WorldWideScience

Sample records for learning style-based adaptive

  1. LEARNING STYLES BASED ADAPTIVE INTELLIGENT TUTORING SYSTEMS: DOCUMENT ANALYSIS OF ARTICLES PUBLISHED BETWEEN 2001. AND 2016.

    Directory of Open Access Journals (Sweden)

    Amit Kumar

    2017-12-01

    Full Text Available Actualizing instructional intercessions to suit learner contrasts has gotten extensive consideration. Among these individual contrast factors, the observational confirmation in regards to the academic benefit of learning styles has been addressed, yet the examination on the issue proceeds. Late improvements in web-based executions have driven researchers to re-examine the learning styles in adaptive tutoring frameworks. Adaptivity in intelligent tutoring systems is strongly influenced by the learning style of a learner. This study involved extensive document analysis of adaptive tutoring systems based on learning styles. Seventy-eight studies in literature from 2001 to 2016 were collected and classified under select parameters such as main focus, purpose, research types, methods, types and levels of participants, field/area of application, learner modelling, data gathering tools used and research findings. The current studies reveal that majority of the studies defined a framework or architecture of adaptive intelligent tutoring system (AITS while others focused on impact of AITS on learner satisfaction and academic outcomes. Currents trends, gaps in literature and ications were discussed.

  2. Playing styles based on experiential learning theory

    NARCIS (Netherlands)

    Bontchev, Boyan; Vassileva, Dessislava; Aleksieva-Petrova, Adelina; Petrov, Milen

    2018-01-01

    In recent years, many researchers have reported positive outcomes and effects from applying computer games to the educational process. The main preconditions for an effective game-based learning process include the presence of high learning interest and the desire to study hard. Therefore,

  3. Learning style-based teaching harvests a superior comprehension of respiratory physiology.

    Science.gov (United States)

    Anbarasi, M; Rajkumar, G; Krishnakumar, S; Rajendran, P; Venkatesan, R; Dinesh, T; Mohan, J; Venkidusamy, S

    2015-09-01

    Students entering medical college generally show vast diversity in their school education. It becomes the responsibility of teachers to motivate students and meet the needs of all diversities. One such measure is teaching students in their own preferred learning style. The present study was aimed to incorporate a learning style-based teaching-learning program for medical students and to reveal its significance and utility. Learning styles of students were assessed online using the visual-auditory-kinesthetic (VAK) learning style self-assessment questionnaire. When respiratory physiology was taught, students were divided into three groups, namely, visual (n = 34), auditory (n = 44), and kinesthetic (n = 28), based on their learning style. A fourth group (the traditional group; n = 40) was formed by choosing students randomly from the above three groups. Visual, auditory, and kinesthetic groups were taught following the appropriate teaching-learning strategies. The traditional group was taught via the routine didactic lecture method. The effectiveness of this intervention was evaluated by a pretest and two posttests, posttest 1 immediately after the intervention and posttest 2 after a month. In posttest 1, one-way ANOVA showed a significant statistical difference (P=0.005). Post hoc analysis showed significance between the kinesthetic group and traditional group (P=0.002). One-way ANOVA showed a significant difference in posttest 2 scores (P learning style-based groups compared with the traditional group [visual vs. traditional groups (p=0.002), auditory vs. traditional groups (p=0.03), and Kinesthetic vs. traditional groups (p=0.001)]. This study emphasizes that teaching methods tailored to students' style of learning definitely improve their understanding, performance, and retrieval of the subject. Copyright © 2015 The American Physiological Society.

  4. Learning Style-Based Teaching Harvests a Superior Comprehension of Respiratory Physiology

    Science.gov (United States)

    Anbarasi, M.; Rajkumar, G.; Krishnakumar, S.; Rajendran, P.; Venkatesan, R.; Dinesh, T.; Mohan, J.; Venkidusamy, S.

    2015-01-01

    Students entering medical college generally show vast diversity in their school education. It becomes the responsibility of teachers to motivate students and meet the needs of all diversities. One such measure is teaching students in their own preferred learning style. The present study was aimed to incorporate a learning style-based…

  5. The Effects of Learning-Style Based Activities on Students' Reading Comprehension Skills and Self-Efficacy Perceptions in English Foreign Language Classes

    Science.gov (United States)

    Balci, Özgül

    2017-01-01

    This study investigated the effects of learning-style based activities on students' reading comprehension skills and self-efficacy perceptions in English foreign language classes. A quasi-experimental, matching-only pretest-posttest control group design was utilized. The study was conducted with freshmen university students majoring in Elementary…

  6. A Learning Style-Based Grouping Collaborative Learning Approach to Improve EFL Students' Performance in English Courses

    Science.gov (United States)

    Kuo, Yu-Chen; Chu, Hui-Chun; Huang, Chi-Hao

    2015-01-01

    Learning English is an important and challenging task for English as Foreign Language (EFL) students. Educators had indicated that, without proper learning support, most EFL students might feel frustrated while learning English, which could significantly affect their learning performance. In the past research, learning usually utilized grouping,…

  7. Adaptation Provisioning with Respect to Learning Styles in a Web-Based Educational System: An Experimental Study

    Science.gov (United States)

    Popescu, E.

    2010-01-01

    Personalized instruction is seen as a desideratum of today's e-learning systems. The focus of this paper is on those platforms that use learning styles as personalization criterion called learning style-based adaptive educational systems. The paper presents an innovative approach based on an integrative set of learning preferences that alleviates…

  8. Predicting Student Performance and Differences in Learning Styles based on Textual Complexity Indices applied on Blog and Microblog Posts

    NARCIS (Netherlands)

    Popescu, Elvira; Dascalu, Mihai; Becheru, Alexandru; Crossley, Scott; Trausan-Matu, Stefan

    2016-01-01

    Social media tools are increasingly popular in Computer Supported Collaborative Learning and the analysis of students' contributions on these tools is an emerging research direction. Previous studies have mainly focused on examining quantitative behavior indicators on social media tools. In

  9. Recommendation System for Adaptive Learning.

    Science.gov (United States)

    Chen, Yunxiao; Li, Xiaoou; Liu, Jingchen; Ying, Zhiliang

    2018-01-01

    An adaptive learning system aims at providing instruction tailored to the current status of a learner, differing from the traditional classroom experience. The latest advances in technology make adaptive learning possible, which has the potential to provide students with high-quality learning benefit at a low cost. A key component of an adaptive learning system is a recommendation system, which recommends the next material (video lectures, practices, and so on, on different skills) to the learner, based on the psychometric assessment results and possibly other individual characteristics. An important question then follows: How should recommendations be made? To answer this question, a mathematical framework is proposed that characterizes the recommendation process as a Markov decision problem, for which decisions are made based on the current knowledge of the learner and that of the learning materials. In particular, two plain vanilla systems are introduced, for which the optimal recommendation at each stage can be obtained analytically.

  10. Adaptive representations for reinforcement learning

    NARCIS (Netherlands)

    Whiteson, S.

    2010-01-01

    This book presents new algorithms for reinforcement learning, a form of machine learning in which an autonomous agent seeks a control policy for a sequential decision task. Since current methods typically rely on manually designed solution representations, agents that automatically adapt their own

  11. Adaptive learning and complex dynamics

    International Nuclear Information System (INIS)

    Gomes, Orlando

    2009-01-01

    In this paper, we explore the dynamic properties of a group of simple deterministic difference equation systems in which the conventional perfect foresight assumption gives place to a mechanism of adaptive learning. These systems have a common feature: under perfect foresight (or rational expectations) they all possess a unique fixed point steady state. This long-term outcome is obtained also under learning if the quality underlying the learning process is high. Otherwise, when the degree of inefficiency of the learning process is relatively strong, nonlinear dynamics (periodic and a-periodic cycles) arise. The specific properties of each one of the proposed systems is explored both in terms of local and global dynamics. One macroeconomic model is used to illustrate how the formation of expectations through learning may eventually lead to awkward long-term outcomes.

  12. Learning to Adapt. Organisational Adaptation to Climate Change Impacts

    International Nuclear Information System (INIS)

    Berkhout, F.; Hertin, J.; Gann, D.M.

    2006-01-01

    Analysis of human adaptation to climate change should be based on realistic models of adaptive behaviour at the level of organisations and individuals. The paper sets out a framework for analysing adaptation to the direct and indirect impacts of climate change in business organisations with new evidence presented from empirical research into adaptation in nine case-study companies. It argues that adaptation to climate change has many similarities with processes of organisational learning. The paper suggests that business organisations face a number of obstacles in learning how to adapt to climate change impacts, especially in relation to the weakness and ambiguity of signals about climate change and the uncertainty about benefits flowing from adaptation measures. Organisations rarely adapt 'autonomously', since their adaptive behaviour is influenced by policy and market conditions, and draws on resources external to the organisation. The paper identifies four adaptation strategies that pattern organisational adaptive behaviour

  13. A New Mobile Learning Adaptation Model

    OpenAIRE

    Mohamd Hassan Hassan; Jehad Al-Sadi

    2009-01-01

    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...

  14. Computerized adaptive testing item selection in computerized adaptive learning systems

    NARCIS (Netherlands)

    Eggen, Theodorus Johannes Hendrikus Maria; Eggen, T.J.H.M.; Veldkamp, B.P.

    2012-01-01

    Item selection methods traditionally developed for computerized adaptive testing (CAT) are explored for their usefulness in item-based computerized adaptive learning (CAL) systems. While in CAT Fisher information-based selection is optimal, for recovering learning populations in CAL systems item

  15. Integrative learning for practicing adaptive resource management

    Directory of Open Access Journals (Sweden)

    Craig A. McLoughlin

    2015-03-01

    Full Text Available Adaptive resource management is a learning-by-doing approach to natural resource management. Its effective practice involves the activation, completion, and regeneration of the "adaptive management cycle" while working toward achieving a flexible set of collaboratively identified objectives. This iterative process requires application of single-, double-, and triple-loop learning, to strategically modify inputs, outputs, assumptions, and hypotheses linked to improving policies, management strategies, and actions, along with transforming governance. Obtaining an appropriate balance between these three modes of learning has been difficult to achieve in practice and building capacity in this area can be achieved through an emphasis on reflexive learning, by employing adaptive feedback systems. A heuristic reflexive learning framework for adaptive resource management is presented in this manuscript. It is built on the conceptual pillars of the following: stakeholder driven adaptive feedback systems; strategic adaptive management (SAM; and hierarchy theory. The SAM Reflexive Learning Framework (SRLF emphasizes the types, roles, and transfer of information within a reflexive learning context. Its adaptive feedback systems enhance the facilitation of single-, double-, and triple-loop learning. Focus on the reflexive learning process is further fostered by streamlining objectives within and across all governance levels; incorporating multiple interlinked adaptive management cycles; having learning as an ongoing, nested process; recognizing when and where to employ the three-modes of learning; distinguishing initiating conditions for this learning; and contemplating practitioner mandates for this learning across governance levels. The SRLF is a key enabler for implementing the "adaptive management cycle," and thereby translating the theory of adaptive resource management into practice. It promotes the heuristics of adaptive management within a cohesive

  16. Computerized adaptive testing in computer assisted learning?

    NARCIS (Netherlands)

    Veldkamp, Bernard P.; Matteucci, Mariagiulia; Eggen, Theodorus Johannes Hendrikus Maria; De Wannemacker, Stefan; Clarebout, Geraldine; De Causmaecker, Patrick

    2011-01-01

    A major goal in computerized learning systems is to optimize learning, while in computerized adaptive tests (CAT) efficient measurement of the proficiency of students is the main focus. There seems to be a common interest to integrate computerized adaptive item selection in learning systems and

  17. Improving Flood Plain Management through Adaptive Learning ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    This project will explore how an adaptive learning approach can improve CBO governance ... for improving resource sustainability and productivity, and facilitate learning and an exchange ... Middlesex University Higher Education Corporation.

  18. Adaptive Learning Systems: Beyond Teaching Machines

    Science.gov (United States)

    Kara, Nuri; Sevim, Nese

    2013-01-01

    Since 1950s, teaching machines have changed a lot. Today, we have different ideas about how people learn, what instructor should do to help students during their learning process. We have adaptive learning technologies that can create much more student oriented learning environments. The purpose of this article is to present these changes and its…

  19. Adaptive vs. eductive learning : Theory and evidence

    NARCIS (Netherlands)

    Bao, T.; Duffy, J.

    2014-01-01

    Adaptive learning and eductive learning are two widely used ways of modeling learning behavior in macroeconomics. Both approaches yield restrictions on model parameters under which agents are able to learn a rational expectation equilibrium (REE) but these restrictions do not always overlap with one

  20. Adaptive competitive learning neural networks

    Directory of Open Access Journals (Sweden)

    Ahmed R. Abas

    2013-11-01

    Full Text Available In this paper, the adaptive competitive learning (ACL neural network algorithm is proposed. This neural network not only groups similar input feature vectors together but also determines the appropriate number of groups of these vectors. This algorithm uses a new proposed criterion referred to as the ACL criterion. This criterion evaluates different clustering structures produced by the ACL neural network for an input data set. Then, it selects the best clustering structure and the corresponding network architecture for this data set. The selected structure is composed of the minimum number of clusters that are compact and balanced in their sizes. The selected network architecture is efficient, in terms of its complexity, as it contains the minimum number of neurons. Synaptic weight vectors of these neurons represent well-separated, compact and balanced clusters in the input data set. The performance of the ACL algorithm is evaluated and compared with the performance of a recently proposed algorithm in the literature in clustering an input data set and determining its number of clusters. Results show that the ACL algorithm is more accurate and robust in both determining the number of clusters and allocating input feature vectors into these clusters than the other algorithm especially with data sets that are sparsely distributed.

  1. Learning for Climate Change Adaptation among Selected ...

    African Journals Online (AJOL)

    Learning for Climate Change Adaptation among Selected Communities of Lusaka ... This research was aimed at surveying perceptions of climate change and ... This work is licensed under a Creative Commons Attribution 3.0 License.

  2. ADAPTIVE E-LEARNING AND ITS EVALUATION

    Directory of Open Access Journals (Sweden)

    KOSTOLÁNYOVÁ, Katerina

    2012-12-01

    Full Text Available This paper introduces a complex plan for a complete system of individualized electronic instruction. The core of the system is a computer program to control teaching, the so called “virtual teacher”. The virtual teacher automatically adapts to individual student’s characteristics and their learning style. It adapts to static as well as to dynamic characteristics of the student. To manage all this it needs a database of various styles and forms of teaching as well as a sufficient amount of information about the learning style, type of memory and other characteristics of the student. The information about these characteristics, the structure of data storage and its use by the virtual teacher are also part of this paper. We also outline a methodology of adaptive study materials. We define basic rules and forms to create adaptive study materials. This adaptive e-learning system was pilot tested in learning of more than 50 students. These students filled in a learning style questionnaire at the beginning of the study and they had the option to fill in an adaptive evaluation questionnaire at the end of the study. Results of these questionnaires were analyzed. Several conclusions were concluded from this analysis to alter the methodology of adaptive study materials.

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

    OpenAIRE

    Burgos, Daniel; Tattersall, Colin; Koper, Rob

    2006-01-01

    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.

  4. Adaptive Social Learning Based on Crowdsourcing

    Science.gov (United States)

    Karataev, Evgeny; Zadorozhny, Vladimir

    2017-01-01

    Many techniques have been developed to enhance learning experience with computer technology. A particularly great influence of technology on learning came with the emergence of the web and adaptive educational hypermedia systems. While the web enables users to interact and collaborate with each other to create, organize, and share knowledge via…

  5. Inference in models with adaptive learning

    NARCIS (Netherlands)

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

    2010-01-01

    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

  6. Different Futures of Adaptive Collaborative Learning Support

    Science.gov (United States)

    Rummel, Nikol; Walker, Erin; Aleven, Vincent

    2016-01-01

    In this position paper we contrast a Dystopian view of the future of adaptive collaborative learning support (ACLS) with a Utopian scenario that--due to better-designed technology, grounded in research--avoids the pitfalls of the Dystopian version and paints a positive picture of the practice of computer-supported collaborative learning 25 years…

  7. Adaptive Hypermedia Systems for E-Learning

    Directory of Open Access Journals (Sweden)

    Aammou Souhaib

    2010-11-01

    Full Text Available The domain of traditional hypermedia is revolutionized by the arrival of the concept of adaptation. Currently the domain of Adaptive Hypermedia Systems (AHS is constantly growing. A major goal of current research is to provide a personalized educational experience that meets the needs specific to each learner (knowledge level, goals, motivation etc.... In this article we have studied the possibility of implementing traditional features of adaptive hypermedia in an open environment, and discussed the standards for describing learning objects and architectural models based on the use of ontologies as a prerequisite for such an adaptation.

  8. Fractal Adaptive Web Service for Mobile Learning

    Directory of Open Access Journals (Sweden)

    Ichraf Tirellil

    2006-06-01

    Full Text Available This paper describes our proposition for adaptive web services which is based on configurable, re-usable adaptive/personalized services. To realize our ideas, we have developed an approach for designing, implementing and maintaining personal service. This approach enables the user to accomplish an activity with a set of services answering to his preferences, his profiles and to a personalized context. In this paper, we describe the principle of our approach that we call fractal adaptation approach, and we discuss the implementation of personalization services in the context of mobile and collaborative scenario of learning. We have realized a platform in this context -a platform for mobile and collaborative learning- based on fractal adaptable web services. The platform is tested with a population of students and tutors, in order to release the gaps and the advantages of the approach suggested.

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

    NARCIS (Netherlands)

    Burgos, Daniel; Tattersall, Colin; Koper, Rob

    2006-01-01

    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:

  10. Adaptive Machine Aids to Learning.

    Science.gov (United States)

    Starkweather, John A.

    With emphasis on man-machine relationships and on machine evolution, computer-assisted instruction (CAI) is examined in this paper. The discussion includes the background of machine assistance to learning, the current status of CAI, directions of development, the development of criteria for successful instruction, meeting the needs of users,…

  11. Learning Transferable Features with Deep Adaptation Networks

    OpenAIRE

    Long, Mingsheng; Cao, Yue; Wang, Jianmin; Jordan, Michael I.

    2015-01-01

    Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation. However, as deep features eventually transition from general to specific along the network, the feature transferability drops significantly in higher layers with increasing domain discrepancy. Hence, it is important to formally reduce the dataset bias and enhance the transferability in task-specific layers. In this paper, we propose a new Deep Adaptation...

  12. Adaptive polymeric system for Hebbian type learning

    OpenAIRE

    2011-01-01

    Abstract We present the experimental realization of an adaptive polymeric system displaying a ?learning behaviour?. The system consists on a statistically organized networks of memristive elements (memory-resitors) based on polyaniline. In a such network the path followed by the current increments its conductivity, a property which makes the system able to mimic Hebbian type learning and have application in hardware neural networks. After discussing the working principle of ...

  13. Adaptive Learning in Weighted Network Games

    NARCIS (Netherlands)

    Bayer, Péter; Herings, P. Jean-Jacques; Peeters, Ronald; Thuijsman, Frank

    2017-01-01

    This paper studies adaptive learning in the class of weighted network games. This class of games includes applications like research and development within interlinked firms, crime within social networks, the economics of pollution, and defense expenditures within allied nations. We show that for

  14. Extensible Adaptive System for STEM Learning

    Science.gov (United States)

    2013-07-16

    Copyright 2013 Raytheon BBN Technologies Corp. All Rights Reserved ONR STEM Grand Challenge Extensible Adaptive System for STEM Learning ...Contract # N00014-12-C-0535 Raytheon BBN Technologies Corp. (BBN) Reference # 14217 In partial fulfillment of contract deliverable item # A001...Quarterly Progress Report #2 April 7, 2013 –July 6, 2013 Submitted July 16, 2013 BBN Technical POC: John Makhoul Raytheon BBN Technologies

  15. Using Data to Understand How to Better Design Adaptive Learning

    Science.gov (United States)

    Liu, Min; Kang, Jina; Zou, Wenting; Lee, Hyeyeon; Pan, Zilong; Corliss, Stephanie

    2017-01-01

    There is much enthusiasm in higher education about the benefits of adaptive learning and using big data to investigate learning processes to make data-informed educational decisions. The benefits of adaptive learning to achieve personalized learning are obvious. Yet, there lacks evidence-based research to understand how data such as user behavior…

  16. MEAT: An Authoring Tool for Generating Adaptable Learning Resources

    Science.gov (United States)

    Kuo, Yen-Hung; Huang, Yueh-Min

    2009-01-01

    Mobile learning (m-learning) is a new trend in the e-learning field. The learning services in m-learning environments are supported by fundamental functions, especially the content and assessment services, which need an authoring tool to rapidly generate adaptable learning resources. To fulfill the imperious demand, this study proposes an…

  17. How Language Supports Adaptive Teaching through a Responsive Learning Culture

    Science.gov (United States)

    Johnston, Peter; Dozier, Cheryl; Smit, Julie

    2016-01-01

    For students to learn optimally, teachers must design classrooms that are responsive to the full range of student development. The teacher must be adaptive, but so must each student and the learning culture itself. In other words, adaptive teaching means constructing a responsive learning culture that accommodates and even capitalizes on diversity…

  18. Adaptive and accelerated tracking-learning-detection

    Science.gov (United States)

    Guo, Pengyu; Li, Xin; Ding, Shaowen; Tian, Zunhua; Zhang, Xiaohu

    2013-08-01

    An improved online long-term visual tracking algorithm, named adaptive and accelerated TLD (AA-TLD) based on Tracking-Learning-Detection (TLD) which is a novel tracking framework has been introduced in this paper. The improvement focuses on two aspects, one is adaption, which makes the algorithm not dependent on the pre-defined scanning grids by online generating scale space, and the other is efficiency, which uses not only algorithm-level acceleration like scale prediction that employs auto-regression and moving average (ARMA) model to learn the object motion to lessen the detector's searching range and the fixed number of positive and negative samples that ensures a constant retrieving time, but also CPU and GPU parallel technology to achieve hardware acceleration. In addition, in order to obtain a better effect, some TLD's details are redesigned, which uses a weight including both normalized correlation coefficient and scale size to integrate results, and adjusts distance metric thresholds online. A contrastive experiment on success rate, center location error and execution time, is carried out to show a performance and efficiency upgrade over state-of-the-art TLD with partial TLD datasets and Shenzhou IX return capsule image sequences. The algorithm can be used in the field of video surveillance to meet the need of real-time video tracking.

  19. Implementing Adaptive Educational Methods with IMS Learning Design

    NARCIS (Netherlands)

    Specht, Marcus; Burgos, Daniel

    2006-01-01

    Please, cite this publication as: Specht, M. & Burgos, D. (2006). Implementing Adaptive Educational Methods with IMS Learning Design. Proceedings of Adaptive Hypermedia. June, Dublin, Ireland. Retrieved June 30th, 2006, from http://dspace.learningnetworks.org

  20. Adaptive and perceptual learning technologies in medical education and training.

    Science.gov (United States)

    Kellman, Philip J

    2013-10-01

    Recent advances in the learning sciences offer remarkable potential to improve medical education and maximize the benefits of emerging medical technologies. This article describes 2 major innovation areas in the learning sciences that apply to simulation and other aspects of medical learning: Perceptual learning (PL) and adaptive learning technologies. PL technology offers, for the first time, systematic, computer-based methods for teaching pattern recognition, structural intuition, transfer, and fluency. Synergistic with PL are new adaptive learning technologies that optimize learning for each individual, embed objective assessment, and implement mastery criteria. The author describes the Adaptive Response-Time-based Sequencing (ARTS) system, which uses each learner's accuracy and speed in interactive learning to guide spacing, sequencing, and mastery. In recent efforts, these new technologies have been applied in medical learning contexts, including adaptive learning modules for initial medical diagnosis and perceptual/adaptive learning modules (PALMs) in dermatology, histology, and radiology. Results of all these efforts indicate the remarkable potential of perceptual and adaptive learning technologies, individually and in combination, to improve learning in a variety of medical domains. Reprint & Copyright © 2013 Association of Military Surgeons of the U.S.

  1. The Dynamics of Learning and the Emergence of Distributed Adaption

    National Research Council Canada - National Science Library

    Crutchfield, James P

    2006-01-01

    .... The second goal was to adapt this single-agent learning theory and associated learning algorithms to the distributed setting in which a population of autonomous agents communicate to achieve a desired group task...

  2. Learning to adapt: Organisational adaptation to climate change impacts

    NARCIS (Netherlands)

    Berkhout, F.G.H.; Hertin, J.; Gann, D.M.

    2006-01-01

    Analysis of human adaptation to climate change should be based on realistic models of adaptive behaviour at the level of organisations and individuals. The paper sets out a framework for analysing adaptation to the direct and indirect impacts of climate change in business organisations with new

  3. M-Learning: Implications in Learning Domain Specificities, Adaptive Learning, Feedback, Augmented Reality, and the Future of Online Learning

    Science.gov (United States)

    Squires, David R.

    2014-01-01

    The aim of this paper is to examine the potential and effectiveness of m-learning in the field of Education and Learning domains. The purpose of this research is to illustrate how mobile technology can and is affecting novel change in instruction, from m-learning and the link to adaptive learning, to the uninitiated learner and capacities of…

  4. Teacher-Led Design of an Adaptive Learning Environment

    Science.gov (United States)

    Mavroudi, Anna; Hadzilacos, Thanasis; Kalles, Dimitris; Gregoriades, Andreas

    2016-01-01

    This paper discusses a requirements engineering process that exemplifies teacher-led design in the case of an envisioned system for adaptive learning. Such a design poses various challenges and still remains an open research issue in the field of adaptive learning. Starting from a scenario-based elicitation method, the whole process was highly…

  5. Designing monitoring arrangements for collaborative learning about adaptation pathways

    NARCIS (Netherlands)

    Hermans, L.M.; Haasnoot, M.; ter Maat, Judith; Kwakkel, J.H.

    2017-01-01

    Adaptation pathways approaches support long-term planning under uncertainty. The use of adaptation pathways implies a systematic monitoring effort to inform future adaptation decisions. Such monitoring should feed into a long-term collaborative learning process between multiple actors at various

  6. A METHODOLOGICAL APPROACH FOR IMPLEMENTATION OF ADAPTIVE E-LEARNING

    OpenAIRE

    Valia Arnaudova; Todorka Terzieva; Asen Rahnev

    2016-01-01

    The purpose of adaptive e-Learning is to ensure effective teaching by providing an opportunity for students to connect with an environment that suits their needs, behavior, and knowledge. The reason adaptive e-Learning is important is that, for a learning process to be successful, it is necessary to consider teaching materials that address specific characteristics of the student, such as their particular goals, preferences, knowledge, and style of studying, to provide an appropriate teaching ...

  7. The Influence of Learning Behaviour on Team Adaptability

    Science.gov (United States)

    Murray, Peter A.; Millett, Bruce

    2011-01-01

    Multiple contexts shape team activities and how they learn, and group learning is a dynamic construct that reflects a repertoire of potential behaviour. The purpose of this developmental paper is to examine how better learning behaviours in semi-autonomous teams improves the level of team adaptability and performance. The discussion suggests that…

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

    Directory of Open Access Journals (Sweden)

    Van Cong Pham

    2011-10-01

    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.

  9. Adaptive Trajectory Tracking Control using Reinforcement Learning for Quadrotor

    Directory of Open Access Journals (Sweden)

    Wenjie Lou

    2016-02-01

    Full Text Available Inaccurate system parameters and unpredicted external disturbances affect the performance of non-linear controllers. In this paper, a new adaptive control algorithm under the reinforcement framework is proposed to stabilize a quadrotor helicopter. Based on a command-filtered non-linear control algorithm, adaptive elements are added and learned by policy-search methods. To predict the inaccurate system parameters, a new kernel-based regression learning method is provided. In addition, Policy learning by Weighting Exploration with the Returns (PoWER and Return Weighted Regression (RWR are utilized to learn the appropriate parameters for adaptive elements in order to cancel the effect of external disturbance. Furthermore, numerical simulations under several conditions are performed, and the ability of adaptive trajectory-tracking control with reinforcement learning are demonstrated.

  10. An Adaptive E-Learning System Based on Students' Learning Styles: An Empirical Study

    Science.gov (United States)

    Drissi, Samia; Amirat, Abdelkrim

    2016-01-01

    Personalized e-learning implementation is recognized as one of the most interesting research areas in the distance web-based education. Since the learning style of each learner is different one must fit e-learning with the different needs of learners. This paper presents an approach to integrate learning styles into adaptive e-learning hypermedia.…

  11. Learning Words through Computer-Adaptive Tool

    DEFF Research Database (Denmark)

    Zhang, Chun

    2005-01-01

    construction, I stress the design of a test theory, namely, a learning algorithm. The learning algorithm is designed under such principles that users experience both 'elaborative rehearsal’ (aspects in receptive and productive learning) and 'expanding rehearsal, (memory-based learning and repetitive act...

  12. INTUITEL and the Hypercube Model - Developing Adaptive Learning Environments

    Directory of Open Access Journals (Sweden)

    Kevin Fuchs

    2016-06-01

    Full Text Available In this paper we introduce an approach for the creation of adaptive learning environments that give human-like recommendations to a learner in the form of a virtual tutor. We use ontologies defining pedagogical, didactic and learner-specific data describing a learner's progress, learning history, capabilities and the learner's current state within the learning environment. Learning recommendations are based on a reasoning process on these ontologies and can be provided in real-time. The ontologies may describe learning content from any domain of knowledge. Furthermore, we describe an approach to store learning histories as spatio-temporal trajectories and to correlate them with influencing didactic factors. We show how such analysis of spatiotemporal data can be used for learning analytics to improve future adaptive learning environments.

  13. Anticipatory Learning for Climate Change Adaptation and Resilience

    Directory of Open Access Journals (Sweden)

    Petra Tschakert

    2010-06-01

    Full Text Available This paper is a methodological contribution to emerging debates on the role of learning, particularly forward-looking (anticipatory learning, as a key element for adaptation and resilience in the context of climate change. First, we describe two major challenges: understanding adaptation as a process and recognizing the inadequacy of existing learning tools, with a specific focus on high poverty contexts and complex livelihood-vulnerability risks. Then, the article examines learning processes from a dynamic systems perspective, comparing theoretical aspects and conceptual advances in resilience thinking and action research/learning (AR/AL. Particular attention is paid to learning loops (cycles, critical reflection, spaces for learning, and power. Finally, we outline a methodological framework to facilitate iterative learning processes and adaptive decision making in practice. We stress memory, monitoring of key drivers of change, scenario planning, and measuring anticipatory capacity as crucial ingredients. Our aim is to identify opportunities and obstacles for forward-looking learning processes at the intersection of climatic uncertainty and development challenges in Africa, with the overarching objective to enhance adaptation and resilient livelihood pathways, rather than learning by shock.

  14. Individual differences in implicit motor learning: task specificity in sensorimotor adaptation and sequence learning.

    Science.gov (United States)

    Stark-Inbar, Alit; Raza, Meher; Taylor, Jordan A; Ivry, Richard B

    2017-01-01

    In standard taxonomies, motor skills are typically treated as representative of implicit or procedural memory. We examined two emblematic tasks of implicit motor learning, sensorimotor adaptation and sequence learning, asking whether individual differences in learning are correlated between these tasks, as well as how individual differences within each task are related to different performance variables. As a prerequisite, it was essential to establish the reliability of learning measures for each task. Participants were tested twice on a visuomotor adaptation task and on a sequence learning task, either the serial reaction time task or the alternating reaction time task. Learning was evident in all tasks at the group level and reliable at the individual level in visuomotor adaptation and the alternating reaction time task but not in the serial reaction time task. Performance variability was predictive of learning in both domains, yet the relationship was in the opposite direction for adaptation and sequence learning. For the former, faster learning was associated with lower variability, consistent with models of sensorimotor adaptation in which learning rates are sensitive to noise. For the latter, greater learning was associated with higher variability and slower reaction times, factors that may facilitate the spread of activation required to form predictive, sequential associations. Interestingly, learning measures of the different tasks were not correlated. Together, these results oppose a shared process for implicit learning in sensorimotor adaptation and sequence learning and provide insight into the factors that account for individual differences in learning within each task domain. We investigated individual differences in the ability to implicitly learn motor skills. As a prerequisite, we assessed whether individual differences were reliable across test sessions. We found that two commonly used tasks of implicit learning, visuomotor adaptation and the

  15. Creating adaptive environment for e-learning courses

    Directory of Open Access Journals (Sweden)

    Bozidar Radenkovic

    2009-06-01

    Full Text Available In this paper we provide an approach to creating adaptive environment for e-learning courses. In the context of e-education, successful adaptation has to be performed upon learners’ characteristics. Currently, modeling and discovering users’ needs, goals, knowledge preferences and motivations is one of the most challenging tasks in e-learning systems that deal with large volumes of information. Primary goal of the research is to perform personalizing of distance education system, according to students’ learning styles. Main steps and requirements in applying business intelligence techniques in process of personalization are identified. In addition, we propose generic model and architecture of an adaptive e-learning system by describing the structure of an adaptive course and exemplify correlations among e-learning course content and different learning styles. Moreover, research that dealt with application of data mining technique in a real e-learning system was carried out. We performed adaptation of our e-learning courses using the results from the research.

  16. Adaptive learning fuzzy control of a mobile robot

    International Nuclear Information System (INIS)

    Tsukada, Akira; Suzuki, Katsuo; Fujii, Yoshio; Shinohara, Yoshikuni

    1989-11-01

    In this report a problem is studied to construct a fuzzy controller for a mobile robot to move autonomously along a given reference direction curve, for which control rules are generated and acquired through an adaptive learning process. An adaptive learning fuzzy controller has been developed for a mobile robot. Good properties of the controller are shown through the travelling experiments of the mobile robot. (author)

  17. Towards adaptation in e-learning 2.0

    Science.gov (United States)

    Cristea, Alexandra I.; Ghali, Fawaz

    2011-04-01

    This paper presents several essential steps from an overall study on shaping new ways of learning and teaching, by using the synergetic merger of three different fields: Web 2.0, e-learning and adaptation (in particular, personalisation to the learner). These novel teaching and learning ways-the latter focus of this paper-are reflected in and finally adding to various versions of the My Online Teacher 2.0 adaptive system. In particular, this paper focuses on a study of how to more effectively use and combine the recommendation of peers and content adaptation to enhance the learning outcome in e-learning systems based on Web 2.0. In order to better isolate and examine the effects of peer recommendation and adaptive content presentation, we designed experiments inspecting collaboration between individuals based on recommendation of peers who have greater knowledge, and compare this to adaptive content recommendation, as well as to "simple" learning in a system with a minimum of Web 2.0 support. Overall, the results of adding peer recommendation and adaptive content presentation were encouraging, and are further discussed in detail in this paper.

  18. Adaptive strategies for cumulative cultural learning.

    Science.gov (United States)

    Ehn, Micael; Laland, Kevin

    2012-05-21

    The demographic and ecological success of our species is frequently attributed to our capacity for cumulative culture. However, it is not yet known how humans combine social and asocial learning to generate effective strategies for learning in a cumulative cultural context. Here we explore how cumulative culture influences the relative merits of various pure and conditional learning strategies, including pure asocial and social learning, critical social learning, conditional social learning and individual refiner strategies. We replicate the Rogers' paradox in the cumulative setting. However, our analysis suggests that strategies that resolved Rogers' paradox in a non-cumulative setting may not necessarily evolve in a cumulative setting, thus different strategies will optimize cumulative and non-cumulative cultural learning. Copyright © 2012 Elsevier Ltd. All rights reserved.

  19. Integrating Adaptive Games in Student-Centered Virtual Learning Environments

    Science.gov (United States)

    del Blanco, Angel; Torrente, Javier; Moreno-Ger, Pablo; Fernandez-Manjon, Baltasar

    2010-01-01

    The increasing adoption of e-Learning technology is facing new challenges, such as how to produce student-centered systems that can be adapted to each student's needs. In this context, educational video games are proposed as an ideal medium to facilitate adaptation and tracking of students' performance for assessment purposes, but integrating the…

  20. Applications of Adaptive Learning Controller to Synthetic Aperture Radar.

    Science.gov (United States)

    1985-02-01

    TERMS (Continue on retuerse if necessary and identify by block num ber) FIELD YGROUP SUB. GR. Adaptive control, aritificial intelligence , synthetic aetr1...application of Artificial Intelligence methods to Synthetic Aperture Radars (SARs) is investigated. It was shown that the neuron-like Adaptive Learning...wavelength Al SE!RI M RADAR DIVISION REFERENCES 1. Barto, A.G. and R.S. Sutton, Goal Seeking Components for Adaptive Intelligence : An Initial Assessment

  1. Learning Experiences Reuse Based on an Ontology Modeling to Improve Adaptation in E-Learning Systems

    Science.gov (United States)

    Hadj M'tir, Riadh; Rumpler, Béatrice; Jeribi, Lobna; Ben Ghezala, Henda

    2014-01-01

    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…

  2. Design Framework for an Adaptive MOOC Enhanced by Blended Learning

    DEFF Research Database (Denmark)

    Gynther, Karsten

    2016-01-01

    The research project has developed a design framework for an adaptive MOOC that complements the MOOC format with blended learning. The design framework consists of a design model and a series of learning design principles which can be used to design in-service courses for teacher professional...

  3. PERSO: Towards an Adaptive e-Learning System

    Science.gov (United States)

    Chorfi, Henda; Jemni, Mohamed

    2004-01-01

    In today's information technology society, members are increasingly required to be up to date on new technologies, particularly for computers, regardless of their background social situation. In this context, our aim is to design and develop an adaptive hypermedia e-learning system, called PERSO (PERSOnalizing e-learning system), where learners…

  4. Student Modelling in Adaptive E-Learning Systems

    Directory of Open Access Journals (Sweden)

    Clemens Bechter

    2011-09-01

    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. Conformal prediction for reliable machine learning theory, adaptations and applications

    CERN Document Server

    Balasubramanian, Vineeth; Vovk, Vladimir

    2014-01-01

    The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detecti

  6. learning for Climate Change adaptation among Selected ...

    African Journals Online (AJOL)

    and coping strategies for, climate change (Gangwar, 2010). In order to adapt to the ..... and forest management were proposed the most among communities. Proposed educational ...... ethical values (McDonald, 2008). The deep meaning of ...

  7. Adaptive e-learning system using ontology

    OpenAIRE

    Yarandi, Maryam; Tawil, Abdel-Rahman; Jahankhani, Hossein

    2011-01-01

    This paper proposes an innovative ontological approach to design a personalised e-learning system which creates a tailored workflow for individual learner. Moreover, the learning content and sequencing logic is separated into content model and pedagogical model to increase the reusability and flexibility of the system.

  8. Using assistive technology adaptations to include students with learning disabilities in cooperative learning activities.

    Science.gov (United States)

    Bryant, D P; Bryant, B R

    1998-01-01

    Cooperative learning (CL) is a common instructional arrangement that is used by classroom teachers to foster academic achievement and social acceptance of students with and without learning disabilities. Cooperative learning is appealing to classroom teachers because it can provide an opportunity for more instruction and feedback by peers than can be provided by teachers to individual students who require extra assistance. Recent studies suggest that students with LD may need adaptations during cooperative learning activities. The use of assistive technology adaptations may be necessary to help some students with LD compensate for their specific learning difficulties so that they can engage more readily in cooperative learning activities. A process for integrating technology adaptations into cooperative learning activities is discussed in terms of three components: selecting adaptations, monitoring the use of the adaptations during cooperative learning activities, and evaluating the adaptations' effectiveness. The article concludes with comments regarding barriers to and support systems for technology integration, technology and effective instructional practices, and the need to consider technology adaptations for students who have learning disabilities.

  9. Adaptive nodes enrich nonlinear cooperative learning beyond traditional adaptation by links.

    Science.gov (United States)

    Sardi, Shira; Vardi, Roni; Goldental, Amir; Sheinin, Anton; Uzan, Herut; Kanter, Ido

    2018-03-23

    Physical models typically assume time-independent interactions, whereas neural networks and machine learning incorporate interactions that function as adjustable parameters. Here we demonstrate a new type of abundant cooperative nonlinear dynamics where learning is attributed solely to the nodes, instead of the network links which their number is significantly larger. The nodal, neuronal, fast adaptation follows its relative anisotropic (dendritic) input timings, as indicated experimentally, similarly to the slow learning mechanism currently attributed to the links, synapses. It represents a non-local learning rule, where effectively many incoming links to a node concurrently undergo the same adaptation. The network dynamics is now counterintuitively governed by the weak links, which previously were assumed to be insignificant. This cooperative nonlinear dynamic adaptation presents a self-controlled mechanism to prevent divergence or vanishing of the learning parameters, as opposed to learning by links, and also supports self-oscillations of the effective learning parameters. It hints on a hierarchical computational complexity of nodes, following their number of anisotropic inputs and opens new horizons for advanced deep learning algorithms and artificial intelligence based applications, as well as a new mechanism for enhanced and fast learning by neural networks.

  10. Evolutionary and adaptive learning in complex markets: a brief summary

    Science.gov (United States)

    Hommes, Cars H.

    2007-06-01

    We briefly review some work on expectations and learning in complex markets, using the familiar demand-supply cobweb model. We discuss and combine two different approaches on learning. According to the adaptive learning approach, agents behave as econometricians using time series observations to form expectations, and update the parameters as more observations become available. This approach has become popular in macro. The second approach has an evolutionary flavor and is sometimes referred to as reinforcement learning. Agents employ different forecasting strategies and evaluate these strategies based upon a fitness measure, e.g. past realized profits. In this framework, boundedly rational agents switch between different, but fixed behavioral rules. This approach has become popular in finance. We combine evolutionary and adaptive learning to model complex markets and discuss whether this theory can match empirical facts and forecasting behavior in laboratory experiments with human subjects.

  11. E-Learning and Personalized Learning Path: A Proposal Based on the Adaptive Educational Hypermedia System

    Directory of Open Access Journals (Sweden)

    Francesco Colace

    2014-03-01

    Full Text Available The E-Learning is becoming an effective approach for the improving of quality of learning. Many institutions are adopting this approach both to improve their traditional courses both to increase the potential audience. In the last period great attention is paid in the introduction of methodologies and techniques for the adaptation of learning process to the real needs of students. In this scenario the Adaptive Educational Hypermedia System can be an effective approach. Adaptive hypermedia is a promising area of research at the crossroads of hypermedia and adaptive systems. One of the most important fields where this approach can be applied is just the e-Learning. In this context the adaptive learning resources selection and sequencing is recognized as among one of the most interesting research questions. An Adaptive Educational Hypermedia System is composed by services for the management of the Knowledge Space, the definition of a User Model, the observation of student during his learning period and, as previously said, the adaptation of the learning path according to the real needs of the students. In particular the use of ontologyཿs formalism for the modeling of the ཿknowledge space࿝ related to the course can increase the sharable of learning objects among similar courses or better contextualize their role in the course. This paper addresses the design problem of an Adaptive hypermedia system by the definition of methodologies able to manage each its components, In particular an original user, learning contents, tracking strategies and adaptation model are developed. The proposed Adaptive Educational Hypermedia System has been integrated in an e-Learning platform and an experimental campaign has been conducted. In particular the proposed approach has been introduced in three different blended courses. A comparison with traditional approach has been described and the obtained results seem to be very promising.

  12. The immune system, adaptation, and machine learning

    Science.gov (United States)

    Farmer, J. Doyne; Packard, Norman H.; Perelson, Alan S.

    1986-10-01

    The immune system is capable of learning, memory, and pattern recognition. By employing genetic operators on a time scale fast enough to observe experimentally, the immune system is able to recognize novel shapes without preprogramming. Here we describe a dynamical model for the immune system that is based on the network hypothesis of Jerne, and is simple enough to simulate on a computer. This model has a strong similarity to an approach to learning and artificial intelligence introduced by Holland, called the classifier system. We demonstrate that simple versions of the classifier system can be cast as a nonlinear dynamical system, and explore the analogy between the immune and classifier systems in detail. Through this comparison we hope to gain insight into the way they perform specific tasks, and to suggest new approaches that might be of value in learning systems.

  13. Adaptive e-learning methods and IMS Learning Design. An integrated approach

    NARCIS (Netherlands)

    Burgos, Daniel; Specht, Marcus

    2006-01-01

    Please, cite this publication as: Burgos, D., & Specht, M. (2006). Adaptive e-learning methods and IMS Learning Design. In Kinshuk, R. Koper, P. Kommers, P. Kirschner, D. G. Sampson & W. Didderen (Eds.), Proceedings of the 6th IEEE International Conference on Advanced Learning Technologies (pp.

  14. Women, Subjectivities and Learning to Be Adaptable

    Science.gov (United States)

    Cavanagh, Jillian

    2010-01-01

    Purpose: The purpose of this paper is to advance understandings of the subjectivities that influence auxiliary-level female employees' work and learning experiences in general legal practice. Moreover, the aim is to maximise the opportunities for these workers. Design/methodology/approach: A broader critical ethnographic study investigated…

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

    Directory of Open Access Journals (Sweden)

    Yuliya V. Vainshtein

    2017-01-01

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

  16. Learning and adaptation: neural and behavioural mechanisms behind behaviour change

    Science.gov (United States)

    Lowe, Robert; Sandamirskaya, Yulia

    2018-01-01

    This special issue presents perspectives on learning and adaptation as they apply to a number of cognitive phenomena including pupil dilation in humans and attention in robots, natural language acquisition and production in embodied agents (robots), human-robot game play and social interaction, neural-dynamic modelling of active perception and neural-dynamic modelling of infant development in the Piagetian A-not-B task. The aim of the special issue, through its contributions, is to highlight some of the critical neural-dynamic and behavioural aspects of learning as it grounds adaptive responses in robotic- and neural-dynamic systems.

  17. Optical implementations of associative networks with versatile adaptive learning capabilities.

    Science.gov (United States)

    Fisher, A D; Lippincott, W L; Lee, J N

    1987-12-01

    Optical associative, parallel-processing architectures are being developed using a multimodule approach, where a number of smaller, adaptive, associative modules are nonlinearly interconnected and cascaded under the guidance of a variety of organizational principles to structure larger architectures for solving specific problems. A number of novel optical implementations with versatile adaptive learning capabilities are presented for the individual associative modules, including holographic configurations and five specific electrooptic configurations. The practical issues involved in real optical architectures are analyzed, and actual laboratory optical implementations of associative modules based on Hebbian and Widrow-Hoff learning rules are discussed, including successful experimental demonstrations of their operation.

  18. Soft systems thinking and social learning for adaptive management.

    Science.gov (United States)

    Cundill, G; Cumming, G S; Biggs, D; Fabricius, C

    2012-02-01

    The success of adaptive management in conservation has been questioned and the objective-based management paradigm on which it is based has been heavily criticized. Soft systems thinking and social-learning theory expose errors in the assumption that complex systems can be dispassionately managed by objective observers and highlight the fact that conservation is a social process in which objectives are contested and learning is context dependent. We used these insights to rethink adaptive management in a way that focuses on the social processes involved in management and decision making. Our approach to adaptive management is based on the following assumptions: action toward a common goal is an emergent property of complex social relationships; the introduction of new knowledge, alternative values, and new ways of understanding the world can become a stimulating force for learning, creativity, and change; learning is contextual and is fundamentally about practice; and defining the goal to be addressed is continuous and in principle never ends. We believe five key activities are crucial to defining the goal that is to be addressed in an adaptive-management context and to determining the objectives that are desirable and feasible to the participants: situate the problem in its social and ecological context; raise awareness about alternative views of a problem and encourage enquiry and deconstruction of frames of reference; undertake collaborative actions; and reflect on learning. ©2011 Society for Conservation Biology.

  19. Biomimetic molecular design tools that learn, evolve, and adapt

    Directory of Open Access Journals (Sweden)

    David A Winkler

    2017-06-01

    Full Text Available A dominant hallmark of living systems is their ability to adapt to changes in the environment by learning and evolving. Nature does this so superbly that intensive research efforts are now attempting to mimic biological processes. Initially this biomimicry involved developing synthetic methods to generate complex bioactive natural products. Recent work is attempting to understand how molecular machines operate so their principles can be copied, and learning how to employ biomimetic evolution and learning methods to solve complex problems in science, medicine and engineering. Automation, robotics, artificial intelligence, and evolutionary algorithms are now converging to generate what might broadly be called in silico-based adaptive evolution of materials. These methods are being applied to organic chemistry to systematize reactions, create synthesis robots to carry out unit operations, and to devise closed loop flow self-optimizing chemical synthesis systems. Most scientific innovations and technologies pass through the well-known “S curve”, with slow beginning, an almost exponential growth in capability, and a stable applications period. Adaptive, evolving, machine learning-based molecular design and optimization methods are approaching the period of very rapid growth and their impact is already being described as potentially disruptive. This paper describes new developments in biomimetic adaptive, evolving, learning computational molecular design methods and their potential impacts in chemistry, engineering, and medicine.

  20. Biomimetic molecular design tools that learn, evolve, and adapt

    Science.gov (United States)

    2017-01-01

    A dominant hallmark of living systems is their ability to adapt to changes in the environment by learning and evolving. Nature does this so superbly that intensive research efforts are now attempting to mimic biological processes. Initially this biomimicry involved developing synthetic methods to generate complex bioactive natural products. Recent work is attempting to understand how molecular machines operate so their principles can be copied, and learning how to employ biomimetic evolution and learning methods to solve complex problems in science, medicine and engineering. Automation, robotics, artificial intelligence, and evolutionary algorithms are now converging to generate what might broadly be called in silico-based adaptive evolution of materials. These methods are being applied to organic chemistry to systematize reactions, create synthesis robots to carry out unit operations, and to devise closed loop flow self-optimizing chemical synthesis systems. Most scientific innovations and technologies pass through the well-known “S curve”, with slow beginning, an almost exponential growth in capability, and a stable applications period. Adaptive, evolving, machine learning-based molecular design and optimization methods are approaching the period of very rapid growth and their impact is already being described as potentially disruptive. This paper describes new developments in biomimetic adaptive, evolving, learning computational molecular design methods and their potential impacts in chemistry, engineering, and medicine. PMID:28694872

  1. Adaptive Landmark-Based Navigation System Using Learning Techniques

    DEFF Research Database (Denmark)

    Zeidan, Bassel; Dasgupta, Sakyasingha; Wörgötter, Florentin

    2014-01-01

    The goal-directed navigational ability of animals is an essential prerequisite for them to survive. They can learn to navigate to a distal goal in a complex environment. During this long-distance navigation, they exploit environmental features, like landmarks, to guide them towards their goal. In...... hexapod robots. As a result, it allows the robots to successfully learn to navigate to distal goals in complex environments.......The goal-directed navigational ability of animals is an essential prerequisite for them to survive. They can learn to navigate to a distal goal in a complex environment. During this long-distance navigation, they exploit environmental features, like landmarks, to guide them towards their goal....... Inspired by this, we develop an adaptive landmark-based navigation system based on sequential reinforcement learning. In addition, correlation-based learning is also integrated into the system to improve learning performance. The proposed system has been applied to simulated simple wheeled and more complex...

  2. Beyond adaptive-critic creative learning for intelligent mobile robots

    Science.gov (United States)

    Liao, Xiaoqun; Cao, Ming; Hall, Ernest L.

    2001-10-01

    Intelligent industrial and mobile robots may be considered proven technology in structured environments. Teach programming and supervised learning methods permit solutions to a variety of applications. However, we believe that to extend the operation of these machines to more unstructured environments requires a new learning method. Both unsupervised learning and reinforcement learning are potential candidates for these new tasks. The adaptive critic method has been shown to provide useful approximations or even optimal control policies to non-linear systems. The purpose of this paper is to explore the use of new learning methods that goes beyond the adaptive critic method for unstructured environments. The adaptive critic is a form of reinforcement learning. A critic element provides only high level grading corrections to a cognition module that controls the action module. In the proposed system the critic's grades are modeled and forecasted, so that an anticipated set of sub-grades are available to the cognition model. The forecasting grades are interpolated and are available on the time scale needed by the action model. The success of the system is highly dependent on the accuracy of the forecasted grades and adaptability of the action module. Examples from the guidance of a mobile robot are provided to illustrate the method for simple line following and for the more complex navigation and control in an unstructured environment. The theory presented that is beyond the adaptive critic may be called creative theory. Creative theory is a form of learning that models the highest level of human learning - imagination. The application of the creative theory appears to not only be to mobile robots but also to many other forms of human endeavor such as educational learning and business forecasting. Reinforcement learning such as the adaptive critic may be applied to known problems to aid in the discovery of their solutions. The significance of creative theory is that it

  3. Adaptive E- Learning System Based on Personalized Learning Style

    African Journals Online (AJOL)

    pc

    2018-03-05

    Mar 5, 2018 ... motivation to this research is to improve the learner performance and achieve the ... valuable factor for enhancing learning process by adopting an effective .... Video. Reflective Intuitive. Primer Test. Verbal Sequential. Tutorial.

  4. Generalized projective synchronization of chaotic systems via adaptive learning control

    International Nuclear Information System (INIS)

    Yun-Ping, Sun; Jun-Min, Li; Hui-Lin, Wang; Jiang-An, Wang

    2010-01-01

    In this paper, a learning control approach is applied to the generalized projective synchronisation (GPS) of different chaotic systems with unknown periodically time-varying parameters. Using the Lyapunov–Krasovskii functional stability theory, a differential-difference mixed parametric learning law and an adaptive learning control law are constructed to make the states of two different chaotic systems asymptotically synchronised. The scheme is successfully applied to the generalized projective synchronisation between the Lorenz system and Chen system. Moreover, numerical simulations results are used to verify the effectiveness of the proposed scheme. (general)

  5. Adaptive learning algorithms for vibration energy harvesting

    International Nuclear Information System (INIS)

    Ward, John K; Behrens, Sam

    2008-01-01

    By scavenging energy from their local environment, portable electronic devices such as MEMS devices, mobile phones, radios and wireless sensors can achieve greater run times with potentially lower weight. Vibration energy harvesting is one such approach where energy from parasitic vibrations can be converted into electrical energy through the use of piezoelectric and electromagnetic transducers. Parasitic vibrations come from a range of sources such as human movement, wind, seismic forces and traffic. Existing approaches to vibration energy harvesting typically utilize a rectifier circuit, which is tuned to the resonant frequency of the harvesting structure and the dominant frequency of vibration. We have developed a novel approach to vibration energy harvesting, including adaptation to non-periodic vibrations so as to extract the maximum amount of vibration energy available. Experimental results of an experimental apparatus using an off-the-shelf transducer (i.e. speaker coil) show mechanical vibration to electrical energy conversion efficiencies of 27–34%

  6. Adaptive Knowledge Management of Project-Based Learning

    Science.gov (United States)

    Tilchin, Oleg; Kittany, Mohamed

    2016-01-01

    The goal of an approach to Adaptive Knowledge Management (AKM) of project-based learning (PBL) is to intensify subject study through guiding, inducing, and facilitating development knowledge, accountability skills, and collaborative skills of students. Knowledge development is attained by knowledge acquisition, knowledge sharing, and knowledge…

  7. Adaptive Learning in Psychology: Wayfinding in the Digital Age

    Science.gov (United States)

    Dziuban, Charles D.; Moskal, Patsy D.; Cassisi, Jeffrey; Fawcett, Alexis

    2016-01-01

    This paper presents the results of a pilot study investigating the use of the Realizeit adaptive learning platform to deliver a fully online General Psychology course across two semesters. Through mutual cooperation, UCF and vendor (CCKF) researchers examined students' affective, behavioral, and cognitive reactions to the system. Student survey…

  8. Learner Profile Management for Collaborative Adaptive eLearning Application

    DEFF Research Database (Denmark)

    Alrifai, Mohammad; Dolog, Peter; Nejdl, Wolfgang

    2006-01-01

    Adaptive Learning Systems would perform better if they would be able to exchange as many relevant fragments of information about the learner as possible. The use of Web Services standards is recently gaining the attention of many researches as a promising solution for the problem of interfacing a...

