Lifelong Transfer Learning for Heterogeneous Teams of Agents in Sequential Decision Processes

DTIC Science & Technology

2016-06-01

making (SDM) tasks in dynamic environments with simulated and physical robots . 15. SUBJECT TERMS Sequential decision making, lifelong learning, transfer...sequential decision-making (SDM) tasks in dynamic environments with both simple benchmark tasks and more complex aerial and ground robot tasks. Our work...and ground robots in the presence of disturbances: We applied our methods to the problem of learning controllers for robots with novel disturbances in

Scaffolding and Integrated Assessment in Computer Assisted Learning (CAL) for Children with Learning Disabilities

ERIC Educational Resources Information Center

Beale, Ivan L.

2005-01-01

Computer assisted learning (CAL) can involve a computerised intelligent learning environment, defined as an environment capable of automatically, dynamically and continuously adapting to the learning context. One aspect of this adaptive capability involves automatic adjustment of instructional procedures in response to each learner's performance,…

Web-Based Learning Support System

NASA Astrophysics Data System (ADS)

Fan, Lisa

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

Animated Pedagogical Agents: A Review of Agent Technology Software in Electronic Learning Environments

ERIC Educational Resources Information Center

Govindasamy, Malliga K.

2014-01-01

Agent technology has become one of the dynamic and most interesting areas of computer science in recent years. The dynamism of this technology has resulted in computer generated characters, known as pedagogical agent, entering the digital learning environments in increasing numbers. Commonly deployed in implementing tutoring strategies, these…

Awareness of Cognitive and Social Behaviour in a CSCL Environment

ERIC Educational Resources Information Center

Kirschner, P. A.; Kreijns, K.; Phielix, C.; Fransen, J.

2015-01-01

Most distributed and virtual online environments for and pedagogies of computer-supported collaborative learning (CSCL) neglect the social and social-emotional aspects underlying the group dynamics of learning and working in a CSCL group. These group dynamics often determine whether the group will develop into a well-performing team and whether a…

A Novel Approach for Enhancing Lifelong Learning Systems by Using Hybrid Recommender System

ERIC Educational Resources Information Center

Kardan, Ahmad A.; Speily, Omid R. B.; Modaberi, Somayyeh

2011-01-01

The majority of current web-based learning systems are closed learning environments where courses and learning materials are fixed, and the only dynamic aspect is the organization of the material that can be adapted to allow a relatively individualized learning environment. In this paper, we propose an evolving web-based learning system which can…

Exploring the Dynamic System of TCFL: Individual Differences, Learning and Instruction

ERIC Educational Resources Information Center

Su, Henghua

2012-01-01

In the setting of TCFL, this dissertation is an exploration of the dynamic development of individual differences and the learning and instruction environment. Major research studies done on motivation and learning strategies are reviewed. The motivation research in foreign language learning is introduced from three different perspectives in…

School Policy on Teaching and School Learning Environment: Direct and Indirect Effects upon Student Outcome Measures

ERIC Educational Resources Information Center

Kyriakides, Leonidas; Creemers, Bert P. M.

2012-01-01

School policy on teaching and the school learning environment (SLE) are the main school factors of the dynamic model of educational effectiveness (Creemers & Kyriakides, 2008). A longitudinal study in which 50 primary schools, 108 classes, and 2369 students participated generated evidence supporting the validity of the dynamic model. This…

Dynamic lighting system for the learning environment: performance of elementary students.

PubMed

Choi, Kyungah; Suk, Hyeon-Jeong

2016-05-16

This study aims to investigate the effects of lighting color temperatures on elementary students' performance, and thereby propose a dynamic lighting system for a smart learning environment. Three empirical studies were conducted: First, physiological responses were measured as a potential mediator of performance. Second, cognitive and behavioral responses were observed during academic and recess activities. Lastly, the experiment was carried out in a real-life setting with prolonged exposure. With a comprehensive analysis of the three studies, three lighting presets-3500 K, 5000 K, and 6500 K-are suggested for easy, standard, and intensive activity, respectively. The study is expected to act as a good stepping stone for developing dynamic lighting systems to support students' performance in learning environments.

Rigor and Support in Racialized Learning Environments: The Case of Graduate Education

ERIC Educational Resources Information Center

Posselt, Julie R.

2018-01-01

Racial and gender dynamics in many learning environments present students from minoritized backgrounds with challenges that must be accounted for in defining both what makes a learning experience rigorous and how faculty can scaffold student growth.

Impacts of Integrating the Repertory Grid into an Augmented Reality-Based Learning Design on Students' Learning Achievements, Cognitive Load and Degree of Satisfaction

ERIC Educational Resources Information Center

Wu, Po-Han; Hwang, Gwo-Jen; Yang, Mei-Ling; Chen, Chih-Hung

2018-01-01

Augmented reality (AR) offers potential advantages for intensifying environmental context awareness and augmenting students' experiences in real-world environments by dynamically overlapping digital materials with a real-world environment. However, some challenges to AR learning environments have been described, such as participants' cognitive…

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

ERIC Educational Resources Information Center

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…

Learning of embodied interaction dynamics with recurrent neural networks: some exploratory experiments.

PubMed

Oubbati, Mohamed; Kord, Bahram; Koprinkova-Hristova, Petia; Palm, Günther

2014-04-01

The new tendency of artificial intelligence suggests that intelligence must be seen as a result of the interaction between brains, bodies and environments. This view implies that designing sophisticated behaviour requires a primary focus on how agents are functionally coupled to their environments. Under this perspective, we present early results with the application of reservoir computing as an efficient tool to understand how behaviour emerges from interaction. Specifically, we present reservoir computing models, that are inspired by imitation learning designs, to extract the essential components of behaviour that results from agent-environment interaction dynamics. Experimental results using a mobile robot are reported to validate the learning architectures.

Learning of embodied interaction dynamics with recurrent neural networks: some exploratory experiments

NASA Astrophysics Data System (ADS)

Oubbati, Mohamed; Kord, Bahram; Koprinkova-Hristova, Petia; Palm, Günther

2014-04-01

The new tendency of artificial intelligence suggests that intelligence must be seen as a result of the interaction between brains, bodies and environments. This view implies that designing sophisticated behaviour requires a primary focus on how agents are functionally coupled to their environments. Under this perspective, we present early results with the application of reservoir computing as an efficient tool to understand how behaviour emerges from interaction. Specifically, we present reservoir computing models, that are inspired by imitation learning designs, to extract the essential components of behaviour that results from agent-environment interaction dynamics. Experimental results using a mobile robot are reported to validate the learning architectures.

The Contribution of Visualization to Learning Computer Architecture

ERIC Educational Resources Information Center

Yehezkel, Cecile; Ben-Ari, Mordechai; Dreyfus, Tommy

2007-01-01

This paper describes a visualization environment and associated learning activities designed to improve learning of computer architecture. The environment, EasyCPU, displays a model of the components of a computer and the dynamic processes involved in program execution. We present the results of a research program that analysed the contribution of…

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

PubMed

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

2017-03-01

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

Humanoids Learning to Walk: A Natural CPG-Actor-Critic Architecture.

PubMed

Li, Cai; Lowe, Robert; Ziemke, Tom

2013-01-01

The identification of learning mechanisms for locomotion has been the subject of much research for some time but many challenges remain. Dynamic systems theory (DST) offers a novel approach to humanoid learning through environmental interaction. Reinforcement learning (RL) has offered a promising method to adaptively link the dynamic system to the environment it interacts with via a reward-based value system. In this paper, we propose a model that integrates the above perspectives and applies it to the case of a humanoid (NAO) robot learning to walk the ability of which emerges from its value-based interaction with the environment. In the model, a simplified central pattern generator (CPG) architecture inspired by neuroscientific research and DST is integrated with an actor-critic approach to RL (cpg-actor-critic). In the cpg-actor-critic architecture, least-square-temporal-difference based learning converges to the optimal solution quickly by using natural gradient learning and balancing exploration and exploitation. Futhermore, rather than using a traditional (designer-specified) reward it uses a dynamic value function as a stability indicator that adapts to the environment. The results obtained are analyzed using a novel DST-based embodied cognition approach. Learning to walk, from this perspective, is a process of integrating levels of sensorimotor activity and value.

Humanoids Learning to Walk: A Natural CPG-Actor-Critic Architecture

PubMed Central

Li, Cai; Lowe, Robert; Ziemke, Tom

2013-01-01

The identification of learning mechanisms for locomotion has been the subject of much research for some time but many challenges remain. Dynamic systems theory (DST) offers a novel approach to humanoid learning through environmental interaction. Reinforcement learning (RL) has offered a promising method to adaptively link the dynamic system to the environment it interacts with via a reward-based value system. In this paper, we propose a model that integrates the above perspectives and applies it to the case of a humanoid (NAO) robot learning to walk the ability of which emerges from its value-based interaction with the environment. In the model, a simplified central pattern generator (CPG) architecture inspired by neuroscientific research and DST is integrated with an actor-critic approach to RL (cpg-actor-critic). In the cpg-actor-critic architecture, least-square-temporal-difference based learning converges to the optimal solution quickly by using natural gradient learning and balancing exploration and exploitation. Futhermore, rather than using a traditional (designer-specified) reward it uses a dynamic value function as a stability indicator that adapts to the environment. The results obtained are analyzed using a novel DST-based embodied cognition approach. Learning to walk, from this perspective, is a process of integrating levels of sensorimotor activity and value. PMID:23675345

Usability Evaluation of an Adaptive 3D Virtual Learning Environment

ERIC Educational Resources Information Center

Ewais, Ahmed; De Troyer, Olga

2013-01-01

Using 3D virtual environments for educational purposes is becoming attractive because of their rich presentation and interaction capabilities. Furthermore, dynamically adapting the 3D virtual environment to the personal preferences, prior knowledge, skills and competence, learning goals, and the personal or (social) context in which the learning…

School Shock: A Psychodynamic View of Learning Disability.

ERIC Educational Resources Information Center

Zitani, E. Alfredo

Learning disability is seen to be a dissociative disorder (school shock) similar to shell shock in wartime. The shell shock model is explained to focus diagnosis and treatment of learning disabilities around the dynamics of the predisposing unconscious conflict, the dynamics in the environment, the mechanism which allows these two conditions to…

Learning Fraction Comparison by Using a Dynamic Mathematics Software--GeoGebra

ERIC Educational Resources Information Center

Poon, Kin Keung

2018-01-01

GeoGebra is a mathematics software system that can serve as a tool for inquiry-based learning. This paper deals with the application of a fraction comparison software, which is constructed by GeoGebra, for use in a dynamic mathematics environment. The corresponding teaching and learning issues have also been discussed.

Learning fraction comparison by using a dynamic mathematics software - GeoGebra

NASA Astrophysics Data System (ADS)

Poon, Kin Keung

2018-04-01

GeoGebra is a mathematics software system that can serve as a tool for inquiry-based learning. This paper deals with the application of a fraction comparison software, which is constructed by GeoGebra, for use in a dynamic mathematics environment. The corresponding teaching and learning issues have also been discussed.

Formation of an internal model of environment dynamics during upper limb reaching movements: a fuzzy approach.

PubMed

MacDonald, Chad; Moussavi, Zahra; Sarkodie-Gyan, Thompson

2007-01-01

This paper presents the development and simulation of a fuzzy logic based learning mechanism to emulate human motor learning. In particular, fuzzy inference was used to develop an internal model of a novel dynamic environment experienced during planar reaching movements with the upper limb. A dynamic model of the human arm was developed and a fuzzy if-then rule base was created to relate trajectory movement and velocity errors to internal model update parameters. An experimental simulation was performed to compare the fuzzy system's performance with that of human subjects. It was found that the dynamic model behaved as expected, and the fuzzy learning mechanism created an internal model that was capable of opposing the environmental force field to regain a trajectory closely resembling the desired ideal.

Modeling and Intervening across Time in Scientific Inquiry Exploratory Learning Environment

ERIC Educational Resources Information Center

Ting, Choo-Yee; Phon-Amnuaisuk, Somnuk; Chong, Yen-Kuan

2008-01-01

This article aims at discussing how Dynamic Decision Network (DDN) can be employed to tackle the challenges in modeling temporally variable scientific inquiry skills and provision of adaptive pedagogical interventions in INQPRO, a scientific inquiry exploratory learning environment for learning O'level Physics. We begin with an overview of INQPRO…

Facilitation of Learning by Social-Emotional Feedback in Humans Is Beta-Noradrenergic-Dependent

ERIC Educational Resources Information Center

Mihov, Yoan; Mayer, Simon; Musshoff, Frank; Maier, Wolfgang; Kendrick, Keith M.; Hurlemann, Rene

2010-01-01

Adaptive behavior in dynamic environments critically depends on the ability to learn rapidly and flexibly from the outcomes of prior choices. In social environments, facial expressions of emotion often serve as performance feedback and thereby guide declarative learning. Abundant evidence implicates beta-noradrenergic signaling in the modulatory…

Young Children Reasoning about Symmetry in a Dynamic Geometry Environment

ERIC Educational Resources Information Center

Ng, Oi-Lam; Sinclair, Nathalie

2015-01-01

In this paper, we investigate children's learning of reflectional symmetry in a dynamic geometry environment. Through a classroom-based intervention involving two 1-h lessons, we analyse the changes in the children's thinking about reflectional symmetry: first, they developed dynamic and embodied ways of thinking about symmetry after working with…

The Effects of a Concept Map-Based Support Tool on Simulation-Based Inquiry Learning

ERIC Educational Resources Information Center

Hagemans, Mieke G.; van der Meij, Hans; de Jong, Ton

2013-01-01

Students often need support to optimize their learning in inquiry learning environments. In 2 studies, we investigated the effects of adding concept-map-based support to a simulation-based inquiry environment on kinematics. The concept map displayed the main domain concepts and their relations, while dynamic color coding of the concepts displayed…

Improving Understanding in Ordinary Differential Equations through Writing in a Dynamical Environment

ERIC Educational Resources Information Center

Habre, Samer

2012-01-01

Research on writing in mathematics has shown that students learn more effectively in an environment that promotes this skill and that writing is most beneficial when it is directed at the learning aspect. Writing, however, necessitates proficiency on the part of the students that may not have been developed at earlier learning stages. Research has…

Modelling Mathematics Teachers' Intention to Use the Dynamic Geometry Environments in Macau: An SEM Approach

ERIC Educational Resources Information Center

Zhou, Mingming; Chan, Kan Kan; Teo, Timothy

2016-01-01

Dynamic geometry environments (DGEs) provide computer-based environments to construct and manipulate geometric figures with great ease. Research has shown that DGEs has positive impact on student motivation, engagement, and achievement in mathematics learning. However, the adoption of DGEs by mathematics teachers varies substantially worldwide.…

Design Approach of Mathematics Learning Activities in a Digital Environment for Children with Autism Spectrum Disorders

ERIC Educational Resources Information Center

Santos, Maria Isabel; Breda, Ana; Almeida, Ana Margarida

2017-01-01

Learning environment on mathematics for autistic children is a prototype of a digital environment with dynamic adaptation features designed to offer activities towards the development of mathematical reasoning in children aged 6-12 years, diagnosed with autism spectrum disorders (ASD), a neurodevelopmental disorder characterized by deficits in…

Neuro-Holistic Learning©: An Integrated Kinesthetic Approach to Cognitive Learning© Using Collaborative Interactive Thought Exchange© in a Blended Environment to Enhance the Learning of Young African American Males

ERIC Educational Resources Information Center

Osler, James Edward, II; Wright, Mark Anthony

2016-01-01

This paper is part two of the article entitled, "Dynamic Neuroscientific Systemology: Using Tri-Squared Meta-Analysis and Innovative Instructional Design to Develop a Novel Distance Education Model for the Systemic Creation of Engaging Online Learning Environments" published in the July-September 2015 issue of i-manager's "Journal…

Learning predictive statistics from temporal sequences: Dynamics and strategies

PubMed Central

Wang, Rui; Shen, Yuan; Tino, Peter; Welchman, Andrew E.; Kourtzi, Zoe

2017-01-01

Human behavior is guided by our expectations about the future. Often, we make predictions by monitoring how event sequences unfold, even though such sequences may appear incomprehensible. Event structures in the natural environment typically vary in complexity, from simple repetition to complex probabilistic combinations. How do we learn these structures? Here we investigate the dynamics of structure learning by tracking human responses to temporal sequences that change in structure unbeknownst to the participants. Participants were asked to predict the upcoming item following a probabilistic sequence of symbols. Using a Markov process, we created a family of sequences, from simple frequency statistics (e.g., some symbols are more probable than others) to context-based statistics (e.g., symbol probability is contingent on preceding symbols). We demonstrate the dynamics with which individuals adapt to changes in the environment's statistics—that is, they extract the behaviorally relevant structures to make predictions about upcoming events. Further, we show that this structure learning relates to individual decision strategy; faster learning of complex structures relates to selection of the most probable outcome in a given context (maximizing) rather than matching of the exact sequence statistics. Our findings provide evidence for alternate routes to learning of behaviorally relevant statistics that facilitate our ability to predict future events in variable environments. PMID:28973111

Structured Kernel Subspace Learning for Autonomous Robot Navigation.

PubMed

Kim, Eunwoo; Choi, Sungjoon; Oh, Songhwai

2018-02-14

This paper considers two important problems for autonomous robot navigation in a dynamic environment, where the goal is to predict pedestrian motion and control a robot with the prediction for safe navigation. While there are several methods for predicting the motion of a pedestrian and controlling a robot to avoid incoming pedestrians, it is still difficult to safely navigate in a dynamic environment due to challenges, such as the varying quality and complexity of training data with unwanted noises. This paper addresses these challenges simultaneously by proposing a robust kernel subspace learning algorithm based on the recent advances in nuclear-norm and l 1 -norm minimization. We model the motion of a pedestrian and the robot controller using Gaussian processes. The proposed method efficiently approximates a kernel matrix used in Gaussian process regression by learning low-rank structured matrix (with symmetric positive semi-definiteness) to find an orthogonal basis, which eliminates the effects of erroneous and inconsistent data. Based on structured kernel subspace learning, we propose a robust motion model and motion controller for safe navigation in dynamic environments. We evaluate the proposed robust kernel learning in various tasks, including regression, motion prediction, and motion control problems, and demonstrate that the proposed learning-based systems are robust against outliers and outperform existing regression and navigation methods.

Learning predictive statistics from temporal sequences: Dynamics and strategies.

PubMed

Wang, Rui; Shen, Yuan; Tino, Peter; Welchman, Andrew E; Kourtzi, Zoe

2017-10-01

Human behavior is guided by our expectations about the future. Often, we make predictions by monitoring how event sequences unfold, even though such sequences may appear incomprehensible. Event structures in the natural environment typically vary in complexity, from simple repetition to complex probabilistic combinations. How do we learn these structures? Here we investigate the dynamics of structure learning by tracking human responses to temporal sequences that change in structure unbeknownst to the participants. Participants were asked to predict the upcoming item following a probabilistic sequence of symbols. Using a Markov process, we created a family of sequences, from simple frequency statistics (e.g., some symbols are more probable than others) to context-based statistics (e.g., symbol probability is contingent on preceding symbols). We demonstrate the dynamics with which individuals adapt to changes in the environment's statistics-that is, they extract the behaviorally relevant structures to make predictions about upcoming events. Further, we show that this structure learning relates to individual decision strategy; faster learning of complex structures relates to selection of the most probable outcome in a given context (maximizing) rather than matching of the exact sequence statistics. Our findings provide evidence for alternate routes to learning of behaviorally relevant statistics that facilitate our ability to predict future events in variable environments.

Persistent Artefacts in an Online Classroom: The Value of a Dynamic Learning Archive

ERIC Educational Resources Information Center

Berry, Stuart C.

2016-01-01

This paper summarizes a multi-year research project that examines the use and value of visible and persistent artefacts within an online learning environment. This study is framed within elements of a business management theory. Changes to an online learning environment are documented as well as an examination of the impact of these changes on the…

Habituation based synaptic plasticity and organismic learning in a quantum perovskite

DOE PAGES

Zuo, Fan; Panda, Priyadarshini; Kotiuga, Michele; ...

2017-08-14

A central characteristic of living beings is the ability to learn from and respond to their environment leading to habit formation and decision making. This behavior, known as habituation, is universal among all forms of life with a central nervous system, and is also observed in single-cell organisms that do not possess a brain. Here, we report the discovery of habituation-based plasticity utilizing a perovskite quantum system by dynamical modulation of electron localization. Microscopic mechanisms and pathways that enable this organismic collective charge-lattice interaction are elucidated by first-principles theory, synchrotron investigations, ab initio molecular dynamics simulations, and in situ environmentalmore » breathing studies. In conclusion, we implement a learning algorithm inspired by the conductance relaxation behavior of perovskites that naturally incorporates habituation, and demonstrate learning to forget: a key feature of animal and human brains. Incorporating this elementary skill in learning boosts the capability of neural computing in a sequential, dynamic environment.« less

Habituation based synaptic plasticity and organismic learning in a quantum perovskite

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zuo, Fan; Panda, Priyadarshini; Kotiuga, Michele

A central characteristic of living beings is the ability to learn from and respond to their environment leading to habit formation and decision making. This behavior, known as habituation, is universal among all forms of life with a central nervous system, and is also observed in single-cell organisms that do not possess a brain. Here, we report the discovery of habituation-based plasticity utilizing a perovskite quantum system by dynamical modulation of electron localization. Microscopic mechanisms and pathways that enable this organismic collective charge-lattice interaction are elucidated by first-principles theory, synchrotron investigations, ab initio molecular dynamics simulations, and in situ environmentalmore » breathing studies. In conclusion, we implement a learning algorithm inspired by the conductance relaxation behavior of perovskites that naturally incorporates habituation, and demonstrate learning to forget: a key feature of animal and human brains. Incorporating this elementary skill in learning boosts the capability of neural computing in a sequential, dynamic environment.« less

Learning about Locomotion Patterns from Visualizations: Effects of Presentation Format and Realism

ERIC Educational Resources Information Center

Imhof, Birgit; Scheiter, Katharina; Gerjets, Peter

2011-01-01

The rapid development of computer graphics technology has made possible an easy integration of dynamic visualizations into computer-based learning environments. This study examines the relative effectiveness of dynamic visualizations, compared either to sequentially or simultaneously presented static visualizations. Moreover, the degree of realism…

A phenomenological research study: Perspectives of student learning through small group work between undergraduate nursing students and educators.

PubMed

Wong, Florence Mei Fung

2018-06-18

Small group work is an effective teaching-learning approach in nursing education to enhance students' learning in theoretical knowledge and skill development. Despite its potential advantageous effects on learning, little is known about its actual effects on students' learning from students' and educators' perspectives. To understand students' learning through small group work from the perspectives of students and educators. A qualitative study with focus group interviews was carried out. Semi-structured interviews with open-ended questions were performed with 13 undergraduate nursing students and 10 educators. Four main themes, "initiative learning", "empowerment of interactive group dynamics", "factors for creating effective learning environment", and "barriers influencing students' learning", were derived regarding students' learning in small group work based on the perspectives of the participants. The results showed the importance of learning attitudes of students in individual and group learning. Factors for creating an effective learning environment, including preference for forming groups, effective group size, and adequacy of discussion, facilitate students' learning with the enhancement of learning engagement in small group work. The identified barriers, such as "excessive group work", "conflicts", and "passive team members" can reduce students' motivation and enjoyment of learning. Small group work is recognized as an effective teaching method for knowledge enhancement and skill development in nursing education. All identified themes are important to understand the initiatives of students and group learning, factors influencing an effective learning environment, and barriers hindering students' learning. Nurse educators should pay more attention to the factors that influence an effective learning environment and reduce students' commitment and group dynamics. Moreover, students may need further support to reduce barriers that impede students' learning motivation and enjoyment. Copyright © 2018 Elsevier Ltd. All rights reserved.

Sublayer-Specific Coding Dynamics during Spatial Navigation and Learning in Hippocampal Area CA1.

PubMed

Danielson, Nathan B; Zaremba, Jeffrey D; Kaifosh, Patrick; Bowler, John; Ladow, Max; Losonczy, Attila

2016-08-03

The mammalian hippocampus is critical for spatial information processing and episodic memory. Its primary output cells, CA1 pyramidal cells (CA1 PCs), vary in genetics, morphology, connectivity, and electrophysiological properties. It is therefore possible that distinct CA1 PC subpopulations encode different features of the environment and differentially contribute to learning. To test this hypothesis, we optically monitored activity in deep and superficial CA1 PCs segregated along the radial axis of the mouse hippocampus and assessed the relationship between sublayer dynamics and learning. Superficial place maps were more stable than deep during head-fixed exploration. Deep maps, however, were preferentially stabilized during goal-oriented learning, and representation of the reward zone by deep cells predicted task performance. These findings demonstrate that superficial CA1 PCs provide a more stable map of an environment, while their counterparts in the deep sublayer provide a more flexible representation that is shaped by learning about salient features in the environment. VIDEO ABSTRACT. Copyright © 2016 Elsevier Inc. All rights reserved.

Normative evidence accumulation in unpredictable environments

PubMed Central

Glaze, Christopher M; Kable, Joseph W; Gold, Joshua I

2015-01-01

In our dynamic world, decisions about noisy stimuli can require temporal accumulation of evidence to identify steady signals, differentiation to detect unpredictable changes in those signals, or both. Normative models can account for learning in these environments but have not yet been applied to faster decision processes. We present a novel, normative formulation of adaptive learning models that forms decisions by acting as a leaky accumulator with non-absorbing bounds. These dynamics, derived for both discrete and continuous cases, depend on the expected rate of change of the statistics of the evidence and balance signal identification and change detection. We found that, for two different tasks, human subjects learned these expectations, albeit imperfectly, then used them to make decisions in accordance with the normative model. The results represent a unified, empirically supported account of decision-making in unpredictable environments that provides new insights into the expectation-driven dynamics of the underlying neural signals. DOI: http://dx.doi.org/10.7554/eLife.08825.001 PMID:26322383

The Design of Learning Experiences: A Connection to Physical Environments.

ERIC Educational Resources Information Center

Stueck, Lawrence E.; Tanner, C. Kenneth

The school environment must create a rich, beautiful, dynamic, meaningful experience for students to learn; however, architects, school boards, and the state focus almost exclusively only on the building when making design decisions. This document lists specific aspects to developing a visionary campus: one that provides a three-dimensional…

Situations, Interaction, Process and Affordances: An Ecological Psychology Perspective.

ERIC Educational Resources Information Center

Young, Michael F.; DePalma, Andrew; Garrett, Steven

2002-01-01

From an ecological psychology perspective, a full analysis of any learning context must acknowledge the complex nonlinear dynamics that unfold as an intentionally-driven learner interacts with a technology-based purposefully designed learning environment. A full situation model would need to incorporate constraints from the environment and also…

Self-Regulated Learning in an Introductory Undergraduate Accounting Course

ERIC Educational Resources Information Center

Becker, Lana Lowe

2011-01-01

Self-regulated learning skills have been shown to positively impact academic achievement in educational settings. This same set of skills becomes critically important as graduates enter today's dynamic work environment. That environment increasingly requires accountants and other professionals to be lifelong learners. This study is a response to…

Establishing Positive Learning Environments for Students of Chinese American and Latino Backgrounds

ERIC Educational Resources Information Center

Wong-Lo, Mickie; Cortez, Gabriel A.

2014-01-01

Cultivating positive learning environments for underrepresented groups such as students of Chinese American and Latino backgrounds require careful planning and consideration. As our society strives to embrace individual differences of all cultures, educators must equip themselves with effective tools to adapt to the ever-changing dynamics within…

Reinforcement learning algorithms for robotic navigation in dynamic environments.

PubMed

Yen, Gary G; Hickey, Travis W

2004-04-01

The purpose of this study was to examine improvements to reinforcement learning (RL) algorithms in order to successfully interact within dynamic environments. The scope of the research was that of RL algorithms as applied to robotic navigation. Proposed improvements include: addition of a forgetting mechanism, use of feature based state inputs, and hierarchical structuring of an RL agent. Simulations were performed to evaluate the individual merits and flaws of each proposal, to compare proposed methods to prior established methods, and to compare proposed methods to theoretically optimal solutions. Incorporation of a forgetting mechanism did considerably improve the learning times of RL agents in a dynamic environment. However, direct implementation of a feature-based RL agent did not result in any performance enhancements, as pure feature-based navigation results in a lack of positional awareness, and the inability of the agent to determine the location of the goal state. Inclusion of a hierarchical structure in an RL agent resulted in significantly improved performance, specifically when one layer of the hierarchy included a feature-based agent for obstacle avoidance, and a standard RL agent for global navigation. In summary, the inclusion of a forgetting mechanism, and the use of a hierarchically structured RL agent offer substantially increased performance when compared to traditional RL agents navigating in a dynamic environment.

An Enjoyable Learning Experience in Personalising Learning Based on Knowledge Management: A Case Study

ERIC Educational Resources Information Center

Xu, Hao; Song, Donglei; Yu, Tao; Tavares, Adriano

2017-01-01

Many attempts at personalisation have been made in education. They all collect learning data and analyse learning behaviours, and ultimately achieve personalised learning dynamically. However, further research is needed on the ways to effectively access and analyse information about learning within an enjoyable environment and with positive…

Controlling Uncertainty: A Review of Human Behavior in Complex Dynamic Environments

ERIC Educational Resources Information Center

Osman, Magda

2010-01-01

Complex dynamic control (CDC) tasks are a type of problem-solving environment used for examining many cognitive activities (e.g., attention, control, decision making, hypothesis testing, implicit learning, memory, monitoring, planning, and problem solving). Because of their popularity, there have been many findings from diverse domains of research…

Role of dopamine D2 receptors in optimizing choice strategy in a dynamic and uncertain environment

PubMed Central

Kwak, Shinae; Huh, Namjung; Seo, Ji-Seon; Lee, Jung-Eun; Han, Pyung-Lim; Jung, Min W.

2014-01-01

In order to investigate roles of dopamine receptor subtypes in reward-based learning, we examined choice behavior of dopamine D1 and D2 receptor-knockout (D1R-KO and D2R-KO, respectively) mice in an instrumental learning task with progressively increasing reversal frequency and a dynamic two-armed bandit task. Performance of D2R-KO mice was progressively impaired in the former as the frequency of reversal increased and profoundly impaired in the latter even with prolonged training, whereas D1R-KO mice showed relatively minor performance deficits. Choice behavior in the dynamic two-armed bandit task was well explained by a hybrid model including win-stay-lose-switch and reinforcement learning terms. A model-based analysis revealed increased win-stay, but impaired value updating and decreased value-dependent action selection in D2R-KO mice, which were detrimental to maximizing rewards in the dynamic two-armed bandit task. These results suggest an important role of dopamine D2 receptors in learning from past choice outcomes for rapid adjustment of choice behavior in a dynamic and uncertain environment. PMID:25389395

Dynamic Interaction between Reinforcement Learning and Attention in Multidimensional Environments.

PubMed

Leong, Yuan Chang; Radulescu, Angela; Daniel, Reka; DeWoskin, Vivian; Niv, Yael

2017-01-18

Little is known about the relationship between attention and learning during decision making. Using eye tracking and multivariate pattern analysis of fMRI data, we measured participants' dimensional attention as they performed a trial-and-error learning task in which only one of three stimulus dimensions was relevant for reward at any given time. Analysis of participants' choices revealed that attention biased both value computation during choice and value update during learning. Value signals in the ventromedial prefrontal cortex and prediction errors in the striatum were similarly biased by attention. In turn, participants' focus of attention was dynamically modulated by ongoing learning. Attentional switches across dimensions correlated with activity in a frontoparietal attention network, which showed enhanced connectivity with the ventromedial prefrontal cortex between switches. Our results suggest a bidirectional interaction between attention and learning: attention constrains learning to relevant dimensions of the environment, while we learn what to attend to via trial and error. Copyright © 2017 Elsevier Inc. All rights reserved.

Multitrophic effects of belowground parasitoid learning

USDA-ARS?s Scientific Manuscript database

The ability to learn allows organisms to take advantage of dynamic and ephemeral opportunities in their environment. Here we show that learning in belowground entomopathogenic nematodes has cascading multitrophic effects on their hosts, other nematodes, and nematophagous fungal predators. In additio...

Safe Exploration Algorithms for Reinforcement Learning Controllers.

PubMed

Mannucci, Tommaso; van Kampen, Erik-Jan; de Visser, Cornelis; Chu, Qiping

2018-04-01

Self-learning approaches, such as reinforcement learning, offer new possibilities for autonomous control of uncertain or time-varying systems. However, exploring an unknown environment under limited prediction capabilities is a challenge for a learning agent. If the environment is dangerous, free exploration can result in physical damage or in an otherwise unacceptable behavior. With respect to existing methods, the main contribution of this paper is the definition of a new approach that does not require global safety functions, nor specific formulations of the dynamics or of the environment, but relies on interval estimation of the dynamics of the agent during the exploration phase, assuming a limited capability of the agent to perceive the presence of incoming fatal states. Two algorithms are presented with this approach. The first is the Safety Handling Exploration with Risk Perception Algorithm (SHERPA), which provides safety by individuating temporary safety functions, called backups. SHERPA is shown in a simulated, simplified quadrotor task, for which dangerous states are avoided. The second algorithm, denominated OptiSHERPA, can safely handle more dynamically complex systems for which SHERPA is not sufficient through the use of safety metrics. An application of OptiSHERPA is simulated on an aircraft altitude control task.

Spatial Integration under Contextual Control in a Virtual Environment

ERIC Educational Resources Information Center

Molet, Mikael; Gambet, Boris; Bugallo, Mehdi; Miller, Ralph R.

2012-01-01

The role of context was examined in the selection and integration of independently learned spatial relationships. Using a dynamic 3D virtual environment, participants learned one spatial relationship between landmarks A and B which was established in one virtual context (e.g., A is left of B) and a different spatial relationship which was…

Designing Collaborative Learning Environments Mediated by Computer Conferencing: Issues and Challenges in the Asian Socio-Cultural Context.

ERIC Educational Resources Information Center

Gunawardena, Charlotte N.

