; Mean in cycle II=2,95. It can be concluded that EL is effective in improving PETs’ teaching competence.

  9. A comparative study of machine learning models for ethnicity classification (United States)

    Trivedi, Advait; Bessie Amali, D. Geraldine


    This paper endeavours to adopt a machine learning approach to solve the problem of ethnicity recognition. Ethnicity identification is an important vision problem with its use cases being extended to various domains. Despite the multitude of complexity involved, ethnicity identification comes naturally to humans. This meta information can be leveraged to make several decisions, be it in target marketing or security. With the recent development of intelligent systems a sub module to efficiently capture ethnicity would be useful in several use cases. Several attempts to identify an ideal learning model to represent a multi-ethnic dataset have been recorded. A comparative study of classifiers such as support vector machines, logistic regression has been documented. Experimental results indicate that the logical classifier provides a much accurate classification than the support vector machine.

  10. Theory and learning protocols for the material tempotron model

    International Nuclear Information System (INIS)

    Baldassi, Carlo; Braunstein, Alfredo; Zecchina, Riccardo


    Neural networks are able to extract information from the timing of spikes. Here we provide new results on the behavior of the simplest neuronal model which is able to decode information embedded in temporal spike patterns, the so-called tempotron. Using statistical physics techniques we compute the capacity for the case of sparse, time-discretized input, and ‘material’ discrete synapses, showing that the device saturates the information theoretic bounds with a statistics of output spikes that is consistent with the statistics of the inputs. We also derive two simple and highly efficient learning algorithms which are able to learn a number of associations which are close to the theoretical limit. The simplest versions of these algorithms correspond to distributed online protocols of interest for neuromorphic devices, and can be adapted to address the more biologically relevant continuous-time version of the classification problem, hopefully allowing the understanding of some aspects of synaptic plasticity. (paper)

  11. Teaching and Learning of Computational Modelling in Creative Shaping Processes

    Directory of Open Access Journals (Sweden)

    Daniela REIMANN


    Full Text Available Today, not only diverse design-related disciplines are required to actively deal with the digitization of information and its potentials and side effects for education processes. In Germany, technology didactics developed in vocational education and computer science education in general education, both separated from media pedagogy as an after-school program. Media education is not a subject in German schools yet. However, in the paper we argue for an interdisciplinary approach to learn about computational modeling in creative processes and aesthetic contexts. It crosses the borders of programming technology, arts and design processes in meaningful contexts. Educational scenarios using smart textile environments are introduced and reflected for project based learning.

  12. Airport Flight Departure Delay Model on Improved BN Structure Learning (United States)

    Cao, Weidong; Fang, Xiangnong

    An high score prior genetic simulated annealing Bayesian network structure learning algorithm (HSPGSA) by combining genetic algorithm(GA) with simulated annealing algorithm(SAA) is developed. The new algorithm provides not only with strong global search capability of GA, but also with strong local hill climb search capability of SAA. The structure with the highest score is prior selected. In the mean time, structures with lower score are also could be choice. It can avoid efficiently prematurity problem by higher score individual wrong direct growing population. Algorithm is applied to flight departure delays analysis in a large hub airport. Based on the flight data a BN model is created. Experiments show that parameters learning can reflect departure delay.

  13. Bottom-up learning of hierarchical models in a class of deterministic POMDP environments

    Directory of Open Access Journals (Sweden)

    Itoh Hideaki


    Full Text Available The theory of partially observable Markov decision processes (POMDPs is a useful tool for developing various intelligent agents, and learning hierarchical POMDP models is one of the key approaches for building such agents when the environments of the agents are unknown and large. To learn hierarchical models, bottom-up learning methods in which learning takes place in a layer-by-layer manner from the lowest to the highest layer are already extensively used in some research fields such as hidden Markov models and neural networks. However, little attention has been paid to bottom-up approaches for learning POMDP models. In this paper, we present a novel bottom-up learning algorithm for hierarchical POMDP models and prove that, by using this algorithm, a perfect model (i.e., a model that can perfectly predict future observations can be learned at least in a class of deterministic POMDP environments

  14. Learning from Your Network of Friends: A Trajectory Representation Learning Model Based on Online Social Ties

    KAUST Repository

    Alharbi, Basma Mohammed


    Location-Based Social Networks (LBSNs) capture individuals whereabouts for a large portion of the population. To utilize this data for user (location)-similarity based tasks, one must map the raw data into a low-dimensional uniform feature space. However, due to the nature of LBSNs, many users have sparse and incomplete check-ins. In this work, we propose to overcome this issue by leveraging the network of friends, when learning the new feature space. We first analyze the impact of friends on individuals\\'s mobility, and show that individuals trajectories are correlated with thoseof their friends and friends of friends (2-hop friends) in an online setting. Based on our observation, we propose a mixed-membership model that infers global mobility patterns from users\\' check-ins and their network of friends, without impairing the model\\'s complexity. Our proposed model infers global patterns and learns new representations for both usersand locations simultaneously. We evaluate the inferred patterns and compare the quality of the new user representation against baseline methods on a social link prediction problem.

  15. The Influence of Problem Based Learning Model toward Students’ Activities and Learning Outcomes on Financial Management Subject

    Directory of Open Access Journals (Sweden)

    Han Tantri Hardini


    Full Text Available This research aims to know the influence of problem based learning model toward students’ activities and achievement on Financial Management subject for undergraduate program students of Accounting Education. It was a quantitative research that used true experimental design. Samples of this study were undergraduate program students of Accounting Education in the year of 2014. Class A were control class and class B were experimental class. Data were analyzed by using t-test in order to determine the differences of learning outcomes between control class and experimental class. Then, questionnaires were distributed to gather students’ activities information in their students’ learning model. Findings show that there is an influence of Problem Based Learning model toward students’ activities and learning outcomes on Financial Management subject for undergraduate program students of Accounting Education since t-count ≥ t-table. It is 6.120 ≥ 1.9904. Students’ learning activities with Problem Based Learning model are better than students who are taught by conventional learning model.

  16. A Learning Framework for Control-Oriented Modeling of Buildings

    Energy Technology Data Exchange (ETDEWEB)

    Rubio-Herrero, Javier; Chandan, Vikas; Siegel, Charles M.; Vishnu, Abhinav; Vrabie, Draguna L.