  9. Active learning and adaptive sampling for non-parametric inference

    NARCIS (Netherlands)

    Castro, R.M.

    2007-01-01

    This thesis presents a general discussion of active learning and adaptive sampling. In many practical scenarios it is possible to use information gleaned from previous observations to focus the sampling process, in the spirit of the "twenty-questions" game. As more samples are collected one can

  10. Heterogeneous Users in MOOC and their Adaptive Learning Needs

    Directory of Open Access Journals (Sweden)

    María Luisa SEIN-ECHALUCE LACLETA

    2017-02-01

    Full Text Available Many research works point out the overcrowding and the heterogeneity of participant’s profiles in Massive Open Online Courses (MOOC as the main causes of their low completion rate. On the other hand, the methodologies of personalization of the learning, along next to the technologies of the information, that allows to realize techniques of adaptativity, appear in international reports as an effective way to improve the learning. This paper explores the participante’ perception of their adaptive needs in this tupe of course, as well as their relationship with different aspects of the participants, such as: profiles (gender, age, geographical location and academic level, previous experience and knowledge about the topic of the MOOC and motivation to enroll the MOOC. The study is carried out through a survey completes by the participants in the MOOC Campus of Educational Innovation. We conclude that the age or gender of the participants does not significantly influence their need for adaptive techniques in a MOOC. However, living in a Latin American country, working as a manager or enrolling in a MOOC with a specific motivation, are some of the factors that influence in the desire for adaptive techniques in a MOOC. The obtained results will contribute to improve the adaptive designs of the MOOC and will be easily transferable to any online training course, in blended or virtual learning.

  11. Supporting Student Learning in Computer Science Education via the Adaptive Learning Environment ALMA

    Directory of Open Access Journals (Sweden)

    Alexandra Gasparinatou

    2015-10-01

    Full Text Available This study presents the ALMA environment (Adaptive Learning Models from texts and Activities. ALMA supports the processes of learning and assessment via: (1 texts differing in local and global cohesion for students with low, medium, and high background knowledge; (2 activities corresponding to different levels of comprehension which prompt the student to practically implement different text-reading strategies, with the recommended activity sequence adapted to the student’s learning style; (3 an overall framework for informing, guiding, and supporting students in performing the activities; and; (4 individualized support and guidance according to student specific characteristics. ALMA also, supports students in distance learning or in blended learning in which students are submitted to face-to-face learning supported by computer technology. The adaptive techniques provided via ALMA are: (a adaptive presentation and (b adaptive navigation. Digital learning material, in accordance with the text comprehension model described by Kintsch, was introduced into the ALMA environment. This material can be exploited in either distance or blended learning.

  12. Intelligent and Adaptive Educational-Learning Systems Achievements and Trends

    CERN Document Server

    2013-01-01

    The Smart Innovation, Systems and Technologies book series encompasses the topics of knowledge, intelligence, innovation and sustainability. The aim of the series is to make available a platform for the publication of books on all aspects of single and multi-disciplinary research on these themes in order to make the latest results available in a readily-accessible form.  This book is devoted to the “Intelligent and Adaptive Educational-Learning Systems”. It privileges works that highlight key achievements and outline trends to inspire future research.  After a rigorous revision process twenty manuscripts were accepted and organized into four parts as follows: ·     Modeling: The first part embraces five chapters oriented to: 1) shape the affective behavior; 2) depict the adaptive learning curriculum; 3) predict learning achievements; 4) mine learner models to outcome optimized and adaptive e-learning objects; 5) classify learning preferences of learners. ·     Content: The second part encompas...

  13. Enhancing Student Adaption to a Case Based Learning Environment

    DEFF Research Database (Denmark)

    Jensen, Lars Peter

    2010-01-01

    these at the end of the semester, showing the development of the student in terms of adapting to the learning model. The idea will be explained more closely in the final paper. RESEARCH METHOD The research part of the experiment was carried out as action research, as the teacher of the course in the same time......INTRODUCTION Since Aalborg University (AAU) was started it has been using an educational model, where Problem Based Learning is the turning point. Each semester the students on the Engineering Educations form groups of 3-6 persons, which uses half of the study time within the semester to solve......) in groups. It appeared to be difficult for the students to adapt to two different PBL approaches at the same time, and with the project being the most popular the learning outcome of the case studies was not satisfactory after the first semester, but improved on the following semesters. In 2009...

  14. Adaptive learning by extremal dynamics and negative feedback

    International Nuclear Information System (INIS)

    Bak, Per; Chialvo, Dante R.

    2001-01-01

    We describe a mechanism for biological learning and adaptation based on two simple principles: (i) Neuronal activity propagates only through the network's strongest synaptic connections (extremal dynamics), and (ii) the strengths of active synapses are reduced if mistakes are made, otherwise no changes occur (negative feedback). The balancing of those two tendencies typically shapes a synaptic landscape with configurations which are barely stable, and therefore highly flexible. This allows for swift adaptation to new situations. Recollection of past successes is achieved by punishing synapses which have once participated in activity associated with successful outputs much less than neurons that have never been successful. Despite its simplicity, the model can readily learn to solve complicated nonlinear tasks, even in the presence of noise. In particular, the learning time for the benchmark parity problem scales algebraically with the problem size N, with an exponent k∼1.4

  15. Adaptive Management of Communication in the Chamilo System of Distant Learning

    OpenAIRE

    Yatsenko Roman Nikolaevich; Polevich Olesya V.

    2012-01-01

    The article considers the communication management within an adaptive system of distance learning. We present two-circuit interaction system of the adaptive system. We consider the implementation of management communication in distance learning system based on the platform Chamilo.

  16. Selecting Learning Tasks: Effects of Adaptation and Shared Control on Learning Efficiency and Task Involvement

    Science.gov (United States)

    Corbalan, Gemma; Kester, Liesbeth; van Merrienboer, Jeroen J. G.

    2008-01-01

    Complex skill acquisition by performing authentic learning tasks is constrained by limited working memory capacity [Baddeley, A. D. (1992). Working memory. "Science, 255", 556-559]. To prevent cognitive overload, task difficulty and support of each newly selected learning task can be adapted to the learner's competence level and perceived task…

  17. Supporting Adaptive Learning Pathways through the Use of Learning Analytics: Developments, Challenges and Future Opportunities

    Science.gov (United States)

    Mavroudi, Anna; Giannakos, Michail; Krogstie, John

    2018-01-01

    Learning Analytics (LA) and adaptive learning are inextricably linked since they both foster technology-supported learner-centred education. This study identifies developments focusing on their interplay and emphasises insufficiently investigated directions which display a higher innovation potential. Twenty-one peer-reviewed studies are…

  18. Learning from sensory and reward prediction errors during motor adaptation.

    Science.gov (United States)

    Izawa, Jun; Shadmehr, Reza

    2011-03-01

    Voluntary motor commands produce two kinds of consequences. Initially, a sensory consequence is observed in terms of activity in our primary sensory organs (e.g., vision, proprioception). Subsequently, the brain evaluates the sensory feedback and produces a subjective measure of utility or usefulness of the motor commands (e.g., reward). As a result, comparisons between predicted and observed consequences of motor commands produce two forms of prediction error. How do these errors contribute to changes in motor commands? Here, we considered a reach adaptation protocol and found that when high quality sensory feedback was available, adaptation of motor commands was driven almost exclusively by sensory prediction errors. This form of learning had a distinct signature: as motor commands adapted, the subjects altered their predictions regarding sensory consequences of motor commands, and generalized this learning broadly to neighboring motor commands. In contrast, as the quality of the sensory feedback degraded, adaptation of motor commands became more dependent on reward prediction errors. Reward prediction errors produced comparable changes in the motor commands, but produced no change in the predicted sensory consequences of motor commands, and generalized only locally. Because we found that there was a within subject correlation between generalization patterns and sensory remapping, it is plausible that during adaptation an individual's relative reliance on sensory vs. reward prediction errors could be inferred. We suggest that while motor commands change because of sensory and reward prediction errors, only sensory prediction errors produce a change in the neural system that predicts sensory consequences of motor commands.

  19. Adaptive Learning and Thinking Style to Improve E-Learning Environment Using Neural Network (ALTENN) Model

    OpenAIRE

    Dagez, Hanan Ettaher; Ambarka, Ali Elghali

    2015-01-01

     In recent years we have witnessed an increasingly heightened awareness of the potential benefits of adaptively in e-learning. This has been mainly driven by the realization that the ideal of individualized learning (i.e., learning tailored to the specific requirements and preferences of the individual) cannot be achieved, especially at a “massive” scale, using traditional approaches. In e-learning when the learning style of the student is not compatible with the teaching style of the teacher...

  20. Adaptive E-learning System in Secondary Education

    Directory of Open Access Journals (Sweden)

    Sofija Tosheva

    2012-02-01

    Full Text Available In this paper we describe an adaptive web application E-school, where students can adjust some features according to their preferences and learning style. This e-learning environment enables monitoring students progress, total time students have spent in the system, their activity on the forums, the overall achievements in lessons learned, tests performed and solutions to given projects. Personalized assistance that teacher provides in a traditional classroom is not easy to implement. Students have regular contact with teachers using e-mail tools and conversation, so teacher get mentoring role for each student. The results of exploitation of the e-learning system show positive impact in acquiring the material and improvement of student’s achievements.

  1. Learning Unknown Structure in CRFs via Adaptive Gradient Projection Method

    Directory of Open Access Journals (Sweden)

    Wei Xue

    2016-08-01

    Full Text Available We study the problem of fitting probabilistic graphical models to the given data when the structure is not known. More specifically, we focus on learning unknown structure in conditional random fields, especially learning both the structure and parameters of a conditional random field model simultaneously. To do this, we first formulate the learning problem as a convex minimization problem by adding an l_2-regularization to the node parameters and a group l_1-regularization to the edge parameters, and then a gradient-based projection method is proposed to solve it which combines an adaptive stepsize selection strategy with a nonmonotone line search. Extensive simulation experiments are presented to show the performance of our approach in solving unknown structure learning problems.

  2. Deep reinforcement learning for automated radiation adaptation in lung cancer.

    Science.gov (United States)

    Tseng, Huan-Hsin; Luo, Yi; Cui, Sunan; Chien, Jen-Tzung; Ten Haken, Randall K; Naqa, Issam El

    2017-12-01

    To investigate deep reinforcement learning (DRL) based on historical treatment plans for developing automated radiation adaptation protocols for nonsmall cell lung cancer (NSCLC) patients that aim to maximize tumor local control at reduced rates of radiation pneumonitis grade 2 (RP2). In a retrospective population of 114 NSCLC patients who received radiotherapy, a three-component neural networks framework was developed for deep reinforcement learning (DRL) of dose fractionation adaptation. Large-scale patient characteristics included clinical, genetic, and imaging radiomics features in addition to tumor and lung dosimetric variables. First, a generative adversarial network (GAN) was employed to learn patient population characteristics necessary for DRL training from a relatively limited sample size. Second, a radiotherapy artificial environment (RAE) was reconstructed by a deep neural network (DNN) utilizing both original and synthetic data (by GAN) to estimate the transition probabilities for adaptation of personalized radiotherapy patients' treatment courses. Third, a deep Q-network (DQN) was applied to the RAE for choosing the optimal dose in a response-adapted treatment setting. This multicomponent reinforcement learning approach was benchmarked against real clinical decisions that were applied in an adaptive dose escalation clinical protocol. In which, 34 patients were treated based on avid PET signal in the tumor and constrained by a 17.2% normal tissue complication probability (NTCP) limit for RP2. The uncomplicated cure probability (P+) was used as a baseline reward function in the DRL. Taking our adaptive dose escalation protocol as a blueprint for the proposed DRL (GAN + RAE + DQN) architecture, we obtained an automated dose adaptation estimate for use at ∼2/3 of the way into the radiotherapy treatment course. By letting the DQN component freely control the estimated adaptive dose per fraction (ranging from 1-5 Gy), the DRL automatically favored dose

  3. Applying perceptual and adaptive learning techniques for teaching introductory histopathology

    Directory of Open Access Journals (Sweden)

    Sally Krasne

    2013-01-01

    Full Text Available Background: Medical students are expected to master the ability to interpret histopathologic images, a difficult and time-consuming process. A major problem is the issue of transferring information learned from one example of a particular pathology to a new example. Recent advances in cognitive science have identified new approaches to address this problem. Methods: We adapted a new approach for enhancing pattern recognition of basic pathologic processes in skin histopathology images that utilizes perceptual learning techniques, allowing learners to see relevant structure in novel cases along with adaptive learning algorithms that space and sequence different categories (e.g. diagnoses that appear during a learning session based on each learner′s accuracy and response time (RT. We developed a perceptual and adaptive learning module (PALM that utilized 261 unique images of cell injury, inflammation, neoplasia, or normal histology at low and high magnification. Accuracy and RT were tracked and integrated into a "Score" that reflected students rapid recognition of the pathologies and pre- and post-tests were given to assess the effectiveness. Results: Accuracy, RT and Scores significantly improved from the pre- to post-test with Scores showing much greater improvement than accuracy alone. Delayed post-tests with previously unseen cases, given after 6-7 weeks, showed a decline in accuracy relative to the post-test for 1 st -year students, but not significantly so for 2 nd -year students. However, the delayed post-test scores maintained a significant and large improvement relative to those of the pre-test for both 1 st and 2 nd year students suggesting good retention of pattern recognition. Student evaluations were very favorable. Conclusion: A web-based learning module based on the principles of cognitive science showed an evidence for improved recognition of histopathology patterns by medical students.

  4. Climate change, mitigation and adaptation with uncertainty and learning

    International Nuclear Information System (INIS)

    Ingham, Alan; Ma Jie; Ulph, Alistair

    2007-01-01

    One of the major issues in climate change policy is how to deal with the considerable uncertainty that surrounds many of the elements. Some of these uncertainties will be resolved through the process of further research. This process of learning raises a crucial timing question: should society delay taking action in anticipation of obtaining better information, or should it accelerate action, because we might learn that climate change is much more serious than expected. Much of the analysis to date has focussed on the case where the actions available to society are just the mitigation of emissions, and where there is little or no role for learning. We extend the analysis to allow for both mitigation and adaptation. We show that including adaptation weakens the effect of the irreversibility constraint and so, for this model, makes it more likely that the prospect of future learning should lead to less action now to deal with climate change. We review the empirical literature on climate change policy with uncertainty, learning, and irreversibility, and show that to date the effects on current policy are rather small, though this may reflect the particular choice of models employed

  5. Adaptation and learning: characteristic time scales of performance dynamics.

    Science.gov (United States)

    Newell, Karl M; Mayer-Kress, Gottfried; Hong, S Lee; Liu, Yeou-Teh

    2009-12-01

    A multiple time scales landscape model is presented that reveals structures of performance dynamics that were not resolved in the traditional power law analysis of motor learning. It shows the co-existence of separate processes during and between practice sessions that evolve in two independent dimensions characterized by time scales that differ by about an order of magnitude. Performance along the slow persistent dimension of learning improves often as much and sometimes more during rest (memory consolidation and/or insight generation processes) than during a practice session itself. In contrast, the process characterized by the fast, transient dimension of adaptation reverses direction between practice sessions, thereby significantly degrading performance at the beginning of the next practice session (warm-up decrement). The theoretical model fits qualitatively and quantitatively the data from Snoddy's [Snoddy, G. S. (1926). Learning and stability. Journal of Applied Psychology, 10, 1-36] classic learning study of mirror tracing and other averaged and individual data sets, and provides a new account of the processes of change in adaptation and learning. 2009 Elsevier B.V. All rights reserved.

  6. A globally convergent MC algorithm with an adaptive learning rate.

    Science.gov (United States)

    Peng, Dezhong; Yi, Zhang; Xiang, Yong; Zhang, Haixian

    2012-02-01

    This brief deals with the problem of minor component analysis (MCA). Artificial neural networks can be exploited to achieve the task of MCA. Recent research works show that convergence of neural networks based MCA algorithms can be guaranteed if the learning rates are less than certain thresholds. However, the computation of these thresholds needs information about the eigenvalues of the autocorrelation matrix of data set, which is unavailable in online extraction of minor component from input data stream. In this correspondence, we introduce an adaptive learning rate into the OJAn MCA algorithm, such that its convergence condition does not depend on any unobtainable information, and can be easily satisfied in practical applications.

  7. Adapting to managed care by becoming a learning organization.

    Science.gov (United States)

    O'Sullivan, M J

    1999-03-01

    In the tumultuous and chaotic environment of managed health care, hospital-based mental health providers need to change in fundamental ways. The traditional view of mental health organizations is a professional-bureaucratic one where actions and outcomes of planning are thought to be highly predictable. The author proposes an alternative paradigm for viewing mental health provider organizations, one based on learning theory, which accepts that the future is unknowable because of its complexity and the probabilistic nature of the world. Within this perspective, mental health care providers need to become "learning organizations" to successfully adapt to the new and evolving conditions.

  8. Sharing and Adaptation of Educational Documents in E-Learning

    Directory of Open Access Journals (Sweden)

    Chekry Abderrahman

    2012-03-01

    Full Text Available Few documents can be reused among the huge number of the educational documents on the web. The exponential increase of these documents makes it almost impossible to search for relevant documents. In addition to this, e-learning is designed for public users who have different levels of knowledge and varied skills so they should be given a content that sees to their needs. This work is about adapting the content of learning with learners preferences, and give the teachers the ability to reuse a given content.

  9. Preference learning with evolutionary Multivariate Adaptive Regression Spline model

    DEFF Research Database (Denmark)

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

    2015-01-01

    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...

  10. Adaptive Learning in Cartesian Product of Reproducing Kernel Hilbert Spaces

    OpenAIRE

    Yukawa, Masahiro

    2014-01-01

    We propose a novel adaptive learning algorithm based on iterative orthogonal projections in the Cartesian product of multiple reproducing kernel Hilbert spaces (RKHSs). The task is estimating/tracking nonlinear functions which are supposed to contain multiple components such as (i) linear and nonlinear components, (ii) high- and low- frequency components etc. In this case, the use of multiple RKHSs permits a compact representation of multicomponent functions. The proposed algorithm is where t...

  11. Adaptation Criteria for the Personalised Delivery of Learning Materials: A Multi-Stage Empirical Investigation

    Science.gov (United States)

    Thalmann, Stefan

    2014-01-01

    Personalised e-Learning represents a major step-change from the one-size-fits-all approach of traditional learning platforms to a more customised and interactive provision of learning materials. Adaptive learning can support the learning process by tailoring learning materials to individual needs. However, this requires the initial preparation of…

  12. Masters of adaptation: learning in late life adjustments.

    Science.gov (United States)

    Roberson, Donald N

    2005-01-01

    The purpose of this research is to understand the relationship between human development in older adults and personal learning. Personal or self-directed learning (SDL) refers to a style of learning where the individual directs, controls, and evaluates what is learned. It may occur with formal classes, but most often takes place in non-formal situations. This study employed a descriptive qualitative design incorporating in-depth, semistructured interviews for data collection. The sample of 10 purposefully selected older adults from a rural area reflected diversity in gender, race, education, and employment. Data analysis was guided by the constant comparative method. The primary late life adjustments of these older adults were in response to having extra time, changes in family, and social and physical loss. This research also indicated that late life adjustments are a primary incentive for self-directed learning. The results of this study indicated that older adults become masters of adaptation through the use of self-directed learning activities.

  13. Solar adaptive optics: specificities, lessons learned, and open alternatives

    Science.gov (United States)

    Montilla, I.; Marino, J.; Asensio Ramos, A.; Collados, M.; Montoya, L.; Tallon, M.

    2016-07-01

    the Strehl and the Point Spread Function used in night time adaptive optics but not really suitable to the solar systems, and new control strategies more complex than the ones used in nowadays solar Multi Conjugate Adaptive Optics systems. In this paper we summarize the lessons learned with past and current solar adaptive optics systems and focus on the discussion on the new alternatives to solve present open issues limiting their performance.

  14. Online Adaptation and Over-Trial Learning in Macaque Visuomotor Control

    Science.gov (United States)

    Braun, Daniel A.; Aertsen, Ad; Paz, Rony; Vaadia, Eilon; Rotter, Stefan; Mehring, Carsten

    2011-01-01

    When faced with unpredictable environments, the human motor system has been shown to develop optimized adaptation strategies that allow for online adaptation during the control process. Such online adaptation is to be contrasted to slower over-trial learning that corresponds to a trial-by-trial update of the movement plan. Here we investigate the interplay of both processes, i.e., online adaptation and over-trial learning, in a visuomotor experiment performed by macaques. We show that simple non-adaptive control schemes fail to perform in this task, but that a previously suggested adaptive optimal feedback control model can explain the observed behavior. We also show that over-trial learning as seen in learning and aftereffect curves can be explained by learning in a radial basis function network. Our results suggest that both the process of over-trial learning and the process of online adaptation are crucial to understand visuomotor learning. PMID:21720526

  15. Evolutionary online behaviour learning and adaptation in real robots.

    Science.gov (United States)

    Silva, Fernando; Correia, Luís; Christensen, Anders Lyhne

    2017-07-01

    Online evolution of behavioural control on real robots is an open-ended approach to autonomous learning and adaptation: robots have the potential to automatically learn new tasks and to adapt to changes in environmental conditions, or to failures in sensors and/or actuators. However, studies have so far almost exclusively been carried out in simulation because evolution in real hardware has required several days or weeks to produce capable robots. In this article, we successfully evolve neural network-based controllers in real robotic hardware to solve two single-robot tasks and one collective robotics task. Controllers are evolved either from random solutions or from solutions pre-evolved in simulation. In all cases, capable solutions are found in a timely manner (1 h or less). Results show that more accurate simulations may lead to higher-performing controllers, and that completing the optimization process in real robots is meaningful, even if solutions found in simulation differ from solutions in reality. We furthermore demonstrate for the first time the adaptive capabilities of online evolution in real robotic hardware, including robots able to overcome faults injected in the motors of multiple units simultaneously, and to modify their behaviour in response to changes in the task requirements. We conclude by assessing the contribution of each algorithmic component on the performance of the underlying evolutionary algorithm.

  16. Style-based classification of Chinese ink and wash paintings

    Science.gov (United States)

    Sheng, Jiachuan; Jiang, Jianmin

    2013-09-01

    Following the fact that a large collection of ink and wash paintings (IWP) is being digitized and made available on the Internet, their automated content description, analysis, and management are attracting attention across research communities. While existing research in relevant areas is primarily focused on image processing approaches, a style-based algorithm is proposed to classify IWPs automatically by their authors. As IWPs do not have colors or even tones, the proposed algorithm applies edge detection to locate the local region and detect painting strokes to enable histogram-based feature extraction and capture of important cues to reflect the styles of different artists. Such features are then applied to drive a number of neural networks in parallel to complete the classification, and an information entropy balanced fusion is proposed to make an integrated decision for the multiple neural network classification results in which the entropy is used as a pointer to combine the global and local features. Evaluations via experiments support that the proposed algorithm achieves good performances, providing excellent potential for computerized analysis and management of IWPs.

  17. Classification of multiple sclerosis lesions using adaptive dictionary learning.

    Science.gov (United States)

    Deshpande, Hrishikesh; Maurel, Pierre; Barillot, Christian

    2015-12-01

    This paper presents a sparse representation and an adaptive dictionary learning based method for automated classification of multiple sclerosis (MS) lesions in magnetic resonance (MR) images. Manual delineation of MS lesions is a time-consuming task, requiring neuroradiology experts to analyze huge volume of MR data. This, in addition to the high intra- and inter-observer variability necessitates the requirement of automated MS lesion classification methods. Among many image representation models and classification methods that can be used for such purpose, we investigate the use of sparse modeling. In the recent years, sparse representation has evolved as a tool in modeling data using a few basis elements of an over-complete dictionary and has found applications in many image processing tasks including classification. We propose a supervised classification approach by learning dictionaries specific to the lesions and individual healthy brain tissues, which include white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The size of the dictionaries learned for each class plays a major role in data representation but it is an even more crucial element in the case of competitive classification. Our approach adapts the size of the dictionary for each class, depending on the complexity of the underlying data. The algorithm is validated using 52 multi-sequence MR images acquired from 13 MS patients. The results demonstrate the effectiveness of our approach in MS lesion classification. Copyright © 2015 Elsevier Ltd. All rights reserved.

  18. Algebraic and adaptive learning in neural control systems

    Science.gov (United States)

    Ferrari, Silvia

    A systematic approach is developed for designing adaptive and reconfigurable nonlinear control systems that are applicable to plants modeled by ordinary differential equations. The nonlinear controller comprising a network of neural networks is taught using a two-phase learning procedure realized through novel techniques for initialization, on-line training, and adaptive critic design. A critical observation is that the gradients of the functions defined by the neural networks must equal corresponding linear gain matrices at chosen operating points. On-line training is based on a dual heuristic adaptive critic architecture that improves control for large, coupled motions by accounting for actual plant dynamics and nonlinear effects. An action network computes the optimal control law; a critic network predicts the derivative of the cost-to-go with respect to the state. Both networks are algebraically initialized based on prior knowledge of satisfactory pointwise linear controllers and continue to adapt on line during full-scale simulations of the plant. On-line training takes place sequentially over discrete periods of time and involves several numerical procedures. A backpropagating algorithm called Resilient Backpropagation is modified and successfully implemented to meet these objectives, without excessive computational expense. This adaptive controller is as conservative as the linear designs and as effective as a global nonlinear controller. The method is successfully implemented for the full-envelope control of a six-degree-of-freedom aircraft simulation. The results show that the on-line adaptation brings about improved performance with respect to the initialization phase during aircraft maneuvers that involve large-angle and coupled dynamics, and parameter variations.

  19. Clinical quality needs complex adaptive systems and machine learning.

    Science.gov (United States)

    Marsland, Stephen; Buchan, Iain

    2004-01-01

    The vast increase in clinical data has the potential to bring about large improvements in clinical quality and other aspects of healthcare delivery. However, such benefits do not come without cost. The analysis of such large datasets, particularly where the data may have to be merged from several sources and may be noisy and incomplete, is a challenging task. Furthermore, the introduction of clinical changes is a cyclical task, meaning that the processes under examination operate in an environment that is not static. We suggest that traditional methods of analysis are unsuitable for the task, and identify complexity theory and machine learning as areas that have the potential to facilitate the examination of clinical quality. By its nature the field of complex adaptive systems deals with environments that change because of the interactions that have occurred in the past. We draw parallels between health informatics and bioinformatics, which has already started to successfully use machine learning methods.

  20. Learning deep features with adaptive triplet loss for person reidentification

    Science.gov (United States)

    Li, Zhiqiang; Sang, Nong; Chen, Kezhou; Gao, Changxin; Wang, Ruolin

    2018-03-01

    Person reidentification (re-id) aims to match a specified person across non-overlapping cameras, which remains a very challenging problem. While previous methods mostly focus on feature extraction or metric learning, this paper makes the attempt in jointly learning both the global full-body and local body-parts features of the input persons with a multichannel convolutional neural network (CNN) model, which is trained by an adaptive triplet loss function that serves to minimize the distance between the same person and maximize the distance between different persons. The experimental results show that our approach achieves very promising results on the large-scale Market-1501 and DukeMTMC-reID datasets.

  1. How adaptive learning affects evolution: reviewing theory on the Baldwin effect

    NARCIS (Netherlands)

    Sznajder, B.; Sabelis, M.W.; Egas, M.

    2012-01-01

    We review models of the Baldwin effect, i.e., the hypothesis that adaptive learning (i.e., learning to improve fitness) accelerates genetic evolution of the phenotype. Numerous theoretical studies scrutinized the hypothesis that a non-evolving ability of adaptive learning accelerates evolution of

  2. The influence of student characteristics on the use of adaptive e-learning material

    NARCIS (Netherlands)

    van Seters, J. R.; Ossevoort, M. A.; Tramper, J.; Goedhart, M. J.

    Adaptive e-learning materials can help teachers to educate heterogeneous student groups. This study provides empirical data about the way academic students differ in their learning when using adaptive e-learning materials. Ninety-four students participated in the study. We determined characteristics

  3. Adapting online learning for Canada's Northern public health workforce

    Directory of Open Access Journals (Sweden)

    Marnie Bell

    2013-08-01

    Full Text Available Background . Canada's North is a diverse, sparsely populated land, where inequalities and public health issues are evident, particularly for Aboriginal people. The Northern public health workforce is a unique mix of professional and paraprofessional workers. Few have formal public health education. From 2009 to 2012, the Public Health Agency of Canada (PHAC collaborated with a Northern Advisory Group to develop and implement a strategy to strengthen public health capacity in Canada's 3 northern territories. Access to relevant, effective continuing education was identified as a key issue. Challenges include diverse educational and cultural backgrounds of public health workers, geographical isolation and variable technological infrastructure across the north. Methods . PHAC's Skills Online program offers Internet-based continuing education modules for public health professionals. In partnership with the Northern Advisory Group, PHAC conducted 3 pilots between 2008 and 2012 to assess the appropriateness of the Skills Online program for Northern/Aboriginal public health workers. Module content and delivery modalities were adapted for the pilots. Adaptations included adding Inuit and Northern public health examples and using video and teleconference discussions to augment the online self-study component. Results . Findings from the pilots were informative and similar to those from previous Skills Online pilots with learners in developing countries. Online learning is effective in bridging the geographical barriers in remote locations. Incorporating content on Northern and Aboriginal health issues facilitates engagement in learning. Employer support facilitates the recruitment and retention of learners in an online program. Facilitator assets included experience as a public health professional from the north, and flexibility to use modified approaches to support and measure knowledge acquisition and application, especially for First Nations, Inuit and

  4. Adapting online learning for Canada's Northern public health workforce.

    Science.gov (United States)

    Bell, Marnie; MacDougall, Karen

    2013-01-01

    Canada's North is a diverse, sparsely populated land, where inequalities and public health issues are evident, particularly for Aboriginal people. The Northern public health workforce is a unique mix of professional and paraprofessional workers. Few have formal public health education. From 2009 to 2012, the Public Health Agency of Canada (PHAC) collaborated with a Northern Advisory Group to develop and implement a strategy to strengthen public health capacity in Canada's 3 northern territories. Access to relevant, effective continuing education was identified as a key issue. Challenges include diverse educational and cultural backgrounds of public health workers, geographical isolation and variable technological infrastructure across the north. PHAC's Skills Online program offers Internet-based continuing education modules for public health professionals. In partnership with the Northern Advisory Group, PHAC conducted 3 pilots between 2008 and 2012 to assess the appropriateness of the Skills Online program for Northern/Aboriginal public health workers. Module content and delivery modalities were adapted for the pilots. Adaptations included adding Inuit and Northern public health examples and using video and teleconference discussions to augment the online self-study component. Findings from the pilots were informative and similar to those from previous Skills Online pilots with learners in developing countries. Online learning is effective in bridging the geographical barriers in remote locations. Incorporating content on Northern and Aboriginal health issues facilitates engagement in learning. Employer support facilitates the recruitment and retention of learners in an online program. Facilitator assets included experience as a public health professional from the north, and flexibility to use modified approaches to support and measure knowledge acquisition and application, especially for First Nations, Inuit and Metis learners. Results demonstrate that

  5. Reinforcement Learning Based Novel Adaptive Learning Framework for Smart Grid Prediction

    Directory of Open Access Journals (Sweden)

    Tian Li

    2017-01-01

    Full Text Available Smart grid is a potential infrastructure to supply electricity demand for end users in a safe and reliable manner. With the rapid increase of the share of renewable energy and controllable loads in smart grid, the operation uncertainty of smart grid has increased briskly during recent years. The forecast is responsible for the safety and economic operation of the smart grid. However, most existing forecast methods cannot account for the smart grid due to the disabilities to adapt to the varying operational conditions. In this paper, reinforcement learning is firstly exploited to develop an online learning framework for the smart grid. With the capability of multitime scale resolution, wavelet neural network has been adopted in the online learning framework to yield reinforcement learning and wavelet neural network (RLWNN based adaptive learning scheme. The simulations on two typical prediction problems in smart grid, including wind power prediction and load forecast, validate the effectiveness and the scalability of the proposed RLWNN based learning framework and algorithm.

  6. Adaptive Learning in Medical Education: The Final Piece of Technology Enhanced Learning?

    Science.gov (United States)

    Sharma, Neel; Doherty, Iain; Dong, Chaoyan

    2017-09-01

    Technology enhanced learning (TEL) is now common practice in the field of medical education. One of the primary examples of its use is that of high fidelity simulation and computerised mannequins. Further examples include online learning modules, electronic portfolios, virtual patient interactions, massive open online courses and the flipped classroom movement. The rise of TEL has occurred primarily due to the ease of internet access enabling the retrieval and sharing of information in an instant. Furthermore, the compact nature of internet ready devices such as smartphones and laptops has meant that access to information can occur anytime and anywhere. From an educational perspective however, the current utilisation of TEL has been hindered by its lack of understanding of learners' needs. This is concerning, particularly as evidence highlights that during medical training, each individual learner has their own learning requirements and often achieves competency at different rates. In view of this, there has been interest in ensuring TEL is more learner aware and that the learning process should be more personalised. Adaptive learning can aim to achieve this by ensuring content is delivered according to the needs of the learner. This commentary highlights the move towards adaptive learning and the benefits of such an intervention.

  7. Breast image feature learning with adaptive deconvolutional networks

    Science.gov (United States)

    Jamieson, Andrew R.; Drukker, Karen; Giger, Maryellen L.

    2012-03-01

    Feature extraction is a critical component of medical image analysis. Many computer-aided diagnosis approaches employ hand-designed, heuristic lesion extracted features. An alternative approach is to learn features directly from images. In this preliminary study, we explored the use of Adaptive Deconvolutional Networks (ADN) for learning high-level features in diagnostic breast mass lesion images with potential application to computer-aided diagnosis (CADx) and content-based image retrieval (CBIR). ADNs (Zeiler, et. al., 2011), are recently-proposed unsupervised, generative hierarchical models that decompose images via convolution sparse coding and max pooling. We trained the ADNs to learn multiple layers of representation for two breast image data sets on two different modalities (739 full field digital mammography (FFDM) and 2393 ultrasound images). Feature map calculations were accelerated by use of GPUs. Following Zeiler et. al., we applied the Spatial Pyramid Matching (SPM) kernel (Lazebnik, et. al., 2006) on the inferred feature maps and combined this with a linear support vector machine (SVM) classifier for the task of binary classification between cancer and non-cancer breast mass lesions. Non-linear, local structure preserving dimension reduction, Elastic Embedding (Carreira-Perpiñán, 2010), was then used to visualize the SPM kernel output in 2D and qualitatively inspect image relationships learned. Performance was found to be competitive with current CADx schemes that use human-designed features, e.g., achieving a 0.632+ bootstrap AUC (by case) of 0.83 [0.78, 0.89] for an ultrasound image set (1125 cases).

  8. Automatic Detection of Tutoring Styles Based on Tutors' Behavior

    Science.gov (United States)

    Bendjebar, Safia; Lafifi, Yacine; Zedadra, Amina

    2016-01-01

    In e-learning systems, tutors have a significant impact on learners' life to increase their knowledge level and to make the learning process more effective. They are characterized by different features. Therefore, identifying tutoring styles is a critical step in understanding the preference of tutors on how to organize and help the learners. In…

  9. Challenges in adapting imitation and reinforcement learning to compliant robots

    Directory of Open Access Journals (Sweden)

    Calinon Sylvain

    2011-12-01

    Full Text Available There is an exponential increase of the range of tasks that robots are forecasted to accomplish. (Reprogramming these robots becomes a critical issue for their commercialization and for their applications to real-world scenarios in which users without expertise in robotics wish to adapt the robot to their needs. This paper addresses the problem of designing userfriendly human-robot interfaces to transfer skills in a fast and efficient manner. This paper presents recent work conducted at the Learning and Interaction group at ADVR-IIT, ranging from skill acquisition through kinesthetic teaching to self-refinement strategies initiated from demonstrations. Our group started to explore the use of imitation and exploration strategies that can take advantage of the compliant capabilities of recent robot hardware and control architectures.

  10. Adaptive Global Innovative Learning Environment for Glioblastoma: GBM AGILE.

    Science.gov (United States)

    Alexander, Brian M; Ba, Sujuan; Berger, Mitchel S; Berry, Donald A; Cavenee, Webster K; Chang, Susan M; Cloughesy, Timothy F; Jiang, Tao; Khasraw, Mustafa; Li, Wenbin; Mittman, Robert; Poste, George H; Wen, Patrick Y; Yung, W K Alfred; Barker, Anna D

    2018-02-15

    Glioblastoma (GBM) is a deadly disease with few effective therapies. Although much has been learned about the molecular characteristics of the disease, this knowledge has not been translated into clinical improvements for patients. At the same time, many new therapies are being developed. Many of these therapies have potential biomarkers to identify responders. The result is an enormous amount of testable clinical questions that must be answered efficiently. The GBM Adaptive Global Innovative Learning Environment (GBM AGILE) is a novel, multi-arm, platform trial designed to address these challenges. It is the result of the collective work of over 130 oncologists, statisticians, pathologists, neurosurgeons, imagers, and translational and basic scientists from around the world. GBM AGILE is composed of two stages. The first stage is a Bayesian adaptively randomized screening stage to identify effective therapies based on impact on overall survival compared with a common control. This stage also finds the population in which the therapy shows the most promise based on clinical indication and biomarker status. Highly effective therapies transition in an inferentially seamless manner in the identified population to a second confirmatory stage. The second stage uses fixed randomization to confirm the findings from the first stage to support registration. Therapeutic arms with biomarkers may be added to the trial over time, while others complete testing. The design of GBM AGILE enables rapid clinical testing of new therapies and biomarkers to speed highly effective therapies to clinical practice. Clin Cancer Res; 24(4); 737-43. ©2017 AACR . ©2017 American Association for Cancer Research.

  11. Evaluation framework based on fuzzy measured method in adaptive learning systems

    OpenAIRE

    Houda Zouari Ounaies, ,; Yassine Jamoussi; Henda Hajjami Ben Ghezala

    2008-01-01

    Currently, e-learning systems are mainly web-based applications and tackle a wide range of users all over the world. Fitting learners’ needs is considered as a key issue to guaranty the success of these systems. Many researches work on providing adaptive systems. Nevertheless, evaluation of the adaptivity is still in an exploratory phase. Adaptation methods are a basic factor to guaranty an effective adaptation. This issue is referred as meta-adaptation in numerous researches. In our research...

  12. Learner Characteristic Based Learning Effort Curve Mode: The Core Mechanism on Developing Personalized Adaptive E-Learning Platform

    Science.gov (United States)

    Hsu, Pi-Shan

    2012-01-01

    This study aims to develop the core mechanism for realizing the development of personalized adaptive e-learning platform, which is based on the previous learning effort curve research and takes into account the learner characteristics of learning style and self-efficacy. 125 university students from Taiwan are classified into 16 groups according…

  13. Swarm Intelligence: New Techniques for Adaptive Systems to Provide Learning Support

    Science.gov (United States)

    Wong, Lung-Hsiang; Looi, Chee-Kit

    2012-01-01

    The notion of a system adapting itself to provide support for learning has always been an important issue of research for technology-enabled learning. One approach to provide adaptivity is to use social navigation approaches and techniques which involve analysing data of what was previously selected by a cluster of users or what worked for…

  14. Exploring the Effects of Intercultural Learning on Cross-Cultural Adaptation in a Study Abroad Context

    Science.gov (United States)

    Tsai, Yau

    2011-01-01

    This study targets Asian students studying abroad and explores the effects of intercultural learning on their cross-cultural adaptation by drawing upon a questionnaire survey. On the one hand, the results of this study find that under the influence of intercultural learning, students respond differently in their cross-cultural adaptation and no…

  15. Effectiveness of Adaptive Assessment versus Learner Control in a Multimedia Learning System

    Science.gov (United States)

    Chen, Ching-Huei; Chang, Shu-Wei

    2015-01-01

    The purpose of this study was to explore the effectiveness of adaptive assessment versus learner control in a multimedia learning system designed to help secondary students learn science. Unlike other systems, this paper presents a workflow of adaptive assessment following instructional materials that better align with learners' cognitive…

  16. The influence of student characteristics on the use of adaptive e-learning material

    NARCIS (Netherlands)

    Seters, van J.R.; Ossevoort, M.A.; Tramper, J.; Goedhart, M.J.

    2012-01-01

    Adaptive e-learning materials can help teachers to educate heterogeneous student groups. This study provides empirical data about the way academic students differ in their learning when using adaptive elearning materials. Ninety-four students participated in the study. We determined characteristics

  17. An Adaptive Approach to Managing Knowledge Development in a Project-Based Learning Environment

    Science.gov (United States)

    Tilchin, Oleg; Kittany, Mohamed

    2016-01-01

    In this paper we propose an adaptive approach to managing the development of students' knowledge in the comprehensive project-based learning (PBL) environment. Subject study is realized by two-stage PBL. It shapes adaptive knowledge management (KM) process and promotes the correct balance between personalized and collaborative learning. The…

  18. Examining the Role of Emotional Intelligence between Organizational Learning and Adaptive Performance in Indian Manufacturing Industries

    Science.gov (United States)

    Pradhan, Rabindra Kumar; Jena, Lalatendu Kesari; Singh, Sanjay Kumar

    2017-01-01

    Purpose: The purpose of this study is to examine the relationship between organisational learning and adaptive performance. Furthermore, the study investigates the moderating role of emotional intelligence in the perspective of organisational learning for addressing adaptive performance of executives employed in manufacturing organisations.…

  19. OPUS One: An Intelligent Adaptive Learning Environment Using Artificial Intelligence Support

    Science.gov (United States)

    Pedrazzoli, Attilio

    2010-06-01

    AI based Tutoring and Learning Path Adaptation are well known concepts in e-Learning scenarios today and increasingly applied in modern learning environments. In order to gain more flexibility and to enhance existing e-learning platforms, the OPUS One LMS Extension package will enable a generic Intelligent Tutored Adaptive Learning Environment, based on a holistic Multidimensional Instructional Design Model (PENTHA ID Model), allowing AI based tutoring and adaptation functionality to existing Web-based e-learning systems. Relying on "real time" adapted profiles, it allows content- / course authors to apply a dynamic course design, supporting tutored, collaborative sessions and activities, as suggested by modern pedagogy. The concept presented combines a personalized level of surveillance, learning activity- and learning path adaptation suggestions to ensure the students learning motivation and learning success. The OPUS One concept allows to implement an advanced tutoring approach combining "expert based" e-tutoring with the more "personal" human tutoring function. It supplies the "Human Tutor" with precise, extended course activity data and "adaptation" suggestions based on predefined subject matter rules. The concept architecture is modular allowing a personalized platform configuration.

  20. The effect of adaptive versus static practicing on student learning - evidence from a randomized field experiment

    NARCIS (Netherlands)

    van Klaveren, Chris; Vonk, Sebastiaan; Cornelisz, Ilja

    2017-01-01

    Schools and governments are increasingly investing in adaptive practice software. To date, the evidence whether adaptivity improves learning outcomes is limited and mixed. A large-scale randomized control trial is conducted in Dutch secondary schools to evaluate the effectiveness of an adaptive

  1. Fundamentals of Adaptive Intelligent Tutoring Systems for Self-Regulated Learning

    Science.gov (United States)

    2015-03-01

    ARL-SR-0318 ● MAR 2015 US Army Research Laboratory Fundamentals of Adaptive Intelligent Tutoring Systems for Self-Regulated...Adaptive Intelligent Tutoring Systems for Self-Regulated Learning by Robert A Sottilare Human Research and Engineering Directorate, ARL...TITLE AND SUBTITLE Fundamentals of Adaptive Intelligent Tutoring Systems for Self-Regulated Learning 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c

  2. The use of and obstacles to social learning in climate change adaptation initiatives in South Africa

    OpenAIRE

    Mudombi, Shakespear; Fabricius, Christo; Van Zyl-Bulitta, Verena; Patt, Anthony

    2017-01-01

    Global environmental change will have major impacts on ecosystems and human livelihoods while challenging the adaptive capacity of individuals and communities. Social learning, an ongoing adaptive process of knowledge generation, reflection and synthesis, may enhance people’s awareness about climate change and its impacts, with positive outcomes for their adaptive capacity. The objectives of this study were to assess the prevalence of factors promoting social learning in climate change adapta...

  3. Context-Adaptive Learning Designs by Using Semantic Web Services

    Science.gov (United States)

    Dietze, Stefan; Gugliotta, Alessio; Domingue, John

    2007-01-01

    IMS Learning Design (IMS-LD) is a promising technology aimed at supporting learning processes. IMS-LD packages contain the learning process metadata as well as the learning resources. However, the allocation of resources--whether data or services--within the learning design is done manually at design-time on the basis of the subjective appraisals…

  4. The Effects of Reflective Activities on Skill Adaptation in a Work-Related Instrumental Learning Setting

    Science.gov (United States)

    Roessger, Kevin M.

    2014-01-01

    In work-related instrumental learning contexts, the role of reflective activities is unclear. Kolb's experiential learning theory and Mezirow's transformative learning theory predict skill adaptation as an outcome. This prediction was tested by manipulating reflective activities and assessing participants' response and error rates during novel…

  5. Constructive, Self-Regulated, Situated, and Collaborative Learning: An Approach for the Acquisition of Adaptive Competence

    Science.gov (United States)

    de Corte, Erik

    2012-01-01

    In today's learning society, education must focus on fostering adaptive competence (AC) defined as the ability to apply knowledge and skills flexibly in different contexts. In this article, four major types of learning are discussed--constructive, self-regulated, situated, and collaborative--in relation to what students must learn in order to…

  6. A Framework for Adaptive Learning Design in a Web-Conferencing Environment

    Science.gov (United States)

    Bower, Matt

    2016-01-01

    Many recent technologies provide the ability to dynamically adjust the interface depending on the emerging cognitive and collaborative needs of the learning episode. This means that educators can adaptively re-design the learning environment during the lesson, rather than purely relying on preemptive learning design thinking. Based on a…

  7. Information-educational environment with adaptive control of learning process

    Science.gov (United States)

    Modjaev, A. D.; Leonova, N. M.

    2017-01-01

    Recent years, a new scientific branch connected with the activities in social sphere management developing intensively and it is called "Social Cybernetics". In the framework of this scientific branch, theory and methods of management of social sphere are formed. Considerable attention is paid to the management, directly in real time. However, the decision of such management tasks is largely constrained by the lack of or insufficiently deep study of the relevant sections of the theory and methods of management. The article discusses the use of cybernetic principles in solving problems of control in social systems. Applying to educational activities a model of composite interrelated objects representing the behaviour of students at various stages of educational process is introduced. Statistical processing of experimental data obtained during the actual learning process is being done. If you increase the number of features used, additionally taking into account the degree and nature of variability of levels of current progress of students during various types of studies, new properties of students' grouping are discovered. L-clusters were identified, reflecting the behaviour of learners with similar characteristics during lectures. It was established that the characteristics of the clusters contain information about the dynamics of learners' behaviour, allowing them to be used in additional lessons. The ways of solving the problem of adaptive control based on the identified dynamic characteristics of the learners are planned.

  8. Learning as You Journey: Anishinaabe Perception of Social-ecological Environments and Adaptive Learning

    Directory of Open Access Journals (Sweden)

    Iain Davidson-Hunt

    2003-12-01

    Full Text Available This paper explores the linkages between social-ecological resilience and adaptive learning. We refer to adaptive learning as a method to capture the two-way relationship between people and their social-ecological environment. In this paper, we focus on traditional ecological knowledge. Research was undertaken with the Anishinaabe people of Iskatewizaagegan No. 39 Independent First Nation, in northwestern Ontario, Canada. The research was carried out over two field seasons, with verification workshops following each field season. The methodology was based on site visits and transects determined by the elders as appropriate to answer a specific question, find specific plants, or locate plant communities. During site visits and transect walks, research themes such as plant nomenclature, plant use, habitat descriptions, biogeophysical landscape vocabulary, and place names were discussed. Working with elders allowed us to record a rich set of vocabulary to describe the spatial characteristics of the biogeophysical landscape. However, elders also directed our attention to places they knew through personal experiences and journeys and remembered from stories and collective history. We documented elders' perceptions of the temporal dynamics of the landscape through discussion of disturbance events and cycles. Again, elders drew our attention to the ways in which time was marked by cultural references to seasons and moons. The social memory of landscape dynamics was documented as a combination of biogeophysical structures and processes, along with the stories by which Iskatewizaagegan people wrote their histories upon the land. Adaptive learning for social-ecological resilience, as suggested by this research, requires maintaining the web of relationships of people and places. Such relationships allow social memory to frame creativity, while allowing knowledge to evolve in the face of change. Social memory does not actually evolve directly out of

  9. How assessment and reflection relate to more effective learning in adaptive management

    Directory of Open Access Journals (Sweden)

    Harry Biggs

    2011-05-01

    Two other studies in the Kruger National Park, which have examined learning specifically, are also discussed. One of them suggests that in a complex environment, learning necessarily has a dual nature, with each component of seven contrasting pairs of the aspects of learning in partial tension with the other. We use these dualities to further probe assessment, reflection, inter-relatedness and learning in the cases presented. Each contrasting aspect of a ‘learning duality’ turns out to emphasise either assessment or reflection, which reinforces the idea that both are needed to facilitate sufficient learning for successful adaptive management. We hope this analysis can act as a springboard for further study, practice and reflection on these important and often underrated components of adaptive management. Conservation implications: The better understanding of assessment and reflection as being largely separate but complementary actions will assist adaptive management practitioners to give explicit attention to both, and to relate them better to each other.

  10. Web Log Pre-processing and Analysis for Generation of Learning Profiles in Adaptive E-learning

    Directory of Open Access Journals (Sweden)

    Radhika M. Pai

    2016-03-01

    Full Text Available Adaptive E-learning Systems (AESs enhance the efficiency of online courses in education by providing personalized contents and user interfaces that changes according to learner’s requirements and usage patterns. This paper presents the approach to generate learning profile of each learner which helps to identify the learning styles and provide Adaptive User Interface which includes adaptive learning components and learning material. The proposed method analyzes the captured web usage data to identify the learning profile of the learners. The learning profiles are identified by an algorithmic approach that is based on the frequency of accessing the materials and the time spent on the various learning components on the portal. The captured log data is pre-processed and converted into standard XML format to generate learners sequence data corresponding to the different sessions and time spent. The learning style model adopted in this approach is Felder-Silverman Learning Style Model (FSLSM. This paper also presents the analysis of learner’s activities, preprocessed XML files and generated sequences.

  11. Web Log Pre-processing and Analysis for Generation of Learning Profiles in Adaptive E-learning

    Directory of Open Access Journals (Sweden)

    Radhika M. Pai

    2016-04-01

    Full Text Available Adaptive E-learning Systems (AESs enhance the efficiency of online courses in education by providing personalized contents and user interfaces that changes according to learner’s requirements and usage patterns. This paper presents the approach to generate learning profile of each learner which helps to identify the learning styles and provide Adaptive User Interface which includes adaptive learning components and learning material. The proposed method analyzes the captured web usage data to identify the learning profile of the learners. The learning profiles are identified by an algorithmic approach that is based on the frequency of accessing the materials and the time spent on the various learning components on the portal. The captured log data is pre-processed and converted into standard XML format to generate learners sequence data corresponding to the different sessions and time spent. The learning style model adopted in this approach is Felder-Silverman Learning Style Model (FSLSM. This paper also presents the analysis of learner’s activities, preprocessed XML files and generated sequences.

  12. Impact of learning adaptability and time management disposition on study engagement among Chinese baccalaureate nursing students.

    Science.gov (United States)

    Liu, Jing-Ying; Liu, Yan-Hui; Yang, Ji-Peng

    2014-01-01

    The aim of this study was to explore the relationships among study engagement, learning adaptability, and time management disposition in a sample of Chinese baccalaureate nursing students. A convenient sample of 467 baccalaureate nursing students was surveyed in two universities in Tianjin, China. Students completed a questionnaire that included their demographic information, Chinese Utrecht Work Engagement Scale-Student Questionnaire, Learning Adaptability Scale, and Adolescence Time Management Disposition Scale. One-way analysis of variance tests were used to assess the relationship between certain characteristics of baccalaureate nursing students. Pearson correlation was performed to test the correlation among study engagement, learning adaptability, and time management disposition. Hierarchical linear regression analyses were performed to explore the mediating role of time management disposition. The results revealed that study engagement (F = 7.20, P < .01) and learning adaptability (F = 4.41, P < .01) differed across grade groups. Learning adaptability (r = 0.382, P < .01) and time management disposition (r = 0.741, P < .01) were positively related with study engagement. Time management disposition had a partially mediating effect on the relationship between study engagement and learning adaptability. The findings implicate that educators should not only promote interventions to increase engagement of baccalaureate nursing students but also focus on development, investment in adaptability, and time management. Copyright © 2014 Elsevier Inc. All rights reserved.

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

    Science.gov (United States)

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

    2017-01-01

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

  14. The use of and obstacles to social learning in climate change adaptation initiatives in South Africa

    Directory of Open Access Journals (Sweden)

    Shakespear Mudombi

    2017-03-01

    Full Text Available Global environmental change will have major impacts on ecosystems and human livelihoods while challenging the adaptive capacity of individuals and communities. Social learning, an ongoing adaptive process of knowledge generation, reflection and synthesis, may enhance people’s awareness about climate change and its impacts, with positive outcomes for their adaptive capacity. The objectives of this study were to assess the prevalence of factors promoting social learning in climate change adaptation initiatives in South Africa. An online survey was used to obtain the views of decision makers in government and non-governmental organisations about the presence of personal factors and organisational factors that promote social learning. Descriptive analysis was used to assess these issues. The findings provide some evidence of social learning in climate change adaptation projects in South Africa, with the majority of respondents indicating that personal social learning indicators were present. Mechanisms for improved conflict resolution were, however, less prevalent. The organisational and governance-related barriers to implementation also presented significant challenges. Some of the main organisational barriers were short timeframes for implementing projects, inadequate financial resources, political interference, shortcomings in governance systems and lack of knowledge and expertise in organisations. There is a need for organisations to promote social learning by ensuring that their organisational environment and governance structures are conducive for their employees to embrace social learning. This will help contribute to the overall success of climate change adaptation initiatives.