1998-01-01

Explores issues related to the design of collaborative-learning environments mediated by computer conferencing from the perspective of challenges faced in the sociocultural context of the Indian sub-continent. Examines the impact of online features on social cohesiveness, group dynamics, interaction, communication anxiety, and participation.…

U-ALS: A Ubiquitous Learning Environment

ERIC Educational Resources Information Center

Piovesan, Sandra Dutra; Passerino, Liliana Maria; Medina, Roseclea Duarte

2012-01-01

The diffusion of the use of the learning virtual environments presents a great potential for the development of an application which meet the necessities in the education area. In view of the importance of a more dynamic application and that can adapt itself continuously to the students' necessities, the "U-ALS" (Ubiquitous Adapted Learning…

Thematic Analysis of the "Games" Students Play in Asynchronous Learning Environments

ERIC Educational Resources Information Center

MacMillan, Thalia; Forte, Michele; Grant, Cynthia

2014-01-01

The dynamics of the student-student relationship within the asynchronous online classroom, as evidenced by conversations in an online discussion board, is a balancing act potentially more complex than those occurring in real-time. In order for learning to truly be considered effective, a collaborative, safe environment needs to exist among…

Challenges Faced by Key Stakeholders Using Educational Online Technologies in Blended Tertiary Environments

ERIC Educational Resources Information Center

Tuapawa, Kimberley

2016-01-01

Traditional learning spaces have evolved into dynamic blended tertiary environments (BTEs), providing a modern means through which tertiary education institutes (TEIs) can augment delivery to meet stakeholder needs. Despite the significant demand for web-enabled learning, there are obstacles concerning the use of EOTs, which challenge the…

Modular Object-Oriented Dynamic Learning Environment: What Open Source Has to Offer

ERIC Educational Resources Information Center

Antonenko, Pavlo; Toy, Serkan; Niederhauser, Dale

2004-01-01

Open source online learning environments have emerged and developed over the past 10 years. In this paper we will analyze the underlying philosophy and features of MOODLE based on the theoretical framework developed by Hannafin and Land (2000). Psychological, pedagogical, technological, cultural, and pragmatic foundations comprise the framework…

Globally Sustainable Management: A Dynamic Model of IHRM Learning and Control

ERIC Educational Resources Information Center

Takeda, Margaret B.; Helms, Marilyn M.

2010-01-01

Purpose: After a thorough literature review on multinational learning, it is apparent organizations "learn" when they capitalize on expatriate management, a "learning strategy" (international work teams, employee involvement and other human resource policies), technology transfer and political environment and cross-cultural adaptation. This…

Space Matters: Experiences of Managing Static Formal Learning Spaces

ERIC Educational Resources Information Center

Montgomery, Tim

2008-01-01

Managing the space in which learning takes place is subject to ongoing debate. Spatial management and movement can impact upon the construction of meaning within education and upon the dynamic of learning. It is suggested that there are now different learning goals and expectations and consequently a need for different learning environments. We…

A Novel Data-Driven Learning Method for Radar Target Detection in Nonstationary Environments

DTIC Science & Technology

2016-05-01

Classifier ensembles for changing environments,” in Multiple Classifier Systems, vol. 3077, F. Roli, J. Kittler and T. Windeatt, Eds. New York, NY...Dec. 2006, pp. 1113–1118. [21] J. Z. Kolter and M. A. Maloof, “Dynamic weighted majority: An ensemble method for drifting concepts,” J. Mach. Learn...Trans. Neural Netw., vol. 22, no. 10, pp. 1517–1531, Oct. 2011. [23] R. Polikar, “ Ensemble learning,” in Ensemble Machine Learning: Methods and

Muscle cocontraction following dynamics learning.

PubMed

Darainy, Mohammad; Ostry, David J

2008-09-01

Coactivation of antagonist muscles is readily observed early in motor learning, in interactions with unstable mechanical environments and in motor system pathologies. Here we present evidence that the nervous system uses coactivation control far more extensively and that patterns of cocontraction during movement are closely tied to the specific requirements of the task. We have examined the changes in cocontraction that follow dynamics learning in tasks that are thought to involve finely sculpted feedforward adjustments to motor commands. We find that, even following substantial training, cocontraction varies in a systematic way that depends on both movement direction and the strength of the external load. The proportion of total activity that is due to cocontraction nevertheless remains remarkably constant. Moreover, long after indices of motor learning and electromyographic measures have reached asymptotic levels, cocontraction still accounts for a significant proportion of total muscle activity in all phases of movement and in all load conditions. These results show that even following dynamics learning in predictable and stable environments, cocontraction forms a central part of the means by which the nervous system regulates movement.

Learning to Manage Intergroup Dynamics in Changing Task Environments: An Experiential Exercise

ERIC Educational Resources Information Center

Hunsaker, Phillip L.

2004-01-01

This article describes an exercise that allows participants to experience the challenges of managing intergroup behavior as an organization's task environment grows and becomes more complex. The article begins with a brief review of models and concepts relating to intergroup dynamics, intergroup conflict, and interventions for effectively managing…

Emerging Trends in Science Education in a Dynamic Academic Environment

ERIC Educational Resources Information Center

Avwiri, H. E.

2016-01-01

Emerging Trends in Science Education in a Dynamic Academic Environment highlights the changes that have occurred in science education particularly in institutions of higher learning in southern Nigeria. Impelled by the fact that most Nigerian Universities and Colleges of Education still adhere to the practices and teaching methodologies of the…

A Social Approach to High-Level Context Generation for Supporting Context-Aware M-Learning

ERIC Educational Resources Information Center

Pan, Xu-Wei; Ding, Ling; Zhu, Xi-Yong; Yang, Zhao-Xiang

2017-01-01

In m-learning environments, context-awareness is for wide use where learners' situations are varied, dynamic and unpredictable. We are facing the challenge of requirements of both generality and depth in generating and processing high-level context. In this paper, we present a social approach which exploits social dynamics and social computing for…

Predicting Student Performance in a Collaborative Learning Environment

ERIC Educational Resources Information Center

Olsen, Jennifer K.; Aleven, Vincent; Rummel, Nikol

2015-01-01

Student models for adaptive systems may not model collaborative learning optimally. Past research has either focused on modeling individual learning or for collaboration, has focused on group dynamics or group processes without predicting learning. In the current paper, we adjust the Additive Factors Model (AFM), a standard logistic regression…

Organisational learning and self-adaptation in dynamic disaster environments.

PubMed

Corbacioglu, Sitki; Kapucu, Naim

2006-06-01

This paper examines the problems associated with inter-organisational learning and adaptation in the dynamic environments that characterise disasters. The research uses both qualitative and quantitative methods to investigate whether organisational learning took place during and in the time in between five disaster response operations in Turkey. The availability of information and its exchange and distribution within and among organisational actors determine whether self-adaptation happens in the course of a disaster response operation. Organisational flexibility supported by an appropriate information infrastructure creates conditions conducive to essential interaction and permits the flow of information. The study found that no significant organisational learning occurred within Turkish disaster management following the earthquakes in Erzincan (1992), Dinar (1995) and Ceyhan (1998). By contrast, the 'symmetry-breaking' Marmara earthquake of 1999 initiated a 'double loop' learning process that led to change in the organisational, technical and cultural aspects of Turkish disaster management, as revealed by the Duzce earthquake response operations.

Interaction Equivalency in Self-Paced Online Learning Environments: An Exploration of Learner Preferences

ERIC Educational Resources Information Center

Rhode, Jason F.

2009-01-01

This mixed methods study explored the dynamics of interaction within a self-paced online learning environment. It used rich media and a mix of traditional and emerging asynchronous computer-mediated communication tools to determine what forms of interaction learners in a self-paced online course value most and what impact they perceive interaction…

Use and Mastery of Virtual Learning Environment in Brazilian Open University

ERIC Educational Resources Information Center

Gomez, Margarita Victoria

2014-01-01

This paper describes and analyses the dynamics of the use and/or mastery of Virtual Learning Environments (VLEs) by educators and students Open University, important part of the Brazilian Educational System. A questionnaire with 32 items was answered by 174 students/instructors/coordinators of the Media in Education and Physics courses, of two…

Movement rehabilitation: are the principles of re-learning in the recovery of function the same as those of original learning?

PubMed

Newell, Karl M; Verhoeven, F Martijn

2017-01-01

This paper addresses the change in movement dynamics in rehabilitation through discussing issues that pertain to the question as to whether the principles of re-learning in functional recovery are the same as those of original learning. The many varieties of disease and injury states lead to significant differences in the constraints to action and these impairments in turn influence the pathway of change in re-learning and/or recovery of function. These altered constraints channel the effectiveness of many conditions and strategies of practice that influence learning and performance. Nevertheless, it is proposed that there is a small set of principles for the change in dynamics of motor learning, which drive the continuously evolving stability and instability of movement forms through the lifespan. However, this common set of dynamical principles is realized in individual pathways of change in the movement dynamics of learning, re-learning and recovery of function. The inherent individual differences of humans and environments insure that the coordination, control and skill of movement rehabilitation are challenged in distinct ways by the changing constraints arising from the many manifestations of disease and injury. Implications for rehabilitation The many varieties of disease and injury states lead to significant differences in the constraints to action that in turn influence the pathway of change in re-learning and/or recovery of function, and the effectiveness of the many conditions/strategies of practice to influence learning and performance. There are a small set of principles for the change in dynamics of motor learning that drive the continuously evolving ebb and flow of stability and instability of movement forms through the lifespan. The inherent individual differences of humans and environments insure that the coordination, control and skill of movement rehabilitation are uniquely challenged by the changing constraints arising from the many manifestations of disease and injury.

Self-esteem, academic self-concept, and achievement: how the learning environment moderates the dynamics of self-concept.

PubMed

Trautwein, Ulrich; Lüdtke, Oliver; Köller, Olaf; Baumert, Jürgen

2006-02-01

The authors examine the directionality of effects between global self-esteem, domain-specific academic self-concepts, and academic achievement. Special emphasis is placed on learning environments as potential moderators of the direction of these effects. According to the meritocracy principle presented here, so-called bottom-up effects (i.e., self-esteem is influenced by academic self-concept) are more pronounced in meritocratic learning environments than in ego-protective learning environments. This hypothesis was examined using a three-wave cross-lagged panel design with a large sample of 7th graders from East and West Germany, a total of 5,648 students who were tested shortly after German reunification. Reciprocal effects were found between self-esteem, academic self-concept, and academic achievement. In conformance with the meritocracy principle, support for bottom-up effects was stronger in the meritocratic learning environment. Copyright 2006 APA, all rights reserved.

Methodological Advances in Research on Learning and Instruction and in the Learning Sciences

ERIC Educational Resources Information Center

Fischer, Frank; Järvelä, Sanna

2014-01-01

Recent years have seen a dynamic growth of research communities addressing conditions, processes and outcomes of learning in formal and informal environments. Two of them have markedly advanced the field: The community on research on learning and instruction that has been organized in the European Association for Research on Learning and…

STEAM by Design

ERIC Educational Resources Information Center

Keane, Linda; Keane, Mark

2016-01-01

We live in a designed world. STEAM by Design presents a transdisciplinary approach to learning that challenges young minds with the task of making a better world. Learning today, like life, is dynamic, connected and engaging. STEAM (Science, Technology, Environment, Engineering, Art, and Math) teaching and learning integrates information in…

Next Generation Online: Advancing Learning through Dynamic Design, Virtual and Web 2.0 Technologies, and Instructor "Attitude"

ERIC Educational Resources Information Center

O'Connor, Eileen

2013-01-01

With the advent of web 2.0 and virtual technologies and new understandings about learning within a global, networked environment, online course design has moved beyond the constraints of text readings, papers, and discussion boards. This next generation of online courses needs to dynamically and actively integrate the wide-ranging distribution of…

Simulation-Based Learning: The Learning-Forgetting-Relearning Process and Impact of Learning History

ERIC Educational Resources Information Center

Davidovitch, Lior; Parush, Avi; Shtub, Avy

2008-01-01

The results of empirical experiments evaluating the effectiveness and efficiency of the learning-forgetting-relearning process in a dynamic project management simulation environment are reported. Sixty-six graduate engineering students performed repetitive simulation-runs with a break period of several weeks between the runs. The students used a…

A Model Driven Framework to Address Challenges in a Mobile Learning Environment

ERIC Educational Resources Information Center

Khaddage, Ferial; Christensen, Rhonda; Lai, Wing; Knezek, Gerald; Norris, Cathie; Soloway, Elliot

2015-01-01

In this paper a review of the pedagogical, technological, policy and research challenges and concepts underlying mobile learning is presented, followed by a brief description of categories of implementations. A model Mobile learning framework and dynamic criteria for mobile learning implementations are proposed, along with a case study of one site…

Psychology of Learning Spaces: Impact on Teaching and Learning

ERIC Educational Resources Information Center

Granito, Vincent J.; Santana, Mary E.

2016-01-01

New research is emerging that focuses on the role the physical classroom space plays in the teaching-learning dynamic. The purpose of this exploratory research is to describe the students' and instructors' perspectives of how the classroom space and environment impact teaching and learning. Focus groups were utilized with data points coming from…

Negotiating Energy Dynamics through Embodied Action in a Materially Structured Environment

ERIC Educational Resources Information Center

Scherr, Rachel E.; Close, Hunter G.; Close, Eleanor W.; Flood, Virginia J.; McKagan, Sarah B.; Robertson, Amy D.; Seeley, Lane; Wittmann, Michael C.; Vokos, Stamatis

2013-01-01

We provide evidence that a learning activity called Energy Theater engages learners with key conceptual issues in the learning of energy, including disambiguating matter flow and energy flow and theorizing mechanisms for energy transformation. A participationist theory of learning, in which learning is indicated by changes in speech and behavior,…

Clinical learning environment at Shiraz Medical School.

PubMed

Rezaee, Rita; Ebrahimi, Sedigheh

2013-01-01

Clinical learning occurs in the context of a dynamic environment. Learning environment found to be one of the most important factors in determining the success of an effective teaching program. To investigate, from the attending and resident's perspective, factors that may affect student leaning in the educational hospital setting at Shiraz University of Medical Sciences (SUMS). This study combined qualitative and quantitative methods to determine factors affecting effective learning in clinical setting. Residents evaluated the perceived effectiveness of the university hospital learning environment. Fifty two faculty members and 132 residents participated in this study. Key determinants that contribute to an effective clinical teaching were autonomy, supervision, social support, workload, role clarity, learning opportunity, work diversity and physical facilities. In a good clinical setting, residents should be appreciated and given appropriate opportunities to study in order to meet their objectives. They require a supportive environment to consolidate their knowledge, skills and judgment. © 2013 Tehran University of Medical Sciences. All rights reserved.

Learning gait of quadruped robot without prior knowledge of the environment

NASA Astrophysics Data System (ADS)

Xu, Tao; Chen, Qijun

2012-09-01

Walking is the basic skill of a legged robot, and one of the promising ways to improve the walking performance and its adaptation to environment changes is to let the robot learn its walking by itself. Currently, most of the walking learning methods are based on robot vision system or some external sensing equipment to estimate the walking performance of certain walking parameters, and therefore are usually only applicable under laboratory condition, where environment can be pre-defined. Inspired by the rhythmic swing movement during walking of legged animals and the behavior of their adjusting their walking gait on different walking surfaces, a concept of walking rhythmic pattern(WRP) is proposed to evaluate the walking specialty of legged robot, which is just based on the walking dynamics of the robot. Based on the onboard acceleration sensor data, a method to calculate WRP using power spectrum in frequency domain and diverse smooth filters is also presented. Since the evaluation of WRP is only based on the walking dynamics data of the robot's body, the proposed method doesn't require prior knowledge of environment and thus can be applied in unknown environment. A gait learning approach of legged robots based on WRP and evolution algorithm(EA) is introduced. By using the proposed approach, a quadruped robot can learn its locomotion by its onboard sensing in an unknown environment, where the robot has no prior knowledge about this place. The experimental result proves proportional relationship exits between WRP match score and walking performance of legged robot, which can be used to evaluate the walking performance in walking optimization under unknown environment.

Comparing the Effects of Traditional Face-to-Face, Technology-Based, and Blended Instructional Strategies in a Post-Secondary Spanish Language Learning Environment

ERIC Educational Resources Information Center

Harris, Richard

2017-01-01

Understanding the way humans communicate linguistically helps to define what proficiency in a particular language is. The general problem is scholars' assumption that the implementation of technology in the language learning environment acts a substitute for the human dynamic in achieving language proficiency. The purpose of this quantitative…

An Algorithm for Automatic Checking of Exercises in a Dynamic Geometry System: iGeom

ERIC Educational Resources Information Center

Isotani, Seiji; de Oliveira Brandao, Leonidas

2008-01-01

One of the key issues in e-learning environments is the possibility of creating and evaluating exercises. However, the lack of tools supporting the authoring and automatic checking of exercises for specifics topics (e.g., geometry) drastically reduces advantages in the use of e-learning environments on a larger scale, as usually happens in Brazil.…

A Typology of Agency in New Generation Learning Environments: Emerging Relational, Ecological and New Material Considerations

ERIC Educational Resources Information Center

Charteris, Jennifer; Smardon, Dianne

2018-01-01

The impetus to move to a new generation learning environments places a spotlight on the relational dynamics of classroom spaces. A key feature is the notion of learner agency. A complex notion, learner agency involves both compliance with and resistance to classroom norms and therefore is far more sophisticated than acting in acquiescence to…

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

PubMed

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

2015-10-01

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

A novel data-driven learning method for radar target detection in nonstationary environments

DOE PAGES

Akcakaya, Murat; Nehorai, Arye; Sen, Satyabrata

2016-04-12

Most existing radar algorithms are developed under the assumption that the environment (clutter) is stationary. However, in practice, the characteristics of the clutter can vary enormously depending on the radar-operational scenarios. If unaccounted for, these nonstationary variabilities may drastically hinder the radar performance. Therefore, to overcome such shortcomings, we develop a data-driven method for target detection in nonstationary environments. In this method, the radar dynamically detects changes in the environment and adapts to these changes by learning the new statistical characteristics of the environment and by intelligibly updating its statistical detection algorithm. Specifically, we employ drift detection algorithms to detectmore » changes in the environment; incremental learning, particularly learning under concept drift algorithms, to learn the new statistical characteristics of the environment from the new radar data that become available in batches over a period of time. The newly learned environment characteristics are then integrated in the detection algorithm. Furthermore, we use Monte Carlo simulations to demonstrate that the developed method provides a significant improvement in the detection performance compared with detection techniques that are not aware of the environmental changes.« less

A graph-based evolutionary algorithm: Genetic Network Programming (GNP) and its extension using reinforcement learning.

PubMed

Mabu, Shingo; Hirasawa, Kotaro; Hu, Jinglu

2007-01-01

This paper proposes a graph-based evolutionary algorithm called Genetic Network Programming (GNP). Our goal is to develop GNP, which can deal with dynamic environments efficiently and effectively, based on the distinguished expression ability of the graph (network) structure. The characteristics of GNP are as follows. 1) GNP programs are composed of a number of nodes which execute simple judgment/processing, and these nodes are connected by directed links to each other. 2) The graph structure enables GNP to re-use nodes, thus the structure can be very compact. 3) The node transition of GNP is executed according to its node connections without any terminal nodes, thus the past history of the node transition affects the current node to be used and this characteristic works as an implicit memory function. These structural characteristics are useful for dealing with dynamic environments. Furthermore, we propose an extended algorithm, "GNP with Reinforcement Learning (GNPRL)" which combines evolution and reinforcement learning in order to create effective graph structures and obtain better results in dynamic environments. In this paper, we applied GNP to the problem of determining agents' behavior to evaluate its effectiveness. Tileworld was used as the simulation environment. The results show some advantages for GNP over conventional methods.

Evolution of learning strategies in temporally and spatially variable environments: A review of theory

PubMed Central

Aoki, Kenichi; Feldman, Marcus W.

2013-01-01

The theoretical literature from 1985 to the present on the evolution of learning strategies in variable environments is reviewed, with the focus on deterministic dynamical models that are amenable to local stability analysis, and on deterministic models yielding evolutionarily stable strategies. Individual learning, unbiased and biased social learning, mixed learning, and learning schedules are considered. A rapidly changing environment or frequent migration in a spatially heterogeneous environment favors individual learning over unbiased social learning. However, results are not so straightforward in the context of learning schedules or when biases in social learning are introduced. The three major methods of modeling temporal environmental change – coevolutionary, two-timescale, and information decay – are compared and shown to sometimes yield contradictory results. The so-called Rogers’ paradox is inherent in the two-timescale method as originally applied to the evolution of pure strategies, but is often eliminated when the other methods are used. Moreover, Rogers’ paradox is not observed for the mixed learning strategies and learning schedules that we review. We believe that further theoretical work is necessary on learning schedules and biased social learning, based on models that are logically consistent and empirically pertinent. PMID:24211681

Evolution of learning strategies in temporally and spatially variable environments: a review of theory.

PubMed

Aoki, Kenichi; Feldman, Marcus W

2014-02-01

The theoretical literature from 1985 to the present on the evolution of learning strategies in variable environments is reviewed, with the focus on deterministic dynamical models that are amenable to local stability analysis, and on deterministic models yielding evolutionarily stable strategies. Individual learning, unbiased and biased social learning, mixed learning, and learning schedules are considered. A rapidly changing environment or frequent migration in a spatially heterogeneous environment favors individual learning over unbiased social learning. However, results are not so straightforward in the context of learning schedules or when biases in social learning are introduced. The three major methods of modeling temporal environmental change--coevolutionary, two-timescale, and information decay--are compared and shown to sometimes yield contradictory results. The so-called Rogers' paradox is inherent in the two-timescale method as originally applied to the evolution of pure strategies, but is often eliminated when the other methods are used. Moreover, Rogers' paradox is not observed for the mixed learning strategies and learning schedules that we review. We believe that further theoretical work is necessary on learning schedules and biased social learning, based on models that are logically consistent and empirically pertinent. Copyright © 2013 Elsevier Inc. All rights reserved.

Social Dynamics Management and Functional Behavioral Assessment

ERIC Educational Resources Information Center

Lee, David L.

2018-01-01

Managing social dynamics is a critical aspect of creating a positive learning environment in classrooms. In this paper three key interrelated ideas, reinforcement, function, and motivating operations, are discussed with relation to managing social behavior.

The evolution of social learning rules: payoff-biased and frequency-dependent biased transmission.

PubMed

Kendal, Jeremy; Giraldeau, Luc-Alain; Laland, Kevin

2009-09-21

Humans and other animals do not use social learning indiscriminately, rather, natural selection has favoured the evolution of social learning rules that make selective use of social learning to acquire relevant information in a changing environment. We present a gene-culture coevolutionary analysis of a small selection of such rules (unbiased social learning, payoff-biased social learning and frequency-dependent biased social learning, including conformism and anti-conformism) in a population of asocial learners where the environment is subject to a constant probability of change to a novel state. We define conditions under which each rule evolves to a genetically polymorphic equilibrium. We find that payoff-biased social learning may evolve under high levels of environmental variation if the fitness benefit associated with the acquired behaviour is either high or low but not of intermediate value. In contrast, both conformist and anti-conformist biases can become fixed when environment variation is low, whereupon the mean fitness in the population is higher than for a population of asocial learners. Our examination of the population dynamics reveals stable limit cycles under conformist and anti-conformist biases and some highly complex dynamics including chaos. Anti-conformists can out-compete conformists when conditions favour a low equilibrium frequency of the learned behaviour. We conclude that evolution, punctuated by the repeated successful invasion of different social learning rules, should continuously favour a reduction in the equilibrium frequency of asocial learning, and propose that, among competing social learning rules, the dominant rule will be the one that can persist with the lowest frequency of asocial learning.

Using Context-Aware Ubiquitous Learning to Support Students' Understanding of Geometry

ERIC Educational Resources Information Center

Crompton, Helen

2015-01-01

In this study, context-aware ubiquitous learning was used to support 4th grade students as they learn angle concepts. Context-aware ubiquitous learning was provided to students primarily through the use of iPads to access real-world connections and a Dynamic Geometry Environment. Gravemeijer and van Eerde's (2009), design-based research (DBR)…

A Case Study of the Dynamics of Scaffolding among ESL Learners and Online Resources in Collaborative Learning

ERIC Educational Resources Information Center

Hsieh, Yi Chin

2017-01-01

Collaborative learning has been widely applied in education, and has been seen as conducive to student learning. The advent of technology and its applications in education have also greatly enhanced the classroom learning environment, leading to increasing research attention on the combination of technology and collaboration. The case study…

A Case Study for Comparing the Effectiveness of a Computer Simulation and a Hands-on Activity on Learning Electric Circuits

ERIC Educational Resources Information Center

Ekmekci, Adem; Gulacar, Ozcan

2015-01-01

Science education reform emphasizes innovative and constructivist views of science teaching and learning that promotes active learning environments, dynamic instructions, and authentic science experiments. Technology-based and hands-on instructional designs are among innovative science teaching and learning methods. Research shows that these two…

Extend Instruction outside the Classroom: Take Advantage of Your Learning Management System

ERIC Educational Resources Information Center

Jensen, Lauren A.

2010-01-01

Numerous institutions of higher education have implemented a learning management system (LMS) or are considering doing so. This web-based software package provides self-service and quick (often personalized) access to content in a dynamic environment. Learning management systems support administrative, reporting, and documentation activities. LMSs…

The Online Classroom: A Thorough Depiction of Distance Learning Spaces

ERIC Educational Resources Information Center

McKenna, Kelly

2018-01-01

This study investigated the online higher education learning space of a doctoral program offered at a distance. It explored the learning space, the stakeholders, utilization, and creators of the space. Developing a successful online classroom experience that incorporates an engaging environment and dynamic community setting conducive to learning…

Learner-Interface Interaction for Technology-Enhanced Active Learning

ERIC Educational Resources Information Center

Sinha, Neelu; Khreisat, Laila; Sharma, Kiron

2009-01-01

Neelu Sinha, Laila Khreisat, and Kiron Sharma describe how learner-interface interaction promotes active learning in computer science education. In a pilot study using technology that combines DyKnow software with a hardware platform of pen-enabled HP Tablet notebook computers, Sinha, Khreisat, and Sharma created dynamic learning environments by…

Learning Is Moving in New Ways: The Ecological Dynamics of Mathematics Education

ERIC Educational Resources Information Center

Abrahamson, Dor; Sánchez-García, Raúl

2016-01-01

Whereas emerging technologies, such as touchscreen tablets, are bringing sensorimotor interaction back into mathematics learning activities, existing educational theory is not geared to inform or analyze passages from action to concept. We present case studies of tutor-student behaviors in an embodied-interaction learning environment, the…

A cholinergic feedback circuit to regulate striatal population uncertainty and optimize reinforcement learning.

PubMed

Franklin, Nicholas T; Frank, Michael J

2015-12-25

Convergent evidence suggests that the basal ganglia support reinforcement learning by adjusting action values according to reward prediction errors. However, adaptive behavior in stochastic environments requires the consideration of uncertainty to dynamically adjust the learning rate. We consider how cholinergic tonically active interneurons (TANs) may endow the striatum with such a mechanism in computational models spanning three Marr's levels of analysis. In the neural model, TANs modulate the excitability of spiny neurons, their population response to reinforcement, and hence the effective learning rate. Long TAN pauses facilitated robustness to spurious outcomes by increasing divergence in synaptic weights between neurons coding for alternative action values, whereas short TAN pauses facilitated stochastic behavior but increased responsiveness to change-points in outcome contingencies. A feedback control system allowed TAN pauses to be dynamically modulated by uncertainty across the spiny neuron population, allowing the system to self-tune and optimize performance across stochastic environments.

DCG & GTE: Dynamic Courseware Generation with Teaching Expertise.

ERIC Educational Resources Information Center

Vassileva, Julita

1998-01-01

Discusses the place of GTE (Generic Tutoring Environment) as an approach to bridging the gap between computer-assisted learning and intelligent tutoring systems; describes DCG (dynamic courseware generation) which allows dynamic planning of the contents of an instructional course; and considers combining GTE with DCG. (Author/LRW)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Akcakaya, Murat; Nehorai, Arye; Sen, Satyabrata

Most existing radar algorithms are developed under the assumption that the environment (clutter) is stationary. However, in practice, the characteristics of the clutter can vary enormously depending on the radar-operational scenarios. If unaccounted for, these nonstationary variabilities may drastically hinder the radar performance. Therefore, to overcome such shortcomings, we develop a data-driven method for target detection in nonstationary environments. In this method, the radar dynamically detects changes in the environment and adapts to these changes by learning the new statistical characteristics of the environment and by intelligibly updating its statistical detection algorithm. Specifically, we employ drift detection algorithms to detectmore » changes in the environment; incremental learning, particularly learning under concept drift algorithms, to learn the new statistical characteristics of the environment from the new radar data that become available in batches over a period of time. The newly learned environment characteristics are then integrated in the detection algorithm. Furthermore, we use Monte Carlo simulations to demonstrate that the developed method provides a significant improvement in the detection performance compared with detection techniques that are not aware of the environmental changes.« less

Design of cognitive engine for cognitive radio based on the rough sets and radial basis function neural network

NASA Astrophysics Data System (ADS)

Yang, Yanchao; Jiang, Hong; Liu, Congbin; Lan, Zhongli

2013-03-01

Cognitive radio (CR) is an intelligent wireless communication system which can dynamically adjust the parameters to improve system performance depending on the environmental change and quality of service. The core technology for CR is the design of cognitive engine, which introduces reasoning and learning methods in the field of artificial intelligence, to achieve the perception, adaptation and learning capability. Considering the dynamical wireless environment and demands, this paper proposes a design of cognitive engine based on the rough sets (RS) and radial basis function neural network (RBF_NN). The method uses experienced knowledge and environment information processed by RS module to train the RBF_NN, and then the learning model is used to reconfigure communication parameters to allocate resources rationally and improve system performance. After training learning model, the performance is evaluated according to two benchmark functions. The simulation results demonstrate the effectiveness of the model and the proposed cognitive engine can effectively achieve the goal of learning and reconfiguration in cognitive radio.

Evolving to organizational learning.

PubMed

Bechtold, B L

2000-02-01

To transform in stride with the business changes, organizations need to think of development as "organizational learning" rather than "training." Companies need to manage learning as a strategic competitive advantage for current and future business rather than as a perk for individuals. To position themselves for success in a dynamic business environment, companies need to reframe their concept of learning and development to a mindset of organizational learning.

Construction and validation of a distance learning module on premedication antisepsis for nursing professionals.

PubMed

Pereira, Barbara Juliana da Costa; Mendes, Isabel Amélia Costa; Beatriz Maria, Jorge; Mazzo, Alessandra

2013-11-01

The aim of this descriptive study, carried out at a public university, was to design, develop, and validate a distance learning module on intramuscular premedication antisepsis. The content was introduced in the Modular Object-Oriented Dynamic Learning Environment, based on the Systematic Model for Web-Based Training projects. Ten nurses and information technologists at work consented to participate, in compliance with ethical guidelines, and answered a questionnaire to validate the Virtual Learning Environment. The educational aspects of the environment interface were mostly evaluated as "excellent," whereas the assessment of didactic resources indicated interactivity difficulties. It is concluded that distance learning is an important tool for the teaching of premedication antisepsis. To ensure its effectiveness, appropriate methods and interactive devices must be used.

Prefrontal Cortex Networks Shift from External to Internal Modes during Learning.

PubMed

Brincat, Scott L; Miller, Earl K

2016-09-14

As we learn about items in our environment, their neural representations become increasingly enriched with our acquired knowledge. But there is little understanding of how network dynamics and neural processing related to external information changes as it becomes laden with "internal" memories. We sampled spiking and local field potential activity simultaneously from multiple sites in the lateral prefrontal cortex (PFC) and the hippocampus (HPC)-regions critical for sensory associations-of monkeys performing an object paired-associate learning task. We found that in the PFC, evoked potentials to, and neural information about, external sensory stimulation decreased while induced beta-band (∼11-27 Hz) oscillatory power and synchrony associated with "top-down" or internal processing increased. By contrast, the HPC showed little evidence of learning-related changes in either spiking activity or network dynamics. The results suggest that during associative learning, PFC networks shift their resources from external to internal processing. As we learn about items in our environment, their representations in our brain become increasingly enriched with our acquired "top-down" knowledge. We found that in the prefrontal cortex, but not the hippocampus, processing of external sensory inputs decreased while internal network dynamics related to top-down processing increased. The results suggest that during learning, prefrontal cortex networks shift their resources from external (sensory) to internal (memory) processing. Copyright © 2016 the authors 0270-6474/16/369739-16$15.00/0.

Prefrontal Cortex Networks Shift from External to Internal Modes during Learning

PubMed Central

Brincat, Scott L.

2016-01-01

As we learn about items in our environment, their neural representations become increasingly enriched with our acquired knowledge. But there is little understanding of how network dynamics and neural processing related to external information changes as it becomes laden with “internal” memories. We sampled spiking and local field potential activity simultaneously from multiple sites in the lateral prefrontal cortex (PFC) and the hippocampus (HPC)—regions critical for sensory associations—of monkeys performing an object paired-associate learning task. We found that in the PFC, evoked potentials to, and neural information about, external sensory stimulation decreased while induced beta-band (∼11–27 Hz) oscillatory power and synchrony associated with “top-down” or internal processing increased. By contrast, the HPC showed little evidence of learning-related changes in either spiking activity or network dynamics. The results suggest that during associative learning, PFC networks shift their resources from external to internal processing. SIGNIFICANCE STATEMENT As we learn about items in our environment, their representations in our brain become increasingly enriched with our acquired “top-down” knowledge. We found that in the prefrontal cortex, but not the hippocampus, processing of external sensory inputs decreased while internal network dynamics related to top-down processing increased. The results suggest that during learning, prefrontal cortex networks shift their resources from external (sensory) to internal (memory) processing. PMID:27629722

Effective Student Learning of Fractions with an Interactive Simulation

ERIC Educational Resources Information Center

Hensberry, Karina K. R.; Moore, Emily B.; Perkins, Katherine K.

2015-01-01

Computer technology, when coupled with reform-based teaching practices, has been shown to be an effective way to support student learning of mathematics. The quality of the technology itself, as well as how it is used, impacts how much students learn. Interactive simulations are dynamic virtual environments similar to virtual manipulatives that…

From Periphery to Core: The Increasing Relevance of Experiential Learning in Undergraduate Business Education

ERIC Educational Resources Information Center

Hodge, Laurin; Proudford, Karen L.; Holt, Harry, Jr.

2014-01-01

Business educators have been challenged to provide a learning experience that prepares graduates to successfully compete in a dynamic business environment. The insistence on building demonstrable competencies prior to entering the workforce has led to a shift in the academic community. Experiential learning has gone from the uncommon, exceptional…

Making Online Learning Accessible for Students with Disabilities

ERIC Educational Resources Information Center

Hashey, Andrew I.; Stahl, Skip

2014-01-01

The growing presence of K-12 online education programs is a trend that promises to increase flexibility, improve efficiency, and foster engagement in learning. Students with disabilities can benefit from dynamic online educational environments, but only to the extent that they can access and participate in the learning process. As students with…

Fundamental Research in Engineering Education. Student Learning in Industrially Situated Virtual Laboratories

ERIC Educational Resources Information Center

Koretsky, Milo D.; Kelly, Christine; Gummer, Edith

2011-01-01

The instructional design and the corresponding research on student learning of two virtual laboratories that provide an engineering task situated in an industrial context are described. In this problem-based learning environment, data are generated dynamically based on each student team's distinct choices of reactor parameters and measurements.…

A Contextualized System for Supporting Active Learning

ERIC Educational Resources Information Center

Gomez, Jorge E.; Huete, Juan F.; Hernandez, Velssy L.