    Buildings consume a significant amount of energy worldwide. Several building optimization and control use cases require models of energy consumption which are control oriented, have high predictive capability, imposes minimal data pre-processing requirements, and have the ability to be adapted continuously to account for changing conditions as new data becomes available. Data driven modeling techniques, that have been investigated so far, while promising in the context of buildings, have been unable to simultaneously satisfy all the requirements mentioned above. In this context, deep learning techniques such as Recurrent Neural Networks (RNNs) hold promise, empowered by advanced computational capabilities and big data opportunities. In this paper, we propose a deep learning based methodology for the development of control oriented models for building energy management and test in on data from a real building. Results show that the proposed methodology outperforms other data driven modeling techniques significantly. We perform a detailed analysis of the proposed methodology along dimensions such as topology, sensitivity, and downsampling. Lastly, we conclude by envisioning a building analytics suite empowered by the proposed deep framework, that can drive several use cases related to building energy management.

  17. Modelling energy technology dynamics: methodology for adaptive expectations models with learning by doing and learning by searching

    International Nuclear Information System (INIS)

    Kouvaritakis, N.; Soria, A.; Isoard, S.


    This paper presents a module endogenising technical change which is capable of being attached to large scale energy models that follow an adaptive-expectations. The formulation includes, apart from the more classical learning by doing effects, quantitative relationships between technology performance and R and D expenditure. It even attempts to go further by partially endogenising the latter by incorporating an optimisation module describing private equipment manufacturers' R and D budget allocation in a context of risk and expectation. Having presented this module in abstract, the paper proceeds to describe how an operational version of it has been constructed and implemented inside a large-scale partial equilibrium world energy model (the POLES model). Concerning learning functions problems associated with the data are alluded to, the hybrid econometric methods used to estimate them are presented as well as the adjustments which had to be effected to ensure a smooth incorporation into the large model. In the final sections is explained the use of the model itself to generate partial foresight parameters for the determination of return expectations particularly in view of CO 2 constraints and associated carbon values. (orig.)

  18. The Learning Organization: A Model for Educational Change. (United States)

    Brown, Rexford


    Analyzes public school bureaucracy and ways to reform institutions into learning communities that value shared knowledge and learning experiences. Describes how a bureaucratic organizational structure impairs learning. Proposes the "learning organization" in which adults learn alongside students, planning is decentralized, families are…

  19. Building Bridges: Seeking Structure and Direction for Higher Education Motivated Learning Strategy Models (United States)

    Fryer, Luke K.


    Many of our current higher education (HE) learning strategy models intersect at important points. At the same time, these theories also often demonstrate important unique perspectives on student learning within HE. Currently, research with one learning strategy model rarely leads to developments in others, as each group of researchers works in…

  20. The Proposed Model of Collaborative Virtual Learning Environment for Introductory Programming Course (United States)

    Othman, Mahfudzah; Othman, Muhaini


    This paper discusses the proposed model of the collaborative virtual learning system for the introductory computer programming course which uses one of the collaborative learning techniques known as the "Think-Pair-Share". The main objective of this study is to design a model for an online learning system that facilitates the…

  1. Beyond the Didactic Classroom: Educational Models to Encourage Active Student Involvement in Learning


    Shreeve, Michael W.


    In a chiropractic college that utilizes a hybrid curriculum model composed of adult-based learning strategies along with traditional lecture-based course delivery, a literature search for educational delivery methods that would integrate the affective domain and the cognitive domain of learning provided some insights into the use of problem-based learning (PBL), experiential learning theory (ELT), and the emerging use of appreciative inquiry (AI) to enhance the learning experience. The purpos...

  2. Unified Deep Learning Architecture for Modeling Biology Sequence. (United States)

    Wu, Hongjie; Cao, Chengyuan; Xia, Xiaoyan; Lu, Qiang


    Prediction of the spatial structure or function of biological macromolecules based on their sequence remains an important challenge in bioinformatics. When modeling biological sequences using traditional sequencing models, characteristics, such as long-range interactions between basic units, the complicated and variable output of labeled structures, and the variable length of biological sequences, usually lead to different solutions on a case-by-case basis. This study proposed the use of bidirectional recurrent neural networks based on long short-term memory or a gated recurrent unit to capture long-range interactions by designing the optional reshape operator to adapt to the diversity of the output labels and implementing a training algorithm to support the training of sequence models capable of processing variable-length sequences. Additionally, the merge and pooling operators enhanced the ability to capture short-range interactions between basic units of biological sequences. The proposed deep-learning model and its training algorithm might be capable of solving currently known biological sequence-modeling problems through the use of a unified framework. We validated our model on one of the most difficult biological sequence-modeling problems currently known, with our results indicating the ability of the model to obtain predictions of protein residue interactions that exceeded the accuracy of current popular approaches by 10% based on multiple benchmarks.

  3. A Combination of Machine Learning and Cerebellar Models for the Motor Control and Learning of a Modular Robot

    DEFF Research Database (Denmark)

    Baira Ojeda, Ismael; Tolu, Silvia; Pacheco, Moises


    We scaled up a bio-inspired control architecture for the motor control and motor learning of a real modular robot. In our approach, the Locally Weighted Projection Regression algorithm (LWPR) and a cerebellar microcircuit coexist, forming a Unit Learning Machine. The LWPR optimizes the input space...... and learns the internal model of a single robot module to command the robot to follow a desired trajectory with its end-effector. The cerebellar microcircuit refines the LWPR output delivering corrective commands. We contrasted distinct cerebellar circuits including analytical models and spiking models...

  4. Learning Hierarchical User Interest Models from Web Pages

    Institute of Scientific and Technical Information of China (English)


    We propose an algorithm for learning hierarchical user interest models according to the Web pages users have browsed. In this algorithm, the interests of a user are represented into a tree which is called a user interest tree, the content and the structure of which can change simultaneously to adapt to the changes in a user's interests. This expression represents a user's specific and general interests as a continuum. In some sense, specific interests correspond to short-term interests, while general interests correspond to long-term interests. So this representation more really reflects the users' interests. The algorithm can automatically model a user's multiple interest domains, dynamically generate the interest models and prune a user interest tree when the number of the nodes in it exceeds given value. Finally, we show the experiment results in a Chinese Web Site.