  15. Enhancing Adaptive Capacity in Food Systems: Learning at Farmers' Markets in Sweden

    Directory of Open Access Journals (Sweden)

    Rebecka Milestad

    2010-09-01

    Full Text Available This article examines how local food systems in the form of farmers' markets can enhance adaptive capacity and build social-ecological resilience. It does this by exploring the learning potential among farmers and customers. Learning can enable actors to adapt successfully and thus build adaptive capacity. Three forms of learning are investigated: instrumental, communicative, and emancipatory. These forms of learning constitute the foundation for lasting changes of behaviors. Local food systems are characterized by close links and opportunities for face-to-face interactions between consumers and producers of food, and are also institutions where farmers and customers can express and act upon their ethical values concerning food. However, local food systems are still a marginal phenomenon and cannot be accessed by all consumers. Interviews were held with customers and farmers, and the interactions between farmers and customers were observed at two farmers' markets in Sweden. Customers and farmers were found to learn and adapt to each other due to the opportunities offered by the farmers' markets. We found that farmers and customers learned in the instrumental and communicative domains, but could not confirm emancipatory learning. We concluded that the feedback between customers and farmers offers the potential for learning, which in turn contributes to adaptive capacity. This can be a driving force for building resilience in the food system.

  16. Learning analytics in practice: The effects of adaptive educational technology Snappet on students' arithmetic skills

    NARCIS (Netherlands)

    Molenaar, I.; Knoop-van Campen, C.A.N.

    2016-01-01

    Even though the recent influx of tablets in primary education goes together with the vision that educational technology empowered with learning analytics will revolutionize education, empirical results supporting this claim are scares. Adaptive educational technology Snappet combines extracted and

  17. ADAPTATION OF TEACHING PROCESS BASED ON A STUDENTS INDIVIDUAL LEARNING NEEDS

    Directory of Open Access Journals (Sweden)

    TAKÁCS, Ondřej

    2011-03-01

    Full Text Available Development of current society requires integration of information technology to every sector, including education. The idea of adaptive teaching in e-learning environment is based on paying attention and giving support to various learning styles. More effective, user friendly thus better quality education can be achieved through such an environment. Learning can be influenced by many factors. In the paper we deal with such factors as student’s personality and qualities – particularly learning style and motivation. In addition we want to prepare study materials and study environment which respects students’ differences. Adaptive e-learning means an automated way of teaching which adapts to different qualities of students which are characteristic for their learning styles. In the last few years we can see a gradual individualization of study not only in distance forms of study but also with full-time study students. Instructional supports, namely those of e-learning, should take this trend into account and adapt the educational processes to individual students’ qualities. The present learning management systems (LMS offers this possibility only to a very limited extent. This paper deals with a design of intelligent virtual tutor behavior, which would adapt its learning ability to both static and dynamically changing student’s qualities. Virtual tutor, in order to manage all that, has to have a sufficiently rich supply of different styles and forms of teaching, with enough information about styles of learning, kinds of memory and other student’s qualities. This paper describes a draft adaptive education model and the results of the first part of the solution – definition of learning styles, pilot testing on students and an outline of further research.

  18. Learn-and-Adapt Stochastic Dual Gradients for Network Resource Allocation

    OpenAIRE

    Chen, Tianyi; Ling, Qing; Giannakis, Georgios B.

    2017-01-01

    Network resource allocation shows revived popularity in the era of data deluge and information explosion. Existing stochastic optimization approaches fall short in attaining a desirable cost-delay tradeoff. Recognizing the central role of Lagrange multipliers in network resource allocation, a novel learn-and-adapt stochastic dual gradient (LA-SDG) method is developed in this paper to learn the sample-optimal Lagrange multiplier from historical data, and accordingly adapt the upcoming resource...

  19. What Can We Learn from a Well-Adapted Enterprise System? A Case Study Approach

    DEFF Research Database (Denmark)

    Svejvig, Per; Jensen, Tina Blegind

    how the system was highly integrated, accepted by its users, and well-aligned to the work processes. This leads to the research question: Why is the enterprise system so well-adapted in SCANDI and what can we learn from this case study? Building on the structural model of technology to investigate...... as a long-term institutionalization and legitimization course of events leading to secondary socialization as the key lessons learned in achieving successful ES adaptations....

  20. Intelligent Adaptation and Personalization Techniques in Computer-Supported Collaborative Learning

    CERN Document Server

    Demetriadis, Stavros; Xhafa, Fatos

    2012-01-01

    Adaptation and personalization have been extensively studied in CSCL research community aiming to design intelligent systems that adaptively support eLearning processes and collaboration. Yet, with the fast development in Internet technologies, especially with the emergence of new data technologies and the mobile technologies, new opportunities and perspectives are opened for advanced adaptive and personalized systems. Adaptation and personalization are posing new research and development challenges to nowadays CSCL systems. In particular, adaptation should be focused in a multi-dimensional way (cognitive, technological, context-aware and personal). Moreover, it should address the particularities of both individual learners and group collaboration. As a consequence, the aim of this book is twofold. On the one hand, it discusses the latest advances and findings in the area of intelligent adaptive and personalized learning systems. On the other hand it analyzes the new implementation perspectives for intelligen...

  1. Stimulating the cerebellum affects visuomotor adaptation but not intermanual transfer of learning.

    Science.gov (United States)

    Block, Hannah; Celnik, Pablo

    2013-12-01

    When systematic movement errors occur, the brain responds with a systematic change in motor behavior. This type of adaptive motor learning can transfer intermanually; adaptation of movements of the right hand in response to training with a perturbed visual signal (visuomotor adaptation) may carry over to the left hand. While visuomotor adaptation has been studied extensively, it is unclear whether the cerebellum, a structure involved in adaptation, is important for intermanual transfer as well. We addressed this question with three experiments in which subjects reached with their right hands as a 30° visuomotor rotation was introduced. Subjects received anodal or sham transcranial direct current stimulation on the trained (experiment 1) or untrained (experiment 2) hemisphere of the cerebellum, or, for comparison, motor cortex (M1). After the training period, subjects reached with their left hand, without visual feedback, to assess intermanual transfer of learning aftereffects. Stimulation of the right cerebellum caused faster adaptation, but none of the stimulation sites affected transfer. To ascertain whether cerebellar stimulation would increase transfer if subjects learned faster as well as a larger amount, in experiment 3 anodal and sham cerebellar groups experienced a shortened training block such that the anodal group learned more than sham. Despite the difference in adaptation magnitude, transfer was similar across these groups, although smaller than in experiment 1. Our results suggest that intermanual transfer of visuomotor learning does not depend on cerebellar activity and that the number of movements performed at plateau is an important predictor of transfer.

  2. A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition

    Science.gov (United States)

    Huang, Qi; Yang, Dapeng; Jiang, Li; Zhang, Huajie; Liu, Hong; Kotani, Kiyoshi

    2017-01-01

    Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC), by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC). We compared PAC performance with incremental support vector classifier (ISVC) and non-adapting SVC (NSVC) in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05) and ISVC (13.38% ± 2.62%, p = 0.001), and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle). PMID:28608824

  3. A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG Pattern Recognition

    Directory of Open Access Journals (Sweden)

    Qi Huang

    2017-06-01

    Full Text Available Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC, by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC. We compared PAC performance with incremental support vector classifier (ISVC and non-adapting SVC (NSVC in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05 and ISVC (13.38% ± 2.62%, p = 0.001, and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle.

  4. Designing an Adaptive Web-Based Learning System Based on Students' Cognitive Styles Identified Online

    Science.gov (United States)

    Lo, Jia-Jiunn; Chan, Ya-Chen; Yeh, Shiou-Wen

    2012-01-01

    This study developed an adaptive web-based learning system focusing on students' cognitive styles. The system is composed of a student model and an adaptation model. It collected students' browsing behaviors to update the student model for unobtrusively identifying student cognitive styles through a multi-layer feed-forward neural network (MLFF).…

  5. Evaluation Framework Based on Fuzzy Measured Method in Adaptive Learning Systems

    Science.gov (United States)

    Ounaies, Houda Zouari; Jamoussi, Yassine; Ben Ghezala, Henda Hajjami

    2008-01-01

    Currently, e-learning systems are mainly web-based applications and tackle a wide range of users all over the world. Fitting learners' needs is considered as a key issue to guaranty the success of these systems. Many researches work on providing adaptive systems. Nevertheless, evaluation of the adaptivity is still in an exploratory phase.…

  6. Professional Learning Communities Assessment: Adaptation, Internal Validity, and Multidimensional Model Testing in Turkish Context

    Science.gov (United States)

    Dogan, Selçuk; Tatik, R. Samil; Yurtseven, Nihal

    2017-01-01

    The main purpose of this study is to adapt and validate the Professional Learning Communities Assessment Revised (PLCA-R) by Olivier, Hipp, and Huffman within the context of Turkish schools. The instrument was translated and adapted to administer to teachers in Turkey. Internal structure of the Turkish version of PLCA-R was investigated by using…

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

    Science.gov (United States)

    Hamza, Lamia; Tlili, Guiassa Yamina

    2018-01-01

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

  8. Learning Motivation and Adaptive Video Caption Filtering for EFL Learners Using Handheld Devices

    Science.gov (United States)

    Hsu, Ching-Kun

    2015-01-01

    The aim of this study was to provide adaptive assistance to improve the listening comprehension of eleventh grade students. This study developed a video-based language learning system for handheld devices, using three levels of caption filtering adapted to student needs. Elementary level captioning excluded 220 English sight words (see Section 1…

  9. Low-Back Pain Patients Learn to Adapt Motor Behavior with Adverse Secondary Consequences

    NARCIS (Netherlands)

    van Dieën, Jaap H.; Flor, Herta; Hodges, Paul W.

    2017-01-01

    ABSTRACT: We hypothesize that changes in motor behavior in individuals with low-back pain are adaptations aimed at minimizing the real or perceived risk of further pain. Through reinforcement learning, pain and subsequent adaptions result in less dynamic motor behavior, leading to increased loading

  10. On the problem of first year students adaptation to the learning ...

    African Journals Online (AJOL)

    The relevance of the studied problem is the fact that successful adaptation of a first year student to life and academic activity in a university is the key to the further development of each student as a personality ... Keywords: Adaptation, students, learning process, means of physical education, sports ans mass sports events.

  11. Learning to push and learning to move: The adaptive control of contact forces

    Directory of Open Access Journals (Sweden)

    Maura eCasadio

    2015-11-01

    Full Text Available To be successful at manipulating objects one needs to apply simultaneously well controlled movements and contact forces. We present a computational theory of how the brain may successfully generate a vast spectrum of interactive behaviors by combining two independent processes. One process is competent to control movements in free space and the other is competent to control contact forces against rigid constraints. Free space and rigid constraints are singularities at the boundaries of a continuum of mechanical impedance. Within this continuum, forces and motions occur in compatible pairs connected by the equations of Newtonian dynamics. The force applied to an object determines its motion. Conversely, inverse dynamics determine a unique force trajectory from a movement trajectory. In this perspective, we describe motor learning as a process leading to the discovery of compatible force/motion pairs. The learned compatible pairs constitute a local representation of the environment's mechanics. Experiments on force field adaptation have already provided us with evidence that the brain is able to predict and compensate the forces encountered when one is attempting to generate a motion. Here, we tested the theory in the dual case, i.e. when one attempts at applying a desired contact force against a simulated rigid surface. If the surface becomes unexpectedly compliant, the contact point moves as a function of the applied force and this causes the applied force to deviate from its desired value. We found that, through repeated attempts at generating the desired contact force, subjects discovered the unique compatible hand motion. When, after learning, the rigid contact was unexpectedly restored, subjects displayed after effects of learning, consistent with the concurrent operation of a motion control system and a force control system. Together, theory and experiment support a new and broader view of modularity in the coordinated control of forces and

  12. Innovations in Lifelong Learning: Capitalising on ADAPT. CEDEFOP Panorama Series.

    Science.gov (United States)

    Janssens, Jos

    A community initiative (called ADAPT) was intended to help the workforce in European Union countries to adapt to industrial change and prepare for the information society, as well as to promote growth, employment, and the competitiveness of companies in the countries. Between 1995 and 1999, 4,000 projects were funded to transform the ways in which…

  13. Adaptive Semantic and Social Web-based learning and assessment environment for the STEM

    Science.gov (United States)

    Babaie, Hassan; Atchison, Chris; Sunderraman, Rajshekhar

    2014-05-01

    We are building a cloud- and Semantic Web-based personalized, adaptive learning environment for the STEM fields that integrates and leverages Social Web technologies to allow instructors and authors of learning material to collaborate in semi-automatic development and update of their common domain and task ontologies and building their learning resources. The semi-automatic ontology learning and development minimize issues related to the design and maintenance of domain ontologies by knowledge engineers who do not have any knowledge of the domain. The social web component of the personal adaptive system will allow individual and group learners to interact with each other and discuss their own learning experience and understanding of course material, and resolve issues related to their class assignments. The adaptive system will be capable of representing key knowledge concepts in different ways and difficulty levels based on learners' differences, and lead to different understanding of the same STEM content by different learners. It will adapt specific pedagogical strategies to individual learners based on their characteristics, cognition, and preferences, allow authors to assemble remotely accessed learning material into courses, and provide facilities for instructors to assess (in real time) the perception of students of course material, monitor their progress in the learning process, and generate timely feedback based on their understanding or misconceptions. The system applies a set of ontologies that structure the learning process, with multiple user friendly Web interfaces. These include the learning ontology (models learning objects, educational resources, and learning goal); context ontology (supports adaptive strategy by detecting student situation), domain ontology (structures concepts and context), learner ontology (models student profile, preferences, and behavior), task ontologies, technological ontology (defines devices and places that surround the

  14. Impact of Nursing Learning Environments on Adaptive Competency Development in Baccalaureate Nursing Students.

    Science.gov (United States)

    Laschinger, Heather K. Spence

    1992-01-01

    Kolb's experiential learning theory was used as a framework to study 179 generic baccalaureate students' perceptions of the different types of learning environments and adaptive competencies. Clinical experience and preceptorships contributed more to competency development than did nursing or nonnursing classes. (JOW)

  15. Towards Increased Relevance: Context-Adapted Models of the Learning Organization

    Science.gov (United States)

    Örtenblad, Anders

    2015-01-01

    Purpose: The purposes of this paper are to take a closer look at the relevance of the idea of the learning organization for organizations in different generalized organizational contexts; to open up for the existence of multiple, context-adapted models of the learning organization; and to suggest a number of such models.…

  16. Fast But Fleeting: Adaptive Motor Learning Processes Associated with Aging and Cognitive Decline

    Science.gov (United States)

    Trewartha, Kevin M.; Garcia, Angeles; Wolpert, Daniel M.

    2014-01-01

    Motor learning has been shown to depend on multiple interacting learning processes. For example, learning to adapt when moving grasped objects with novel dynamics involves a fast process that adapts and decays quickly—and that has been linked to explicit memory—and a slower process that adapts and decays more gradually. Each process is characterized by a learning rate that controls how strongly motor memory is updated based on experienced errors and a retention factor determining the movement-to-movement decay in motor memory. Here we examined whether fast and slow motor learning processes involved in learning novel dynamics differ between younger and older adults. In addition, we investigated how age-related decline in explicit memory performance influences learning and retention parameters. Although the groups adapted equally well, they did so with markedly different underlying processes. Whereas the groups had similar fast processes, they had different slow processes. Specifically, the older adults exhibited decreased retention in their slow process compared with younger adults. Within the older group, who exhibited considerable variation in explicit memory performance, we found that poor explicit memory was associated with reduced retention in the fast process, as well as the slow process. These findings suggest that explicit memory resources are a determining factor in impairments in the both the fast and slow processes for motor learning but that aging effects on the slow process are independent of explicit memory declines. PMID:25274819

  17. Educational Multimedia Profiling Recommendations for Device-Aware Adaptive Mobile Learning

    Science.gov (United States)

    Moldovan, Arghir-Nicolae; Ghergulescu, Ioana; Muntean, Cristina Hava

    2014-01-01

    Mobile learning is seeing a fast adoption with the increasing availability and affordability of mobile devices such as smartphones and tablets. As the creation and consumption of educational multimedia content on mobile devices is also increasing fast, educators and mobile learning providers are faced with the challenge to adapt multimedia type…

  18. An adaptive deep Q-learning strategy for handwritten digit recognition.

    Science.gov (United States)

    Qiao, Junfei; Wang, Gongming; Li, Wenjing; Chen, Min

    2018-02-22

    Handwritten digits recognition is a challenging problem in recent years. Although many deep learning-based classification algorithms are studied for handwritten digits recognition, the recognition accuracy and running time still need to be further improved. In this paper, an adaptive deep Q-learning strategy is proposed to improve accuracy and shorten running time for handwritten digit recognition. The adaptive deep Q-learning strategy combines the feature-extracting capability of deep learning and the decision-making of reinforcement learning to form an adaptive Q-learning deep belief network (Q-ADBN). First, Q-ADBN extracts the features of original images using an adaptive deep auto-encoder (ADAE), and the extracted features are considered as the current states of Q-learning algorithm. Second, Q-ADBN receives Q-function (reward signal) during recognition of the current states, and the final handwritten digits recognition is implemented by maximizing the Q-function using Q-learning algorithm. Finally, experimental results from the well-known MNIST dataset show that the proposed Q-ADBN has a superiority to other similar methods in terms of accuracy and running time. Copyright © 2018 Elsevier Ltd. All rights reserved.

  19. Fast but fleeting: adaptive motor learning processes associated with aging and cognitive decline.

    Science.gov (United States)

    Trewartha, Kevin M; Garcia, Angeles; Wolpert, Daniel M; Flanagan, J Randall

    2014-10-01

    Motor learning has been shown to depend on multiple interacting learning processes. For example, learning to adapt when moving grasped objects with novel dynamics involves a fast process that adapts and decays quickly-and that has been linked to explicit memory-and a slower process that adapts and decays more gradually. Each process is characterized by a learning rate that controls how strongly motor memory is updated based on experienced errors and a retention factor determining the movement-to-movement decay in motor memory. Here we examined whether fast and slow motor learning processes involved in learning novel dynamics differ between younger and older adults. In addition, we investigated how age-related decline in explicit memory performance influences learning and retention parameters. Although the groups adapted equally well, they did so with markedly different underlying processes. Whereas the groups had similar fast processes, they had different slow processes. Specifically, the older adults exhibited decreased retention in their slow process compared with younger adults. Within the older group, who exhibited considerable variation in explicit memory performance, we found that poor explicit memory was associated with reduced retention in the fast process, as well as the slow process. These findings suggest that explicit memory resources are a determining factor in impairments in the both the fast and slow processes for motor learning but that aging effects on the slow process are independent of explicit memory declines. Copyright © 2014 the authors 0270-6474/14/3413411-11$15.00/0.

  20. Rich media content adaptation in e-learning systems

    OpenAIRE

    Mirri, Silvia

    2007-01-01

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

  1. A service based adaptive U-learning system using UX.

    Science.gov (United States)

    Jeong, Hwa-Young; Yi, Gangman

    2014-01-01

    In recent years, traditional development techniques for e-learning systems have been changing to become more convenient and efficient. One new technology in the development of application systems includes both cloud and ubiquitous computing. Cloud computing can support learning system processes by using services while ubiquitous computing can provide system operation and management via a high performance technical process and network. In the cloud computing environment, a learning service application can provide a business module or process to the user via the internet. This research focuses on providing the learning material and processes of courses by learning units using the services in a ubiquitous computing environment. And we also investigate functions that support users' tailored materials according to their learning style. That is, we analyzed the user's data and their characteristics in accordance with their user experience. We subsequently applied the learning process to fit on their learning performance and preferences. Finally, we demonstrate how the proposed system outperforms learning effects to learners better than existing techniques.

  2. A Service Based Adaptive U-Learning System Using UX

    Directory of Open Access Journals (Sweden)

    Hwa-Young Jeong

    2014-01-01

    Full Text Available In recent years, traditional development techniques for e-learning systems have been changing to become more convenient and efficient. One new technology in the development of application systems includes both cloud and ubiquitous computing. Cloud computing can support learning system processes by using services while ubiquitous computing can provide system operation and management via a high performance technical process and network. In the cloud computing environment, a learning service application can provide a business module or process to the user via the internet. This research focuses on providing the learning material and processes of courses by learning units using the services in a ubiquitous computing environment. And we also investigate functions that support users’ tailored materials according to their learning style. That is, we analyzed the user’s data and their characteristics in accordance with their user experience. We subsequently applied the learning process to fit on their learning performance and preferences. Finally, we demonstrate how the proposed system outperforms learning effects to learners better than existing techniques.

  3. Development Model of Basic Technique Skills Training Shot-Put Obrien Style Based Biomechanics Review

    Directory of Open Access Journals (Sweden)

    danang rohmat hidayanto

    2018-03-01

    Full Text Available The background of this research is the unavailability of learning model of basic technique technique of O'Brien style force that integrated in skill program based on biomechanics study which is used as a reference to build the basic technique skill of the O'Brien style force among students. The purpose of this study is to develop a model of basic-style technique of rejecting the O'Brien-style shot put based on biomechanical studies for beginner levels, including basic prefix technique, glide, final stage, repulsion, further motion and repulsion performance of O'Brien style, all of which arranged in a medium that is easily accessible whenever, by anyone and anywhere, especially in SMK Negeri 1 Kalijambe Sragen . The research method used is "Reasearch and Developement" approach. "Preliminary studies show that 43.0% of respondents considered that the O'Brien style was very important to be developed with a model of skill-based exercise based on biomechanics, as many as 40.0% ressponden stated that it is important to be developed with biomechanics based learning media. Therefore, it is deemed necessary to develop the learning media of the O'Brien style-based training skills based on biomechanical studies. Development of media starts from the design of the storyboard and script form that will be used as media. The design of this model is called the draft model. Draft models that have been prepared are reviewed by the multimedia expert and the O'Brien style expert to get the product's validity. A total of 78.24% of experts declare a viable product with some input. In small groups with n = 6, earned value 72.2% was obtained or valid enough to be tested in large groups. In the large group test with n = 12,values obtained 70.83% or quite feasible to be tested in the field. In the field test, experimental group was prepared with treatment according to media and control group with free treatment. From result of counting of significance test can be

  4. Children and adolescents with migratory experience at risk in language learning and psychosocial adaptation contexts.

    OpenAIRE

    Figueiredo, Sandra; Silva, Carlos Fernandes da; Monteiro, Sara

    2007-01-01

    A compelling body of evidence shows a strong association between psychological, affective and learning variables, related also with the age and gender factors, which are involved in the language learning development process. Children and adolescents with migratory experience (direct/indirect) can develop behaviours at risk in their academic learning and psychosocial adaptation, according to several stressors as anxiety, low motivation, negative attitudes, within a stressed internal l...

  5. Learning to bridge the gap between adaptive management and organisational culture

    Directory of Open Access Journals (Sweden)

    Richard J. Stirzaker

    2011-05-01

    Full Text Available Adaptive management is the problem-solving approach of choice proposed for complex and multistakeholder environments, which are, at best, only partly predictable. We discuss the implications of this approach as applicable to scientists, who have to overcome certain entrained behaviour patterns in order to participate effectively in an adaptive management process. The challenge does not end there. Scientists and managers soon discover that an adaptive management approach does not only challenge conventional scientific and management behaviour but also clashes with contemporary organisational culture. We explore the shortcomings and requirements of organisations with regard to enabling adaptive management. Our overall conclusion relates to whether organisations are learning-centred or not. Do we continue to filter out unfamiliar information which does not fit our world view and avoid situations where we might fail, or do we use new and challenging situations to reframe the question and prepare ourselves for continued learning? Conservation implications: For an organisation to effectively embrace adaptive management, its mangers and scientists may first have to adapt their own beliefs regarding their respective roles. Instead of seeking certainty for guiding decisions, managers and scientists should acknowledge a degree of uncertainty inherent to complex social and ecological systems and seek to learn from the patterns emerging from every decision and action. The required organisational culture is one of ongoing and purposeful learning with all relevant stakeholders. Such a learning culture is often talked about but rarely practised in the organisational environment.

  6. Using Ontology to Drive an Adaptive Learning Interface

    Directory of Open Access Journals (Sweden)

    Andrew Crapo

    2004-10-01

    Full Text Available Intelligent, adaptive interfaces are a pre-requisite to elevating computer-based applications to the realm of collaborative decision support in complex, relatively open-ended domains such as logistics and planning. This is because the composition and effective presentation of even the most useful information must be tailored to constantly changing circumstances. Our objective is to not only achieve an adaptive human-machine interface, but to imbue the software with a significant portion of the responsibility for effectively controlling the adaptation, freeing the user from unnecessary distraction and making the human-machine relationship more collaborative in nature. The foundational concepts of interface adaptation are discussed and a specific logistics application is described as an example.

  7. Adaptative Peer to Peer Data Sharing for Technology Enhanced Learning

    Science.gov (United States)

    Angelaccio, Michele; Buttarazzi, Berta

    Starting from the hypothesis that P2P Data Sharing in a direct teaching scenario (e.g.: a classroom lesson) may lead to relevant benefits, this paper explores the features of EduSHARE a Collaborative Learning System useful for Enhanced Learning Process.

  8. Psychosocial and Adaptive Deficits Associated with Learning Disability Subtypes

    Science.gov (United States)

    Backenson, Erica M.; Holland, Sara C.; Kubas, Hanna A.; Fitzer, Kim R.; Wilcox, Gabrielle; Carmichael, Jessica A.; Fraccaro, Rebecca L.; Smith, Amanda D.; Macoun, Sarah J.; Harrison, Gina L.; Hale, James B.

    2015-01-01

    Children with specific learning disabilities (SLD) have deficits in the basic psychological processes that interfere with learning and academic achievement, and for some SLD subtypes, these deficits can also lead to emotional and/or behavior problems. This study examined psychosocial functioning in 123 students, aged 6 to 11, who underwent…

  9. Complex Mobile Learning That Adapts to Learners' Cognitive Load

    Science.gov (United States)

    Deegan, Robin

    2015-01-01

    Mobile learning is cognitively demanding and frequently the ubiquitous nature of mobile computing means that mobile devices are used in cognitively demanding environments. This paper examines the use of mobile devices from a Learning, Usability and Cognitive Load Theory perspective. It suggests scenarios where these fields interact and presents an…

  10. Adaptation of elaborated feedback in e-learning

    NARCIS (Netherlands)

    Vasilyeva, E.; Pechenizkiy, M.; De Bra, P.M.E.; Nejdl, W.; Kay, J.; Pu, P.; Herder, E.

    2008-01-01

    Design of feedback is a critical issue of online assessment development within Web-based Learning Systems (WBLSs). In our work we demonstrate the possibilities of tailoring the feedback to the students’ learning style (LS), certitude in response and its correctness. We observe in the experimental

  11. Using a social learning configuration to increase Vietnamese smallholder farmers’ adaptive capacity to respond to climate change

    NARCIS (Netherlands)

    Phuong, Le Thi Hong; Wals, Arjen; Sen, Le Thi Hoa; Hoa, Nguyen Quoc; Lu, Van Phan; Biesbroek, Robbert

    2018-01-01

    Social learning is crucial for local smallholder farmers in developing countries to improve their adaptive capacity and to adapt to the current and projected impacts of climate change. While it is widely acknowledged that social learning is a necessary condition for adaptation, few studies have

  12. A neural learning classifier system with self-adaptive constructivism for mobile robot control.

    Science.gov (United States)

    Hurst, Jacob; Bull, Larry

    2006-01-01

    For artificial entities to achieve true autonomy and display complex lifelike behavior, they will need to exploit appropriate adaptable learning algorithms. In this context adaptability implies flexibility guided by the environment at any given time and an open-ended ability to learn appropriate behaviors. This article examines the use of constructivism-inspired mechanisms within a neural learning classifier system architecture that exploits parameter self-adaptation as an approach to realize such behavior. The system uses a rule structure in which each rule is represented by an artificial neural network. It is shown that appropriate internal rule complexity emerges during learning at a rate controlled by the learner and that the structure indicates underlying features of the task. Results are presented in simulated mazes before moving to a mobile robot platform.

  13. Specialized hybrid learners resolve Rogers' paradox about the adaptive value of social learning.

    Science.gov (United States)

    Kharratzadeh, Milad; Montrey, Marcel; Metz, Alex; Shultz, Thomas R

    2017-02-07

    Culture is considered an evolutionary adaptation that enhances reproductive fitness. A common explanation is that social learning, the learning mechanism underlying cultural transmission, enhances mean fitness by avoiding the costs of individual learning. This explanation was famously contradicted by Rogers (1988), who used a simple mathematical model to show that cheap social learning can invade a population without raising its mean fitness. He concluded that some crucial factor remained unaccounted for, which would reverse this surprising result. Here we extend this model to include a more complex environment and limited resources, where individuals cannot reliably learn everything about the environment on their own. Under such conditions, cheap social learning evolves and enhances mean fitness, via hybrid learners capable of specializing their individual learning. We then show that while spatial or social constraints hinder the evolution of hybrid learners, a novel social learning strategy, complementary copying, can mitigate these effects. Copyright © 2016 Elsevier Ltd. All rights reserved.

  14. Adapted Lethality: What We Can Learn from Guinea Pig-Adapted Ebola Virus Infection Model.

    Science.gov (United States)

    Cheresiz, S V; Semenova, E A; Chepurnov, A A

    2016-01-01

    Establishment of small animal models of Ebola virus (EBOV) infection is important both for the study of genetic determinants involved in the complex pathology of EBOV disease and for the preliminary screening of antivirals, production of therapeutic heterologic immunoglobulins, and experimental vaccine development. Since the wild-type EBOV is avirulent in rodents, the adaptation series of passages in these animals are required for the virulence/lethality to emerge in these models. Here, we provide an overview of our several adaptation series in guinea pigs, which resulted in the establishment of guinea pig-adapted EBOV (GPA-EBOV) variants different in their characteristics, while uniformly lethal for the infected animals, and compare the virologic, genetic, pathomorphologic, and immunologic findings with those obtained in the adaptation experiments of the other research groups.

  15. Adapted Lethality: What We Can Learn from Guinea Pig-Adapted Ebola Virus Infection Model

    Directory of Open Access Journals (Sweden)

    S. V. Cheresiz

    2016-01-01

    Full Text Available Establishment of small animal models of Ebola virus (EBOV infection is important both for the study of genetic determinants involved in the complex pathology of EBOV disease and for the preliminary screening of antivirals, production of therapeutic heterologic immunoglobulins, and experimental vaccine development. Since the wild-type EBOV is avirulent in rodents, the adaptation series of passages in these animals are required for the virulence/lethality to emerge in these models. Here, we provide an overview of our several adaptation series in guinea pigs, which resulted in the establishment of guinea pig-adapted EBOV (GPA-EBOV variants different in their characteristics, while uniformly lethal for the infected animals, and compare the virologic, genetic, pathomorphologic, and immunologic findings with those obtained in the adaptation experiments of the other research groups.

  16. Lessons Learned from the First Decade of Adaptive Management in Comprehensive Everglades Restoration

    Directory of Open Access Journals (Sweden)

    Andrew J. LoSchiavo

    2013-12-01

    Full Text Available Although few successful examples of large-scale adaptive management applications are available to ecosystem restoration scientists and managers, examining where and how the components of an adaptive management program have been successfully implemented yields insight into what approaches have and have not worked. We document five key lessons learned during the decade-long development and implementation of the Comprehensive Everglades Restoration Plan (CERP Collaborative Adaptive Management Program that might be useful to other adaptive management practitioners. First, legislative and regulatory authorities that require the development of an adaptive management program are necessary to maintain funding and support to set up and implement adaptive management. Second, integration of adaptive management activities into existing institutional processes, and development of technical guidance, helps to ensure that adaptive management activities are understood and roles and responsibilities are clearly articulated so that adaptive management activities are implemented successfully. Third, a strong applied science framework is critical for establishing a prerestoration ecosystem reference condition and understanding of how the system works, as well as for providing a conduit for incorporating new scientific information into the decision-making process. Fourth, clear identification of uncertainties that pose risks to meeting restoration goals helps with the development of hypothesis-driven strategies to inform restoration planning and implementation. Tools such as management options matrices can provide a coherent way to link hypotheses to specific monitoring efforts and options to adjust implementation if performance goals are not achieved. Fifth, independent external peer review of an adaptive management program provides important feedback critical to maintaining and improving adaptive management implementation for ecosystem restoration. These lessons

  17. Adapting Parameterized Motions using Iterative Learning and Online Collision Detection

    DEFF Research Database (Denmark)

    Laursen, Johan Sund; Sørensen, Lars Carøe; Schultz, Ulrik Pagh

    2018-01-01

    utilizing Gaussian Process learning. This allows for motion parameters to be optimized using real world trials which incorporate all uncertainties inherent in the assembly process without requiring advanced robot and sensor setups. The result is a simple and straightforward system which helps the user...... automatically find robust and uncertainty-tolerant motions. We present experiments for an assembly case showing both detection and learning in the real world and how these combine to a robust robot system....

  18. Robust Visual Knowledge Transfer via Extreme Learning Machine Based Domain Adaptation.

    Science.gov (United States)

    Zhang, Lei; Zhang, David

    2016-08-10

    We address the problem of visual knowledge adaptation by leveraging labeled patterns from source domain and a very limited number of labeled instances in target domain to learn a robust classifier for visual categorization. This paper proposes a new extreme learning machine based cross-domain network learning framework, that is called Extreme Learning Machine (ELM) based Domain Adaptation (EDA). It allows us to learn a category transformation and an ELM classifier with random projection by minimizing the -norm of the network output weights and the learning error simultaneously. The unlabeled target data, as useful knowledge, is also integrated as a fidelity term to guarantee the stability during cross domain learning. It minimizes the matching error between the learned classifier and a base classifier, such that many existing classifiers can be readily incorporated as base classifiers. The network output weights cannot only be analytically determined, but also transferrable. Additionally, a manifold regularization with Laplacian graph is incorporated, such that it is beneficial to semi-supervised learning. Extensively, we also propose a model of multiple views, referred as MvEDA. Experiments on benchmark visual datasets for video event recognition and object recognition, demonstrate that our EDA methods outperform existing cross-domain learning methods.

  19. Iterative learning-based decentralized adaptive tracker for large-scale systems: a digital redesign approach.

    Science.gov (United States)

    Tsai, Jason Sheng-Hong; Du, Yan-Yi; Huang, Pei-Hsiang; Guo, Shu-Mei; Shieh, Leang-San; Chen, Yuhua

    2011-07-01

    In this paper, a digital redesign methodology of the iterative learning-based decentralized adaptive tracker is proposed to improve the dynamic performance of sampled-data linear large-scale control systems consisting of N interconnected multi-input multi-output subsystems, so that the system output will follow any trajectory which may not be presented by the analytic reference model initially. To overcome the interference of each sub-system and simplify the controller design, the proposed model reference decentralized adaptive control scheme constructs a decoupled well-designed reference model first. Then, according to the well-designed model, this paper develops a digital decentralized adaptive tracker based on the optimal analog control and prediction-based digital redesign technique for the sampled-data large-scale coupling system. In order to enhance the tracking performance of the digital tracker at specified sampling instants, we apply the iterative learning control (ILC) to train the control input via continual learning. As a result, the proposed iterative learning-based decentralized adaptive tracker not only has robust closed-loop decoupled property but also possesses good tracking performance at both transient and steady state. Besides, evolutionary programming is applied to search for a good learning gain to speed up the learning process of ILC. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

  20. Synthesizing Learning on Adaptation to Climate Change | IDRC ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Climate Change Adaptation in Africa (CCAA), a program is supported by IDRC and the United Kingdom's Department for International Development (DFID), supports three kinds of activity: research, capacity building and networking. Since 2006, CCAA has supported more than 30 participatory action research projects.

  1. Adaptive WTA with an analog VLSI neuromorphic learning chip.

    Science.gov (United States)

    Häfliger, Philipp

    2007-03-01

    In this paper, we demonstrate how a particular spike-based learning rule (where exact temporal relations between input and output spikes of a spiking model neuron determine the changes of the synaptic weights) can be tuned to express rate-based classical Hebbian learning behavior (where the average input and output spike rates are sufficient to describe the synaptic changes). This shift in behavior is controlled by the input statistic and by a single time constant. The learning rule has been implemented in a neuromorphic very large scale integration (VLSI) chip as part of a neurally inspired spike signal image processing system. The latter is the result of the European Union research project Convolution AER Vision Architecture for Real-Time (CAVIAR). Since it is implemented as a spike-based learning rule (which is most convenient in the overall spike-based system), even if it is tuned to show rate behavior, no explicit long-term average signals are computed on the chip. We show the rule's rate-based Hebbian learning ability in a classification task in both simulation and chip experiment, first with artificial stimuli and then with sensor input from the CAVIAR system.

  2. Learning and adaptation in the management of waterfowl harvests

    Science.gov (United States)

    Johnson, Fred A.

    2011-01-01

    A formal framework for the adaptive management of waterfowl harvests was adopted by the U.S. Fish and Wildlife Service in 1995. The process admits competing models of waterfowl population dynamics and harvest impacts, and relies on model averaging to compute optimal strategies for regulating harvest. Model weights, reflecting the relative ability of the alternative models to predict changes in population size, are used in the model averaging and are updated each year based on a comparison of model predictions and observations of population size. Since its inception the adaptive harvest program has focused principally on mallards (Anas platyrhynchos), which constitute a large portion of the U.S. waterfowl harvest. Four competing models, derived from a combination of two survival and two reproductive hypotheses, were originally assigned equal weights. In the last year of available information (2007), model weights favored the weakly density-dependent reproductive hypothesis over the strongly density-dependent one, and the additive mortality hypothesis over the compensatory one. The change in model weights led to a more conservative harvesting policy than what was in effect in the early years of the program. Adaptive harvest management has been successful in many ways, but nonetheless has exposed the difficulties in defining management objectives, in predicting and regulating harvests, and in coping with the tradeoffs inherent in managing multiple waterfowl stocks exposed to a common harvest. The key challenge now facing managers is whether adaptive harvest management as an institution can be sufficiently adaptive, and whether the knowledge and experience gained from the process can be reflected in higher-level policy decisions.

  3. Adaptive memory: animacy effects persist in paired-associate learning.

    Science.gov (United States)

    VanArsdall, Joshua E; Nairne, James S; Pandeirada, Josefa N S; Cogdill, Mindi

    2015-01-01

    Recent evidence suggests that animate stimuli are remembered better than matched inanimate stimuli. Two experiments tested whether this animacy effect persists in paired-associate learning of foreign words. Experiment 1 randomly paired Swahili words with matched animate and inanimate English words. Participants were told simply to learn the English "translations" for a later test. Replicating earlier findings using free recall, a strong animacy advantage was found in this cued-recall task. Concerned that the effect might be due to enhanced accessibility of the individual responses (e.g., animates represent a more accessible category), Experiment 2 selected animate and inanimate English words from two more constrained categories (four-legged animals and furniture). Once again, an advantage was found for pairs using animate targets. These results argue against organisational accounts of the animacy effect and potentially have implications for foreign language vocabulary learning.

  4. Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels.

    Science.gov (United States)

    Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J

    2014-01-01

    This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively "hiding" its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research.

  5. Intelligent Adaptable e-Assessment for Inclusive e-Learning

    Science.gov (United States)

    Nacheva-Skopalik, Lilyana; Green, Steve

    2016-01-01

    Access to education is one of the main human rights. Everyone should have access to education and be capable of benefiting from it. However there are a number who are excluded, not because of a lack of ability but simply because they have a disability or specific need which current education systems do not address. A learning system in which…

  6. The utility of adaptive eLearning in cervical cytopathology education.

    Science.gov (United States)

    Samulski, T Danielle; Taylor, Laura A; La, Teresa; Mehr, Chelsea R; McGrath, Cindy M; Wu, Roseann I

    2018-02-01

    Adaptive eLearning allows students to experience a self-paced, individualized curriculum based on prior knowledge and learning ability. The authors investigated the effectiveness of adaptive online modules in teaching cervical cytopathology. eLearning modules were created that covered basic concepts in cervical cytopathology, including artifacts and infections, squamous lesions (SL), and glandular lesions (GL). The modules used student responses to individualize the educational curriculum and provide real-time feedback. Pathology trainees and faculty from the authors' institution were randomized into 2 groups (SL or GL), and identical pre-tests and post-tests were used to compare the efficacy of eLearning modules versus traditional study methods (textbooks and slide sets). User experience was assessed with a Likert scale and free-text responses. Sixteen of 17 participants completed the SL module, and 19 of 19 completed the GL module. Participants in both groups had improved post-test scores for content in the adaptive eLearning module. Users indicated that the module was effective in presenting content and concepts (Likert scale [from 1 to 5], 4.3 of 5.0), was an efficient and convenient way to review the material (Likert scale, 4.4 of 5.0), and was more engaging than lectures and texts (Likert scale, 4.6 of 5.0). Users favored the immediate feedback and interactivity of the module. Limitations included the inability to review prior content and slow upload time for images. Learners demonstrated improvement in their knowledge after the use of adaptive eLearning modules compared with traditional methods. Overall, the modules were viewed positively by participants. Adaptive eLearning modules can provide an engaging and effective adjunct to traditional teaching methods in cervical cytopathology. Cancer Cytopathol 2018;126:129-35. © 2017 American Cancer Society. © 2017 American Cancer Society.

  7. Becoming a Coach in Developmental Adaptive Sailing: A Lifelong Learning Perspective.

    Science.gov (United States)

    Duarte, Tiago; Culver, Diane M

    2014-10-02

    Life-story methodology and innovative methods were used to explore the process of becoming a developmental adaptive sailing coach. Jarvis's (2009) lifelong learning theory framed the thematic analysis. The findings revealed that the coach, Jenny, was exposed from a young age to collaborative environments. Social interactions with others such as mentors, colleagues, and athletes made major contributions to her coaching knowledge. As Jenny was exposed to a mixture of challenges and learning situations, she advanced from recreational para-swimming instructor to developmental adaptive sailing coach. The conclusions inform future research in disability sport coaching, coach education, and applied sport psychology.

  8. Lost in Translation: Adapting a Face-to-Face Course Into an Online Learning Experience.

    Science.gov (United States)

    Kenzig, Melissa J

    2015-09-01

    Online education has grown dramatically over the past decade. Instructors who teach face-to-face courses are being called on to adapt their courses to the online environment. Many instructors do not have sufficient training to be able to effectively move courses to an online format. This commentary discusses the growth of online learning, common challenges faced by instructors adapting courses from face-to-face to online, and best practices for translating face-to-face courses into online learning opportunities. © 2015 Society for Public Health Education.

  9. Modeling the behavioral substrates of associate learning and memory - Adaptive neural models

    Science.gov (United States)

    Lee, Chuen-Chien

    1991-01-01

    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.

  10. Designing and Developing a Novel Hybrid Adaptive Learning Path Recommendation System (ALPRS) for Gamification Mathematics Geometry Course

    Science.gov (United States)

    Su, Chung-Ho

    2017-01-01

    Since recommendation systems possess the advantage of adaptive recommendation, they have gradually been applied to e-learning systems to recommend subsequent learning content for learners. However, problems exist in current learning recommender systems available to students in that they are often general learning content and unable to offer…

  11. A framework for adaptive e-learning for continuum mechanics and structural analysis

    OpenAIRE

    Mosquera Feijoo, Juan Carlos; Plaza Beltrán, Luis Francisco; González Rodrigo, Beatriz

    2015-01-01

    This paper presents a project for providing the students of Structural Engineering with the flexibility to learn outside classroom schedules. The goal is a framework for adaptive E-learning based on a repository of open educational courseware with a set of basic Structural Engineering concepts and fundamentals. These are paramount for students to expand their technical knowledge and skills in structural analysis and design of tall buildings, arch-type structures as well as bridges. Thus, conc...

  12. Adaptive Learning Rule for Hardware-based Deep Neural Networks Using Electronic Synapse Devices

    OpenAIRE

    Lim, Suhwan; Bae, Jong-Ho; Eum, Jai-Ho; Lee, Sungtae; Kim, Chul-Heung; Kwon, Dongseok; Park, Byung-Gook; Lee, Jong-Ho

    2017-01-01

    In this paper, we propose a learning rule based on a back-propagation (BP) algorithm that can be applied to a hardware-based deep neural network (HW-DNN) using electronic devices that exhibit discrete and limited conductance characteristics. This adaptive learning rule, which enables forward, backward propagation, as well as weight updates in hardware, is helpful during the implementation of power-efficient and high-speed deep neural networks. In simulations using a three-layer perceptron net...

  13. Managing Cognitive Load in Adaptive ICT-Based Learning

    Directory of Open Access Journals (Sweden)

    Slava Kalyuga

    2009-10-01

    Full Text Available The history of technological innovations in education has many examples of failed high expectations. To avoid becoming another one, current multimedia ICT tools need to be designed in accordance with how the human mind works. There are well established characteristics of its architecture that should be taken into account when evaluating, selecting, and using educational technology. This paper starts with a review of the most important features of human cognitive architecture and their implications for ICT-based learning. Expertise reversal effect relates to the interactions between levels of learner prior knowledge and effectiveness of different instructional techniques and procedures. Designs and techniques that are effective with low-knowledge learners can lose their effectiveness and even have negative consequences for more proficient learners. The paper describes recent empirical findings associated with the expertise reversal effect in multimedia and hypermedia learning environments, their interpretation within a cognitive load framework, and implications for the design of learner-tailored multimedia.

  14. Adaptive Sampling for Nonlinear Dimensionality Reduction Based on Manifold Learning

    DEFF Research Database (Denmark)

    Franz, Thomas; Zimmermann, Ralf; Goertz, Stefan

    2017-01-01

    We make use of the non-intrusive dimensionality reduction method Isomap in order to emulate nonlinear parametric flow problems that are governed by the Reynolds-averaged Navier-Stokes equations. Isomap is a manifold learning approach that provides a low-dimensional embedding space that is approxi...... to detect and fill up gaps in the sampling in the embedding space. The performance of the proposed manifold filling method will be illustrated by numerical experiments, where we consider nonlinear parameter-dependent steady-state Navier-Stokes flows in the transonic regime.......We make use of the non-intrusive dimensionality reduction method Isomap in order to emulate nonlinear parametric flow problems that are governed by the Reynolds-averaged Navier-Stokes equations. Isomap is a manifold learning approach that provides a low-dimensional embedding space...

  15. Thai nursing students' adaption to problem-based learning: a qualitative study.

    Science.gov (United States)

    Klunklin, Areewan; Subpaiboongid, Pornpun; Keitlertnapha, Pongsri; Viseskul, Nongkran; Turale, Sue

    2011-11-01

    Student-centred forms of learning have gained favour internationally over the last few decades including problem based learning, an approach now incorporated in medicine, nursing and other disciplines' education in many countries. However, it is still new in Thailand and being piloted to try to offset traditional forms of didactic, teacher-centred forms of teaching. In this qualitative study, 25 undergraduate nursing students in northern Thailand were interviewed about their experiences with problem-based learning in a health promotion subject. Content analysis was used to interrogate interview data, which revealed four categories: adapting, seeking assistance, self-development, and thinking process development. Initially participants had mixed emotions of confusion, negativity or boredom in the adaption process, but expressed satisfaction with creativity in learning, group work, and leadership development. They described increased abilities to problem solve and think critically, but struggled to develop questioning behaviours in learning. Socio-culturally in Thai education, students have great respect for teachers, but rarely question or challenge them or their learning. We conclude that problem-based learning has great potential in Thai nursing education, but educators and systems need to systematically prepare appropriate learning environments, their staff and students, to incorporate this within curricula. Copyright © 2011 Elsevier Ltd. All rights reserved.

  16. Multiple Kernel Learning for adaptive graph regularized nonnegative matrix factorization

    KAUST Repository

    Wang, Jim Jing-Yan; AbdulJabbar, Mustafa Abdulmajeed

    2012-01-01

    Nonnegative Matrix Factorization (NMF) has been continuously evolving in several areas like pattern recognition and information retrieval methods. It factorizes a matrix into a product of 2 low-rank non-negative matrices that will define parts-based, and linear representation of non-negative data. Recently, Graph regularized NMF (GrNMF) is proposed to find a compact representation, which uncovers the hidden semantics and simultaneously respects the intrinsic geometric structure. In GNMF, an affinity graph is constructed from the original data space to encode the geometrical information. In this paper, we propose a novel idea which engages a Multiple Kernel Learning approach into refining the graph structure that reflects the factorization of the matrix and the new data space. The GrNMF is improved by utilizing the graph refined by the kernel learning, and then a novel kernel learning method is introduced under the GrNMF framework. Our approach shows encouraging results of the proposed algorithm in comparison to the state-of-the-art clustering algorithms like NMF, GrNMF, SVD etc.

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

    Science.gov (United States)

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

    2018-06-01

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

  18. The Study of Reinforcement Learning for Traffic Self-Adaptive Control under Multiagent Markov Game Environment

    Directory of Open Access Journals (Sweden)

    Lun-Hui Xu

    2013-01-01

    Full Text Available Urban traffic self-adaptive control problem is dynamic and uncertain, so the states of traffic environment are hard to be observed. Efficient agent which controls a single intersection can be discovered automatically via multiagent reinforcement learning. However, in the majority of the previous works on this approach, each agent needed perfect observed information when interacting with the environment and learned individually with less efficient coordination. This study casts traffic self-adaptive control as a multiagent Markov game problem. The design employs traffic signal control agent (TSCA for each signalized intersection that coordinates with neighboring TSCAs. A mathematical model for TSCAs’ interaction is built based on nonzero-sum markov game which has been applied to let TSCAs learn how to cooperate. A multiagent Markov game reinforcement learning approach is constructed on the basis of single-agent Q-learning. This method lets each TSCA learn to update its Q-values under the joint actions and imperfect information. The convergence of the proposed algorithm is analyzed theoretically. The simulation results show that the proposed method is convergent and effective in realistic traffic self-adaptive control setting.

  19. AN ADAPTIVE ACO-DRIVEN SCHEME FOR LEARNING AIM ORIENTED PERSONALIZED E-LEARNING

    Directory of Open Access Journals (Sweden)

    Sushma Hans

    2014-10-01

    Full Text Available The e-learning paradigm is now a well-established vehicle of modern education. It caters to a wide spectrum of students with diverse backgrounds who enroll with their own learning aims. A core challenge under this scenario is to generate personalized learning paths so that each student can achieve her learning aim most effectively. Prior works used static attributes such as prior knowledge level, learning ability, browsing preferences, learning style etc. to generate personalized learning paths. In this paper, we take an entirely new route by taking into account the continuous improvement of a learner in the light of her own learning aim, to redefine her learning path at each level of the course. We introduce the concept of personalized examination system that systematically evaluates the dynamic learning ability of every student according to her pre-set goals. The proposed intelligent e-learning system uses Ant Colony Optimization to iteratively optimize the forward learning paths. Experimental results reveal that the system is able to tap a student’s improved learning ability to choose more difficult paths that contribute highly towards her own aims. We demonstrate that the overall learning success of weaker students doubles as compared to statically generated paths while there is considerable improvement of 50% in the learning success for average students as well. This clearly indicates that our approach gives realistic benefits to initially weak students who gradually evolve as the course progresses.

  20. SU-D-BRB-05: Quantum Learning for Knowledge-Based Response-Adaptive Radiotherapy

    Energy Technology Data Exchange (ETDEWEB)

    El Naqa, I; Ten, R [Haken University of Michigan, Ann Arbor, MI (United States)

    2016-06-15

    Purpose: There is tremendous excitement in radiotherapy about applying data-driven methods to develop personalized clinical decisions for real-time response-based adaptation. However, classical statistical learning methods lack in terms of efficiency and ability to predict outcomes under conditions of uncertainty and incomplete information. Therefore, we are investigating physics-inspired machine learning approaches by utilizing quantum principles for developing a robust framework to dynamically adapt treatments to individual patient’s characteristics and optimize outcomes. Methods: We studied 88 liver SBRT patients with 35 on non-adaptive and 53 on adaptive protocols. Adaptation was based on liver function using a split-course of 3+2 fractions with a month break. The radiotherapy environment was modeled as a Markov decision process (MDP) of baseline and one month into treatment states. The patient environment was modeled by a 5-variable state represented by patient’s clinical and dosimetric covariates. For comparison of classical and quantum learning methods, decision-making to adapt at one month was considered. The MDP objective was defined by the complication-free tumor control (P{sup +}=TCPx(1-NTCP)). A simple regression model represented state-action mapping. Single bit in classical MDP and a qubit of 2-superimposed states in quantum MDP represented the decision actions. Classical decision selection was done using reinforcement Q-learning and quantum searching was performed using Grover’s algorithm, which applies uniform superposition over possible states and yields quadratic speed-up. Results: Classical/quantum MDPs suggested adaptation (probability amplitude ≥0.5) 79% of the time for splitcourses and 100% for continuous-courses. However, the classical MDP had an average adaptation probability of 0.5±0.22 while the quantum algorithm reached 0.76±0.28. In cases where adaptation failed, classical MDP yielded 0.31±0.26 average amplitude while the

  1. Adaptation, postpartum concerns, and learning needs in the first two weeks after caesarean birth.

    Science.gov (United States)

    Weiss, Marianne; Fawcett, Jacqueline; Aber, Cynthia

    2009-11-01

    The purpose of this Roy Adaptation Model-based study was to describe women's physical, emotional, functional and social adaptation; postpartum concerns; and learning needs during the first two weeks following caesarean birth and identify relevant nursing interventions. Studies of caesarean-delivered women indicated a trend toward normalisation of the caesarean birth experience. Escalating caesarean birth rates mandate continued study of contemporary caesarean-delivered women. Mixed methods (qualitative and quantitative) descriptive research design. Nursing students collected data from 233 culturally diverse caesarean-delivered women in urban areas of the Midwestern and Northeastern USA between 2002-2004. The focal stimulus was the planned or unplanned caesarean birth; contextual stimuli were cultural identity and parity. Adaptation was measured by open-ended interview questions, fixed choice questionnaires about postpartum concerns and learning needs and nurse assessment of post-discharge problems. Potential interventions were identified using the Omaha System Intervention Scheme. More positive than negative responses were reported for functional and social adaptation than for physical and emotional adaptation. Women with unplanned caesarean births and primiparous women reported less favourable adaptation than planned caesarean mothers and multiparas. Black women reported lower social adaptation, Hispanic women had more role function concerns and Black and Hispanic women had more learning needs than White women. Post-discharge nursing assessments revealed that actual problems accounted for 40% of identified actual or potential problems or needs. Health teaching was the most commonly recommended postpartum intervention strategy followed by case management, treatment and surveillance interventions. Caesarean-delivered women continue to experience some problems with adapting to childbirth. Recommended intervention strategies reflect the importance of health teaching

  2. An adaptive deep learning approach for PPG-based identification.

    Science.gov (United States)

    Jindal, V; Birjandtalab, J; Pouyan, M Baran; Nourani, M

    2016-08-01

    Wearable biosensors have become increasingly popular in healthcare due to their capabilities for low cost and long term biosignal monitoring. This paper presents a novel two-stage technique to offer biometric identification using these biosensors through Deep Belief Networks and Restricted Boltzman Machines. Our identification approach improves robustness in current monitoring procedures within clinical, e-health and fitness environments using Photoplethysmography (PPG) signals through deep learning classification models. The approach is tested on TROIKA dataset using 10-fold cross validation and achieved an accuracy of 96.1%.