2016-01-01

The dynamics of the world today demands a change in traditional education paradigms to enable the creation of more efficient learning environments, where students will learn more effectively and will play a more active role in their education. They should interact with the knowledge at anytime-anywhere. In order to tackle this problem we should…

Transforming the Social Practices of Learning with Representations: A Study of Disciplinary Discourse

ERIC Educational Resources Information Center

Nichols, Kim; Hanan, Jim; Ranasinghe, Muditha

2013-01-01

This study used an interactive dynamic simulation of action potential to explore social practices of learning among first year undergraduate biology students. It aimed to create a learning environment that fosters knowledge building discourse through working with multiple concept-specific representations. Three hundred and eighty-nine students and…

Architectures for Distributed and Complex M-Learning Systems: Applying Intelligent Technologies

ERIC Educational Resources Information Center

Caballe, Santi, Ed.; Xhafa, Fatos, Ed.; Daradoumis, Thanasis, Ed.; Juan, Angel A., Ed.

2009-01-01

Over the last decade, the needs of educational organizations have been changing in accordance with increasingly complex pedagogical models and with the technological evolution of e-learning environments with very dynamic teaching and learning requirements. This book explores state-of-the-art software architectures and platforms used to support…

Intelligent Agents for Dynamic Optimization of Learner Performances in an Online System

ERIC Educational Resources Information Center

Kamsa, Imane; Elouahbi, Rachid; El Khoukhi, Fatima

2017-01-01

Aim/Purpose: To identify and rectify the learning difficulties of online learners. Background: The major cause of learners' failure and non-acquisition of knowledge relates to their weaknesses in certain areas necessary for optimal learning. We focus on e-learning because, within this environment, the learner is mostly affected by these…

PUPIL-TEACHER ADJUSTEMENT AND MUTUAL ADAPTATION IN CREATING CLASSROOM LEARNING ENVIRONMENTS.

ERIC Educational Resources Information Center

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…

Facilitating Conversational Learning in a Project Team Practice

ERIC Educational Resources Information Center

Sense, Andrew J.

2005-01-01

Purpose: This paper seeks to provide an empirical insight into the facilitation dilemmas for conversational learning in a project team environment. Design/methodology/approach: This paper is an outcome of a participative action research process into the dynamics of situated learning activity in a case study project team. As part of their…

Hold it! The influence of lingering rewards on choice diversification and persistence.

PubMed

Schulze, Christin; van Ravenzwaaij, Don; Newell, Ben R

2017-11-01

Learning to choose adaptively when faced with uncertain and variable outcomes is a central challenge for decision makers. This study examines repeated choice in dynamic probability learning tasks in which outcome probabilities changed either as a function of the choices participants made or independently of those choices. This presence/absence of sequential choice-outcome dependencies was implemented by manipulating a single task aspect between conditions: the retention/withdrawal of reward across individual choice trials. The study addresses how people adapt to these learning environments and to what extent they engage in 2 choice strategies often contrasted as paradigmatic examples of striking violation of versus nominal adherence to rational choice: diversification and persistent probability maximizing, respectively. Results show that decisions approached adaptive choice diversification and persistence when sufficient feedback was provided on the dynamic rules of the probabilistic environments. The findings of divergent behavior in the 2 environments indicate that diversified choices represented a response to the reward retention manipulation rather than to the mere variability of outcome probabilities. Choice in both environments was well accounted for by the generalized matching law, and computational modeling-based strategy analyses indicated that adaptive choice arose mainly from reliance on reinforcement learning strategies. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Full Spectrum Operations: An Analysis of Course Content at the Command and General Staff College

DTIC Science & Technology

2008-05-01

Dynamics of Military Revolutions 1300-2050. Cambridge: Cambridge University Press, 2001. Kolb , David A. Experiential Learning : Experience as the... experiential learning provides latitude for the student or instructor to deviate from the structured lesson plan and pursue alternate avenues to...their contribution. The cognitive learning environment of CGSC is best understood by reviewing the adult learning model and the experiential learning

Note-taking and Handouts in The Digital Age.

PubMed

Stacy, Elizabeth Moore; Cain, Jeff

2015-09-25

Most educators consider note-taking a critical component of formal classroom learning. Advancements in technology such as tablet computers, mobile applications, and recorded lectures are altering classroom dynamics and affecting the way students compose and review class notes. These tools may improve a student's ability to take notes, but they also may hinder learning. In an era of dynamic technology developments, it is important for educators to routinely examine and evaluate influences on formal and informal learning environments. This paper discusses key background literature on student note-taking, identifies recent trends and potential implications of mobile technologies on classroom note-taking and student learning, and discusses future directions for note-taking in the context of digitally enabled lifelong learning.

Needs, Pains, and Motivations in Autonomous Agents.

PubMed

Starzyk, Janusz A; Graham, James; Puzio, Leszek

This paper presents the development of a motivated learning (ML) agent with symbolic I/O. Our earlier work on the ML agent was enhanced, giving it autonomy for interaction with other agents. Specifically, we equipped the agent with drives and pains that establish its motivations to learn how to respond to desired and undesired events and create related abstract goals. The purpose of this paper is to explore the autonomous development of motivations and memory in agents within a simulated environment. The ML agent has been implemented in a virtual environment created within the NeoAxis game engine. Additionally, to illustrate the benefits of an ML-based agent, we compared the performance of our algorithm against various reinforcement learning (RL) algorithms in a dynamic test scenario, and demonstrated that our ML agent learns better than any of the tested RL agents.This paper presents the development of a motivated learning (ML) agent with symbolic I/O. Our earlier work on the ML agent was enhanced, giving it autonomy for interaction with other agents. Specifically, we equipped the agent with drives and pains that establish its motivations to learn how to respond to desired and undesired events and create related abstract goals. The purpose of this paper is to explore the autonomous development of motivations and memory in agents within a simulated environment. The ML agent has been implemented in a virtual environment created within the NeoAxis game engine. Additionally, to illustrate the benefits of an ML-based agent, we compared the performance of our algorithm against various reinforcement learning (RL) algorithms in a dynamic test scenario, and demonstrated that our ML agent learns better than any of the tested RL agents.

Designing Geometry 2.0 learning environments: a preliminary study with primary school students

NASA Astrophysics Data System (ADS)

Joglar Prieto, Nuria; María Sordo Juanena, José; Star, Jon R.

2014-04-01

The information and communication technologies of Web 2.0 are arriving in our schools, allowing the design and implementation of new learning environments with great educational potential. This article proposes a pedagogical model based on a new geometry technology-integrated learning environment, called Geometry 2.0, which was tested with 39 sixth grade students from a public school in Madrid (Spain). The main goals of the study presented here were to describe an optimal role for the mathematics teacher within Geometry 2.0, and to analyse how dynamic mathematics and communication might affect young students' learning of basic figural concepts in a real setting. The analyses offered in this article illustrate how our Geometry 2.0 model facilitates deeply mathematical tasks which encourage students' exploration, cooperation and communication, improving their learning while fostering geometrical meanings.

Dynamic Visualizations: How Attraction, Motivation and Communication Affect Streaming Video Tutorial Implementation

ERIC Educational Resources Information Center

Boger, Claire

2011-01-01

The rapid advancement in the capabilities of computer technologies has made it easier to design and deploy dynamic visualizations in web-based learning environments; yet, the implementation of these dynamic visuals has been met with mixed results. While many guidelines exist to assist instructional designers in the design and application of…

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

NASA Astrophysics Data System (ADS)

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.

Environmental Learning in Regions: A Social Capital Based Approach. The Case of Latvia

ERIC Educational Resources Information Center

Sechi, Guido; Borri, Dino; De Lucia, Caterina; Celmins, Viesturs

2018-01-01

How do people learn about the environment and behave accordingly? What is the cognitive process at the base of this learning mechanism? The present paper is a pilot work investigating the dynamics of individual environmental knowledge on the basis of social capital theory. Using Tsai and Ghoshal's findings, a well known framework widely accepted…

Designing Science Laboratories: Learning Environments, School Architecture and Teaching and Learning Models

ERIC Educational Resources Information Center

Veloso, Luísa; Marques, Joana S.

2017-01-01

This article on secondary schools science laboratories in Portugal focuses on how school space functions as a pedagogical and political instrument by contributing to shape the conditions for teaching and learning dynamics. The article places the impact of changes to school layouts within the larger context of a public school renovation programme,…

Context Becomes Content: Sensor Data for Computer-Supported Reflective Learning

ERIC Educational Resources Information Center

Muller, Lars; Divitini, Monica; Mora, Simone; Rivera-Pelayo, Veronica; Stork, Wilhelm

2015-01-01

Wearable devices and ambient sensors can monitor a growing number of aspects of daily life and work. We propose to use this context data as content for learning applications in workplace settings to enable employees to reflect on experiences from their work. Learning by reflection is essential for today's dynamic work environments, as employees…

Dynamic and Interactive Mathematics Learning Environments: Opportunities and Challenges for Future Research

ERIC Educational Resources Information Center

Olive, John

2013-01-01

New networking and social interaction technologies offer new media for learning and teaching both inside and outside the classroom. How and what kind of learning may take place in these new media is the main focus of this paper. An integrative theoretical framework for investigating these questions is posed based on the Didactic Tetrahedron (Olive…

Modularization and Structured Markup for Learning Content in an Academic Environment

ERIC Educational Resources Information Center

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

2006-01-01

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

A Methodology for Assessing Learning in Complex and Ill-Structured Task Domains

ERIC Educational Resources Information Center

Spector, J. Michael

2006-01-01

New information and communications technologies and research in cognitive science have led to new ways to think about and implement learning environments. Among these new approaches to instruction and new methods to support learning and performance is an interest in and emphasis on complex subject matter (e.g., complex and dynamic systems…

Dynamic Fuzzy Logic-Based Quality of Interaction within Blended-Learning: The Rare and Contemporary Dance Cases

ERIC Educational Resources Information Center

Dias, Sofia B.; Diniz, José A.; Hadjileontiadis, Leontios J.

2014-01-01

The combination of the process of pedagogical planning within the Blended (b-) learning environment with the users' quality of interaction ("QoI") with the Learning Management System (LMS) is explored here. The required "QoI" (both for professors and students) is estimated by adopting a fuzzy logic-based modeling approach,…

The Online Writing Lab (OWL) and the Forum: A Tool for Writers in Distance Education Environments.

ERIC Educational Resources Information Center

Terryberry, Karl

2002-01-01

Demonstrates how to integrate static web pages with the dynamic forum for an effective learning experience on the online writing lab (OWL). Explains why asynchronous feedback provides effective, individualized writing instruction to students with various learning styles and how collaborative learning is fostered through threaded discussion groups.…

Pupil-Teacher Adjustment and Mutual Adaptation in Creating Classroom Learning Environments. Final Report.

ERIC Educational Resources Information Center

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…

Student-Centred Teaching Methods: Can They Optimise Students' Approaches to Learning in Professional Higher Education?

ERIC Educational Resources Information Center

Baeten, Marlies; Struyven, Katrien; Dochy, Filip

2013-01-01

This paper investigates dynamics in approaches to learning within different learning environments. Two quasi-experimental studies were conducted with first-year student teachers (N[subscript Study 1] = 496, N[subscript Study 2] = 1098) studying a child development course. Data collection was carried out using a pre-test/post-test design by means…

A Dynamic Programming Approach to Identifying the Shortest Path in Virtual Learning Environments

ERIC Educational Resources Information Center

Fazlollahtabar, Hamed

2008-01-01

E-learning has been widely adopted as a promising solution by many organizations to offer learning-on-demand opportunities to individual employees (learners) in order to reduce training time and cost. While successful information systems models have received much attention among researchers, little research has been conducted to assess the success…

Enhancing the Motor Skills of Children with Autism Spectrum Disorders: A Pool-Based Approach

ERIC Educational Resources Information Center

Lee, Jihyun; Porretta, David L.

2013-01-01

Children with autism spectrum disorders (ASDs) often experience difficulties with motor skill learning and performance. The pool is a unique learning environment that can help children with ASDs learn or improve aquatic skills, fitness, and social skills. A pool-based approach is also aligned with the elements of dynamic systems theory, which…

Group dynamics and social interaction in a South Asian online learning forum for faculty development of medical teachers.

PubMed

Anshu; Sharma, M; Burdick, W P; Singh, T

2010-04-01

Group dynamics of online medical faculty development programs have not been analyzed and reported in literature. Knowledge of the types of content of posted messages will help to understand group dynamics and promote participation in an asynchronous learning environment. This paper assesses group dynamics and social interactivity in an online learning environment for medical teachers in the South Asian context. Participants of a medical education fellowship program conducted by the Foundation for Advancement of International Medical Education and Research (FAIMER) Regional Institute at Christian Medical College, Ludhiana (CMCL) in India interact on a listserv called the Mentoring-Learning Web (ML-Web). Monthly topics for online discussion are chosen by fellows through a standard tool called "multi-voting". Fellows volunteer to moderate sessions and direct the pace of the discussion. We analyzed the content and process of the discussion of one particular month. The emails were categorized as those that reflected cognitive presence (dealing with construction and exploration of knowledge), teacher presence (dealing with instructional material and learning resources), and social presence, or were administrative in nature. Social emails were further classified as: affective, cohesive and interactive. Social emails constituted one-third of the total emails. Another one-quarter of the emails dealt with sharing of resources and teacher presence, while cognitive emails comprised 36.2% of the total. More than half of the social emails were affective, while a little less than one-third were cohesive. Social posts are an inevitable part of online learning. These posts promote bonding between learners and contribute to better interaction and collaboration in online learning. Moderators should be aware of their presence and use them as tools to promote interactivity.

Opportunistic Behavior in Motivated Learning Agents.

PubMed

Graham, James; Starzyk, Janusz A; Jachyra, Daniel

2015-08-01

This paper focuses on the novel motivated learning (ML) scheme and opportunistic behavior of an intelligent agent. It extends previously developed ML to opportunistic behavior in a multitask situation. Our paper describes the virtual world implementation of autonomous opportunistic agents learning in a dynamically changing environment, creating abstract goals, and taking advantage of arising opportunities to improve their performance. An opportunistic agent achieves better results than an agent based on ML only. It does so by minimizing the average value of all need signals rather than a dominating need. This paper applies to the design of autonomous embodied systems (robots) learning in real-time how to operate in a complex environment.

Dynamic Neuroscientific Systemology: Using Tri-Squared Meta-Analysis and Innovative Instructional Design to Develop a Novel Distance Education Model for the Systemic Creation of Engaging Online Learning Environments

ERIC Educational Resources Information Center

Osler, James Edward, II.; Wright, Mark Anthony

2015-01-01

The purpose of this research investigation was to look at the factors that lead to isolation, lack of student inspiration and motivation, lack of student engagement and lack of student retention in the asynchronous online learning environment. The study further delves into how the use of cognitive and neuroscience research can inform the design of…

Short Term Gains, Long Term Pains: How Cues About State Aid Learning in Dynamic Environments

PubMed Central

Gureckis, Todd M.; Love, Bradley C.

2009-01-01

Successful investors seeking returns, animals foraging for food, and pilots controlling aircraft all must take into account how their current decisions will impact their future standing. One challenge facing decision makers is that options that appear attractive in the short-term may not turn out best in the long run. In this paper, we explore human learning in a dynamic decision-making task which places short- and long-term rewards in conflict. Our goal in these studies was to evaluate how people’s mental representation of a task affects their ability to discover an optimal decision strategy. We find that perceptual cues that readily align with the underlying state of the task environment help people overcome the impulsive appeal of short-term rewards. Our experimental manipulations, predictions, and analyses are motivated by current work in reinforcement learning which details how learners value delayed outcomes in sequential tasks and the importance that “state” identification plays in effective learning. PMID:19427635

Reinforcement learning or active inference?

PubMed

Friston, Karl J; Daunizeau, Jean; Kiebel, Stefan J

2009-07-29

This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain.

A cholinergic feedback circuit to regulate striatal population uncertainty and optimize reinforcement learning

PubMed Central

Franklin, Nicholas T; Frank, Michael J

2015-01-01

Convergent evidence suggests that the basal ganglia support reinforcement learning by adjusting action values according to reward prediction errors. However, adaptive behavior in stochastic environments requires the consideration of uncertainty to dynamically adjust the learning rate. We consider how cholinergic tonically active interneurons (TANs) may endow the striatum with such a mechanism in computational models spanning three Marr's levels of analysis. In the neural model, TANs modulate the excitability of spiny neurons, their population response to reinforcement, and hence the effective learning rate. Long TAN pauses facilitated robustness to spurious outcomes by increasing divergence in synaptic weights between neurons coding for alternative action values, whereas short TAN pauses facilitated stochastic behavior but increased responsiveness to change-points in outcome contingencies. A feedback control system allowed TAN pauses to be dynamically modulated by uncertainty across the spiny neuron population, allowing the system to self-tune and optimize performance across stochastic environments. DOI: http://dx.doi.org/10.7554/eLife.12029.001 PMID:26705698

Functionally dissociable influences on learning rate in a dynamic environment

PubMed Central

McGuire, Joseph T.; Nassar, Matthew R.; Gold, Joshua I.; Kable, Joseph W.

2015-01-01

Summary Maintaining accurate beliefs in a changing environment requires dynamically adapting the rate at which one learns from new experiences. Beliefs should be stable in the face of noisy data, but malleable in periods of change or uncertainty. Here we used computational modeling, psychophysics and fMRI to show that adaptive learning is not a unitary phenomenon in the brain. Rather, it can be decomposed into three computationally and neuroanatomically distinct factors that were evident in human subjects performing a spatial-prediction task: (1) surprise-driven belief updating, related to BOLD activity in visual cortex; (2) uncertainty-driven belief updating, related to anterior prefrontal and parietal activity; and (3) reward-driven belief updating, a context-inappropriate behavioral tendency related to activity in ventral striatum. These distinct factors converged in a core system governing adaptive learning. This system, which included dorsomedial frontal cortex, responded to all three factors and predicted belief updating both across trials and across individuals. PMID:25459409

Cultural entrainment of motor skill development: Learning to write hiragana in Japanese primary school

PubMed Central

2017-01-01

Abstract The aim of the present study was to examine how the social norms shared in a classroom environment influence the development of movement dynamics of handwriting of children who participate in the environment. To look into this issue, the following aspects of the entire period of classroom learning of hiragana letters in Japanese 1st graders who had just entered primary school were studied: First, the structure of classroom events and the specific types of interaction and learning within such environment were described. Second, in the experiment involving 6‐year‐old children who participated in the class, writing movements of children and their changes over the period of hiragana education were analyzed for each stroke composing letters. It was found that writing movement of children became differentiated in a manner specific to the different types of stroke endings, to which children were systematically encouraged to attend in the classroom. The results provide a detailed description of the process of how dynamics of fine motor movement of children is modulated by the social norms of a populated, classroom environment in a non‐Latin alphabet writing system. PMID:28608521

Cultural entrainment of motor skill development: Learning to write hiragana in Japanese primary school.

PubMed

Nonaka, Tetsushi

2017-09-01

The aim of the present study was to examine how the social norms shared in a classroom environment influence the development of movement dynamics of handwriting of children who participate in the environment. To look into this issue, the following aspects of the entire period of classroom learning of hiragana letters in Japanese 1st graders who had just entered primary school were studied: First, the structure of classroom events and the specific types of interaction and learning within such environment were described. Second, in the experiment involving 6-year-old children who participated in the class, writing movements of children and their changes over the period of hiragana education were analyzed for each stroke composing letters. It was found that writing movement of children became differentiated in a manner specific to the different types of stroke endings, to which children were systematically encouraged to attend in the classroom. The results provide a detailed description of the process of how dynamics of fine motor movement of children is modulated by the social norms of a populated, classroom environment in a non-Latin alphabet writing system. © 2017 The Authors. Developmental Psychobiology Published by Wiley Periodicals, Inc.

Note-taking and Handouts in The Digital Age

PubMed Central

Stacy, Elizabeth Moore

2015-01-01

Most educators consider note-taking a critical component of formal classroom learning. Advancements in technology such as tablet computers, mobile applications, and recorded lectures are altering classroom dynamics and affecting the way students compose and review class notes. These tools may improve a student’s ability to take notes, but they also may hinder learning. In an era of dynamic technology developments, it is important for educators to routinely examine and evaluate influences on formal and informal learning environments. This paper discusses key background literature on student note-taking, identifies recent trends and potential implications of mobile technologies on classroom note-taking and student learning, and discusses future directions for note-taking in the context of digitally enabled lifelong learning. PMID:27168620

Can Students Collaboratively Use Hypermedia to Learn Science? The Dynamics of Self-And Other-Regulatory Processes in an Ecology Classroom

ERIC Educational Resources Information Center

Azevedo, Roger; Winters, Fielding I.; Moos, Daniel C.

2004-01-01

This classroom study examined the role of low-achieving students' self-regulated learning (SRL) behaviors and their teacher's scaffolding of SRL while using a Web-based water quality simulation environment to learn about ecological systems. Forty-nine 11th and 12th grade students learned about ecology and the effects of land use on water quality…

Ecological Dynamics as a Theoretical Framework for Development of Sustainable Behaviours towards the Environment

ERIC Educational Resources Information Center

Brymer, Eric; Davids, Keith

2013-01-01

This paper proposes how the theoretical framework of ecological dynamics can provide an influential model of the learner and the learning process to pre-empt effective behaviour changes. Here we argue that ecological dynamics supports a well-established model of the learner ideally suited to the environmental education context because of its…

Instructional changes based on cogenerative physics reform

NASA Astrophysics Data System (ADS)

Samuels, Natan; Brewe, Eric; Kramer, Laird

2013-01-01

We describe changes in a physics teacher's pedagogy and cultural awareness that resulted from her students' involvement in reforming their classroom. For this case study, we examined a veteran high school teacher's semester-long use of CMPLE (the Cogenerative Mediation Process for Learning Environments) in her Modeling Instruction classroom. CMPLE is a formative intervention designed to help students and instructors collaborate to change classroom dynamics, based on how closely the environment matches their learning preferences. Analysis of classroom videos, interviews, and other artifacts indicates that adapting the environment to align with the preferences of that shared culture affected the instructor in complex ways. We will trace her teaching practices and her self-described awareness of the culture of learning, to highlight notable changes. The teacher espoused deeper understanding of her students' physics learning experience, which she gained from including students in responding to their own individual and collective learning preferences.

Category Learning by Clustering with Extension to Dynamic Environments

DTIC Science & Technology

2010-03-05

and decision making when short- and long-term rewards are in conflict. In a paper published in Psychonomic Bulletin & Review , we examined whether...Navigating through Abstract Decision Spaces: Evaluating the Role of State Generalization in a Dynamic Decision-Making Task. Psychonomic Bulletin & Review , 16

In-Factory Learning - Qualification For The Factory Of The Future

NASA Astrophysics Data System (ADS)

Quint, Fabian; Mura, Katharina; Gorecky, Dominic

2015-07-01

The Industry 4.0 vision anticipates that internet technologies will find their way into future factories replacing traditional components by dynamic and intelligent cyber-physical systems (CPS) that combine the physical objects with their digital representation. Reducing the gap between the real and digital world makes the factory environment more flexible, more adaptive, but also more complex for the human workers. Future workers require interdisciplinary competencies from engineering, information technology, and computer science in order to understand and manage the diverse interrelations between physical objects and their digital counterpart. This paper proposes a mixed-reality based learning environment, which combines physical objects and visualisation of digital content via Augmented Reality. It uses reality-based interaction in order to make the dynamic interrelations between real and digital factory visible and tangible. We argue that our learning system does not work as a stand-alone solution, but should fit into existing academic and advanced training curricula.

Perception-action map learning in controlled multiscroll systems applied to robot navigation.

PubMed

Arena, Paolo; De Fiore, Sebastiano; Fortuna, Luigi; Patané, Luca

2008-12-01

In this paper a new technique for action-oriented perception in robots is presented. The paper starts from exploiting the successful implementation of the basic idea that perceptual states can be embedded into chaotic attractors whose dynamical evolution can be associated with sensorial stimuli. In this way, it can be possible to encode, into the chaotic dynamics, environment-dependent patterns. These have to be suitably linked to an action, executed by the robot, to fulfill an assigned mission. This task is addressed here: the action-oriented perception loop is closed by introducing a simple unsupervised learning stage, implemented via a bio-inspired structure based on the motor map paradigm. In this way, perceptual meanings, useful for solving a given task, can be autonomously learned, based on the environment-dependent patterns embedded into the controlled chaotic dynamics. The presented framework has been tested on a simulated robot and the performance have been successfully compared with other traditional navigation control paradigms. Moreover an implementation of the proposed architecture on a Field Programmable Gate Array is briefly outlined and preliminary experimental results on a roving robot are also reported.

Different Identity Revelation Modes in an Online Peer-Assessment Learning Environment: Effects on Perceptions toward Assessors, Classroom Climate and Learning Activities

ERIC Educational Resources Information Center

Yu, Fu-Yun; Wu, Chun-Ping

2011-01-01

The effects of four different identity revelation modes (three fixed modes: real-name, anonymity, nickname and one dynamic user self-choice mode) on participants' perceptions toward their assessors, classroom climate, and past experience with the learning activity in which they were engaged were examined. A pretest-posttest quasi-experimental…

Dynamic Educational e-Content Selection Using Multiple Criteria in Web-Based Personalized Learning Environments.

ERIC Educational Resources Information Center

Manouselis, Nikos; Sampson, Demetrios

This paper focuses on the way a multi-criteria decision making methodology is applied in the case of agent-based selection of offered learning objects. The problem of selection is modeled as a decision making one, with the decision variables being the learner model and the learning objects' educational description. In this way, selection of…

Model learning for robot control: a survey.

PubMed

Nguyen-Tuong, Duy; Peters, Jan

2011-11-01

Models are among the most essential tools in robotics, such as kinematics and dynamics models of the robot's own body and controllable external objects. It is widely believed that intelligent mammals also rely on internal models in order to generate their actions. However, while classical robotics relies on manually generated models that are based on human insights into physics, future autonomous, cognitive robots need to be able to automatically generate models that are based on information which is extracted from the data streams accessible to the robot. In this paper, we survey the progress in model learning with a strong focus on robot control on a kinematic as well as dynamical level. Here, a model describes essential information about the behavior of the environment and the influence of an agent on this environment. In the context of model-based learning control, we view the model from three different perspectives. First, we need to study the different possible model learning architectures for robotics. Second, we discuss what kind of problems these architecture and the domain of robotics imply for the applicable learning methods. From this discussion, we deduce future directions of real-time learning algorithms. Third, we show where these scenarios have been used successfully in several case studies.

Science, School Science, and School: Looking at Science Learning in Classrooms from the Perspective of Basil Bernstein's Theory of the Structure of Pedagogic Discourse

NASA Astrophysics Data System (ADS)

Campbell, Ralph Ian

This analytic paper asks one question: How does Basil Bernstein's concept of the structure of pedagogic discourse (SPD) contribute to our understanding of the role of teacher-student interactions in science learning in the classroom? Applying Bernstein's theory of the SPD to an analysis of current research in science education explores the structure of Bernstein's theory as a tool for understanding the challenges and questions related to current concerns about classroom science learning. This analysis applies Bernstein's theory of the SPD as a heuristic through a secondary reading of selected research from the past fifteen years and prompts further consideration of Bernstein's ideas. This leads to a reevaluation of the categories of regulative discourse (RD) and instructional discourse (ID) as structures that frame learning environments and the dynamics of student-teacher interactions, which determine learning outcomes. The SPD becomes a simple but flexible heuristic, offering a useful deconstruction of teaching and learning dynamics in three different classroom environments. Understanding the framing interactions of RD and ID provides perspectives on the balance of agency and expectation, suggesting some causal explanations for the student learning outcomes described by the authors. On one hand, forms of open inquiry and student-driven instruction may lack the structure to ensure the appropriation of desired forms of scientific thinking. On the other hand, well-designed pathways towards the understanding of fundamental concepts in science may lack the forms of more open-ended inquiry that develop transferable understanding. Important ideas emerge about the complex dynamics of learning communities, the materials of learning, and the dynamic role of the teacher as facilitator and expert. Simultaneously, the SPD as a flexible heuristic proves ambiguous, prompting a reevaluation of Bernstein's organization of RD and ID. The hierarchical structure of pedagogic discourse becomes a problematic distinction. Regulative discourse is often more instructional and instructional discourse more instrumental in shaping roles and relationships within the learning community. This analysis suggests an agenda for future classroom research and the education of teachers, capitalizing on the SPD as heuristic and reevaluating the ways that social dynamics and structures for domain-specific learning interact in the realization of classroom learning.

Incremental learning of concept drift in nonstationary environments.

PubMed

Elwell, Ryan; Polikar, Robi

2011-10-01

We introduce an ensemble of classifiers-based approach for incremental learning of concept drift, characterized by nonstationary environments (NSEs), where the underlying data distributions change over time. The proposed algorithm, named Learn(++). NSE, learns from consecutive batches of data without making any assumptions on the nature or rate of drift; it can learn from such environments that experience constant or variable rate of drift, addition or deletion of concept classes, as well as cyclical drift. The algorithm learns incrementally, as other members of the Learn(++) family of algorithms, that is, without requiring access to previously seen data. Learn(++). NSE trains one new classifier for each batch of data it receives, and combines these classifiers using a dynamically weighted majority voting. The novelty of the approach is in determining the voting weights, based on each classifier's time-adjusted accuracy on current and past environments. This approach allows the algorithm to recognize, and act accordingly, to the changes in underlying data distributions, as well as to a possible reoccurrence of an earlier distribution. We evaluate the algorithm on several synthetic datasets designed to simulate a variety of nonstationary environments, as well as a real-world weather prediction dataset. Comparisons with several other approaches are also included. Results indicate that Learn(++). NSE can track the changing environments very closely, regardless of the type of concept drift. To allow future use, comparison and benchmarking by interested researchers, we also release our data used in this paper. © 2011 IEEE

Category Learning by Clustering with Extension to Dynamic Environments

DTIC Science & Technology

2010-05-03

making when short- and long-term rewards are in conflict. In a paper published in Psychonomic Bulletin & Review , we examined whether state cues make...through Abstract Decision Spaces: Evaluating the Role of State Generalization in a Dynamic Decision-Making Task. Psychonomic Bulletin & Review , 16, 957

Capturing the complexity: Content, type, and amount of instruction and quality of the classroom learning environment synergistically predict third graders’ vocabulary and reading comprehension outcomes

PubMed Central

Connor, Carol McDonald; Spencer, Mercedes; Day, Stephanie L.; Giuliani, Sarah; Ingebrand, Sarah W.; McLean, Leigh; Morrison, Frederick J.

2014-01-01

We examined classrooms as complex systems that affect students’ literacy learning through interacting effects of content and amount of time individual students spent in literacy instruction along with the global quality of the classroom-learning environment. We observed 27 third grade classrooms serving 315 target students using two different observation systems. The first assessed instruction at a more micro-level; specifically, the amount of time individual students spent in literacy instruction defined by the type of instruction, role of the teacher, and content. The second assessed the quality of the classroom-learning environment at a more macro level focusing on classroom organization, teacher responsiveness, and support for vocabulary and language. Results revealed that both global quality of the classroom learning environment and time individual students spent in specific types of literacy instruction covering specific content interacted to predict students’ comprehension and vocabulary gains whereas neither system alone did. These findings support a dynamic systems model of how individual children learn in the context of classroom literacy instruction and the classroom-learning environment, which can help to improve observations systems, advance research, elevate teacher evaluation and professional development, and enhance student achievement. PMID:25400293

Geometrical Constructions in Dynamic and Interactive Mathematics Learning Environment

ERIC Educational Resources Information Center

Kondratieva, Margo

2013-01-01

This paper concerns teaching Euclidean geometry at the university level. It is based on the authors' personal experience. It describes a sequence of learning activities that combine geometrical constructions with explorations, observations, and explanations of facts related to the geometry of triangle. Within this approach, a discussion of the…

The Role of Affective and Motivational Factors in Designing Personalized Learning Environments

ERIC Educational Resources Information Center

Kim, ChanMin

2012-01-01

In this paper, guidelines for designing virtual change agents (VCAs) are proposed to support students' affective and motivational needs in order to promote personalized learning in online remedial mathematics courses. Automated, dynamic, and personalized support is emphasized in the guidelines through maximizing "interactions" between VCAs and…

Leadership in Multiplayer Online Gaming Environments

ERIC Educational Resources Information Center

Lisk, Timothy C.; Kaplancali, Ugur T.; Riggio, Ronald E.

2012-01-01

With their increased popularity, games open up possibilities for simultaneous learning on multiple levels; players may learn from contextual information embedded in the narrative of the game and through the risks, benefits, costs, outcomes, and rewards of the alternative strategies that result from fast-paced decision making. Such dynamics also…

Social Aspects of CSCL Environments: A Research Framework

ERIC Educational Resources Information Center

Kreijns, Karel; Kirschner, Paul A.; Vermeulen, Marjan

2013-01-01

Although there are research findings supporting the positive effects of computer-supported collaborative learning (CSCL), problems have been reported regarding the learning process itself, group formation, and group dynamics. These problems can be traced back to impeded social interaction between group members. Social interaction is necessary (a)…

Cheater or Collaborator?

ERIC Educational Resources Information Center

Jakes, David

2009-01-01

As more social technologies and processes enter the classroom, new questions arise about how these tools and processes serve teaching and learning. Many have the potential to create dynamic learning environments. They also have the potential to cause distraction. In this article, the author describes one scenario that relates only to the social…

Evaluating the Design and Development of an Adaptive E-Tutorial Module: A Rasch-Measurement Approach

ERIC Educational Resources Information Center

Barefah, Allaa; McKay, Elspeth

2016-01-01

Courseware designers aim to innovate information communications technology (ICT) tools to increase learning experiences, spending many hours developing eLearning programmes. This effort gives rise to a dynamic technological pedagogical environment. However, it is difficult to recognise whether these online programmes reflect an instructional…

Extending human potential in a technical learning environment

NASA Astrophysics Data System (ADS)

Fielden, Kay A.

This thesis is a report of a participatory inquiry process looking at enhancing the learning process in a technical academic field in high education by utilising tools and techniques which go beyond the rational/logical, intellectual domain in a functional, objective world. By empathising with, nurturing and sustaining the whole person, and taking account of past patterning as well as future visions including technological advances to augment human awareness, the scene is set for depth learning. Depth learning in a tertiary environment can only happen as a result of the dynamic that exists between the dominant, logical/rational, intellectual paradigm and the experiential extension of the boundaries surrounding this domain. Any experiences which suppress the full, holistic expression of our being alienate us from the fullness of the expression and hence from depth learning. Depth learning is indicated by intrinsic motivation, which is more likely to occur in a trusting and supporting environment. The research took place within a systemic intellectual framework, where emergence is the prime characteristic used to evaluate results.

Clipping in neurocontrol by adaptive dynamic programming.

PubMed

Fairbank, Michael; Prokhorov, Danil; Alonso, Eduardo

2014-10-01

In adaptive dynamic programming, neurocontrol, and reinforcement learning, the objective is for an agent to learn to choose actions so as to minimize a total cost function. In this paper, we show that when discretized time is used to model the motion of the agent, it can be very important to do clipping on the motion of the agent in the final time step of the trajectory. By clipping, we mean that the final time step of the trajectory is to be truncated such that the agent stops exactly at the first terminal state reached, and no distance further. We demonstrate that when clipping is omitted, learning performance can fail to reach the optimum, and when clipping is done properly, learning performance can improve significantly. The clipping problem we describe affects algorithms that use explicit derivatives of the model functions of the environment to calculate a learning gradient. These include backpropagation through time for control and methods based on dual heuristic programming. However, the clipping problem does not significantly affect methods based on heuristic dynamic programming, temporal differences learning, or policy-gradient learning algorithms.