  5. Machine learning based switching model for electricity load forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Fan, Shu; Lee, Wei-Jen [Energy Systems Research Center, The University of Texas at Arlington, 416 S. College Street, Arlington, TX 76019 (United States); Chen, Luonan [Department of Electronics, Information and Communication Engineering, Osaka Sangyo University, 3-1-1 Nakagaito, Daito, Osaka 574-0013 (Japan)


    In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts an integrated architecture to handle the non-stationarity of time series. Firstly, a BCD classifier is applied to cluster the input data set into several subsets by the dynamics of the time series in an unsupervised manner. Then, groups of SVRs are used to fit the training data of each subset in a supervised way. The effectiveness of the proposed model is demonstrated with actual data taken from the New York ISO and the Western Farmers Electric Cooperative in Oklahoma. (author)

  6. Machine learning based switching model for electricity load forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Fan Shu [Energy Systems Research Center, University of Texas at Arlington, 416 S. College Street, Arlington, TX 76019 (United States); Chen Luonan [Department of Electronics, Information and Communication Engineering, Osaka Sangyo University, 3-1-1 Nakagaito, Daito, Osaka 574-0013 (Japan); Lee, Weijen [Energy Systems Research Center, University of Texas at Arlington, 416 S. College Street, Arlington, TX 76019 (United States)], E-mail:


    In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts an integrated architecture to handle the non-stationarity of time series. Firstly, a BCD classifier is applied to cluster the input data set into several subsets by the dynamics of the time series in an unsupervised manner. Then, groups of SVRs are used to fit the training data of each subset in a supervised way. The effectiveness of the proposed model is demonstrated with actual data taken from the New York ISO and the Western Farmers Electric Cooperative in Oklahoma.

  7. Machine learning based switching model for electricity load forecasting

    International Nuclear Information System (INIS)

    Fan Shu; Chen Luonan; Lee, Weijen


    In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts an integrated architecture to handle the non-stationarity of time series. Firstly, a BCD classifier is applied to cluster the input data set into several subsets by the dynamics of the time series in an unsupervised manner. Then, groups of SVRs are used to fit the training data of each subset in a supervised way. The effectiveness of the proposed model is demonstrated with actual data taken from the New York ISO and the Western Farmers Electric Cooperative in Oklahoma

  8. A Multidimensional/Non-Linear Teaching and Learning Model: Teaching and Learning Music in an Authentic and Holistic Context (United States)

    Crawford, Renée


    This article discusses the conceptual framework that leads to the design of a teaching and learning model as part of a recent ethnographic study that considered the effectiveness of current Victorian government secondary school music teaching and learning practices when engaged with technology. The philosophical and theoretical basis for this…

  9. Students' Critical Thinking Skills in Chemistry Learning Using Local Culture-Based 7E Learning Cycle Model (United States)

    Suardana, I. Nyoman; Redhana, I. Wayan; Sudiatmika, A. A. Istri Agung Rai; Selamat, I. Nyoman


    This research aimed at describing the effectiveness of the local culture-based 7E learning cycle model in improving students' critical thinking skills in chemistry learning. It was an experimental research with post-test only control group design. The population was the eleventh-grade students of senior high schools in Singaraja, Indonesia. The…

  10. Personal recommender systems for learners in lifelong learning: requirements, techniques and model

    NARCIS (Netherlands)

    Drachsler, Hendrik; Hummel, Hans; Koper, Rob


    Drachsler, H., Hummel, H. G. K., & Koper, R. (2008). Personal recommender systems for learners in lifelong learning: requirements, techniques and model. International Journal of Learning Technology, 3(4), 404-423.

  11. Impact of statistical learning methods on the predictive power of multivariate normal tissue complication probability models

    NARCIS (Netherlands)

    Xu, Cheng-Jian; van der Schaaf, Arjen; Schilstra, Cornelis; Langendijk, Johannes A.; van t Veld, Aart A.


    PURPOSE: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. METHODS AND MATERIALS: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator

  12. A modelling approach to study learning processes with a focus on knowledge creation

    NARCIS (Netherlands)

    Naeve, Ambjorn; Yli-Luoma, Pertti; Kravcik, Milos; Lytras, Miltiadis


    Naeve, A., Yli-Luoma, P., Kravcik, M., & Lytras, M. D. (2008). A modelling approach to study learning processes with a focus on knowledge creation. International Journal Technology Enhanced Learning, 1(1/2), 1–34.

  13. The MVP Model as an Organizing Framework for Neuroscience Findings Related to Learning (United States)

    Zakrajsek, Todd M.


    This chapter describes the ways in which the MVP model relates to recent research on neuroscience and learning, and demonstrates how those relationships may be used to better understand physiological impacts on motivation, and to facilitate improved learning.

  14. Learning from Your Network of Friends: A Trajectory Representation Learning Model Based on Online Social Ties

    KAUST Repository

    Alharbi, Basma Mohammed; Zhang, Xiangliang


    Location-Based Social Networks (LBSNs) capture individuals whereabouts for a large portion of the population. To utilize this data for user (location)-similarity based tasks, one must map the raw data into a low-dimensional uniform feature space. However, due to the nature of LBSNs, many users have sparse and incomplete check-ins. In this work, we propose to overcome this issue by leveraging the network of friends, when learning the new feature space. We first analyze the impact of friends on individuals's mobility, and show that individuals trajectories are correlated with thoseof their friends and friends of friends (2-hop friends) in an online setting. Based on our observation, we propose a mixed-membership model that infers global mobility patterns from users' check-ins and their network of friends, without impairing the model's complexity. Our proposed model infers global patterns and learns new representations for both usersand locations simultaneously. We evaluate the inferred patterns and compare the quality of the new user representation against baseline methods on a social link prediction problem.

  15. A Wittgenstein Approach to the Learning of OO-modeling (United States)

    Holmboe, Christian


    The paper uses Ludwig Wittgenstein's theories about the relationship between thought, language, and objects of the world to explore the assumption that OO-thinking resembles natural thinking. The paper imports from research in linguistic philosophy to computer science education research. I show how UML class diagrams (i.e., an artificial context-free language) correspond to the logically perfect languages described in Tractatus Logico-Philosophicus. In Philosophical Investigations Wittgenstein disputes his previous theories by showing that natural languages are not constructed by rules of mathematical logic, but are language games where the meaning of a word is constructed through its use in social contexts. Contradicting the claim that OO-thinking is easy to learn because of its similarity to natural thinking, I claim that OO-thinking is difficult to learn because of its differences from natural thinking. The nature of these differences is not currently well known or appreciated. I suggest how explicit attention to the nature and implications of different language games may improve the teaching and learning of OO-modeling as well as programming.