  3. Impact of Adapted Hypermedia on Undergraduate Students' Learning of Astronomy in an Elearning Environment

    Science.gov (United States)

    Zuel, Brian

    The purpose of this dissertation was to examine the effectiveness of matching learners' optimal learning styles to their overall knowledge retention. The study attempted to determine if learners who are placed in an online learning environment that matches their optimal learning styles will retain the information at a higher rate than those learners who are not in an adapted learning environment. There were 56 participants that took one of two lessons; the first lesson was textual based, had no hypertext, and was not influenced heavily by the coherence principle, while the second lesson was multimedia based utilizing hypermedia guided by the coherence principle. Each participant took Felder and Soloman's (1991, 2000) Index of Learning Styles (ILS) questionnaire and was classified using the Felder-Silverman Learning Style Model (FSLSM; 1998) into four individual categories. Groups were separated using the Visual/Verbal section of the FSLSM with 55% (n = 31) of participants going to the adapted group, and 45% (n =25) of participants going to the non-adapted group. Each participant completed an immediate posttest directly after the lesson and a retention posttest a week later. Several repeated measures MANOVA tests were conducted to measure the significance of differences in the tests between groups and within groups. Repeated measures MANOVA tests were conducted to determine if significance existed between the immediate posttest results and the retention posttest results. Also, participants were asked their perspectives if the lesson type they received was beneficial to their perceived learning of the material. Of the 56 students who took part in this study, 31 students were placed in the adapted group and 25 in the non-adapted group based on outcomes of the ILS and the FLSSM. No significant differences were found between groups taking the multimedia lesson and the textual lesson in the immediate posttest. No significant differences were found between the adapted and

  4. Adaptive learning compressive tracking based on Markov location prediction

    Science.gov (United States)

    Zhou, Xingyu; Fu, Dongmei; Yang, Tao; Shi, Yanan

    2017-03-01

    Object tracking is an interdisciplinary research topic in image processing, pattern recognition, and computer vision which has theoretical and practical application value in video surveillance, virtual reality, and automatic navigation. Compressive tracking (CT) has many advantages, such as efficiency and accuracy. However, when there are object occlusion, abrupt motion and blur, similar objects, and scale changing, the CT has the problem of tracking drift. We propose the Markov object location prediction to get the initial position of the object. Then CT is used to locate the object accurately, and the classifier parameter adaptive updating strategy is given based on the confidence map. At the same time according to the object location, extract the scale features, which is able to deal with object scale variations effectively. Experimental results show that the proposed algorithm has better tracking accuracy and robustness than current advanced algorithms and achieves real-time performance.

  5. Neuromorphic adaptive plastic scalable electronics: analog learning systems.

    Science.gov (United States)

    Srinivasa, Narayan; Cruz-Albrecht, Jose

    2012-01-01

    Decades of research to build programmable intelligent machines have demonstrated limited utility in complex, real-world environments. Comparing their performance with biological systems, these machines are less efficient by a factor of 1 million1 billion in complex, real-world environments. The Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) program is a multifaceted Defense Advanced Research Projects Agency (DARPA) project that seeks to break the programmable machine paradigm and define a new path for creating useful, intelligent machines. Since real-world systems exhibit infinite combinatorial complexity, electronic neuromorphic machine technology would be preferable in a host of applications, but useful and practical implementations still do not exist. HRL Laboratories LLC has embarked on addressing these challenges, and, in this article, we provide an overview of our project and progress made thus far.

  6. Strategies for Adapting WebQuests for Students with Learning Disabilities

    Science.gov (United States)

    Skylar, Ashley A.; Higgins, Kyle; Boone, Randall

    2007-01-01

    WebQuests are gaining popularity as teachers explore using the Internet for guided learning activities. A WebQuest involves students working on a task that is broken down into clearly defined steps. Students often work in groups to actively conduct the research. This article suggests a variety of methods for adapting WebQuests for students with…

  7. Adaptations for Culturally and Linguistically Diverse Families of English Language Learning Students with Autisim Spectrum Disorders

    Science.gov (United States)

    Mitchell, Deborah J.

    2012-01-01

    The purpose of this qualitative, grounded theory study was to describe adaptations for culturally and linguistically diverse families of English language learning students with autism spectrum disorders. Each family's parent was interviewed three separate times to gather information to understand the needs and experiences regarding their…

  8. Adapting the Survivor Game to Create a Group Learning Term Project in Business Finance

    Science.gov (United States)

    Campbell, Robert D.

    2017-01-01

    A large and growing body of research supports the view that the small-group learning structure can be an effective tool to enhance student performance and encourage innovative problem solving. This paper explains in detail how the framework of the popular television reality show Survivor has been adapted to form a vehicle for a college level group…

  9. Adapting and Evaluating a Tree of Life Group for Women with Learning Disabilities

    Science.gov (United States)

    Randle-Phillips, Cathy; Farquhar, Sarah; Thomas, Sally

    2016-01-01

    Background: This study describes how a specific narrative therapy approach called 'the tree of life' was adapted to run a group for women with learning disabilities. The group consisted of four participants and ran for five consecutive weeks. Materials and Methods: Participants each constructed a tree to represent their lives and presented their…

  10. Japanese English Education and Learning: A History of Adapting Foreign Cultures

    Science.gov (United States)

    Shimizu, Minoru

    2010-01-01

    This essay is a history that relates the Japanese tradition of accepting and adapting aspects of foreign culture, especially as it applies to the learning of foreign languages. In particular, the essay describes the history of English education in Japan by investigating its developments after the Meiji era. The author addresses the issues from the…

  11. Quality Assessment of Adaptive Bitrate Videos using Image Metrics and Machine Learning

    DEFF Research Database (Denmark)

    Søgaard, Jacob; Forchhammer, Søren; Brunnström, Kjell

    2015-01-01

    Adaptive bitrate (ABR) streaming is widely used for distribution of videos over the internet. In this work, we investigate how well we can predict the quality of such videos using well-known image metrics, information about the bitrate levels, and a relatively simple machine learning method...

  12. The Dynamic Interplay among EFL Learners' Ambiguity Tolerance, Adaptability, Cultural Intelligence, Learning Approach, and Language Achievement

    Science.gov (United States)

    Alahdadi, Shadi; Ghanizadeh, Afsaneh

    2017-01-01

    A key objective of education is to prepare individuals to be fully-functioning learners. This entails developing the cognitive, metacognitive, motivational, cultural, and emotional competencies. The present study aimed to examine the interrelationships among adaptability, tolerance of ambiguity, cultural intelligence, learning approach, and…

  13. Bridging Scientific Reasoning and Conceptual Change through Adaptive Web-Based Learning

    Science.gov (United States)

    She, Hsiao-Ching; Liao, Ya-Wen

    2010-01-01

    This study reports an adaptive digital learning project, Scientific Concept Construction and Reconstruction (SCCR), and examines its effects on 108 8th grade students' scientific reasoning and conceptual change through mixed methods. A one-group pre-, post-, and retention quasi-experimental design was used in the study. All students received tests…

  14. Co-operative learning and adaptive instruction in a mathematics curriculum

    NARCIS (Netherlands)

    Terwel, J.; Herfs, P.G.P.; Mertens, E.H.M.; Perrenet, J.Chr.

    1994-01-01

    The AGO 12 to 16 Project (the acronym AGO stands for the Dutch equivalent of 'Adaptive Instruction and Co-operative Learning') seeks to develop and evaluate a mathematics curriculum which is suitable for mixed-ability groups in secondary education. The research questions we will address here are,

  15. Negotiating Service Learning through Community Engagement: Adaptive Leadership, Knowledge, Dialogue and Power

    Science.gov (United States)

    Preece, Julia

    2016-01-01

    This article builds on two recent publications (Preece 2013; 2013a) concerning the application of asset-based community development and adaptive leadership theories when negotiating university service learning placements with community organisations in one South African province. The first publication introduced the concept of 'adaptive…

  16. A Hybrid Approach for Supporting Adaptivity in E-Learning Environments

    Science.gov (United States)

    Al-Omari, Mohammad; Carter, Jenny; Chiclana, Francisco

    2016-01-01

    Purpose: The purpose of this paper is to identify a framework to support adaptivity in e-learning environments. The framework reflects a novel hybrid approach incorporating the concept of the event-condition-action (ECA) model and intelligent agents. Moreover, a system prototype is developed reflecting the hybrid approach to supporting adaptivity…

  17. Adaptation to Altitude as a Vehicle for Experiential Learning of Physiology by University Undergraduates

    Science.gov (United States)

    Weigle, David S.; Buben, Amelia; Burke, Caitlin C.; Carroll, Nels D.; Cook, Brett M.; Davis, Benjamin S.; Dubowitz, Gerald; Fisher, Rian E.; Freeman, Timothy C.; Gibbons, Stephen M.; Hansen, Hale A.; Heys, Kimberly A.; Hopkins, Brittany; Jordan, Brittany L.; McElwain, Katherine L.; Powell, Frank L.; Reinhart, Katherine E.; Robbins, Charles D.; Summers, Cameron C.; Walker, Jennifer D.; Weber, Steven S.; Weinheimer, Caroline J.

    2007-01-01

    In this article, an experiential learning activity is described in which 19 university undergraduates made experimental observations on each other to explore physiological adaptations to high altitude. Following 2 wk of didactic sessions and baseline data collection at sea level, the group ascended to a research station at 12,500-ft elevation.…

  18. A Factor-Analytic Study of Adaptive Behavior and Intellectual Functioning in Learning Disabled Children.

    Science.gov (United States)

    Yeargan, Dollye R.

    The factorial structure of intellectual functioning and adaptive behavior was examined in 160 learning disabled students (6 to 16 years old). Ss were administered the Wechsler Intelligence Scale for Children-Revised (WISC-R) and the Coping Inventory (CI). Factor analysis of WISC-R scores revealed three factors: verbal comprehenson, perceptual…

  19. Development of an Advanced, Automatic, Ultrasonic NDE Imaging System via Adaptive Learning Network Signal Processing Techniques

    Science.gov (United States)

    1981-03-13

    UNCLASSIFIED SECURITY CLAS,:FtfC ’i OF TH*!’ AGC W~ct P- A* 7~9r1) 0. ABSTRACT (continued) onuing in concert with a sophisticated detector has...and New York, 1969. Whalen, M.F., L.J. O’Brien, and A.N. Mucciardi, "Application of Adaptive Learning Netowrks for the Characterization of Two

  20. A Framework for Creating Semantically Adaptive Collaborative E-learning Environments

    Directory of Open Access Journals (Sweden)

    Marija Cubric

    2009-09-01

    Full Text Available In this paper we present a framework that can be used to generate web-based, semantically adaptive, e-learning and computer-assisted assessment (CAA tools for any given knowledge domain, based upon dynamic ontological modeling. We accomplish this by generating “learning ontologies” for a given knowledge domain. The generated learning ontologies are built upon our previous work on a domain “Glossary” ontology and augmented with additional conceptual relations from the WordNet 3.0 lexical database, using Text2Onto, an open source ontology extraction tool. The main novelty of this work is in “on the fly” generation of computer assisted assessments based on the underlying ontology and pre-defined question templates that are founded on the Bloom’s taxonomy of educational objectives. The main deployment scenario for the framework is a web-service providing collaborative e- learning and knowledge management capabilities to various learning communities. The framework can be extended to provide collection and exploitation of the users’ learning behaviour metrics, in order to further adapt the generated e-learning environment to the learners’ needs.

  1. A New Approach to Teaching Biomechanics Through Active, Adaptive, and Experiential Learning.

    Science.gov (United States)

    Singh, Anita

    2017-07-01

    Demand of biomedical engineers continues to rise to meet the needs of healthcare industry. Current training of bioengineers follows the traditional and dominant model of theory-focused curricula. However, the unmet needs of the healthcare industry warrant newer skill sets in these engineers. Translational training strategies such as solving real world problems through active, adaptive, and experiential learning hold promise. In this paper, we report our findings of adding a real-world 4-week problem-based learning unit into a biomechanics capstone course for engineering students. Surveys assessed student perceptions of the activity and learning experience. While students, across three cohorts, felt challenged to solve a real-world problem identified during the simulation lab visit, they felt more confident in utilizing knowledge learned in the biomechanics course and self-directed research. Instructor evaluations indicated that the active and experiential learning approach fostered their technical knowledge and life-long learning skills while exposing them to the components of adaptive learning and innovation.

  2. Sleep benefits consolidation of visuo-motor adaptation learning in older adults.

    Science.gov (United States)

    Mantua, Janna; Baran, Bengi; Spencer, Rebecca M C

    2016-02-01

    Sleep is beneficial for performance across a range of memory tasks in young adults, but whether memories are similarly consolidated in older adults is less clear. Performance benefits have been observed following sleep in older adults for declarative learning tasks, but this benefit may be reduced for non-declarative, motor skill learning tasks. To date, studies of sleep-dependent consolidation of motor learning in older adults are limited to motor sequence tasks. To examine whether reduced sleep-dependent consolidation in older adults is generalizable to other forms of motor skill learning, we examined performance changes over intervals of sleep and wake in young (n = 62) and older adults (n = 61) using a mirror-tracing task, which assesses visuo-motor adaptation learning. Participants learned the task either in the morning or in evening, and performance was assessed following a 12-h interval containing overnight sleep or daytime wake. Contrary to our prediction, both young adults and older adults exhibited sleep-dependent gains in visuo-motor adaptation. There was a correlation between performance improvement over sleep and percent of the night in non-REM stage 2 sleep. These results indicate that motor skill consolidation remains intact with increasing age although this relationship may be limited to specific forms of motor skill learning.

  3. Alignment Condition-Based Robust Adaptive Iterative Learning Control of Uncertain Robot System

    Directory of Open Access Journals (Sweden)

    Guofeng Tong

    2014-04-01

    Full Text Available This paper proposes an adaptive iterative learning control strategy integrated with saturation-based robust control for uncertain robot system in presence of modelling uncertainties, unknown parameter, and external disturbance under alignment condition. An important merit is that it achieves adaptive switching of gain matrix both in conventional PD-type feedforward control and robust adaptive control in the iteration domain simultaneously. The analysis of convergence of proposed control law is based on Lyapunov's direct method under alignment initial condition. Simulation results demonstrate the faster learning rate and better robust performance with proposed algorithm by comparing with other existing robust controllers. The actual experiment on three-DOF robot manipulator shows its better practical effectiveness.

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

    Directory of Open Access Journals (Sweden)

    Gimenez López Jose Luis

    2009-07-01

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

  5. Adaptive rival penalized competitive learning and combined linear predictor model for financial forecast and investment.

    Science.gov (United States)

    Cheung, Y M; Leung, W M; Xu, L

    1997-01-01

    We propose a prediction model called Rival Penalized Competitive Learning (RPCL) and Combined Linear Predictor method (CLP), which involves a set of local linear predictors such that a prediction is made by the combination of some activated predictors through a gating network (Xu et al., 1994). Furthermore, we present its improved variant named Adaptive RPCL-CLP that includes an adaptive learning mechanism as well as a data pre-and-post processing scheme. We compare them with some existing models by demonstrating their performance on two real-world financial time series--a China stock price and an exchange-rate series of US Dollar (USD) versus Deutschmark (DEM). Experiments have shown that Adaptive RPCL-CLP not only outperforms the other approaches with the smallest prediction error and training costs, but also brings in considerable high profits in the trading simulation of foreign exchange market.

  6. Strategies for adding adaptive learning mechanisms to rule-based diagnostic expert systems

    Science.gov (United States)

    Stclair, D. C.; Sabharwal, C. L.; Bond, W. E.; Hacke, Keith

    1988-01-01

    Rule-based diagnostic expert systems can be used to perform many of the diagnostic chores necessary in today's complex space systems. These expert systems typically take a set of symptoms as input and produce diagnostic advice as output. The primary objective of such expert systems is to provide accurate and comprehensive advice which can be used to help return the space system in question to nominal operation. The development and maintenance of diagnostic expert systems is time and labor intensive since the services of both knowledge engineer(s) and domain expert(s) are required. The use of adaptive learning mechanisms to increment evaluate and refine rules promises to reduce both time and labor costs associated with such systems. This paper describes the basic adaptive learning mechanisms of strengthening, weakening, generalization, discrimination, and discovery. Next basic strategies are discussed for adding these learning mechanisms to rule-based diagnostic expert systems. These strategies support the incremental evaluation and refinement of rules in the knowledge base by comparing the set of advice given by the expert system (A) with the correct diagnosis (C). Techniques are described for selecting those rules in the in the knowledge base which should participate in adaptive learning. The strategies presented may be used with a wide variety of learning algorithms. Further, these strategies are applicable to a large number of rule-based diagnostic expert systems. They may be used to provide either immediate or deferred updating of the knowledge base.

  7. Parameters identification of photovoltaic models using self-adaptive teaching-learning-based optimization

    International Nuclear Information System (INIS)

    Yu, Kunjie; Chen, Xu; Wang, Xin; Wang, Zhenlei

    2017-01-01

    Highlights: • SATLBO is proposed to identify the PV model parameters efficiently. • In SATLBO, the learners self-adaptively select different learning phases. • An elite learning is developed in teacher phase to perform local searching. • A diversity learning is proposed in learner phase to maintain population diversity. • SATLBO achieves the first in ranking on overall performance among nine algorithms. - Abstract: Parameters identification of photovoltaic (PV) model based on measured current-voltage characteristic curves plays an important role in the simulation and evaluation of PV systems. To accurately and reliably identify the PV model parameters, a self-adaptive teaching-learning-based optimization (SATLBO) is proposed in this paper. In SATLBO, the learners can self-adaptively select different learning phases based on their knowledge level. The better learners are more likely to choose the learner phase for improving the population diversity, while the worse learners tend to choose the teacher phase to enhance the convergence rate. Thus, learners at different levels focus on different searching abilities to efficiently enhance the performance of algorithm. In addition, to improve the searching ability of different learning phases, an elite learning strategy and a diversity learning method are introduced into the teacher phase and learner phase, respectively. The performance of SATLBO is firstly evaluated on 34 benchmark functions, and experimental results show that SATLBO achieves the first in ranking on the overall performance among nine algorithms. Then, SATLBO is employed to identify parameters of different PV models, i.e., single diode, double diode, and PV module. Experimental results indicate that SATLBO exhibits high accuracy and reliability compared with other parameter extraction methods.

  8. Adaptive learning in agents behaviour: A framework for electricity markets simulation

    DEFF Research Database (Denmark)

    Pinto, Tiago; Vale, Zita; Sousa, Tiago M.

    2014-01-01

    decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology...... that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management...... allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides...

  9. Cooperative Learning Groups and the Evolution of Human Adaptability : (Another Reason) Why Hermits Are Rare in Tonga and Elsewhere.

    Science.gov (United States)

    Bell, Adrian Viliami; Hernandez, Daniel

    2017-03-01

    Understanding the prevalence of adaptive culture in part requires understanding the dynamics of learning. Here we explore the adaptive value of social learning in groups and how formal social groups function as effective mediums of information exchange. We discuss the education literature on Cooperative Learning Groups (CLGs), which outlines the potential of group learning for enhancing learning outcomes. Four qualities appear essential for CLGs to enhance learning: (1) extended conversations, (2) regular interactions, (3) gathering of experts, and (4) incentives for sharing knowledge. We analyze these four qualities within the context of a small-scale agricultural society using data we collected in 2010 and 2012. Through an analysis of surveys, interviews, and observations in the Tongan islands, we describe the role CLGs likely plays in facilitating individuals' learning of adaptive information. Our analysis of group affiliation, membership, and topics of conversation suggest that the first three CLG qualities reflect conditions for adaptive learning in groups. We utilize ethnographic anecdotes to suggest the fourth quality is also conducive to adaptive group learning. Using an evolutionary model, we further explore the scope for CLGs outside the Tongan socioecological context. Model analysis shows that environmental volatility and migration rates among human groups mediate the scope for CLGs. We call for wider attention to how group structure facilitates learning in informal settings, which may be key to assessing the contribution of groups to the evolution of complex, adaptive culture.

  10. Adaptation.

    Science.gov (United States)

    Broom, Donald M

    2006-01-01

    The term adaptation is used in biology in three different ways. It may refer to changes which occur at the cell and organ level, or at the individual level, or at the level of gene action and evolutionary processes. Adaptation by cells, especially nerve cells helps in: communication within the body, the distinguishing of stimuli, the avoidance of overload and the conservation of energy. The time course and complexity of these mechanisms varies. Adaptive characters of organisms, including adaptive behaviours, increase fitness so this adaptation is evolutionary. The major part of this paper concerns adaptation by individuals and its relationships to welfare. In complex animals, feed forward control is widely used. Individuals predict problems and adapt by acting before the environmental effect is substantial. Much of adaptation involves brain control and animals have a set of needs, located in the brain and acting largely via motivational mechanisms, to regulate life. Needs may be for resources but are also for actions and stimuli which are part of the mechanism which has evolved to obtain the resources. Hence pigs do not just need food but need to be able to carry out actions like rooting in earth or manipulating materials which are part of foraging behaviour. The welfare of an individual is its state as regards its attempts to cope with its environment. This state includes various adaptive mechanisms including feelings and those which cope with disease. The part of welfare which is concerned with coping with pathology is health. Disease, which implies some significant effect of pathology, always results in poor welfare. Welfare varies over a range from very good, when adaptation is effective and there are feelings of pleasure or contentment, to very poor. A key point concerning the concept of individual adaptation in relation to welfare is that welfare may be good or poor while adaptation is occurring. Some adaptation is very easy and energetically cheap and

  11. Anatomy of Student Models in Adaptive Learning Systems: A Systematic Literature Review of Individual Differences from 2001 to 2013

    Science.gov (United States)

    Nakic, Jelena; Granic, Andrina; Glavinic, Vlado

    2015-01-01

    This study brings an evidence-based review of user individual characteristics employed as sources of adaptation in recent adaptive learning systems. Twenty-two user individual characteristics were explored in a systematically designed search procedure, while 17 of them were identified as sources of adaptation in final selection. The content…

  12. Study of Adolescents Perceived Parenting Styles Based on their Gender and Age

    OpenAIRE

    صادق تقی لو

    2017-01-01

    Parenting styles play a major role in determining the life styles of adolescents and that is why they share a special significance. The present study was done with the aim to investigate adolescents’ perceived parenting styles based on their gender and age. The study was conducted by a post-event method and with a sample size of 623 subjects (311 female and 312 male), who were selected by the multistage sampling method. Data were analyzed, after being collected by the Baumrind Parenting Style...

  13. A Case-Study for Life-Long Learning and Adaptation in Cooperative Robot Teams

    International Nuclear Information System (INIS)

    Parker, L.E.

    1999-01-01

    While considerable progress has been made in recent years toward the development of multi-robot teams, much work remains to be done before these teams are used widely in real-world applications. Two particular needs toward this end are the development of mechanisms that enable robot teams to generate cooperative behaviors on their own, and the development of techniques that allow these teams to autonomously adapt their behavior over time as the environment or the robot team changes. This paper proposes the use of the Cooperative Multi-Robot Observation of Multiple Moving Targets (CMOMMT) application as a rich domain for studying the issues of multi-robot learning and adaptation. After discussing the need for learning and adaptation in multi-robot teams, this paper describes the CMOMMT application and its relevance to multi-robot learning. We discuss the results of the previously- developed, hand-generated algorithm for CMOMMT and the potential for learning that was discovered from the hand-generated approach. We then describe the early work that has been done (by us and others) to generate multi- robot learning techniques for the CMOMMT application, as well as our ongoing research to develop approaches that give performance as good, or better, than the hand-generated approach. The ultimate goal of this research is to develop techniques for multi-robot learning and adaptation in the CMOMMT application domain that will generalize to cooperative robot applications in other domains, thus making the practical use of multi-robot teams in a wide variety of real-world applications much closer to reality

  14. Studying citizen science through adaptive management and learning feedbacks as mechanisms for improving conservation.

    Science.gov (United States)

    Jordan, Rebecca; Gray, Steven; Sorensen, Amanda; Newman, Greg; Mellor, David; Newman, Greg; Hmelo-Silver, Cindy; LaDeau, Shannon; Biehler, Dawn; Crall, Alycia

    2016-06-01

    Citizen science has generated a growing interest among scientists and community groups, and citizen science programs have been created specifically for conservation. We examined collaborative science, a highly interactive form of citizen science, which we developed within a theoretically informed framework. In this essay, we focused on 2 aspects of our framework: social learning and adaptive management. Social learning, in contrast to individual-based learning, stresses collaborative and generative insight making and is well-suited for adaptive management. Adaptive-management integrates feedback loops that are informed by what is learned and is guided by iterative decision making. Participants engaged in citizen science are able to add to what they are learning through primary data collection, which can result in the real-time information that is often necessary for conservation. Our work is particularly timely because research publications consistently report a lack of established frameworks and evaluation plans to address the extent of conservation outcomes in citizen science. To illustrate how our framework supports conservation through citizen science, we examined how 2 programs enacted our collaborative science framework. Further, we inspected preliminary conservation outcomes of our case-study programs. These programs, despite their recent implementation, are demonstrating promise with regard to positive conservation outcomes. To date, they are independently earning funds to support research, earning buy-in from local partners to engage in experimentation, and, in the absence of leading scientists, are collecting data to test ideas. We argue that this success is due to citizen scientists being organized around local issues and engaging in iterative, collaborative, and adaptive learning. © 2016 Society for Conservation Biology.

  15. MRSA model of learning and adaptation: a qualitative study among the general public

    Science.gov (United States)

    2012-01-01

    Background More people in the US now die from Methicillin Resistant Staphylococcus aureus (MRSA) infections than from HIV/AIDS. Often acquired in healthcare facilities or during healthcare procedures, the extremely high incidence of MRSA infections and the dangerously low levels of literacy regarding antibiotic resistance in the general public are on a collision course. Traditional medical approaches to infection control and the conventional attitude healthcare practitioners adopt toward public education are no longer adequate to avoid this collision. This study helps us understand how people acquire and process new information and then adapt behaviours based on learning. Methods Using constructivist theory, semi-structured face-to-face and phone interviews were conducted to gather pertinent data. This allowed participants to tell their stories so their experiences could deepen our understanding of this crucial health issue. Interview transcripts were analysed using grounded theory and sensitizing concepts. Results Our findings were classified into two main categories, each of which in turn included three subthemes. First, in the category of Learning, we identified how individuals used their Experiences with MRSA, to answer the questions: What was learned? and, How did learning occur? The second category, Adaptation gave us insights into Self-reliance, Reliance on others, and Reflections on the MRSA journey. Conclusions This study underscores the critical importance of educational programs for patients, and improved continuing education for healthcare providers. Five specific results of this study can reduce the vacuum that currently exists between the knowledge and information available to healthcare professionals, and how that information is conveyed to the public. These points include: 1) a common model of MRSA learning and adaptation; 2) the self-directed nature of adult learning; 3) the focus on general MRSA information, care and prevention, and antibiotic

  16. MRSA model of learning and adaptation: a qualitative study among the general public

    Directory of Open Access Journals (Sweden)

    Rohde Rodney E

    2012-04-01

    Full Text Available Abstract Background More people in the US now die from Methicillin Resistant Staphylococcus aureus (MRSA infections than from HIV/AIDS. Often acquired in healthcare facilities or during healthcare procedures, the extremely high incidence of MRSA infections and the dangerously low levels of literacy regarding antibiotic resistance in the general public are on a collision course. Traditional medical approaches to infection control and the conventional attitude healthcare practitioners adopt toward public education are no longer adequate to avoid this collision. This study helps us understand how people acquire and process new information and then adapt behaviours based on learning. Methods Using constructivist theory, semi-structured face-to-face and phone interviews were conducted to gather pertinent data. This allowed participants to tell their stories so their experiences could deepen our understanding of this crucial health issue. Interview transcripts were analysed using grounded theory and sensitizing concepts. Results Our findings were classified into two main categories, each of which in turn included three subthemes. First, in the category of Learning, we identified how individuals used their Experiences with MRSA, to answer the questions: What was learned? and, How did learning occur? The second category, Adaptation gave us insights into Self-reliance, Reliance on others, and Reflections on the MRSA journey. Conclusions This study underscores the critical importance of educational programs for patients, and improved continuing education for healthcare providers. Five specific results of this study can reduce the vacuum that currently exists between the knowledge and information available to healthcare professionals, and how that information is conveyed to the public. These points include: 1 a common model of MRSA learning and adaptation; 2 the self-directed nature of adult learning; 3 the focus on general MRSA information, care and

  17. Preventing KPI Violations in Business Processes based on Decision Tree Learning and Proactive Runtime Adaptation

    Directory of Open Access Journals (Sweden)

    Dimka Karastoyanova

    2012-01-01

    Full Text Available The performance of business processes is measured and monitored in terms of Key Performance Indicators (KPIs. If the monitoring results show that the KPI targets are violated, the underlying reasons have to be identified and the process should be adapted accordingly to address the violations. In this paper we propose an integrated monitoring, prediction and adaptation approach for preventing KPI violations of business process instances. KPIs are monitored continuously while the process is executed. Additionally, based on KPI measurements of historical process instances we use decision tree learning to construct classification models which are then used to predict the KPI value of an instance while it is still running. If a KPI violation is predicted, we identify adaptation requirements and adaptation strategies in order to prevent the violation.

  18. Towards more efficient e-learning, intelligence and adapted teaching material

    Directory of Open Access Journals (Sweden)

    Damir Kalpić

    2010-12-01

    Full Text Available This article presents results of a research project in which we attempted to determine the relationship between efficient E-learning and teaching materials adapted based on students’ structure of intelligence. The project was conducted on approximately 500 students, 23 classes, nine elementary schools, with ten teachers of history, informatics and several licensed psychologists. E-teaching material was prepared for the subject of History for eight-grade students of elementary school. Students were tested for the structure of intelligence, and based on their most prominent component, they were divided into groups, using teaching materials adapted to their most prominent intelligence component. The results have shown that use of the adapted teaching materials achieved 6-12% better results than E-materials not adapted to students’ structure of intelligence.

  19. Adaptation

    International Development Research Centre (IDRC) Digital Library (Canada)

    building skills, knowledge or networks on adaptation, ... the African partners leading the AfricaAdapt network, together with the UK-based Institute of Development Studies; and ... UNCCD Secretariat, Regional Coordination Unit for Africa, Tunis, Tunisia .... 26 Rural–urban Cooperation on Water Management in the Context of.

  20. Towards Individualized Online Learning: The Design and Development of an Adaptive Web Based Learning Environment

    Science.gov (United States)

    Inan, Fethi A.; Flores, Raymond; Ari, Fatih; Arslan-Ari, Ismahan

    2011-01-01

    The purpose of this study was to document the design and development of an adaptive system which individualizes instruction such as content, interfaces, instructional strategies, and resources dependent on two factors, namely student motivation and prior knowledge levels. Combining adaptive hypermedia methods with strategies proposed by…

  1. Older adults learn less, but still reduce metabolic cost, during motor adaptation

    Science.gov (United States)

    Huang, Helen J.

    2013-01-01

    The ability to learn new movements and dynamics is important for maintaining independence with advancing age. Age-related sensorimotor changes and increased muscle coactivation likely alter the trial-and-error-based process of adapting to new movement demands (motor adaptation). Here, we asked, to what extent is motor adaptation to novel dynamics maintained in older adults (≥65 yr)? We hypothesized that older adults would adapt to the novel dynamics less well than young adults. Because older adults often use muscle coactivation, we expected older adults to use greater muscle coactivation during motor adaptation than young adults. Nevertheless, we predicted that older adults would reduce muscle activity and metabolic cost with motor adaptation, similar to young adults. Seated older (n = 11, 73.8 ± 5.6 yr) and young (n = 15, 23.8 ± 4.7 yr) adults made targeted reaching movements while grasping a robotic arm. We measured their metabolic rate continuously via expired gas analysis. A force field was used to add novel dynamics. Older adults had greater movement deviations and compensated for just 65% of the novel dynamics compared with 84% in young adults. As expected, older adults used greater muscle coactivation than young adults. Last, older adults reduced muscle activity with motor adaptation and had consistent reductions in metabolic cost later during motor adaptation, similar to young adults. These results suggest that despite increased muscle coactivation, older adults can adapt to the novel dynamics, albeit less accurately. These results also suggest that reductions in metabolic cost may be a fundamental feature of motor adaptation. PMID:24133222

  2. Effects of practice schedule and task specificity on the adaptive process of motor learning.

    Science.gov (United States)

    Barros, João Augusto de Camargo; Tani, Go; Corrêa, Umberto Cesar

    2017-10-01

    This study investigated the effects of practice schedule and task specificity based on the perspective of adaptive process of motor learning. For this purpose, tasks with temporal and force control learning requirements were manipulated in experiments 1 and 2, respectively. Specifically, the task consisted of touching with the dominant hand the three sequential targets with specific movement time or force for each touch. Participants were children (N=120), both boys and girls, with an average age of 11.2years (SD=1.0). The design in both experiments involved four practice groups (constant, random, constant-random, and random-constant) and two phases (stabilisation and adaptation). The dependent variables included measures related to the task goal (accuracy and variability of error of the overall movement and force patterns) and movement pattern (macro- and microstructures). Results revealed a similar error of the overall patterns for all groups in both experiments and that they adapted themselves differently in terms of the macro- and microstructures of movement patterns. The study concludes that the effects of practice schedules on the adaptive process of motor learning were both general and specific to the task. That is, they were general to the task goal performance and specific regarding the movement pattern. Copyright © 2017 Elsevier B.V. All rights reserved.

  3. Basal ganglia-dependent processes in recalling learned visual-motor adaptations.

    Science.gov (United States)

    Bédard, Patrick; Sanes, Jerome N

    2011-03-01

    Humans learn and remember motor skills to permit adaptation to a changing environment. During adaptation, the brain develops new sensory-motor relationships that become stored in an internal model (IM) that may be retained for extended periods. How the brain learns new IMs and transforms them into long-term memory remains incompletely understood since prior work has mostly focused on the learning process. A current model suggests that basal ganglia, cerebellum, and their neocortical targets actively participate in forming new IMs but that a cerebellar cortical network would mediate automatization. However, a recent study (Marinelli et al. 2009) reported that patients with Parkinson's disease (PD), who have basal ganglia dysfunction, had similar adaptation rates as controls but demonstrated no savings at recall tests (24 and 48 h). Here, we assessed whether a longer training session, a feature known to increase long-term retention of IM in healthy individuals, could allow PD patients to demonstrate savings. We recruited PD patients and age-matched healthy adults and used a visual-motor adaptation paradigm similar to the study by Marinelli et al. (2009), doubling the number of training trials and assessed recall after a short and a 24-h delay. We hypothesized that a longer training session would allow PD patients to develop an enhanced representation of the IM as demonstrated by savings at the recall tests. Our results showed that PD patients had similar adaptation rates as controls but did not demonstrate savings at both recall tests. We interpret these results as evidence that fronto-striatal networks have involvement in the early to late phase of motor memory formation, but not during initial learning.

  4. Evaluation of an Adaptive Learning Technology in a First-year Extended Curriculum Programme Physics course

    Directory of Open Access Journals (Sweden)

    Moses Mushe Basitere

    2017-12-01

    Full Text Available Personalised, adaptive online learning platforms that form part of web-based proficiency tests play a major role in the improvement of the quality of learning in physics and assist learners in building proficiency, preparing for tests and using their time more effectively. In this study, the effectiveness of an adaptive learning platform, Wiley Plus ORION, was evaluated using proficiency test scores compared to paper-based test scores in a first-year introductory engineering physics course. Learners’ performance activities on the adaptive learning platform as well as their performance on the proficiency tests and their impact on the paper-based midterm averaged test were investigated using both qualitative and quantitative methods of data collection. A comparison between learners’ performance on the proficiency tests and a paper-based midterm test was done to evaluate whether there was a correlation between their performance on the proficiency tests and the midterm test. Focus group interviews were carried out with three categories of learners to elicit their experiences. Results showed that there was a positive relationship between high-performing learners’ proficiency score in the midterm averaged test and that the proficiency test enhanced learners’ performance in the paper-based midterm averaged test.

  5. Investigating the Impact of Formal Reflective Activities on Skill Adaptation in a Work-Related Instrumental Learning Setting

    Science.gov (United States)

    Roessger, Kevin M.

    2013-01-01

    In work-related, instrumental learning contexts the role of reflective activities is unclear. Kolb's (1985) experiential learning theory and Mezirow's transformative learning theory (2000) predict skill-adaptation as a possible outcome. This prediction was experimentally explored by manipulating reflective activities and assessing participants'…

  6. A single-rate context-dependent learning process underlies rapid adaptation to familiar object dynamics.

    Science.gov (United States)

    Ingram, James N; Howard, Ian S; Flanagan, J Randall; Wolpert, Daniel M

    2011-09-01

    Motor learning has been extensively studied using dynamic (force-field) perturbations. These induce movement errors that result in adaptive changes to the motor commands. Several state-space models have been developed to explain how trial-by-trial errors drive the progressive adaptation observed in such studies. These models have been applied to adaptation involving novel dynamics, which typically occurs over tens to hundreds of trials, and which appears to be mediated by a dual-rate adaptation process. In contrast, when manipulating objects with familiar dynamics, subjects adapt rapidly within a few trials. Here, we apply state-space models to familiar dynamics, asking whether adaptation is mediated by a single-rate or dual-rate process. Previously, we reported a task in which subjects rotate an object with known dynamics. By presenting the object at different visual orientations, adaptation was shown to be context-specific, with limited generalization to novel orientations. Here we show that a multiple-context state-space model, with a generalization function tuned to visual object orientation, can reproduce the time-course of adaptation and de-adaptation as well as the observed context-dependent behavior. In contrast to the dual-rate process associated with novel dynamics, we show that a single-rate process mediates adaptation to familiar object dynamics. The model predicts that during exposure to the object across multiple orientations, there will be a degree of independence for adaptation and de-adaptation within each context, and that the states associated with all contexts will slowly de-adapt during exposure in one particular context. We confirm these predictions in two new experiments. Results of the current study thus highlight similarities and differences in the processes engaged during exposure to novel versus familiar dynamics. In both cases, adaptation is mediated by multiple context-specific representations. In the case of familiar object dynamics

  7. A single-rate context-dependent learning process underlies rapid adaptation to familiar object dynamics.

    Directory of Open Access Journals (Sweden)

    James N Ingram

    2011-09-01

    Full Text Available Motor learning has been extensively studied using dynamic (force-field perturbations. These induce movement errors that result in adaptive changes to the motor commands. Several state-space models have been developed to explain how trial-by-trial errors drive the progressive adaptation observed in such studies. These models have been applied to adaptation involving novel dynamics, which typically occurs over tens to hundreds of trials, and which appears to be mediated by a dual-rate adaptation process. In contrast, when manipulating objects with familiar dynamics, subjects adapt rapidly within a few trials. Here, we apply state-space models to familiar dynamics, asking whether adaptation is mediated by a single-rate or dual-rate process. Previously, we reported a task in which subjects rotate an object with known dynamics. By presenting the object at different visual orientations, adaptation was shown to be context-specific, with limited generalization to novel orientations. Here we show that a multiple-context state-space model, with a generalization function tuned to visual object orientation, can reproduce the time-course of adaptation and de-adaptation as well as the observed context-dependent behavior. In contrast to the dual-rate process associated with novel dynamics, we show that a single-rate process mediates adaptation to familiar object dynamics. The model predicts that during exposure to the object across multiple orientations, there will be a degree of independence for adaptation and de-adaptation within each context, and that the states associated with all contexts will slowly de-adapt during exposure in one particular context. We confirm these predictions in two new experiments. Results of the current study thus highlight similarities and differences in the processes engaged during exposure to novel versus familiar dynamics. In both cases, adaptation is mediated by multiple context-specific representations. In the case of familiar

  8. Active and Adaptive Learning from Biased Data with Applications in Astronomy

    DEFF Research Database (Denmark)

    Kremer, Jan

    This thesis addresses the problem of machine learning from biased datasets in the context of astronomical applications. In astronomy there are many cases in which the training sample does not follow the true distribution. The thesis examines different types of biases and proposes algorithms...... set. Against this background, the thesis begins with a survey of active learning algorithms for the support vector machine. If the cost of additional labeling is prohibitive, unlabeled data can often be utilized instead and the sample selection bias can be overcome through domain adaptation, that is...... to handle them. During learning and when applying the predictive model, active learning enables algorithms to select training examples from a pool of unlabeled data and to request the labels. This allows for selecting examples that maximize the algorithm's accuracy despite an initial bias in the training...

  9. Peer Pressure and Adaptive Behavior Learning: A Study of Adolescents in Gujrat City

    OpenAIRE

    Asma Yunus; Shahzad Khaver Mushtaq; Sobia Qaiser

    2012-01-01

    The study aims at discovering the influences of Peer Pressure on adaptive behavior learning in the adolescents. For the purpose two scales, Adaptive behavior scale (ABS) and Peer Pressure Scale (PPS) were developed to measure both variables. The Sample of the study was purposive in nature and comprised of late adolescents (n=120) i.e. 60 males and 60 females, from Gujrat city. Cronbach alpha was calculated and found to be significant for Peer Pressure Scale(PPS) and its subscales i.e. Belongi...

  10. The cerebellum does more than sensory prediction error-based learning in sensorimotor adaptation tasks.

    Science.gov (United States)

    Butcher, Peter A; Ivry, Richard B; Kuo, Sheng-Han; Rydz, David; Krakauer, John W; Taylor, Jordan A

    2017-09-01

    Individuals with damage to the cerebellum perform poorly in sensorimotor adaptation paradigms. This deficit has been attributed to impairment in sensory prediction error-based updating of an internal forward model, a form of implicit learning. These individuals can, however, successfully counter a perturbation when instructed with an explicit aiming strategy. This successful use of an instructed aiming strategy presents a paradox: In adaptation tasks, why do individuals with cerebellar damage not come up with an aiming solution on their own to compensate for their implicit learning deficit? To explore this question, we employed a variant of a visuomotor rotation task in which, before executing a movement on each trial, the participants verbally reported their intended aiming location. Compared with healthy control participants, participants with spinocerebellar ataxia displayed impairments in both implicit learning and aiming. This was observed when the visuomotor rotation was introduced abruptly ( experiment 1 ) or gradually ( experiment 2 ). This dual deficit does not appear to be related to the increased movement variance associated with ataxia: Healthy undergraduates showed little change in implicit learning or aiming when their movement feedback was artificially manipulated to produce similar levels of variability ( experiment 3 ). Taken together the results indicate that a consequence of cerebellar dysfunction is not only impaired sensory prediction error-based learning but also a difficulty in developing and/or maintaining an aiming solution in response to a visuomotor perturbation. We suggest that this dual deficit can be explained by the cerebellum forming part of a network that learns and maintains action-outcome associations across trials. NEW & NOTEWORTHY Individuals with cerebellar pathology are impaired in sensorimotor adaptation. This deficit has been attributed to an impairment in error-based learning, specifically, from a deficit in using sensory

  11. Learning from Learning: the experiences with implementing Adaptive Collaborative Forest Management in Zimbabwe.

    NARCIS (Netherlands)

    Mutimukuru, T.; Almekinders, C.J.M.

    2011-01-01

    Convinced that participatory resource management is the way forward in the conservation of natural resources, despite the increasing criticism of participatory approaches, the Centre for International Forestry Research (CIFOR) initiated a multi-country adaptive collaborative management (ACM)

  12. Organising for Learning - Adaptive and Innovative Learning in Customer-Supplier Relationships

    DEFF Research Database (Denmark)

    Christensen, Poul Rind; Damgaard, Torben; Munksgaard, Kristin B.

    2004-01-01

    Based on studies of supplier associations, the concepts of adaptived and innovative learning in an interoganisational setting are defined and discussed.......Based on studies of supplier associations, the concepts of adaptived and innovative learning in an interoganisational setting are defined and discussed....

  13. A Fuzzy Logic-Based Personalized Learning System for Supporting Adaptive English Learning

    Science.gov (United States)

    Hsieh, Tung-Cheng; Wang, Tzone-I; Su, Chien-Yuan; Lee, Ming-Che

    2012-01-01

    As a nearly global language, English as a Foreign Language (EFL) programs are essential for people wishing to learn English. Researchers have noted that extensive reading is an effective way to improve a person's command of English. Choosing suitable articles in accordance with a learner's needs, interests and ability using an e-learning system…

  14. Teaching and Learning Hand in Hand: Adaptive Teaching and Self-Regulated Learning

    Science.gov (United States)

    Randi, Judi

    2017-01-01

    This article presents case studies of two novice teachers and their mentors who, without formal knowledge of self-regulation theory, established a classroom environment that promoted self-regulated learning. This case was drawn from a larger descriptive study of novice teachers learning to integrate a student-centered visual literacy instructional…

  15. Adaptive enhancement of learning protocol in hippocampal cultured networks grown on multielectrode arrays

    Science.gov (United States)

    Pimashkin, Alexey; Gladkov, Arseniy; Mukhina, Irina; Kazantsev, Victor

    2013-01-01

    Learning in neuronal networks can be investigated using dissociated cultures on multielectrode arrays supplied with appropriate closed-loop stimulation. It was shown in previous studies that weakly respondent neurons on the electrodes can be trained to increase their evoked spiking rate within a predefined time window after the stimulus. Such neurons can be associated with weak synaptic connections in nearby culture network. The stimulation leads to the increase in the connectivity and in the response. However, it was not possible to perform the learning protocol for the neurons on electrodes with relatively strong synaptic inputs and responding at higher rates. We proposed an adaptive closed-loop stimulation protocol capable to achieve learning even for the highly respondent electrodes. It means that the culture network can reorganize appropriately its synaptic connectivity to generate a desired response. We introduced an adaptive reinforcement condition accounting for the response variability in control stimulation. It significantly enhanced the learning protocol to a large number of responding electrodes independently on its base response level. We also found that learning effect preserved after 4–6 h after training. PMID:23745105

  16. Adaptive learning in a compartmental model of visual cortex—how feedback enables stable category learning and refinement

    Science.gov (United States)

    Layher, Georg; Schrodt, Fabian; Butz, Martin V.; Neumann, Heiko

    2014-01-01

    The categorization of real world objects is often reflected in the similarity of their visual appearances. Such categories of objects do not necessarily form disjunct sets of objects, neither semantically nor visually. The relationship between categories can often be described in terms of a hierarchical structure. For instance, tigers and leopards build two separate mammalian categories, both of which are subcategories of the category Felidae. In the last decades, the unsupervised learning of categories of visual input stimuli has been addressed by numerous approaches in machine learning as well as in computational neuroscience. However, the question of what kind of mechanisms might be involved in the process of subcategory learning, or category refinement, remains a topic of active investigation. We propose a recurrent computational network architecture for the unsupervised learning of categorial and subcategorial visual input representations. During learning, the connection strengths of bottom-up weights from input to higher-level category representations are adapted according to the input activity distribution. In a similar manner, top-down weights learn to encode the characteristics of a specific stimulus category. Feedforward and feedback learning in combination realize an associative memory mechanism, enabling the selective top-down propagation of a category's feedback weight distribution. We suggest that the difference between the expected input encoded in the projective field of a category node and the current input pattern controls the amplification of feedforward-driven representations. Large enough differences trigger the recruitment of new representational resources and the establishment of additional (sub-) category representations. We demonstrate the temporal evolution of such learning and show how the proposed combination of an associative memory with a modulatory feedback integration successfully establishes category and subcategory representations

  17. Adaptive learning in a compartmental model of visual cortex - how feedback enables stable category learning and refinement

    Directory of Open Access Journals (Sweden)

    Georg eLayher

    2014-12-01

    Full Text Available The categorization of real world objects is often reflected in the similarity of their visual appearances. Such categories of objects do not necessarily form disjunct sets of objects, neither semantically nor visually. The relationship between categories can often be described in terms of a hierarchical structure. For instance, tigers and leopards build two separate mammalian categories, but both belong to the category of felines. In other words, tigers and leopards are subcategories of the category Felidae. In the last decades, the unsupervised learning of categories of visual input stimuli has been addressed by numerous approaches in machine learning as well as in the computational neurosciences. However, the question of what kind of mechanisms might be involved in the process of subcategory learning, or category refinement, remains a topic of active investigation. We propose a recurrent computational network architecture for the unsupervised learning of categorial and subcategorial visual input representations. During learning, the connection strengths of bottom-up weights from input to higher-level category representations are adapted according to the input activity distribution. In a similar manner, top-down weights learn to encode the characteristics of a specific stimulus category. Feedforward and feedback learning in combination realize an associative memory mechanism, enabling the selective top-down propagation of a category's feedback weight distribution. We suggest that the difference between the expected input encoded in the projective field of a category node and the current input pattern controls the amplification of feedforward-driven representations. Large enough differences trigger the recruitment of new representational resources and the establishment of (sub- category representations. We demonstrate the temporal evolution of such learning and show how the approach successully establishes category and subcategory

  18. Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope

    Directory of Open Access Journals (Sweden)

    Bin Xu

    2017-01-01

    Full Text Available This paper investigates an adaptive neural sliding mode controller for MEMS gyroscopes with minimal-learning-parameter technique. Considering the system uncertainty in dynamics, neural network is employed for approximation. Minimal-learning-parameter technique is constructed to decrease the number of update parameters, and in this way the computation burden is greatly reduced. Sliding mode control is designed to cancel the effect of time-varying disturbance. The closed-loop stability analysis is established via Lyapunov approach. Simulation results are presented to demonstrate the effectiveness of the method.

  19. Error-Induced Learning as a Resource-Adaptive Process in Young and Elderly Individuals

    Science.gov (United States)

    Ferdinand, Nicola K.; Weiten, Anja; Mecklinger, Axel; Kray, Jutta

    Thorndike described in his law of effect [44] that actions followed by positive events are more likely to be repeated in the future, whereas actions that are followed by negative outcomes are less likely to be repeated. This implies that behavior is evaluated in the light of its potential consequences, and non-reward events (i.e., errors) must be detected for reinforcement learning to take place. In short, humans have to monitor their performance in order to detect and correct errors, and this allows them to successfully adapt their behavior to changing environmental demands and acquire new behavior, i.e., to learn.

  20. AN INDUCTIVE, INTERACTIVE AND ADAPTIVE HYBRID PROBLEM-BASED LEARNING METHODOLOGY: APPLICATION TO STATISTICS

    Directory of Open Access Journals (Sweden)

    ADA ZHENG

    2011-10-01

    Full Text Available We have developed an innovative hybrid problem-based learning (PBL methodology. The methodology has the following distinctive features: i Each complex question was decomposed into a set of coherent finer subquestions by following the carefully designed criteria to maintain a delicate balance between guiding the students and inspiring them to think independently. This learning methodology enabled the students to solve the complex questions progressively in an inductive context. ii Facilitated by the utilization of our web-based learning systems, the teacher was able to interact with the students intensively and could allocate more teaching time to provide tailor-made feedback for individual student. The students were actively engaged in the learning activities, stimulated by the intensive interaction. iii The answers submitted by the students could be automatically consolidated in the report of the Moodle system in real-time. The teacher could adjust the teaching schedule and focus of the class to adapt to the learning progress of the students by analysing the automatically generated report and log files of the web-based learning system. As a result, the attendance rate of the students increased from about 50% to more than 90%, and the students’ learning motivation have been significantly enhanced.

  1. Adaptive eLearning modules for cytopathology education: A review and approach.

    Science.gov (United States)

    Samulski, T Danielle; La, Teresa; Wu, Roseann I

    2016-11-01

    Clinical training imposes time and resource constraints on educators and learners, making it difficult to provide and absorb meaningful instruction. Additionally, innovative and personalized education has become an expectation of adult learners. Fortunately, the development of web-based educational tools provides a possible solution to these challenges. Within this review, we introduce the utility of adaptive eLearning platforms in pathology education. In addition to a review of the current literature, we provide the reader with a suggested approach for module creation, as well as a critical assessment of an available platform, based on our experience in creating adaptive eLearning modules for teaching basic concepts in gynecologic cytopathology. Diagn. Cytopathol. 2016;44:944-951. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  2. Adaptive and Energy Efficient Walking in a Hexapod Robot under Neuromechanical Control and Sensorimotor Learning

    DEFF Research Database (Denmark)

    Xiong, Xiaofeng; Wörgötter, Florentin; Manoonpong, Poramate

    2016-01-01

    The control of multilegged animal walking is a neuromechanical process, and to achieve this in an adaptive and energy efficient way is a difficult and challenging problem. This is due to the fact that this process needs in real time: 1) to coordinate very many degrees of freedom of jointed legs; 2......) to generate the proper leg stiffness (i.e., compliance); and 3) to determine joint angles that give rise to particular positions at the endpoints of the legs. To tackle this problem for a robotic application, here we present a neuromechanical controller coupled with sensorimotor learning. The controller...... energy efficient walking, compared to other small legged robots. In addition, this paper also shows that the tight combination of neural control with tunable muscle-like functions, guided by sensory feedback and coupled with sensorimotor learning, is a way forward to better understand and solve adaptive...

  3. Distance learning education for mitigation/adaptation policy: a case study

    Science.gov (United States)

    Slini, T.; Giama, E.; Papadopoulou, Ch.-O.

    2016-02-01

    The efficient training of young environmental scientists has proven to be a challenging goal over the last years, while several dynamic initiatives have been developed aiming to provide complete and consistent education. A successful example is the e-learning course for participants mainly coming from emerging economy countries 'Development of mitigation/adaptation policy portfolios' organised in the frame of the project Promitheas4: Knowledge transfer and research needs for preparing mitigation/adaptation policy portfolios, aiming to provide knowledge transfer, enhance new skills and competencies, using modern didactic approaches and learning technologies. The present paper addresses the experience and the results of these actions, which seem promising and encouraging and were broadly welcomed by the participants.

  4. Do students’ styles of learning affect how they adapt to learning methods and to the learning environment?

    OpenAIRE

    Topal, Kenan; Sarıkaya, Özlem; Basturk, Ramazan; Buke, Akile

    2015-01-01

    Objectives: The process of development and evaluation of undergraduate medical education programs should include analysis of learners’ characteristics, needs, and perceptions about learning methods. This study aims to evaluate medical students’ perceptions about problem-based learning methods and to compare these results with their individual learning styles.Materials and Methods: The survey was conducted at Marmara University Medical School where problem-based learning was implemented in the...

  5. Perceptions of Preservice Teachers about Adaptive Learning Programs in K-8 Mathematics Education

    OpenAIRE

    Smith, Kevin

    2018-01-01

    Adaptivelearning programs are frequently used in the K-8 mathematics classroom. Theseprograms provide instruction to students at the appropriate level of difficultyby presenting content, providing feedback, and allowing students to masterskills before progressing. The purpose of the study was to seek to interprethow preservice teachers’ experiences influence their perceptions and plans tointegrate adaptive learning programs in their future K-8 mathematics classroom.This was a qualitative stud...

  6. Adapt

    Science.gov (United States)

    Bargatze, L. F.