Infant Statistical Learning

PubMed Central

Saffran, Jenny R.; Kirkham, Natasha Z.

2017-01-01

Perception involves making sense of a dynamic, multimodal environment. In the absence of mechanisms capable of exploiting the statistical patterns in the natural world, infants would face an insurmountable computational problem. Infant statistical learning mechanisms facilitate the detection of structure. These abilities allow the infant to compute across elements in their environmental input, extracting patterns for further processing and subsequent learning. In this selective review, we summarize findings that show that statistical learning is both a broad and flexible mechanism (supporting learning from different modalities across many different content areas) and input specific (shifting computations depending on the type of input and goal of learning). We suggest that statistical learning not only provides a framework for studying language development and object knowledge in constrained laboratory settings, but also allows researchers to tackle real-world problems, such as multilingualism, the role of ever-changing learning environments, and differential developmental trajectories. PMID:28793812

Technology, Methodology, and Business Education. National Business Education Association Yearbook, 2002.

ERIC Educational Resources Information Center

Remp, Ann M., Ed.

This document contains 20 papers on the challenges that business educators face in integrating technology into the learning environment. The following papers are included: "Teaching in a Dynamic Technology Environment: An Overview and Introduction" (Ann M. Remp); "Learners and Technology: Experience, Attitudes, and…

Transfer of Dynamic Learning Across Postures

PubMed Central

Wolpert, Daniel M.

2009-01-01

When learning a difficult motor task, we often decompose the task so that the control of individual body segments is practiced in isolation. But on re-composition, the combined movements can result in novel and possibly complex internal forces between the body segments that were not experienced (or did not need to be compensated for) during isolated practice. Here we investigate whether dynamics learned in isolation by one part of the body can be used by other parts of the body to immediately predict and compensate for novel forces between body segments. Subjects reached to targets while holding the handle of a robotic, force-generating manipulandum. One group of subjects was initially exposed to the novel robot dynamics while seated and was then tested in a standing position. A second group was tested in the reverse order: standing then sitting. Both groups adapted their arm dynamics to the novel environment, and this movement learning transferred between seated and standing postures and vice versa. Both groups also generated anticipatory postural adjustments when standing and exposed to the force field for several trials. In the group that had learned the dynamics while seated, the appropriate postural adjustments were observed on the very first reach on standing. These results suggest that the CNS can immediately anticipate the effect of learned movement dynamics on a novel whole-body posture. The results support the existence of separate mappings for posture and movement, which encode similar dynamics but can be adapted independently. PMID:19710374

Fuzzy Q-Learning for Generalization of Reinforcement Learning

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1996-01-01

Fuzzy Q-Learning, introduced earlier by the author, is an extension of Q-Learning into fuzzy environments. GARIC is a methodology for fuzzy reinforcement learning. In this paper, we introduce GARIC-Q, a new method for doing incremental Dynamic Programming using a society of intelligent agents which are controlled at the top level by Fuzzy Q-Learning and at the local level, each agent learns and operates based on GARIC. GARIC-Q improves the speed and applicability of Fuzzy Q-Learning through generalization of input space by using fuzzy rules and bridges the gap between Q-Learning and rule based intelligent systems.

A fast and precise indoor localization algorithm based on an online sequential extreme learning machine.

PubMed

Zou, Han; Lu, Xiaoxuan; Jiang, Hao; Xie, Lihua

2015-01-15

Nowadays, developing indoor positioning systems (IPSs) has become an attractive research topic due to the increasing demands on location-based service (LBS) in indoor environments. WiFi technology has been studied and explored to provide indoor positioning service for years in view of the wide deployment and availability of existing WiFi infrastructures in indoor environments. A large body of WiFi-based IPSs adopt fingerprinting approaches for localization. However, these IPSs suffer from two major problems: the intensive costs of manpower and time for offline site survey and the inflexibility to environmental dynamics. In this paper, we propose an indoor localization algorithm based on an online sequential extreme learning machine (OS-ELM) to address the above problems accordingly. The fast learning speed of OS-ELM can reduce the time and manpower costs for the offline site survey. Meanwhile, its online sequential learning ability enables the proposed localization algorithm to adapt in a timely manner to environmental dynamics. Experiments under specific environmental changes, such as variations of occupancy distribution and events of opening or closing of doors, are conducted to evaluate the performance of OS-ELM. The simulation and experimental results show that the proposed localization algorithm can provide higher localization accuracy than traditional approaches, due to its fast adaptation to various environmental dynamics.

Interactive knowledge networks for interdisciplinary course navigation within Moodle.

PubMed

Scherl, Andre; Dethleffsen, Kathrin; Meyer, Michael

2012-12-01

Web-based hypermedia learning environments are widely used in modern education and seem particularly well suited for interdisciplinary learning. Previous work has identified guidance through these complex environments as a crucial problem of their acceptance and efficiency. We reasoned that map-based navigation might provide straightforward and effortless orientation. To achieve this, we developed a clickable and user-oriented concept map-based navigation plugin. This tool is implemented as an extension of Moodle, a widely used learning management system. It visualizes inner and interdisciplinary relations between learning objects and is generated dynamically depending on user set parameters and interactions. This plugin leaves the choice of navigation type to the user and supports direct guidance. Previously developed and evaluated face-to-face interdisciplinary learning materials bridging physiology and physics courses of a medical curriculum were integrated as learning objects, the relations of which were defined by metadata. Learning objects included text pages, self-assessments, videos, animations, and simulations. In a field study, we analyzed the effects of this learning environment on physiology and physics knowledge as well as the transfer ability of third-term medical students. Data were generated from pre- and posttest questionnaires and from tracking student navigation. Use of the hypermedia environment resulted in a significant increase of knowledge and transfer capability. Furthermore, the efficiency of learning was enhanced. We conclude that hypermedia environments based on Moodle and enriched by concept map-based navigation tools can significantly support interdisciplinary learning. Implementation of adaptivity may further strengthen this approach.

ENERGY-NET (Energy, Environment and Society Learning Network): Enhancing opportunities for learning using an Earth systems science framework

NASA Astrophysics Data System (ADS)

Elliott, E. M.; Bain, D. J.; Divers, M. T.; Crowley, K. J.; Povis, K.; Scardina, A.; Steiner, M.

2012-12-01

We describe a newly funded collaborative NSF initiative, ENERGY-NET (Energy, Environment and Society Learning Network), that brings together the Carnegie Museum of Natural History (CMNH) with the Learning Science and Geoscience research strengths at the University of Pittsburgh. ENERGY-NET aims to create rich opportunities for participatory learning and public education in the arena of energy, the environment, and society using an Earth systems science framework. We build upon a long-established teen docent program at CMNH and to form Geoscience Squads comprised of underserved teens. Together, the ENERGY-NET team, including museum staff, experts in informal learning sciences, and geoscientists spanning career stage (undergraduates, graduate students, faculty) provides inquiry-based learning experiences guided by Earth systems science principles. Together, the team works with Geoscience Squads to design "Exploration Stations" for use with CMNH visitors that employ an Earth systems science framework to explore the intersecting lenses of energy, the environment, and society. The goals of ENERGY-NET are to: 1) Develop a rich set of experiential learning activities to enhance public knowledge about the complex dynamics between Energy, Environment, and Society for demonstration at CMNH; 2) Expand diversity in the geosciences workforce by mentoring underrepresented teens, providing authentic learning experiences in earth systems science and life skills, and providing networking opportunities with geoscientists; and 3) Institutionalize ENERGY-NET collaborations among geosciences expert, learning researchers, and museum staff to yield long-term improvements in public geoscience education and geoscience workforce recruiting.

Using an improved virtual learning environment for engineering students

NASA Astrophysics Data System (ADS)

Lourdes Martínez Cartas, Ma

2012-06-01

In recent years, e-learning has been used in a chemical engineering subject in the final course of a mining engineering degree, a subject concerned with fuel technology. The low results obtained by students in this subject have led the teacher to search for new strategies to increase grades. Such strategies have consisted of incorporating into the existing virtual environment a dynamics of work with conceptual maps and a consideration of the different learning styles in the classroom. In an attempt to adapt teaching to the individual methods of learning for each student, various activities aimed at strengthening different learning styles have been proposed and concept maps have been used to create meaningful learning experiences. In addition, different modalities of assessment have been proposed, which can be selected by each student according to his or her particular method of learning to avoid penalising one style preference in contrast to another. This combination of e-learning, use of concept maps and catering for different learning styles has involved the implementation of the improved virtual learning environment. This has led to an increase in participation in the subject and has improved student assessment results.

Movement Issues Identified in Movement ABC2 Checklist Parent Ratings for Students with Persisting Dysgraphia, Dyslexia, and OWL LD and Typical Literacy Learners.

PubMed

Nielsen, Kathleen; Henderson, Sheila; Barnett, Anna L; Abbott, Robert D; Berninger, Virginia

2018-01-01

Movement, which draws on motor skills and executive functions for managing them, plays an important role in literacy learning (e.g., movement of mouth during oral reading and movement of hand and fingers during writing); but relatively little research has focused on movement skills in students with specific learning disabilities as the current study did. Parents completed normed Movement Assessment Battery for Children Checklist, 2nd edition (ABC-2), ratings and their children in grades 4 to 9 ( M = 11 years, 11 months; 94 boys, 61 girls) completed diagnostic assessment used to assign them to diagnostic groups: control typical language learning ( N = 42), dysgraphia (impaired handwriting) ( N = 29), dyslexia (impaired word decoding/reading and spelling) ( N = 65), or oral and written language learning disability (OWL LD) (impaired syntax in oral and written language) ( N = 19). The research aims were to (a) correlate the Movement ABC-2 parent ratings for Scale A Static/Predictable Environment (15 items) and Scale B Dynamic/Unpredictable Environment (15 items) with reading and writing achievement in total sample varying within and across different skills; and (b) compare each specific learning disability group with the control group on Movement ABC-2 parent ratings for Scale A, Scale B, and Scale C Movement-Related (Non-Motor Executive Functions, or Self-Efficacy, or Affect) (13 items). At least one Movement ABC-2 parent rating was correlated with each assessed literacy achievement skill. Each of three specific learning disability groups differed from the control group on two Scale A (static/predictable environment) items (fastens buttons and forms letters with pencil or pen) and on three Scale C items (distractibility, overactive, and underestimates own ability); but only OWL LD differed from control on Scale B (dynamic/unpredictable environment) items. Applications of findings to assessment and instruction for students ascertained for and diagnosed with persisting specific learning disabilities in literacy learning, and future research directions are discussed.

Homeostatic Agent for General Environment

NASA Astrophysics Data System (ADS)

Yoshida, Naoto

2018-03-01

One of the essential aspect in biological agents is dynamic stability. This aspect, called homeostasis, is widely discussed in ethology, neuroscience and during the early stages of artificial intelligence. Ashby's homeostats are general-purpose learning machines for stabilizing essential variables of the agent in the face of general environments. However, despite their generality, the original homeostats couldn't be scaled because they searched their parameters randomly. In this paper, first we re-define the objective of homeostats as the maximization of a multi-step survival probability from the view point of sequential decision theory and probabilistic theory. Then we show that this optimization problem can be treated by using reinforcement learning algorithms with special agent architectures and theoretically-derived intrinsic reward functions. Finally we empirically demonstrate that agents with our architecture automatically learn to survive in a given environment, including environments with visual stimuli. Our survival agents can learn to eat food, avoid poison and stabilize essential variables through theoretically-derived single intrinsic reward formulations.

Evaluation of Distance Course Effectiveness - Exploring the Quality of Interactive Processes

NASA Astrophysics Data System (ADS)

Botelho, Francisco Villa Ulhôa; Vicari, Rosa Maria

Understanding the dynamics of learning processes implies an understanding of their components: individuals, environment or context and mediation. It is known that distance learning (DL) has a distinctive characteristic in relation to the mediation component. Due to the need of overcoming the barriers of distance and time, DL intensively uses information and communication technologies (ICT) to perform interactive processes. Construction of effective learning environments depends on human relationships. It also depends on the emotionality placed on such relationships. Therefore, knowing how to act in virtual environments in the sense of creating the required ambiance for animation of learning processes has a unique importance. This is the theme of this study. Its general objectives were achieved and can be summarized as follows: analyze indexes that are significant for evaluations of distance course effectiveness; investigate to which extent effectiveness of DL courses is correlated with quality of interactive processes; search characteristics of the conversations by individuals interacting in study groups that are formed in virtual environments, which may contribute to effectiveness of distance courses.

Instructional Management for Adaptive Training and Education in Support of the US Army Learning Model-Research Outline

DTIC Science & Technology

2015-11-01

within adaptive training environments. This line of research associates with tenets of Social Cognitive Theory in that learning is theorized to be an...Challenges 17 6.1 Guidance and Scaffolding 17 6.2 Social Dynamics and Virtual Humans 21 6.3 Metacognition and Self-Regulated Learning 23 6.4...and develop prototype authoring tools grounded in learning and instructional theory and informed by empirical research to assist training managers

The Effect of Geogebra on Students' Conceptual and Procedural Knowledge: The Case of Applications of Derivative

ERIC Educational Resources Information Center

Ocal, Mehmet Fatih

2017-01-01

Integrating the properties of computer algebra systems and dynamic geometry environments, Geogebra became an effective and powerful tool for teaching and learning mathematics. One of the reasons that teachers use Geogebra in mathematics classrooms is to make students learn mathematics meaningfully and conceptually. From this perspective, the…

Coordinating Decentralized Learning and Conflict Resolution across Agent Boundaries

ERIC Educational Resources Information Center

Cheng, Shanjun

2012-01-01

It is crucial for embedded systems to adapt to the dynamics of open environments. This adaptation process becomes especially challenging in the context of multiagent systems because of scalability, partial information accessibility and complex interaction of agents. It is a challenge for agents to learn good policies, when they need to plan and…

"I'm Ambivalent about It": The Dilemmas of PowerPoint

ERIC Educational Resources Information Center

Hill, Andrea; Arford, Tammi; Lubitow, Amy; Smollin, Leandra M.

2012-01-01

The increasing ubiquity of PowerPoint in the university classroom raises complex questions about pedagogy and the creation of dynamic and effective learning environments. Though much of the sociological teaching literature has focused on engagement and active learning, very little of this work has addressed the presence of PowerPoint in sociology…

GeoGebra in Professional Development: The Experience of Rural Inservice Elementary School (K-8) Teachers

ERIC Educational Resources Information Center

Bu, Ligguo; Mumba, Frackson; Henson, Harvey; Wright, Mary

2013-01-01

GeoGebra is an emergent open-source Dynamic and Interactive Mathematics Learning Environment (DIMLE) (Martinovic & Karadag, 2012) that invites a modeling perspective in mathematics teaching and learning. In springs of 2010 and 2012, GeoGebra was integrated respectively into two online professional development courses on mathematical problem…

Teaching and Learning for Intercultural Sensitivity: A Cross-Cultural Examination of American Domestic Students and Japanese Exchange Students

ERIC Educational Resources Information Center

Sakurauchi, Yoko Hwang

2014-01-01

Global student mobility has become a dynamic force in American higher education. Integrating international students into diverse campus environments provides domestic as well as foreign students with enriched learning opportunities. However, a diverse campus climate itself will not make college students interculturally competent. Intentional…

Sustainability Learning through Gaming: An Exploratory Study

ERIC Educational Resources Information Center

Fabricatore, Carlo; Lopez, Ximena

2012-01-01

This study explored the potential of digital games as learning environments to develop mindsets capable of dealing with complexity in the domain of sustainability. Building sustainable futures requires the ability to deal with the complex dynamics that characterize the world in which we live. As central elements in this system, we must develop the…

In-Service Teachers' Internet Self-Efficacy: A Re-Examination of Gender Differences

ERIC Educational Resources Information Center

Kahraman, Sakip; Yilmaz, Zeynel Abidin

2018-01-01

Teachers' Internet self-efficacy plays a critical role in their web-based professional development and on their students' learning outcomes in Internet-based learning environments. It is therefore important to periodically measure and evaluate teachers' self-efficacy regarding the Internet, which is a dynamic technology, using an instrument that…

Hey, We See It Differently! Lessons on Team Dynamics.

ERIC Educational Resources Information Center

Walz, Lynn; Vandercook, Terri; Medwetz, Laura; Nelson, Marilyn; Thurlow, Martha

This monograph summarizes lessons learned from the 5 years that the Together We're Better (TWB) program worked to create inclusive learning environments in four Minnesota school districts. Each of the partner districts established a collaborative core planning team to provide leadership and management of efforts toward school change and inclusive…

Success, Failure and Emotions: Examining the Relationship between Performance Feedback and Emotions in Diagnostic Reasoning

ERIC Educational Resources Information Center

Jarrell, Amanda; Harley, Jason M.; Lajoie, Susanne; Naismith, Laura

2017-01-01

Students experience a variety of emotions following achievement outcomes which stand to influence how they learn and perform in academic settings. However, little is known about the link between student outcome emotions and dimensions of performance feedback in computer-based learning environments (CBLEs). Understanding the dynamics of this…

Not Your Mother's View: The Dynamics of Toddler Visual Experience

ERIC Educational Resources Information Center

Smith, Linda B.; Yu, Chen; Pereira, Alfredo F.

2011-01-01

Human toddlers learn about objects through second-by-second, minute-by-minute sensory-motor interactions. In an effort to understand how toddlers' bodily actions structure the visual learning environment, mini-video cameras were placed low on the foreheads of toddlers, and for comparison also on the foreheads of their parents, as they jointly…

Implementation of Personalized E-Assessment for Remedial Teaching in an E-Learning Environment

ERIC Educational Resources Information Center

Lin, Chen-Yu; Wang, Tzu-Hua

2017-01-01

This research explored how different models of Web-based dynamic assessment in remedial teaching improved junior high school student learning achievement and their misconceptions about the topic of "Weather and Climate." This research adopted a quasi-experimental design. A total of 58 7th graders participated in this research.…

The Teaching-Learning Environment, an Information-Dynamic Approach

ERIC Educational Resources Information Center

De Blasio, Cataldo; Järvinen, Mika

2014-01-01

In the present study a generalized approach is given for the description of acquisition procedures with a particular focus on the knowledge acquisition process. The learning progression is given as an example here letting the theory to be applied to different situations. An analytical approach is performed starting from the generalized fundamental…

Modeling eating behaviors: The role of environment and positive food association learning via a Ratatouille effect.

PubMed

Murillo, Anarina L; Safan, Muntaser; Castillo-Chavez, Carlos; Phillips, Elizabeth D Capaldi; Wadhera, Devina

2016-08-01

Eating behaviors among a large population of children are studied as a dynamic process driven by nonlinear interactions in the sociocultural school environment. The impact of food association learning on diet dynamics, inspired by a pilot study conducted among Arizona children in Pre-Kindergarten to 8th grades, is used to build simple population-level learning models. Qualitatively, mathematical studies are used to highlight the possible ramifications of instruction, learning in nutrition, and health at the community level. Model results suggest that nutrition education programs at the population-level have minimal impact on improving eating behaviors, findings that agree with prior field studies. Hence, the incorporation of food association learning may be a better strategy for creating resilient communities of healthy and non-healthy eaters. A Ratatouille effect can be observed when food association learners become food preference learners, a potential sustainable behavioral change, which in turn, may impact the overall distribution of healthy eaters. In short, this work evaluates the effectiveness of population-level intervention strategies and the importance of institutionalizing nutrition programs that factor in economical, social, cultural, and environmental elements that mesh well with the norms and values in the community.

The dynamics of student learning within a high school virtual reality design class

NASA Astrophysics Data System (ADS)

Morales, Teresa M.

This mixed method study investigated knowledge and skill development of high school students in a project-based VR design class, in which 3-D projects were developed within a student-centered, student-directed environment. This investigation focused on student content learning, and problem solving. Additionally the social dynamics of the class and the role of peer mentoring were examined to determine how these factors influenced student behavior and learning. Finally, parent and teachers perceptions of the influence of the class were examined. The participants included freshmen through senior students, parents, teachers and the high school principal. Student interviews and classroom observations were used to collect data from students, while teachers and parents completed surveys. The results of this study suggested that this application of virtual reality (VR) learning environment promoted the development of; meaningful cognitive experiences, creativity, leadership, global socialization, problem solving and a deeper understanding of academic content. Further theoretical implications for 3-D virtual reality technology are exceedingly promising, and warrant additional research and development as an instructional tool for practical use.

Intelligent Sensing in Dynamic Environments Using Markov Decision Process

PubMed Central

Nanayakkara, Thrishantha; Halgamuge, Malka N.; Sridhar, Prasanna; Madni, Asad M.

2011-01-01

In a network of low-powered wireless sensors, it is essential to capture as many environmental events as possible while still preserving the battery life of the sensor node. This paper focuses on a real-time learning algorithm to extend the lifetime of a sensor node to sense and transmit environmental events. A common method that is generally adopted in ad-hoc sensor networks is to periodically put the sensor nodes to sleep. The purpose of the learning algorithm is to couple the sensor’s sleeping behavior to the natural statistics of the environment hence that it can be in optimal harmony with changes in the environment, the sensors can sleep when steady environment and stay awake when turbulent environment. This paper presents theoretical and experimental validation of a reward based learning algorithm that can be implemented on an embedded sensor. The key contribution of the proposed approach is the design and implementation of a reward function that satisfies a trade-off between the above two mutually contradicting objectives, and a linear critic function to approximate the discounted sum of future rewards in order to perform policy learning. PMID:22346624

Incremental learning of skill collections based on intrinsic motivation

PubMed Central

Metzen, Jan H.; Kirchner, Frank

2013-01-01

Life-long learning of reusable, versatile skills is a key prerequisite for embodied agents that act in a complex, dynamic environment and are faced with different tasks over their lifetime. We address the question of how an agent can learn useful skills efficiently during a developmental period, i.e., when no task is imposed on him and no external reward signal is provided. Learning of skills in a developmental period needs to be incremental and self-motivated. We propose a new incremental, task-independent skill discovery approach that is suited for continuous domains. Furthermore, the agent learns specific skills based on intrinsic motivation mechanisms that determine on which skills learning is focused at a given point in time. We evaluate the approach in a reinforcement learning setup in two continuous domains with complex dynamics. We show that an intrinsically motivated, skill learning agent outperforms an agent which learns task solutions from scratch. Furthermore, we compare different intrinsic motivation mechanisms and how efficiently they make use of the agent's developmental period. PMID:23898265

Local Learning Strategies for Wake Identification

NASA Astrophysics Data System (ADS)

Colvert, Brendan; Alsalman, Mohamad; Kanso, Eva

2017-11-01

Swimming agents, biological and engineered alike, must navigate the underwater environment to survive. Tasks such as autonomous navigation, foraging, mating, and predation require the ability to extract critical cues from the hydrodynamic environment. A substantial body of evidence supports the hypothesis that biological systems leverage local sensing modalities, including flow sensing, to gain knowledge of their global surroundings. The nonlinear nature and high degree of complexity of fluid dynamics makes the development of algorithms for implementing localized sensing in bioinspired engineering systems essentially intractable for many systems of practical interest. In this work, we use techniques from machine learning for training a bioinspired swimmer to learn from its environment. We demonstrate the efficacy of this strategy by learning how to sense global characteristics of the wakes of other swimmers measured only from local sensory information. We conclude by commenting on the advantages and limitations of this data-driven, machine learning approach and its potential impact on broader applications in underwater sensing and navigation.

Causal learning is collaborative: Examining explanation and exploration in social contexts.

PubMed

Legare, Cristine H; Sobel, David M; Callanan, Maureen

2017-10-01

Causal learning in childhood is a dynamic and collaborative process of explanation and exploration within complex physical and social environments. Understanding how children learn causal knowledge requires examining how they update beliefs about the world given novel information and studying the processes by which children learn in collaboration with caregivers, educators, and peers. The objective of this article is to review evidence for how children learn causal knowledge by explaining and exploring in collaboration with others. We review three examples of causal learning in social contexts, which elucidate how interaction with others influences causal learning. First, we consider children's explanation-seeking behaviors in the form of "why" questions. Second, we examine parents' elaboration of meaning about causal relations. Finally, we consider parents' interactive styles with children during free play, which constrains how children explore. We propose that the best way to understand children's causal learning in social context is to combine results from laboratory and natural interactive informal learning environments.

Elucidating the electron transport in semiconductors via Monte Carlo simulations: an inquiry-driven learning path for engineering undergraduates

NASA Astrophysics Data System (ADS)

Persano Adorno, Dominique; Pizzolato, Nicola; Fazio, Claudio

2015-09-01

Within the context of higher education for science or engineering undergraduates, we present an inquiry-driven learning path aimed at developing a more meaningful conceptual understanding of the electron dynamics in semiconductors in the presence of applied electric fields. The electron transport in a nondegenerate n-type indium phosphide bulk semiconductor is modelled using a multivalley Monte Carlo approach. The main characteristics of the electron dynamics are explored under different values of the driving electric field, lattice temperature and impurity density. Simulation results are presented by following a question-driven path of exploration, starting from the validation of the model and moving up to reasoned inquiries about the observed characteristics of electron dynamics. Our inquiry-driven learning path, based on numerical simulations, represents a viable example of how to integrate a traditional lecture-based teaching approach with effective learning strategies, providing science or engineering undergraduates with practical opportunities to enhance their comprehension of the physics governing the electron dynamics in semiconductors. Finally, we present a general discussion about the advantages and disadvantages of using an inquiry-based teaching approach within a learning environment based on semiconductor simulations.

Creating learning environments.

PubMed

Ollier, D

1995-01-01

The Healthcare Forum Journal has compiled this compendium to serve as a resource in building learning organizations. Our aim is to help healthcare organizations, policymakers, and others (payers, providers, patients, physicians, and citizens) rethink the system of healthcare delivery by opening up a dialogue--the ideas presented in Sandra Seagal's interview, ¿The Pillars of Learning¿, provide the groundwork for understanding how human dynamics impact learning, and the further resources section offers readers an annotated bibliography on the subject, as well as a listing of organizations that focus on systems thinking and how to create organizations that continually learn.

A Faculty-Based Mentorship Circle: Positioning New Faculty for Success

ERIC Educational Resources Information Center

Waddell, Janice; Martin, Jennifer; Schwind, Jasna K.; Lapum, Jennifer L.

2016-01-01

Multiple and competing priorities within a dynamic and changing academic environment can pose significant challenges for new faculty. Mentorship has been identified as an important strategy to help socialize new faculty to their roles and the expectations of the academic environment. It also helps them learn new skills that will position them to…

Adaptive functional systems: learning with chaos.

PubMed

Komarov, M A; Osipov, G V; Burtsev, M S

2010-12-01

We propose a new model of adaptive behavior that combines a winnerless competition principle and chaos to learn new functional systems. The model consists of a complex network of nonlinear dynamical elements producing sequences of goal-directed actions. Each element describes dynamics and activity of the functional system which is supposed to be a distributed set of interacting physiological elements such as nerve or muscle that cooperates to obtain certain goal at the level of the whole organism. During "normal" behavior, the dynamics of the system follows heteroclinic channels, but in the novel situation chaotic search is activated and a new channel leading to the target state is gradually created simulating the process of learning. The model was tested in single and multigoal environments and had demonstrated a good potential for generation of new adaptations. © 2010 American Institute of Physics.

Debating Whether Dinosaurs Should Be "Cloned" from Ancient DNA To Promote Cooperative Learning in an Introductory Evolution Course.

ERIC Educational Resources Information Center

Soja, Constance M.; Huerta, Deborah

2001-01-01

Describes an interactive internet exercise that enables students to engage in cooperative library and web research on a controversial topic in science, specifically the cloning of extinct lifeforms. Creates a dynamic learning environment in a large introductory geology course and demonstrates the importance of scientific literacy. (Author/SAH)

Multiple Views of Space: Continuous Visual Flow Enhances Small-Scale Spatial Learning

ERIC Educational Resources Information Center

Holmes, Corinne A.; Marchette, Steven A.; Newcombe, Nora S.

2017-01-01

In the real word, we perceive our environment as a series of static and dynamic views, with viewpoint transitions providing a natural link from one static view to the next. The current research examined if experiencing such transitions is fundamental to learning the spatial layout of small-scale displays. In Experiment 1, participants viewed a…

An Equitable Balance: Designing Quality Thinking Systems in Art Education

ERIC Educational Resources Information Center

Ingalls Vanada, Delane

2016-01-01

Dynamic learning environments in the arts that nurture all students' capacities for deep meaning, synthesis and connection-making have the best chance of standing in the gap toward educational justice. New paradigms for teaching and learning are needed that elevate all students' capacities--not just the select few who excel in narrow subsets of…

Engineering a Dynamic Science Learning Environment for K-12 Teachers

ERIC Educational Resources Information Center

Hardre, Patricia L.; Nanny, Mark; Refai, Hazen; Ling, Chen; Slater, Janis

2010-01-01

The present study follows a cohort of 17 K-12 teachers through a six-week resident learning experience in science and engineering, and on into the planning and implementation of applications for their classrooms. This Research Experiences for Teachers (RET) program was examined using the strategic approach of design-based research, with its fluid,…

Quality Teaching: Building a Flexible and Dynamic Approach. GEC Working Paper Series. Number 2

ERIC Educational Resources Information Center

Leu, Elizabeth; Hays, Frances; LeCzel, Donna Kay; O'Grady, Barbara

2005-01-01

Good basic education depends on several factors working in harmony. The first is that students be healthy, safe, and ready to learn. Other essentials include an enabling policy environment and transparent management; a curriculum that reflects the society's values and aspirations for learning; and community support for education and parents'…

Mechanism for Promoting Motivation, Confidence, and Autonomy through Synchronic Communication Sessions in Virtual Learning Environments

ERIC Educational Resources Information Center

Valencia, Jorge Andrick Parra; Dallos, Adriana Rocío Lizcano; Ballesteros, Eliécer Pineda

2017-01-01

This study presents a mechanism which explains the effect of synchronous communication on students' perception of the training process in virtual learning methodology used in a postgraduate programme at the University of Santander. We use System Dynamics to design a mechanism that integrates motivation, confidence, trust, and autonomy in students.…

From Orientation Needs to Developmental Realities: The Honors First-Year Seminar in a National Context

ERIC Educational Resources Information Center

Vander Zee, Anton; Folds-Bennett, Trisha; Meyer-Bernstein, Elizabeth; Reardon, Brendan

2016-01-01

The transition into college remains one of the most formative and complex phases in an individual's life. Institutions of higher learning have responded to the challenges facing first-year students in myriad ways, most often by offering summer orientation programs, dynamic living-learning environments, tailored academic and psychological support…

Improving the English Proficiency of Native Japanese via Digital Storytelling, Blogs, and E-Mobile Technologies

ERIC Educational Resources Information Center

Obari, Hiroyuki; Lambacher, Stephen

2012-01-01

This paper reports on the use of digital storytelling and blog activities to make CALL classes more dynamic and personalized for both instructors and learners alike. An empirical research study was carried out to determine if a blended-learning environment incorporating m-learning could help improve the English listening, presentation, and…

Exploring the Reciprocal Relationship between a Comprehensive Living-Learning Program and Institutional Culture: A Narrative Inquiry Case Study

ERIC Educational Resources Information Center

Marquart, Christopher P.

2017-01-01

Over the past 50 years, living-learning programs (LLPs) have emerged as a dynamic curricular innovation in higher education. These programs are residentially based, seeking to seamlessly integrate the classroom and residence hall environments and blur the traditional boundaries between the academic and residential experiences for students (Kuh,…

Exploring Reading: Empowering Readers with Special Needs.

ERIC Educational Resources Information Center

Builder, Philip

Focusing on the affective aspects of children's learning, this book presents a view of learners with special needs in the widest sense, including their responses to their environments at school and at home; and the dynamics of the literacy-learning processes--how to view them and what to look for. The book also introduces BRAT (Building Reading…

Building a Trusted Environment for Education Technology Products

ERIC Educational Resources Information Center

Levin, Douglas

2016-01-01

Data about learning, about teaching and about school operations helps to generate information that can and is being used to benefit students. This, in fact, is one of the key advances powering the dynamic movement to use technology in schools to support student learning. At the same time, not all parents and privacy advocates are equally…

From Chalk and Talk to Walking the Walk: Facilitating Dynamic Learning Contexts for Entrepreneurship Students in Fast-Tracking Innovations

ERIC Educational Resources Information Center

Gilbert, David H.

2012-01-01

Purpose: The purpose of this paper is to examine the notion of designing and developing applied, industry-engaged learning environments that embrace ambiguity and uncertainty in overcoming pedagogical inertia in educating young entrepreneurs and innovators. The research reported on proposes a solution to the dual expectations of producing…

It's All Happening at the Zoo: Children's Environmental Learning after School

ERIC Educational Resources Information Center

Douglas, Jason A.; Katz, Cindi

2009-01-01

Pairing dynamic out-of-school-time (OST) programs with zoos can encourage young people's relationships with and sense of responsibility for animals and the environment. The project presented in this article, Animal Rescuers, gave the authors the opportunity to examine how such a pairing can work. OST programs enable learning in settings that are…

Dual-Schemata Model

NASA Astrophysics Data System (ADS)

Taniguchi, Tadahiro; Sawaragi, Tetsuo

In this paper, a new machine-learning method, called Dual-Schemata model, is presented. Dual-Schemata model is a kind of self-organizational machine learning methods for an autonomous robot interacting with an unknown dynamical environment. This is based on Piaget's Schema model, that is a classical psychological model to explain memory and cognitive development of human beings. Our Dual-Schemata model is developed as a computational model of Piaget's Schema model, especially focusing on sensori-motor developing period. This developmental process is characterized by a couple of two mutually-interacting dynamics; one is a dynamics formed by assimilation and accommodation, and the other dynamics is formed by equilibration and differentiation. By these dynamics schema system enables an agent to act well in a real world. This schema's differentiation process corresponds to a symbol formation process occurring within an autonomous agent when it interacts with an unknown, dynamically changing environment. Experiment results obtained from an autonomous facial robot in which our model is embedded are presented; an autonomous facial robot becomes able to chase a ball moving in various ways without any rewards nor teaching signals from outside. Moreover, emergence of concepts on the target movements within a robot is shown and discussed in terms of fuzzy logics on set-subset inclusive relationships.

Dynamics of EEG functional connectivity during statistical learning.