  16. Human-Guided Learning for Probabilistic Logic Models

    Directory of Open Access Journals (Sweden)

    Phillip Odom


    Full Text Available Advice-giving has been long explored in the artificial intelligence community to build robust learning algorithms when the data is noisy, incorrect or even insufficient. While logic based systems were effectively used in building expert systems, the role of the human has been restricted to being a “mere labeler” in recent times. We hypothesize and demonstrate that probabilistic logic can provide an effective and natural way for the expert to specify domain advice. Specifically, we consider different types of advice-giving in relational domains where noise could arise due to systematic errors or class-imbalance inherent in the domains. The advice is provided as logical statements or privileged features that are thenexplicitly considered by an iterative learning algorithm at every update. Our empirical evidence shows that human advice can effectively accelerate learning in noisy, structured domains where so far humans have been merely used as labelers or as designers of the (initial or final structure of the model.

  17. Innovation and entrepreneurship as pathways for new teaching / learning models

    Directory of Open Access Journals (Sweden)

    Helen Kelle dos Santos Costa


    Full Text Available Introduction: Modern times demand from society a new attitude, new attitudes, a new way of thinking and seeing the world. It is thus necessary that Education, the foundation for building a society, once again reinvents, innovates and adapts the demands that the process of human development requires. Objective: To emphasize the importance of Innovation and Entrepreneurship as tools for the development of new models of teaching / learning so that there is an education that meets the new social demands. Methodology: The article was structured from a Bibliographic research on theories and models of teaching / learning through an analytical reading, able to identify the characteristics for the effective realization of entrepreneurship in education in an innovative way. Results: The models of education are in constant process of evolution, the adoption of good practices and new resources that can help in teachinglearning as motivating agent of entrepreneurship in education through innovation is a reality to be reviewed by society as a whole. Conclusions: This study is expected to be an important tool for behavioral and / or economic change, with the aim of making the results successful for all parties involved in the attempt to corroborate with the entrepreneurship ecosystem through continuous and increasing multiplication of knowledge.

  18. Advanced Machine Learning Emulators of Radiative Transfer Models (United States)

    Camps-Valls, G.; Verrelst, J.; Martino, L.; Vicent, J.


    Physically-based model inversion methodologies are based on physical laws and established cause-effect relationships. A plethora of remote sensing applications rely on the physical inversion of a Radiative Transfer Model (RTM), which lead to physically meaningful bio-geo-physical parameter estimates. The process is however computationally expensive, needs expert knowledge for both the selection of the RTM, its parametrization and the the look-up table generation, as well as its inversion. Mimicking complex codes with statistical nonlinear machine learning algorithms has become the natural alternative very recently. Emulators are statistical constructs able to approximate the RTM, although at a fraction of the computational cost, providing an estimation of uncertainty, and estimations of the gradient or finite integral forms. We review the field and recent advances of emulation of RTMs with machine learning models. We posit Gaussian processes (GPs) as the proper framework to tackle the problem. Furthermore, we introduce an automatic methodology to construct emulators for costly RTMs. The Automatic Gaussian Process Emulator (AGAPE) methodology combines the interpolation capabilities of GPs with the accurate design of an acquisition function that favours sampling in low density regions and flatness of the interpolation function. We illustrate the good capabilities of our emulators in toy examples, leaf and canopy levels PROSPECT and PROSAIL RTMs, and for the construction of an optimal look-up-table for atmospheric correction based on MODTRAN5.

  19. A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning

    DEFF Research Database (Denmark)

    Fraccaro, Marco; Kamronn, Simon Due; Paquet, Ulrich


    This paper takes a step towards temporal reasoning in a dynamically changing video, not in the pixel space that constitutes its frames, but in a latent space that describes the non-linear dynamics of the objects in its world. We introduce the Kalman variational auto-encoder, a framework...... for unsupervised learning of sequential data that disentangles two latent representations: an object’s representation, coming from a recognition model, and a latent state describing its dynamics. As a result, the evolution of the world can be imagined and missing data imputed, both without the need to generate...

  20. Generative Models in Deep Learning: Constraints for Galaxy Evolution (United States)

    Turp, Maximilian Dennis; Schawinski, Kevin; Zhang, Ce; Weigel, Anna K.


    New techniques are essential to make advances in the field of galaxy evolution. Recent developments in the field of artificial intelligence and machine learning have proven that these tools can be applied to problems far more complex than simple image recognition. We use these purely data driven approaches to investigate the process of star formation quenching. We show that Variational Autoencoders provide a powerful method to forward model the process of galaxy quenching. Our results imply that simple changes in specific star formation rate and bulge to disk ratio cannot fully describe the properties of the quenched population.

  1. Modeling Music Emotion Judgments Using Machine Learning Methods

    Directory of Open Access Journals (Sweden)

    Naresh N. Vempala


    Full Text Available Emotion judgments and five channels of physiological data were obtained from 60 participants listening to 60 music excerpts. Various machine learning (ML methods were used to model the emotion judgments inclusive of neural networks, linear regression, and random forests. Input for models of perceived emotion consisted of audio features extracted from the music recordings. Input for models of felt emotion consisted of physiological features extracted from the physiological recordings. Models were trained and interpreted with consideration of the classic debate in music emotion between cognitivists and emotivists. Our models supported a hybrid position wherein emotion judgments were influenced by a combination of perceived and felt emotions. In comparing the different ML approaches that were used for modeling, we conclude that neural networks were optimal, yielding models that were flexible as well as interpretable. Inspection of a committee machine, encompassing an ensemble of networks, revealed that arousal judgments were predominantly influenced by felt emotion, whereas valence judgments were predominantly influenced by perceived emotion.

  2. Strategies to Enhance Online Learning Teams. Team Assessment and Diagnostics Instrument and Agent-based Modeling (United States)


    Strategies to Enhance Online Learning Teams Team Assessment and Diagnostics Instrument and Agent-based Modeling Tristan E. Johnson, Ph.D. Learning ...REPORT DATE AUG 2010 2. REPORT TYPE 3. DATES COVERED 00-00-2010 to 00-00-2010 4. TITLE AND SUBTITLE Strategies to Enhance Online Learning ...TeamsTeam Strategies to Enhance Online Learning Teams: Team Assessment and Diagnostics Instrument and Agent-based Modeling 5a. CONTRACT NUMBER 5b. GRANT

  3. Model-based and model-free Pavlovian reward learning: revaluation, revision, and revelation. (United States)

    Dayan, Peter; Berridge, Kent C


    Evidence supports at least two methods for learning about reward and punishment and making predictions for guiding actions. One method, called model-free, progressively acquires cached estimates of the long-run values of circumstances and actions from retrospective experience. The other method, called model-based, uses representations of the environment, expectations, and prospective calculations to make cognitive predictions of future value. Extensive attention has been paid to both methods in computational analyses of instrumental learning. By contrast, although a full computational analysis has been lacking, Pavlovian learning and prediction has typically been presumed to be solely model-free. Here, we revise that presumption and review compelling evidence from Pavlovian revaluation experiments showing that Pavlovian predictions can involve their own form of model-based evaluation. In model-based Pavlovian evaluation, prevailing states of the body and brain influence value computations, and thereby produce powerful incentive motivations that can sometimes be quite new. We consider the consequences of this revised Pavlovian view for the computational landscape of prediction, response, and choice. We also revisit differences between Pavlovian and instrumental learning in the control of incentive motivation.