    2015-12-01

    Active Data Archive Product Tracking (ADAPT) is a collection of software routines that permits one to generate XML metadata files to describe and register data products in support of the NASA Heliophysics Virtual Observatory VxO effort. ADAPT is also a philosophy. The ADAPT concept is to use any and all available metadata associated with scientific data to produce XML metadata descriptions in a consistent, uniform, and organized fashion to provide blanket access to the full complement of data stored on a targeted data server. In this poster, we present an application of ADAPT to describe all of the data products that are stored by using the Common Data File (CDF) format served out by the CDAWEB and SPDF data servers hosted at the NASA Goddard Space Flight Center. These data servers are the primary repositories for NASA Heliophysics data. For this purpose, the ADAPT routines have been used to generate data resource descriptions by using an XML schema named Space Physics Archive, Search, and Extract (SPASE). SPASE is the designated standard for documenting Heliophysics data products, as adopted by the Heliophysics Data and Model Consortium. The set of SPASE XML resource descriptions produced by ADAPT includes high-level descriptions of numerical data products, display data products, or catalogs and also includes low-level "Granule" descriptions. A SPASE Granule is effectively a universal access metadata resource; a Granule associates an individual data file (e.g. a CDF file) with a "parent" high-level data resource description, assigns a resource identifier to the file, and lists the corresponding assess URL(s). The CDAWEB and SPDF file systems were queried to provide the input required by the ADAPT software to create an initial set of SPASE metadata resource descriptions. Then, the CDAWEB and SPDF data repositories were queried subsequently on a nightly basis and the CDF file lists were checked for any changes such as the occurrence of new, modified, or deleted

  7. A planning quality evaluation tool for prostate adaptive IMRT based on machine learning

    International Nuclear Information System (INIS)

    Zhu Xiaofeng; Ge Yaorong; Li Taoran; Thongphiew, Danthai; Yin Fangfang; Wu, Q Jackie

    2011-01-01

    Purpose: To ensure plan quality for adaptive IMRT of the prostate, we developed a quantitative evaluation tool using a machine learning approach. This tool generates dose volume histograms (DVHs) of organs-at-risk (OARs) based on prior plans as a reference, to be compared with the adaptive plan derived from fluence map deformation. Methods: Under the same configuration using seven-field 15 MV photon beams, DVHs of OARs (bladder and rectum) were estimated based on anatomical information of the patient and a model learned from a database of high quality prior plans. In this study, the anatomical information was characterized by the organ volumes and distance-to-target histogram (DTH). The database consists of 198 high quality prostate plans and was validated with 14 cases outside the training pool. Principal component analysis (PCA) was applied to DVHs and DTHs to quantify their salient features. Then, support vector regression (SVR) was implemented to establish the correlation between the features of the DVH and the anatomical information. Results: DVH/DTH curves could be characterized sufficiently just using only two or three truncated principal components, thus, patient anatomical information was quantified with reduced numbers of variables. The evaluation of the model using the test data set demonstrated its accuracy ∼80% in prediction and effectiveness in improving ART planning quality. Conclusions: An adaptive IMRT plan quality evaluation tool based on machine learning has been developed, which estimates OAR sparing and provides reference in evaluating ART.

  8. Providing QoS through machine-learning-driven adaptive multimedia applications.

    Science.gov (United States)

    Ruiz, Pedro M; Botía, Juan A; Gómez-Skarmeta, Antonio

    2004-06-01

    We investigate the optimization of the quality of service (QoS) offered by real-time multimedia adaptive applications through machine learning algorithms. These applications are able to adapt in real time their internal settings (i.e., video sizes, audio and video codecs, among others) to the unpredictably changing capacity of the network. Traditional adaptive applications just select a set of settings to consume less than the available bandwidth. We propose a novel approach in which the selected set of settings is the one which offers a better user-perceived QoS among all those combinations which satisfy the bandwidth restrictions. We use a genetic algorithm to decide when to trigger the adaptation process depending on the network conditions (i.e., loss-rate, jitter, etc.). Additionally, the selection of the new set of settings is done according to a set of rules which model the user-perceived QoS. These rules are learned using the SLIPPER rule induction algorithm over a set of examples extracted from scores provided by real users. We will demonstrate that the proposed approach guarantees a good user-perceived QoS even when the network conditions are constantly changing.

  9. The dynamic interplay among EFL learners’ ambiguity tolerance, adaptability, cultural intelligence, learning approach, and language achievement

    Directory of Open Access Journals (Sweden)

    Shadi Alahdadi

    2017-01-01

    Full Text Available A key objective of education is to prepare individuals to be fully-functioning learners. This entails developing the cognitive, metacognitive, motivational, cultural, and emotional competencies. The present study aimed to examine the interrelationships among adaptability, tolerance of ambiguity, cultural intelligence, learning approach, and language achievement as manifestations of the above competencies within a single model. The participants comprised one hundred eighty BA and MA Iranian university students studying English language teaching and translation. The instruments used in this study consisted of the translated versions of four questionnaires: second language tolerance of ambiguity scale, adaptability taken from emotional intelligence inventory, cultural intelligence (CQ inventory, and the revised study process questionnaire measuring surface and deep learning. The results estimated via structural equation modeling (SEM revealed that the proposed model containing the variables under study had a good fit with the data. It was found that all the variables except adaptability directly influenced language achievement with deep approach having the highest impact and ambiguity tolerance having the lowest influence. In addition, ambiguity tolerance was a positive and significant predictor of deep approach. CQ was found to be under the influence of both ambiguity tolerance and adaptability. The findings were discussed in the light of the yielded results.

  10. Neural robust stabilization via event-triggering mechanism and adaptive learning technique.

    Science.gov (United States)

    Wang, Ding; Liu, Derong

    2018-06-01

    The robust control synthesis of continuous-time nonlinear systems with uncertain term is investigated via event-triggering mechanism and adaptive critic learning technique. We mainly focus on combining the event-triggering mechanism with adaptive critic designs, so as to solve the nonlinear robust control problem. This can not only make better use of computation and communication resources, but also conduct controller design from the view of intelligent optimization. Through theoretical analysis, the nonlinear robust stabilization can be achieved by obtaining an event-triggered optimal control law of the nominal system with a newly defined cost function and a certain triggering condition. The adaptive critic technique is employed to facilitate the event-triggered control design, where a neural network is introduced as an approximator of the learning phase. The performance of the event-triggered robust control scheme is validated via simulation studies and comparisons. The present method extends the application domain of both event-triggered control and adaptive critic control to nonlinear systems possessing dynamical uncertainties. Copyright © 2018 Elsevier Ltd. All rights reserved.

  11. Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments

    OpenAIRE

    Kidziński, Łukasz; Mohanty, Sharada Prasanna; Ong, Carmichael; Huang, Zhewei; Zhou, Shuchang; Pechenko, Anton; Stelmaszczyk, Adam; Jarosik, Piotr; Pavlov, Mikhail; Kolesnikov, Sergey; Plis, Sergey; Chen, Zhibo; Zhang, Zhizheng; Chen, Jiale; Shi, Jun

    2018-01-01

    In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course. Top participants were invited to describe their algorithms. In this work, we present eight solutions that used deep reinforcement learning approaches, based on algorithms such as Deep Deterministic Policy Gradient, Proximal Policy Optimization, and Trust Region Policy Optimization. Many solutions use similar ...

  12. L1-norm locally linear representation regularization multi-source adaptation learning.

    Science.gov (United States)

    Tao, Jianwen; Wen, Shiting; Hu, Wenjun

    2015-09-01

    In most supervised domain adaptation learning (DAL) tasks, one has access only to a small number of labeled examples from target domain. Therefore the success of supervised DAL in this "small sample" regime needs the effective utilization of the large amounts of unlabeled data to extract information that is useful for generalization. Toward this end, we here use the geometric intuition of manifold assumption to extend the established frameworks in existing model-based DAL methods for function learning by incorporating additional information about the target geometric structure of the marginal distribution. We would like to ensure that the solution is smooth with respect to both the ambient space and the target marginal distribution. In doing this, we propose a novel L1-norm locally linear representation regularization multi-source adaptation learning framework which exploits the geometry of the probability distribution, which has two techniques. Firstly, an L1-norm locally linear representation method is presented for robust graph construction by replacing the L2-norm reconstruction measure in LLE with L1-norm one, which is termed as L1-LLR for short. Secondly, considering the robust graph regularization, we replace traditional graph Laplacian regularization with our new L1-LLR graph Laplacian regularization and therefore construct new graph-based semi-supervised learning framework with multi-source adaptation constraint, which is coined as L1-MSAL method. Moreover, to deal with the nonlinear learning problem, we also generalize the L1-MSAL method by mapping the input data points from the input space to a high-dimensional reproducing kernel Hilbert space (RKHS) via a nonlinear mapping. Promising experimental results have been obtained on several real-world datasets such as face, visual video and object. Copyright © 2015 Elsevier Ltd. All rights reserved.

  13. Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization

    KAUST Repository

    Wang, Jim Jing-Yan

    2014-09-20

    Nonnegative matrix factorization (NMF), a popular part-based representation technique, does not capture the intrinsic local geometric structure of the data space. Graph regularized NMF (GNMF) was recently proposed to avoid this limitation by regularizing NMF with a nearest neighbor graph constructed from the input data set. However, GNMF has two main bottlenecks. First, using the original feature space directly to construct the graph is not necessarily optimal because of the noisy and irrelevant features and nonlinear distributions of data samples. Second, one possible way to handle the nonlinear distribution of data samples is by kernel embedding. However, it is often difficult to choose the most suitable kernel. To solve these bottlenecks, we propose two novel graph-regularized NMF methods, AGNMFFS and AGNMFMK, by introducing feature selection and multiple-kernel learning to the graph regularized NMF, respectively. Instead of using a fixed graph as in GNMF, the two proposed methods learn the nearest neighbor graph that is adaptive to the selected features and learned multiple kernels, respectively. For each method, we propose a unified objective function to conduct feature selection/multi-kernel learning, NMF and adaptive graph regularization simultaneously. We further develop two iterative algorithms to solve the two optimization problems. Experimental results on two challenging pattern classification tasks demonstrate that the proposed methods significantly outperform state-of-the-art data representation methods.

  14. EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation

    Directory of Open Access Journals (Sweden)

    Suwicha Jirayucharoensak

    2014-01-01

    Full Text Available Automatic emotion recognition is one of the most challenging tasks. To detect emotion from nonstationary EEG signals, a sophisticated learning algorithm that can represent high-level abstraction is required. This study proposes the utilization of a deep learning network (DLN to discover unknown feature correlation between input signals that is crucial for the learning task. The DLN is implemented with a stacked autoencoder (SAE using hierarchical feature learning approach. Input features of the network are power spectral densities of 32-channel EEG signals from 32 subjects. To alleviate overfitting problem, principal component analysis (PCA is applied to extract the most important components of initial input features. Furthermore, covariate shift adaptation of the principal components is implemented to minimize the nonstationary effect of EEG signals. Experimental results show that the DLN is capable of classifying three different levels of valence and arousal with accuracy of 49.52% and 46.03%, respectively. Principal component based covariate shift adaptation enhances the respective classification accuracy by 5.55% and 6.53%. Moreover, DLN provides better performance compared to SVM and naive Bayes classifiers.

  15. Adoption, adaptation, and abandonment: Appropriation of science education professional development learning

    Science.gov (United States)

    Longhurst, Max L.

    Understanding factors that impact teacher utilization of learning from professional development is critical in order maximize the educational and financial investment in teacher professional learning. This study used a multicase mixed quantitative and qualitative methodology to investigate the factors that influence teacher adoption, adaption, or abandonment of learning from science teacher professional development. The theoretical framework of activity theory was identified as a useful way to investigate the phenomenon of teacher appropriation of pedagogical practices from professional development. This framework has the capacity to account for a multitude of elements in the context of a learning experience. In this study educational appropriation is understood through a continuum of how an educator acquires and implements both practical and conceptual aspects of learning from professional development within localized context. The variability associated with instructional changes made from professional development drives this inquiry to search for better understandings of the appropriation of pedagogical practices. Purposeful sampling was used to identify two participants from a group of eighth-grade science teachers engaged in professional development designed to investigate how cyber-enabled technologies might enhance instruction and learning in integrated science classrooms. The data from this investigation add to the literature of appropriation of instructional practices by connecting eight factors that influence conceptual and practical tools with the development of ownership of pedagogical practices in the appropriation hierarchy. Recommendations are shared with professional development developers, providers, and participants in anticipation that future science teaching experiences might be informed by findings from this study.

  16. Adaptive structured dictionary learning for image fusion based on group-sparse-representation

    Science.gov (United States)

    Yang, Jiajie; Sun, Bin; Luo, Chengwei; Wu, Yuzhong; Xu, Limei

    2018-04-01

    Dictionary learning is the key process of sparse representation which is one of the most widely used image representation theories in image fusion. The existing dictionary learning method does not use the group structure information and the sparse coefficients well. In this paper, we propose a new adaptive structured dictionary learning algorithm and a l1-norm maximum fusion rule that innovatively utilizes grouped sparse coefficients to merge the images. In the dictionary learning algorithm, we do not need prior knowledge about any group structure of the dictionary. By using the characteristics of the dictionary in expressing the signal, our algorithm can automatically find the desired potential structure information that hidden in the dictionary. The fusion rule takes the physical meaning of the group structure dictionary, and makes activity-level judgement on the structure information when the images are being merged. Therefore, the fused image can retain more significant information. Comparisons have been made with several state-of-the-art dictionary learning methods and fusion rules. The experimental results demonstrate that, the dictionary learning algorithm and the fusion rule both outperform others in terms of several objective evaluation metrics.

  17. Adaptive Load Balancing of Parallel Applications with Multi-Agent Reinforcement Learning on Heterogeneous Systems

    Directory of Open Access Journals (Sweden)

    Johan Parent

    2004-01-01

    Full Text Available We report on the improvements that can be achieved by applying machine learning techniques, in particular reinforcement learning, for the dynamic load balancing of parallel applications. The applications being considered in this paper are coarse grain data intensive applications. Such applications put high pressure on the interconnect of the hardware. Synchronization and load balancing in complex, heterogeneous networks need fast, flexible, adaptive load balancing algorithms. Viewing a parallel application as a one-state coordination game in the framework of multi-agent reinforcement learning, and by using a recently introduced multi-agent exploration technique, we are able to improve upon the classic job farming approach. The improvements are achieved with limited computation and communication overhead.

  18. QUALITY ASSURANCE IN RWANDAN HIGHER LEARNING EDUCATION: IS THE SYSTEM ADAPTIVE OR COMPLEX?

    Directory of Open Access Journals (Sweden)

    Nathan Kanuma Taremwa

    2014-01-01

    Full Text Available Developing knowledge infrastructure by massive investments in education and training are taken as a benchmark in facilitating the acceleration and possible increases in skills, capacities and competences of Rwandan people has become apriority issue in the recent years. This notion is relevant to vision 2020 where human resource development and building of a knowledge based economy are fundamental pillars. In the past years, several policy reforms have taken place in education sector. However, the overarching question is if such reforms are becoming adaptive or complex and if such reforms will not compromise the quality of education in higher learning education in Rwanda? The main objective of the study was to investigate the impact of changes in Higher Learning Institutions on the quality of education in Rwanda. This research had three hypotheses, namely; there is an impact of changes in Higher Learning Institutions on quality of education in Rwanda; the current complexity in Rwandan education system is affecting the quality of education in HLIs; Tailoring education system to the regional reforms and implementation strategies is affecting the quality of education in Rwanda. This study was carried out in 10 higher learning institutions (5 public, 5 private and 2 Ministry of Education directorates (HEC and REB. Key informants were the senior management/head of institutions, experienced academic staff, and students. The parameters considered included; the learning methods, assessment styles, workloads, language of instruction, merging of public HLIs, curriculum, and the transformation of some private higher learning institutions into company forms. Main research instruments were questionnaires and interview guides. Both qualitative and quantitative research was collected. Analyses were done using SPSS and excel packages. Major findings indicate that the system is still in transition with indicative gaps. Ample time would therefore be necessary for

  19. Self-organizing adaptive map: autonomous learning of curves and surfaces from point samples.

    Science.gov (United States)

    Piastra, Marco

    2013-05-01

    Competitive Hebbian Learning (CHL) (Martinetz, 1993) is a simple and elegant method for estimating the topology of a manifold from point samples. The method has been adopted in a number of self-organizing networks described in the literature and has given rise to related studies in the fields of geometry and computational topology. Recent results from these fields have shown that a faithful reconstruction can be obtained using the CHL method only for curves and surfaces. Within these limitations, these findings constitute a basis for defining a CHL-based, growing self-organizing network that produces a faithful reconstruction of an input manifold. The SOAM (Self-Organizing Adaptive Map) algorithm adapts its local structure autonomously in such a way that it can match the features of the manifold being learned. The adaptation process is driven by the defects arising when the network structure is inadequate, which cause a growth in the density of units. Regions of the network undergo a phase transition and change their behavior whenever a simple, local condition of topological regularity is met. The phase transition is eventually completed across the entire structure and the adaptation process terminates. In specific conditions, the structure thus obtained is homeomorphic to the input manifold. During the adaptation process, the network also has the capability to focus on the acquisition of input point samples in critical regions, with a substantial increase in efficiency. The behavior of the network has been assessed experimentally with typical data sets for surface reconstruction, including suboptimal conditions, e.g. with undersampling and noise. Copyright © 2012 Elsevier Ltd. All rights reserved.

  20. Third Age Learning: Adapting the Idea to a Thailand Context of Lifelong Learning

    Science.gov (United States)

    Ratana-Ubol, Archanya; Richards, Cameron

    2016-01-01

    The concept of the university of the third age (U3A) is well established overseas and a key international focus for emerging global networks of senior citizen (i.e. seniors) lifelong learning. However it is yet to become so in Thailand although it too is in the process of becoming an ageing society. Moreover, this is despite the extent to which…

  1. The local enhancement conundrum: in search of the adaptive value of a social learning mechanism.

    Science.gov (United States)

    Arbilly, Michal; Laland, Kevin N

    2014-02-01

    Social learning mechanisms are widely thought to vary in their degree of complexity as well as in their prevalence in the natural world. While learning the properties of a stimulus that generalize to similar stimuli at other locations (stimulus enhancement) prima facie appears more useful to an animal than learning about a specific stimulus at a specific location (local enhancement), empirical evidence suggests that the latter is much more widespread in nature. Simulating populations engaged in a producer-scrounger game, we sought to deploy mathematical models to identify the adaptive benefits of reliance on local enhancement and/or stimulus enhancement, and the alternative conditions favoring their evolution. Surprisingly, we found that while stimulus enhancement readily evolves, local enhancement is advantageous only under highly restricted conditions: when generalization of information was made unreliable or when error in social learning was high. Our results generate a conundrum over how seemingly conflicting empirical and theoretical findings can be reconciled. Perhaps the prevalence of local enhancement in nature is due to stimulus enhancement costs independent of the learning task itself (e.g. predation risk), perhaps natural habitats are often characterized by unreliable yet highly rewarding payoffs, or perhaps local enhancement occurs less frequently, and stimulus enhancement more frequently, than widely believed. Copyright © 2013 Elsevier Inc. All rights reserved.

  2. Using concept similarity in cross ontology for adaptive e-Learning systems

    Directory of Open Access Journals (Sweden)

    B. Saleena

    2015-01-01

    Full Text Available e-Learning is one of the most preferred media of learning by the learners. The learners search the web to gather knowledge about a particular topic from the information in the repositories. Retrieval of relevant materials from a domain can be easily implemented if the information is organized and related in some way. Ontologies are a key concept that helps us to relate information for providing the more relevant lessons to the learner. This paper proposes an adaptive e-Learning system, which generates a user specific e-Learning content by comparing the concepts with more than one system using similarity measures. A cross ontology measure is defined, which consists of fuzzy domain ontology as the primary ontology and the domain expert’s ontology as the secondary ontology, for the comparison process. A personalized document is provided to the user with a user profile, which includes the data obtained from the processing of the proposed method under a User score, which is obtained through the user evaluation. The results of the proposed e-Learning system under the designed cross ontology similarity measure show a significant increase in performance and accuracy under different conditions. The assessment of the comparative analysis, showed the difference in performance of our proposed method over other methods. Based on the assessment results it is proved that the proposed approach is effective over other methods.

  3. DYNAMIC AND INCREMENTAL EXPLORATION STRATEGY IN FUSION ADAPTIVE RESONANCE THEORY FOR ONLINE REINFORCEMENT LEARNING

    Directory of Open Access Journals (Sweden)

    Budhitama Subagdja

    2016-06-01

    Full Text Available One of the fundamental challenges in reinforcement learning is to setup a proper balance between exploration and exploitation to obtain the maximum cummulative reward in the long run. Most protocols for exploration bound the overall values to a convergent level of performance. If new knowledge is inserted or the environment is suddenly changed, the issue becomes more intricate as the exploration must compromise the pre-existing knowledge. This paper presents a type of multi-channel adaptive resonance theory (ART neural network model called fusion ART which serves as a fuzzy approximator for reinforcement learning with inherent features that can regulate the exploration strategy. This intrinsic regulation is driven by the condition of the knowledge learnt so far by the agent. The model offers a stable but incremental reinforcement learning that can involve prior rules as bootstrap knowledge for guiding the agent to select the right action. Experiments in obstacle avoidance and navigation tasks demonstrate that in the configuration of learning wherein the agent learns from scratch, the inherent exploration model in fusion ART model is comparable to the basic E-greedy policy. On the other hand, the model is demonstrated to deal with prior knowledge and strike a balance between exploration and exploitation.

  4. An Adaptive Learning Based Network Selection Approach for 5G Dynamic Environments

    Directory of Open Access Journals (Sweden)

    Xiaohong Li

    2018-03-01

    Full Text Available Networks will continue to become increasingly heterogeneous as we move toward 5G. Meanwhile, the intelligent programming of the core network makes the available radio resource be more changeable rather than static. In such a dynamic and heterogeneous network environment, how to help terminal users select optimal networks to access is challenging. Prior implementations of network selection are usually applicable for the environment with static radio resources, while they cannot handle the unpredictable dynamics in 5G network environments. To this end, this paper considers both the fluctuation of radio resources and the variation of user demand. We model the access network selection scenario as a multiagent coordination problem, in which a bunch of rationally terminal users compete to maximize their benefits with incomplete information about the environment (no prior knowledge of network resource and other users’ choices. Then, an adaptive learning based strategy is proposed, which enables users to adaptively adjust their selections in response to the gradually or abruptly changing environment. The system is experimentally shown to converge to Nash equilibrium, which also turns out to be both Pareto optimal and socially optimal. Extensive simulation results show that our approach achieves significantly better performance compared with two learning and non-learning based approaches in terms of load balancing, user payoff and the overall bandwidth utilization efficiency. In addition, the system has a good robustness performance under the condition with non-compliant terminal users.

  5. Adaptive critic learning techniques for engine torque and air-fuel ratio control.

    Science.gov (United States)

    Liu, Derong; Javaherian, Hossein; Kovalenko, Olesia; Huang, Ting

    2008-08-01

    A new approach for engine calibration and control is proposed. In this paper, we present our research results on the implementation of adaptive critic designs for self-learning control of automotive engines. A class of adaptive critic designs that can be classified as (model-free) action-dependent heuristic dynamic programming is used in this research project. The goals of the present learning control design for automotive engines include improved performance, reduced emissions, and maintained optimum performance under various operating conditions. Using the data from a test vehicle with a V8 engine, we developed a neural network model of the engine and neural network controllers based on the idea of approximate dynamic programming to achieve optimal control. We have developed and simulated self-learning neural network controllers for both engine torque (TRQ) and exhaust air-fuel ratio (AFR) control. The goal of TRQ control and AFR control is to track the commanded values. For both control problems, excellent neural network controller transient performance has been achieved.

  6. Reinforcement learning design-based adaptive tracking control with less learning parameters for nonlinear discrete-time MIMO systems.

    Science.gov (United States)

    Liu, Yan-Jun; Tang, Li; Tong, Shaocheng; Chen, C L Philip; Li, Dong-Juan

    2015-01-01

    Based on the neural network (NN) approximator, an online reinforcement learning algorithm is proposed for a class of affine multiple input and multiple output (MIMO) nonlinear discrete-time systems with unknown functions and disturbances. In the design procedure, two networks are provided where one is an action network to generate an optimal control signal and the other is a critic network to approximate the cost function. An optimal control signal and adaptation laws can be generated based on two NNs. In the previous approaches, the weights of critic and action networks are updated based on the gradient descent rule and the estimations of optimal weight vectors are directly adjusted in the design. Consequently, compared with the existing results, the main contributions of this paper are: 1) only two parameters are needed to be adjusted, and thus the number of the adaptation laws is smaller than the previous results and 2) the updating parameters do not depend on the number of the subsystems for MIMO systems and the tuning rules are replaced by adjusting the norms on optimal weight vectors in both action and critic networks. It is proven that the tracking errors, the adaptation laws, and the control inputs are uniformly bounded using Lyapunov analysis method. The simulation examples are employed to illustrate the effectiveness of the proposed algorithm.

  7. The Link between Age, Career Goals, and Adaptive Development for Work-Related Learning among Local Government Employees

    Science.gov (United States)

    Tones, Megan; Pillay, Hitendra; Kelly, Kathy

    2011-01-01

    More recently, lifespan development psychology models of adaptive development have been applied to the workforce to investigate ageing worker and lifespan issues. The current study uses the Learning and Development Survey (LDS) to investigate employee selection and engagement of learning and development goals and opportunities and constraints for…

  8. The added value of a gaming context and intelligent adaptation for a mobile application for vocabulary learning

    NARCIS (Netherlands)

    Sandberg, J.; Maris, M.; Hoogendoorn, P.

    2014-01-01

    Two groups participated in a study on the added value of a gaming context and intelligent adaptation for a mobile learning application. The control group worked at home for a fortnight with the original Mobile English Learning application (MEL-original) developed in a previous project. The

  9. Implementation of an Automated Grading System with an Adaptive Learning Component to Affect Student Feedback and Response Time

    Science.gov (United States)

    Matthews, Kevin; Janicki, Thomas; He, Ling; Patterson, Laurie

    2012-01-01

    This research focuses on the development and implementation of an adaptive learning and grading system with a goal to increase the effectiveness and quality of feedback to students. By utilizing various concepts from established learning theories, the goal of this research is to improve the quantity, quality, and speed of feedback as it pertains…

  10. Chaos Synchronization Using Adaptive Dynamic Neural Network Controller with Variable Learning Rates

    Directory of Open Access Journals (Sweden)

    Chih-Hong Kao

    2011-01-01

    Full Text Available This paper addresses the synchronization of chaotic gyros with unknown parameters and external disturbance via an adaptive dynamic neural network control (ADNNC system. The proposed ADNNC system is composed of a neural controller and a smooth compensator. The neural controller uses a dynamic RBF (DRBF network to online approximate an ideal controller. The DRBF network can create new hidden neurons online if the input data falls outside the hidden layer and prune the insignificant hidden neurons online if the hidden neuron is inappropriate. The smooth compensator is designed to compensate for the approximation error between the neural controller and the ideal controller. Moreover, the variable learning rates of the parameter adaptation laws are derived based on a discrete-type Lyapunov function to speed up the convergence rate of the tracking error. Finally, the simulation results which verified the chaotic behavior of two nonlinear identical chaotic gyros can be synchronized using the proposed ADNNC scheme.

  11. Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine

    Science.gov (United States)

    Luo, Guangchun; Qin, Ke; Wang, Nan; Niu, Weina

    2018-01-01

    Sensor drift is a common issue in E-Nose systems and various drift compensation methods have received fruitful results in recent years. Although the accuracy for recognizing diverse gases under drift conditions has been largely enhanced, few of these methods considered online processing scenarios. In this paper, we focus on building online drift compensation model by transforming two domain adaptation based methods into their online learning versions, which allow the recognition models to adapt to the changes of sensor responses in a time-efficient manner without losing the high accuracy. Experimental results using three different settings confirm that the proposed methods save large processing time when compared with their offline versions, and outperform other drift compensation methods in recognition accuracy. PMID:29494543

  12. Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Zhiyuan Ma

    2018-03-01

    Full Text Available Sensor drift is a common issue in E-Nose systems and various drift compensation methods have received fruitful results in recent years. Although the accuracy for recognizing diverse gases under drift conditions has been largely enhanced, few of these methods considered online processing scenarios. In this paper, we focus on building online drift compensation model by transforming two domain adaptation based methods into their online learning versions, which allow the recognition models to adapt to the changes of sensor responses in a time-efficient manner without losing the high accuracy. Experimental results using three different settings confirm that the proposed methods save large processing time when compared with their offline versions, and outperform other drift compensation methods in recognition accuracy.

  13. Collaborative Education in Climate Change Sciences and Adaptation through Interactive Learning

    Science.gov (United States)

    Ozbay, G.; Sriharan, S.; Fan, C.

    2014-12-01

    As a result of several funded climate change education grants, collaboration between VSU, DSU, and MSU, was established to provide the innovative and cohesive education and research opportunities to underrepresented groups in the climate related sciences. Prior to offering climate change and adaptation related topics to the students, faculty members of the three collaborating institutions participated at a number of faculty training and preparation workshops for teaching climate change sciences (i.e. AMS Diversity Project Workshop, NCAR Faculty-Student Team on Climate Change, NASA-NICE Program). In order to enhance the teaching and student learning on various issues in the Environmental Sciences Programs, Climatology, Climate Change Sciences and Adaptation or related courses were developed at Delaware State University and its partner institutions (Virginia State University and Morgan State University). These courses were prepared to deliver information on physical basis for the earth's climate system and current climate change instruction modules by AMS and historic climate information (NOAA Climate Services, U.S. and World Weather Data, NCAR and NASA Climate Models). By using Global Seminar as a Model, faculty members worked in teams to engage students in videoconferencing on climate change through Contemporary Global Studies and climate courses including Climate Change and Adaptation Science, Sustainable Agriculture, Introduction to Environmental Sciences, Climatology, and Ecology and Adaptation courses. All climate change courses have extensive hands-on practices and research integrated into the student learning experiences. Some of these students have presented their classroom projects during Earth Day, Student Climate Change Symposium, Undergraduate Summer Symposium, and other national conferences.

  14. Improvement of defect characterization in ultrasonic testing by adaptative learning network

    International Nuclear Information System (INIS)

    Bieth, M.; Adamonis, D.C.; Jusino, A.

    1982-01-01

    Numerous methods exist now for signal analysis in ultrasonic testing. These methods give more or less accurate information for defects characterization. In this paper is presented the development of a particular system based on a computer Signal processing: the Adaptative Learning Network (ALN) allowing the discrimination of defects in function of their nature. The ultrasonic signal is sampled and characterized by parameters amplitude-time and amplitude-frequency. The method was tested on stainless steel tubes welds showing fatigue cracks. The ALN model developed allows, under certain conditions, the discrimination of cracks from other defects [fr

  15. Adaptive Neuron Model: An architecture for the rapid learning of nonlinear topological transformations

    Science.gov (United States)

    Tawel, Raoul (Inventor)

    1994-01-01

    A method for the rapid learning of nonlinear mappings and topological transformations using a dynamically reconfigurable artificial neural network is presented. This fully-recurrent Adaptive Neuron Model (ANM) network was applied to the highly degenerate inverse kinematics problem in robotics, and its performance evaluation is bench-marked. Once trained, the resulting neuromorphic architecture was implemented in custom analog neural network hardware and the parameters capturing the functional transformation downloaded onto the system. This neuroprocessor, capable of 10(exp 9) ops/sec, was interfaced directly to a three degree of freedom Heathkit robotic manipulator. Calculation of the hardware feed-forward pass for this mapping was benchmarked at approximately 10 microsec.

  16. Machine learning for adaptive many-core machines a practical approach

    CERN Document Server

    Lopes, Noel

    2015-01-01

    The overwhelming data produced everyday and the increasing performance and cost requirements of applications?are transversal to a wide range of activities in society, from science to industry. In particular, the magnitude and complexity of the tasks that Machine Learning (ML) algorithms have to solve are driving the need to devise adaptive many-core machines that scale well with the volume of data, or in other words, can handle Big Data.This book gives a concise view on how to extend the applicability of well-known ML algorithms in Graphics Processing Unit (GPU) with data scalability in mind.

  17. Growing adaptive machines combining development and learning in artificial neural networks

    CERN Document Server

    Bredeche, Nicolas; Doursat, René

    2014-01-01

    The pursuit of artificial intelligence has been a highly active domain of research for decades, yielding exciting scientific insights and productive new technologies. In terms of generating intelligence, however, this pursuit has yielded only limited success. This book explores the hypothesis that adaptive growth is a means of moving forward. By emulating the biological process of development, we can incorporate desirable characteristics of natural neural systems into engineered designs, and thus move closer towards the creation of brain-like systems. The particular focus is on how to design artificial neural networks for engineering tasks. The book consists of contributions from 18 researchers, ranging from detailed reviews of recent domains by senior scientists, to exciting new contributions representing the state of the art in machine learning research. The book begins with broad overviews of artificial neurogenesis and bio-inspired machine learning, suitable both as an introduction to the domains and as a...

  18. Effectiveness of Adaptive Contextual Learning Model of Integrated Science by Integrating Digital Age Literacy on Grade VIII Students

    Science.gov (United States)

    Asrizal, A.; Amran, A.; Ananda, A.; Festiyed, F.

    2018-04-01

    Educational graduates should have good competencies to compete in the 21st century. Integrated learning is a good way to develop competence of students in this century. Besides that, literacy skills are very important for students to get success in their learning and daily life. For this reason, integrated science learning and literacy skills are important in 2013 curriculum. However, integrated science learning and integration of literacy in learning can’t be implemented well. Solution of this problem is to develop adaptive contextual learning model by integrating digital age literacy. The purpose of the research is to determine the effectiveness of adaptive contextual learning model to improve competence of grade VIII students in junior high school. This research is a part of the research and development or R&D. Research design which used in limited field testing was before and after treatment. The research instruments consist of three parts namely test sheet of learning outcome for assessing knowledge competence, observation sheet for assessing attitudes, and performance sheet for assessing skills of students. Data of student’s competence were analyzed by three kinds of analysis, namely descriptive statistics, normality test and homogeneity test, and paired comparison test. From the data analysis result, it can be stated that the implementation of adaptive contextual learning model of integrated science by integrating digital age literacy is effective to improve the knowledge, attitude, and literacy skills competences of grade VIII students in junior high school at 95% confidence level.

  19. An associative model of adaptive inference for learning word-referent mappings.

    Science.gov (United States)

    Kachergis, George; Yu, Chen; Shiffrin, Richard M

    2012-04-01

    People can learn word-referent pairs over a short series of individually ambiguous situations containing multiple words and referents (Yu & Smith, 2007, Cognition 106: 1558-1568). Cross-situational statistical learning relies on the repeated co-occurrence of words with their intended referents, but simple co-occurrence counts cannot explain the findings. Mutual exclusivity (ME: an assumption of one-to-one mappings) can reduce ambiguity by leveraging prior experience to restrict the number of word-referent pairings considered but can also block learning of non-one-to-one mappings. The present study first trained learners on one-to-one mappings with varying numbers of repetitions. In late training, a new set of word-referent pairs were introduced alongside pretrained pairs; each pretrained pair consistently appeared with a new pair. Results indicate that (1) learners quickly infer new pairs in late training on the basis of their knowledge of pretrained pairs, exhibiting ME; and (2) learners also adaptively relax the ME bias and learn two-to-two mappings involving both pretrained and new words and objects. We present an associative model that accounts for both results using competing familiarity and uncertainty biases.

  20. Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems.

    Science.gov (United States)

    Grisafi, Andrea; Wilkins, David M; Csányi, Gábor; Ceriotti, Michele

    2018-01-19

    Statistical learning methods show great promise in providing an accurate prediction of materials and molecular properties, while minimizing the need for computationally demanding electronic structure calculations. The accuracy and transferability of these models are increased significantly by encoding into the learning procedure the fundamental symmetries of rotational and permutational invariance of scalar properties. However, the prediction of tensorial properties requires that the model respects the appropriate geometric transformations, rather than invariance, when the reference frame is rotated. We introduce a formalism that extends existing schemes and makes it possible to perform machine learning of tensorial properties of arbitrary rank, and for general molecular geometries. To demonstrate it, we derive a tensor kernel adapted to rotational symmetry, which is the natural generalization of the smooth overlap of atomic positions kernel commonly used for the prediction of scalar properties at the atomic scale. The performance and generality of the approach is demonstrated by learning the instantaneous response to an external electric field of water oligomers of increasing complexity, from the isolated molecule to the condensed phase.

  1. Adaptation and validation of the instrument Clinical Learning Environment and Supervision for medical students in primary health care

    Directory of Open Access Journals (Sweden)

    Eva Öhman

    2016-12-01

    Full Text Available Abstract Background Clinical learning takes place in complex socio-cultural environments that are workplaces for the staff and learning places for the students. In the clinical context, the students learn by active participation and in interaction with the rest of the community at the workplace. Clinical learning occurs outside the university, therefore is it important for both the university and the student that the student is given opportunities to evaluate the clinical placements with an instrument that allows evaluation from many perspectives. The instrument Clinical Learning Environment and Supervision (CLES was originally developed for evaluation of nursing students’ clinical learning environment. The aim of this study was to adapt and validate the CLES instrument to measure medical students’ perceptions of their learning environment in primary health care. Methods In the adaptation process the face validity was tested by an expert panel of primary care physicians, who were also active clinical supervisors. The adapted CLES instrument with 25 items and six background questions was sent electronically to 1,256 medical students from one university. Answers from 394 students were eligible for inclusion. Exploratory factor analysis based on principal component methods followed by oblique rotation was used to confirm the adequate number of factors in the data. Construct validity was assessed by factor analysis. Confirmatory factor analysis was used to confirm the dimensions of CLES instrument. Results The construct validity showed a clearly indicated four-factor model. The cumulative variance explanation was 0.65, and the overall Cronbach’s alpha was 0.95. All items loaded similarly with the dimensions in the non-adapted CLES except for one item that loaded to another dimension. The CLES instrument in its adapted form had high construct validity and high reliability and internal consistency. Conclusion CLES, in its adapted form, appears

  2. Estimation of the Driving Style Based on the Users' Activity and Environment Influence.

    Science.gov (United States)

    Sysoev, Mikhail; Kos, Andrej; Guna, Jože; Pogačnik, Matevž

    2017-10-21

    New models and methods have been designed to predict the influence of the user's environment and activity information to the driving style in standard automotive environments. For these purposes, an experiment was conducted providing two types of analysis: (i) the evaluation of a self-assessment of the driving style; (ii) the prediction of aggressive driving style based on drivers' activity and environment parameters. Sixty seven h of driving data from 10 drivers were collected for analysis in this study. The new parameters used in the experiment are the car door opening and closing manner, which were applied to improve the prediction accuracy. An Android application called Sensoric was developed to collect low-level smartphone data about the users' activity. The driving style was predicted from the user's environment and activity data collected before driving. The prediction was tested against the actual driving style, calculated from objective driving data. The prediction has shown encouraging results, with precision values ranging from 0.727 up to 0.909 for aggressive driving recognition rate. The obtained results lend support to the hypothesis that user's environment and activity data could be used for the prediction of the aggressive driving style in advance, before the driving starts.

  3. Study of Adolescents Perceived Parenting Styles Based on their Gender and Age

    Directory of Open Access Journals (Sweden)

    صادق تقی لو

    2017-12-01

    Full Text Available Parenting styles play a major role in determining the life styles of adolescents and that is why they share a special significance. The present study was done with the aim to investigate adolescents’ perceived parenting styles based on their gender and age. The study was conducted by a post-event method and with a sample size of 623 subjects (311 female and 312 male, who were selected by the multistage sampling method. Data were analyzed, after being collected by the Baumrind Parenting Styles Questionnaire, using multivariate analysis of variance. The results indicated that the main effects of gender and age are significant at 0.01 level. Parents’ interaction with boys is more authoritarian and with girls more authoritative; also compared with adolescents less than 17 years, they interact with adolescents 17 years old more permissively. The interaction effects of gender and age were significant at 0.05 level only in the permissive parenting style. This means that unlike the girls, parents use more and more the permissive parenting style along with increasing age of adolescent teenage boys. It was concluded that the interaction patterns of parents with children are not fixed and these patterns vary according to gender and age of the children.

  4. Estimation of the Driving Style Based on the Users’ Activity and Environment Influence

    Science.gov (United States)

    Sysoev, Mikhail; Kos, Andrej; Guna, Jože; Pogačnik, Matevž

    2017-01-01

    New models and methods have been designed to predict the influence of the user’s environment and activity information to the driving style in standard automotive environments. For these purposes, an experiment was conducted providing two types of analysis: (i) the evaluation of a self-assessment of the driving style; (ii) the prediction of aggressive driving style based on drivers’ activity and environment parameters. Sixty seven h of driving data from 10 drivers were collected for analysis in this study. The new parameters used in the experiment are the car door opening and closing manner, which were applied to improve the prediction accuracy. An Android application called Sensoric was developed to collect low-level smartphone data about the users’ activity. The driving style was predicted from the user’s environment and activity data collected before driving. The prediction was tested against the actual driving style, calculated from objective driving data. The prediction has shown encouraging results, with precision values ranging from 0.727 up to 0.909 for aggressive driving recognition rate. The obtained results lend support to the hypothesis that user’s environment and activity data could be used for the prediction of the aggressive driving style in advance, before the driving starts. PMID:29065476

  5. Improving preschooler conduct adaptation by using a social learning program based on motion games

    Directory of Open Access Journals (Sweden)

    Zsuzsa Szilárda

    2017-03-01

    Full Text Available Being aware of the changes which occur under the influence of environmental conditions, education, culture and social roles upon the child is indispensable with a view to build up a conduct adapted to the social environment. For any preschooler child, entering kindergarten is an important social event and getting adapted to the new situation is not easy. Broadening the relational framework with objects, other individuals, with one’s own self, results in disciplining preschooler conducts and increasing the number of socially desirable conducts. Relying upon the above statements, this study is aimed at working out a social learning programme made up of motion games involving socialization/cooperation elements intended for inducing amelioration in terms of the child’s conduct during the process of adaptation to the kindergarten environment. The experiment was conducted using a sample of “little group” preschoolers (children 3-4 years of age. As research methods, the following have been used: studying the reference literature, the method of pedagogical observation, the method of experiment and the method of playing. Further to the practical application of the programme worked out with a view to enhance the adaptation conduct in the said subjects, the experimental group proved to have undergone a significant positive evolution and each subject showed improvements considering the conduct of adaptation to kindergarten conditions, as highlighted by the change i.e. higher values in terms of the individual scores achieved at the final test. Preschool education is meant to provide all possible ways and means to enable any child’s integration into groups of children of a peer age, to develop sociability in children and to create favorable conditions for building out inter-children networks.

  6. Adapting a Technology-Based Implementation Support Tool for Community Mental Health: Challenges and Lessons Learned.

    Science.gov (United States)

    Livet, Melanie; Fixsen, Amanda

    2018-01-01

    With mental health services shifting to community-based settings, community mental health (CMH) organizations are under increasing pressure to deliver effective services. Despite availability of evidence-based interventions, there is a gap between effective mental health practices and the care that is routinely delivered. Bridging this gap requires availability of easily tailorable implementation support tools to assist providers in implementing evidence-based intervention with quality, thereby increasing the likelihood of achieving the desired client outcomes. This study documents the process and lessons learned from exploring the feasibility of adapting such a technology-based tool, Centervention, as the example innovation, for use in CMH settings. Mixed-methods data on core features, innovation-provider fit, and organizational capacity were collected from 44 CMH providers. Lessons learned included the need to augment delivery through technology with more personal interactions, the importance of customizing and integrating the tool with existing technologies, and the need to incorporate a number of strategies to assist with adoption and use of Centervention-like tools in CMH contexts. This study adds to the current body of literature on the adaptation process for technology-based tools and provides information that can guide additional innovations for CMH settings.

  7. One-Shot Learning of Human Activity With an MAP Adapted GMM and Simplex-HMM.

    Science.gov (United States)

    Rodriguez, Mario; Orrite, Carlos; Medrano, Carlos; Makris, Dimitrios

    2016-05-10

    This paper presents a novel activity class representation using a single sequence for training. The contribution of this representation lays on the ability to train an one-shot learning recognition system, useful in new scenarios where capturing and labeling sequences is expensive or impractical. The method uses a universal background model of local descriptors obtained from source databases available on-line and adapts it to a new sequence in the target scenario through a maximum a posteriori adaptation. Each activity sample is encoded in a sequence of normalized bag of features and modeled by a new hidden Markov model formulation, where the expectation-maximization algorithm for training is modified to deal with observations consisting in vectors in a unit simplex. Extensive experiments in recognition have been performed using one-shot learning over the public datasets Weizmann, KTH, and IXMAS. These experiments demonstrate the discriminative properties of the representation and the validity of application in recognition systems, achieving state-of-the-art results.

  8. An Adaptive Privacy Protection Method for Smart Home Environments Using Supervised Learning

    Directory of Open Access Journals (Sweden)

    Jingsha He

    2017-03-01

    Full Text Available In recent years, smart home technologies have started to be widely used, bringing a great deal of convenience to people’s daily lives. At the same time, privacy issues have become particularly prominent. Traditional encryption methods can no longer meet the needs of privacy protection in smart home applications, since attacks can be launched even without the need for access to the cipher. Rather, attacks can be successfully realized through analyzing the frequency of radio signals, as well as the timestamp series, so that the daily activities of the residents in the smart home can be learnt. Such types of attacks can achieve a very high success rate, making them a great threat to users’ privacy. In this paper, we propose an adaptive method based on sample data analysis and supervised learning (SDASL, to hide the patterns of daily routines of residents that would adapt to dynamically changing network loads. Compared to some existing solutions, our proposed method exhibits advantages such as low energy consumption, low latency, strong adaptability, and effective privacy protection.

  9. Interacting orientations and instrumentalities to adapt a learning tool for health professionals

    Directory of Open Access Journals (Sweden)

    Kathrine L. Nygård

    2015-09-01

    Full Text Available Web-based instructional software offers new opportunities for collaborative, task-oriented in-service training. Planning and negotiation of content to adapt a web-based learning resource for nursing is the topic of this paper. We draw from Cultural Historical Activity Theory to elaborate the dialectical relationship of changing and stabilizing organizational practice. Local adaptation to create a domain-specific resource plays out as interactions of orientations and instrumentalities. Our analysis traces how orientations, i.e., in situ selection of knowledge and mobilization of experiences, and instrumentality, i.e., interpreted affordances of available cultural tools, interact. The adaptation processes are mediated by a set of new and current tools that interact with multiple orientations to ensure stability and promote change. Practice and project are introduced as intermediate, analytic concepts to assess tensions in the observed activity. Our analysis shows three central tensions, how they are introduced, addressed and subsequently resolved. Considering the opportunities help understand how engagement with technology can lead to new representations for introduction to a local knowledge domain.

  10. Data mining methods application in reflexive adaptation realization in e-learning systems

    Directory of Open Access Journals (Sweden)

    A. S. Bozhday

    2017-01-01

    Full Text Available In recent years, e-learning technologies are rapidly gaining momentum in their evolution. In this regard, issues related to improving the quality of software for virtual educational systems are becoming topical: increasing the period of exploitation of programs, increasing their reliability and flexibility. The above characteristics directly depend on the ability of the software system to adapt to changes in the domain, environment and user characteristics. In some cases, this ability is reduced to the timely optimization of the program’s own interfaces and data structure. At present, several approaches to creating mechanisms for self-optimization of software systems are known, but all of them have an insufficient degree of formalization and, as a consequence, weak universality. The purpose of this work is to develop the basics of the technology of self-optimization of software systems in the structure of e-learning. The proposed technology is based on the formulated and formalized principle of reflexive adaptation of software, applicable to a wide class of software systems and based on the discovery of new knowledge in the behavioral products of the system.To solve this problem, methods of data mining were applied. Data mining allows finding regularities in the functioning of software systems, which may not be obvious at the stage of their development. Finding such regularities and their subsequent analysis will make it possible to reorganize the structure of the system in a more optimal way and without human intervention, which will prolong the life cycle of the software and reduce the costs of its maintenance. Achieving this effect is important for e-learning systems, since they are quite expensive.The main results of the work include: the proposed classification of software adaptation mechanisms, taking into account the latest trends in the IT field in general and in the field of e-learning in particular; Formulation and formalization of

  11. Statistical Learning Framework with Adaptive Retraining for Condition-Based Maintenance

    International Nuclear Information System (INIS)

    An, Sang Ha; Chang, Soon Heung; Heo, Gyun Young; Seo, Ho Joon; Kim, Su Young

    2009-01-01

    As systems become more complex and more critical in our daily lives, the need for the maintenance based on the reliable monitoring and diagnosis has become more apparent. However, in reality, the general opinion has been that 'maintenance is a necessary evil' or 'nothing can be done to improve maintenance costs'. Perhaps these were true statements twenty years ago when many of the diagnostic technologies were not fully developed. The developments of microprocessor or computer based instrumentation that can be used to monitor the operating condition of plant equipment, machinery and systems have provided the means to manage the maintenance operation. They have provided the means to reduce or eliminate unnecessary repairs, prevent catastrophic machine failures and reduce the negative impact of the maintenance operation on the profitability of manufacturing and production plants. Condition-based maintenance (CBM) techniques help determine the condition of in-service equipment in order to predict when maintenance should be performed. Most of the statistical learning techniques are only valid as long as the physics of a system does not change. If any significant change such as the replacement of a component or equipment occurs in the system, the statistical learning model should be re-trained or re-developed to adapt the new system. In this research, authors will propose a statistical learning framework which can be applicable for various CBMs, and the concept of the adaptive retraining technique will be described to support the execution of the framework so that the monitoring system does not need to be re-developed or re-trained even though there are any significant changes in the system or component

  12. 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.

    2000-01-01

    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.)

  13. Usability of clinical decision support system as a facilitator for learning the assistive technology adaptation process.

    Science.gov (United States)

    Danial-Saad, Alexandra; Kuflik, Tsvi; Weiss, Patrice L Tamar; Schreuer, Naomi

    2016-01-01

    The aim of this study was to evaluate the usability of Ontology Supported Computerized Assistive Technology Recommender (OSCAR), a Clinical Decision Support System (CDSS) for the assistive technology adaptation process, its impact on learning the matching process, and to determine the relationship between its usability and learnability. Two groups of expert and novice clinicians (total, n = 26) took part in this study. Each group filled out system usability scale (SUS) to evaluate OSCAR's usability. The novice group completed a learning questionnaire to assess OSCAR's effect on their ability to learn the matching process. Both groups rated OSCAR's usability as "very good", (M [SUS] = 80.7, SD = 11.6, median = 83.7) by the novices, and (M [SUS] = 81.2, SD = 6.8, median = 81.2) by the experts. The Mann-Whitney results indicated that no significant differences were found between the expert and novice groups in terms of OSCAR's usability. A significant positive correlation existed between the usability of OSCAR and the ability to learn the adaptation process (rs = 0.46, p = 0.04). Usability is an important factor in the acceptance of a system. The successful application of user-centered design principles during the development of OSCAR may serve as a case study that models the significant elements to be considered, theoretically and practically in developing other systems. Implications for Rehabilitation Creating a CDSS with a focus on its usability is an important factor for its acceptance by its users. Successful usability outcomes can impact the learning process of the subject matter in general, and the AT prescription process in particular. The successful application of User-Centered Design principles during the development of OSCAR may serve as a case study that models the significant elements to be considered, theoretically and practically. The study emphasizes the importance of close collaboration between the developers and

  14. Multilevel learning in the adaptive management of waterfowl harvests: 20 years and counting

    Science.gov (United States)

    Johnson, Fred A.; Boomer, G. Scott; Williams, Byron K.; Nichols, James D.; Case, David J.

    2015-01-01

    In 1995, the U.S. Fish and Wildlife Service implemented an adaptive harvest management program (AHM) for the sport harvest of midcontinent mallards (Anas platyrhynchos). The program has been successful in reducing long-standing contentiousness in the regulatory process, while integrating science and policy in a coherent, rigorous, and transparent fashion. After 20 years, much has been learned about the relationship among waterfowl populations, their environment, and hunting regulations, with each increment of learning contributing to better management decisions. At the same time, however, much has been changing in the social, institutional, and environmental arenas that provide context for the AHM process. Declines in hunter numbers, competition from more pressing conservation issues, and global-change processes are increasingly challenging waterfowl managers to faithfully reflect the needs and desires of stakeholders, to account for an increasing number of institutional constraints, and to (probabilistically) predict the consequences of regulatory policy in a changing environment. We review the lessons learned from the AHM process so far, and describe emerging challenges and ways in which they may be addressed. We conclude that the practice of AHM has greatly increased an awareness of the roles of social values, trade-offs, and attitudes toward risk in regulatory decision-making. Nevertheless, going forward the waterfowl management community will need to focus not only on the relationships among habitat, harvest, and waterfowl populations, but on the ways in which society values waterfowl and how those values can change over time. 

  15. Online learning control using adaptive critic designs with sparse kernel machines.

    Science.gov (United States)

    Xu, Xin; Hou, Zhongsheng; Lian, Chuanqiang; He, Haibo

    2013-05-01

    In the past decade, adaptive critic designs (ACDs), including heuristic dynamic programming (HDP), dual heuristic programming (DHP), and their action-dependent ones, have been widely studied to realize online learning control of dynamical systems. However, because neural networks with manually designed features are commonly used to deal with continuous state and action spaces, the generalization capability and learning efficiency of previous ACDs still need to be improved. In this paper, a novel framework of ACDs with sparse kernel machines is presented by integrating kernel methods into the critic of ACDs. To improve the generalization capability as well as the computational efficiency of kernel machines, a sparsification method based on the approximately linear dependence analysis is used. Using the sparse kernel machines, two kernel-based ACD algorithms, that is, kernel HDP (KHDP) and kernel DHP (KDHP), are proposed and their performance is analyzed both theoretically and empirically. Because of the representation learning and generalization capability of sparse kernel machines, KHDP and KDHP can obtain much better performance than previous HDP and DHP with manually designed neural networks. Simulation and experimental results of two nonlinear control problems, that is, a continuous-action inverted pendulum problem and a ball and plate control problem, demonstrate the effectiveness of the proposed kernel ACD methods.

  16. System level mechanisms of adaptation, learning, memory formation and evolvability: the role of chaperone and other networks.

    Science.gov (United States)

    Gyurko, David M; Soti, Csaba; Stetak, Attila; Csermely, Peter

    2014-05-01

    During the last decade, network approaches became a powerful tool to describe protein structure and dynamics. Here, we describe first the protein structure networks of molecular chaperones, then characterize chaperone containing sub-networks of interactomes called as chaperone-networks or chaperomes. We review the role of molecular chaperones in short-term adaptation of cellular networks in response to stress, and in long-term adaptation discussing their putative functions in the regulation of evolvability. We provide a general overview of possible network mechanisms of adaptation, learning and memory formation. We propose that changes of network rigidity play a key role in learning and memory formation processes. Flexible network topology provides ' learning-competent' state. Here, networks may have much less modular boundaries than locally rigid, highly modular networks, where the learnt information has already been consolidated in a memory formation process. Since modular boundaries are efficient filters of information, in the 'learning-competent' state information filtering may be much smaller, than after memory formation. This mechanism restricts high information transfer to the 'learning competent' state. After memory formation, modular boundary-induced segregation and information filtering protect the stored information. The flexible networks of young organisms are generally in a 'learning competent' state. On the contrary, locally rigid networks of old organisms have lost their 'learning competent' state, but store and protect their learnt information efficiently. We anticipate that the above mechanism may operate at the level of both protein-protein interaction and neuronal networks.