PubMed

Tóth, Brigitta; Janacsek, Karolina; Takács, Ádám; Kóbor, Andrea; Zavecz, Zsófia; Nemeth, Dezso

2017-10-01

Statistical learning is a fundamental mechanism of the brain, which extracts and represents regularities of our environment. Statistical learning is crucial in predictive processing, and in the acquisition of perceptual, motor, cognitive, and social skills. Although previous studies have revealed competitive neurocognitive processes underlying statistical learning, the neural communication of the related brain regions (functional connectivity, FC) has not yet been investigated. The present study aimed to fill this gap by investigating FC networks that promote statistical learning in humans. Young adults (N=28) performed a statistical learning task while 128-channels EEG was acquired. The task involved probabilistic sequences, which enabled to measure incidental/implicit learning of conditional probabilities. Phase synchronization in seven frequency bands was used to quantify FC between cortical regions during the first, second, and third periods of the learning task, respectively. Here we show that statistical learning is negatively correlated with FC of the anterior brain regions in slow (theta) and fast (beta) oscillations. These negative correlations increased as the learning progressed. Our findings provide evidence that dynamic antagonist brain networks serve a hallmark of statistical learning. Copyright © 2017 Elsevier Inc. All rights reserved.

Task path planning, scheduling and learning for free-ranging robot systems

NASA Technical Reports Server (NTRS)

Wakefield, G. Steve

1987-01-01

The development of robotics applications for space operations is often restricted by the limited movement available to guided robots. Free ranging robots can offer greater flexibility than physically guided robots in these applications. Presented here is an object oriented approach to path planning and task scheduling for free-ranging robots that allows the dynamic determination of paths based on the current environment. The system also provides task learning for repetitive jobs. This approach provides a basis for the design of free-ranging robot systems which are adaptable to various environments and tasks.

Impedance learning for robotic contact tasks using natural actor-critic algorithm.

PubMed

Kim, Byungchan; Park, Jooyoung; Park, Shinsuk; Kang, Sungchul

2010-04-01

Compared with their robotic counterparts, humans excel at various tasks by using their ability to adaptively modulate arm impedance parameters. This ability allows us to successfully perform contact tasks even in uncertain environments. This paper considers a learning strategy of motor skill for robotic contact tasks based on a human motor control theory and machine learning schemes. Our robot learning method employs impedance control based on the equilibrium point control theory and reinforcement learning to determine the impedance parameters for contact tasks. A recursive least-square filter-based episodic natural actor-critic algorithm is used to find the optimal impedance parameters. The effectiveness of the proposed method was tested through dynamic simulations of various contact tasks. The simulation results demonstrated that the proposed method optimizes the performance of the contact tasks in uncertain conditions of the environment.

A Fast and Precise Indoor Localization Algorithm Based on an Online Sequential Extreme Learning Machine †

PubMed Central

Zou, Han; Lu, Xiaoxuan; Jiang, Hao; Xie, Lihua

2015-01-01

Nowadays, developing indoor positioning systems (IPSs) has become an attractive research topic due to the increasing demands on location-based service (LBS) in indoor environments. WiFi technology has been studied and explored to provide indoor positioning service for years in view of the wide deployment and availability of existing WiFi infrastructures in indoor environments. A large body of WiFi-based IPSs adopt fingerprinting approaches for localization. However, these IPSs suffer from two major problems: the intensive costs of manpower and time for offline site survey and the inflexibility to environmental dynamics. In this paper, we propose an indoor localization algorithm based on an online sequential extreme learning machine (OS-ELM) to address the above problems accordingly. The fast learning speed of OS-ELM can reduce the time and manpower costs for the offline site survey. Meanwhile, its online sequential learning ability enables the proposed localization algorithm to adapt in a timely manner to environmental dynamics. Experiments under specific environmental changes, such as variations of occupancy distribution and events of opening or closing of doors, are conducted to evaluate the performance of OS-ELM. The simulation and experimental results show that the proposed localization algorithm can provide higher localization accuracy than traditional approaches, due to its fast adaptation to various environmental dynamics. PMID:25599427

The Application of System Dynamics to the Integration of National Laboratory Research and K-12 Education

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mills, James Ignatius; Zounar Harbour, Elda D

2001-08-01

The Idaho National Engineering and Environmental Laboratory (INEEL) is dedicated to finding solutions to problems related to the environment, energy, economic competitiveness, and national security. In an effort to attract and retain the expertise needed to accomplish these challenges, the INEEL is developing a program of broad educational opportunities that makes continuing education readily available to all laboratory employees, beginning in the K–12 environment and progressing through post-graduate education and beyond. One of the most innovative educational approaches being implemented at the laboratory is the application of STELLA© dynamic learning environments, which facilitate captivating K–12 introductions to the complex energymore » and environmental challenges faced by global societies. These simulations are integrated into lesson plans developed by teachers in collaboration with INEEL scientists and engineers. This approach results in an enjoyable and involved learning experience, and an especially positive introduction to the application of science to emerging problems of great social and environmental consequence.« less

Simulator technology as a tool for education in cardiac care.

PubMed

Hravnak, Marilyn; Beach, Michael; Tuite, Patricia

2007-01-01

Assisting nurses in gaining the cognitive and psychomotor skills necessary to safely and effectively care for patients with cardiovascular disease can be challenging for educators. Ideally, nurses would have the opportunity to synthesize and practice these skills in a protected training environment before application in the dynamic clinical setting. Recently, a technology known as high fidelity human simulation was introduced, which permits learners to interact with a simulated patient. The dynamic physiologic parameters and physical assessment capabilities of the simulated patient provide for a realistic learning environment. This article describes the High Fidelity Human Simulation Laboratory at the University of Pittsburgh School of Nursing and presents strategies for using this technology as a tool in teaching complex cardiac nursing care at the basic and advanced practice nursing levels. The advantages and disadvantages of high fidelity human simulation in learning are discussed.

The charged particle accelerators subsystems modeling

NASA Astrophysics Data System (ADS)

Averyanov, G. P.; Kobylyatskiy, A. V.

2017-01-01

Presented web-based resource for information support the engineering, science and education in Electrophysics, containing web-based tools for simulation subsystems charged particle accelerators. Formulated the development motivation of Web-Environment for Virtual Electrophysical Laboratories. Analyzes the trends of designs the dynamic web-environments for supporting of scientific research and E-learning, within the framework of Open Education concept.

Taking Control: Stealth Assessment of Deterministic Behaviors within a Game-Based System

ERIC Educational Resources Information Center

Snow, Erica L.; Likens, Aaron D.; Allen, Laura K.; McNamara, Danielle S.

2016-01-01

Game-based environments frequently afford students the opportunity to exert agency over their learning paths by making various choices within the environment. The combination of log data from these systems and dynamic methodologies may serve as a stealth means to assess how students behave (i.e., deterministic or random) within these learning…

Taking Control: Stealth Assessment of Deterministic Behaviors within a Game-Based System

ERIC Educational Resources Information Center

Snow, Erica L.; Likens, Aaron D.; Allen, Laura K.; McNamara, Danielle S.

2015-01-01

Game-based environments frequently afford students the opportunity to exert agency over their learning paths by making various choices within the environment. The combination of log data from these systems and dynamic methodologies may serve as a stealth means to assess how students behave (i.e., deterministic or random) within these learning…

Nonlinear dynamics in the study of birdsong

NASA Astrophysics Data System (ADS)

Mindlin, Gabriel B.

2017-09-01

Birdsong, a rich and complex behavior, is a stellar model to understand a variety of biological problems, from motor control to learning. It also enables us to study how behavior emerges when a nervous system, a biomechanical device and the environment interact. In this review, I will show that many questions in the field can benefit from the approach of nonlinear dynamics, and how birdsong can inspire new directions for research in dynamics.

ICT Integration in Education: Incorporation for Teaching & Learning Improvement

ERIC Educational Resources Information Center

Ghavifekr, Simin; Razak, Ahmad Zabidi Abd; Ghani, Muhammad Faizal A.; Ran, Ng Yan; Meixi, Yao; Tengyue, Zhang

2014-01-01

Over the last two decades, the rapid growth of ICT has become one of the most important topics discussed by the scholars in education. This is due to the capability of ICT in providing a dynamic and proactive teaching and learning environment. In line with the current digital era, teachers are required to integrate ICT in their daily teaching and…

Learning under Conditions of Hierarchy and Discipline: The Case of the German Army, 1939-1940

ERIC Educational Resources Information Center

Visser, Max

2008-01-01

To survive in and adapt to dynamic, turbulent, and complex environments, organizations need to engage in learning. This truism is particularly relevant for army organizations in times of war and armed conflict. In this article a case of army operations during World War II is analyzed on the basis of Ortenblad's integrated model of the learning…

Patterns of Control over the Teaching-Studying-Learning Process and Classrooms as Complex Dynamic Environments: A Theoretical Framework

ERIC Educational Resources Information Center

Harjunen, Elina

2012-01-01

In this theoretical paper the role of power in classroom interactions is examined in terms of a dominance continuum to advance a theoretical framework justifying the emergence of three ways of distributing power when it comes to dealing with the control over the teaching-studying-learning (TSL) "pattern of teacher domination," "pattern of…

Designing After-School Learning Using the Massively Multiplayer Online Role-Playing Game

ERIC Educational Resources Information Center

King, Elizabeth M.

2015-01-01

Digital games have become popular for engaging students in a range of learning goals, both in the classroom and the after-school space. In this article, I discuss a specific genre of video game, the massively multiplayer online role-playing game (MMO), which has been identified as a dynamic environment for encountering 21st-century workplace…

E-Learning for Elementary Students: The Web 2.0 Tool Google Drive as Teaching and Learning Practice

ERIC Educational Resources Information Center

Apergi, Angeliki; Anagnostopoulou, Angeliki; Athanasiou, Alexandra

2015-01-01

It is a well-known fact that during recent years, the new economic and technological environment, which has emerged from the dynamic impacts of globalization, has given rise to the increased development of information and communication technologies that have immensely influenced education and training all over Europe. Within this framework, there…

e-Learning, e-Books and Virtual Reference Service: The Nexus between the Library and Education

ERIC Educational Resources Information Center

Nicholas, Pauline; White, Thelma

2012-01-01

The society relies on institutions of higher education to produce a literate workforce; one that is able to function in a dynamic, technological, information-overload environment. In support of this new thrust, most universities have incorporated the use of new media and ICTs in the teaching learning process resulting in a multi-modal approach…

Matching Navy Recruiting Needs with Social Network Profiles Using Lexical Link Analysis. N1 FY10 Research Project

DTIC Science & Technology

2010-01-01

recruiting needs and candidate profiles – Link the features in context of dynamic social network environments, learn from on-going market...universities, companies, etc.) • Friends list fandom (fan of) , • Endorsements (supporter of) • Navy Enlisted Rating descriptions – Hard Cards...the samples into a validation and a learning set Set aside . the validation set. Use the learning set to match the recruit ratings with the

Versatile, Immersive, Creative and Dynamic Virtual 3-D Healthcare Learning Environments: A Review of the Literature

PubMed Central

2008-01-01

The author provides a critical overview of three-dimensional (3-D) virtual worlds and “serious gaming” that are currently being developed and used in healthcare professional education and medicine. The relevance of this e-learning innovation for teaching students and professionals is debatable and variables influencing adoption, such as increased knowledge, self-directed learning, and peer collaboration, by academics, healthcare professionals, and business executives are examined while looking at various Web 2.0/3.0 applications. There is a need for more empirical research in order to unearth the pedagogical outcomes and advantages associated with this e-learning technology. A brief description of Roger’s Diffusion of Innovations Theory and Siemens’ Connectivism Theory for today’s learners is presented as potential underlying pedagogical tenets to support the use of virtual 3-D learning environments in higher education and healthcare. PMID:18762473

Versatile, immersive, creative and dynamic virtual 3-D healthcare learning environments: a review of the literature.

PubMed

Hansen, Margaret M

2008-09-01

The author provides a critical overview of three-dimensional (3-D) virtual worlds and "serious gaming" that are currently being developed and used in healthcare professional education and medicine. The relevance of this e-learning innovation for teaching students and professionals is debatable and variables influencing adoption, such as increased knowledge, self-directed learning, and peer collaboration, by academics, healthcare professionals, and business executives are examined while looking at various Web 2.0/3.0 applications. There is a need for more empirical research in order to unearth the pedagogical outcomes and advantages associated with this e-learning technology. A brief description of Roger's Diffusion of Innovations Theory and Siemens' Connectivism Theory for today's learners is presented as potential underlying pedagogical tenets to support the use of virtual 3-D learning environments in higher education and healthcare.

PlayPhysics: An Emotional Games Learning Environment for Teaching Physics

NASA Astrophysics Data System (ADS)

Muñoz, Karla; Kevitt, Paul Mc; Lunney, Tom; Noguez, Julieta; Neri, Luis

To ensure learning, game-based learning environments must incorporate assessment mechanisms, e.g. Intelligent Tutoring Systems (ITSs). ITSs are focused on recognising and influencing the learner's emotional or motivational states. This research focuses on designing and implementing an affective student model for intelligent gaming, which reasons about the learner's emotional state from cognitive and motivational variables using observable behaviour. A Probabilistic Relational Models (PRMs) approach is employed to derive Dynamic Bayesian Networks (DBNs). The model uses the Control-Value theory of 'achievement emotions' as a basis. A preliminary test was conducted to recognise the students' prospective-outcome emotions with results presented and discussed. PlayPhysics is an emotional games learning environment for teaching Physics. Once the affective student model proves effective it will be incorporated into PlayPhysics' architecture. The design, evaluation and postevaluation of PlayPhysics are also discussed. Future work will focus on evaluating the affective student model with a larger population of students, and on providing affective feedback.

Beyond the blank slate: routes to learning new coordination patterns depend on the intrinsic dynamics of the learner—experimental evidence and theoretical model

PubMed Central

Kostrubiec, Viviane; Zanone, Pier-Giorgio; Fuchs, Armin; Kelso, J. A. Scott

2012-01-01

Using an approach that combines experimental studies of bimanual movements to visual stimuli and theoretical modeling, the present paper develops a dynamical account of sensorimotor learning, that is, how new skills are acquired and old ones modified. A significant aspect of our approach is the focus on the individual learner as the basic unit of analysis, in particular the quantification of predispositions and capabilities that the individual learner brings to the learning environment. Such predispositions constitute the learner's behavioral repertoire, captured here theoretically as a dynamical landscape (“intrinsic dynamics”). The learning process is demonstrated to not only lead to a relatively permanent improvement of performance in the required task—the usual outcome—but also to alter the individual's entire repertoire. Changes in the dynamical landscape due to learning are shown to result from two basic mechanisms or “routes”: bifurcation and shift. Which mechanism is selected depends the initial individual repertoire before new learning begins. Both bifurcation and shift mechanisms are accommodated by a dynamical model, a relatively straightforward development of the well-established HKB model of movement coordination. Model simulations show that although environmental or task demands may be met equally well using either mechanism, the bifurcation route results in greater stabilization of the to-be-learned behavior. Thus, stability not (or not only) error is demonstrated to be the basis of selection, both of a new pattern of behavior and the path (smooth shift versus abrupt qualitative change) that learning takes. In line with these results, recent neurophysiological evidence indicates that stability is a relevant feature around which brain activity is organized while an individual performs a coordination task. Finally, we explore the consequences of the dynamical approach to learning for theories of biological change. PMID:22876227

Examining the Results of an Intervention to Influence Factors of Group Dynamics in Video Conferencing Learning Environments

ERIC Educational Resources Information Center

Cain, William Christopher

2017-01-01

The following study was framed around a simple question: when a group of people is engaged in video conferencing, "what sort of things can they do to improve their group dynamics?" This is an important question for current and future educational practice because web-based video conferencing has increasingly become an important tool for…

The training schedule affects the stability, not the magnitude, of the interlimb transfer of learned dynamics

PubMed Central

Joiner, Wilsaan M.; Brayanov, Jordan B.

2013-01-01

The way that a motor adaptation is trained, for example, the manner in which it is introduced or the duration of the training period, can influence its internal representation. However, recent studies examining the gradual versus abrupt introduction of a novel environment have produced conflicting results. Here we examined how these effects determine the effector specificity of motor adaptation during visually guided reaching. After adaptation to velocity-dependent dynamics in the right arm, we estimated the amount of adaptation transferred to the left arm, using error-clamp measurement trials to directly measure changes in learned dynamics. We found that a small but significant amount of generalization to the untrained arm occurs under three different training schedules: a short-duration (15 trials) abrupt presentation, a long-duration (160 trials) abrupt presentation, and a long-duration gradual presentation of the novel dynamic environment. Remarkably, we found essentially no difference between the amount of interlimb generalization when comparing these schedules, with 9–12% transfer of the trained adaptation for all three. However, the duration of training had a pronounced effect on the stability of the interlimb transfer: The transfer elicited from short-duration training decayed rapidly, whereas the transfer from both long-duration training schedules was considerably more persistent (<50% vs. >90% retention over the first 20 trials). These results indicate that the amount of interlimb transfer is similar for gradual versus abrupt training and that interlimb transfer of learned dynamics can occur after even a brief training period but longer training is required for an enduring effect. PMID:23719204

The training schedule affects the stability, not the magnitude, of the interlimb transfer of learned dynamics.

PubMed

Joiner, Wilsaan M; Brayanov, Jordan B; Smith, Maurice A

2013-08-01

The way that a motor adaptation is trained, for example, the manner in which it is introduced or the duration of the training period, can influence its internal representation. However, recent studies examining the gradual versus abrupt introduction of a novel environment have produced conflicting results. Here we examined how these effects determine the effector specificity of motor adaptation during visually guided reaching. After adaptation to velocity-dependent dynamics in the right arm, we estimated the amount of adaptation transferred to the left arm, using error-clamp measurement trials to directly measure changes in learned dynamics. We found that a small but significant amount of generalization to the untrained arm occurs under three different training schedules: a short-duration (15 trials) abrupt presentation, a long-duration (160 trials) abrupt presentation, and a long-duration gradual presentation of the novel dynamic environment. Remarkably, we found essentially no difference between the amount of interlimb generalization when comparing these schedules, with 9-12% transfer of the trained adaptation for all three. However, the duration of training had a pronounced effect on the stability of the interlimb transfer: The transfer elicited from short-duration training decayed rapidly, whereas the transfer from both long-duration training schedules was considerably more persistent (<50% vs. >90% retention over the first 20 trials). These results indicate that the amount of interlimb transfer is similar for gradual versus abrupt training and that interlimb transfer of learned dynamics can occur after even a brief training period but longer training is required for an enduring effect.

DEGAS: Dynamic Exascale Global Address Space Programming Environments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Demmel, James

The Dynamic, Exascale Global Address Space programming environment (DEGAS) project will develop the next generation of programming models and runtime systems to meet the challenges of Exascale computing. The Berkeley part of the project concentrated on communication-optimal code generation to optimize speed and energy efficiency by reducing data movement. Our work developed communication lower bounds, and/or communication avoiding algorithms (that either meet the lower bound, or do much less communication than their conventional counterparts) for a variety of algorithms, including linear algebra, machine learning and genomics. The Berkeley part of the project concentrated on communication-optimal code generation to optimize speedmore » and energy efficiency by reducing data movement. Our work developed communication lower bounds, and/or communication avoiding algorithms (that either meet the lower bound, or do much less communication than their conventional counterparts) for a variety of algorithms, including linear algebra, machine learning and genomics.« less

Learning Extrema Problems Using a Non-Differential Approach in a Digital Dynamic Environment: The Case of High-Track yet Low-Achievers

ERIC Educational Resources Information Center

Dvir, Assaf; Tabach, Michal

2017-01-01

High schools commonly use a differential approach to teach minima and maxima geometric problems. Although calculus serves as a systematic and powerful technique, this rigorous instrument might hinder students' ability to understand the behavior and constraints of the objective function. The proliferation of digital environments allowed us to adopt…

Teacher Behaviors Contributing to Student Content Engagement: A Socially Constructed Consensus of Undergraduate Students in a College of Agriculture

ERIC Educational Resources Information Center

Estepp, Christopher M.; Roberts, T. Grady

2013-01-01

Students in colleges of agriculture will face a dynamically changing workplace. In order to learn the skills needed to succeed in such an environment, students must be cognitively engaged in the college classroom. Engagement with instructional content is a precursor to learning, and teachers in colleges of agriculture must shift towards using more…

Analysis of the Cognitive Unity or Rupture between Conjecture and Proof When Learning to Prove on a Grade 10 Trigonometry Course

ERIC Educational Resources Information Center

Fiallo, Jorge; Gutiérrez, Angel

2017-01-01

We present results from a classroom-based intervention designed to help a class of grade 10 students (14-15 years old) learn proof while studying trigonometry in a dynamic geometry software environment. We analysed some students' solutions to conjecture-and-proof problems that let them gain experience in stating conjectures and developing proofs.…

Towards representation of a perceptual color manifold using associative memory for color constancy.

PubMed

Seow, Ming-Jung; Asari, Vijayan K

2009-01-01

In this paper, we propose the concept of a manifold of color perception through empirical observation that the center-surround properties of images in a perceptually similar environment define a manifold in the high dimensional space. Such a manifold representation can be learned using a novel recurrent neural network based learning algorithm. Unlike the conventional recurrent neural network model in which the memory is stored in an attractive fixed point at discrete locations in the state space, the dynamics of the proposed learning algorithm represent memory as a nonlinear line of attraction. The region of convergence around the nonlinear line is defined by the statistical characteristics of the training data. This learned manifold can then be used as a basis for color correction of the images having different color perception to the learned color perception. Experimental results show that the proposed recurrent neural network learning algorithm is capable of color balance the lighting variations in images captured in different environments successfully.

Toward a neural basis for peer-interaction: what makes peer-learning tick?

PubMed Central

Clark, Ian; Dumas, Guillaume

2015-01-01

Many of the instructional practices that have been advanced as intrinsically motivating are inherent in socio-constructivist learning environments. There is now emerging scientific evidence to explain why interactive learning environments promote the intrinsic motivation to learn. The “two-body” and “second person” approaches have begun to explore the “dark matter” of social neuroscience: the intra- and inter-individual brain dynamics during social interaction. Moreover, studies indicate that when young learners are given expanded opportunities to actively and equitably participate in collaborative learning activities they experienced feelings of well-being, contentment, or even excitement. Neuroscience starts demonstrating how this naturally rewarding aspect is strongly associated with the implication of the mesolimbic dopaminergic pathway during social interaction. The production of dopamine reinforces the desire to continue the interaction, and heightens feelings of anticipation for future peer-learning activities. Here we review how cooperative learning and problem-solving interactions can bring about the “intrinsic” motivation to learn. Overall, the reported theoretical arguments and neuroscientific results have clear implications for school and organization approaches and support social constructivist perspectives. PMID:25713542

Self-Learning Embedded System for Object Identification in Intelligent Infrastructure Sensors.

PubMed

Villaverde, Monica; Perez, David; Moreno, Felix

2015-11-17

The emergence of new horizons in the field of travel assistant management leads to the development of cutting-edge systems focused on improving the existing ones. Moreover, new opportunities are being also presented since systems trend to be more reliable and autonomous. In this paper, a self-learning embedded system for object identification based on adaptive-cooperative dynamic approaches is presented for intelligent sensor's infrastructures. The proposed system is able to detect and identify moving objects using a dynamic decision tree. Consequently, it combines machine learning algorithms and cooperative strategies in order to make the system more adaptive to changing environments. Therefore, the proposed system may be very useful for many applications like shadow tolls since several types of vehicles may be distinguished, parking optimization systems, improved traffic conditions systems, etc.

Dynamic learning from adaptive neural network control of a class of nonaffine nonlinear systems.

PubMed

Dai, Shi-Lu; Wang, Cong; Wang, Min

2014-01-01

This paper studies the problem of learning from adaptive neural network (NN) control of a class of nonaffine nonlinear systems in uncertain dynamic environments. In the control design process, a stable adaptive NN tracking control design technique is proposed for the nonaffine nonlinear systems with a mild assumption by combining a filtered tracking error with the implicit function theorem, input-to-state stability, and the small-gain theorem. The proposed stable control design technique not only overcomes the difficulty in controlling nonaffine nonlinear systems but also relaxes constraint conditions of the considered systems. In the learning process, the partial persistent excitation (PE) condition of radial basis function NNs is satisfied during tracking control to a recurrent reference trajectory. Under the PE condition and an appropriate state transformation, the proposed adaptive NN control is shown to be capable of acquiring knowledge on the implicit desired control input dynamics in the stable control process and of storing the learned knowledge in memory. Subsequently, an NN learning control design technique that effectively exploits the learned knowledge without re-adapting to the controller parameters is proposed to achieve closed-loop stability and improved control performance. Simulation studies are performed to demonstrate the effectiveness of the proposed design techniques.

A plastic corticostriatal circuit model of adaptation in perceptual decision making

PubMed Central

Hsiao, Pao-Yueh; Lo, Chung-Chuan

2013-01-01

The ability to optimize decisions and adapt them to changing environments is a crucial brain function that increase survivability. Although much has been learned about the neuronal activity in various brain regions that are associated with decision making, and about how the nervous systems may learn to achieve optimization, the underlying neuronal mechanisms of how the nervous systems optimize decision strategies with preference given to speed or accuracy, and how the systems adapt to changes in the environment, remain unclear. Based on extensive empirical observations, we addressed the question by extending a previously described cortico-basal ganglia circuit model of perceptual decisions with the inclusion of a dynamic dopamine (DA) system that modulates spike-timing dependent plasticity (STDP). We found that, once an optimal model setting that maximized the reward rate was selected, the same setting automatically optimized decisions across different task environments through dynamic balancing between the facilitating and depressing components of the DA dynamics. Interestingly, other model parameters were also optimal if we considered the reward rate that was weighted by the subject's preferences for speed or accuracy. Specifically, the circuit model favored speed if we increased the phasic DA response to the reward prediction error, whereas the model favored accuracy if we reduced the tonic DA activity or the phasic DA responses to the estimated reward probability. The proposed model provides insight into the roles of different components of DA responses in decision adaptation and optimization in a changing environment. PMID:24339814

On learning navigation behaviors for small mobile robots with reservoir computing architectures.

PubMed

Antonelo, Eric Aislan; Schrauwen, Benjamin

2015-04-01

This paper proposes a general reservoir computing (RC) learning framework that can be used to learn navigation behaviors for mobile robots in simple and complex unknown partially observable environments. RC provides an efficient way to train recurrent neural networks by letting the recurrent part of the network (called reservoir) be fixed while only a linear readout output layer is trained. The proposed RC framework builds upon the notion of navigation attractor or behavior that can be embedded in the high-dimensional space of the reservoir after learning. The learning of multiple behaviors is possible because the dynamic robot behavior, consisting of a sensory-motor sequence, can be linearly discriminated in the high-dimensional nonlinear space of the dynamic reservoir. Three learning approaches for navigation behaviors are shown in this paper. The first approach learns multiple behaviors based on the examples of navigation behaviors generated by a supervisor, while the second approach learns goal-directed navigation behaviors based only on rewards. The third approach learns complex goal-directed behaviors, in a supervised way, using a hierarchical architecture whose internal predictions of contextual switches guide the sequence of basic navigation behaviors toward the goal.

Intermediate Greek EFL Learners' Attitudes to On-Line Teaching Practices: A Blended Task-Based English Language Learning Approach

ERIC Educational Resources Information Center

Liontou, Trisevgeni

2015-01-01

This paper reports on a one-year longitudinal study that adopted a blended teaching approach based on designing and implementing an online EFL course to be used by Greek students aged 13-14 years old along their more traditional face-to-face lessons. The reason for creating a more dynamic learning environment aligned with the rest of the…

Social Fear Learning: from Animal Models to Human Function.

PubMed

Debiec, Jacek; Olsson, Andreas

2017-07-01

Learning about potential threats is critical for survival. Learned fear responses are acquired either through direct experiences or indirectly through social transmission. Social fear learning (SFL), also known as vicarious fear learning, is a paradigm successfully used for studying the transmission of threat information between individuals. Animal and human studies have begun to elucidate the behavioral, neural and molecular mechanisms of SFL. Recent research suggests that social learning mechanisms underlie a wide range of adaptive and maladaptive phenomena, from supporting flexible avoidance in dynamic environments to intergenerational transmission of trauma and anxiety disorders. This review discusses recent advances in SFL studies and their implications for basic, social and clinical sciences. Copyright © 2017 Elsevier Ltd. All rights reserved.

Dynamic gesture recognition using neural networks: a fundament for advanced interaction construction

NASA Astrophysics Data System (ADS)

Boehm, Klaus; Broll, Wolfgang; Sokolewicz, Michael A.

1994-04-01

Interaction in virtual reality environments is still a challenging task. Static hand posture recognition is currently the most common and widely used method for interaction using glove input devices. In order to improve the naturalness of interaction, and thereby decrease the user-interface learning time, there is a need to be able to recognize dynamic gestures. In this paper we describe our approach to overcoming the difficulties of dynamic gesture recognition (DGR) using neural networks. Backpropagation neural networks have already proven themselves to be appropriate and efficient for posture recognition. However, the extensive amount of data involved in DGR requires a different approach. Because of features such as topology preservation and automatic-learning, Kohonen Feature Maps are particularly suitable for the reduction of the high dimensional data space that is the result of a dynamic gesture, and are thus implemented for this task.

CliniSpace: a multiperson 3D online immersive training environment accessible through a browser.

PubMed

Dev, Parvati; Heinrichs, W LeRoy; Youngblood, Patricia

2011-01-01

Immersive online medical environments, with dynamic virtual patients, have been shown to be effective for scenario-based learning (1). However, ease of use and ease of access have been barriers to their use. We used feedback from prior evaluation of these projects to design and develop CliniSpace. To improve usability, we retained the richness of prior virtual environments but modified the user interface. To improve access, we used a Software-as-a-Service (SaaS) approach to present a richly immersive 3D environment within a web browser.

Negotiating energy dynamics through embodied action in a materially structured environment

NASA Astrophysics Data System (ADS)

Scherr, Rachel E.; Close, Hunter G.; Close, Eleanor W.; Flood, Virginia J.; McKagan, Sarah B.; Robertson, Amy D.; Seeley, Lane; Wittmann, Michael C.; Vokos, Stamatis

2013-12-01

We provide evidence that a learning activity called Energy Theater engages learners with key conceptual issues in the learning of energy, including disambiguating matter flow and energy flow and theorizing mechanisms for energy transformation. A participationist theory of learning, in which learning is indicated by changes in speech and behavior, supports ethnographic analysis of learners’ embodied interactions with each other and the material setting. We conduct detailed analysis to build plausible causal links between specific features of Energy Theater and the conceptual engagement that we observe. Disambiguation of matter and energy appears to be promoted especially by the material structure of the Energy Theater environment, in which energy is represented by participants, while objects are represented by areas demarcated by loops of rope. Theorizing mechanisms of energy transformation is promoted especially by Energy Theater’s embodied action, which necessitates modeling the time ordering of energy transformations.

Intelligent control based on fuzzy logic and neural net theory

NASA Technical Reports Server (NTRS)

Lee, Chuen-Chien

1991-01-01

In the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment.

Developing critical care skills for nurses in the ward environment: a work-based learning approach.

PubMed

Thorne, Linda; Hackwood, Helen

2002-01-01

An account of collaborative working between an NHS trust and university in responding to the critical care agenda. An 'Introduction to Critical Care Skills' course initiative, which addresses the needs of nurses caring for level 1 and 2 patients in ward areas, is discussed. Work-based learning forms the focus of skills development using core competencies related to a holistic approach to caring for patients with complex needs. A dynamic evolving process of course development is promoting quality care for patients and closely reflects the needs of those caring for acutely ill patients outside the designated critical care environment.

Laser-Based Slam with Efficient Occupancy Likelihood Map Learning for Dynamic Indoor Scenes

NASA Astrophysics Data System (ADS)

Li, Li; Yao, Jian; Xie, Renping; Tu, Jinge; Feng, Chen

2016-06-01

Location-Based Services (LBS) have attracted growing attention in recent years, especially in indoor environments. The fundamental technique of LBS is the map building for unknown environments, this technique also named as simultaneous localization and mapping (SLAM) in robotic society. In this paper, we propose a novel approach for SLAMin dynamic indoor scenes based on a 2D laser scanner mounted on a mobile Unmanned Ground Vehicle (UGV) with the help of the grid-based occupancy likelihood map. Instead of applying scan matching in two adjacent scans, we propose to match current scan with the occupancy likelihood map learned from all previous scans in multiple scales to avoid the accumulation of matching errors. Due to that the acquisition of the points in a scan is sequential but not simultaneous, there unavoidably exists the scan distortion at different extents. To compensate the scan distortion caused by the motion of the UGV, we propose to integrate a velocity of a laser range finder (LRF) into the scan matching optimization framework. Besides, to reduce the effect of dynamic objects such as walking pedestrians often existed in indoor scenes as much as possible, we propose a new occupancy likelihood map learning strategy by increasing or decreasing the probability of each occupancy grid after each scan matching. Experimental results in several challenged indoor scenes demonstrate that our proposed approach is capable of providing high-precision SLAM results.

Evaluation of linearly solvable Markov decision process with dynamic model learning in a mobile robot navigation task.

PubMed

Kinjo, Ken; Uchibe, Eiji; Doya, Kenji

2013-01-01

Linearly solvable Markov Decision Process (LMDP) is a class of optimal control problem in which the Bellman's equation can be converted into a linear equation by an exponential transformation of the state value function (Todorov, 2009b). In an LMDP, the optimal value function and the corresponding control policy are obtained by solving an eigenvalue problem in a discrete state space or an eigenfunction problem in a continuous state using the knowledge of the system dynamics and the action, state, and terminal cost functions. In this study, we evaluate the effectiveness of the LMDP framework in real robot control, in which the dynamics of the body and the environment have to be learned from experience. We first perform a simulation study of a pole swing-up task to evaluate the effect of the accuracy of the learned dynamics model on the derived the action policy. The result shows that a crude linear approximation of the non-linear dynamics can still allow solution of the task, despite with a higher total cost. We then perform real robot experiments of a battery-catching task using our Spring Dog mobile robot platform. The state is given by the position and the size of a battery in its camera view and two neck joint angles. The action is the velocities of two wheels, while the neck joints were controlled by a visual servo controller. We test linear and bilinear dynamic models in tasks with quadratic and Guassian state cost functions. In the quadratic cost task, the LMDP controller derived from a learned linear dynamics model performed equivalently with the optimal linear quadratic regulator (LQR). In the non-quadratic task, the LMDP controller with a linear dynamics model showed the best performance. The results demonstrate the usefulness of the LMDP framework in real robot control even when simple linear models are used for dynamics learning.

The Effect of Dynamic and Interactive Mathematics Learning Environments (DIMLE), Supporting Multiple Representations, on Perceptions of Elementary Mathematics Pre-Service Teachers in Problem Solving Process

ERIC Educational Resources Information Center

Ozdemir, S.; Reis, Z. Ayvaz

2013-01-01

Mathematics is an important discipline, providing crucial tools, such as problem solving, to improve our cognitive abilities. In order to solve a problem, it is better to envision and represent through multiple means. Multiple representations can help a person to redefine a problem with his/her own words in that envisioning process. Dynamic and…

Improving health care quality and safety: the role of collective learning.