  4. Tool Support for Collaborative Teaching and Learning of Object-Oriented Modelling

    DEFF Research Database (Denmark)

    Hansen, Klaus Marius; Ratzer, Anne Vinter


    Modeling is central to doing and learning object-oriented development. We present a new tool, Ideogramic UML, for gesture-based collaborative modeling with the Unified Modeling Language (UML), which can be used to collaboratively teach and learn modeling. Furthermore, we discuss how we have...

  5. Design e-learning with flipped learning model to improve layout understanding the concepts basic of the loop control structure (United States)

    Handayani, D. P.; Sutarno, H.; Wihardi, Y.


    This study aimed in design and build e-learning with classroom flipped model to improve the concept of understanding of SMK students on the basic programming subject. Research and development obtained research data from survey questionnaire given to students of SMK class X RPL in SMK Negeri 2 Bandung and interviews to RPL productive teacher. Data also obtained from questionnaire of expert validation and students' assessment from e-learning with flipped classroom models. Data also obtained from multiple-choice test to measure improvements in conceptual understanding. The results of this research are: 1) Developed e- learning with flipped classroom model considered good and worthy of use by the average value of the percentage of 86,3% by media experts, and 85,5% by subjects matter experts, then students gave judgment is very good on e-learning either flipped classroom model with a percentage of 79,15% votes. 2) e-learning with classroom flipped models show an increase in the average value of pre-test before using e-learning 26.67 compared to the average value post-test after using e- learning at 63.37 and strengthened by the calculation of the index gains seen Increased understanding of students 'concepts by 50% with moderate criteria indicating that students' understanding is improving.


    Directory of Open Access Journals (Sweden)

    Sri Rosepda Sebayang


    Full Text Available This study aims: 1 to determine whether the student learning outcomes using discovery learning is better than conventional learning 2 To determine whether the learning outcomes of students who have a high initial concept understanding better then of low initial concept understanding, and 3 to determine the effect of interaction discovery learning and understanding of the initial concept of the learning outcomes of students. The samples in this study was taken by cluster random sampling two classes where class X PIA 3 as a class experiment with applying discovery learning and class X PIA 2 as a control class by applying conventional learning. The instrument used in this study is a test of learning outcomes in the form of multiple-choice comprehension test initial concept description form. The results of research are: 1 learning outcomes of students who were taught with discovery learning is better than the learning outcomes of students who are taught by conventional learning, 2 student learning outcomes with high initial conceptual understanding better than the learning outcomes of students with low initial conceptual understanding, and 3 there was no interaction between discovery learning and understanding of initial concepts for the student learning outcomes.

  7. Learning

    Directory of Open Access Journals (Sweden)

    Mohsen Laabidi


    Full Text Available Nowadays learning technologies transformed educational systems with impressive progress of Information and Communication Technologies (ICT. Furthermore, when these technologies are available, affordable and accessible, they represent more than a transformation for people with disabilities. They represent real opportunities with access to an inclusive education and help to overcome the obstacles they met in classical educational systems. In this paper, we will cover basic concepts of e-accessibility, universal design and assistive technologies, with a special focus on accessible e-learning systems. Then, we will present recent research works conducted in our research Laboratory LaTICE toward the development of an accessible online learning environment for persons with disabilities from the design and specification step to the implementation. We will present, in particular, the accessible version “MoodleAcc+” of the well known e-learning platform Moodle as well as new elaborated generic models and a range of tools for authoring and evaluating accessible educational content.

  8. Influence of Discussion Rating in Cooperative Learning Type Numbered Head Together on Learning Results Students VII MTSN Model Padang (United States)

    Sasmita, E.; Edriati, S.; Yunita, A.


    Related to the math score of the first semester in class at seventh grade of MTSN Model Padang which much the score that low (less than KKM). It because of the students who feel less involved in learning process because the teacher don't do assessment the discussions. The solution of the problem is discussion assessment in Cooperative Learning Model type Numbered Head Together. This study aims to determine whether the discussion assessment in NHT effect on student learning outcomes of class VII MTsN Model Padang. The instrument used in this study is discussion assessment and final tests. The data analysis technique used is the simple linear regression analysis. Hypothesis test results Fcount greater than the value of Ftable then the hypothesis in this study received. So it concluded that the assessment of the discussion in NHT effect on student learning outcomes of class VII MTsN Model Padang.

  9. "The Spiral Model for the Development of Coordination": A Learning Model Based on Eshkol-Wachman Movement Notation (EWMN) (United States)

    Al-Dor, Nira


    The objective of this study is to present "The Spiral Model for the Development of Coordination" (SMDC), a learning model that reflects the complexity and possibilities embodied in the learning of movement notation Eshkol-Wachman (EWMN), an Israeli invention. This model constituted the infrastructure for a comprehensive study that examined the…

  10. Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework. (United States)

    El-Assady, Mennatallah; Sevastjanova, Rita; Sperrle, Fabian; Keim, Daniel; Collins, Christopher


    Topic modeling algorithms are widely used to analyze the thematic composition of text corpora but remain difficult to interpret and adjust. Addressing these limitations, we present a modular visual analytics framework, tackling the understandability and adaptability of topic models through a user-driven reinforcement learning process which does not require a deep understanding of the underlying topic modeling algorithms. Given a document corpus, our approach initializes two algorithm configurations based on a parameter space analysis that enhances document separability. We abstract the model complexity in an interactive visual workspace for exploring the automatic matching results of two models, investigating topic summaries, analyzing parameter distributions, and reviewing documents. The main contribution of our work is an iterative decision-making technique in which users provide a document-based relevance feedback that allows the framework to converge to a user-endorsed topic distribution. We also report feedback from a two-stage study which shows that our technique results in topic model quality improvements on two independent measures.