  17. Comparison of the Psychological Characteristics of Adaptation in Orphan Students of Initial Learning Stage to Adaptation Potential in Students Brought up in Families

    Directory of Open Access Journals (Sweden)

    Zamorueva V.V.,

    2014-08-01

    Full Text Available We present a study of psychological characteristics of preadult orphans, their psychological adaptation to the conditions of learning in high school compared to the norm population (students living in family. We assumed that the level of adaptation of the orphan students is significantly smaller than in other students, because of their special life circumstances (maternal deprivation, living in residential care institutions, sometimes bad heredity, lack of life skills in everyday issues, personal problems. The results of the survey of 49 orphan students (26 girls and 23 boys and 49 first-year students brought up by parents (28 girls and 21 boys, confirmed this hypothesis and allow us to tell that orphan students need special psychological help in the learning process in high school to grow at a personal and professional level.

  18. Adaptive Education.

    Science.gov (United States)

    Anderson, Lorin W.

    1979-01-01

    Schools have devised several ways to adapt instruction to a wide variety of student abilities and needs. Judged by criteria for what adaptive education should be, most learning for mastery programs look good. (Author/JM)

  19. MO-G-17A-05: PET Image Deblurring Using Adaptive Dictionary Learning

    International Nuclear Information System (INIS)

    Valiollahzadeh, S; Clark, J; Mawlawi, O

    2014-01-01

    Purpose: The aim of this work is to deblur PET images while suppressing Poisson noise effects using adaptive dictionary learning (DL) techniques. Methods: The model that relates a blurred and noisy PET image to the desired image is described as a linear transform y=Hm+n where m is the desired image, H is a blur kernel, n is Poisson noise and y is the blurred image. The approach we follow to recover m involves the sparse representation of y over a learned dictionary, since the image has lots of repeated patterns, edges, textures and smooth regions. The recovery is based on an optimization of a cost function having four major terms: adaptive dictionary learning term, sparsity term, regularization term, and MLEM Poisson noise estimation term. The optimization is solved by a variable splitting method that introduces additional variables. We simulated a 128×128 Hoffman brain PET image (baseline) with varying kernel types and sizes (Gaussian 9×9, σ=5.4mm; Uniform 5×5, σ=2.9mm) with additive Poisson noise (Blurred). Image recovery was performed once when the kernel type was included in the model optimization and once with the model blinded to kernel type. The recovered image was compared to the baseline as well as another recovery algorithm PIDSPLIT+ (Setzer et. al.) by calculating PSNR (Peak SNR) and normalized average differences in pixel intensities (NADPI) of line profiles across the images. Results: For known kernel types, the PSNR of the Gaussian (Uniform) was 28.73 (25.1) and 25.18 (23.4) for DL and PIDSPLIT+ respectively. For blinded deblurring the PSNRs were 25.32 and 22.86 for DL and PIDSPLIT+ respectively. NADPI between baseline and DL, and baseline and blurred for the Gaussian kernel was 2.5 and 10.8 respectively. Conclusion: PET image deblurring using dictionary learning seems to be a good approach to restore image resolution in presence of Poisson noise. GE Health Care

  20. MO-G-17A-05: PET Image Deblurring Using Adaptive Dictionary Learning

    Energy Technology Data Exchange (ETDEWEB)

    Valiollahzadeh, S [RICE University, Houston, Tx (United States); Clark, J [MD Anderson Cancer Ctr., Houston, TX (United States); Mawlawi, O

    2014-06-15

    Purpose: The aim of this work is to deblur PET images while suppressing Poisson noise effects using adaptive dictionary learning (DL) techniques. Methods: The model that relates a blurred and noisy PET image to the desired image is described as a linear transform y=Hm+n where m is the desired image, H is a blur kernel, n is Poisson noise and y is the blurred image. The approach we follow to recover m involves the sparse representation of y over a learned dictionary, since the image has lots of repeated patterns, edges, textures and smooth regions. The recovery is based on an optimization of a cost function having four major terms: adaptive dictionary learning term, sparsity term, regularization term, and MLEM Poisson noise estimation term. The optimization is solved by a variable splitting method that introduces additional variables. We simulated a 128×128 Hoffman brain PET image (baseline) with varying kernel types and sizes (Gaussian 9×9, σ=5.4mm; Uniform 5×5, σ=2.9mm) with additive Poisson noise (Blurred). Image recovery was performed once when the kernel type was included in the model optimization and once with the model blinded to kernel type. The recovered image was compared to the baseline as well as another recovery algorithm PIDSPLIT+ (Setzer et. al.) by calculating PSNR (Peak SNR) and normalized average differences in pixel intensities (NADPI) of line profiles across the images. Results: For known kernel types, the PSNR of the Gaussian (Uniform) was 28.73 (25.1) and 25.18 (23.4) for DL and PIDSPLIT+ respectively. For blinded deblurring the PSNRs were 25.32 and 22.86 for DL and PIDSPLIT+ respectively. NADPI between baseline and DL, and baseline and blurred for the Gaussian kernel was 2.5 and 10.8 respectively. Conclusion: PET image deblurring using dictionary learning seems to be a good approach to restore image resolution in presence of Poisson noise. GE Health Care.

  1. Moving Past Curricula and Strategies: Language and the Development of Adaptive Pedagogy for Immersive Learning Environments

    Science.gov (United States)

    Hand, Brian; Cavagnetto, Andy; Chen, Ying-Chih; Park, Soonhye

    2016-04-01

    Given current concerns internationally about student performance in science and the need to shift how science is being learnt in schools, as a community, we need to shift how we approach the issue of learning and teaching in science. In the future, we are going to have to close the gap between how students construct and engage with knowledge in a media-rich environment, and how school classroom environments engage them. This is going to require a shift to immersive environments where attention is paid to the knowledge bases and resources students bring into the classroom. Teachers will have to adopt adaptive pedagogical approaches that are framed around a more nuanced understanding of epistemological orientation, language and the nature of prosocial environments.

  2. Populists as Chameleons? An Adaptive Learning Approach to the Rise of Populist Politicians

    Directory of Open Access Journals (Sweden)

    Jasper Muis

    2015-04-01

    Full Text Available This paper envisions populism as a vote- and attention-maximizing strategy. It applies an adaptive learning approach to understand successes of populist party leaders. I assume that populists are ideologically flexible and continually search for a more beneficial policy position, in terms of both electoral support and media attention, by retaining political claims that yield positive feedback and discard those that encounter negative feedback. This idea is empirically tested by analyzing the Dutch populist leader Pim Fortuyn and the development of his stance about immigration and integration issues. In contrast to the conventional wisdom, the results do not show any empirical support for the claim that Fortuyn was ideologically driven by the opinion polls or by media publicity during the 2002 Dutch parliamentary election campaign. The findings thus suggest that populist parties are perhaps less distinctive in their strategies from mainstream parties than often claimed.

  3. Mild-moderate TBI: clinical recommendations to optimize neurobehavioral functioning, learning, and adaptation.

    Science.gov (United States)

    Chen, Anthony J-W; Loya, Fred

    2014-11-01

    Traumatic brain injury (TBI) can result in functional deficits that persist long after acute injury. The authors present a case study of an individual who experienced some of the most common debilitating problems that characterize the chronic phase of mild-to-moderate TBI-difficulties with neurobehavioral functions that manifest via complaints of distractibility, poor memory, disorganization, poor frustration tolerance, and feeling easily overwhelmed. They present a rational strategy for management that addresses important domain-general targets likely to have far-ranging benefits. This integrated, longitudinal, and multifaceted approach first addresses approachable targets and provides an important foundation to enhance the success of other, more specific interventions requiring specialty intervention. The overall approach places an emphasis on accomplishing two major categories of clinical objectives: optimizing current functioning and enhancing learning and adaptation to support improvement of functioning in the long-term for individuals living with brain injury. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

  4. Reinforcement learning for adaptive optimal control of unknown continuous-time nonlinear systems with input constraints

    Science.gov (United States)

    Yang, Xiong; Liu, Derong; Wang, Ding

    2014-03-01

    In this paper, an adaptive reinforcement learning-based solution is developed for the infinite-horizon optimal control problem of constrained-input continuous-time nonlinear systems in the presence of nonlinearities with unknown structures. Two different types of neural networks (NNs) are employed to approximate the Hamilton-Jacobi-Bellman equation. That is, an recurrent NN is constructed to identify the unknown dynamical system, and two feedforward NNs are used as the actor and the critic to approximate the optimal control and the optimal cost, respectively. Based on this framework, the action NN and the critic NN are tuned simultaneously, without the requirement for the knowledge of system drift dynamics. Moreover, by using Lyapunov's direct method, the weights of the action NN and the critic NN are guaranteed to be uniformly ultimately bounded, while keeping the closed-loop system stable. To demonstrate the effectiveness of the present approach, simulation results are illustrated.

  5. The influence of demographics and work related goals on adaptive development for work related learning amongst private hospital employees.

    Science.gov (United States)

    Tones, Megan; Pillay, Hitendra; Fraser, Jennifer

    2010-01-01

    Contemporary lifespan development models of adaptive development have been applied to the workforce to examine characteristics of the ageing employee. Few studies have examined adaptive development in terms of worker perceptions of workplace, or their learning and development issues. This study used the recently developed Revised Learning and Development Survey to investigate employee selection and engagement of learning and development goals, opportunities for learning and development at work, and constraints to learning and development at work. Demographic and career goal variables were tested amongst a sample of private hospital employees, almost all of whom were nurses. Workers under 45 years of age perceived greater opportunities for training and development than more mature aged workers. Age and physical demands interacted such that physical demands of work were associated with lower engagement in learning and development goals in mature aged workers. The opposite was observed amongst younger workers. Engagement in learning and development goals at work predicted goals associated with an intention to decrease work hours or change jobs to a different industry when opportunities to learn via work tasks were limited. At the same time limited opportunities for training and development and perceptions of constraints to development at work predicted the intention to change jobs. Results indicate consideration must be paid to employee perceptions in the workplace in relation to goals. They may be important factors in designing strategies to retain workers.

  6. Adaptive neural network/expert system that learns fault diagnosis for different structures

    Science.gov (United States)

    Simon, Solomon H.

    1992-08-01

    Corporations need better real-time monitoring and control systems to improve productivity by watching quality and increasing production flexibility. The innovative technology to achieve this goal is evolving in the form artificial intelligence and neural networks applied to sensor processing, fusion, and interpretation. By using these advanced Al techniques, we can leverage existing systems and add value to conventional techniques. Neural networks and knowledge-based expert systems can be combined into intelligent sensor systems which provide real-time monitoring, control, evaluation, and fault diagnosis for production systems. Neural network-based intelligent sensor systems are more reliable because they can provide continuous, non-destructive monitoring and inspection. Use of neural networks can result in sensor fusion and the ability to model highly, non-linear systems. Improved models can provide a foundation for more accurate performance parameters and predictions. We discuss a research software/hardware prototype which integrates neural networks, expert systems, and sensor technologies and which can adapt across a variety of structures to perform fault diagnosis. The flexibility and adaptability of the prototype in learning two structures is presented. Potential applications are discussed.

  7. Bio-inspired adaptive feedback error learning architecture for motor control.

    Science.gov (United States)

    Tolu, Silvia; Vanegas, Mauricio; Luque, Niceto R; Garrido, Jesús A; Ros, Eduardo

    2012-10-01

    This study proposes an adaptive control architecture based on an accurate regression method called Locally Weighted Projection Regression (LWPR) and on a bio-inspired module, such as a cerebellar-like engine. This hybrid architecture takes full advantage of the machine learning module (LWPR kernel) to abstract an optimized representation of the sensorimotor space while the cerebellar component integrates this to generate corrective terms in the framework of a control task. Furthermore, we illustrate how the use of a simple adaptive error feedback term allows to use the proposed architecture even in the absence of an accurate analytic reference model. The presented approach achieves an accurate control with low gain corrective terms (for compliant control schemes). We evaluate the contribution of the different components of the proposed scheme comparing the obtained performance with alternative approaches. Then, we show that the presented architecture can be used for accurate manipulation of different objects when their physical properties are not directly known by the controller. We evaluate how the scheme scales for simulated plants of high Degrees of Freedom (7-DOFs).

  8. Linear hypergeneralization of learned dynamics across movement speeds reveals anisotropic, gain-encoding primitives for motor adaptation.

    Science.gov (United States)

    Joiner, Wilsaan M; Ajayi, Obafunso; Sing, Gary C; Smith, Maurice A

    2011-01-01

    The ability to generalize learned motor actions to new contexts is a key feature of the motor system. For example, the ability to ride a bicycle or swing a racket is often first developed at lower speeds and later applied to faster velocities. A number of previous studies have examined the generalization of motor adaptation across movement directions and found that the learned adaptation decays in a pattern consistent with the existence of motor primitives that display narrow Gaussian tuning. However, few studies have examined the generalization of motor adaptation across movement speeds. Following adaptation to linear velocity-dependent dynamics during point-to-point reaching arm movements at one speed, we tested the ability of subjects to transfer this adaptation to short-duration higher-speed movements aimed at the same target. We found near-perfect linear extrapolation of the trained adaptation with respect to both the magnitude and the time course of the velocity profiles associated with the high-speed movements: a 69% increase in movement speed corresponded to a 74% extrapolation of the trained adaptation. The close match between the increase in movement speed and the corresponding increase in adaptation beyond what was trained indicates linear hypergeneralization. Computational modeling shows that this pattern of linear hypergeneralization across movement speeds is not compatible with previous models of adaptation in which motor primitives display isotropic Gaussian tuning of motor output around their preferred velocities. Instead, we show that this generalization pattern indicates that the primitives involved in the adaptation to viscous dynamics display anisotropic tuning in velocity space and encode the gain between motor output and motion state rather than motor output itself.

  9. Adaptive learning can result in a failure to profit from good conditions: implications for understanding depression.

    Science.gov (United States)

    Trimmer, Pete C; Higginson, Andrew D; Fawcett, Tim W; McNamara, John M; Houston, Alasdair I

    2015-04-26

    Depression is a major medical problem diagnosed in an increasing proportion of people and for which commonly prescribed psychoactive drugs are frequently ineffective. Development of treatment options may be facilitated by an evolutionary perspective; several adaptive reasons for proneness to depression have been proposed. A common feature of many explanations is that depressive behaviour is a way to avoid costly effort where benefits are small and/or unlikely. However, this viewpoint fails to explain why low mood persists when the situation improves. We investigate whether a behavioural rule that is adapted to a stochastically changing world can cause inactivity which appears similar to the effect of depression, in that it persists after the situation has improved. We develop an adaptive learning model in which an individual has repeated choices of whether to invest costly effort that may result in a net benefit. Investing effort also provides information about the current conditions and rates of change of the conditions. An individual following the optimal behavioural strategy may sometimes remain inactive when conditions are favourable (i.e. when it would be better to invest effort) when it is poorly informed about the current environmental state. Initially benign conditions can predispose an individual to inactivity after a relatively brief period of negative experiences. Our approach suggests that the antecedent factors causing depressed behaviour could go much further back in an individual s history than is currently appreciated. The insights from our approach have implications for the ongoing debate about best treatment options for patients with depressive symptoms. © The Author(s) 2015. Published by Oxford University Press on behalf of the Foundation for Evolution, Medicine, and Public Health.

  10. Adaptive template generation for amyloid PET using a deep learning approach.

    Science.gov (United States)

    Kang, Seung Kwan; Seo, Seongho; Shin, Seong A; Byun, Min Soo; Lee, Dong Young; Kim, Yu Kyeong; Lee, Dong Soo; Lee, Jae Sung

    2018-05-11

    Accurate spatial normalization (SN) of amyloid positron emission tomography (PET) images for Alzheimer's disease assessment without coregistered anatomical magnetic resonance imaging (MRI) of the same individual is technically challenging. In this study, we applied deep neural networks to generate individually adaptive PET templates for robust and accurate SN of amyloid PET without using matched 3D MR images. Using 681 pairs of simultaneously acquired 11 C-PIB PET and T1-weighted 3D MRI scans of AD, MCI, and cognitively normal subjects, we trained and tested two deep neural networks [convolutional auto-encoder (CAE) and generative adversarial network (GAN)] that produce adaptive best PET templates. More specifically, the networks were trained using 685,100 pieces of augmented data generated by rotating 527 randomly selected datasets and validated using 154 datasets. The input to the supervised neural networks was the 3D PET volume in native space and the label was the spatially normalized 3D PET image using the transformation parameters obtained from MRI-based SN. The proposed deep learning approach significantly enhanced the quantitative accuracy of MRI-less amyloid PET assessment by reducing the SN error observed when an average amyloid PET template is used. Given an input image, the trained deep neural networks rapidly provide individually adaptive 3D PET templates without any discontinuity between the slices (in 0.02 s). As the proposed method does not require 3D MRI for the SN of PET images, it has great potential for use in routine analysis of amyloid PET images in clinical practice and research. © 2018 Wiley Periodicals, Inc.

  11. Dynamic Learning from Adaptive Neural Control of Uncertain Robots with Guaranteed Full-State Tracking Precision

    Directory of Open Access Journals (Sweden)

    Min Wang

    2017-01-01

    Full Text Available A dynamic learning method is developed for an uncertain n-link robot with unknown system dynamics, achieving predefined performance attributes on the link angular position and velocity tracking errors. For a known nonsingular initial robotic condition, performance functions and unconstrained transformation errors are employed to prevent the violation of the full-state tracking error constraints. By combining two independent Lyapunov functions and radial basis function (RBF neural network (NN approximator, a novel and simple adaptive neural control scheme is proposed for the dynamics of the unconstrained transformation errors, which guarantees uniformly ultimate boundedness of all the signals in the closed-loop system. In the steady-state control process, RBF NNs are verified to satisfy the partial persistent excitation (PE condition. Subsequently, an appropriate state transformation is adopted to achieve the accurate convergence of neural weight estimates. The corresponding experienced knowledge on unknown robotic dynamics is stored in NNs with constant neural weight values. Using the stored knowledge, a static neural learning controller is developed to improve the full-state tracking performance. A comparative simulation study on a 2-link robot illustrates the effectiveness of the proposed scheme.

  12. Envisioning the future of wildlife in a changing climate: Collaborative learning for adaptation planning

    Science.gov (United States)

    LeDee, Olivia E.; Karasov, W.H.; Martin, Karl J.; Meyer, Michael W.; Ribic, Christine; Van Deelen, Timothy R.

    2011-01-01

    Natural resource managers are tasked with assessing the impacts of climate change on conservation targets and developing adaptation strategies to meet agency goals. The complex, transboundary nature of climate change demands the collaboration of scientists, managers, and stakeholders in this effort. To share, integrate, and apply knowledge from these diverse perspectives, we must engage in social learning. In 2009, we initiated a process to engage university researchers and agency scientists and managers in collaborative learning to assess the impacts of climate change on terrestrial fauna in the state of Wisconsin, USA. We constructed conceptual Bayesian networks to depict the influence of climate change, key biotic and abiotic factors, and existing stressors on the distribution and abundance of 3 species: greater prairie-chicken (Tympanuchus cupido), wood frog (Lithobates sylvaticus), and Karner blue butterfly (Plebejus melissa samuelis). For each species, we completed a 2-stage expert review that elicited dialogue on information gaps, management opportunities, and research priorities. From our experience, collaborative network modeling proved to be a powerful tool to develop a common vision of the potential impacts of climate change on conservation targets.

  13. Automated Detection of Microaneurysms Using Scale-Adapted Blob Analysis and Semi-Supervised Learning

    Energy Technology Data Exchange (ETDEWEB)

    Adal, Kedir M. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Sidebe, Desire [Univ. of Burgundy, Dijon (France); Ali, Sharib [Univ. of Burgundy, Dijon (France); Chaum, Edward [Univ. of Tennessee, Knoxville, TN (United States); Karnowski, Thomas Paul [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Meriaudeau, Fabrice [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

    2014-01-07

    Despite several attempts, automated detection of microaneurysm (MA) from digital fundus images still remains to be an open issue. This is due to the subtle nature of MAs against the surrounding tissues. In this paper, the microaneurysm detection problem is modeled as finding interest regions or blobs from an image and an automatic local-scale selection technique is presented. Several scale-adapted region descriptors are then introduced to characterize these blob regions. A semi-supervised based learning approach, which requires few manually annotated learning examples, is also proposed to train a classifier to detect true MAs. The developed system is built using only few manually labeled and a large number of unlabeled retinal color fundus images. The performance of the overall system is evaluated on Retinopathy Online Challenge (ROC) competition database. A competition performance measure (CPM) of 0.364 shows the competitiveness of the proposed system against state-of-the art techniques as well as the applicability of the proposed features to analyze fundus images.

  14. Learning-Based Adaptive Imputation Methodwith kNN Algorithm for Missing Power Data

    Directory of Open Access Journals (Sweden)

    Minkyung Kim

    2017-10-01

    Full Text Available This paper proposes a learning-based adaptive imputation method (LAI for imputing missing power data in an energy system. This method estimates the missing power data by using the pattern that appears in the collected data. Here, in order to capture the patterns from past power data, we newly model a feature vector by using past data and its variations. The proposed LAI then learns the optimal length of the feature vector and the optimal historical length, which are significant hyper parameters of the proposed method, by utilizing intentional missing data. Based on a weighted distance between feature vectors representing a missing situation and past situation, missing power data are estimated by referring to the k most similar past situations in the optimal historical length. We further extend the proposed LAI to alleviate the effect of unexpected variation in power data and refer to this new approach as the extended LAI method (eLAI. The eLAI selects a method between linear interpolation (LI and the proposed LAI to improve accuracy under unexpected variations. Finally, from a simulation under various energy consumption profiles, we verify that the proposed eLAI achieves about a 74% reduction of the average imputation error in an energy system, compared to the existing imputation methods.

  15. Automated detection of microaneurysms using scale-adapted blob analysis and semi-supervised learning.

    Science.gov (United States)

    Adal, Kedir M; Sidibé, Désiré; Ali, Sharib; Chaum, Edward; Karnowski, Thomas P; Mériaudeau, Fabrice

    2014-04-01

    Despite several attempts, automated detection of microaneurysm (MA) from digital fundus images still remains to be an open issue. This is due to the subtle nature of MAs against the surrounding tissues. In this paper, the microaneurysm detection problem is modeled as finding interest regions or blobs from an image and an automatic local-scale selection technique is presented. Several scale-adapted region descriptors are introduced to characterize these blob regions. A semi-supervised based learning approach, which requires few manually annotated learning examples, is also proposed to train a classifier which can detect true MAs. The developed system is built using only few manually labeled and a large number of unlabeled retinal color fundus images. The performance of the overall system is evaluated on Retinopathy Online Challenge (ROC) competition database. A competition performance measure (CPM) of 0.364 shows the competitiveness of the proposed system against state-of-the art techniques as well as the applicability of the proposed features to analyze fundus images. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  16. Learning effects of interactive decision-making processes for climate change adaptation

    NARCIS (Netherlands)

    Baird, J.; Plummer, R.; Haug, C.C.; Huitema, D.

    2014-01-01

    Learning is gaining attention in relation to governance processes for contemporary environmental challenges; however, scholarship at the nexus of learning and environmental governance lacks clarity and understanding about how to define and measure learning, and the linkages between learning, social

  17. Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection

    Energy Technology Data Exchange (ETDEWEB)

    Park, Sang Hyun [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Gao, Yaozong, E-mail: yzgao@cs.unc.edu [Department of Computer Science, Department of Radiology, and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Shi, Yinghuan, E-mail: syh@nju.edu.cn [State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023 (China); Shen, Dinggang, E-mail: dgshen@med.unc.edu [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul 136-713 (Korea, Republic of)

    2014-11-01

    Purpose: Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correct the segmentations from any type of automatic or interactive segmentation methods. Methods: The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm. Results: The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in order to

  18. Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection

    International Nuclear Information System (INIS)

    Park, Sang Hyun; Gao, Yaozong; Shi, Yinghuan; Shen, Dinggang

    2014-01-01

    Purpose: Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correct the segmentations from any type of automatic or interactive segmentation methods. Methods: The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm. Results: The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in order to

  19. Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection.

    Science.gov (United States)

    Park, Sang Hyun; Gao, Yaozong; Shi, Yinghuan; Shen, Dinggang

    2014-11-01

    Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correct the segmentations from any type of automatic or interactive segmentation methods. The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm. The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in order to evaluate both the efficiency

  20. Effects of Adaptive Training on Working Memory and Academic Achievement of Children with Learning Disabilities: A School-Based Study

    Science.gov (United States)

    Cunningham, Rhonda Phillips

    2013-01-01

    Research has suggested many children with learning disabilities (LD) have deficits in working memory (WM) that hinder their academic achievement. Cogmed RM, a computerized intervention, uses adaptive training over 25 sessions and has shown efficacy in improving WM in children with attention deficit hyperactivity disorder (ADHD) and a variety of…

  1. Psychosocial Adjustment over a Two-Year Period in Children Referred for Learning Problems: Risk, Resilience, and Adaptation.

    Science.gov (United States)

    Sorensen, Lisa G.; Forbes, Peter W.; Bernstein, Jane H.; Weiler, Michael D.; Mitchell, William M.; Waber, Deborah P.

    2003-01-01

    A 2-year study evaluated the relationship among psychosocial adjustment, changes in academic skills, and contextual factors in 100 children (ages 7-11) with learning problems. Contextual variables were significantly associated with psychosocial adaptation, including the effectiveness of the clinical assessment, extent of academic support, and the…

  2. A Model of Successful Adaptation to Online Learning for College-Bound Native American High School Students

    Science.gov (United States)

    Kaler, Collier Butler

    2012-01-01

    Purpose: The purpose of this paper is to examine the conditions for Native American high school students that result in successful adaptation to an online learning environment. Design/methodology/approach: In total, eight Native American students attending high schools located on Montana Indian reservations, and one urban city, were interviewed.…

  3. Methods of Psychological and Pedagogical Accompaniment of First-Year Students in Process of Adapting to Learning at University

    Science.gov (United States)

    Maralova, Tatyana P.; Filipenkova, Olesya G.; Galitskikh, Elena O.; Shulga, Tatiana I.; Sidyacheva, Natalya V.; Ovsyanik, Olga A.

    2016-01-01

    The relevance of the study is conditioned by the complexity of students' adaptation to learning at University due to the change of social environment, an alarming feelings about the precise self-determination, lack of knowledge in opportunities for self-expression in art, science, sport and public life. The purpose of the paper is to identify…

  4. Impacts of Organizational Knowledge Sharing Practices on Employees' Job Satisfaction: Mediating Roles of Learning Commitment and Interpersonal Adaptability

    Science.gov (United States)

    Malik, Muhammad Shaukat; Kanwal, Maria

    2018-01-01

    Purpose: The purpose of this paper is to investigate empirically impacts of organizational knowledge-sharing practices (KSP) on employees' job satisfaction (JS), interpersonal adaptability (IA) and learning commitment (LC). Indirect effects of KSP on JS are also confirmed through mediating factors (LC and IA). Design/methodology/approach:…

  5. Theta synchronization between medial prefrontal cortex and cerebellum is associated with adaptive performance of associative learning behavior

    Science.gov (United States)

    Chen, Hao; Wang, Yi-jie; Yang, Li; Sui, Jian-feng; Hu, Zhi-an; Hu, Bo

    2016-01-01

    Associative learning is thought to require coordinated activities among distributed brain regions. For example, to direct behavior appropriately, the medial prefrontal cortex (mPFC) must encode and maintain sensory information and then interact with the cerebellum during trace eyeblink conditioning (TEBC), a commonly-used associative learning model. However, the mechanisms by which these two distant areas interact remain elusive. By simultaneously recording local field potential (LFP) signals from the mPFC and the cerebellum in guinea pigs undergoing TEBC, we found that theta-frequency (5.0–12.0 Hz) oscillations in the mPFC and the cerebellum became strongly synchronized following presentation of auditory conditioned stimulus. Intriguingly, the conditioned eyeblink response (CR) with adaptive timing occurred preferentially in the trials where mPFC-cerebellum theta coherence was stronger. Moreover, both the mPFC-cerebellum theta coherence and the adaptive CR performance were impaired after the disruption of endogenous orexins in the cerebellum. Finally, association of the mPFC -cerebellum theta coherence with adaptive CR performance was time-limited occurring in the early stage of associative learning. These findings suggest that the mPFC and the cerebellum may act together to contribute to the adaptive performance of associative learning behavior by means of theta synchronization. PMID:26879632

  6. Discriminating Children with Autism from Children with Learning Difficulties with an Adaptation of the Short Sensory Profile

    Science.gov (United States)

    O'Brien, Justin; Tsermentseli, Stella; Cummins, Omar; Happe, Francesca; Heaton, Pamela; Spencer, Janine

    2009-01-01

    In this article, we examine the extent to which children with autism and children with learning difficulties can be discriminated from their responses to different patterns of sensory stimuli. Using an adapted version of the Short Sensory Profile (SSP), sensory processing was compared in 34 children with autism to 33 children with typical…

  7. Learning-based adaptive prescribed performance control of postcapture space robot-target combination without inertia identifications

    Science.gov (United States)

    Wei, Caisheng; Luo, Jianjun; Dai, Honghua; Bian, Zilin; Yuan, Jianping

    2018-05-01

    In this paper, a novel learning-based adaptive attitude takeover control method is investigated for the postcapture space robot-target combination with guaranteed prescribed performance in the presence of unknown inertial properties and external disturbance. First, a new static prescribed performance controller is developed to guarantee that all the involved attitude tracking errors are uniformly ultimately bounded by quantitatively characterizing the transient and steady-state performance of the combination. Then, a learning-based supplementary adaptive strategy based on adaptive dynamic programming is introduced to improve the tracking performance of static controller in terms of robustness and adaptiveness only utilizing the input/output data of the combination. Compared with the existing works, the prominent advantage is that the unknown inertial properties are not required to identify in the development of learning-based adaptive control law, which dramatically decreases the complexity and difficulty of the relevant controller design. Moreover, the transient and steady-state performance is guaranteed a priori by designer-specialized performance functions without resorting to repeated regulations of the controller parameters. Finally, the three groups of illustrative examples are employed to verify the effectiveness of the proposed control method.

  8. Regulation of Emotions in Socially Challenging Learning Situations: An Instrument to Measure the Adaptive and Social Nature of the Regulation Process

    Science.gov (United States)

    Jarvenoja, Hanna; Volet, Simone; Jarvela, Sanna

    2013-01-01

    Self-regulated learning (SRL) research has conventionally relied on measures, which treat SRL as an aptitude. To study self-regulation and motivation in learning contexts as an ongoing adaptive process, situation-specific methods are needed in addition to static measures. This article presents an "Adaptive Instrument for Regulation of Emotions"…

  9. Seamless Integration of Desktop and Mobile Learning Experience through an Ontology-Based Adaptation Engine: Report of a Pilot-Project

    Science.gov (United States)

    Mercurio, Marco; Torre, Ilaria; Torsani, Simone

    2014-01-01

    The paper describes a module within the distance language learning environment of the Language Centre at the Genoa University which adapts, through an ontology, learning activities to the device in use. Adaptation means not simply resizing a page but also the ability to transform the nature of a task so that it fits the device with the smallest…

  10. Using an adapted form of the picture exchange communication system to increase independent requesting in deafblind adults with learning disabilities.

    Science.gov (United States)

    Bracken, Maeve; Rohrer, Nicole

    2014-02-01

    The current study assessed the effectiveness of an adapted form of the Picture Exchange Communication System (PECS) in increasing independent requesting in deafblind adults with learning disabilities. PECS cards were created to accommodate individual needs, including adaptations such as enlarging photographs and using swelled images which consisted of images created on raised line drawing paper. Training included up to Phase III of PECS and procedures ensuring generalizations across individuals and contexts were included. The effects of the intervention were evaluated using a multiple baseline design across participants. Results demonstrated an increase in independent requesting with each of the participants reaching mastery criterion. These results suggest that PECS, in combination with some minor adaptations, may be an effective communicative alternative for individuals who are deafblind and have learning impairments. Copyright © 2013 Elsevier Ltd. All rights reserved.

  11. Adaptive online monitoring for ICU patients by combining just-in-time learning and principal component analysis.

    Science.gov (United States)

    Li, Xuejian; Wang, Youqing

    2016-12-01

    Offline general-type models are widely used for patients' monitoring in intensive care units (ICUs), which are developed by using past collected datasets consisting of thousands of patients. However, these models may fail to adapt to the changing states of ICU patients. Thus, to be more robust and effective, the monitoring models should be adaptable to individual patients. A novel combination of just-in-time learning (JITL) and principal component analysis (PCA), referred to learning-type PCA (L-PCA), was proposed for adaptive online monitoring of patients in ICUs. JITL was used to gather the most relevant data samples for adaptive modeling of complex physiological processes. PCA was used to build an online individual-type model and calculate monitoring statistics, and then to judge whether the patient's status is normal or not. The adaptability of L-PCA lies in the usage of individual data and the continuous updating of the training dataset. Twelve subjects were selected from the Physiobank's Multi-parameter Intelligent Monitoring for Intensive Care II (MIMIC II) database, and five vital signs of each subject were chosen. The proposed method was compared with the traditional PCA and fast moving-window PCA (Fast MWPCA). The experimental results demonstrated that the fault detection rates respectively increased by 20 % and 47 % compared with PCA and Fast MWPCA. L-PCA is first introduced into ICU patients monitoring and achieves the best monitoring performance in terms of adaptability to changes in patient status and sensitivity for abnormality detection.

  12. Adaptation Challenges in Complex River Basins: Lessons Learned and Unlearned for the Colorado

    Science.gov (United States)

    Pulwarty, R. S.

    2008-12-01

    management (e.g. through metering and pricing), and institutional changes that improve the tradability of water rights. The co-evolution of climate history and adaptation did not start with the release of IPCC scenarios. The development of the Colorado River Basin was itself influenced by water resources planners from around the world (including the Middle East) in the late 1800s. As such lessons identified, but not always learned, abound. These hold considerable promise for water savings and the reallocation of water to highly valued uses. Supply-side strategies generally involve increases in storage capacity, abstraction from watercourses, and water transfers. Integrated water resources management provides an important governance framework to achieve adaptation measures across socio-economic, environmental and administrative systems. However, several paradoxes in water management and governance mitigate against the effectiveness of scientific information for meeting short term needs in the context of reducing longer-term vulnerabilities and for providing water to meet environmental needs. Consequently a complete analysis of the effects of climate change on human water uses would consider cross-sector interactions, including the impacts of changes in water use efficiency and intentional transfers of the use of water from one sector to another.

  13. Neuromodulatory adaptive combination of correlation-based learning in cerebellum and reward-based learning in basal ganglia for goal-directed behavior control.

    Science.gov (United States)

    Dasgupta, Sakyasingha; Wörgötter, Florentin; Manoonpong, Poramate

    2014-01-01

    Goal-directed decision making in biological systems is broadly based on associations between conditional and unconditional stimuli. This can be further classified as classical conditioning (correlation-based learning) and operant conditioning (reward-based learning). A number of computational and experimental studies have well established the role of the basal ganglia in reward-based learning, where as the cerebellum plays an important role in developing specific conditioned responses. Although viewed as distinct learning systems, recent animal experiments point toward their complementary role in behavioral learning, and also show the existence of substantial two-way communication between these two brain structures. Based on this notion of co-operative learning, in this paper we hypothesize that the basal ganglia and cerebellar learning systems work in parallel and interact with each other. We envision that such an interaction is influenced by reward modulated heterosynaptic plasticity (RMHP) rule at the thalamus, guiding the overall goal directed behavior. Using a recurrent neural network actor-critic model of the basal ganglia and a feed-forward correlation-based learning model of the cerebellum, we demonstrate that the RMHP rule can effectively balance the outcomes of the two learning systems. This is tested using simulated environments of increasing complexity with a four-wheeled robot in a foraging task in both static and dynamic configurations. Although modeled with a simplified level of biological abstraction, we clearly demonstrate that such a RMHP induced combinatorial learning mechanism, leads to stabler and faster learning of goal-directed behaviors, in comparison to the individual systems. Thus, in this paper we provide a computational model for adaptive combination of the basal ganglia and cerebellum learning systems by way of neuromodulated plasticity for goal-directed decision making in biological and bio-mimetic organisms.

  14. Simultaneous learning and filtering without delusions: a Bayes-optimal combination of Predictive Inference and Adaptive Filtering.

    Science.gov (United States)

    Kneissler, Jan; Drugowitsch, Jan; Friston, Karl; Butz, Martin V

    2015-01-01

    Predictive coding appears to be one of the fundamental working principles of brain processing. Amongst other aspects, brains often predict the sensory consequences of their own actions. Predictive coding resembles Kalman filtering, where incoming sensory information is filtered to produce prediction errors for subsequent adaptation and learning. However, to generate prediction errors given motor commands, a suitable temporal forward model is required to generate predictions. While in engineering applications, it is usually assumed that this forward model is known, the brain has to learn it. When filtering sensory input and learning from the residual signal in parallel, a fundamental problem arises: the system can enter a delusional loop when filtering the sensory information using an overly trusted forward model. In this case, learning stalls before accurate convergence because uncertainty about the forward model is not properly accommodated. We present a Bayes-optimal solution to this generic and pernicious problem for the case of linear forward models, which we call Predictive Inference and Adaptive Filtering (PIAF). PIAF filters incoming sensory information and learns the forward model simultaneously. We show that PIAF is formally related to Kalman filtering and to the Recursive Least Squares linear approximation method, but combines these procedures in a Bayes optimal fashion. Numerical evaluations confirm that the delusional loop is precluded and that the learning of the forward model is more than 10-times faster when compared to a naive combination of Kalman filtering and Recursive Least Squares.

  15. Simultaneous Learning and Filtering without Delusions: A Bayes-Optimal Derivation of Combining Predictive Inference and AdaptiveFiltering

    Directory of Open Access Journals (Sweden)

    Jan eKneissler

    2015-04-01

    Full Text Available Predictive coding appears to be one of the fundamental working principles of brain processing. Amongst other aspects, brains often predict the sensory consequences of their own actions. Predictive coding resembles Kalman filtering, where incoming sensory information is filtered to produce prediction errors for subsequent adaptation and learning. However, to generate prediction errors given motor commands, a suitable temporal forward model is required to generate predictions. While in engineering applications, it is usually assumed that this forward model is known, the brain has to learn it. When filtering sensory input and learning from the residual signal in parallel, a fundamental problem arises: the system can enter a delusional loop when filtering the sensory information using an overly trusted forward model. In this case, learning stalls before accurate convergence because uncertainty about the forward model is not properly accommodated. We present a Bayes-optimal solution to this generic and pernicious problem for the case of linear forward models, which we call Predictive Inference and Adaptive Filtering (PIAF. PIAF filters incoming sensory information and learns the forward model simultaneously. We show that PIAF is formally related to Kalman filtering and to the Recursive Least Squares linear approximation method, but combines these procedures in a Bayes optimal fashion. Numerical evaluations confirm that the delusional loop is precluded and that the learning of the forward model is more than ten-times faster when compared to a naive combination of Kalman filtering and Recursive Least Squares.

  16. Adaptive Kalman filtering for histogram-based appearance learning in infrared imagery.

    Science.gov (United States)

    Venkataraman, Vijay; Fan, Guoliang; Havlicek, Joseph P; Fan, Xin; Zhai, Yan; Yeary, Mark B

    2012-11-01

    Targets of interest in video acquired from imaging infrared sensors often exhibit profound appearance variations due to a variety of factors, including complex target maneuvers, ego-motion of the sensor platform, background clutter, etc., making it difficult to maintain a reliable detection process and track lock over extended time periods. Two key issues in overcoming this problem are how to represent the target and how to learn its appearance online. In this paper, we adopt a recent appearance model that estimates the pixel intensity histograms as well as the distribution of local standard deviations in both the foreground and background regions for robust target representation. Appearance learning is then cast as an adaptive Kalman filtering problem where the process and measurement noise variances are both unknown. We formulate this problem using both covariance matching and, for the first time in a visual tracking application, the recent autocovariance least-squares (ALS) method. Although convergence of the ALS algorithm is guaranteed only for the case of globally wide sense stationary process and measurement noises, we demonstrate for the first time that the technique can often be applied with great effectiveness under the much weaker assumption of piecewise stationarity. The performance advantages of the ALS method relative to the classical covariance matching are illustrated by means of simulated stationary and nonstationary systems. Against real data, our results show that the ALS-based algorithm outperforms the covariance matching as well as the traditional histogram similarity-based methods, achieving sub-pixel tracking accuracy against the well-known AMCOM closure sequences and the recent SENSIAC automatic target recognition dataset.

  17. Adaptive high learning rate probabilistic disruption predictors from scratch for the next generation of tokamaks

    International Nuclear Information System (INIS)

    Vega, J.; Moreno, R.; Pereira, A.; Acero, A.; Murari, A.; Dormido-Canto, S.

    2014-01-01

    The development of accurate real-time disruption predictors is a pre-requisite to any mitigation action. Present theoretical models of disruptions do not reliably cope with the disruption issues. This article deals with data-driven predictors and a review of existing machine learning techniques, from both physics and engineering points of view, is provided. All these methods need large training datasets to develop successful predictors. However, ITER or DEMO cannot wait for hundreds of disruptions to have a reliable predictor. So far, the attempts to extrapolate predictors between different tokamaks have not shown satisfactory results. In addition, it is not clear how valid this approach can be between present devices and ITER/DEMO, due to the differences in their respective scales and possibly underlying physics. Therefore, this article analyses the requirements to create adaptive predictors from scratch to learn from the data of an individual machine from the beginning of operation. A particular algorithm based on probabilistic classifiers has been developed and it has been applied to the database of the three first ITER-like wall campaigns of JET (1036 non-disruptive and 201 disruptive discharges). The predictions start from the first disruption and only 12 re-trainings have been necessary as a consequence of missing 12 disruptions only. Almost 10 000 different predictors have been developed (they differ in their features) and after the chronological analysis of the 1237 discharges, the predictors recognize 94% of all disruptions with an average warning time (AWT) of 654 ms. This percentage corresponds to the sum of tardy detections (11%), valid alarms (76%) and premature alarms (7%). The false alarm rate is 4%. If only valid alarms are considered, the AWT is 244 ms and the standard deviation is 205 ms. The average probability interval about the reliability and accuracy of all the individual predictions is 0.811 ± 0.189. (paper)

  18. Adaptive high learning rate probabilistic disruption predictors from scratch for the next generation of tokamaks

    Science.gov (United States)

    Vega, J.; Murari, A.; Dormido-Canto, S.; Moreno, R.; Pereira, A.; Acero, A.; Contributors, JET-EFDA

    2014-12-01

    The development of accurate real-time disruption predictors is a pre-requisite to any mitigation action. Present theoretical models of disruptions do not reliably cope with the disruption issues. This article deals with data-driven predictors and a review of existing machine learning techniques, from both physics and engineering points of view, is provided. All these methods need large training datasets to develop successful predictors. However, ITER or DEMO cannot wait for hundreds of disruptions to have a reliable predictor. So far, the attempts to extrapolate predictors between different tokamaks have not shown satisfactory results. In addition, it is not clear how valid this approach can be between present devices and ITER/DEMO, due to the differences in their respective scales and possibly underlying physics. Therefore, this article analyses the requirements to create adaptive predictors from scratch to learn from the data of an individual machine from the beginning of operation. A particular algorithm based on probabilistic classifiers has been developed and it has been applied to the database of the three first ITER-like wall campaigns of JET (1036 non-disruptive and 201 disruptive discharges). The predictions start from the first disruption and only 12 re-trainings have been necessary as a consequence of missing 12 disruptions only. Almost 10 000 different predictors have been developed (they differ in their features) and after the chronological analysis of the 1237 discharges, the predictors recognize 94% of all disruptions with an average warning time (AWT) of 654 ms. This percentage corresponds to the sum of tardy detections (11%), valid alarms (76%) and premature alarms (7%). The false alarm rate is 4%. If only valid alarms are considered, the AWT is 244 ms and the standard deviation is 205 ms. The average probability interval about the reliability and accuracy of all the individual predictions is 0.811 ± 0.189.

  19. A learning heuristic for space mapping and searching self-organizing systems using adaptive mesh refinement

    Science.gov (United States)

    Phillips, Carolyn L.

    2014-09-01

    In a complex self-organizing system, small changes in the interactions between the system's components can result in different emergent macrostructures or macrobehavior. In chemical engineering and material science, such spontaneously self-assembling systems, using polymers, nanoscale or colloidal-scale particles, DNA, or other precursors, are an attractive way to create materials that are precisely engineered at a fine scale. Changes to the interactions can often be described by a set of parameters. Different contiguous regions in this parameter space correspond to different ordered states. Since these ordered states are emergent, often experiment, not analysis, is necessary to create a diagram of ordered states over the parameter space. By issuing queries to points in the parameter space (e.g., performing a computational or physical experiment), ordered states can be discovered and mapped. Queries can be costly in terms of resources or time, however. In general, one would like to learn the most information using the fewest queries. Here we introduce a learning heuristic for issuing queries to map and search a two-dimensional parameter space. Using a method inspired by adaptive mesh refinement, the heuristic iteratively issues batches of queries to be executed in parallel based on past information. By adjusting the search criteria, different types of searches (for example, a uniform search, exploring boundaries, sampling all regions equally) can be flexibly implemented. We show that this method will densely search the space, while preferentially targeting certain features. Using numerical examples, including a study simulating the self-assembly of complex crystals, we show how this heuristic can discover new regions and map boundaries more accurately than a uniformly distributed set of queries.

  20. Lessons Learned in Designing and Implementing a Computer-Adaptive Test for English

    Directory of Open Access Journals (Sweden)

    Jack Burston

    2014-09-01

    Full Text Available This paper describes the lessons learned in designing and implementing a computer-adaptive test (CAT for English. The early identification of students with weak L2 English proficiency is of critical importance in university settings that have compulsory English language course graduation requirements. The most efficient means of diagnosing the L2 English ability of incoming students is by means of a computer-based test since such evaluation can be administered quickly, automatically corrected, and the outcome known as soon as the test is completed. While the option of using a commercial CAT is available to institutions with the ability to pay substantial annual fees, or the means of passing these expenses on to their students, language instructors without these resources can only avail themselves of the advantages of CAT evaluation by creating their own tests.  As is demonstrated by the E-CAT project described in this paper, this is a viable alternative even for those lacking any computer programing expertise.  However, language teaching experience and testing expertise are critical to such an undertaking, which requires considerable effort and, above all, collaborative teamwork to succeed. A number of practical skills are also required. Firstly, the operation of a CAT authoring programme must be learned. Once this is done, test makers must master the art of creating a question database and assigning difficulty levels to test items. Lastly, if multimedia resources are to be exploited in a CAT, test creators need to be able to locate suitable copyright-free resources and re-edit them as needed.

  1. Arousal regulation and affective adaptation to human responsiveness by a robot that explores and learns a novel environment.

    Science.gov (United States)

    Hiolle, Antoine; Lewis, Matthew; Cañamero, Lola

    2014-01-01

    In the context of our work in developmental robotics regarding robot-human caregiver interactions, in this paper we investigate how a "baby" robot that explores and learns novel environments can adapt its affective regulatory behavior of soliciting help from a "caregiver" to the preferences shown by the caregiver in terms of varying responsiveness. We build on two strands of previous work that assessed independently (a) the differences between two "idealized" robot profiles-a "needy" and an "independent" robot-in terms of their use of a caregiver as a means to regulate the "stress" (arousal) produced by the exploration and learning of a novel environment, and (b) the effects on the robot behaviors of two caregiving profiles varying in their responsiveness-"responsive" and "non-responsive"-to the regulatory requests of the robot. Going beyond previous work, in this paper we (a) assess the effects that the varying regulatory behavior of the two robot profiles has on the exploratory and learning patterns of the robots; (b) bring together the two strands previously investigated in isolation and take a step further by endowing the robot with the capability to adapt its regulatory behavior along the "needy" and "independent" axis as a function of the varying responsiveness of the caregiver; and (c) analyze the effects that the varying regulatory behavior has on the exploratory and learning patterns of the adaptive robot.

  2. Adaptive e-learning to improve dietary behaviour: a systematic review and cost-effectiveness analysis.

    Science.gov (United States)

    Harris, J; Felix, L; Miners, A; Murray, E; Michie, S; Ferguson, E; Free, C; Lock, K; Landon, J; Edwards, P

    2011-10-01

    UK public health policy strongly advocates dietary change for the improvement of population health and emphasises the importance of individual empowerment to improve health. A new and evolving area in the promotion of dietary behavioural change is 'e-learning', the use of interactive electronic media to facilitate teaching and learning on a range of issues including health. The high level of accessibility, combined with emerging advances in computer processing power, data transmission and data storage, makes interactive e-learning a potentially powerful and cost-effective medium for improving dietary behaviour. This review aims to assess the effectiveness and cost-effectiveness of adaptive e-learning interventions for dietary behaviour change, and also to explore potential psychological mechanisms of action and components of effective interventions. Electronic bibliographic databases (Cumulative Index to Nursing and Allied Health Literature, The Cochrane Library, Dissertation Abstracts, EMBASE, Education Resources Information Center, Global Health, Health Economic Evaluations Database, Health Management Information Consortium, MEDLINE, PsycINFO and Web of Science) were searched for the period January 1990 to November 2009. Reference lists of included studies and previous reviews were also screened; authors were contacted and trial registers were searched. Studies were included if they were randomised controlled trials, involving participants aged ≥ 13 years, which evaluated the effectiveness of interactive software programs for improving dietary behaviour. Primary outcomes were measures of dietary behaviours, including estimated intakes or changes in intake of energy, nutrients, dietary fibre, foods or food groups. Secondary outcome measures were clinical outcomes such as anthropometry or blood biochemistry. Psychological mediators of dietary behaviour change were also investigated. Two review authors independently screened results and extracted data from

  3. Conflict Adaptation and Cue Competition during Learning in an Eriksen Flanker Task

    Science.gov (United States)

    Ghinescu, Rodica; Ramsey, Ashley K.; Gratton, Gabriele; Fabiani, Monica

    2016-01-01

    Two experiments investigated competition between cues that predicted the correct target response to a target stimulus in a response conflict procedure using a flanker task. Subjects received trials with five-character arrays with a central target character and distractor flanker characters that matched (compatible) or did not match (incompatible) the central target. Subjects’ expectancies for compatible and incompatible trials were manipulated by presenting pre-trial cues that signaled the occurrence of compatible or incompatible trials. On some trials, a single cue predicted the target stimulus and the required target response. On other trials, a second redundant, predictive cue was also present on such trials. The results showed an effect of competition between cues for control over strategic responding to the target stimuli, a finding that is predicted by associative learning theories. The finding of competition between pre-trial cues that predict incompatible trials, but not cues that predict compatible trials, suggests that different strategic processes may occur during adaptation to conflict when different kinds of trials are expected. PMID:27941977

  4. Adaptive metric learning with deep neural networks for video-based facial expression recognition

    Science.gov (United States)

    Liu, Xiaofeng; Ge, Yubin; Yang, Chao; Jia, Ping

    2018-01-01

    Video-based facial expression recognition has become increasingly important for plenty of applications in the real world. Despite that numerous efforts have been made for the single sequence, how to balance the complex distribution of intra- and interclass variations well between sequences has remained a great difficulty in this area. We propose the adaptive (N+M)-tuplet clusters loss function and optimize it with the softmax loss simultaneously in the training phrase. The variations introduced by personal attributes are alleviated using the similarity measurements of multiple samples in the feature space with many fewer comparison times as conventional deep metric learning approaches, which enables the metric calculations for large data applications (e.g., videos). Both the spatial and temporal relations are well explored by a unified framework that consists of an Inception-ResNet network with long short term memory and the two fully connected layer branches structure. Our proposed method has been evaluated with three well-known databases, and the experimental results show that our method outperforms many state-of-the-art approaches.

  5. Perceptron learning rule derived from spike-frequency adaptation and spike-time-dependent plasticity.

    Science.gov (United States)

    D'Souza, Prashanth; Liu, Shih-Chii; Hahnloser, Richard H R

    2010-03-09

    It is widely believed that sensory and motor processing in the brain is based on simple computational primitives rooted in cellular and synaptic physiology. However, many gaps remain in our understanding of the connections between neural computations and biophysical properties of neurons. Here, we show that synaptic spike-time-dependent plasticity (STDP) combined with spike-frequency adaptation (SFA) in a single neuron together approximate the well-known perceptron learning rule. Our calculations and integrate-and-fire simulations reveal that delayed inputs to a neuron endowed with STDP and SFA precisely instruct neural responses to earlier arriving inputs. We demonstrate this mechanism on a developmental example of auditory map formation guided by visual inputs, as observed in the external nucleus of the inferior colliculus (ICX) of barn owls. The interplay of SFA and STDP in model ICX neurons precisely transfers the tuning curve from the visual modality onto the auditory modality, demonstrating a useful computation for multimodal and sensory-guided processing.

  6. An Adaptive Bacterial Foraging Optimization Algorithm with Lifecycle and Social Learning

    Directory of Open Access Journals (Sweden)

    Xiaohui Yan

    2012-01-01

    Full Text Available Bacterial Foraging Algorithm (BFO is a recently proposed swarm intelligence algorithm inspired by the foraging and chemotactic phenomenon of bacteria. However, its optimization ability is not so good compared with other classic algorithms as it has several shortages. This paper presents an improved BFO Algorithm. In the new algorithm, a lifecycle model of bacteria is founded. The bacteria could split, die, or migrate dynamically in the foraging processes, and population size varies as the algorithm runs. Social learning is also introduced so that the bacteria will tumble towards better directions in the chemotactic steps. Besides, adaptive step lengths are employed in chemotaxis. The new algorithm is named BFOLS and it is tested on a set of benchmark functions with dimensions of 2 and 20. Canonical BFO, PSO, and GA algorithms are employed for comparison. Experiment results and statistic analysis show that the BFOLS algorithm offers significant improvements than original BFO algorithm. Particulary with dimension of 20, it has the best performance among the four algorithms.

  7. Sparing of descending axons rescues interneuron plasticity in the lumbar cord to allow adaptive learning after thoracic spinal cord injury

    Directory of Open Access Journals (Sweden)

    Christopher Nelson Hansen

    2016-03-01

    Full Text Available This study evaluated the role of spared axons on structural and behavioral neuroplasticity in the lumbar enlargement after a thoracic spinal cord injury (SCI. Previous work has demonstrated that recovery in the presence of spared axons after an incomplete lesion increases behavioral output after a subsequent complete spinal cord transection (TX. This suggests that spared axons direct adaptive changes in below-level neuronal networks of the lumbar cord. In response to spared fibers, we postulate that lumbar neuron networks support behavioral gains by preventing aberrant plasticity. As such, the present study measured histological and functional changes in the isolated lumbar cord after complete TX or incomplete contusion (SCI. To measure functional plasticity in the lumbar cord, we used an established instrumental learning paradigm. In this paradigm, neural circuits within isolated lumbar segments demonstrate learning by an increase in flexion duration that reduces exposure to a noxious leg shock. We employed this model using a proof-of-principle design to evaluate the role of sparing on lumbar learning and plasticity early (7 days or late (42 days after midthoracic SCI in a rodent model. Early after SCI or TX at 7d, spinal learning was unattainable regardless of whether the animal recovered with or without axonal substrate. Failed learning occurred alongside measures of cell soma atrophy and aberrant dendritic spine expression within interneuron populations responsible for sensorimotor integration and learning. Alternatively, exposure of the lumbar cord to a small amount of spared axons for 6 weeks produced near-normal learning late after SCI. This coincided with greater cell soma volume and fewer aberrant dendritic spines on interneurons. Thus, an opportunity to influence activity-based learning in locomotor networks depends on spared axons limiting maladaptive plasticity. Together, this work identifies a time dependent interaction between

  8. A knowledge representation approach using fuzzy cognitive maps for better navigation support in an adaptive learning system.