PubMed

Singer, Sara J; Benzer, Justin K; Hamdan, Sami U

2015-01-01

Despite decades of effort to improve quality and safety in health care, this goal feels increasingly elusive. Successful examples of improvement are infrequently replicated. This scoping review synthesizes 76 empirical or conceptual studies (out of 1208 originally screened) addressing learning in quality or safety improvement, that were published in selected health care and management journals between January 2000 and December 2014 to deepen understanding of the role that collective learning plays in quality and safety improvement. We categorize learning activities using a theoretical model that shows how leadership and environmental factors support collective learning processes and practices, and in turn team and organizational improvement outcomes. By focusing on quality and safety improvement, our review elaborates the premise of learning theory that leadership, environment, and processes combine to create conditions that promote learning. Specifically, we found that learning for quality and safety improvement includes experimentation (including deliberate experimentation, improvisation, learning from failures, exploration, and exploitation), internal and external knowledge acquisition, performance monitoring and comparison, and training. Supportive learning environments are characterized by team characteristics like psychological safety, appreciation of differences, openness to new ideas social motivation, and team autonomy; team contextual factors including learning resources like time for reflection, access to knowledge, organizational capabilities; incentives; and organizational culture, strategy, and structure; and external environmental factors including institutional pressures, environmental dynamism and competitiveness and learning collaboratives. Lastly learning in the context of quality and safety improvement requires leadership that reinforces learning through actions and behaviors that affect people, such as coaching and trust building, and through influencing contextual factors, including providing resources, developing culture, and taking strategic actions that support improvement. Our review highlights the importance of leadership in both promoting a supportive learning environment and implementing learning processes.

Improving health care quality and safety: the role of collective learning

PubMed Central

Singer, Sara J; Benzer, Justin K; Hamdan, Sami U

2015-01-01

Despite decades of effort to improve quality and safety in health care, this goal feels increasingly elusive. Successful examples of improvement are infrequently replicated. This scoping review synthesizes 76 empirical or conceptual studies (out of 1208 originally screened) addressing learning in quality or safety improvement, that were published in selected health care and management journals between January 2000 and December 2014 to deepen understanding of the role that collective learning plays in quality and safety improvement. We categorize learning activities using a theoretical model that shows how leadership and environmental factors support collective learning processes and practices, and in turn team and organizational improvement outcomes. By focusing on quality and safety improvement, our review elaborates the premise of learning theory that leadership, environment, and processes combine to create conditions that promote learning. Specifically, we found that learning for quality and safety improvement includes experimentation (including deliberate experimentation, improvisation, learning from failures, exploration, and exploitation), internal and external knowledge acquisition, performance monitoring and comparison, and training. Supportive learning environments are characterized by team characteristics like psychological safety, appreciation of differences, openness to new ideas social motivation, and team autonomy; team contextual factors including learning resources like time for reflection, access to knowledge, organizational capabilities; incentives; and organizational culture, strategy, and structure; and external environmental factors including institutional pressures, environmental dynamism and competitiveness and learning collaboratives. Lastly learning in the context of quality and safety improvement requires leadership that reinforces learning through actions and behaviors that affect people, such as coaching and trust building, and through influencing contextual factors, including providing resources, developing culture, and taking strategic actions that support improvement. Our review highlights the importance of leadership in both promoting a supportive learning environment and implementing learning processes. PMID:29355197

A universal strategy for the creation of machine learning-based atomistic force fields

NASA Astrophysics Data System (ADS)

Huan, Tran Doan; Batra, Rohit; Chapman, James; Krishnan, Sridevi; Chen, Lihua; Ramprasad, Rampi

2017-09-01

Emerging machine learning (ML)-based approaches provide powerful and novel tools to study a variety of physical and chemical problems. In this contribution, we outline a universal strategy to create ML-based atomistic force fields, which can be used to perform high-fidelity molecular dynamics simulations. This scheme involves (1) preparing a big reference dataset of atomic environments and forces with sufficiently low noise, e.g., using density functional theory or higher-level methods, (2) utilizing a generalizable class of structural fingerprints for representing atomic environments, (3) optimally selecting diverse and non-redundant training datasets from the reference data, and (4) proposing various learning approaches to predict atomic forces directly (and rapidly) from atomic configurations. From the atomistic forces, accurate potential energies can then be obtained by appropriate integration along a reaction coordinate or along a molecular dynamics trajectory. Based on this strategy, we have created model ML force fields for six elemental bulk solids, including Al, Cu, Ti, W, Si, and C, and show that all of them can reach chemical accuracy. The proposed procedure is general and universal, in that it can potentially be used to generate ML force fields for any material using the same unified workflow with little human intervention. Moreover, the force fields can be systematically improved by adding new training data progressively to represent atomic environments not encountered previously.

Machine learning molecular dynamics for the simulation of infrared spectra.

PubMed

Gastegger, Michael; Behler, Jörg; Marquetand, Philipp

2017-10-01

Machine learning has emerged as an invaluable tool in many research areas. In the present work, we harness this power to predict highly accurate molecular infrared spectra with unprecedented computational efficiency. To account for vibrational anharmonic and dynamical effects - typically neglected by conventional quantum chemistry approaches - we base our machine learning strategy on ab initio molecular dynamics simulations. While these simulations are usually extremely time consuming even for small molecules, we overcome these limitations by leveraging the power of a variety of machine learning techniques, not only accelerating simulations by several orders of magnitude, but also greatly extending the size of systems that can be treated. To this end, we develop a molecular dipole moment model based on environment dependent neural network charges and combine it with the neural network potential approach of Behler and Parrinello. Contrary to the prevalent big data philosophy, we are able to obtain very accurate machine learning models for the prediction of infrared spectra based on only a few hundreds of electronic structure reference points. This is made possible through the use of molecular forces during neural network potential training and the introduction of a fully automated sampling scheme. We demonstrate the power of our machine learning approach by applying it to model the infrared spectra of a methanol molecule, n -alkanes containing up to 200 atoms and the protonated alanine tripeptide, which at the same time represents the first application of machine learning techniques to simulate the dynamics of a peptide. In all of these case studies we find an excellent agreement between the infrared spectra predicted via machine learning models and the respective theoretical and experimental spectra.

Probing Teachers' Lesson Planning: Promoting Metacognition

ERIC Educational Resources Information Center

Eilam, Billie

2017-01-01

Classrooms are complex systems, with dynamic interactions of different kinds among their composing varied elements. Such complex interactions lead to the system's unpredictable emergent learning behaviors. To support teachers' lesson planning and monitoring in the complex environment of classrooms, the present article examines the core…

Central Limit Theorem: New SOCR Applet and Demonstration Activity

ERIC Educational Resources Information Center

Dinov, Ivo D.; Christou, Nicholas; Sanchez, Juana

2008-01-01

Modern approaches for information technology based blended education utilize a variety of novel instructional, computational and network resources. Such attempts employ technology to deliver integrated, dynamically linked, interactive content and multi-faceted learning environments, which may facilitate student comprehension and information…

Mars Science Laboratory Sample Acquisition, Sample Processing and Handling: Subsystem Design and Test Challenges

NASA Technical Reports Server (NTRS)

Jandura, Louise

2010-01-01

The Sample Acquisition/Sample Processing and Handling subsystem for the Mars Science Laboratory is a highly-mechanized, Rover-based sampling system that acquires powdered rock and regolith samples from the Martian surface, sorts the samples into fine particles through sieving, and delivers small portions of the powder into two science instruments inside the Rover. SA/SPaH utilizes 17 actuated degrees-of-freedom to perform the functions needed to produce 5 sample pathways in support of the scientific investigation on Mars. Both hardware redundancy and functional redundancy are employed in configuring this sampling system so some functionality is retained even with the loss of a degree-of-freedom. Intentional dynamic environments are created to move sample while vibration isolators attenuate this environment at the sensitive instruments located near the dynamic sources. In addition to the typical flight hardware qualification test program, two additional types of testing are essential for this kind of sampling system: characterization of the intentionally-created dynamic environment and testing of the sample acquisition and processing hardware functions using Mars analog materials in a low pressure environment. The overall subsystem design and configuration are discussed along with some of the challenges, tradeoffs, and lessons learned in the areas of fault tolerance, intentional dynamic environments, and special testing

Memory Transformation Enhances Reinforcement Learning in Dynamic Environments.

PubMed

Santoro, Adam; Frankland, Paul W; Richards, Blake A

2016-11-30

Over the course of systems consolidation, there is a switch from a reliance on detailed episodic memories to generalized schematic memories. This switch is sometimes referred to as "memory transformation." Here we demonstrate a previously unappreciated benefit of memory transformation, namely, its ability to enhance reinforcement learning in a dynamic environment. We developed a neural network that is trained to find rewards in a foraging task where reward locations are continuously changing. The network can use memories for specific locations (episodic memories) and statistical patterns of locations (schematic memories) to guide its search. We find that switching from an episodic to a schematic strategy over time leads to enhanced performance due to the tendency for the reward location to be highly correlated with itself in the short-term, but regress to a stable distribution in the long-term. We also show that the statistics of the environment determine the optimal utilization of both types of memory. Our work recasts the theoretical question of why memory transformation occurs, shifting the focus from the avoidance of memory interference toward the enhancement of reinforcement learning across multiple timescales. As time passes, memories transform from a highly detailed state to a more gist-like state, in a process called "memory transformation." Theories of memory transformation speak to its advantages in terms of reducing memory interference, increasing memory robustness, and building models of the environment. However, the role of memory transformation from the perspective of an agent that continuously acts and receives reward in its environment is not well explored. In this work, we demonstrate a view of memory transformation that defines it as a way of optimizing behavior across multiple timescales. Copyright © 2016 the authors 0270-6474/16/3612228-15$15.00/0.

To Exist as a Case Manager Is to Constantly Change; to Be Successful, You Must Constantly Adapt.

PubMed

Tahan, Hussein M

Change is inevitable whether in personal or professional lives. Case management practice is always evolving on the basis of the dynamic nature of the U.S. health care environment. Effective case managers are those who possess an adaptive mind-set, recognize the importance to change to maintain success, and remain relevant. They also demonstrate a sense of accountability and responsibility for own learning, professional development, and acquisition of new skills and knowledge. This editorial discusses the nature of change and adaptation and presents key strategies for case managers to remain relevant and effective in dynamic practice environments.

Static force field representation of environments based on agents' nonlinear motions

NASA Astrophysics Data System (ADS)

Campo, Damian; Betancourt, Alejandro; Marcenaro, Lucio; Regazzoni, Carlo

2017-12-01

This paper presents a methodology that aims at the incremental representation of areas inside environments in terms of attractive forces. It is proposed a parametric representation of velocity fields ruling the dynamics of moving agents. It is assumed that attractive spots in the environment are responsible for modifying the motion of agents. A switching model is used to describe near and far velocity fields, which in turn are used to learn attractive characteristics of environments. The effect of such areas is considered radial over all the scene. Based on the estimation of attractive areas, a map that describes their effects in terms of their localizations, ranges of action, and intensities is derived in an online way. Information of static attractive areas is added dynamically into a set of filters that describes possible interactions between moving agents and an environment. The proposed approach is first evaluated on synthetic data; posteriorly, the method is applied on real trajectories coming from moving pedestrians in an indoor environment.

A meta-learning system based on genetic algorithms

NASA Astrophysics Data System (ADS)

Pellerin, Eric; Pigeon, Luc; Delisle, Sylvain

2004-04-01

The design of an efficient machine learning process through self-adaptation is a great challenge. The goal of meta-learning is to build a self-adaptive learning system that is constantly adapting to its specific (and dynamic) environment. To that end, the meta-learning mechanism must improve its bias dynamically by updating the current learning strategy in accordance with its available experiences or meta-knowledge. We suggest using genetic algorithms as the basis of an adaptive system. In this work, we propose a meta-learning system based on a combination of the a priori and a posteriori concepts. A priori refers to input information and knowledge available at the beginning in order to built and evolve one or more sets of parameters by exploiting the context of the system"s information. The self-learning component is based on genetic algorithms and neural Darwinism. A posteriori refers to the implicit knowledge discovered by estimation of the future states of parameters and is also applied to the finding of optimal parameters values. The in-progress research presented here suggests a framework for the discovery of knowledge that can support human experts in their intelligence information assessment tasks. The conclusion presents avenues for further research in genetic algorithms and their capability to learn to learn.

Free-energy and the brain

PubMed Central

Friston, Karl J.; Stephan, Klaas E.

2009-01-01

If one formulates Helmholtz’s ideas about perception in terms of modern-day theories one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts. Using constructs from statistical physics it can be shown that the problems of inferring what cause our sensory input and learning causal regularities in the sensorium can be resolved using exactly the same principles. Furthermore, inference and learning can proceed in a biologically plausible fashion. The ensuing scheme rests on Empirical Bayes and hierarchical models of how sensory information is generated. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of the brain’s organisation and responses. In this paper, we suggest that these perceptual processes are just one emergent property of systems that conform to a free-energy principle. The free-energy considered here represents a bound on the surprise inherent in any exchange with the environment, under expectations encoded by its state or configuration. A system can minimise free-energy by changing its configuration to change the way it samples the environment, or to change its expectations. These changes correspond to action and perception respectively and lead to an adaptive exchange with the environment that is characteristic of biological systems. This treatment implies that the system’s state and structure encode an implicit and probabilistic model of the environment. We will look at models entailed by the brain and how minimisation of free-energy can explain its dynamics and structure. PMID:19325932

Law of Large Numbers: The Theory, Applications and Technology-Based Education

ERIC Educational Resources Information Center

Dinov, Ivo D.; Christou, Nicolas; Gould, Robert

2009-01-01

Modern approaches for technology-based blended education utilize a variety of recently developed novel pedagogical, computational and network resources. Such attempts employ technology to deliver integrated, dynamically-linked, interactive-content and heterogeneous learning environments, which may improve student comprehension and information…

Faults and Fractures in the Subseafloor Environment tell a Different Story than They do at the Seafloor

NASA Astrophysics Data System (ADS)

Hayman, N. W.

2018-05-01

Planetary studies can benefit from a lesson learned in the research of Mid-Ocean Ridges, wherein the subsurface view of faulting and fracturing contrasts with surface observations, important for the dynamics and chemistry of hydrothermal systems.

"WGL," a Web Laboratory for Geometry

ERIC Educational Resources Information Center

Quaresma, Pedro; Santos, Vanda; Maric, Milena

2018-01-01

The role of information and communication technologies (ICT) in education is nowadays well recognised. The "Web Geometry Laboratory," is an e-learning, collaborative and adaptive, Web environment for geometry, integrating a well known dynamic geometry system. In a collaborative session, teachers and students, engaged in solving…

The Social Organization of Schooling

ERIC Educational Resources Information Center

Hedges, Larry V., Ed.; Schneider, Barbara, Ed.

2005-01-01

Schools are complex social settings where students, teachers, administrators, and parents interact to shape a child's educational experience. Any effort to improve educational outcomes for America's children requires a dynamic understanding of the environments in which children learn. In "The Social Organization of Schooling", editors Larry Hedges…

Pulse!! The Virtual Clinical Learning Lab and Center of Excellence

DTIC Science & Technology

2011-08-01

environments, physiological assets and case-authoring tools using state- of-the art technologies common to the videogame industry but here appropriated...interior processes (e.g., fluid dynamics) are beyond the current reach of the videogame industry. c. Concise Accomplishments (limit 200 words/170

Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis

PubMed Central

Dura-Bernal, S.; Neymotin, S. A.; Kerr, C. C.; Sivagnanam, S.; Majumdar, A.; Francis, J. T.; Lytton, W. W.

2017-01-01

Biomimetic simulation permits neuroscientists to better understand the complex neuronal dynamics of the brain. Embedding a biomimetic simulation in a closed-loop neuroprosthesis, which can read and write signals from the brain, will permit applications for amelioration of motor, psychiatric, and memory-related brain disorders. Biomimetic neuroprostheses require real-time adaptation to changes in the external environment, thus constituting an example of a dynamic data-driven application system. As model fidelity increases, so does the number of parameters and the complexity of finding appropriate parameter configurations. Instead of adapting synaptic weights via machine learning, we employed major biological learning methods: spike-timing dependent plasticity and reinforcement learning. We optimized the learning metaparameters using evolutionary algorithms, which were implemented in parallel and which used an island model approach to obtain sufficient speed. We employed these methods to train a cortical spiking model to utilize macaque brain activity, indicating a selected target, to drive a virtual musculoskeletal arm with realistic anatomical and biomechanical properties to reach to that target. The optimized system was able to reproduce macaque data from a comparable experimental motor task. These techniques can be used to efficiently tune the parameters of multiscale systems, linking realistic neuronal dynamics to behavior, and thus providing a useful tool for neuroscience and neuroprosthetics. PMID:29200477

Multi Car Elevator Control by using Learning Automaton

NASA Astrophysics Data System (ADS)

Shiraishi, Kazuaki; Hamagami, Tomoki; Hirata, Hironori

We study an adaptive control technique for multi car elevators (MCEs) by adopting learning automatons (LAs.) The MCE is a high performance and a near-future elevator system with multi shafts and multi cars. A strong point of the system is that realizing a large carrying capacity in small shaft area. However, since the operation is too complicated, realizing an efficient MCE control is difficult for top-down approaches. For example, “bunching up together" is one of the typical phenomenon in a simple traffic environment like the MCE. Furthermore, an adapting to varying environment in configuration requirement is a serious issue in a real elevator service. In order to resolve these issues, having an autonomous behavior is required to the control system of each car in MCE system, so that the learning automaton, as the solutions for this requirement, is supposed to be appropriate for the simple traffic control. First, we assign a stochastic automaton (SA) to each car control system. Then, each SA varies its stochastic behavior distributions for adapting to environment in which its policy is evaluated with each passenger waiting times. That is LA which learns the environment autonomously. Using the LA based control technique, the MCE operation efficiency is evaluated through simulation experiments. Results show the technique enables reducing waiting times efficiently, and we confirm the system can adapt to the dynamic environment.

Probabilistic reversal learning is impaired in Parkinson's disease

PubMed Central

Peterson, David A.; Elliott, Christian; Song, David D.; Makeig, Scott; Sejnowski, Terrence J.; Poizner, Howard

2009-01-01

In many everyday settings, the relationship between our choices and their potentially rewarding outcomes is probabilistic and dynamic. In addition, the difficulty of the choices can vary widely. Although a large body of theoretical and empirical evidence suggests that dopamine mediates rewarded learning, the influence of dopamine in probabilistic and dynamic rewarded learning remains unclear. We adapted a probabilistic rewarded learning task originally used to study firing rates of dopamine cells in primate substantia nigra pars compacta (Morris et al. 2006) for use as a reversal learning task with humans. We sought to investigate how the dopamine depletion in Parkinson's disease (PD) affects probabilistic reward learning and adaptation to a reversal in reward contingencies. Over the course of 256 trials subjects learned to choose the more favorable from among pairs of images with small or large differences in reward probabilities. During a subsequent otherwise identical reversal phase, the reward probability contingencies for the stimuli were reversed. Seventeen Parkinson's disease (PD) patients of mild to moderate severity were studied off of their dopaminergic medications and compared to 15 age-matched controls. Compared to controls, PD patients had distinct pre- and post-reversal deficiencies depending upon the difficulty of the choices they had to learn. The patients also exhibited compromised adaptability to the reversal. A computational model of the subjects’ trial-by-trial choices demonstrated that the adaptability was sensitive to the gain with which patients weighted pre-reversal feedback. Collectively, the results implicate the nigral dopaminergic system in learning to make choices in environments with probabilistic and dynamic reward contingencies. PMID:19628022

Nonparametric Online Learning Control for Soft Continuum Robot: An Enabling Technique for Effective Endoscopic Navigation

PubMed Central

Lee, Kit-Hang; Fu, Denny K.C.; Leong, Martin C.W.; Chow, Marco; Fu, Hing-Choi; Althoefer, Kaspar; Sze, Kam Yim; Yeung, Chung-Kwong

2017-01-01

Abstract Bioinspired robotic structures comprising soft actuation units have attracted increasing research interest. Taking advantage of its inherent compliance, soft robots can assure safe interaction with external environments, provided that precise and effective manipulation could be achieved. Endoscopy is a typical application. However, previous model-based control approaches often require simplified geometric assumptions on the soft manipulator, but which could be very inaccurate in the presence of unmodeled external interaction forces. In this study, we propose a generic control framework based on nonparametric and online, as well as local, training to learn the inverse model directly, without prior knowledge of the robot's structural parameters. Detailed experimental evaluation was conducted on a soft robot prototype with control redundancy, performing trajectory tracking in dynamically constrained environments. Advanced element formulation of finite element analysis is employed to initialize the control policy, hence eliminating the need for random exploration in the robot's workspace. The proposed control framework enabled a soft fluid-driven continuum robot to follow a 3D trajectory precisely, even under dynamic external disturbance. Such enhanced control accuracy and adaptability would facilitate effective endoscopic navigation in complex and changing environments. PMID:29251567

Nonparametric Online Learning Control for Soft Continuum Robot: An Enabling Technique for Effective Endoscopic Navigation.

PubMed

Lee, Kit-Hang; Fu, Denny K C; Leong, Martin C W; Chow, Marco; Fu, Hing-Choi; Althoefer, Kaspar; Sze, Kam Yim; Yeung, Chung-Kwong; Kwok, Ka-Wai

2017-12-01

Bioinspired robotic structures comprising soft actuation units have attracted increasing research interest. Taking advantage of its inherent compliance, soft robots can assure safe interaction with external environments, provided that precise and effective manipulation could be achieved. Endoscopy is a typical application. However, previous model-based control approaches often require simplified geometric assumptions on the soft manipulator, but which could be very inaccurate in the presence of unmodeled external interaction forces. In this study, we propose a generic control framework based on nonparametric and online, as well as local, training to learn the inverse model directly, without prior knowledge of the robot's structural parameters. Detailed experimental evaluation was conducted on a soft robot prototype with control redundancy, performing trajectory tracking in dynamically constrained environments. Advanced element formulation of finite element analysis is employed to initialize the control policy, hence eliminating the need for random exploration in the robot's workspace. The proposed control framework enabled a soft fluid-driven continuum robot to follow a 3D trajectory precisely, even under dynamic external disturbance. Such enhanced control accuracy and adaptability would facilitate effective endoscopic navigation in complex and changing environments.

Use of an automated learning management system to validate nursing competencies.

PubMed

Dumpe, Michelle L; Kanyok, Nancy; Hill, Kristin

2007-01-01

Maintaining nurse competencies in a dynamic environment is not an easy task and requires the use of resources already strained. An online learning management system was created, and 24 annual competencies were redesigned for online validation. As a result of this initiative, competencies have been standardized across many disciplines and are completed in a more timely manner, nurses and managers are more satisfied with this method of annual assessments, and cost savings have been realized.

Human Exposure to Dynamic Air Pollutants: Ozone in Airplanes and Ultrafine Particles in Homes

DTIC Science & Technology

2010-01-01

original source of my love of learning. All her grandchildren have had to tolerate, at least temporarily, being ignored while she was immersed in a book ...Indoor Air 17, 372-383. Klepeis NE, Nazaroff WW, 2006a. Modeling residential exposure to secondhand tobacco smoke. Atmospheric Environment 40, 4393...4407. Klepeis NE, Nazaroff WW, 2006b. Mitigating residential exposure to secondhand tobacco smoke. Atmospheric Environment 40, 4408-4422. 84

Distributed interactive virtual environments for collaborative experiential learning and training independent of distance over Internet2.

PubMed

Alverson, Dale C; Saiki, Stanley M; Jacobs, Joshua; Saland, Linda; Keep, Marcus F; Norenberg, Jeffrey; Baker, Rex; Nakatsu, Curtis; Kalishman, Summers; Lindberg, Marlene; Wax, Diane; Mowafi, Moad; Summers, Kenneth L; Holten, James R; Greenfield, John A; Aalseth, Edward; Nickles, David; Sherstyuk, Andrei; Haines, Karen; Caudell, Thomas P

2004-01-01

Medical knowledge and skills essential for tomorrow's healthcare professionals continue to change faster than ever before creating new demands in medical education. Project TOUCH (Telehealth Outreach for Unified Community Health) has been developing methods to enhance learning by coupling innovations in medical education with advanced technology in high performance computing and next generation Internet2 embedded in virtual reality environments (VRE), artificial intelligence and experiential active learning. Simulations have been used in education and training to allow learners to make mistakes safely in lieu of real-life situations, learn from those mistakes and ultimately improve performance by subsequent avoidance of those mistakes. Distributed virtual interactive environments are used over distance to enable learning and participation in dynamic, problem-based, clinical, artificial intelligence rules-based, virtual simulations. The virtual reality patient is programmed to dynamically change over time and respond to the manipulations by the learner. Participants are fully immersed within the VRE platform using a head-mounted display and tracker system. Navigation, locomotion and handling of objects are accomplished using a joy-wand. Distribution is managed via the Internet2 Access Grid using point-to-point or multi-casting connectivity through which the participants can interact. Medical students in Hawaii and New Mexico (NM) participated collaboratively in problem solving and managing of a simulated patient with a closed head injury in VRE; dividing tasks, handing off objects, and functioning as a team. Students stated that opportunities to make mistakes and repeat actions in the VRE were extremely helpful in learning specific principles. VRE created higher performance expectations and some anxiety among VRE users. VRE orientation was adequate but students needed time to adapt and practice in order to improve efficiency. This was also demonstrated successfully between Western Australia and UNM. We successfully demonstrated the ability to fully immerse participants in a distributed virtual environment independent of distance for collaborative team interaction in medical simulation designed for education and training. The ability to make mistakes in a safe environment is well received by students and has a positive impact on their understanding, as well as memory of the principles involved in correcting those mistakes. Bringing people together as virtual teams for interactive experiential learning and collaborative training, independent of distance, provides a platform for distributed "just-in-time" training, performance assessment and credentialing. Further validation is necessary to determine the potential value of the distributed VRE in knowledge transfer, improved future performance and should entail training participants to competence in using these tools.

SuperSchools: Education in the Information Age and Beyond.

ERIC Educational Resources Information Center

Ameritech Foundation, Chicago, IL.

This document discusses how improvements in the capabilities of the intelligent communications network are making new enhancements and advances available to educators, administrators, students, parents, and the community, focusing on the role of Ameritech. Modern technologies can create dynamic and appropriate learning environments for children…

Strategically Fostering Dynamic Interactive Environments

ERIC Educational Resources Information Center

Özgün-Koca, S. Asli

2016-01-01

The Common Core State Standards (CCSSI 2010) and NCTM's (2014) "Principles to Actions" agree that "for meaningful learning of mathematics, tools and technology must be indispensable features of the classroom . . . that support students in exploring mathematics as well as in making sense of concepts and procedures and engaging in…

Object-location training elicits an overlapping but temporally distinct transcriptional profile from contextual fear conditioning.

PubMed

Poplawski, Shane G; Schoch, Hannah; Wimmer, Mathieu; Hawk, Joshua D; Walsh, Jennifer L; Giese, Karl P; Abel, Ted

2014-12-01

Hippocampus-dependent learning is known to induce changes in gene expression, but information on gene expression differences between different learning paradigms that require the hippocampus is limited. The bulk of studies investigating RNA expression after learning use the contextual fear conditioning task, which couples a novel environment with a footshock. Although contextual fear conditioning has been useful in discovering gene targets, gene expression after spatial memory tasks has received less attention. In this study, we used the object-location memory task and studied gene expression at two time points after learning in a high-throughput manner using a microfluidic qPCR approach. We found that expression of the classic immediate-early genes changes after object-location training in a fashion similar to that observed after contextual fear conditioning. However, the temporal dynamics of gene expression are different between the two tasks, with object-location memory producing gene expression changes that last at least 2 hours. Our findings indicate that different training paradigms may give rise to distinct temporal dynamics of gene expression after learning. Copyright © 2014 Elsevier Inc. All rights reserved.

Object-Location Training Elicits an Overlapping but Temporally Distinct Transcriptional Profile from Contextual Fear Conditioning

PubMed Central

Wimmer, Mathieu; Hawk, Joshua D.; Walsh, Jennifer L.; Giese, Karl P.; Abel, Ted

2014-01-01

Hippocampus-dependent learning is known to induce changes in gene expression, but information on gene expression differences between different learning paradigms that require the hippocampus is limited. The bulk of studies investigating RNA expression after learning use the contextual fear conditioning task, which couples a novel environment with a footshock. Although contextual fear conditioning has been useful in discovering gene targets, gene expression after spatial memory tasks has received less attention. In this study, we used the object-location memory task and studied gene expression at two time points after learning in a high-throughput manner using a microfluidic qPCR approach. We found that expression of the classic immediate-early genes changes after object-location training in a fashion similar to that observed after contextual fear conditioning. However, the temporal dynamics of gene expression are different between the two tasks, with object-location memory producing gene expression changes that last at least 2 hours. Our findings indicate that different training paradigms may give rise to distinct temporal dynamics of gene expression after learning. PMID:25242102

Foreign language learning in immersive virtual environments

NASA Astrophysics Data System (ADS)

Chang, Benjamin; Sheldon, Lee; Si, Mei; Hand, Anton

2012-03-01

Virtual reality has long been used for training simulations in fields from medicine to welding to vehicular operation, but simulations involving more complex cognitive skills present new design challenges. Foreign language learning, for example, is increasingly vital in the global economy, but computer-assisted education is still in its early stages. Immersive virtual reality is a promising avenue for language learning as a way of dynamically creating believable scenes for conversational training and role-play simulation. Visual immersion alone, however, only provides a starting point. We suggest that the addition of social interactions and motivated engagement through narrative gameplay can lead to truly effective language learning in virtual environments. In this paper, we describe the development of a novel application for teaching Mandarin using CAVE-like VR, physical props, human actors and intelligent virtual agents, all within a semester-long multiplayer mystery game. Students travel (virtually) to China on a class field trip, which soon becomes complicated with intrigue and mystery surrounding the lost manuscript of an early Chinese literary classic. Virtual reality environments such as the Forbidden City and a Beijing teahouse provide the setting for learning language, cultural traditions, and social customs, as well as the discovery of clues through conversation in Mandarin with characters in the game.

Learning to perceive in the sensorimotor approach: Piaget’s theory of equilibration interpreted dynamically

PubMed Central

Di Paolo, Ezequiel Alejandro; Barandiaran, Xabier E.; Beaton, Michael; Buhrmann, Thomas

2014-01-01

Learning to perceive is faced with a classical paradox: if understanding is required for perception, how can we learn to perceive something new, something we do not yet understand? According to the sensorimotor approach, perception involves mastery of regular sensorimotor co-variations that depend on the agent and the environment, also known as the “laws” of sensorimotor contingencies (SMCs). In this sense, perception involves enacting relevant sensorimotor skills in each situation. It is important for this proposal that such skills can be learned and refined with experience and yet up to this date, the sensorimotor approach has had no explicit theory of perceptual learning. The situation is made more complex if we acknowledge the open-ended nature of human learning. In this paper we propose Piaget’s theory of equilibration as a potential candidate to fulfill this role. This theory highlights the importance of intrinsic sensorimotor norms, in terms of the closure of sensorimotor schemes. It also explains how the equilibration of a sensorimotor organization faced with novelty or breakdowns proceeds by re-shaping pre-existing structures in coupling with dynamical regularities of the world. This way learning to perceive is guided by the equilibration of emerging forms of skillful coping with the world. We demonstrate the compatibility between Piaget’s theory and the sensorimotor approach by providing a dynamical formalization of equilibration to give an explicit micro-genetic account of sensorimotor learning and, by extension, of how we learn to perceive. This allows us to draw important lessons in the form of general principles for open-ended sensorimotor learning, including the need for an intrinsic normative evaluation by the agent itself. We also explore implications of our micro-genetic account at the personal level. PMID:25126065

Learning from adaptive neural dynamic surface control of strict-feedback systems.

PubMed

Wang, Min; Wang, Cong

2015-06-01

Learning plays an essential role in autonomous control systems. However, how to achieve learning in the nonstationary environment for nonlinear systems is a challenging problem. In this paper, we present learning method for a class of n th-order strict-feedback systems by adaptive dynamic surface control (DSC) technology, which achieves the human-like ability of learning by doing and doing with learned knowledge. To achieve the learning, this paper first proposes stable adaptive DSC with auxiliary first-order filters, which ensures the boundedness of all the signals in the closed-loop system and the convergence of tracking errors in a finite time. With the help of DSC, the derivative of the filter output variable is used as the neural network (NN) input instead of traditional intermediate variables. As a result, the proposed adaptive DSC method reduces greatly the dimension of NN inputs, especially for high-order systems. After the stable DSC design, we decompose the stable closed-loop system into a series of linear time-varying perturbed subsystems. Using a recursive design, the recurrent property of NN input variables is easily verified since the complexity is overcome using DSC. Subsequently, the partial persistent excitation condition of the radial basis function NN is satisfied. By combining a state transformation, accurate approximations of the closed-loop system dynamics are recursively achieved in a local region along recurrent orbits. Then, the learning control method using the learned knowledge is proposed to achieve the closed-loop stability and the improved control performance. Simulation studies are performed to demonstrate the proposed scheme can not only reuse the learned knowledge to achieve the better control performance with the faster tracking convergence rate and the smaller tracking error but also greatly alleviate the computational burden because of reducing the number and complexity of NN input variables.

Real-time maneuver optimization of space-based robots in a dynamic environment: Theory and on-orbit experiments

NASA Astrophysics Data System (ADS)

Chamitoff, Gregory E.; Saenz-Otero, Alvar; Katz, Jacob G.; Ulrich, Steve; Morrell, Benjamin J.; Gibbens, Peter W.

2018-01-01

This paper presents the development of a real-time path-planning optimization approach to controlling the motion of space-based robots. The algorithm is capable of planning three dimensional trajectories for a robot to navigate within complex surroundings that include numerous static and dynamic obstacles, path constraints and performance limitations. The methodology employs a unique transformation that enables rapid generation of feasible solutions for complex geometries, making it suitable for application to real-time operations and dynamic environments. This strategy was implemented on the Synchronized Position Hold Engage Reorient Experimental Satellite (SPHERES) test-bed on the International Space Station (ISS), and experimental testing was conducted onboard the ISS during Expedition 17 by the first author. Lessons learned from the on-orbit tests were used to further refine the algorithm for future implementations.

Undergraduate nursing students' perspectives on clinical assessment at transition to practice.

PubMed

Wu, Xi Vivien; Wang, Wenru; Pua, Lay Hoon; Heng, Doreen Gek Noi; Enskär, Karin

2015-01-01

Assessment of clinical competence requires explicitly defined standards meeting the national standards of the nursing profession. This is a complex process because of the diverse nature of nursing practice. To explore the perceptions of final-year undergraduate nursing students regarding clinical assessment at transition to practice. An exploratory qualitative approach was adopted. Twenty-four students participated in three focus group discussions. Thematic analysis was conducted. Five themes emerged: the need for a valid and reliable clinical assessment tool, the need for a flexible style of reflection and specific feedback, the dynamic clinical learning environment, students' efforts in learning and assessment, and the unclear support system for preceptors. Workload, time, resource availability, adequate preparation of preceptors, and the provision of valid and reliable clinical assessment tools were deemed to influence the quality of students' clinical learning and assessment. Nursing leadership in hospitals and educational institutions has a joint responsibility in shaping the clinical learning environment and providing clinical assessments for the students.