  11. L.I.M.E. A recommendation model for informal and formal learning, engaged

    Directory of Open Access Journals (Sweden)

    Daniel Burgos


    Full Text Available In current eLearning models and implementations (e.g. Learning Management Systems-LMS there is a lack of engagement between formal and informal activities. Furthermore, the online methodology focuses on a standard set of units of learning and learning objects, along with pre-defined tests, and collateral resources like, i.e. discussion fora and message wall. They miss the huge potential of learning via the interlacement of social networks, LMS and external sources. Thanks to user behaviour, user interaction, and personalised counselling by a tutor, learning performance can be improved. We design and develop an adaptation eLearning model for restricted social networks, which supports this approach. In addition, we build an eLearning module that implements this conceptual model in a real application case, and present the preliminary analysis and positive results.

  12. Learning Particle Physics with DIY Play Dough Model (United States)

    Thunyaniti, T.; Toedtanya, K.; Wuttiprom, S.


    The scientists once believed an atom was the smallest particle, nothing was smaller than this tiny particle. Later, they discovered an atom which consists of protons, neutrons and electrons, and they believed that these particles cannot be broken into the smaller particles. According to advanced technology, the scientists have discovered these particles are consisted of a smaller particles. The new particles are called quarks leptons and bosons which we called fundamental particle. Atomic structure cannot be observed directly, so it is complicated for studying these particles. To help the students get more understanding of its properties, so the researcher develops the learning pattern of fundamental particles from Play Dough Model for high school to graduate students. Four step of learning are 1) to introduces the concept of the fundamental particles discovery 2) to play the Happy Families game by using fundamental particles cards 3) to design and make their particle in a way that reflects its properties 4) to represents their particles from Play Dough Model. After doing activities, the students had more conceptual understanding and better memorability on fundamental particles. In addition, the students gained collaborative working experience among their friends also.

  13. A reinforcement learning model of joy, distress, hope and fear (United States)

    Broekens, Joost; Jacobs, Elmer; Jonker, Catholijn M.


    In this paper we computationally study the relation between adaptive behaviour and emotion. Using the reinforcement learning framework, we propose that learned state utility, ?, models fear (negative) and hope (positive) based on the fact that both signals are about anticipation of loss or gain. Further, we propose that joy/distress is a signal similar to the error signal. We present agent-based simulation experiments that show that this model replicates psychological and behavioural dynamics of emotion. This work distinguishes itself by assessing the dynamics of emotion in an adaptive agent framework - coupling it to the literature on habituation, development, extinction and hope theory. Our results support the idea that the function of emotion is to provide a complex feedback signal for an organism to adapt its behaviour. Our work is relevant for understanding the relation between emotion and adaptation in animals, as well as for human-robot interaction, in particular how emotional signals can be used to communicate between adaptive agents and humans.

  14. Learners' Ensemble Based Security Conceptual Model for M-Learning System in Malaysian Higher Learning Institution (United States)

    Mahalingam, Sheila; Abdollah, Faizal Mohd; Sahib, Shahrin


    M-Learning has a potential to improve efficiency in the education sector and has a tendency to grow advance and transform the learning environment in the future. Yet there are challenges in many areas faced when introducing and implementing m-learning. The learner centered attribute in mobile learning implies deployment in untrustworthy learning…

  15. Surveying and Modeling Students' Motivation and Learning Strategies for Mobile-Assisted Seamless Chinese Language Learning (United States)

    Chai, Ching Sing; Wong, Lung-Hsiang; King, Ronnel B.


    Seamless language learning promises to be an effective learning approach that addresses the limitations of classroom-only language learning. It leverages mobile technologies to facilitate holistic and perpetual learning experiences that bridge different locations, times, technologies or social settings. Despite the emergence of studies on seamless…

  16. Learning Experiences Reuse Based on an Ontology Modeling to Improve Adaptation in E-Learning Systems (United States)

    Hadj M'tir, Riadh; Rumpler, Béatrice; Jeribi, Lobna; Ben Ghezala, Henda


    Current trends in e-Learning focus mainly on personalizing and adapting the learning environment and learning process. Although their increasingly number, theses researches often ignore the concepts of capitalization and reuse of learner experiences which can be exploited later by other learners. Thus, the major challenge of distance learning is…

  17. Common Mobile Learning Characteristics--An Analysis of Mobile Learning Models and Frameworks (United States)

    Imtinan, Umera; Chang, Vanessa; Issa, Tomayess


    Mobile learning offers learning opportunities to learners without the limitations of time and space. Mobile learning has introduced a number of flexible options to the learners across disciplines and at different educational levels. However, designing mobile learning content is an equally challenging task for the instructional designers.…

  18. Models of Learning Space: Integrating Research on Space, Place and Learning in Higher Education (United States)

    Ellis, R. A.; Goodyear, P.


    Learning space research is a relatively new field of study that seeks to inform the design, evaluation and management of learning spaces. This paper reviews a dispersed and fragmented literature relevant to understanding connections between university learning spaces and student learning activities. From this review, the paper distils a number of…

  19. Supporting Teachers in Identifying Students' Learning Styles in Learning Management Systems: An Automatic Student Modelling Approach (United States)

    Graf, Sabine; Kinshuk; Liu, Tzu-Chien


    In learning management systems (LMSs), teachers have more difficulties to notice and know how individual students behave and learn in a course, compared to face-to-face education. Enabling teachers to know their students' learning styles and making students aware of their own learning styles increases teachers' and students' understanding about…

  20. A model for learning development | Kilfoil | South African Journal of ...

    African Journals Online (AJOL)

    This article looks at the way in which people perceive learning and the impact of these perceptions on teaching methods within the context of learning development in distance education. The context could, in fact, be any type of teaching and learning environment. The point is to balance approaches to teaching and learning ...

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

    DEFF Research Database (Denmark)

    Cheng, Jade Yu


    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...