    Science.gov (United States)

    Chrysafiadi, Konstantina; Virvou, Maria

    2013-12-01

    In this paper a knowledge representation approach of an adaptive and/or personalized tutoring system is presented. The domain knowledge should be represented in a more realistic way in order to allow the adaptive and/or personalized tutoring system to deliver the learning material to each individual learner dynamically taking into account her/his learning needs and her/his different learning pace. To succeed this, the domain knowledge representation has to depict the possible increase or decrease of the learner's knowledge. Considering that the domain concepts that constitute the learning material are not independent from each other, the knowledge representation approach has to allow the system to recognize either the domain concepts that are already partly or completely known for a learner, or the domain concepts that s/he has forgotten, taking into account the learner's knowledge level of the related concepts. In other words, the system should be informed about the knowledge dependencies that exist among the domain concepts of the learning material, as well as the strength on impact of each domain concept on others. Fuzzy Cognitive Maps (FCMs) seem to be an ideal way for representing graphically this kind of information. The suggested knowledge representation approach has been implemented in an e-learning adaptive system for teaching computer programming. The particular system was used by the students of a postgraduate program in the field of Informatics in the University of Piraeus and was compared with a corresponding system, in which the domain knowledge was represented using the most common used technique of network of concepts. The results of the evaluation were very encouraging.

  9. Applying Adaptive Swarm Intelligence Technology with Structuration in Web-Based Collaborative Learning

    Science.gov (United States)

    Huang, Yueh-Min; Liu, Chien-Hung

    2009-01-01

    One of the key challenges in the promotion of web-based learning is the development of effective collaborative learning environments. We posit that the structuration process strongly influences the effectiveness of technology used in web-based collaborative learning activities. In this paper, we propose an ant swarm collaborative learning (ASCL)…

  10. Controlling the chaotic discrete-Hénon system using a feedforward neural network with an adaptive learning rate

    OpenAIRE

    GÖKCE, Kürşad; UYAROĞLU, Yılmaz

    2013-01-01

    This paper proposes a feedforward neural network-based control scheme to control the chaotic trajectories of a discrete-Hénon map in order to stay within an acceptable distance from the stable fixed point. An adaptive learning back propagation algorithm with online training is employed to improve the effectiveness of the proposed method. The simulation study carried in the discrete-Hénon system verifies the validity of the proposed control system.

  11. Individual Learner and Team Modeling for Adaptive Training and Education in Support of the US Army Learning Model: Research Outline

    Science.gov (United States)

    2015-09-01

    system to include the learner, domain, and pedagogical models needed to deliver this training via an ITS. 4 3.1 Self-Regulated Learning and the US...elements and also to highlight their relationships : Adaptive Tutoring: also known as intelligent tutoring; tailored instructional methods to...asserts that through the use of case study examples, instruction can provide the pedagogical foundation for decision-making under uncertainty

  12. Collaborative adaptations in social work intervention research in real-world settings: lessons learned from the field.

    Science.gov (United States)

    Blank Wilson, Amy; Farkas, Kathleen

    2014-01-01

    Social work research has identified the crucial role that service practitioners play in the implementation of evidence-based practices. This has led some researchers to suggest that intervention research needs to incorporate collaborative adaptation strategies in the design and implementation of studies focused on adapting evidence-based practices to real-world practice settings. This article describes a collaborative approach to service adaptations that was used in an intervention study that integrated evidence-based mental health and correctional services in a jail reentry program for people with serious mental illness. This description includes a discussion of the nature of the collaboration engaged in this study, the implementation strategies that were used to support this collaboration, and the lessons that the research team has learned about engaging a collaborative approach to implementing interventions in research projects being conducted in real-world social service delivery settings.

  13. Fast adaptation of the internal model of gravity for manual interceptions: evidence for event-dependent learning.

    Science.gov (United States)

    Zago, Myrka; Bosco, Gianfranco; Maffei, Vincenzo; Iosa, Marco; Ivanenko, Yuri P; Lacquaniti, Francesco

    2005-02-01

    We studied how subjects learn to deal with two conflicting sensory environments as a function of the probability of each environment and the temporal distance between repeated events. Subjects were asked to intercept a visual target moving downward on a screen with randomized laws of motion. We compared five protocols that differed in the probability of constant speed (0g) targets and accelerated (1g) targets. Probability ranged from 9 to 100%, and the time interval between consecutive repetitions of the same target ranged from about 1 to 20 min. We found that subjects systematically timed their responses consistent with the assumption of gravity effects, for both 1 and 0g trials. With training, subjects rapidly adapted to 0g targets by shifting the time of motor activation. Surprisingly, the adaptation rate was independent of both the probability of 0g targets and their temporal distance. Very few 0g trials sporadically interspersed as catch trials during immersive practice with 1g trials were sufficient for learning and consolidation in long-term memory, as verified by retesting after 24 h. We argue that the memory store for adapted states of the internal gravity model is triggered by individual events and can be sustained for prolonged periods of time separating sporadic repetitions. This form of event-related learning could depend on multiple-stage memory, with exponential rise and decay in the initial stages followed by a sample-and-hold module.

  14. Effects of the amount and schedule of varied practice after constant practice on the adaptive process of motor learning

    Directory of Open Access Journals (Sweden)

    Umberto Cesar Corrêa

    2014-12-01

    Full Text Available This study investigated the effects of different amounts and schedules of varied practice, after constant practice, on the adaptive process of motor learning. Participants were one hundred and seven children with a mean age of 11.1 ± 0.9 years. Three experiments were carried out using a complex anticipatory timing task manipulating the following components in the varied practice: visual stimulus speed (experiment 1; sequential response pattern (experiment 2; and visual stimulus speed plus sequential response pattern (experiment 3. In all experiments the design involved three amounts (18, 36, and 63 trials, and two schedules (random and blocked of varied practice. The experiments also involved two learning phases: stabilization and adaptation. The dependent variables were the absolute, variable, and constant errors related to the task goal, and the relative timing of the sequential response. Results showed that all groups worsened the performances in the adaptation phase, and no difference was observed between them. Altogether, the results of the three experiments allow the conclusion that the amounts of trials manipulated in the random and blocked practices did not promote the diversification of the skill since no adaptation was observed.

  15. Adaptive social learning strategies in temporally and spatially varying environments : how temporal vs. spatial variation, number of cultural traits, and costs of learning influence the evolution of conformist-biased transmission, payoff-biased transmission, and individual learning.

    Science.gov (United States)

    Nakahashi, Wataru; Wakano, Joe Yuichiro; Henrich, Joseph

    2012-12-01

    Long before the origins of agriculture human ancestors had expanded across the globe into an immense variety of environments, from Australian deserts to Siberian tundra. Survival in these environments did not principally depend on genetic adaptations, but instead on evolved learning strategies that permitted the assembly of locally adaptive behavioral repertoires. To develop hypotheses about these learning strategies, we have modeled the evolution of learning strategies to assess what conditions and constraints favor which kinds of strategies. To build on prior work, we focus on clarifying how spatial variability, temporal variability, and the number of cultural traits influence the evolution of four types of strategies: (1) individual learning, (2) unbiased social learning, (3) payoff-biased social learning, and (4) conformist transmission. Using a combination of analytic and simulation methods, we show that spatial-but not temporal-variation strongly favors the emergence of conformist transmission. This effect intensifies when migration rates are relatively high and individual learning is costly. We also show that increasing the number of cultural traits above two favors the evolution of conformist transmission, which suggests that the assumption of only two traits in many models has been conservative. We close by discussing how (1) spatial variability represents only one way of introducing the low-level, nonadaptive phenotypic trait variation that so favors conformist transmission, the other obvious way being learning errors, and (2) our findings apply to the evolution of conformist transmission in social interactions. Throughout we emphasize how our models generate empirical predictions suitable for laboratory testing.

  16. An adaptive multi-agent memetic system for personalizing e-learning experiences

    NARCIS (Netherlands)

    Acampora, G.; Gaeta, M.; Munoz, E.; Vitiello, A.

    2011-01-01

    The rapid changes in modern knowledge, due to exponential growth of information sources, are complicating learners' activity. For this reason, novel approaches are necessary to obtain suitable learning solutions able to generate efficient, personalized and flexible learning experiences. From this

  17. Revisiting the Blended Learning Literature: Using a Complex Adaptive Systems Framework

    Science.gov (United States)

    Wang, Yuping; Han, Xibin; Yang, Juan

    2015-01-01

    This research has two aims: (1) to bridge a gap in blended learning research--the lack of a systems approach to the understanding of blended learning research and practice, and (2) to promote a more comprehensive understanding of what has been achieved and what needs to be achieved in blended learning research and practice. To achieve these aims,…

  18. QoS-based experience-aware adaptation in multimedia e-learning - A learner, is a learner, is a user, is a customer

    OpenAIRE

    Moebs, Sabine

    2011-01-01

    One of the challenges for the future of technology-enhanced learning is the retention of learners. On-line learning environments should engage learners and provide an appropriate “Quality of Experience” (QoE). For more than a decade, adaptive hypermedia systems have been used to adapt content and instruction to individual knowledge, goals and preferences in an effort to engage learners. However, even if the content is highly engaging it can be very difficult to achieve good Quality ...

  19. Towards a Finer-Grained Classification of Translation Styles Based on Eye-Tracking, Key-Logging and RTP Data

    DEFF Research Database (Denmark)

    Feng, Jia; Carl, Michael

    This research endeavors to reach a finer-grained classification of translation styles based on observations of Translation Progression Graphs that integrate translation process data and translation product data. Translation styles are first coded based on the findings and classification of Jakobsen...... for the translation tasks. Each translation task is immediately followed by a retrospective protocol with the eye-tracking replay as the cue. We are also interested to see whether translation directionality and source text difficulty would have an impact on translation styles. We try to explore 1) the translation...... styles in terms of different ways of allocating attention to the three phases of translation process, 2) the translation styles in the orientation phase, 3) the translation styles in the drafting phase, with a special focus on online-planning, backtracking, online-revision, as well as the distribution...

  20. Transfer of memory trace of cerebellum-dependent motor learning in human prism adaptation: a model study.

    Science.gov (United States)

    Nagao, Soichi; Honda, Takeru; Yamazaki, Tadashi

    2013-11-01

    Accumulating experimental evidence suggests that the memory trace of ocular reflex adaptation is initially encoded in the cerebellar cortex, and later transferred to the cerebellar nuclei for consolidation through repetitions of training. However, the memory transfer is not well characterized in the learning of voluntary movement. Here, we implement our model of memory transfer to interpret the data of prism adaptation (Martin, Keating, Goodkin, Bastian, & Thach, 1996a, 1996b), assuming that the cerebellar nuclear memory formed by memory transfer is used for normal throwing. When the subject was trained to throw darts wearing prisms in 30-40 trials, the short-term memory for recalibrating the throwing direction by gaze would be formed in the cerebellar cortex, which was extinguished by throwing with normal vision in a similar number of trials. After weeks of repetitions of short-term prism adaptation, the long-term memory would be formed in the cerebellar nuclei through memory transfer, which enabled one to throw darts to the center wearing prisms without any training. These two long-term memories, one for throwing with normal vision and the other for throwing wearing prisms, are assumed to be utilized automatically under volitional control. Moreover, when the prisms were changed to new prisms, a new memory for adapting to the new prisms would be formed in the cerebellar cortex, just to counterbalance the nuclear memory of long-term adaptation to the original prisms in a similar number of trials. These results suggest that memory transfer may occur in the learning of voluntary movements. Copyright © 2013 Elsevier Ltd. All rights reserved.

  1. Cerebellar and prefrontal cortex contributions to adaptation, strategies, and reinforcement learning.

    Science.gov (United States)

    Taylor, Jordan A; Ivry, Richard B

    2014-01-01

    Traditionally, motor learning has been studied as an implicit learning process, one in which movement errors are used to improve performance in a continuous, gradual manner. The cerebellum figures prominently in this literature given well-established ideas about the role of this system in error-based learning and the production of automatized skills. Recent developments have brought into focus the relevance of multiple learning mechanisms for sensorimotor learning. These include processes involving repetition, reinforcement learning, and strategy utilization. We examine these developments, considering their implications for understanding cerebellar function and how this structure interacts with other neural systems to support motor learning. Converging lines of evidence from behavioral, computational, and neuropsychological studies suggest a fundamental distinction between processes that use error information to improve action execution or action selection. While the cerebellum is clearly linked to the former, its role in the latter remains an open question. © 2014 Elsevier B.V. All rights reserved.

  2. Innovations in advanced technology for learning : authoring for adaptive educational hypermedia

    NARCIS (Netherlands)

    Cristea, A.I.

    2004-01-01

    Invited editor's note. - Why should we look at the authoring process in adaptive educational hypermedia design? How does detecting authoring patterns help the process? Why do we need to consider cognitive styles in adaptive hypermedia? What do these seemingly unrelated topics have in common? These

  3. ADAPTATION OF THE STUDENTS' MOTIVATION TOWARDS SCIENCE LEARNING QUESTIONNAIRE TO MEASURE GREEK STUDENTS’ MOTIVATION TOWARDS BIOLOGY LEARNING

    OpenAIRE

    Andressa, Helen; Mavrikaki, Evangelia; Dermitzaki, Irini

    2015-01-01

    The purpose of this study was to investigate students’ motivation towards biology learning and to determine the factors that are related to it: students’ gender and their parents’ occupation (relevant with biology or not) were investigated. The sample of the study consisted of 360 Greek high school students of the 10th grade (178 boys and 182 girls). The data were collected through Students’ Motivation Toward Science Learning (SMTSL) questionnaire. It was found that it was a valid and reliabl...

  4. A cerebellar learning model of vestibulo-ocular reflex adaptation in wild-type and mutant mice.

    Science.gov (United States)

    Clopath, Claudia; Badura, Aleksandra; De Zeeuw, Chris I; Brunel, Nicolas

    2014-05-21

    Mechanisms of cerebellar motor learning are still poorly understood. The standard Marr-Albus-Ito theory posits that learning involves plasticity at the parallel fiber to Purkinje cell synapses under control of the climbing fiber input, which provides an error signal as in classical supervised learning paradigms. However, a growing body of evidence challenges this theory, in that additional sites of plasticity appear to contribute to motor adaptation. Here, we consider phase-reversal training of the vestibulo-ocular reflex (VOR), a simple form of motor learning for which a large body of experimental data is available in wild-type and mutant mice, in which the excitability of granule cells or inhibition of Purkinje cells was affected in a cell-specific fashion. We present novel electrophysiological recordings of Purkinje cell activity measured in naive wild-type mice subjected to this VOR adaptation task. We then introduce a minimal model that consists of learning at the parallel fibers to Purkinje cells with the help of the climbing fibers. Although the minimal model reproduces the behavior of the wild-type animals and is analytically tractable, it fails at reproducing the behavior of mutant mice and the electrophysiology data. Therefore, we build a detailed model involving plasticity at the parallel fibers to Purkinje cells' synapse guided by climbing fibers, feedforward inhibition of Purkinje cells, and plasticity at the mossy fiber to vestibular nuclei neuron synapse. The detailed model reproduces both the behavioral and electrophysiological data of both the wild-type and mutant mice and allows for experimentally testable predictions. Copyright © 2014 the authors 0270-6474/14/347203-13$15.00/0.

  5. Developing the learning physical science curriculum: Adapting a small enrollment, laboratory and discussion based physical science course for large enrollments

    Science.gov (United States)

    Goldberg, Fred; Price, Edward; Robinson, Stephen; Boyd-Harlow, Danielle; McKean, Michael

    2012-06-01

    We report on the adaptation of the small enrollment, lab and discussion based physical science course, Physical Science and Everyday Thinking (PSET), for a large-enrollment, lecture-style setting. Like PSET, the new Learning Physical Science (LEPS) curriculum was designed around specific principles based on research on learning to meet the needs of nonscience students, especially prospective and practicing elementary and middle school teachers. We describe the structure of the two curricula and the adaptation process, including a detailed comparison of similar activities from the two curricula and a case study of a LEPS classroom implementation. In LEPS, short instructor-guided lessons replace lengthier small group activities, and movies, rather than hands-on investigations, provide the evidence used to support and test ideas. LEPS promotes student peer interaction as an important part of sense making via “clicker” questions, rather than small group and whole class discussions typical of PSET. Examples of student dialog indicate that this format is capable of generating substantive student discussion and successfully enacting the design principles. Field-test data show similar student content learning gains with the two curricula. Nevertheless, because of classroom constraints, some important practices of science that were an integral part of PSET were not included in LEPS.

  6. Developing the learning physical science curriculum: Adapting a small enrollment, laboratory and discussion based physical science course for large enrollments

    Directory of Open Access Journals (Sweden)

    Fred Goldberg1

    2012-05-01

    Full Text Available We report on the adaptation of the small enrollment, lab and discussion based physical science course, Physical Science and Everyday Thinking (PSET, for a large-enrollment, lecture-style setting. Like PSET, the new Learning Physical Science (LEPS curriculum was designed around specific principles based on research on learning to meet the needs of nonscience students, especially prospective and practicing elementary and middle school teachers. We describe the structure of the two curricula and the adaptation process, including a detailed comparison of similar activities from the two curricula and a case study of a LEPS classroom implementation. In LEPS, short instructor-guided lessons replace lengthier small group activities, and movies, rather than hands-on investigations, provide the evidence used to support and test ideas. LEPS promotes student peer interaction as an important part of sense making via “clicker” questions, rather than small group and whole class discussions typical of PSET. Examples of student dialog indicate that this format is capable of generating substantive student discussion and successfully enacting the design principles. Field-test data show similar student content learning gains with the two curricula. Nevertheless, because of classroom constraints, some important practices of science that were an integral part of PSET were not included in LEPS.

  7. Adaptation Stories

    International Development Research Centre (IDRC) Digital Library (Canada)

    By Reg'

    adaptation to climate change from various regions of the Sahel. Their .... This simple system, whose cost and maintenance were financially sustainable, brought ... method that enables him to learn from experience and save time, which he ...

  8. Adaptations and accommodations: The use of the WAIS III with people with a Learning Disability

    OpenAIRE

    McKenzie, Karen; Murray, George; Wright, Jenny

    2004-01-01

    Evidence of significant impairment in cognitive functioning has always been one of the main criteria of a learning disability (Pulsifer, 1996) and intellectual assessment is, therefore, one of the tasks of clinical psychologists working within learning disability services. Such assessments are commonly used to help establish of an individual’s cognitive strengths and weaknesses, support needs and more specifically, to help determine if an individual falls within the remit of learning disabili...

  9. Facilitating peer based learning through summative assessment - An adaptation of the Objective Structured Clinical Assessment tool for the blended learning environment.

    Science.gov (United States)

    Wikander, Lolita; Bouchoucha, Stéphane L

    2018-01-01

    Adapting a course from face to face to blended delivery necessitates that assessments are modified accordingly. In Australia the Objective Structured Clinical Assessment tool, as a derivative from the Objective Structured Clinical Examination, has been used in the face-to-face delivery mode as a formative or summative assessment tool in medicine and nursing since 1990. The Objective Structured Clinical Assessment has been used at Charles Darwin University to assess nursing students' simulated clinical skills prior to the commencement of their clinical placements since 2008. Although the majority of the course is delivered online, students attend a one-week intensive clinical simulation block yearly, prior to attending clinical placements. Initially, the Objective Structured Clinical Assessment was introduced as a lecturer assessed summative assessment, over time it was adapted to better suit the blended learning environment. The modification of the tool from an academic to peer assessed assessment tool, was based on the empirical literature, student feedback and a cross-sectional, qualitative study exploring academics' perceptions of the Objective Structured Clinical Assessment (Bouchoucha et al., 2013a, b). This paper presents an overview of the process leading to the successful adaptation of the Objective Structured Clinical Assessment to suit the requirements of a preregistration nursing course delivered through blended learning. This is significant as many universities are moving their curriculum to fully online or blended delivery, yet little attention has been paid to adapting the assessment of simulated clinical skills. The aim is to identify the benefits and drawbacks of using the peer assessed Objective Structured Clinical Assessment and share recommendations for successful implementation. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Closed-loop adaptation of neurofeedback based on mental effort facilitates reinforcement learning of brain self-regulation.

    Science.gov (United States)

    Bauer, Robert; Fels, Meike; Royter, Vladislav; Raco, Valerio; Gharabaghi, Alireza

    2016-09-01

    Considering self-rated mental effort during neurofeedback may improve training of brain self-regulation. Twenty-one healthy, right-handed subjects performed kinesthetic motor imagery of opening their left hand, while threshold-based classification of beta-band desynchronization resulted in proprioceptive robotic feedback. The experiment consisted of two blocks in a cross-over design. The participants rated their perceived mental effort nine times per block. In the adaptive block, the threshold was adjusted on the basis of these ratings whereas adjustments were carried out at random in the other block. Electroencephalography was used to examine the cortical activation patterns during the training sessions. The perceived mental effort was correlated with the difficulty threshold of neurofeedback training. Adaptive threshold-setting reduced mental effort and increased the classification accuracy and positive predictive value. This was paralleled by an inter-hemispheric cortical activation pattern in low frequency bands connecting the right frontal and left parietal areas. Optimal balance of mental effort was achieved at thresholds significantly higher than maximum classification accuracy. Rating of mental effort is a feasible approach for effective threshold-adaptation during neurofeedback training. Closed-loop adaptation of the neurofeedback difficulty level facilitates reinforcement learning of brain self-regulation. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  11. Teacher Effectiveness in Adapting Instruction to the Needs of Pupils With Learning Difficulties in Regular Primary Schools in Ghana

    Directory of Open Access Journals (Sweden)

    Abdul-Razak Kuyini Alhassan

    2014-01-01

    Full Text Available Ghana education system has failed to effectively address the needs of pupils with learning difficulties (LDs in regular classrooms. Underachievement, school dropout, streetism, and antisocial behaviors are the consequences. Teachers’ lack of adequate competence in adaptive instruction is one of the fundamental reasons responsible for this anomaly. This study aims to examine teachers’ competence in adapting instructions to teach pupils with LDs in the regular classroom in Ghana. The data were gathered from 387 sampled teachers in a cross-sectional survey using questionnaires and structured observation methods. We analyzed the data using descriptive statistic, chi-square test, correlation, t test, and ANOVA. The results show that (a teachers have limited to moderate competence in adaptive instruction, (b adaptive teaching is strongly associated with teachers’ competence in teaching pupils with LDs in the regular classroom, and (c apart from gender and class size, teachers’ background variables such as school location and teaching experience differ significantly. The study has serious implications for Ghana’s inclusive education policy and teaching practice.

  12. Technique adaptation, strategic replanning, and team learning during implementation of MR-guided brachytherapy for cervical cancer.

    Science.gov (United States)

    Skliarenko, Julia; Carlone, Marco; Tanderup, Kari; Han, Kathy; Beiki-Ardakani, Akbar; Borg, Jette; Chan, Kitty; Croke, Jennifer; Rink, Alexandra; Simeonov, Anna; Ujaimi, Reem; Xie, Jason; Fyles, Anthony; Milosevic, Michael

    MR-guided brachytherapy (MRgBT) with interstitial needles is associated with improved outcomes in cervical cancer patients. However, there are implementation barriers, including magnetic resonance (MR) access, practitioner familiarity/comfort, and efficiency. This study explores a graded MRgBT implementation strategy that included the adaptive use of needles, strategic use of MR imaging/planning, and team learning. Twenty patients with cervical cancer were treated with high-dose-rate MRgBT (28 Gy in four fractions, two insertions, daily MR imaging/planning). A tandem/ring applicator alone was used for the first insertion in most patients. Needles were added for the second insertion based on evaluation of the initial dosimetry. An interdisciplinary expert team reviewed and discussed the MR images and treatment plans. Dosimetry-trigger technique adaptation with the addition of needles for the second insertion improved target coverage in all patients with suboptimal dosimetry initially without compromising organ-at-risk (OAR) sparing. Target and OAR planning objectives were achieved in most patients. There were small or no systematic differences in tumor or OAR dosimetry between imaging/planning once per insertion vs. daily and only small random variations. Peer review and discussion of images, contours, and plans promoted learning and process development. Technique adaptation based on the initial dosimetry is an efficient approach to implementing MRgBT while gaining comfort with the use of needles. MR imaging and planning once per insertion is safe in most patients as long as applicator shifts, and large anatomical changes are excluded. Team learning is essential to building individual and programmatic competencies. Copyright © 2017 American Brachytherapy Society. Published by Elsevier Inc. All rights reserved.

  13. Enhancing adaptive capacity for restoring fire-dependent ecosystems: the Fire Learning Network's Prescribed Fire Training Exchanges

    Directory of Open Access Journals (Sweden)

    Andrew G. Spencer

    2015-09-01

    Full Text Available Prescribed fire is a critical tool for promoting restoration and increasing resilience in fire-adapted ecosystems, but there are barriers to its use, including a shortage of personnel with adequate ecological knowledge and operational expertise to implement prescribed fire across multijurisdictional landscapes. In the United States, recognized needs for both professional development and increased use of fire are not being met, often because of institutional limitations. The Fire Learning Network has been characterized as a multiscalar, collaborative network that works to enhance the adaptive capacity of fire management institutions, and this network developed the Prescribed Fire Training Exchanges (TREXs to address persistent challenges in increasing the capacity for prescribed fire implementation. Our research was designed to investigate where fire professionals face professional barriers, how the TREX addresses these, and in what ways the TREX may be contributing to the adaptive capacity of fire management institutions. We evaluated the training model using surveys, interviews, focus groups, and participant observation. We found that, although the training events cannot overcome all institutional barriers, they incorporate the key components of professional development in fire; foster collaboration, learning, and network building; and provide flexible opportunities with an emphasis on local context to train a variety of professionals with disparate needs. The strategy also offers an avenue for overcoming barriers faced by contingent and nonfederal fire professionals in attaining training and operational experience, thereby increasing the variety of actors and resources involved in fire management. Although it is an incremental step, the TREX is contributing to the adaptive capacity of institutions in social-ecological systems in which fire is a critical ecological process.

  14. Adaptation of lessons learned from the Eurotunnel Project and CDM magnet production to super collider main ring installation

    International Nuclear Information System (INIS)

    Belding, J.; Di Domenico, P.; Gillin, J.; Hahn, W.; Naventi, R.; Nielsen, M.; Seely, M.; Hopkins, J.; Patterson, L.R.

    1994-01-01

    This paper will present preliminary findings from the Phase I Collider Installation contract studies performed by the Bechtel/General Dynamics/Belding Team related to the installation of technical systems for the SSC main ring north and south arcs. Specific focus is given to the adaptation of lessons learned during construction of the Eurotunnel, including equipment and personnel logistics and transportation. The incorporation of Collider Dipole Magnet manufacturing techniques and process methodologies as related to the handling and interconnection of main ring components is also discussed

  15. Adapting pediatric psychology interventions: lessons learned in treating families from the Middle East.

    Science.gov (United States)

    Hilliard, Marisa E; Ernst, Michelle M; Gray, Wendy N; Saeed, Shehzad A; Cortina, Sandra

    2012-09-01

    Pediatric psychologists are increasingly called upon to treat children from non-Western countries, whose cultures may contrast with a Western medical setting. Research on cultural adaptations of evidence-based treatments (EBTs), particularly for individuals from the Middle East, is sparse. To address this need, we discuss clinical issues encountered when working with patients from the Middle East. Synthesis of the literature regarding culturally adapted EBTs and common themes in Middle Eastern culture. Case vignettes illustrate possible EBT adaptations. Integrating cultural values in treatment is an opportunity to join with patients and families to optimize care. Expectations for medical and psychological treatment vary, and collaborations with cultural liaisons are beneficial. Critical next steps include systematic development, testing, and training in culturally adapting EBTs in pediatric medical settings. Increased dialogue between clinicians, researchers, and cultural liaisons is needed to share knowledge and experiences to enhance patient care.

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

    Science.gov (United States)

    Zhao, Xinyou; Okamoto, Toshio

    2011-01-01

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

  17. Adapting Team-Based Learning for Application in the Basic Electric Circuit Theory Sequence

    Science.gov (United States)

    O'Connell, Robert M.

    2015-01-01

    Team-based learning (TBL) is a form of student-centered active learning in which students independently study new conceptual material before it is treated in the classroom, and then subsequently spend considerable classroom time working in groups on increasingly challenging problems and applications based on that new material. TBL provides…

  18. Combining multi agent paradigm and memetic computing for personalized and adaptive learning experiences

    NARCIS (Netherlands)

    Acampora, G.; Gaeta, M.; Loia, V.

    2011-01-01

    Learning is a critical support mechanism for industrial and academic organizations to enhance the skills of employees and students and, consequently, the overall competitiveness in the new economy. The remarkable velocity and volatility of modern knowledge require novel learning methods offering

  19. Pupil-Teacher Adjustment and Mutual Adaptation in Creating Classroom Learning Environments. Final Report.

    Science.gov (United States)

    Fox, Robert S.; And Others

    This investigation is directed toward an analysis of the dynamics of the learning situations in a variety of public school elementary and secondary classrooms. The focus of the project is to make a comparative analysis of the patterns of cooperation or alienation among parents, teachers, peers and individual pupils which create learning cultures…

  20. PUPIL-TEACHER ADJUSTEMENT AND MUTUAL ADAPTATION IN CREATING CLASSROOM LEARNING ENVIRONMENTS.

    Science.gov (United States)

    FOX, ROBERT S.; AND OTHERS

    AN ANALYSIS OF THE DYNAMICS OF THE LEARNING SITUATIONS IN A VARIETY OF PUBLIC SCHOOL CLASSROOMS WAS UNDERTAKEN. THE PROJECT MADE A COMPARATIVE ANALYSIS OF THE PATTERNS OF COOPERATION OR ALIENATION AMONG PARENTS, TEACHERS, PEERS, AND INDIVIDUAL PUPILS. THE PATTERNS CREATE LEARNING CULTURES OF DIFFERENT PRODUCTIVITY IN VARIOUS CLASSROOMS. THE DATA…

  1. Adaptive Advice in Learning With a Computer-Based Knowledge Management Simulation Game

    NARCIS (Netherlands)

    Leemkuil, Hendrik H.; de Jong, Anthonius J.M.

    2012-01-01

    Despite the long tradition of game-based learning, there are still many unanswered questions regarding the instructional design of educational games. An important issue is the support that learners can be given in a game to enhance their learning. One recommended type of support is “advice,” which

  2. Second Language Learning: Investigating Domain-Specific Adaptation in Advanced L2 Production

    NARCIS (Netherlands)

    Kerz, E.; Wiechmann, D.

    2016-01-01

    Usage-based (UB) accounts conceive of language learning as continuous, locally contingent construction learning, i.e., a lifelong process of developing and honing the repertoire of constructional patterns geared to the optimization of a language user’s communicative ability across a wide range of

  3. Using "big data" to guide implementation of a web and mobile adaptive learning platform for medical students.

    Science.gov (United States)

    Menon, Ashwin; Gaglani, Shiv; Haynes, M Ryan; Tackett, Sean

    2017-09-01

    Adaptive learning platforms (ALPs) can revolutionize medical education by making learning more efficient, but their potential has not been realized because students do not use them persistently. We applied educational data mining methods to study United States medical students who used an ALP called Osmosis ( www.osmosis.org ) from 1 August 2014 to 31 July 2015. Multivariate logistic regressions modeled persistence on Osmosis as the dependent variable and Osmosis-collected variables as predictors. The 6787 students included in our analysis responded to a total of 887,193 items, with 2138 (31.5%) using Osmosis persistently. Number of items per student, mobile device use, subscription payment, and group membership were independently associated with persisting (p data medical education research and provides guidance for improving implementation of ALPs and further investigation.

  4. Adaptive Traffic Signal Control: Deep Reinforcement Learning Algorithm with Experience Replay and Target Network

    OpenAIRE

    Gao, Juntao; Shen, Yulong; Liu, Jia; Ito, Minoru; Shiratori, Norio

    2017-01-01

    Adaptive traffic signal control, which adjusts traffic signal timing according to real-time traffic, has been shown to be an effective method to reduce traffic congestion. Available works on adaptive traffic signal control make responsive traffic signal control decisions based on human-crafted features (e.g. vehicle queue length). However, human-crafted features are abstractions of raw traffic data (e.g., position and speed of vehicles), which ignore some useful traffic information and lead t...

  5. Embracing Change: Adapting and Evolving Your Distance Learning Library Services to Meet the New ACRL Distance Learning Library Services Standards

    Science.gov (United States)

    Marcum, Brad

    2016-01-01

    This article examines the update and revision of the current Association of College and Research Libraries (ACRL) Distance Learning Standards that has been proposed and submitted to the ACRL Standards Committee. An in-depth analysis of the update is included, along with some comparisons between the old and new. Practical advice detailing…

  6. How basic psychological needs and motivation affect vitality and lifelong learning adaptability of pharmacists: a structural equation model.

    Science.gov (United States)

    Tjin A Tsoi, Sharon L N M; de Boer, Anthonius; Croiset, Gerda; Koster, Andries S; van der Burgt, Stéphanie; Kusurkar, Rashmi A

    2018-01-31

    Insufficient professional development may lead to poor performance of healthcare professionals. Therefore, continuing education (CE) and continuing professional development (CPD) are needed to secure safe and good quality healthcare. The aim of the study was to investigate the hypothesized associations and their directions between pharmacists' basic psychological needs in CE, their academic motivation, well-being, learning outcomes. Self-determination theory was used as a theoretical framework for this study. Data were collected through four questionnaires measuring: academic motivation, basic psychological needs (BPN), vitality and lifelong learning adaptability of pharmacists in the CE/CPD learning context. Structural equation modelling was used to analyze the data. Demographic factors like gender and working environment influenced the observed scores for frustration of BPN and factors like training status and working experience influenced the observed scores for academic motivation. A good model fit could be found only for a part of the hypothesized pathway. Frustration of BPN is positively directly related to the less desirable type of academic motivation, controlled motivation (0.88) and negatively directly related to vitality (- 1.61) and negatively indirectly related to learning outcomes in CE. Fulfillment or frustration of BPN are important predictors for well-being and learning outcomes. Further research should be conducted to discover how we can prevent these needs from being frustrated in order to design a motivating, vitalizing and sustainable CE/CPD system for pharmacists and other healthcare professionals. Basic psychological needs are very important predictors for well-being and learning outcomes. Further research should be conducted to discover how we can prevent these needs from being frustrated in order to design a motivating, vitalizing and sustainable CE/CPD system for pharmacists and other healthcare professionals.

  7. Implementasi Jaringan Syaraf Tiruan Recurrent Menggunakan Gradient Descent Adaptive Learning Rate and Momentum Untuk Pendugaan Curah Hujan

    Directory of Open Access Journals (Sweden)

    Afan Galih Salman

    2011-06-01

    Full Text Available The artificial neural network (ANN technology in rainfall prediction can be done using the learning approach. The ANN prediction accuracy is measured by the determination coefficient (R2 and root mean square error (RMSE. This research implements Elman’s Recurrent ANN which is heuristically optimized based on el-nino southern oscilation (ENSO variables: wind, southern oscillation index (SOI, sea surface temperatur (SST dan outgoing long wave radiation (OLR to forecast regional monthly rainfall in Bongan Bali. The heuristic learning optimization done is basically a performance development of standard gradient descent learning algorithm into training algorithms: gradient descent momentum and adaptive learning rate. The patterns of input data affect the performance of Recurrent Elman neural network in estimation process. The first data group that is 75% training data and 25% testing data produce the maximum R2 leap 74,6% while the second data group that is 50% training data and 50% testing data produce the maximum R2 leap 49,8%.

  8. Adaptive Strategy for Online Gait Learning Evaluated on the Polymorphic Robotic LocoKit

    DEFF Research Database (Denmark)

    Christensen, David Johan; Larsen, Jørgen Christian; Stoy, Kasper

    2012-01-01

    This paper presents experiments with a morphologyindependent, life-long strategy for online learning of locomotion gaits, performed on a quadruped robot constructed from the LocoKit modular robot. The learning strategy applies a stochastic optimization algorithm to optimize eight open parameters...... of a central pattern generator based gait implementation. We observe that the strategy converges in roughly ten minutes to gaits of similar or higher velocity than a manually designed gait and that the strategy readapts in the event of failed actuators. In future work we plan to study co-learning...

  9. Adaptive local learning in sampling based motion planning for protein folding.

    Science.gov (United States)

    Ekenna, Chinwe; Thomas, Shawna; Amato, Nancy M

    2016-08-01

    Simulating protein folding motions is an important problem in computational biology. Motion planning algorithms, such as Probabilistic Roadmap Methods, have been successful in modeling the folding landscape. Probabilistic Roadmap Methods and variants contain several phases (i.e., sampling, connection, and path extraction). Most of the time is spent in the connection phase and selecting which variant to employ is a difficult task. Global machine learning has been applied to the connection phase but is inefficient in situations with varying topology, such as those typical of folding landscapes. We develop a local learning algorithm that exploits the past performance of methods within the neighborhood of the current connection attempts as a basis for learning. It is sensitive not only to different types of landscapes but also to differing regions in the landscape itself, removing the need to explicitly partition the landscape. We perform experiments on 23 proteins of varying secondary structure makeup with 52-114 residues. We compare the success rate when using our methods and other methods. We demonstrate a clear need for learning (i.e., only learning methods were able to validate against all available experimental data) and show that local learning is superior to global learning producing, in many cases, significantly higher quality results than the other methods. We present an algorithm that uses local learning to select appropriate connection methods in the context of roadmap construction for protein folding. Our method removes the burden of deciding which method to use, leverages the strengths of the individual input methods, and it is extendable to include other future connection methods.

  10. Resilience design: toward a synthesis of cognition, learning, and collaboration for adaptive problem solving in conservation and natural resource stewardship

    Directory of Open Access Journals (Sweden)

    Charles G. Curtin

    2014-06-01

    Full Text Available Through the resilience design approach, I propose to extend the resilience paradigm by re-examining the components of adaptive decision-making and governance processes. The approach can be divided into three core components: (1 equity design, i.e., the integration of collaborative approaches to conservation and adaptive governance that generates effective self-organization and emergence in conservation and natural resource stewardship; (2 process design, i.e., the generation of more effective knowledge through strategic development of information inputs; and (3 outcome design, i.e., the pragmatic synthesis of the previous two approaches, generating a framework for developing durable and dynamic conservation and stewardship. The design of processes that incorporate perception and learning is critical to generating durable solutions, especially in developing linkages between wicked social and ecological challenges. Starting from first principles based on human cognition, learning, and collaboration, coupled with nearly two decades of practical experience designing and implementing ecosystem-level conservation and restoration programs, I present how design-based approaches to conservation and stewardship can be achieved. This context is critical in helping practitioners and resources managers undertake more effective policy and practice.

  11. Collaborative Game-based Learning - Automatized Adaptation Mechanics for Game-based Collaborative Learning using Game Mastering Concepts

    OpenAIRE

    Wendel, Viktor Matthias

    2015-01-01

    Learning and playing represent two core aspects of the information and communication society nowadays. Both issues are subsumed in Digital Education Games, one major field of Serious Games. Serious Games combine concepts of gaming with a broad range of application fields: among others, educational sectors and training or health and sports, but also marketing, advertisement, political education, and other societally relevant areas such as climate, energy, and safety. This work focuses on colla...

  12. Materials learning from life: concepts for active, adaptive and autonomous molecular systems.

    Science.gov (United States)

    Merindol, Rémi; Walther, Andreas

    2017-09-18

    Bioinspired out-of-equilibrium systems will set the scene for the next generation of molecular materials with active, adaptive, autonomous, emergent and intelligent behavior. Indeed life provides the best demonstrations of complex and functional out-of-equilibrium systems: cells keep track of time, communicate, move, adapt, evolve and replicate continuously. Stirred by the understanding of biological principles, artificial out-of-equilibrium systems are emerging in many fields of soft matter science. Here we put in perspective the molecular mechanisms driving biological functions with the ones driving synthetic molecular systems. Focusing on principles that enable new levels of functionalities (temporal control, autonomous structures, motion and work generation, information processing) rather than on specific material classes, we outline key cross-disciplinary concepts that emerge in this challenging field. Ultimately, the goal is to inspire and support new generations of autonomous and adaptive molecular devices fueled by self-regulating chemistry.

  13. A Novel Framework for Adaptation in Agriculture: Lessons Learned from California's Wine Industry (Invited)

    Science.gov (United States)

    Nicholas, K. A.

    2010-12-01

    While crop yields are threatened by climate change, the management decisions of growers, including their practices to modify the microclimate experienced by the crop, can partially or even completely offset these damages. However, there have been few evaluations of adaptation on the farm scale, where managers are on the front lines of responding to global change. I will present a framework for classifying potential adaptations based on their temporal and spatial scale, their ease of implementation, and their effectiveness in altering or maintaining crop production. Applying this framework to the winegrowing industry in California, it appears that many strategies suggested in the literature for adaptation will either be of limited effectiveness, likely to be cost-prohibitive, or are not compatible with the current values of growers. However, interviews with and observations of winegrowers reveal that novel adaptations, not widely discussed in the literature, are already being employed, often by individuals in an experimental capacity and without community coordination. For example, in addition to irrigation, water is used to modify the vine microclimate for both heating (frost protection) and evaporative cooling. An analysis of responses to past environmental stresses in the wine industry revealed that growers tended to respond to stresses individually rather than collectively, except for severe, novel pests and diseases. Responses may be reactive or proactive; most proactive strategies have been short-term, in response to imminent stress. Growers tend to rely on their own experience to guide their management decisions, which may offer poor guidance under novel climate regimes. These findings highlight some of the difficulties expected in adapting to global change, as well as areas for strategic investments to enhance agricultural resilience to climate change. In particular, strategies to enhance the potential for effective proactive, collective responses could

  14. Proposition and Organization of an Adaptive Learning Domain Based on Fusion from the Web

    Science.gov (United States)

    Chaoui, Mohammed; Laskri, Mohamed Tayeb

    2013-01-01

    The Web allows self-navigated education through interaction with large amounts of Web resources. While enjoying the flexibility of Web tools, authors may suffer from research and filtering Web resources, when they face various resources formats and complex structures. An adaptation of extracted Web resources must be assured by authors, to give…

  15. Adaptive Game Based Learning Using Brain Measures for Attention--Some Explorations

    Science.gov (United States)

    van der Pal, Jelke; Roos, Christopher; Sewnath, Ghanshaam; Rosheuvel, Christian

    2016-01-01

    The prospective use of low fidelity simulation and gaming in aviation training is high, and may facilitate individual, personal training needs in usually asynchronous training setting. Without direct feedback from, or intervention by, an instructor, adaptivity of the training environment is in high demand to ensure training sessions maintain an…

  16. A Cognitive and Neural Model for Adaptive Emotion Reading by Mirroring Preparation States and Hebbian Learning

    NARCIS (Netherlands)

    Bosse, T.; Memon, Z.A.; Treur, J.

    2012-01-01

    Two types of modelling approaches exist to reading an observed person's emotions: with or without making use of the observing person's own emotions. This paper focuses on an integrated approach that combines both types of approaches in an adaptive manner. The proposed models were inspired by recent

  17. People-Centered Development of a Smart Learning Ecosystem of Adaptive Robots

    DEFF Research Database (Denmark)

    Fischer, Daniel Kjær Bonde; Kristiansen, Jakob; Mariager, Casper

    2019-01-01

    Robots are currently moving out of the laboratory and company floor into more human and social contexts including care, rehabilitation and education. While those robots are usually envisioned as a kind of social interaction partner, we suggest a different approach, where robots become adaptive...

  18. Adapting Total Quality Doesn't Mean "Turning Learning into a Business."

    Science.gov (United States)

    Schmoker, Mike; Wilson, Richard B.

    1993-01-01

    Although Alfie Kohn is a first-rate thinker, his article in the same "Educational Leadership" issue confuses adopting Total Quality Management methods with intelligently adapting them. Kohn wrestles too hard with the "worker/student" metaphor and wrongly disparages Deming's emphasis on data and performance. Schools can definitely benefit from…

  19. Learning to walk with an adaptive gain proportional myoelectric controller for a robotic ankle exoskeleton.

    Science.gov (United States)

    Koller, Jeffrey R; Jacobs, Daniel A; Ferris, Daniel P; Remy, C David

    2015-11-04

    Robotic ankle exoskeletons can provide assistance to users and reduce metabolic power during walking. Our research group has investigated the use of proportional myoelectric control for controlling robotic ankle exoskeletons. Previously, these controllers have relied on a constant gain to map user's muscle activity to actuation control signals. A constant gain may act as a constraint on the user, so we designed a controller that dynamically adapts the gain to the user's myoelectric amplitude. We hypothesized that an adaptive gain proportional myoelectric controller would reduce metabolic energy expenditure compared to walking with the ankle exoskeleton unpowered because users could choose their preferred control gain. We tested eight healthy subjects walking with the adaptive gain proportional myoelectric controller with bilateral ankle exoskeletons. The adaptive gain was updated each stride such that on average the user's peak muscle activity was mapped to maximal power output of the exoskeleton. All subjects participated in three identical training sessions where they walked on a treadmill for 50 minutes (30 minutes of which the exoskeleton was powered) at 1.2 ms(-1). We calculated and analyzed metabolic energy consumption, muscle recruitment, inverse kinematics, inverse dynamics, and exoskeleton mechanics. Using our controller, subjects achieved a metabolic reduction similar to that seen in previous work in about a third of the training time. The resulting controller gain was lower than that seen in previous work (β=1.50±0.14 versus a constant β=2). The adapted gain allowed users more total ankle joint power than that of unassisted walking, increasing ankle power in exchange for a decrease in hip power. Our findings indicate that humans prefer to walk with greater ankle mechanical power output than their unassisted gait when provided with an ankle exoskeleton using an adaptive controller. This suggests that robotic assistance from an exoskeleton can allow

  20. An Adaptive Web-Based Learning Environment for the Application of Remote Sensing in Schools

    Science.gov (United States)

    Wolf, N.; Fuchsgruber, V.; Riembauer, G.; Siegmund, A.

    2016-06-01

    Satellite images have great educational potential for teaching on environmental issues and can promote the motivation of young people to enter careers in natural science and technology. Due to the importance and ubiquity of remote sensing in science, industry and the public, the use of satellite imagery has been included into many school curricular in Germany. However, its implementation into school practice is still hesitant, mainly due to lack of teachers' know-how and education materials that align with the curricula. In the project "Space4Geography" a web-based learning platform is developed with the aim to facilitate the application of satellite imagery in secondary school teaching and to foster effective student learning experiences in geography and other related subjects in an interdisciplinary way. The platform features ten learning modules demonstrating the exemplary application of original high spatial resolution remote sensing data (RapidEye and TerraSAR-X) to examine current environmental issues such as droughts, deforestation and urban sprawl. In this way, students will be introduced into the versatile applications of spaceborne earth observation and geospatial technologies. The integrated web-based remote sensing software "BLIF" equips the students with a toolset to explore, process and analyze the satellite images, thereby fostering the competence of students to work on geographical and environmental questions without requiring prior knowledge of remote sensing. This contribution presents the educational concept of the learning environment and its realization by the example of the learning module "Deforestation of the rainforest in Brasil".

  1. Adaptable Web Modules to Stimulate Active Learning in Engineering Hydrology using Data and Model Simulations of Three Regional Hydrologic Systems

    Science.gov (United States)

    Habib, E. H.; Tarboton, D. G.; Lall, U.; Bodin, M.; Rahill-Marier, B.; Chimmula, S.; Meselhe, E. A.; Ali, A.; Williams, D.; Ma, Y.

    2013-12-01

    server-based system. Open source web technologies and community-based tools are used to facilitate wide dissemination and adaptation by diverse, independent institutions. The new hydrologic learning modules are based on recent developments in hydrologic modeling, data, and resources. The modules are embedded in three regional-scale ecosystems, Coastal Louisiana, Florida Everglades, and Utah Great Salt Lake Basin. These sites provide a wealth of hydrologic concepts and scenarios that can be used in most water resource and hydrology curricula. The study develops several learning modules based on the three hydro-systems covering subjects such as: water-budget analysis, effects of human and natural changes, climate-hydrology teleconnections, and water-resource management scenarios. The new developments include an instructional interface to give critical guidance and support to the learner and an instructor's guide containing adaptation and implementation procedures to assist instructors in adopting and integrating the material into courses and provide a consistent experience. The design of the new hydrologic education developments will be transferable to independent institutions and adaptable both instructionally and technically through a server system capable of supporting additional developments by the educational community.

  2. Assessing the effectiveness and cost effectiveness of adaptive e-Learning to improve dietary behaviour: protocol for a systematic review

    Directory of Open Access Journals (Sweden)

    Michie Susan

    2010-04-01

    Full Text Available Abstract Background The composition of habitual diets is associated with adverse or protective effects on aspects of health. Consequently, UK public health policy strongly advocates dietary change for the improvement of population health and emphasises the importance of individual empowerment to improve health. A new and evolving area in the promotion of dietary behavioural change is e-Learning, the use of interactive electronic media to facilitate teaching and learning on a range of issues, including diet and health. The aims of this systematic review are to determine the effectiveness and cost-effectiveness of adaptive e-Learning for improving dietary behaviours. Methods/Design The research will consist of a systematic review and a cost-effectiveness analysis. Studies will be considered for the review if they are randomised controlled trials, involving participants aged 13 or over, which evaluate the effectiveness or efficacy of interactive software programmes for improving dietary behaviour. Primary outcome measures will be those related to dietary behaviours, including estimated intakes of energy, nutrients and dietary fibre, or the estimated number of servings per day of foods or food groups. Secondary outcome measures will be objective clinical measures that are likely to respond to changes in dietary behaviours, such as anthropometry or blood biochemistry. Knowledge, self-efficacy, intention and emotion will be examined as mediators of dietary behaviour change in order to explore potential mechanisms of action. Databases will be searched using a comprehensive four-part search strategy, and the results exported to a bibliographic database. Two review authors will independently screen results to identify potentially eligible studies, and will independently extract data from included studies, with any discrepancies at each stage settled by a third author. Standardised forms and criteria will be used. A descriptive analysis of included

  3. Assessing the effectiveness and cost effectiveness of adaptive e-Learning to improve dietary behaviour: protocol for a systematic review.

    Science.gov (United States)

    Edwards, Phil; Felix, Lambert; Harris, Jody; Ferguson, Elaine; Free, Caroline; Landon, Jane; Lock, Karen; Michie, Susan; Miners, Alec; Murray, Elizabeth

    2010-04-21

    The composition of habitual diets is associated with adverse or protective effects on aspects of health. Consequently, UK public health policy strongly advocates dietary change for the improvement of population health and emphasises the importance of individual empowerment to improve health. A new and evolving area in the promotion of dietary behavioural change is e-Learning, the use of interactive electronic media to facilitate teaching and learning on a range of issues, including diet and health. The aims of this systematic review are to determine the effectiveness and cost-effectiveness of adaptive e-Learning for improving dietary behaviours. The research will consist of a systematic review and a cost-effectiveness analysis. Studies will be considered for the review if they are randomised controlled trials, involving participants aged 13 or over, which evaluate the effectiveness or efficacy of interactive software programmes for improving dietary behaviour. Primary outcome measures will be those related to dietary behaviours, including estimated intakes of energy, nutrients and dietary fibre, or the estimated number of servings per day of foods or food groups. Secondary outcome measures will be objective clinical measures that are likely to respond to changes in dietary behaviours, such as anthropometry or blood biochemistry. Knowledge, self-efficacy, intention and emotion will be examined as mediators of dietary behaviour change in order to explore potential mechanisms of action. Databases will be searched using a comprehensive four-part search strategy, and the results exported to a bibliographic database. Two review authors will independently screen results to identify potentially eligible studies, and will independently extract data from included studies, with any discrepancies at each stage settled by a third author. Standardised forms and criteria will be used.A descriptive analysis of included studies will describe study design, participants, the

  4. A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images.

    Science.gov (United States)

    Janowczyk, Andrew; Doyle, Scott; Gilmore, Hannah; Madabhushi, Anant

    2018-01-01

    Deep learning (DL) has recently been successfully applied to a number of image analysis problems. However, DL approaches tend to be inefficient for segmentation on large image data, such as high-resolution digital pathology slide images. For example, typical breast biopsy images scanned at 40× magnification contain billions of pixels, of which usually only a small percentage belong to the class of interest. For a typical naïve deep learning scheme, parsing through and interrogating all the image pixels would represent hundreds if not thousands of hours of compute time using high performance computing environments. In this paper, we present a resolution adaptive deep hierarchical (RADHicaL) learning scheme wherein DL networks at lower resolutions are leveraged to determine if higher levels of magnification, and thus computation, are necessary to provide precise results. We evaluate our approach on a nuclear segmentation task with a cohort of 141 ER+ breast cancer images and show we can reduce computation time on average by about 85%. Expert annotations of 12,000 nuclei across these 141 images were employed for quantitative evaluation of RADHicaL. A head-to-head comparison with a naïve DL approach, operating solely at the highest magnification, yielded the following performance metrics: .9407 vs .9854 Detection Rate, .8218 vs .8489 F -score, .8061 vs .8364 true positive rate and .8822 vs 0.8932 positive predictive value. Our performance indices compare favourably with state of the art nuclear segmentation approaches for digital pathology images.

  5. RLAM: A Dynamic and Efficient Reinforcement Learning-Based Adaptive Mapping Scheme in Mobile WiMAX Networks

    Directory of Open Access Journals (Sweden)

    M. Louta

    2014-01-01

    Full Text Available WiMAX (Worldwide Interoperability for Microwave Access constitutes a candidate networking technology towards the 4G vision realization. By adopting the Orthogonal Frequency Division Multiple Access (OFDMA technique, the latest IEEE 802.16x amendments manage to provide QoS-aware access services with full mobility support. A number of interesting scheduling and mapping schemes have been proposed in research literature. However, they neglect a considerable asset of the OFDMA-based wireless systems: the dynamic adjustment of the downlink-to-uplink width ratio. In order to fully exploit the supported mobile WiMAX features, we design, develop, and evaluate a rigorous adaptive model, which inherits its main aspects from the reinforcement learning field. The model proposed endeavours to efficiently determine the downlink-to-uplinkwidth ratio, on a frame-by-frame basis, taking into account both the downlink and uplink traffic in the Base Station (BS. Extensive evaluation results indicate that the model proposed succeeds in providing quite accurate estimations, keeping the average error rate below 15% with respect to the optimal sub-frame configurations. Additionally, it presents improved performance compared to other learning methods (e.g., learning automata and notable improvements compared to static schemes that maintain a fixed predefined ratio in terms of service ratio and resource utilization.