The Effects of Educational Delivery Methods on Knowledge Retention

ERIC Educational Resources Information Center

Turner, Craig; Turner, Kyle Dean

2017-01-01

In today's dynamic learning environment, educational delivery methods have become increasingly diverse. Using a unique opportunity to assess three types of course delivery--face-to-face, interactive television (iTV), and purely online delivery--the authors look at both initial knowledge acquisition and the retention of this knowledge. The results…

High Thinking Processes (HTP): Elements of Curricula and Teaching Able-Learners.

ERIC Educational Resources Information Center

Kaniel, Shlomo

2002-01-01

This article discusses preparing able learners for the technologically dynamic future by teaching High Thinking Processes (HTP). It describes components of HTP and four main elements for developing HTP: well organized and justified curricula with appropriate tasks; metacognitive teaching; learning communities and challenging environments; and…

Dynamic Evaluation of the Multimedia Interface in Computer Supported Learning

ERIC Educational Resources Information Center

Zaidel, Mark

2007-01-01

As information technology applications become widespread in education, new innovations in computer systems and communication technologies stimulate changes in students' visual preferences. In a university environment each new cohort of students is more comfortable in the digital world, expecting that new technology will enhance teaching and…

Paintbrush of Discovery: Using Java Applets to Enhance Mathematics Education

ERIC Educational Resources Information Center

Eason, Ray; Heath, Garrett

2004-01-01

This article addresses the enhancement of the learning environment by using Java applets in the mathematics classroom. Currently, the first year mathematics program at the United States Military Academy involves one semester of modeling with discrete dynamical systems (DDS). Several faculty members from the Academy have integrated Java applets…

Sensory Cues, Visualization and Physics Learning

ERIC Educational Resources Information Center

Reiner, Miriam

2009-01-01

Bodily manipulations, such as juggling, suggest a well-synchronized physical interaction as if the person were a physics expert. The juggler uses "knowledge" that is rooted in bodily experience, to interact with the environment. Such enacted bodily knowledge is powerful, efficient, predictive, and relates to sensory perception of the dynamics of…

Change Detection, Multiple Controllers, and Dynamic Environments: Insights from the Brain

ERIC Educational Resources Information Center

Pearson, John M.; Platt, Michael L.

2013-01-01

Foundational studies in decision making focused on behavior as the most accessible and reliable data on which to build theories of choice. More recent work, however, has incorporated neural data to provide insights unavailable from behavior alone. Among other contributions, these studies have validated reinforcement learning models by…

From Discipline to Dynamic Pedagogy: A Re-Conceptualization of Classroom Management

ERIC Educational Resources Information Center

Davis, Jonathan Ryan

2017-01-01

The purpose of this article is to re-conceptualize the definition of classroom management, moving away from its traditional definition rooted in discipline and control toward a definition that focuses on the creation of a positive learning environment. Integrating innovative, culturally responsive classroom management theories, frameworks, and…

Classroom Learning Environments and the Mental Health of First Grade Children

ERIC Educational Resources Information Center

Milkie, Melissa A.; Warner, Catharine H.

2011-01-01

Sociological research focuses on how poverty, family, and neighborhood dynamics shape children's problems, but knowledge about how school is related to children's mental health is underdeveloped, despite its central presence in children's lives. Using a social structure and personality-stress contagion perspective, the authors use a nationally…

Effects of Virtual Manipulatives with Different Approaches on Students' Knowledge of Slope

ERIC Educational Resources Information Center

Demir, Mustafa

2018-01-01

Virtual Manipulatives (VMs) are computer-based, dynamic, and visual representations of mathematical concepts, provide interactive learning environments to advance mathematics instruction (Moyer et al., 2002). Despite their broad use, few research explored the integration of VMs into mathematics instruction (Moyer-Packenham & Westenskow, 2013).…

Dynamic User Modeling within a Game-Based ITS

ERIC Educational Resources Information Center

Snow, Erica L.

2015-01-01

Intelligent tutoring systems are adaptive learning environments designed to support individualized instruction. The adaptation embedded within these systems is often guided by user models that represent one or more aspects of students' domain knowledge, actions, or performance. The proposed project focuses on the development and testing of user…

Big data analytics : predicting traffic flow regimes from simulated connected vehicle messages using data analytics and machine learning.

DOT National Transportation Integrated Search

2016-12-25

The key objectives of this study were to: 1. Develop advanced analytical techniques that make use of a dynamically configurable connected vehicle message protocol to predict traffic flow regimes in near-real time in a virtual environment and examine ...

CLEANing the Reward: Counterfactual Actions to Remove Exploratory Action Noise in Multiagent Learning

NASA Technical Reports Server (NTRS)

HolmesParker, Chris; Taylor, Mathew E.; Tumer, Kagan; Agogino, Adrian

2014-01-01

Learning in multiagent systems can be slow because agents must learn both how to behave in a complex environment and how to account for the actions of other agents. The inability of an agent to distinguish between the true environmental dynamics and those caused by the stochastic exploratory actions of other agents creates noise in each agent's reward signal. This learning noise can have unforeseen and often undesirable effects on the resultant system performance. We define such noise as exploratory action noise, demonstrate the critical impact it can have on the learning process in multiagent settings, and introduce a reward structure to effectively remove such noise from each agent's reward signal. In particular, we introduce Coordinated Learning without Exploratory Action Noise (CLEAN) rewards and empirically demonstrate their benefits

Event detection and localization for small mobile robots using reservoir computing.

PubMed

Antonelo, E A; Schrauwen, B; Stroobandt, D

2008-08-01

Reservoir Computing (RC) techniques use a fixed (usually randomly created) recurrent neural network, or more generally any dynamic system, which operates at the edge of stability, where only a linear static readout output layer is trained by standard linear regression methods. In this work, RC is used for detecting complex events in autonomous robot navigation. This can be extended to robot localization tasks which are solely based on a few low-range, high-noise sensory data. The robot thus builds an implicit map of the environment (after learning) that is used for efficient localization by simply processing the input stream of distance sensors. These techniques are demonstrated in both a simple simulation environment and in the physically realistic Webots simulation of the commercially available e-puck robot, using several complex and even dynamic environments.

Behavior generation strategy of artificial behavioral system by self-learning paradigm for autonomous robot tasks

NASA Astrophysics Data System (ADS)

Dağlarli, Evren; Temeltaş, Hakan

2008-04-01

In this study, behavior generation and self-learning paradigms are investigated for the real-time applications of multi-goal mobile robot tasks. The method is capable to generate new behaviors and it combines them in order to achieve multi goal tasks. The proposed method is composed from three layers: Behavior Generating Module, Coordination Level and Emotion -Motivation Level. Last two levels use Hidden Markov models to manage dynamical structure of behaviors. The kinematics and dynamic model of the mobile robot with non-holonomic constraints are considered in the behavior based control architecture. The proposed method is tested on a four-wheel driven and four-wheel steered mobile robot with constraints in simulation environment and results are obtained successfully.

Designing Science Learning Environments That Support Emerging Bilingual Students to Problematize Electrical Phenomena

NASA Astrophysics Data System (ADS)

Suarez, Enrique A.

This dissertation investigates how emerging bilingual students make sense of natural phenomena through engaging in certain epistemic practices of science, and the elements of the learning environment that created those opportunities. Specifically, the dissertation focuses on how emerging bilingual students problematized electrical phenomena, like electric flow and electrical resistance, and how the design features of the environment (e.g., sequencing of activities, linguistic practices) may have supported students as they made sense of phenomena. The first study describes how for students presented and evaluated mechanistic models of electric flow, focusing specifically on how students identified and negotiated a disagreement between their explanatory models. The results from this study highlight the complexity of students' disagreements, not only because of the epistemological aspects related to presenting and evaluating knowledge, but also due to interpersonal dynamics and the discomfort associated with disagreeing with another person. The second study focuses on the design features of the learning environment that supported emerging bilingual students' investigations of electrical phenomena. The findings from this study highlight how a carefully designed set of activities, with the appropriate material resources (e.g., experimental tools), could support students to problematize electrical resistance. The third study describes how emerging bilingual students engaged in translanguaging practices and the contextual features of the learning environment that created and hindered opportunities for translanguaging. The findings from this study identify and articulate how emerging bilingual students engaged in translanguaging practices when problematizing electrical resistance, and strengthen the perspective that, in order to be equitable for emerging bilingual students, science learning environments need to act as translanguaging spaces. This dissertation makes three contributions to how science educators understand how elementary-aged emerging bilingual students learning science. First, I offer a detailed account of how emerging bilingual students engaged in epistemic practices to problematize electrical phenomena. Secondly, I argue learning environments need to create opportunities for emerging bilingual students to engage in productive epistemic work through leveraging multiple kinds of resources from their semiotic repertoires. Finally, this dissertation contributes to our understanding of how emerging bilingual students engage in translanguaging practices as they investigate and talk about the natural world.

Enhanced Learning Methodologies and the Implementation of an Identification Course

NASA Astrophysics Data System (ADS)

Guidorzi, Roberto

This paper proposes some considerations on the role played by information and communication technologies in the evolution of educational systems and describes the design philosophy and the realization of a basic course on dynamic system identification that relies on constructivist methodologies and on the use of e-learning environments. It reports also some of the opinions formulated by the students on the effectiveness of the available tools and on their role in acquiring proficiency in the application of identification techniques in modeling real processes.

The Value of Indirect Teaching Strategies in Enhancing Student-Coaches’ Learning Engagement

PubMed Central

Mesquita, Isabel; Coutinho, Patrícia; De Martin-Silva, Luciana; Parente, Bruno; Faria, Mário; Afonso, José

2015-01-01

This study aimed to examine the indirect teaching strategies adopted by a coach educator in terms of promoting student-coaches’ engagement in a positive and active learning environment. The participants were an expert coach educator and seven student-coaches from an academic coaching setting. A mix method approach was used to collect data. Whilst video-recording and participant observations were used to collect data from the lessons, focus groups were adopted to recall the perceptions of student-coaches. The results showed that indirect teaching strategies (i.e., asking questions, showing signs of autonomy by monitoring the pace at which they completed tasks and actively engaging in the search for solutions to tasks) implemented by the coach educator promoted a supportive and challenging learning environment which, in turn, encouraged student-coaches to be more actively involved in the lessons. Additionally, the affective aspects of the relationship established with student-coaches (tone of voice, gestures, facial expressions, eye contact, physical contact and humor) led them to feel confident in exposing their doubts and opinions, and in learning in a more autonomous manner. Moreover, the practical lessons proved to be crucial in helping student-coaches to reach broader and deeper forms of understanding by allowing the application of theory to coaching practice. In conclusion, this study reinforces the value of indirect teaching strategies to stimulate an active learning environment. It further highlights the value of practical learning environments to better prepare neophyte coaches for dealing with the complex and dynamic nature of their professional reality. Key points Both instructional and affective teaching indirect strategies used by the coach educator promoted a positive and challenging learning environment to student-coaches. The directness profile used by this coach educator (questioning, giving autonomy for problem solving and responsibility to regulate the learning tasks development) promoted the awareness and the ability of student-coaches to explore alternative solutions and self-regulate their own learning. Using humor, touch, gestures and tone of voice, the coach educator showed great care for student-coaches, which impacted positively on their enthusiasm, confidence and desire to be actively engaged in their own learning. PMID:26336354

Student Engagement: A Principle-Based Concept Analysis.

PubMed

Bernard, Jean S

2015-08-04

A principle-based concept analysis of student engagement was used to examine the state of the science across disciplines. Four major perspectives of philosophy of science guided analysis and provided a framework for study of interrelationships and integration of conceptual components which then resulted in formulation of a theoretical definition. Findings revealed student engagement as a dynamic reiterative process marked by positive behavioral, cognitive, and affective elements exhibited in pursuit of deep learning. This process is influenced by a broader sociocultural environment bound by contextual preconditions of self-investment, motivation, and a valuing of learning. Outcomes of student engagement include satisfaction, sense of well-being, and personal development. Findings of this analysis prove relevant to nursing education as faculty transition from traditional teaching paradigms, incorporate learner-centered strategies, and adopt innovative pedagogical methodologies. It lends support for curricula reform, development of more accurate evaluative measures, and creation of meaningful teaching-learning environments within the discipline.

Firing rate dynamics in the hippocampus induced by trajectory learning.

PubMed

Ji, Daoyun; Wilson, Matthew A

2008-04-30

The hippocampus is essential for spatial navigation, which may involve sequential learning. However, how the hippocampus encodes new sequences in familiar environments is unknown. To study the impact of novel spatial sequences on the activity of hippocampal neurons, we monitored hippocampal ensembles while rats learned to switch from two familiar trajectories to a new one in a familiar environment. Here, we show that this novel spatial experience induces two types of changes in firing rates, but not locations of hippocampal place cells. First, place-cell firing rates on the two familiar trajectories start to change before the actual behavioral switch to the new trajectory. Second, repeated exposure on the new trajectory is associated with an increased dependence of place-cell firing rates on immediate past locations. The result suggests that sequence encoding in the hippocampus may involve integration of information about the recent past into current state.

Firing Rate Dynamics in the Hippocampus Induced by Trajectory Learning

PubMed Central

Wilson, Matthew A.

2008-01-01

The hippocampus is essential for spatial navigation, which may involve sequential learning. However, how the hippocampus encodes new sequences in familiar environments is unknown. To study the impact of novel spatial sequences on the activity of hippocampal neurons, we monitored hippocampal ensembles while rats learned to switch from two familiar trajectories to a new one in a familiar environment. Here, we show that this novel spatial experience induces two types of changes in firing rates, but not locations of hippocampal place cells. First, place-cell firing rates on the two familiar trajectories start to change before the actual behavioral switch to the new trajectory. Second, repeated exposure on the new trajectory is associated with an increased dependence of place-cell firing rates on immediate past locations. The result suggests that sequence encoding in the hippocampus may involve integration of information about the recent past into current state. PMID:18448645

Multi-Instance Learning Models for Automated Support of Analysts in Simulated Surveillance Environments

NASA Technical Reports Server (NTRS)

Birisan, Mihnea; Beling, Peter

2011-01-01

New generations of surveillance drones are being outfitted with numerous high definition cameras. The rapid proliferation of fielded sensors and supporting capacity for processing and displaying data will translate into ever more capable platforms, but with increased capability comes increased complexity and scale that may diminish the usefulness of such platforms to human operators. We investigate methods for alleviating strain on analysts by automatically retrieving content specific to their current task using a machine learning technique known as Multi-Instance Learning (MIL). We use MIL to create a real time model of the analysts' task and subsequently use the model to dynamically retrieve relevant content. This paper presents results from a pilot experiment in which a computer agent is assigned analyst tasks such as identifying caravanning vehicles in a simulated vehicle traffic environment. We compare agent performance between MIL aided trials and unaided trials.

Lifelong learning in nursing: a Delphi study.

PubMed

Davis, Lisa; Taylor, Heidi; Reyes, Helen

2014-03-01

In order to foster a culture of lifelong learning in nursing, it is important to identify what the concept means in the nursing profession as well as the characteristics of a lifelong learner. The purpose of this Delphi study was to conceptualize lifelong learning from the perspective of nursing, and to identify characteristics and essential elements of lifelong learning. A Delphi Study technique in three phases was completed using an online survey tool. Data were analyzed for conceptual description, ratings of characteristics and attributes, and expert consensus in these three phases. An online survey tool was used in this study. Recognized experts in nursing education, administration and public policy participated in this study. Lifelong learning in nursing is defined as a dynamic process, which encompasses both personal and professional life. This learning process is also both formal and informal. Lifelong learning involves seeking and appreciating new worlds or ideas in order to gain a new perspective as well as questioning one's environment, knowledge, skills and interactions. The most essential characteristics of a lifelong learner are reflection, questioning, enjoying learning, understanding the dynamic nature of knowledge, and engaging in learning by actively seeking learning opportunities. Keeping the mind active is essential to both lifelong learning and being able to translate knowledge into the capacity to deliver high quality nursing care. It is hoped that a clearer understanding of lifelong learning in nursing will foster more discussion and research about intentional, active inclusion of lifelong learning behaviors in nursing curricula. Copyright © 2013 Elsevier Ltd. All rights reserved.

Learning to perceive in the sensorimotor approach: Piaget's theory of equilibration interpreted dynamically.

PubMed

Di Paolo, Ezequiel Alejandro; Barandiaran, Xabier E; Beaton, Michael; Buhrmann, Thomas

2014-01-01

if understanding is required for perception, how can we learn to perceive something new, something we do not yet understand? According to the sensorimotor approach, perception involves mastery of regular sensorimotor co-variations that depend on the agent and the environment, also known as the "laws" of sensorimotor contingencies (SMCs). In this sense, perception involves enacting relevant sensorimotor skills in each situation. It is important for this proposal that such skills can be learned and refined with experience and yet up to this date, the sensorimotor approach has had no explicit theory of perceptual learning. The situation is made more complex if we acknowledge the open-ended nature of human learning. In this paper we propose Piaget's theory of equilibration as a potential candidate to fulfill this role. This theory highlights the importance of intrinsic sensorimotor norms, in terms of the closure of sensorimotor schemes. It also explains how the equilibration of a sensorimotor organization faced with novelty or breakdowns proceeds by re-shaping pre-existing structures in coupling with dynamical regularities of the world. This way learning to perceive is guided by the equilibration of emerging forms of skillful coping with the world. We demonstrate the compatibility between Piaget's theory and the sensorimotor approach by providing a dynamical formalization of equilibration to give an explicit micro-genetic account of sensorimotor learning and, by extension, of how we learn to perceive. This allows us to draw important lessons in the form of general principles for open-ended sensorimotor learning, including the need for an intrinsic normative evaluation by the agent itself. We also explore implications of our micro-genetic account at the personal level.

Inter-level Scaffolding and Sequences of Representational Activities in Teaching a Chemical System with Graphical Simulations

NASA Astrophysics Data System (ADS)

Li, Na; Black, John B.

2016-10-01

Chemistry knowledge can be represented at macro-, micro- and symbolic levels, and learning a chemistry topic requires students to engage in multiple representational activities. This study focused on scaffolding for inter-level connection-making in learning chemistry knowledge with graphical simulations. We also tested whether different sequences of representational activities produced different student learning outcomes in learning a chemistry topic. A sample of 129 seventh graders participated in this study. In a simulation-based environment, participants completed three representational activities to learn several ideal gas law concepts. We conducted a 2 × 3 factorial design experiment. We compared two scaffolding conditions: (1) the inter- level scaffolding condition in which participants received inter-level questions and experienced the dynamic link function in the simulation-based environment and (2) the intra- level scaffolding condition in which participants received intra-level questions and did not experience the dynamic link function. We also compared three different sequences of representational activities: macro-symbolic-micro, micro-symbolic-macro and symbolic-micro-macro. For the scaffolding variable, we found that the inter- level scaffolding condition produced significantly better performance in both knowledge comprehension and application, compared to the intra- level scaffolding condition. For the sequence variable, we found that the macro-symbolic-micro sequence produced significantly better knowledge comprehension performance than the other two sequences; however, it did not benefit knowledge application performance. There was a trend that the treatment group who experienced inter- level scaffolding and the micro-symbolic-macro sequence achieved the best knowledge application performance.

Space Vehicle Terrestrial Environment Design Requirements Guidelines

NASA Technical Reports Server (NTRS)

Johnson, Dale L.; Keller, Vernon W.; Vaughan, William W.

2006-01-01

The terrestrial environment is an important driver of space vehicle structural, control, and thermal system design. NASA is currently in the process of producing an update to an earlier Terrestrial Environment Guidelines for Aerospace Vehicle Design and Development Handbook. This paper addresses the contents of this updated handbook, with special emphasis on new material being included in the areas of atmospheric thermodynamic models, wind dynamics, atmospheric composition, atmospheric electricity, cloud phenomena, atmospheric extremes, and sea state. In addition, the respective engineering design elements are discussed relative to terrestrial environment inputs that require consideration. Specific lessons learned that have contributed to the advancements made in the application and awareness of terrestrial environment inputs for aerospace engineering applications are presented.

The Neural Correlates of Hierarchical Predictions for Perceptual Decisions.

PubMed

Weilnhammer, Veith A; Stuke, Heiner; Sterzer, Philipp; Schmack, Katharina

2018-05-23

Sensory information is inherently noisy, sparse, and ambiguous. In contrast, visual experience is usually clear, detailed, and stable. Bayesian theories of perception resolve this discrepancy by assuming that prior knowledge about the causes underlying sensory stimulation actively shapes perceptual decisions. The CNS is believed to entertain a generative model aligned to dynamic changes in the hierarchical states of our volatile sensory environment. Here, we used model-based fMRI to study the neural correlates of the dynamic updating of hierarchically structured predictions in male and female human observers. We devised a crossmodal associative learning task with covertly interspersed ambiguous trials in which participants engaged in hierarchical learning based on changing contingencies between auditory cues and visual targets. By inverting a Bayesian model of perceptual inference, we estimated individual hierarchical predictions, which significantly biased perceptual decisions under ambiguity. Although "high-level" predictions about the cue-target contingency correlated with activity in supramodal regions such as orbitofrontal cortex and hippocampus, dynamic "low-level" predictions about the conditional target probabilities were associated with activity in retinotopic visual cortex. Our results suggest that our CNS updates distinct representations of hierarchical predictions that continuously affect perceptual decisions in a dynamically changing environment. SIGNIFICANCE STATEMENT Bayesian theories posit that our brain entertains a generative model to provide hierarchical predictions regarding the causes of sensory information. Here, we use behavioral modeling and fMRI to study the neural underpinnings of such hierarchical predictions. We show that "high-level" predictions about the strength of dynamic cue-target contingencies during crossmodal associative learning correlate with activity in orbitofrontal cortex and the hippocampus, whereas "low-level" conditional target probabilities were reflected in retinotopic visual cortex. Our findings empirically corroborate theorizations on the role of hierarchical predictions in visual perception and contribute substantially to a longstanding debate on the link between sensory predictions and orbitofrontal or hippocampal activity. Our work fundamentally advances the mechanistic understanding of perceptual inference in the human brain. Copyright © 2018 the authors 0270-6474/18/385008-14$15.00/0.

Sex differences in the inference and perception of causal relations within a video game

PubMed Central

Young, Michael E.

2014-01-01

The learning of immediate causation within a dynamic environment was examined. Participants encountered seven decision points in which they needed to choose, which of three possible candidates was the cause of explosions in the environment. Each candidate was firing a weapon at random every few seconds, but only one of them produced an immediate effect. Some participants showed little learning, but most demonstrated increases in accuracy across time. On average, men showed higher accuracy and shorter latencies that were not explained by differences in self-reported prior video game experience. This result suggests that prior reports of sex differences in causal choice in the game are not specific to situations involving delayed or probabilistic causal relations. PMID:25202293

Sex differences in the inference and perception of causal relations within a video game.

PubMed

Young, Michael E

2014-01-01

The learning of immediate causation within a dynamic environment was examined. Participants encountered seven decision points in which they needed to choose, which of three possible candidates was the cause of explosions in the environment. Each candidate was firing a weapon at random every few seconds, but only one of them produced an immediate effect. Some participants showed little learning, but most demonstrated increases in accuracy across time. On average, men showed higher accuracy and shorter latencies that were not explained by differences in self-reported prior video game experience. This result suggests that prior reports of sex differences in causal choice in the game are not specific to situations involving delayed or probabilistic causal relations.

Behavioural variation in 172 small-scale societies indicates that social learning is the main mode of human adaptation

PubMed Central

Mathew, Sarah; Perreault, Charles

2015-01-01

The behavioural variation among human societies is vast and unmatched in the animal world. It is unclear whether this variation is due to variation in the ecological environment or to differences in cultural traditions. Underlying this debate is a more fundamental question: is the richness of humans’ behavioural repertoire due to non-cultural mechanisms, such as causal reasoning, inventiveness, reaction norms, trial-and-error learning and evoked culture, or is it due to the population-level dynamics of cultural transmission? Here, we measure the relative contribution of environment and cultural history in explaining the behavioural variation of 172 Native American tribes at the time of European contact. We find that the effect of cultural history is typically larger than that of environment. Behaviours also persist over millennia within cultural lineages. This indicates that human behaviour is not predominantly determined by single-generation adaptive responses, contra theories that emphasize non-cultural mechanisms as determinants of human behaviour. Rather, the main mode of human adaptation is social learning mechanisms that operate over multiple generations. PMID:26085589

Decision-making dynamics in parasitoids of Drosophila.

PubMed

Thiel, Andra; Hoffmeister, Thomas S

2009-01-01

Drosophilids and their associated parasitoids live in environments that vary in resource availability and quality within and between generations. The use of information to adapt behavior to the current environment is a key feature under such circumstances and Drosophila parasitic wasps are excellent model systems to study learning and information use. They are among the few parasitoid model species that have been tested in a wide array of situations. Moreover, several related species have been tested under similar conditions, allowing the analysis of within and between species variability, the effect of natural selection in a typical environment, the current physiological status, and previous experience of the individual. This holds for host habitat and host location as well as for host choice and search time allocation. Here, we review patterns of learning and memory, of information use and updating mechanisms, and we point out that information use itself is under strong selective pressure and thus, optimized by parasitic wasps.

A "Sweet 16" of Rules About Teamwork

NASA Technical Reports Server (NTRS)

Laufer, Alexander (Editor)

2002-01-01

The following "Sweet 16" rules included in this paper derive from a longer paper by APPL Director Dr. Edward Hoffman and myself entitled " 99 Rules for Managing Faster, Better, Cheaper Projects." Our sources consisted mainly of "war stories" told by master project managers in my book Simultaneous Management: Managing Projects in a Dynamic Environment (AMACOM, The American Management Association, 1996). The Simultaneous Management model was a result of 10 years of intensive research and testing conducted with the active participation of master project managers from leading private organizations such as AT&T, DuPont, Exxon, General Motors, IBM, Motorola and Procter & Gamble. In a more recent study, led by Dr. Hoffman, we learned that master project managers in leading public organizations employ most of these rules as well. Both studies, in private and public organizations, found that a dynamic environment calls for dynamic management, and that is especially clear in how successful project managers think about their teams.

And So It Grows: Using a Computer-Based Simulation of a Population Growth Model to Integrate Biology & Mathematics

ERIC Educational Resources Information Center

Street, Garrett M.; Laubach, Timothy A.

2013-01-01

We provide a 5E structured-inquiry lesson so that students can learn more of the mathematics behind the logistic model of population biology. By using models and mathematics, students understand how population dynamics can be influenced by relatively simple changes in the environment.

Affinities and beyond! Developing Ways of Seeing in Online Spaces

ERIC Educational Resources Information Center

Davies, Julia

2006-01-01

This article presents an insider view of an online community of adults involved in sharing digital photography through a host website, Flickr. It describes how reciprocal teaching and learning partnerships in a dynamic multimodal environment are achieved through the creation of a "Third Space" or "Affinity Space", where "Funds of Knowledge" are…

A DGS Gesture Dictionary for Modelling on Mobile Devices

ERIC Educational Resources Information Center

Isotani, Seiji; Reis, Helena M.; Alvares, Danilo; Brandão, Anarosa A. F.; Brandão, Leônidas O.

2018-01-01

Interactive or Dynamic Geometry System (DGS) is a tool that help to teach and learn geometry using a computer-based interactive environment. Traditionally, the interaction with DGS is based on keyboard and mouse events where the functionalities are accessed using a menu of icons. Nevertheless, recent findings suggest that such a traditional model…

The Impact of Humanities-Based Teaching and Learning Strategies on Critical Thinking and Clinical Reasoning Development among BSN Students

ERIC Educational Resources Information Center

Brodhead, Josette

2016-01-01

The ability to function effectively in a dynamic, culturally diverse healthcare environment requires both critical thinking and clinical reasoning skills. The American Association of Colleges of Nursing (AACN, 2008) recognizes the importance of humanities in the baccalaureate nursing curriculum. This quasi-experimental, nonrandomized…

Collection Directions: The Evolution of Library Collections and Collecting

ERIC Educational Resources Information Center

Dempsey, Lorcan; Malpas, Constance; Lavoie, Brian

2014-01-01

This article takes a broad view of the evolution of collecting behaviors in a network environment and suggests some future directions based on various simple models. The authors look at the changing dynamics of print collections, at the greater engagement with research and learning behaviors, and at trends in scholarly communication. The goal is…

Visual NNet: An Educational ANN's Simulation Environment Reusing Matlab Neural Networks Toolbox

ERIC Educational Resources Information Center

Garcia-Roselló, Emilio; González-Dacosta, Jacinto; Lado, Maria J.; Méndez, Arturo J.; Garcia Pérez-Schofield, Baltasar; Ferrer, Fátima

2011-01-01

Artificial Neural Networks (ANN's) are nowadays a common subject in different curricula of graduate and postgraduate studies. Due to the complex algorithms involved and the dynamic nature of ANN's, simulation software has been commonly used to teach this subject. This software has usually been developed specifically for learning purposes, because…

Dynamic Group Formation as an Approach to Collaborative Learning Support

ERIC Educational Resources Information Center

Srba, Ivan; Bielikova, Maria

2015-01-01

In the current time of globalization, collaboration among people in virtual environments is becoming an important precondition of success. This trend is reflected also in the educational domain where students collaborate in various short-term groups created repetitively but changing in each round (e.g. in MOOCs). Students in these kind of dynamic…

Leadership and Storytelling: Promoting a Culture of Learning, Positive Change, and Community

ERIC Educational Resources Information Center

Aidman, Barry; Long, Tanya Alyson

2017-01-01

Educational leaders work in increasingly complex, high pressure environments with people who have diverse backgrounds, interests, and goals. To be effective, these leaders must understand the dynamic process of creating and managing culture and change. Stories have the potential to influence culture and to help people connect, develop genuine…

Sociotechnical Systems Approach: An Internal Assessment of a Blended Doctoral Program

ERIC Educational Resources Information Center

Erichsen, Elizabeth Anne; DeLorme, Lyn; Connelley, Rosalinda; Okurut-Ibore, Christine; McNamara, Lisa; Aljohani, Obaidalah

2013-01-01

An internal assessment was conducted utilizing a sociotechnical systems approach and cultural lens as a means of exploring the dynamics of a blended doctoral program. Blended learning environments were conceived of as sociotechnical systems, and blended programs were defined as programs that utilize multimodal means for the mediation of…

Culture, Role and Group Work: A Social Network Analysis Perspective on an Online Collaborative Course

ERIC Educational Resources Information Center

Stepanyan, Karen; Mather, Richard; Dalrymple, Roger

2014-01-01

This paper discusses the patterns of network dynamics within a multicultural online collaborative learning environment. It analyses the interaction of participants (both students and facilitators) within a discussion board that was established as part of a 3-month online collaborative course. The study employs longitudinal probabilistic social…

Evaluation of Theoretical and Empirical Characteristics of the Communication, Language, and Statistics Survey (CLASS)

ERIC Educational Resources Information Center

Wagler, Amy E.; Lesser, Lawrence M.

2018-01-01

The interaction between language and the learning of statistical concepts has been receiving increased attention. The Communication, Language, And Statistics Survey (CLASS) was developed in response to the need to focus on dynamics of language in light of the culturally and linguistically diverse environments of introductory statistics classrooms.…

The Use of the Microcomposter to Study the Dynamics of a Mini-Ecosystem

ERIC Educational Resources Information Center

Stoeber, Rodelyn Padua; Saurette, Fernand; Dubois-Jacques, Daniele; Gravel, Deny

2010-01-01

Compost bins are beginning to make their way into home gardens and classrooms, allowing students to actively participate in greening their environment. However, do they really understand the process of composting? According to the "National Science Education Standards" (National Research Council [NRC] 1996, 20), "Learning science is something…

Building interactive virtual environments for simulated training in medicine using VRML and Java/JavaScript.

PubMed

Korocsec, D; Holobar, A; Divjak, M; Zazula, D

2005-12-01

Medicine is a difficult thing to learn. Experimenting with real patients should not be the only option; simulation deserves a special attention here. Virtual Reality Modelling Language (VRML) as a tool for building virtual objects and scenes has a good record of educational applications in medicine, especially for static and animated visualisations of body parts and organs. However, to create computer simulations resembling situations in real environments the required level of interactivity and dynamics is difficult to achieve. In the present paper we describe some approaches and techniques which we used to push the limits of the current VRML technology further toward dynamic 3D representation of virtual environments (VEs). Our demonstration is based on the implementation of a virtual baby model, whose vital signs can be controlled from an external Java application. The main contributions of this work are: (a) outline and evaluation of the three-level VRML/Java implementation of the dynamic virtual environment, (b) proposal for a modified VRML Timesensor node, which greatly improves the overall control of system performance, and (c) architecture of the prototype distributed virtual environment for training in neonatal resuscitation comprising the interactive virtual newborn, active bedside monitor for vital signs and full 3D representation of the surgery room.

Q-Learning and p-persistent CSMA based rendezvous protocol for cognitive radio networks operating with shared spectrum activity

NASA Astrophysics Data System (ADS)

Watson, Clifton L.; Biswas, Subir

2014-06-01

With an increasing demand for spectrum, dynamic spectrum access (DSA) has been proposed as viable means for providing the flexibility and greater access to spectrum necessary to meet this demand. Within the DSA concept, unlicensed secondary users temporarily "borrow" or access licensed spectrum, while respecting the licensed primary user's rights to that spectrum. As key enablers for DSA, cognitive radios (CRs) are based on software-defined radios which allow them to sense, learn, and adapt to the spectrum environment. These radios can operate independently and rapidly switch channels. Thus, the initial setup and maintenance of cognitive radio networks are dependent upon the ability of CR nodes to find each other, in a process known as rendezvous, and create a link on a common channel for the exchange of data and control information. In this paper, we propose a novel rendezvous protocol, known as QLP, which is based on Q-learning and the p-persistent CSMA protocol. With the QLP protocol, CR nodes learn which channels are best for rendezvous and thus adapt their behavior to visit those channels more frequently. We demonstrate through simulation that the QLP protocol provides a rendevous capability for DSA environments with different dynamics of PU activity, while attempting to achieve the following performance goals: (1) minimize the average time-to-rendezvous, (2) maximize system throughput, (3) minimize primary user interference, and (4) minimize collisions among CR nodes.