    Directory of Open Access Journals (Sweden)

    Teguh Febri Sudarma


    Full Text Available Research was aimed to determine: (1 Students’ learning outcomes that was taught with just in time teaching based STAD cooperative learning method and STAD cooperative learning method (2 Students’ outcomes on Physics subject that had high learning activity compared with low learning activity. The research sample was random by raffling four classes to get two classes. The first class taught with just in time teaching based STAD cooperative learning method, while the second class was taught with STAD cooperative learning method. The instrument used was conceptual understanding that had been validated with 7 essay questions. The average gain values of students learning results with just in time teaching based STAD cooperative learning method 0,47 higher than average gain values of students learning results with STAD cooperative learning method. The high learning activity and low learning activity gave different learning results. In this case the average gain values of students learning results with just in time teaching based STAD cooperative learning method 0,48 higher than average gain values of students learning results with STAD cooperative learning method. There was interaction between learning model and learning activity to the physics learning result test in students

  3. Computational Cognitive Neuroscience Modeling of Sequential Skill Learning (United States)


    learning during declarative control. 8. Journal of Experimental Psychology : Learning, Memory , and Cognition . 9. Crossley, M. J., Ashby, F. G., & Maddox...learning: Sensitivity to feedback timing. Frontiers in Psychology – Cognitive Science, 5, article 643, 1-9. 15. Worthy, D.A. & Maddox, W.T. (2014). A...Learning, Memory , and Cognition . Crossley, M. J., Ashby, F. G., & Maddox, W. T. (2014). Context-dependent savings in procedural category learning

  4. Learning by Doing vs Learning by Researching in a Model of Climate Change Policy Analysis

    International Nuclear Information System (INIS)

    Castelnuovo, E.; Galeotti, M.; Vergalli, S.; Gambarelli, G.


    Many predictions and conclusions in the climate change literature have been made and drawn on the basis of theoretical analyses and quantitative models that assume exogenous technological change. One is naturally led to wonder whether those conclusions and policy prescriptions hold in the more realistic case of endogenously evolving technologies. In previous work we took a popular integrated assessment model and modified it so as to allow for an explicit role of the stock of knowledge which accumulates through R and D investment. In our formulation knowledge affects both the output production technology and the emission-output ratio. In this paper we make further progress in our efforts aimed to model the process of technological change. In keeping with recent theories of endogenous growth, we specify two ways in which knowledge accumulates: via a deliberate, optimally selected R and D decision or via experience, giving rise to Learning by Doing. As an illustration, we simulate the model under the two versions of endogenous technical change and look at the dynamics of a selected number of relevant variables, including growth rates of GDP and physical capital, as well as total emissions and rate of domestic abatement. Keywords: Climate Policy, Environmental Modeling, Integrated Assessment, Technical Change

  5. Knowledge models as agents of meaninful learning and knowledge creation.

    Directory of Open Access Journals (Sweden)

    Fermín María González García


    Full Text Available 0 0 1 172 952 USAL 7 2 1122 14.0 Normal 0 21 false false false ES JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:Calibri; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-ansi-language:ES; mso-fareast-language:EN-US;} The educational change that pushes the current context requires a shift in the unfortunately predominant positivist-behaviourist model that favours mechanical      memoristic learning, ideal breeding ground for the existence and maintenance of conceptual errors, to another cognitive-constructivist that stimulates meaningful learning to allow students to build and master knowledge, therefore to be more creative and critical. We present here a model of knowledge where students construct new knowledge as a result of significant learning. Students play an active role, learning not only about the product, but also about the process itself (meta-cognition. We also show how to promote teacher activity primarily in order to create the conditions that facilitate the student to transform the information in useful, substantive knowledge, to be  incorporated in his knowledge structure and in his long-term memory. Finally, we provide elements to measure what the student knows and to assess how their cognitive structure has changed regarding their ancient knowledge; that is, to assess the necessary conceptual change.

  6. The POL Model: Using a Social Constructivist Framework to Develop Blended and Online Learning

    DEFF Research Database (Denmark)

    Dalsgaard, Christian; Godsk, Mikkel


    The paper presents a model for developing blended and online learning based on a given curriculum and typical learning objectives for university courses. The model consists of a three-step-process in which the instructor formulates product-oriented tasks, develops and structures the learning...... materials and tools, outlines a schedule, and supports the students' learning activity in developing a product. The model is based on our experiences with transforming traditional lecture-based lessons into problem-based blended and online learning using a social constructivist approach and a standard...... virtual learning environment (VLE). Our initial experiments indicate that our model is useful to develop blended and online modules and, furthermore, it seems fruitful to use a social constructivist framework and orienting learning activities towards the development of products....

  7. Functional networks inference from rule-based machine learning models. (United States)

    Lazzarini, Nicola; Widera, Paweł; Williamson, Stuart; Heer, Rakesh; Krasnogor, Natalio; Bacardit, Jaume


    Functional networks play an important role in the analysis of biological processes and systems. The inference of these networks from high-throughput (-omics) data is an area of intense research. So far, the similarity-based inference paradigm (e.g. gene co-expression) has been the most popular approach. It assumes a functional relationship between genes which are expressed at similar levels across different samples. An alternative to this paradigm is the inference of relationships from the structure of machine learning models. These models are able to capture complex relationships between variables, that often are different/complementary to the similarity-based methods. We propose a protocol to infer functional networks from machine learning models, called FuNeL. It assumes, that genes used together within a rule-based machine learning model to classify the samples, might also be functionally related at a biological level. The protocol is first tested on synthetic datasets and then evaluated on a test suite of 8 real-world datasets related to human cancer. The networks inferred from the real-world data are compared against gene co-expression networks of equal size, generated with 3 different methods. The comparison is performed from two different points of view. We analyse the enriched biological terms in the set of network nodes and the relationships between known disease-associated genes in a context of the network topology. The comparison confirms both the biological relevance and the complementary character of the knowledge captured by the FuNeL networks in relation to similarity-based methods and demonstrates its potential to identify known disease associations as core elements of the network. Finally, using a prostate cancer dataset as a case study, we confirm that the biological knowledge captured by our method is relevant to the disease and consistent with the specialised literature and with an independent dataset not used in the inference process. The

  8. A Model of e-Learning by Constructivism Approach Using Problem-Based Learning to Develop Thinking Skills for Students in Rajaghat University (United States)

    Shutimarrungson, Werayut; Pumipuntu, Sangkom; Noirid, Surachet


    This research aimed to develop a model of e-learning by using Problem-Based Learning--PBL to develop thinking skills for students in Rajabhat University. The research is divided into three phases through the e-learning model via PBL with Constructivism approach as follows: Phase 1 was to study characteristics and factors through the model to…