  6. A Robust Cooperated Control Method with Reinforcement Learning and Adaptive H∞ Control

    Science.gov (United States)

    Obayashi, Masanao; Uchiyama, Shogo; Kuremoto, Takashi; Kobayashi, Kunikazu

    This study proposes a robust cooperated control method combining reinforcement learning with robust control to control the system. A remarkable characteristic of the reinforcement learning is that it doesn't require model formula, however, it doesn't guarantee the stability of the system. On the other hand, robust control system guarantees stability and robustness, however, it requires model formula. We employ both the actor-critic method which is a kind of reinforcement learning with minimal amount of computation to control continuous valued actions and the traditional robust control, that is, H∞ control. The proposed system was compared method with the conventional control method, that is, the actor-critic only used, through the computer simulation of controlling the angle and the position of a crane system, and the simulation result showed the effectiveness of the proposed method.

  7. Adapting physics courses in an engineering school to the b-learning philosophy

    Science.gov (United States)

    Borondo, J.; Benito, R. M.; Losada, J. C.

    2014-09-01

    In this paper we introduce the methodology that we have followed to convert traditional notes into interactive online materials. The idea behind this has been to make self-consistent and interactive online materials capable of motivating the students to get involved in the learning process. For this purpose, we have used the e-learning environment Moodle, which is a platform with a high interactivity potential. We conclude that the academic performance reaches its maximum when correctly combining self-organising with minimum teacher guidance.

  8. Semi-supervised learning and domain adaptation in natural language processing

    CERN Document Server

    Søgaard, Anders

    2013-01-01

    This book introduces basic supervised learning algorithms applicable to natural language processing (NLP) and shows how the performance of these algorithms can often be improved by exploiting the marginal distribution of large amounts of unlabeled data. One reason for that is data sparsity, i.e., the limited amounts of data we have available in NLP. However, in most real-world NLP applications our labeled data is also heavily biased. This book introduces extensions of supervised learning algorithms to cope with data sparsity and different kinds of sampling bias.This book is intended to be both

  9. An economic evaluation of adaptive e-learning devices to promote weight loss via dietary change for people with obesity.

    Science.gov (United States)

    Miners, Alec; Harris, Jody; Felix, Lambert; Murray, Elizabeth; Michie, Susan; Edwards, Phil

    2012-07-07

    The prevalence of obesity is over 25 % in many developed countries. Obesity is strongly associated with an increased risk of fatal and chronic conditions such as cardiovascular disease and type 2 diabetes. Therefore it has become a major public health concern for many economies. E-learning devices are a relatively novel approach to promoting dietary change. The new generation of devices are 'adaptive' and use interactive electronic media to facilitate teaching and learning. E-Learning has grown out of recent developments in information and communication technology, such as the Internet, interactive computer programmes, interactive television and mobile phones. The aim of this study is to assess the cost-effectiveness of e-learning devices as a method of promoting weight loss via dietary change. An economic evaluation was performed using decision modelling techniques. Outcomes were expressed in terms of Quality-Adjusted Life-Years (QALYs) and costs were estimated from a health services perspective. All parameter estimates were derived from the literature. A systematic review was undertaken to derive the estimate of relative treatment effect. The base case results from the e-Learning Economic Evaluation Model (e-LEEM) suggested that the incremental cost-effectiveness ratio was approximately £102,000 per Quality-Adjusted Life-Year (QALY) compared to conventional care. This finding was robust to most alternative assumptions, except a much lower fixed cost of providing e-learning devices. Expected value of perfect information (EVPI) analysis showed that while the individual level EVPI was arguably negligible, the population level value was between £37 M and £170 M at a willingness to pay between £20,000 to £30,000 per additional QALY. The current economic evidence base suggests that e-learning devices for managing the weight of obese individuals are unlikely to be cost-effective unless their fixed costs are much lower than estimated or future devices prove to

  10. Pre-Service Teachers' Constructivist Teaching Scores Based on Their Learning Styles

    Science.gov (United States)

    Kablan, Zeynel; Kaya, Sibel

    2014-01-01

    This study examined the relationship between pre-service teachers' constructivist teaching and their learning styles based on Kolb's Experiential Learning Theory. The Learning Styles Inventory-3 was administered at the beginning of the semester to determine preferred learning style. The Constructivist Teaching Evaluation Form was filled out by…

  11. A Remote Sensing Image Fusion Method based on adaptive dictionary learning

    Science.gov (United States)

    He, Tongdi; Che, Zongxi

    2018-01-01

    This paper discusses using a remote sensing fusion method, based on' adaptive sparse representation (ASP)', to provide improved spectral information, reduce data redundancy and decrease system complexity. First, the training sample set is formed by taking random blocks from the images to be fused, the dictionary is then constructed using the training samples, and the remaining terms are clustered to obtain the complete dictionary by iterated processing at each step. Second, the self-adaptive weighted coefficient rule of regional energy is used to select the feature fusion coefficients and complete the reconstruction of the image blocks. Finally, the reconstructed image blocks are rearranged and an average is taken to obtain the final fused images. Experimental results show that the proposed method is superior to other traditional remote sensing image fusion methods in both spectral information preservation and spatial resolution.

  12. An adaptive-learning approach to affect regulation: strategic influences on evaluative priming.

    Science.gov (United States)

    Freytag, Peter; Bluemke, Matthias; Fiedler, Klaus

    2011-04-01

    An adaptive cognition approach to evaluative priming is not compatible with the view that the entire process is automatically determined by prime stimulus valence alone. In addition to the evaluative congruity of individual prime-target pairs, an adaptive regulation function should be sensitive to the base rates of positive and negative stimuli as well as to the perceived contingency between prime and target valence. The present study was particularly concerned with pseudocontingent inferences that offer a proxy for the assessment of contingencies from degraded or incomplete stimulus input. As expected, response latencies were shorter for the more prevalent target valence and for evaluatively congruent trials. However, crucially, the congruity effect was eliminated and overridden by pseudocontingencies inferred from the stimulus environment. These strategic inferences were further enhanced when the task called for the evaluation of both prime stimuli and target stimuli. © 2011 Psychology Press, an imprint of the Taylor & Francis Group, an Informa business

  13. QoS Adaptation in Multimedia Multicast Conference Applications for E-Learning Services

    Science.gov (United States)

    Deusdado, Sérgio; Carvalho, Paulo

    2006-01-01

    The evolution of the World Wide Web service has incorporated new distributed multimedia conference applications, powering a new generation of e-learning development and allowing improved interactivity and prohuman relations. Groupware applications are increasingly representative in the Internet home applications market, however, the Quality of…

  14. Potentiating mGluR5 Function with a Positive Allosteric Modulator Enhances Adaptive Learning

    Science.gov (United States)

    Xu, Jian; Zhu, Yongling; Kraniotis, Stephen; He, Qionger; Marshall, John J.; Nomura, Toshihiro; Stauffer, Shaun R.; Lindsley, Craig W.; Conn, P. Jeffrey; Contractor, Anis

    2013-01-01

    Metabotropic glutamate receptor 5 (mGluR5) plays important roles in modulating neural activity and plasticity and has been associated with several neuropathological disorders. Previous work has shown that genetic ablation or pharmacological inhibition of mGluR5 disrupts fear extinction and spatial reversal learning, suggesting that mGluR5…

  15. Reinforcement learning for partially observable dynamic processes: adaptive dynamic programming using measured output data.

    Science.gov (United States)

    Lewis, F L; Vamvoudakis, Kyriakos G

    2011-02-01

    Approximate dynamic programming (ADP) is a class of reinforcement learning methods that have shown their importance in a variety of applications, including feedback control of dynamical systems. ADP generally requires full information about the system internal states, which is usually not available in practical situations. In this paper, we show how to implement ADP methods using only measured input/output data from the system. Linear dynamical systems with deterministic behavior are considered herein, which are systems of great interest in the control system community. In control system theory, these types of methods are referred to as output feedback (OPFB). The stochastic equivalent of the systems dealt with in this paper is a class of partially observable Markov decision processes. We develop both policy iteration and value iteration algorithms that converge to an optimal controller that requires only OPFB. It is shown that, similar to Q -learning, the new methods have the important advantage that knowledge of the system dynamics is not needed for the implementation of these learning algorithms or for the OPFB control. Only the order of the system, as well as an upper bound on its "observability index," must be known. The learned OPFB controller is in the form of a polynomial autoregressive moving-average controller that has equivalent performance with the optimal state variable feedback gain.

  16. Game-as-Teacher: Modification by Adaptation in Learning through Game-Play

    Science.gov (United States)

    Hopper, Tim

    2011-01-01

    This paper will explore how game-play in video games as well as game centered approaches in physical education (PE) such as Teaching Games for Understanding (TGfU) can draw on complexity thinking to inform the learning process in physical education. Using the video game concept of game-as-teacher (Gee, 2007), ideas such as enabling constraints…

  17. Moving Past Curricula and Strategies: Language and the Development of Adaptive Pedagogy for Immersive Learning Environments

    Science.gov (United States)

    Hand, Brian; Cavagnetto, Andy; Chen, Ying-Chih; Park, Soonhye

    2016-01-01

    Given current concerns internationally about student performance in science and the need to shift how science is being learnt in schools, as a community, we need to shift how we approach the issue of learning and teaching in science. In the future, we are going to have to close the gap between how students construct and engage with knowledge in a…

  18. Enriching Adaptation in E-Learning Systems through a Situation-Aware Ontology Network

    Science.gov (United States)

    Pernas, Ana Marilza; Diaz, Alicia; Motz, Regina; de Oliveira, Jose Palazzo Moreira

    2012-01-01

    Purpose: The broader adoption of the internet along with web-based systems has defined a new way of exchanging information. That advance added by the multiplication of mobile devices has required systems to be even more flexible and personalized. Maybe because of that, the traditional teaching-controlled learning style has given up space to a new…

  19. Disaster Preparedness, Adaptive Politics and Lifelong Learning: A Case of Japan

    Science.gov (United States)

    Kitagawa, Kaori

    2016-01-01

    Preparedness for disaster scenarios is progressively becoming an educational agenda for governments because of diversifying risks and threats worldwide. In disaster-prone Japan, disaster preparedness has been a prioritised national agenda, and preparedness education has been undertaken in both formal schooling and lifelong learning settings. This…

  20. Adapting to Student Learning Styles: Engaging Students with Cell Phone Technology in Organic Chemistry Instruction

    Science.gov (United States)

    Pursell, David P.

    2009-01-01

    Students of organic chemistry traditionally make 3 x 5 in. flash cards to assist learning nomenclature, structures, and reactions. Advances in educational technology have enabled flash cards to be viewed on computers, offering an endless array of drilling and feedback for students. The current generation of students is less inclined to use…

  1. Adapting research-based curricula at Seattle Pacific University: Results on student learning

    Science.gov (United States)

    Close, Eleanor; Vokos, Stamatis; Lindberg, John; Seeley, Lane

    2004-05-01

    Seattle Pacific University is the recent recipient of a NSF CCLI grant to improve student learning in introductory physics and calculus courses. This talk will outline the goals of this collaborative project and present some initial results on student performance. Results from research-based assessments will be presented as well as specific examples of successes and challenges from mechanics and electricity and magnetism.

  2. Human tracking in thermal images using adaptive particle filters with online random forest learning

    Science.gov (United States)

    Ko, Byoung Chul; Kwak, Joon-Young; Nam, Jae-Yeal

    2013-11-01

    This paper presents a fast and robust human tracking method to use in a moving long-wave infrared thermal camera under poor illumination with the existence of shadows and cluttered backgrounds. To improve the human tracking performance while minimizing the computation time, this study proposes an online learning of classifiers based on particle filters and combination of a local intensity distribution (LID) with oriented center-symmetric local binary patterns (OCS-LBP). Specifically, we design a real-time random forest (RF), which is the ensemble of decision trees for confidence estimation, and confidences of the RF are converted into a likelihood function of the target state. First, the target model is selected by the user and particles are sampled. Then, RFs are generated using the positive and negative examples with LID and OCS-LBP features by online learning. The learned RF classifiers are used to detect the most likely target position in the subsequent frame in the next stage. Then, the RFs are learned again by means of fast retraining with the tracked object and background appearance in the new frame. The proposed algorithm is successfully applied to various thermal videos as tests and its tracking performance is better than those of other methods.

  3. Adaptive Educational Hypermedia Accommodating Learning Styles: A Content Analysis of Publications from 2000 to 2011

    Science.gov (United States)

    Akbulut, Yavuz; Cardak, Cigdem Suzan

    2012-01-01

    Implementing instructional interventions to accommodate learner differences has received considerable attention. Among these individual difference variables, the empirical evidence regarding the pedagogical value of learning styles has been questioned, but the research on the issue continues. Recent developments in Web-based implementations have…

  4. Ask yeast how to burn your fats: lessons learned from the metabolic adaptation to salt stress.

    Science.gov (United States)

    Pascual-Ahuir, Amparo; Manzanares-Estreder, Sara; Timón-Gómez, Alba; Proft, Markus

    2018-02-01

    Here, we review and update the recent advances in the metabolic control during the adaptive response of budding yeast to hyperosmotic and salt stress, which is one of the best understood signaling events at the molecular level. This environmental stress can be easily applied and hence has been exploited in the past to generate an impressively detailed and comprehensive model of cellular adaptation. It is clear now that this stress modulates a great number of different physiological functions of the cell, which altogether contribute to cellular survival and adaptation. Primary defense mechanisms are the massive induction of stress tolerance genes in the nucleus, the activation of cation transport at the plasma membrane, or the production and intracellular accumulation of osmolytes. At the same time and in a coordinated manner, the cell shuts down the expression of housekeeping genes, delays the progression of the cell cycle, inhibits genomic replication, and modulates translation efficiency to optimize the response and to avoid cellular damage. To this fascinating interplay of cellular functions directly regulated by the stress, we have to add yet another layer of control, which is physiologically relevant for stress tolerance. Salt stress induces an immediate metabolic readjustment, which includes the up-regulation of peroxisomal biomass and activity in a coordinated manner with the reinforcement of mitochondrial respiratory metabolism. Our recent findings are consistent with a model, where salt stress triggers a metabolic shift from fermentation to respiration fueled by the enhanced peroxisomal oxidation of fatty acids. We discuss here the regulatory details of this stress-induced metabolic shift and its possible roles in the context of the previously known adaptive functions.

  5. Effects of culture shock and cross-cultural adaptation on learning satisfaction of mainland China students studying in Taiwan

    Directory of Open Access Journals (Sweden)

    Shieh, Chich-Jen

    2014-11-01

    Full Text Available With the national impact of low fertility, the enrollment of higher education in Taiwan is facing a dilemma. To cope with such a problem, the government has actively promoted Mainland China students to study in Taiwan. In addition to enhancing the international competitiveness of domestic universities, cross-strait education, and real academic exchange, it is expected to solve the enrollment shortage of colleges. However, the situations and pressures of Culture Shock, Cross-Cultural Adaptation, and Learning Satisfaction are critical for Mainland China students. Taking Mainland China students who study in Taiwan for more than four months (about a semester as the research participants, a total of 250 questionnaires were distributed and 167 valid ones were retrieved, with a retrieval rate of 67%. The research findings show significant correlations between Cross-Cultural Adaptation and Culture Shock, Culture Shock and Learning Satisfaction, and Cross-Cultural Adaptation and Learning Satisfaction.Debido al impacto de la baja fertilidad en el país, Taiwán afronta un dilema en relación con la inscripción en la enseñanza superior. Para enfrentarse al problema el gobierno ha promovido activamente que estudiantes de la China continental estudien en Taiwán. Además de incrementar la competitividad internacional de las universidades taiwanesas, la formación a ambos lados del estrecho y un verdadero intercambio académico, se espera que ello solucione la escasez de inscripciones en las facultades. Sin embargo, las situaciones y las presiones que generan el choque cultural, la adaptación multicultural y la satisfacción con el aprendizaje resultan críticas para los estudiantes de la China continental. Tomando como muestra de investigación a estudiantes de la China continental que estudian en Taiwán durante más de cuatro meses (aproximadamente un semestre, se distribuyó un total de 250 cuestionarios, de los cuales 167 fueron válidos, con una tasa

  6. Adaptive Control Using Fully Online Sequential-Extreme Learning Machine and a Case Study on Engine Air-Fuel Ratio Regulation

    Directory of Open Access Journals (Sweden)

    Pak Kin Wong

    2014-01-01

    Full Text Available Most adaptive neural control schemes are based on stochastic gradient-descent backpropagation (SGBP, which suffers from local minima problem. Although the recently proposed regularized online sequential-extreme learning machine (ReOS-ELM can overcome this issue, it requires a batch of representative initial training data to construct a base model before online learning. The initial data is usually difficult to collect in adaptive control applications. Therefore, this paper proposes an improved version of ReOS-ELM, entitled fully online sequential-extreme learning machine (FOS-ELM. While retaining the advantages of ReOS-ELM, FOS-ELM discards the initial training phase, and hence becomes suitable for adaptive control applications. To demonstrate its effectiveness, FOS-ELM was applied to the adaptive control of engine air-fuel ratio based on a simulated engine model. Besides, controller parameters were also analyzed, in which it is found that large hidden node number with small regularization parameter leads to the best performance. A comparison among FOS-ELM and SGBP was also conducted. The result indicates that FOS-ELM achieves better tracking and convergence performance than SGBP, since FOS-ELM tends to learn the unknown engine model globally whereas SGBP tends to “forget” what it has learnt. This implies that FOS-ELM is more preferable for adaptive control applications.

  7. SpikeTemp: An Enhanced Rank-Order-Based Learning Approach for Spiking Neural Networks With Adaptive Structure.

    Science.gov (United States)

    Wang, Jinling; Belatreche, Ammar; Maguire, Liam P; McGinnity, Thomas Martin

    2017-01-01

    This paper presents an enhanced rank-order-based learning algorithm, called SpikeTemp, for spiking neural networks (SNNs) with a dynamically adaptive structure. The trained feed-forward SNN consists of two layers of spiking neurons: 1) an encoding layer which temporally encodes real-valued features into spatio-temporal spike patterns and 2) an output layer of dynamically grown neurons which perform spatio-temporal classification. Both Gaussian receptive fields and square cosine population encoding schemes are employed to encode real-valued features into spatio-temporal spike patterns. Unlike the rank-order-based learning approach, SpikeTemp uses the precise times of the incoming spikes for adjusting the synaptic weights such that early spikes result in a large weight change and late spikes lead to a smaller weight change. This removes the need to rank all the incoming spikes and, thus, reduces the computational cost of SpikeTemp. The proposed SpikeTemp algorithm is demonstrated on several benchmark data sets and on an image recognition task. The results show that SpikeTemp can achieve better classification performance and is much faster than the existing rank-order-based learning approach. In addition, the number of output neurons is much smaller when the square cosine encoding scheme is employed. Furthermore, SpikeTemp is benchmarked against a selection of existing machine learning algorithms, and the results demonstrate the ability of SpikeTemp to classify different data sets after just one presentation of the training samples with comparable classification performance.

  8. BROA: An agent-based model to recommend relevant Learning Objects from Repository Federations adapted to learner profile

    Directory of Open Access Journals (Sweden)

    Paula A. Rodríguez

    2013-03-01

    Full Text Available Learning Objects (LOs are distinguished from traditional educational resources for their easy and quickly availability through Web-based repositories, from which they are accessed through their metadata. In addition, having a user profile allows an educational recommender system to help the learner to find the most relevant LOs based on their needs and preferences. The aim of this paper is to propose an agent-based model so-called BROA to recommend relevant LOs recovered from Repository Federations as well as LOs adapted to learner profile. The model proposed uses both role and service models of GAIA methodology, and the analysis models of the MAS-CommonKADS methodology. A prototype was built based on this model and validated to obtain some assessing results that are finally presented.

  9. Adapting to Student Learning Styles: Using Cell Phone Technology in Undergraduate Science Instruction

    Directory of Open Access Journals (Sweden)

    Richard Pennington

    2010-10-01

    Full Text Available Students of science traditionally make 3x5 flash cards to assist learning nomenclature, structures, and reactions. Advances in educational technology have enabled flashcards viewed on computers, offering an endless array of drilling and feedback opportunities for students. The current generation of students is less inclined to use computers, but they use their cell phones 24 hours a day. This report outlines these trends and an even more recent educational technology initiative, that of using cell phone flash cards to help students learn biology and chemistry nomenclature, structures, and reactions. Students responded positively to cell phone flash cards in a pilot study and a more detailed study is planned for the coming year.

  10. LEARNING VECTOR QUANTIZATION FOR ADAPTED GAUSSIAN MIXTURE MODELS IN AUTOMATIC SPEAKER IDENTIFICATION

    Directory of Open Access Journals (Sweden)

    IMEN TRABELSI

    2017-05-01

    Full Text Available Speaker Identification (SI aims at automatically identifying an individual by extracting and processing information from his/her voice. Speaker voice is a robust a biometric modality that has a strong impact in several application areas. In this study, a new combination learning scheme has been proposed based on Gaussian mixture model-universal background model (GMM-UBM and Learning vector quantization (LVQ for automatic text-independent speaker identification. Features vectors, constituted by the Mel Frequency Cepstral Coefficients (MFCC extracted from the speech signal are used to train the New England subset of the TIMIT database. The best results obtained (90% for gender- independent speaker identification, 97 % for male speakers and 93% for female speakers for test data using 36 MFCC features.

  11. Adaptive Tutoring for Self-Regulated Learning: A Tutorial on Tutoring Systems

    Science.gov (United States)

    2014-12-01

    impact learning with effect sizes equivalent to raising average (“C”) students to experts (“A” students) through tailored instruction and...classification using physiological sensors (Brawner and Goldberg, 2012; Goldberg & Brawner, 2012; Kokini, et al, 2012) • EEGs – Advanced Brain ... IQ , EQ, adaptability…) Merrill, D. , Reiser, B, Ranney, M., and Trafton, J. (1992). Effective Tutoring Techniques: A Comparison of Human Tutors and

  12. Statistical Learning and Adaptive Decision-Making Underlie Human Response Time Variability in Inhibitory Control

    Directory of Open Access Journals (Sweden)

    Ning eMa

    2015-08-01

    Full Text Available Response time (RT is an oft-reported behavioral measure in psychological and neurocognitive experiments, but the high level of observed trial-to-trial variability in this measure has often limited its usefulness. Here, we combine computational modeling and psychophysics to examine the hypothesis that fluctuations in this noisy measure reflect dynamic computations in human statistical learning and corresponding cognitive adjustments. We present data from the stop-signal task, in which subjects respond to a go stimulus on each trial, unless instructed not to by a subsequent, infrequently presented stop signal. We model across-trial learning of stop signal frequency, P(stop, and stop-signal onset time, SSD (stop-signal delay, with a Bayesian hidden Markov model, and within-trial decision-making with an optimal stochastic control model. The combined model predicts that RT should increase with both expected P(stop and SSD. The human behavioral data (n=20 bear out this prediction, showing P(stop and SSD both to be significant, independent predictors of RT, with P(stop being a more prominent predictor in 75% of the subjects, and SSD being more prominent in the remaining 25%. The results demonstrate that humans indeed readily internalize environmental statistics and adjust their cognitive/behavioral strategy accordingly, and that subtle patterns in RT variability can serve as a valuable tool for validating models of statistical learning and decision-making. More broadly, the modeling tools presented in this work can be generalized to a large body of behavioral paradigms, in order to extract insights about cognitive and neural processing from apparently quite noisy behavioral measures. We also discuss how this behaviorally validated model can then be used to conduct model-based analysis of neural data, in order to help identify specific brain areas for representing and encoding key computational quantities in learning and decision-making.

  13. A Study on Online Education Model using Location Based Adaptive Mobile Learning

    OpenAIRE

    K. Krishna Prasad; P. S. Aithal

    2017-01-01

    Online educations are gaining more scope due to the busy schedule of working groups and their interest to acquire knowledge in new fields. Working group people find difficult to get admission in top institutions for their interested course due to competition and lack of time flexibility. Regular full-time university affiliated courses become lack of interest for the working group due to outdated curriculum, lack of innovation in teaching, unchanged learning and evaluation environment and lack...

  14. Adaptive learning with covariate shift-detection for motor imagery-based brain–computer interface

    OpenAIRE

    Raza, H; Cecotti, H; Li, Y; Prasad, G

    2015-01-01

    A common assumption in traditional supervised learning is the similar probability distribution of data between the training phase and the testing/operating phase. When transitioning from the training to testing phase, a shift in the probability distribution of input data is known as a covariate shift. Covariate shifts commonly arise in a wide range of real-world systems such as electroencephalogram-based brain–computer interfaces (BCIs). In such systems, there is a necessity for continuous mo...

  15. Looking through the Lens: Adapting and Modifying Photovoice Projects for Active Learning and Engagement in Biology

    OpenAIRE

    Karobi Moitra

    2016-01-01

    Photovoice projects traditionally include original visual imagery and minimal text to tell a powerful story. Photovoice student projects have been utilized in the health and social sciences to involve students with the local community and also in community-based projects to help at-risk youth call attention to their community problems, such as disease and drug use. The tool of the photovoice can be used in the biology classroom to engage students in the active learning process through communi...

  16. Implementing the adapted physical education E-learning program into physical education teacher education program.

    Science.gov (United States)

    Kwon, Eun Hye; Block, Martin E

    2017-10-01

    According to the Ministry of Education Korea (2014), the approximately 70.4% of all students with disabilities are included in general schools in Korea. However, studies show that Korean GPE teachers do not feel comforatble or prepared to include students with disabilities (Oh & Lee, 1999; Roh, 2002; Roh & Oh, 2005). The purpose of this study was to explore whether an APE e-learning supplement would have an impact on the level of self-efficacy and content knowledge of pre-service teachers related to including students with intellectual disabilities. An APE supplement was developed based on the Instructional Design Model (Dick, Carey, & Carey, 2005) to provide three sources of self-efficacy, mastery experience, vicarious experience, and social persuasions. Three groups of pre-service teachers (N=75) took the same content supplement with different delivery system, E-learning group (n=25) with online, traditional group (n=25) with printed handout, and control group (n=25) without supplement. Two instruments, the Physical Educators' Situation-Specific Self-efficacy and Inclusion Student with Disabilities in Physical Education (SE-PETE-D) and the content knowledge test, were given to all participants twice (i.e., pretest and posttest). A 3×2 mixed effect ANOVA revealed that pre-service teachers' perceived self-efficacy (p=0.023) improved after taking the e-learning supplement. However, there was no significant difference in the level of content knowledge (p=0.248) between the learning group and tranditional group. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. Statistical learning and adaptive decision-making underlie human response time variability in inhibitory control.

    Science.gov (United States)

    Ma, Ning; Yu, Angela J

    2015-01-01

    Response time (RT) is an oft-reported behavioral measure in psychological and neurocognitive experiments, but the high level of observed trial-to-trial variability in this measure has often limited its usefulness. Here, we combine computational modeling and psychophysics to examine the hypothesis that fluctuations in this noisy measure reflect dynamic computations in human statistical learning and corresponding cognitive adjustments. We present data from the stop-signal task (SST), in which subjects respond to a go stimulus on each trial, unless instructed not to by a subsequent, infrequently presented stop signal. We model across-trial learning of stop signal frequency, P(stop), and stop-signal onset time, SSD (stop-signal delay), with a Bayesian hidden Markov model, and within-trial decision-making with an optimal stochastic control model. The combined model predicts that RT should increase with both expected P(stop) and SSD. The human behavioral data (n = 20) bear out this prediction, showing P(stop) and SSD both to be significant, independent predictors of RT, with P(stop) being a more prominent predictor in 75% of the subjects, and SSD being more prominent in the remaining 25%. The results demonstrate that humans indeed readily internalize environmental statistics and adjust their cognitive/behavioral strategy accordingly, and that subtle patterns in RT variability can serve as a valuable tool for validating models of statistical learning and decision-making. More broadly, the modeling tools presented in this work can be generalized to a large body of behavioral paradigms, in order to extract insights about cognitive and neural processing from apparently quite noisy behavioral measures. We also discuss how this behaviorally validated model can then be used to conduct model-based analysis of neural data, in order to help identify specific brain areas for representing and encoding key computational quantities in learning and decision-making.

  18. Adaptive gain modulation in V1 explains contextual modifications during bisection learning.

    Directory of Open Access Journals (Sweden)

    Roland Schäfer

    2009-12-01

    Full Text Available The neuronal processing of visual stimuli in primary visual cortex (V1 can be modified by perceptual training. Training in bisection discrimination, for instance, changes the contextual interactions in V1 elicited by parallel lines. Before training, two parallel lines inhibit their individual V1-responses. After bisection training, inhibition turns into non-symmetric excitation while performing the bisection task. Yet, the receptive field of the V1 neurons evaluated by a single line does not change during task performance. We present a model of recurrent processing in V1 where the neuronal gain can be modulated by a global attentional signal. Perceptual learning mainly consists in strengthening this attentional signal, leading to a more effective gain modulation. The model reproduces both the psychophysical results on bisection learning and the modified contextual interactions observed in V1 during task performance. It makes several predictions, for instance that imagery training should improve the performance, or that a slight stimulus wiggling can strongly affect the representation in V1 while performing the task. We conclude that strengthening a top-down induced gain increase can explain perceptual learning, and that this top-down signal can modify lateral interactions within V1, without significantly changing the classical receptive field of V1 neurons.

  19. Talker-specific learning in amnesia: Insight into mechanisms of adaptive speech perception.

    Science.gov (United States)

    Trude, Alison M; Duff, Melissa C; Brown-Schmidt, Sarah

    2014-05-01

    A hallmark of human speech perception is the ability to comprehend speech quickly and effortlessly despite enormous variability across talkers. However, current theories of speech perception do not make specific claims about the memory mechanisms involved in this process. To examine whether declarative memory is necessary for talker-specific learning, we tested the ability of amnesic patients with severe declarative memory deficits to learn and distinguish the accents of two unfamiliar talkers by monitoring their eye-gaze as they followed spoken instructions. Analyses of the time-course of eye fixations showed that amnesic patients rapidly learned to distinguish these accents and tailored perceptual processes to the voice of each talker. These results demonstrate that declarative memory is not necessary for this ability and points to the involvement of non-declarative memory mechanisms. These results are consistent with findings that other social and accommodative behaviors are preserved in amnesia and contribute to our understanding of the interactions of multiple memory systems in the use and understanding of spoken language. Copyright © 2014 Elsevier Ltd. All rights reserved.

  20. Noradrenergic control of gene expression and long-term neuronal adaptation evoked by learned vocalizations in songbirds.

    Directory of Open Access Journals (Sweden)

    Tarciso A F Velho

    Full Text Available Norepinephrine (NE is thought to play important roles in the consolidation and retrieval of long-term memories, but its role in the processing and memorization of complex acoustic signals used for vocal communication has yet to be determined. We have used a combination of gene expression analysis, electrophysiological recordings and pharmacological manipulations in zebra finches to examine the role of noradrenergic transmission in the brain's response to birdsong, a learned vocal behavior that shares important features with human speech. We show that noradrenergic transmission is required for both the expression of activity-dependent genes and the long-term maintenance of stimulus-specific electrophysiological adaptation that are induced in central auditory neurons by stimulation with birdsong. Specifically, we show that the caudomedial nidopallium (NCM, an area directly involved in the auditory processing and memorization of birdsong, receives strong noradrenergic innervation. Song-responsive neurons in this area express α-adrenergic receptors and are in close proximity to noradrenergic terminals. We further show that local α-adrenergic antagonism interferes with song-induced gene expression, without affecting spontaneous or evoked electrophysiological activity, thus dissociating the molecular and electrophysiological responses to song. Moreover, α-adrenergic antagonism disrupts the maintenance but not the acquisition of the adapted physiological state. We suggest that the noradrenergic system regulates long-term changes in song-responsive neurons by modulating the gene expression response that is associated with the electrophysiological activation triggered by song. We also suggest that this mechanism may be an important contributor to long-term auditory memories of learned vocalizations.

  1. "They Have to Adapt to Learn": Surgeons' Perspectives on the Role of Procedural Variation in Surgical Education.

    Science.gov (United States)

    Apramian, Tavis; Cristancho, Sayra; Watling, Chris; Ott, Michael; Lingard, Lorelei

    2016-01-01

    Clinical research increasingly acknowledges the existence of significant procedural variation in surgical practice. This study explored surgeons' perspectives regarding the influence of intersurgeon procedural variation on the teaching and learning of surgical residents. This qualitative study used a grounded theory-based analysis of observational and interview data. Observational data were collected in 3 tertiary care teaching hospitals in Ontario, Canada. Semistructured interviews explored potential procedural variations arising during the observations and prompts from an iteratively refined guide. Ongoing data analysis refined the theoretical framework and informed data collection strategies, as prescribed by the iterative nature of grounded theory research. Our sample included 99 hours of observation across 45 cases with 14 surgeons. Semistructured, audio-recorded interviews (n = 14) occurred immediately following observational periods. Surgeons endorsed the use of intersurgeon procedural variations to teach residents about adapting to the complexity of surgical practice and the norms of surgical culture. Surgeons suggested that residents' efforts to identify thresholds of principle and preference are crucial to professional development. Principles that emerged from the study included the following: (1) knowing what comes next, (2) choosing the right plane, (3) handling tissue appropriately, (4) recognizing the abnormal, and (5) making safe progress. Surgeons suggested that learning to follow these principles while maintaining key aspects of surgical culture, like autonomy and individuality, are important social processes in surgical education. Acknowledging intersurgeon variation has important implications for curriculum development and workplace-based assessment in surgical education. Adapting to intersurgeon procedural variations may foster versatility in surgical residents. However, the existence of procedural variations and their active use in surgeons

  2. IPCC Climate Change 2013: Impacts, Adaptation and Vulnerability: Key findings and lessons learned

    Science.gov (United States)

    Giorgi, Filippo; Field, Christopher; Barros, Vicente

    2014-05-01

    The Working Group II contribution to the Fifth Assessment Report of the Intergivernmental Panel on Climate Change, Impacts, Adaptation and Vulnerability, will be completed and approved in March 2014. It includes two parts, Part A covering Global and Sectoral Aspects, and Part B, covering Regional Aspects. The WGII report spans a very broad range of topics which are approached in a strong interdisciplinary context. It highlights how observed impacts of climate change are now widespread and consequential, particularly for natural systems, and can be observed on all continents and across the oceans. Vulnerability to climate change depends on interactions with non-climatic stressors and inequalities, resulting in highly differential risks associated with climate change. It is also found that adaptation is already occurring across scales and is embedded in many planning processes. Continued sustained warming thrughout the 21st century will exacerbate risks and vulnerabilities across multiple sectors, such as freshwater resources, terrestrial and inland water systems, coastal and marine systems, food production, human health, security and livelihood. The report stresses how risks and vulnerabilities need to be assessed within a multi-stressor and regionally specific context, and can be reduced and managed by adopting climate-resilient pathwyas combining suitable adaptation and mitigation options with synergies and tradeoffs occurring both within and across regions. The Working group II report includes a large number of Chapters (30) and contributors (310 including authors and review editors), with expertise in a broad range of disciplines, from the physical science to the impact and socio-economic sciences. The communication across chapters and disciplines has been a challenge, and will continue to be one as the Global Change problem will increasingly require a fully integrated and holistic approach. Note that text on this abstract is not approved at the time its

  3. Medial prefrontal cortex and the adaptive regulation of reinforcement learning parameters.

    Science.gov (United States)

    Khamassi, Mehdi; Enel, Pierre; Dominey, Peter Ford; Procyk, Emmanuel

    2013-01-01

    Converging evidence suggest that the medial prefrontal cortex (MPFC) is involved in feedback categorization, performance monitoring, and task monitoring, and may contribute to the online regulation of reinforcement learning (RL) parameters that would affect decision-making processes in the lateral prefrontal cortex (LPFC). Previous neurophysiological experiments have shown MPFC activities encoding error likelihood, uncertainty, reward volatility, as well as neural responses categorizing different types of feedback, for instance, distinguishing between choice errors and execution errors. Rushworth and colleagues have proposed that the involvement of MPFC in tracking the volatility of the task could contribute to the regulation of one of RL parameters called the learning rate. We extend this hypothesis by proposing that MPFC could contribute to the regulation of other RL parameters such as the exploration rate and default action values in case of task shifts. Here, we analyze the sensitivity to RL parameters of behavioral performance in two monkey decision-making tasks, one with a deterministic reward schedule and the other with a stochastic one. We show that there exist optimal parameter values specific to each of these tasks, that need to be found for optimal performance and that are usually hand-tuned in computational models. In contrast, automatic online regulation of these parameters using some heuristics can help producing a good, although non-optimal, behavioral performance in each task. We finally describe our computational model of MPFC-LPFC interaction used for online regulation of the exploration rate and its application to a human-robot interaction scenario. There, unexpected uncertainties are produced by the human introducing cued task changes or by cheating. The model enables the robot to autonomously learn to reset exploration in response to such uncertain cues and events. The combined results provide concrete evidence specifying how prefrontal

  4. Vineland-II adaptive behavior profile of children with attention-deficit/hyperactivity disorder or specific learning disorders.

    Science.gov (United States)

    Balboni, Giulia; Incognito, Oriana; Belacchi, Carmen; Bonichini, Sabrina; Cubelli, Roberto

    2017-02-01

    The evaluation of adaptive behavior is informative in children with attention-deficit/hyperactivity disorder (ADHD) or specific learning disorders (SLD). However, the few investigations available have focused only on the gross level of domains of adaptive behavior. To investigate which item subsets of the Vineland-II can discriminate children with ADHD or SLD from peers with typical development. Student's t-tests, ROC analysis, logistic regression, and linear discriminant function analysis were used to compare 24 children with ADHD, 61 elementary students with SLD, and controls matched on age, sex, school level attended, and both parents' education level. Several item subsets that address not only ADHD core symptoms, but also understanding in social context and development of interpersonal relationships, allowed discrimination of children with ADHD from controls. The combination of four item subsets (Listening and attending, Expressing complex ideas, Social communication, and Following instructions) classified children with ADHD with both sensitivity and specificity of 87.5%. Only Reading skills, Writing skills, and Time and dates discriminated children with SLD from controls. Evaluation of Vineland-II scores at the level of item content categories is a useful procedure for an efficient clinical description. Copyright © 2016 Elsevier Ltd. All rights reserved.

  5. Design of robust adaptive controller and feedback error learning for rehabilitation in Parkinson's disease: a simulation study.

    Science.gov (United States)

    Rouhollahi, Korosh; Emadi Andani, Mehran; Karbassi, Seyed Mahdi; Izadi, Iman

    2017-02-01

    Deep brain stimulation (DBS) is an efficient therapy to control movement disorders of Parkinson's tremor. Stimulation of one area of basal ganglia (BG) by DBS with no feedback is the prevalent opinion. Reduction of additional stimulatory signal delivered to the brain is the advantage of using feedback. This results in reduction of side effects caused by the excessive stimulation intensity. In fact, the stimulatory intensity of controllers is decreased proportional to reduction of hand tremor. The objective of this study is to design a new controller structure to decrease three indicators: (i) the hand tremor; (ii) the level of delivered stimulation in disease condition; and (iii) the ratio of the level of delivered stimulation in health condition to disease condition. For this purpose, the authors offer a new closed-loop control structure to stimulate two areas of BG simultaneously. One area (STN: subthalamic nucleus) is stimulated by an adaptive controller with feedback error learning. The other area (GPi: globus pallidus internal) is stimulated by a partial state feedback (PSF) controller. Considering the three indicators, the results show that, stimulating two areas simultaneously leads to better performance compared with stimulating one area only. It is shown that both PSF and adaptive controllers are robust regarding system parameter uncertainties. In addition, a method is proposed to update the parameters of the BG model in real time. As a result, the parameters of the controllers can be updated based on the new parameters of the BG model.

  6. Hybrid fitness, adaptation and evolutionary diversification: lessons learned from Louisiana Irises.

    Science.gov (United States)

    Arnold, M L; Ballerini, E S; Brothers, A N

    2012-03-01

    Estimates of hybrid fitness have been used as either a platform for testing the potential role of natural hybridization in the evolution of species and species complexes or, alternatively, as a rationale for dismissing hybridization events as being of any evolutionary significance. From the time of Darwin's publication of The Origin, through the neo-Darwinian synthesis, to the present day, the observation of variability in hybrid fitness has remained a challenge for some models of speciation. Yet, Darwin and others have reported the elevated fitness of hybrid genotypes under certain environmental conditions. In modern scientific terminology, this observation reflects the fact that hybrid genotypes can demonstrate genotype × environment interactions. In the current review, we illustrate the development of one plant species complex, namely the Louisiana Irises, into a 'model system' for investigating hybrid fitness and the role of genetic exchange in adaptive evolution and diversification. In particular, we will argue that a multitude of approaches, involving both experimental and natural environments, and incorporating both manipulative analyses and surveys of natural populations, are necessary to adequately test for the evolutionary significance of introgressive hybridization. An appreciation of the variability of hybrid fitness leads to the conclusion that certain genetic signatures reflect adaptive evolution. Furthermore, tests of the frequency of allopatric versus sympatric/parapatric divergence (that is, divergence with ongoing gene flow) support hybrid genotypes as a mechanism of evolutionary diversification in numerous species complexes.

  7. Learning and adaptation in waterfowl conservation: By chance or by design?

    Science.gov (United States)

    Johnson, Fred A.; Case, David J.; Humburg, Dale H.

    2016-01-01

    The most recent revision of the North American Waterfowl Management Plan seeks to increase the adaptive capacity of the management enterprise to cope with accelerating changes in climate, land-use patterns, agency priorities, and the waterfowl and wetlands constituency. Institutional and cultural changes of the magnitude envisioned are necessarily slow, messy processes, involving many actors who at a minimum must agree on the need for change. Waterfowl conservation now finds itself in the transition zone between business as usual and some new mode of operation. There are at least 2 different perspectives of this transition: one focuses on process, accountability, and planning for change; another focuses on solutions generated from an organic process of creativity, information sharing, and risk-taking. Both of these views have something to contribute, but some in the wildlife management enterprise may tend to focus more on the first view. We suggest that ideas from panarchy theory, especially those related to the behaviors of complex adaptive systems, can help waterfowl managers better understand and foster the institutional changes they seek.

  8. Barrier Function-Based Neural Adaptive Control With Locally Weighted Learning and Finite Neuron Self-Growing Strategy.

    Science.gov (United States)

    Jia, Zi-Jun; Song, Yong-Duan

    2017-06-01

    This paper presents a new approach to construct neural adaptive control for uncertain nonaffine systems. By integrating locally weighted learning with barrier Lyapunov function (BLF), a novel control design method is presented to systematically address the two critical issues in neural network (NN) control field: one is how to fulfill the compact set precondition for NN approximation, and the other is how to use varying rather than a fixed NN structure to improve the functionality of NN control. A BLF is exploited to ensure the NN inputs to remain bounded during the entire system operation. To account for system nonlinearities, a neuron self-growing strategy is proposed to guide the process for adding new neurons to the system, resulting in a self-adjustable NN structure for better learning capabilities. It is shown that the number of neurons needed to accomplish the control task is finite, and better performance can be obtained with less number of neurons as compared with traditional methods. The salient feature of the proposed method also lies in the continuity of the control action everywhere. Furthermore, the resulting control action is smooth almost everywhere except for a few time instants at which new neurons are added. Numerical example illustrates the effectiveness of the proposed approach.

  9. Defect characterization by an adaptive learning classifier in ultrasonic inspection of ferritic materials

    International Nuclear Information System (INIS)

    Grozellier, M.; Bieth, M.; Romy, D.

    1985-01-01

    The purpose of the work presented here is to determine the efficiency and reliability of the discrimination method ''Adapatative Learning Network''. This process is used to identify the nature of defects detected during Ultrasonic Testing (UT), by analysing some of the echo parameters calculated in temporal and frequency domains. The survey which dealt with several hundreds of echoes, revealed that the machined artificial reflectors, well defined geometrically, were identified with a probability exceeding 95 %. The fatigue cracks, producing less characteristic echoes, are identified with a slightly lower probability (90 % - 95 %)

  10. Enhancing an adaptive e-learning system with didactic test assessment using an expert system

    Science.gov (United States)

    Bradáč, Vladimír; Kostolányová, Kateřina

    2017-07-01

    The paper deals with a follow-up research on intelligent tutoring systems that were studied in authors' previous papers from the point of view of describing their advantages. In this paper, the authors make use of the fuzzy logic expert system, which assesses student's knowledge, and integrate it into the intelligent tutoring system called Barborka. The goal is to create an even more personal student's study plan, which is tailored both to student's sensory/learning preferences and the level of knowledge of the given subject.

  11. Guiding Learning by Creating an Adaptive User-Model of Knowledge, Memory and its Decay

    OpenAIRE

    Dahlin, Anders Oliver

    2015-01-01

    Background As time pass, education has been an ever growing enterprise. The past 150 years obligatory education has grown from almost non-existent to a project that takes most of our childhood. Adding more years to an already long education is out of the questions, but there are other improvements that can be done to make sure our children learn what they need to know. Objective The main objective of this paper is to find out how computer systems can help this improvement process. ...

  12. An adaptive case management system to support integrated care services: Lessons learned from the NEXES project.

    Science.gov (United States)

    Cano, Isaac; Alonso, Albert; Hernandez, Carme; Burgos, Felip; Barberan-Garcia, Anael; Roldan, Jim; Roca, Josep

    2015-06-01

    Extensive deployment and sustainability of integrated care services (ICS) constitute an unmet need to reduce the burden of chronic conditions. The European Union project NEXES (2008-2013) assessed the deployment of four ICS encompassing the spectrum of severity of chronic patients. The current study aims to (i) describe the open source Adaptive Case Management (ACM) system (Linkcare®) developed to support the deployment of ICS at the level of healthcare district; (ii) to evaluate its performance; and, (iii) to identify key challenges for regional deployment of ICS. We first defined a conceptual model for ICS management and execution composed of five main stages. We then specified an associated logical model considering the dynamic runtime of ACM. Finally, we implemented the four ICS as a physical model with an ICS editor to allow professionals (case managers) to play active roles in adapting the system to their needs. Instances of ICS were then run in Linkcare®. Four ICS provided a framework for evaluating the system: Wellness and Rehabilitation (W&R) (number of patients enrolled in the study (n)=173); Enhanced Care (EC) in frail chronic patients to prevent hospital admissions, (n=848); Home Hospitalization and Early Discharge (HH/ED) (n=2314); and, Support to remote diagnosis (Support) (n=7793). The method for assessment of telemedicine applications (MAST) was used for iterative evaluation. Linkcare® supports ACM with shared-care plans across healthcare tiers and offers integration with provider-specific electronic health records. Linkcare® successfully contributed to the deployment of the four ICS: W&R facilitated long-term sustainability of training effects (p<0.01) and active life style (p<0.03); EC showed significant positive outcomes (p<0.05); HH/ED reduced on average 5 in-hospital days per patient with a 30-d re-admission rate of 10%; and, Support, enhanced community-based quality forced spirometry testing (p<0.01). Key challenges for regional deployment

  13. Gamification and e-learning: study of a university context for the adaptation of the design

    Directory of Open Access Journals (Sweden)

    Mario Germán Almonte Moreno

    2016-05-01

    Full Text Available Gamification, applied to educational contexts, can increase the motivation and engagement of students. First analysis of the bibliography tells that there is not enough research done in this topic, and there are few guidelines marked to implement gamification. This work aims to generate hypotheses that guide the design of a future pilot study in e-learning high education. Students and teachers from the Master of Education and New Technologies at the Madrid Open University (UDIMA have been tested. The methodology includes qualitative and quantitative aspects and two different on-line questionnaires. Student’s questionnaire included a translation of the BrainHex test. Conclusions about the gamification characteristics which are more suitable in this e-learning high education context are generated using these results and through a review of existing research: related to the acceptance of gamification by students and teachers; related to the type of gamification elements that are more suitable for use in the context at the UDIMA and related to aspects or behaviors that need to be changed through the motivation of students.

  14. Image Captioning with Word Gate and Adaptive Self-Critical Learning

    Directory of Open Access Journals (Sweden)

    Xinxin Zhu

    2018-06-01

    Full Text Available Although the policy-gradient methods for reinforcement learning have shown significant improvement in image captioning, how to achieve high performance during the reinforcement optimizing process is still not a simple task. There are at least two difficulties: (1 The large size of vocabulary leads to a large action space, which makes it difficult for the model to accurately predict the current word. (2 The large variance of gradient estimation in reinforcement learning usually causes severe instabilities in the training process. In this paper, we propose two innovations to boost the performance of self-critical sequence training (SCST. First, we modify the standard long short-term memory (LSTMbased decoder by introducing a gate function to reduce the search scope of the vocabulary for any given image, which is termed the word gate decoder. Second, instead of only considering current maximum actions greedily, we propose a stabilized gradient estimation method whose gradient variance is controlled by the difference between the sampling reward from the current model and the expectation of the historical reward. We conducted extensive experiments, and results showed that our method could accelerate the training process and increase the prediction accuracy. Our method was validated on MS COCO datasets and yielded state-of-the-art performance.

  15. Secondary Special Education. Part I: The "Stepping Stone Model" Designed for Secondary Learning Disabled Students. Part II: Adapting Materials and Curriculum.

    Science.gov (United States)

    Fox, Barbara

    The paper describes the Stepping Stone Model, a model for the remediation and mainstreaming of secondary learning disabled students and the adaptation of curriculum and materials for the model. The Stepping Stone Model is designed to establish the independence of students in the mainstream through content reading. Five areas of concern common to…

  16. Resilience through adaptation

    NARCIS (Netherlands)

    Broeke, ten Guus; Voorn, van George A.K.; Ligtenberg, Arend; Molenaar, Jaap

    2017-01-01

    Adaptation of agents through learning or evolution is an important component of the resilience of Complex Adaptive Systems (CAS). Without adaptation, the flexibility of such systems to cope with outside pressures would be much lower. To study the capabilities of CAS to adapt, social simulations

  17. A learning algorithm for adaptive canonical correlation analysis of several data sets.

    Science.gov (United States)

    Vía, Javier; Santamaría, Ignacio; Pérez, Jesús

    2007-01-01

    Canonical correlation analysis (CCA) is a classical tool in statistical analysis to find the projections that maximize the correlation between two data sets. In this work we propose a generalization of CCA to several data sets, which is shown to be equivalent to the classical maximum variance (MAXVAR) generalization proposed by Kettenring. The reformulation of this generalization as a set of coupled least squares regression problems is exploited to develop a neural structure for CCA. In particular, the proposed CCA model is a two layer feedforward neural network with lateral connections in the output layer to achieve the simultaneous extraction of all the CCA eigenvectors through deflation. The CCA neural model is trained using a recursive least squares (RLS) algorithm. Finally, the convergence of the proposed learning rule is proved by means of stochastic approximation techniques and their performance is analyzed through simulations.

  18. Adaptive ethnography

    DEFF Research Database (Denmark)

    Berth, Mette

    2005-01-01

    This paper focuses on the use of an adaptive ethnography when studying such phenomena as young people's use of mobile media in a learning perspective. Mobile media such as PDAs and mobile phones have a number of affordances which make them potential tools for learning. However, before we begin to...... formal and informal learning contexts. The paper also proposes several adaptive methodological techniques for studying young people's interaction with mobiles.......This paper focuses on the use of an adaptive ethnography when studying such phenomena as young people's use of mobile media in a learning perspective. Mobile media such as PDAs and mobile phones have a number of affordances which make them potential tools for learning. However, before we begin...... to design and develop educational materials for mobile media platforms we must first understand everyday use and behaviour with a medium such as a mobile phone. The paper outlines the research design for a PhD project on mobile learning which focuses on mobile phones as a way to bridge the gap between...

  19. Statistical learning methods for aero-optic wavefront prediction and adaptive-optic latency compensation

    Science.gov (United States)

    Burns, W. Robert

    Since the early 1970's research in airborne laser systems has been the subject of continued interest. Airborne laser applications depend on being able to propagate a near diffraction-limited laser beam from an airborne platform. Turbulent air flowing over the aircraft produces density fluctuations through which the beam must propagate. Because the index of refraction of the air is directly related to the density, the turbulent flow imposes aberrations on the beam passing through it. This problem is referred to as Aero-Optics. Aero-Optics is recognized as a major technical issue that needs to be solved before airborne optical systems can become routinely fielded. This dissertation research specifically addresses an approach to mitigating the deleterious effects imposed on an airborne optical system by aero-optics. A promising technology is adaptive optics: a feedback control method that measures optical aberrations and imprints the conjugate aberrations onto an outgoing beam. The challenge is that it is a computationally-difficult problem, since aero-optic disturbances are on the order of kilohertz for practical applications. High control loop frequencies and high disturbance frequencies mean that adaptive-optic systems are sensitive to latency in sensors, mirrors, amplifiers, and computation. These latencies build up to result in a dramatic reduction in the system's effective bandwidth. This work presents two variations of an algorithm that uses model reduction and data-driven predictors to estimate the evolution of measured wavefronts over a short temporal horizon and thus compensate for feedback latency. The efficacy of the two methods are compared in this research, and evaluated against similar algorithms that have been previously developed. The best version achieved over 75% disturbance rejection in simulation in the most optically active flow region in the wake of a turret, considerably outperforming conventional approaches. The algorithm is shown to be

  20. Adaptive management and the value of information: learning via intervention in epidemiology

    Science.gov (United States)

    Shea, Katriona; Tildesley, Michael J.; Runge, Michael C.; Fonnesbeck, Christopher J.; Ferrari, Matthew J.

    2014-01-01

    Optimal intervention for disease outbreaks is often impeded by severe scientific uncertainty. Adaptive management (AM), long-used in natural resource management, is a structured decision-making approach to solving dynamic problems that accounts for the value of resolving uncertainty via real-time evaluation of alternative models. We propose an AM approach to design and evaluate intervention strategies in epidemiology, using real-time surveillance to resolve model uncertainty as management proceeds, with foot-and-mouth disease (FMD) culling and measles vaccination as case studies. We use simulations of alternative intervention strategies under competing models to quantify the effect of model uncertainty on decision making, in terms of the value of information, and quantify the benefit of adaptive versus static intervention strategies. Culling decisions during the 2001 UK FMD outbreak were contentious due to uncertainty about the spatial scale of transmission. The expected benefit of resolving this uncertainty prior to a new outbreak on a UK-like landscape would be £45–£60 million relative to the strategy that minimizes livestock losses averaged over alternate transmission models. AM during the outbreak would be expected to recover up to £20.1 million of this expected benefit. AM would also recommend a more conservative initial approach (culling of infected premises and dangerous contact farms) than would a fixed strategy (which would additionally require culling of contiguous premises). For optimal targeting of measles vaccination, based on an outbreak in Malawi in 2010, AM allows better distribution of resources across the affected region; its utility depends on uncertainty about both the at-risk population and logistical capacity. When daily vaccination rates are highly constrained, the optimal initial strategy is to conduct a small, quick campaign; a reduction in expected burden of approximately 10,000 cases could result if campaign targets can be updated on