  9. Pragmatically Framed Cross-Situational Noun Learning Using Computational Reinforcement Models. (United States)

    Najnin, Shamima; Banerjee, Bonny


    Cross-situational learning and social pragmatic theories are prominent mechanisms for learning word meanings (i.e., word-object pairs). In this paper, the role of reinforcement is investigated for early word-learning by an artificial agent. When exposed to a group of speakers, the agent comes to understand an initial set of vocabulary items belonging to the language used by the group. Both cross-situational learning and social pragmatic theory are taken into account. As social cues, joint attention and prosodic cues in caregiver's speech are considered. During agent-caregiver interaction, the agent selects a word from the caregiver's utterance and learns the relations between that word and the objects in its visual environment. The "novel words to novel objects" language-specific constraint is assumed for computing rewards. The models are learned by maximizing the expected reward using reinforcement learning algorithms [i.e., table-based algorithms: Q-learning, SARSA, SARSA-λ, and neural network-based algorithms: Q-learning for neural network (Q-NN), neural-fitted Q-network (NFQ), and deep Q-network (DQN)]. Neural network-based reinforcement learning models are chosen over table-based models for better generalization and quicker convergence. Simulations are carried out using mother-infant interaction CHILDES dataset for learning word-object pairings. Reinforcement is modeled in two cross-situational learning cases: (1) with joint attention (Attentional models), and (2) with joint attention and prosodic cues (Attentional-prosodic models). Attentional-prosodic models manifest superior performance to Attentional ones for the task of word-learning. The Attentional-prosodic DQN outperforms existing word-learning models for the same task.

  10. The Implementation of C-ID, R2D2 Model on Learning Reading Comprehension (United States)

    Rayanto, Yudi Hari; Rusmawan, Putu Ngurah


    The purposes of this research are to find out, (1) whether C-ID, R2D2 model is effective to be implemented on learning Reading comprehension, (2) college students' activity during the implementation of C-ID, R2D2 model on learning Reading comprehension, and 3) college students' learning achievement during the implementation of C-ID, R2D2 model on…

  11. A "Uses and Gratification Expectancy Model" to Predict Students' "Perceived e-Learning Experience" (United States)

    Mondi, Makingu; Woods, Peter; Rafi, Ahmad


    This study investigates "how and why" students' "Uses and Gratification Expectancy" (UGE) for e-learning resources influences their "Perceived e-Learning Experience." A "Uses and Gratification Expectancy Model" (UGEM) framework is proposed to predict students' "Perceived e-Learning Experience," and…

  12. A User-Centered Educational Modeling Language Improving the Controllability of Learning Design Quality (United States)

    Zendi, Asma; Bouhadada, Tahar; Bousbia, Nabila


    Semiformal EMLs are developed to facilitate the adoption of educational modeling languages (EMLs) and to address practitioners' learning design concerns, such as reusability and readability. In this article, SDLD (Structure Dialogue Learning Design) is presented, which is a semiformal EML that aims to improve controllability of learning design…

  13. Agent-oriented Modeling for Collaborative Learning Environments: A Peer-to-Peer Helpdesk Case Study

    NARCIS (Netherlands)

    Guizzardi-Silva Souza, R.; Wagner, G.; Aroyo, L.M.


    In this paper, we present the analysis and modelling of Help&Learn, an agent-based peer-to-peer helpdesk system to support extra-class interactions among students and teachers. Help&Learn expands the student’s possibility of solving problems, getting involved in a cooperative learning experience

  14. The Development of a Peer Assisted Learning Model for the Clinical Education of Physiotherapy Students (United States)

    Sevenhuysen, Samantha L.; Nickson, Wendy; Farlie, Melanie K.; Raitman, Lyn; Keating, Jennifer L.; Molloy, Elizabeth; Skinner, Elizabeth; Maloney, Stephen; Haines, Terry P.


    Demand for clinical placements in physiotherapy education continues to outstrip supply. Peer assisted learning, in various formats, has been trialled to increase training capacity and facilitate student learning during clinical education. There are no documented examples of measurable or repeatable peer assisted learning models to aid clinicians…

  15. Mixed Methods Study Using Constructive Learning Team Model for Secondary Mathematics Teachers (United States)

    Ritter, Kristy L.


    The constructive learning team model for secondary mathematics teachers (CLTM) was created to provide students with learning opportunities and experiences that address deficiencies in oral and written communication, logical processes and analysis, mathematical operations, independent learning, teamwork, and technology utilization. This study…

  16. How Levels of Interactivity in Tutorials Affect Students' Learning of Modeling Transportation Problems in a Spreadsheet (United States)

    Seal, Kala Chand; Przasnyski, Zbigniew H.; Leon, Linda A.


    Do students learn to model OR/MS problems better by using computer-based interactive tutorials and, if so, does increased interactivity in the tutorials lead to better learning? In order to determine the effect of different levels of interactivity on student learning, we used screen capture technology to design interactive support materials for…

  17. The Effectiveness of Learning Model of Basic Education with Character-Based at Universitas Muslim Indonesia (United States)

    Rosmiati, Rosmiati; Mahmud, Alimuddin; Talib, Syamsul B.


    The purpose of this study was to determine the effectiveness of the basic education learning model with character-based through learning in the Universitas Muslim Indonesia. In addition, the research specifically examines the character of discipline, curiosity and responsibility. The specific target is to produce a basic education learning model…

  18. Investigating Students' Acceptance of a Statistics Learning Platform Using Technology Acceptance Model (United States)

    Song, Yanjie; Kong, Siu-Cheung


    The study aims at investigating university students' acceptance of a statistics learning platform to support the learning of statistics in a blended learning context. Three kinds of digital resources, which are simulations, online videos, and online quizzes, were provided on the platform. Premised on the technology acceptance model, we adopted a…

  19. A Primer on the Statistical Modelling of Learning Curves in Health Professions Education (United States)

    Pusic, Martin V.; Boutis, Kathy; Pecaric, Martin R.; Savenkov, Oleksander; Beckstead, Jason W.; Jaber, Mohamad Y.


    Learning curves are a useful way of representing the rate of learning over time. Features include an index of baseline performance (y-intercept), the efficiency of learning over time (slope parameter) and the maximal theoretical performance achievable (upper asymptote). Each of these parameters can be statistically modelled on an individual and…

  20. Utilizing the Active and Collaborative Learning Model in the Introductory Physics Course (United States)

    Nam, Nguyen Hoai


    Model of active and collaborative learning (ACLM) applied in training specific subject makes clear advantage due to the goals of knowledge, skills that students got to develop successful future job. The author exploits the learning management system (LMS) of Hanoi National University of Education (HNUE) to establish a learning environment in the…