An efficient incremental learning mechanism for tracking concept drift in spam filtering

PubMed Central

Sheu, Jyh-Jian; Chu, Ko-Tsung; Li, Nien-Feng; Lee, Cheng-Chi

2017-01-01

This research manages in-depth analysis on the knowledge about spams and expects to propose an efficient spam filtering method with the ability of adapting to the dynamic environment. We focus on the analysis of email’s header and apply decision tree data mining technique to look for the association rules about spams. Then, we propose an efficient systematic filtering method based on these association rules. Our systematic method has the following major advantages: (1) Checking only the header sections of emails, which is different from those spam filtering methods at present that have to analyze fully the email’s content. Meanwhile, the email filtering accuracy is expected to be enhanced. (2) Regarding the solution to the problem of concept drift, we propose a window-based technique to estimate for the condition of concept drift for each unknown email, which will help our filtering method in recognizing the occurrence of spam. (3) We propose an incremental learning mechanism for our filtering method to strengthen the ability of adapting to the dynamic environment. PMID:28182691

Supporting students' knowledge integration with technology-enhanced inquiry curricula

NASA Astrophysics Data System (ADS)

Chiu, Jennifer Lopseen

Dynamic visualizations of scientific phenomena have the potential to transform how students learn and understand science. Dynamic visualizations enable interaction and experimentation with unobservable atomic-level phenomena. A series of studies clarify the conditions under which embedding dynamic visualizations in technology-enhanced inquiry instruction can help students develop robust and durable chemistry knowledge. Using the knowledge integration perspective, I designed Chemical Reactions, a technology-enhanced curriculum unit, with a partnership of teachers, educational researchers, and chemists. This unit guides students in an exploration of how energy and chemical reactions relate to climate change. It uses powerful dynamic visualizations to connect atomic level interactions to the accumulation of greenhouse gases. The series of studies were conducted in typical classrooms in eleven high schools across the country. This dissertation describes four studies that contribute to understanding of how visualizations can be used to transform chemistry learning. The efficacy study investigated the impact of the Chemical Reactions unit compared to traditional instruction using pre-, post- and delayed posttest assessments. The self-monitoring study used self-ratings in combination with embedded assessments to explore how explanation prompts help students learn from dynamic visualizations. The self-regulation study used log files of students' interactions with the learning environment to investigate how external feedback and explanation prompts influence students' exploration of dynamic visualizations. The explanation study compared specific and general explanation prompts to explore the processes by which explanations benefit learning with dynamic visualizations. These studies delineate the conditions under which dynamic visualizations embedded in inquiry instruction can enhance student outcomes. The studies reveal that visualizations can be deceptively clear, deterring learners from exploring details. Asking students to generate explanations helps them realize what they don't understand and can spur students to revisit visualizations to remedy gaps in their knowledge. The studies demonstrate that science instruction focused on complex topics can succeed by combining visualizations with generative activities to encourage knowledge integration. Students are more successful at monitoring their progress and remedying gaps in knowledge when required to distinguish among alternative explanations. The results inform the design of technology-enhanced science instruction for typical classrooms.

Effects of learning climate and registered nurse staffing on medication errors.

PubMed

Chang, Yunkyung; Mark, Barbara

2011-01-01

Despite increasing recognition of the significance of learning from errors, little is known about how learning climate contributes to error reduction. The purpose of this study was to investigate whether learning climate moderates the relationship between error-producing conditions and medication errors. A cross-sectional descriptive study was done using data from 279 nursing units in 146 randomly selected hospitals in the United States. Error-producing conditions included work environment factors (work dynamics and nurse mix), team factors (communication with physicians and nurses' expertise), personal factors (nurses' education and experience), patient factors (age, health status, and previous hospitalization), and medication-related support services. Poisson models with random effects were used with the nursing unit as the unit of analysis. A significant negative relationship was found between learning climate and medication errors. It also moderated the relationship between nurse mix and medication errors: When learning climate was negative, having more registered nurses was associated with fewer medication errors. However, no relationship was found between nurse mix and medication errors at either positive or average levels of learning climate. Learning climate did not moderate the relationship between work dynamics and medication errors. The way nurse mix affects medication errors depends on the level of learning climate. Nursing units with fewer registered nurses and frequent medication errors should examine their learning climate. Future research should be focused on the role of learning climate as related to the relationships between nurse mix and medication errors.

Necessary Contributions of Human Frontal Lobe Subregions to Reward Learning in a Dynamic, Multidimensional Environment.

PubMed

Vaidya, Avinash R; Fellows, Lesley K

2016-09-21

Real-world decisions are typically made between options that vary along multiple dimensions, requiring prioritization of the important dimensions to support optimal choice. Learning in this setting depends on attributing decision outcomes to the dimensions with predictive relevance rather than to dimensions that are irrelevant and nonpredictive. This attribution problem is computationally challenging, and likely requires an interplay between selective attention and reward learning. Both these processes have been separately linked to the prefrontal cortex, but little is known about how they combine to support learning the reward value of multidimensional stimuli. Here, we examined the necessary contributions of frontal lobe subregions in attributing feedback to relevant and irrelevant dimensions on a trial-by-trial basis in humans. Patients with focal frontal lobe damage completed a demanding reward learning task where options varied on three dimensions, only one of which predicted reward. Participants with left lateral frontal lobe damage attributed rewards to irrelevant dimensions, rather than the relevant dimension. Damage to the ventromedial frontal lobe also impaired learning about the relevant dimension, but did not increase reward attribution to irrelevant dimensions. The results argue for distinct roles for these two regions in learning the value of multidimensional decision options under dynamic conditions, with the lateral frontal lobe required for selecting the relevant dimension to associate with reward, and the ventromedial frontal lobe required to learn the reward association itself. The real world is complex and multidimensional; how do we attribute rewards to predictive features when surrounded by competing cues? Here, we tested the critical involvement of human frontal lobe subregions in a probabilistic, multidimensional learning environment, asking whether focal lesions affected trial-by-trial attribution of feedback to relevant and irrelevant dimensions. The left lateral frontal lobe was required for filtering option dimensions to allow appropriate feedback attribution, while the ventromedial frontal lobe was necessary for learning the value of features in the relevant dimension. These findings argue that selective attention and associative learning processes mediated by anatomically distinct frontal lobe subregions are both critical for adaptive choice in more complex, ecologically valid settings. Copyright © 2016 the authors 0270-6474/16/369843-16$15.00/0.

Biologically Inspired SNN for Robot Control.

PubMed

Nichols, Eric; McDaid, Liam J; Siddique, Nazmul

2013-02-01

This paper proposes a spiking-neural-network-based robot controller inspired by the control structures of biological systems. Information is routed through the network using facilitating dynamic synapses with short-term plasticity. Learning occurs through long-term synaptic plasticity which is implemented using the temporal difference learning rule to enable the robot to learn to associate the correct movement with the appropriate input conditions. The network self-organizes to provide memories of environments that the robot encounters. A Pioneer robot simulator with laser and sonar proximity sensors is used to verify the performance of the network with a wall-following task, and the results are presented.

A survey of automated methods for sensemaking support

NASA Astrophysics Data System (ADS)

Llinas, James

2014-05-01

Complex, dynamic problems in general present a challenge for the design of analysis support systems and tools largely because there is limited reliable a priori procedural knowledge descriptive of the dynamic processes in the environment. Problem domains that are non-cooperative or adversarial impute added difficulties involving suboptimal observational data and/or data containing the effects of deception or covertness. The fundamental nature of analysis in these environments is based on composite approaches involving mining or foraging over the evidence, discovery and learning processes, and the synthesis of fragmented hypotheses; together, these can be labeled as sensemaking procedures. This paper reviews and analyzes the features, benefits, and limitations of a variety of automated techniques that offer possible support to sensemaking processes in these problem domains.

Risk assessment and predator learning in a changing world: understanding the impacts of coral reef degradation.

PubMed

Chivers, Douglas P; McCormick, Mark I; Allan, Bridie J M; Ferrari, Maud C O

2016-09-09

Habitat degradation is among the top drivers of the loss of global biodiversity. This problem is particularly acute in coral reef system. Here we investigated whether coral degradation influences predator risk assessment and learning for damselfish. When in a live coral environment, Ambon damselfish were able to learn the identity of an unknown predator upon exposure to damselfish alarm cues combined with predator odour and were able to socially transmit this learned recognition to naïve conspecifics. However, in the presence of dead coral water, damselfish failed to learn to recognize the predator through alarm cue conditioning and hence could not transmit the information socially. Unlike alarm cues of Ambon damselfish that appear to be rendered unusable in degraded coral habitats, alarm cues of Nagasaki damselfish remain viable in this same environment. Nagasaki damselfish were able to learn predators through conditioning with alarm cues in degraded habitats and subsequently transmit the information socially to Ambon damselfish. Predator-prey dynamics may be profoundly affected as habitat degradation proceeds; the success of one species that appears to have compromised predation assessment and learning, may find itself reliant on other species that are seemingly unaffected by the same degree of habitat degradation.

Efficacy of a Meiosis Learning Module Developed for the Virtual Cell Animation Collection

PubMed Central

Goff, Eric E.; Reindl, Katie M.; Johnson, Christina; McClean, Phillip; Offerdahl, Erika G.; Schroeder, Noah L.; White, Alan R.

2017-01-01

Recent reports calling for change in undergraduate biology education have resulted in the redesign of many introductory biology courses. Reports on one common change to course structure, the active-learning environment, have placed an emphasis on student preparation, noting that the positive outcomes of active learning in the classroom depend greatly on how well the student prepares before class. As a possible preparatory resource, we test the efficacy of a learning module developed for the Virtual Cell Animation Collection. This module presents the concepts of meiosis in an interactive, dynamic environment that has previously been shown to facilitate learning in introductory biology students. Participants (n = 534) were enrolled in an introductory biology course and were presented the concepts of meiosis in one of two treatments: the interactive-learning module or a traditional lecture session. Analysis of student achievement shows that students who viewed the learning module as their only means of conceptual presentation scored significantly higher (d = 0.40, p < 0.001) than students who only attended a traditional lecture on the topic. Our results show the animation-based learning module effectively conveyed meiosis conceptual understanding, which suggests that it may facilitate student learning outside the classroom. Moreover, these results have implications for instructors seeking to expand their arsenal of tools for “flipping” undergraduate biology courses. PMID:28188282

Controlling uncertainty: a review of human behavior in complex dynamic environments.

PubMed

Osman, Magda

2010-01-01

Complex dynamic control (CDC) tasks are a type of problem-solving environment used for examining many cognitive activities (e.g., attention, control, decision making, hypothesis testing, implicit learning, memory, monitoring, planning, and problem solving). Because of their popularity, there have been many findings from diverse domains of research (economics, engineering, ergonomics, human-computer interaction, management, psychology), but they remain largely disconnected from each other. The objective of this article is to review theoretical developments and empirical work on CDC tasks, and to introduce a novel framework (monitoring and control framework) as a tool for integrating theory and findings. The main thesis of the monitoring and control framework is that CDC tasks are characteristically uncertain environments, and subjective judgments of uncertainty guide the way in which monitoring and control behaviors attempt to reduce it. The article concludes by discussing new insights into continuing debates and future directions for research on CDC tasks.

The relationship between learning organization and organizational commitment among nursing managers in educational hospitals of Isfahan University of Medical Sciences in 2008-9

PubMed Central

Yaghoubi, Maryam; Raeisi, Ahmad Reza; Afshar, Mina; Yarmohammadian, Mohammad Hossein; Hasanzadeh, Akbar; Javadi, Marzi; Ansary, Maryam

2010-01-01

BACKGROUND: Old methods of administrating can’t cover the rapid changes of today. These changes redounded new organizations like learning organizations to be formed. The purpose of this research was to study the relationship between learning organization and organizational commitment among nursing managers. METHODS: This was a descriptive analytic survey. The population of study included 90 nursing managers of 9 educational hospitals. Data gathering was done via learning organizational (LO) and organizational commitment (OC) questionnaires. Data analysis was done using SPSS software. RESULTS: The mean score of LO was 56.9 ± 18.1 among nursing mangers, and the mean score of OC was 62.3 ± 10.1. In general, there was a significant relationship between LO and OC and there was a significant relationship between LO and job experience based on ANOVA test. CONCLUSIONS: In today’s changing environment of very rapid changes which have been seen in different areas of science and technology and the increasing complexity and dynamics of environmental factors, only organizations with active adaptation (dynamic equilibrium) can survive and remain capable of growth. This aim can be fulfilled just in learning organizations. PMID:21589785

Evolutionary games under incompetence.

PubMed

Kleshnina, Maria; Filar, Jerzy A; Ejov, Vladimir; McKerral, Jody C

2018-02-26

The adaptation process of a species to a new environment is a significant area of study in biology. As part of natural selection, adaptation is a mutation process which improves survival skills and reproductive functions of species. Here, we investigate this process by combining the idea of incompetence with evolutionary game theory. In the sense of evolution, incompetence and training can be interpreted as a special learning process. With focus on the social side of the problem, we analyze the influence of incompetence on behavior of species. We introduce an incompetence parameter into a learning function in a single-population game and analyze its effect on the outcome of the replicator dynamics. Incompetence can change the outcome of the game and its dynamics, indicating its significance within what are inherently imperfect natural systems.

Temporal structure of motor variability is dynamically regulated and predicts motor learning ability.

PubMed

Wu, Howard G; Miyamoto, Yohsuke R; Gonzalez Castro, Luis Nicolas; Ölveczky, Bence P; Smith, Maurice A

2014-02-01

Individual differences in motor learning ability are widely acknowledged, yet little is known about the factors that underlie them. Here we explore whether movement-to-movement variability in motor output, a ubiquitous if often unwanted characteristic of motor performance, predicts motor learning ability. Surprisingly, we found that higher levels of task-relevant motor variability predicted faster learning both across individuals and across tasks in two different paradigms, one relying on reward-based learning to shape specific arm movement trajectories and the other relying on error-based learning to adapt movements in novel physical environments. We proceeded to show that training can reshape the temporal structure of motor variability, aligning it with the trained task to improve learning. These results provide experimental support for the importance of action exploration, a key idea from reinforcement learning theory, showing that motor variability facilitates motor learning in humans and that our nervous systems actively regulate it to improve learning.

Temporal structure of motor variability is dynamically regulated and predicts motor learning ability

PubMed Central

Wu, Howard G; Miyamoto, Yohsuke R; Castro, Luis Nicolas Gonzalez; Ölveczky, Bence P; Smith, Maurice A

2015-01-01

Individual differences in motor learning ability are widely acknowledged, yet little is known about the factors that underlie them. Here we explore whether movement-to-movement variability in motor output, a ubiquitous if often unwanted characteristic of motor performance, predicts motor learning ability. Surprisingly, we found that higher levels of task-relevant motor variability predicted faster learning both across individuals and across tasks in two different paradigms, one relying on reward-based learning to shape specific arm movement trajectories and the other relying on error-based learning to adapt movements in novel physical environments. We proceeded to show that training can reshape the temporal structure of motor variability, aligning it with the trained task to improve learning. These results provide experimental support for the importance of action exploration, a key idea from reinforcement learning theory, showing that motor variability facilitates motor learning in humans and that our nervous systems actively regulate it to improve learning. PMID:24413700

Toward cognitive robotics

NASA Astrophysics Data System (ADS)

Laird, John E.

2009-05-01

Our long-term goal is to develop autonomous robotic systems that have the cognitive abilities of humans, including communication, coordination, adapting to novel situations, and learning through experience. Our approach rests on the recent integration of the Soar cognitive architecture with both virtual and physical robotic systems. Soar has been used to develop a wide variety of knowledge-rich agents for complex virtual environments, including distributed training environments and interactive computer games. For development and testing in robotic virtual environments, Soar interfaces to a variety of robotic simulators and a simple mobile robot. We have recently made significant extensions to Soar that add new memories and new non-symbolic reasoning to Soar's original symbolic processing, which should significantly improve Soar abilities for control of robots. These extensions include episodic memory, semantic memory, reinforcement learning, and mental imagery. Episodic memory and semantic memory support the learning and recalling of prior events and situations as well as facts about the world. Reinforcement learning provides the ability of the system to tune its procedural knowledge - knowledge about how to do things. Mental imagery supports the use of diagrammatic and visual representations that are critical to support spatial reasoning. We speculate on the future of unmanned systems and the need for cognitive robotics to support dynamic instruction and taskability.

Environment as a witness: Selective proliferation of information and emergence of objectivity in a quantum universe

NASA Astrophysics Data System (ADS)

Ollivier, Harold; Poulin, David; Zurek, Wojciech H.

2005-10-01

We study the role of the information deposited in the environment of an open quantum system in the course of the decoherence process. Redundant spreading of information—the fact that some observables of the system can be independently read off from many distinct fragments of the environment—is investigated as the key to effective objectivity, the essential ingredient of classical reality. This focus on the environment as a communication channel through which observers learn about physical systems underscores the importance of quantum Darwinism—selective proliferation of information about “the fittest states” chosen by the dynamics of decoherence at the expense of their superpositions—as redundancy imposes the existence of preferred observables. We demonstrate that the only observables that can leave multiple imprints in the environment are the familiar pointer observables singled out by environment-induced superselection (einselection) for their predictability. Many independent observers monitoring the environment will therefore agree on properties of the system as they can only learn about preferred observables. In this operational sense, the selective spreading of information leads to appearance of an objective classical reality from within the quantum substrate.

Prioritising the relevant information for learning and decision making within orbital and ventromedial prefrontal cortex.

PubMed

Walton, Mark E; Chau, Bolton K H; Kennerley, Steven W

2015-02-01

Our environment and internal states are frequently complex, ambiguous and dynamic, meaning we need to have selection mechanisms to ensure we are basing our decisions on currently relevant information. Here, we review evidence that orbitofrontal (OFC) and ventromedial prefrontal cortex (VMPFC) play conserved, critical but distinct roles in this process. While OFC may use specific sensory associations to enhance task-relevant information, particularly in the context of learning, VMPFC plays a role in ensuring irrelevant information does not impinge on the decision in hand.

Motor-Skill Learning in an Insect Inspired Neuro-Computational Control System

PubMed Central

Arena, Eleonora; Arena, Paolo; Strauss, Roland; Patané, Luca

2017-01-01

In nature, insects show impressive adaptation and learning capabilities. The proposed computational model takes inspiration from specific structures of the insect brain: after proposing key hypotheses on the direct involvement of the mushroom bodies (MBs) and on their neural organization, we developed a new architecture for motor learning to be applied in insect-like walking robots. The proposed model is a nonlinear control system based on spiking neurons. MBs are modeled as a nonlinear recurrent spiking neural network (SNN) with novel characteristics, able to memorize time evolutions of key parameters of the neural motor controller, so that existing motor primitives can be improved. The adopted control scheme enables the structure to efficiently cope with goal-oriented behavioral motor tasks. Here, a six-legged structure, showing a steady-state exponentially stable locomotion pattern, is exposed to the need of learning new motor skills: moving through the environment, the structure is able to modulate motor commands and implements an obstacle climbing procedure. Experimental results on a simulated hexapod robot are reported; they are obtained in a dynamic simulation environment and the robot mimicks the structures of Drosophila melanogaster. PMID:28337138

Fostering soft skills in project-oriented learning within an agile atmosphere

NASA Astrophysics Data System (ADS)

Chassidim, Hadas; Almog, Dani; Mark, Shlomo

2018-07-01

The project-oriented and Agile approaches have motivated a new generation of software engineers. Within the academic curriculum, the issue of whether students are being sufficiently prepared for the future has been raised. The objective of this work is to present the project-oriented environment as an influential factor that software engineering profession requires, using the second year course 'Software Development and Management in Agile Approach' as a case-study. This course combines academic topics, self-learned and soft skills implementation, the call for creativity, and the recognition of updated technologies and dynamic circumstances. The results of a survey that evaluated the perceived value of the course showed that the highest contribution of our environment was in the effectiveness of the team-work and the overall development process of the project.

Towards high-speed autonomous navigation of unknown environments

NASA Astrophysics Data System (ADS)

Richter, Charles; Roy, Nicholas

2015-05-01

In this paper, we summarize recent research enabling high-speed navigation in unknown environments for dynamic robots that perceive the world through onboard sensors. Many existing solutions to this problem guarantee safety by making the conservative assumption that any unknown portion of the map may contain an obstacle, and therefore constrain planned motions to lie entirely within known free space. In this work, we observe that safety constraints may significantly limit performance and that faster navigation is possible if the planner reasons about collision with unobserved obstacles probabilistically. Our overall approach is to use machine learning to approximate the expected costs of collision using the current state of the map and the planned trajectory. Our contribution is to demonstrate fast but safe planning using a learned function to predict future collision probabilities.

Exploring the limits of learning: Segregation of information integration and response selection is required for learning a serial reversal task

PubMed Central

Zanutto, B. Silvano

2017-01-01

Animals are proposed to learn the latent rules governing their environment in order to maximize their chances of survival. However, rules may change without notice, forcing animals to keep a memory of which one is currently at work. Rule switching can lead to situations in which the same stimulus/response pairing is positively and negatively rewarded in the long run, depending on variables that are not accessible to the animal. This fact raises questions on how neural systems are capable of reinforcement learning in environments where the reinforcement is inconsistent. Here we address this issue by asking about which aspects of connectivity, neural excitability and synaptic plasticity are key for a very general, stochastic spiking neural network model to solve a task in which rules change without being cued, taking the serial reversal task (SRT) as paradigm. Contrary to what could be expected, we found strong limitations for biologically plausible networks to solve the SRT. Especially, we proved that no network of neurons can learn a SRT if it is a single neural population that integrates stimuli information and at the same time is responsible of choosing the behavioural response. This limitation is independent of the number of neurons, neuronal dynamics or plasticity rules, and arises from the fact that plasticity is locally computed at each synapse, and that synaptic changes and neuronal activity are mutually dependent processes. We propose and characterize a spiking neural network model that solves the SRT, which relies on separating the functions of stimuli integration and response selection. The model suggests that experimental efforts to understand neural function should focus on the characterization of neural circuits according to their connectivity, neural dynamics, and the degree of modulation of synaptic plasticity with reward. PMID:29077735

The Effect of Dynamic Web Technologies on Student Academic Achievement in Problem-Based Collaborative Learning Environment

ERIC Educational Resources Information Center

Korucu, Agâh Tugrul; Cakir, Hasan

2018-01-01

Some of the 21st century proficiencies expected from people are determined as collaborative working and problem solving. One way to gain these proficiencies is by using collaborative problem solving based on social constructivism theory. Collaborative problem solving is one of the methods allowing for social constructivism in the class. In…

"Walking Yourself around as a Teacher": Gender and Embodiment in Student Teachers' Working Lives

ERIC Educational Resources Information Center

Braun, Annette

2011-01-01

This paper considers the psychic and social dynamics reported by student teachers when learning to embody their teacher persona in the secondary school environment. Focusing on gender dimensions of embodiment and drawing on qualitative interview data from a UK study of postgraduate teacher-training students, teaching is examined as a physical…

The Changing Academic Ecology of Sociology: Learning to Live with More Frogs in the Pond

ERIC Educational Resources Information Center

Clark, Robert A.

2008-01-01

Sociology exists in a dynamic academic environment that influences how students view and evaluate the discipline. This essay explores the changing academic context of sociology through the author's experience as a professor and department chair over a span of four decades. Increased co-curricular programming, changing student goals, and more…

Spawning Ideas--Moving from Ideas to Action: Quality Tools for Collective Problem-Solving and Continuous Learning.

ERIC Educational Resources Information Center

Flor, Richard F.; Troskey, Matthew D.

This paper explores the dynamics of managing collective problem solving and decision making, and the application of tools and strategies to deal with the emergent complexity of systems in which educators work. Schools and educational programs are complex adaptive systems that respond to changes in internal and external environments. Functioning…

Signifying the Accumulation Graph in a Dynamic and Multi-Representation Environment

ERIC Educational Resources Information Center

Yerushalmy, Michal; Swidan, Osama

2012-01-01

The present study focuses on the accumulation process involved in the integration of a single-variable function. Observing the work of two high-school calculus students who had not yet learned any other integral-related ideas, we analyze the emergence of the semiotic relationship between personal and mathematical meanings, as expressed through the…

Achievement of Joint Perception in a Computer Supported Collaborative Learning Environment: A Case Study

ERIC Educational Resources Information Center

Afacan Adanir, Gulgun

2017-01-01

The case study focuses on the interactional mechanisms through which online collaborative teams co-construct a shared understanding of an analytical geometry problem by using dynamic geometry representations. The collaborative study consisted of an assignment on which the learners worked together in groups to solve a ship navigation problem as…

The Development of a Virtual Dinosaur Museum

ERIC Educational Resources Information Center

Tarng, Wernhuar; Liou, Hsin-Hun

2007-01-01

The objective of this article is to study the network and virtual reality technologies for developing a virtual dinosaur museum, which provides a Web-learning environment for students of all ages and the general public to know more about dinosaurs. We first investigate the method for building the 3D dynamic models of dinosaurs, and then describe…

Collection Directions: Some Reflections on the Future of Library Collections and Collecting

ERIC Educational Resources Information Center

Dempsey, Lorcan; Malpas, Constance; Lavoie, Brian

2014-01-01

This article takes a broad view of the evolution of collecting behaviors in a network environment and suggests some future directions based on various simple models. The authors look at the changing dynamics of print collections, at the greater engagement with research and learning behaviors, and at trends in scholarly communication. The goal is…

Who You Know and What You Know: Student Interaction in Online Discussions

ERIC Educational Resources Information Center

Stevens, Tony

2013-01-01

The dynamics of how students respond to each other during online discussions in a blended learning environment remains under-explored in the literature. How this technology shapes interaction when used in conjunction with traditional teaching methods and the practices of learners in these multi-site situations is a significant educational issue.…

ICT Training of University Teachers in a Personal Learning Environment. Project DIPRO 2.0.

ERIC Educational Resources Information Center

Cabero Almenara, Julio; Marín Díaz, Verónica

2012-01-01

Developing an information and knowledge society involves the incorporation of technological tools into education. This can only happen if teachers are properly qualified to include such tools into the classroom dynamics. The present article brings to the forefront a training proposal framed within an R&D project funded by the Spanish Ministry…

A Head in Virtual Reality: Development of A Dynamic Head and Neck Model

ERIC Educational Resources Information Center

Nguyen, Ngan; Wilson, Timothy D.

2009-01-01

Advances in computer and interface technologies have made it possible to create three-dimensional (3D) computerized models of anatomical structures for visualization, manipulation, and interaction in a virtual 3D environment. In the past few decades, a multitude of digital models have been developed to facilitate complex spatial learning of the…

Teaching Outside the Box: How to Grab Your Students By Their Brains

ERIC Educational Resources Information Center

Johnson, LouAnne

2005-01-01

This book offers strategies to help both new teachers and seasoned veterans create dynamic classroom environments where students enjoy learning and teachers enjoy teaching. In addition to no-nonsense advice, checklists, and handouts, the book includes: (1) A step-by-step plan to make the first week of school a success; (2) Approaches for creating…

Making Sense of How Physician Preceptors Interact with Medical Students: Discourses of Dialogue, Good Medical Practice, and Relationship Trajectories

ERIC Educational Resources Information Center

van der Zwet, J.; Dornan, T.; Teunissen, P. W.; de Jonge, L. P. J. W. M.; Scherpbier, A. J. J. A.

2014-01-01

Work based learning and teaching in health care settings are complex and dynamic. Sociocultural theory addresses this complexity by focusing on interaction between learners, teachers, and their environment as learners develop their professional identity. Although social interaction between doctors and students plays a crucial role in this…

The Role of Collaborative Scholarship in the Mentorship of Doctoral Students

ERIC Educational Resources Information Center

Zipp, Genevieve Pinto; Cahill, Terrance; Clark, MaryAnn

2009-01-01

The work of a professor is the "scholarship of teaching" (Boyer, 1990). The strength of the teaching and learning environment is fostered by a dynamic interplay between the mentor (scholar) and the mentee (student). Boyer (1990) suggests that in order to be a scholar, one must have "a recognition that knowledge is acquired through research,…

Short-Term Memories in "Drosophila" Are Governed by General and Specific Genetic Systems

ERIC Educational Resources Information Center

Zars, Troy

2010-01-01

In a dynamic environment, there is an adaptive value in the ability of animals to acquire and express memories. That both simple and complex animals can learn is therefore not surprising. How animals have solved this problem genetically and anatomically probably lies somewhere in a range between a single molecular/anatomical mechanism that applies…

Probabilistic Learning by Rodent Grid Cells

PubMed Central

Cheung, Allen

2016-01-01

Mounting evidence shows mammalian brains are probabilistic computers, but the specific cells involved remain elusive. Parallel research suggests that grid cells of the mammalian hippocampal formation are fundamental to spatial cognition but their diverse response properties still defy explanation. No plausible model exists which explains stable grids in darkness for twenty minutes or longer, despite being one of the first results ever published on grid cells. Similarly, no current explanation can tie together grid fragmentation and grid rescaling, which show very different forms of flexibility in grid responses when the environment is varied. Other properties such as attractor dynamics and grid anisotropy seem to be at odds with one another unless additional properties are assumed such as a varying velocity gain. Modelling efforts have largely ignored the breadth of response patterns, while also failing to account for the disastrous effects of sensory noise during spatial learning and recall, especially in darkness. Here, published electrophysiological evidence from a range of experiments are reinterpreted using a novel probabilistic learning model, which shows that grid cell responses are accurately predicted by a probabilistic learning process. Diverse response properties of probabilistic grid cells are statistically indistinguishable from rat grid cells across key manipulations. A simple coherent set of probabilistic computations explains stable grid fields in darkness, partial grid rescaling in resized arenas, low-dimensional attractor grid cell dynamics, and grid fragmentation in hairpin mazes. The same computations also reconcile oscillatory dynamics at the single cell level with attractor dynamics at the cell ensemble level. Additionally, a clear functional role for boundary cells is proposed for spatial learning. These findings provide a parsimonious and unified explanation of grid cell function, and implicate grid cells as an accessible neuronal population readout of a set of probabilistic spatial computations. PMID:27792723

Deep Direct Reinforcement Learning for Financial Signal Representation and Trading.

PubMed

Deng, Yue; Bao, Feng; Kong, Youyong; Ren, Zhiquan; Dai, Qionghai

2017-03-01

Can we train the computer to beat experienced traders for financial assert trading? In this paper, we try to address this challenge by introducing a recurrent deep neural network (NN) for real-time financial signal representation and trading. Our model is inspired by two biological-related learning concepts of deep learning (DL) and reinforcement learning (RL). In the framework, the DL part automatically senses the dynamic market condition for informative feature learning. Then, the RL module interacts with deep representations and makes trading decisions to accumulate the ultimate rewards in an unknown environment. The learning system is implemented in a complex NN that exhibits both the deep and recurrent structures. Hence, we propose a task-aware backpropagation through time method to cope with the gradient vanishing issue in deep training. The robustness of the neural system is verified on both the stock and the commodity future markets under broad testing conditions.

Who is Crossing Where?: Infants’ Discrimination of Figures and Grounds in Events

PubMed Central

Göksun, Tilbe; Hirsh-Pasek, Kathy; Golinkoff, Roberta Michnick; Imai, Mutsumi; Konishi, Haruka; Okada, Hiroyuki

2011-01-01

To learn relational terms such as verbs and prepositions, children must first dissect and process dynamic event components. This paper investigates the way in which 8- to 14-month-old English-reared infants notice the event components, figure (i.e., the moving entity) and ground (i.e., stationary setting), in both dynamic (Experiment 1) and static representations of events (Experiment 2) for categorical ground distinctions expressed in Japanese, but not in English. We then compare both 14- and 19-month-old English- and Japanese-reared infants’ processing of grounds to understand how language learning interacts with the conceptualization of these constructs (Experiment 3). Results suggest that 1) infants distinguish between figures and grounds in events; 2) they do so differently for static vs. dynamic displays; 3) early in the second year, children from diverse language environments form nonnative - perhaps universal - event categories; and 4) these event categories shift over time as children have more exposure to their native tongue. PMID:21839990

Protocol for a realist review of workplace learning in postgraduate medical education and training.

PubMed

Wiese, Anel; Kilty, Caroline; Bergin, Colm; Flood, Patrick; Fu, Na; Horgan, Mary; Higgins, Agnes; Maher, Bridget; O'Kane, Grainne; Prihodova, Lucia; Slattery, Dubhfeasa; Bennett, Deirdre

2017-01-19

Postgraduate medical education and training (PGMET) is a complex social process which happens predominantly during the delivery of patient care. The clinical learning environment (CLE), the context for PGMET, shapes the development of the doctors who learn and work within it, ultimately impacting the quality and safety of patient care. Clinical workplaces are complex, dynamic systems in which learning emerges from non-linear interactions within a network of related factors and activities. Those tasked with the design and delivery of postgraduate medical education and training need to understand the relationship between the processes of medical workplace learning and these contextual elements in order to optimise conditions for learning. We propose to conduct a realist synthesis of the literature to address the overarching questions; how, why and in what circumstances do doctors learn in clinical environments? This review is part of a funded projected with the overall aim of producing guidelines and recommendations for the design of high quality clinical learning environments for postgraduate medical education and training. We have chosen realist synthesis as a methodology because of its suitability for researching complexity and producing answers useful to policymakers and practitioners. This realist synthesis will follow the steps and procedures outlined by Wong et al. in the RAMESES Publication Standards for Realist Synthesis and the Realist Synthesis RAMESES Training Materials. The core research team is a multi-disciplinary group of researchers, clinicians and health professions educators. The wider research group includes experts in organisational behaviour and human resources management as well as the key stakeholders; doctors in training, patient representatives and providers of PGMET. This study will draw from the published literature and programme, and substantive, theories of workplace learning, to describe context, mechanism and outcome configurations for PGMET. This information will be useful to policymakers and practitioners in PGMET, who will be able to apply our findings within their own contexts. Improving the quality of clinical learning environments can improve the performance, humanism and wellbeing of learners and improve the quality and safety of patient care. The review is not registered with the PROSPERO International Prospective Register of Systematic Reviews as the review objectives relate solely to education outcomes.

Bayesian deterministic decision making: a normative account of the operant matching law and heavy-tailed reward history dependency of choices.

PubMed

Saito, Hiroshi; Katahira, Kentaro; Okanoya, Kazuo; Okada, Masato

2014-01-01

The decision making behaviors of humans and animals adapt and then satisfy an "operant matching law" in certain type of tasks. This was first pointed out by Herrnstein in his foraging experiments on pigeons. The matching law has been one landmark for elucidating the underlying processes of decision making and its learning in the brain. An interesting question is whether decisions are made deterministically or probabilistically. Conventional learning models of the matching law are based on the latter idea; they assume that subjects learn choice probabilities of respective alternatives and decide stochastically with the probabilities. However, it is unknown whether the matching law can be accounted for by a deterministic strategy or not. To answer this question, we propose several deterministic Bayesian decision making models that have certain incorrect beliefs about an environment. We claim that a simple model produces behavior satisfying the matching law in static settings of a foraging task but not in dynamic settings. We found that the model that has a belief that the environment is volatile works well in the dynamic foraging task and exhibits undermatching, which is a slight deviation from the matching law observed in many experiments. This model also demonstrates the double-exponential reward history dependency of a choice and a heavier-tailed run-length distribution, as has recently been reported in experiments on monkeys.

GeoSpatial Workforce Development: enhancing the traditional learning environment in geospatial information technology

NASA Astrophysics Data System (ADS)

Lawhead, Pamela B.; Aten, Michelle L.

2003-04-01

The Center for GeoSpatial Workforce Development is embarking on a new era in education by developing a repository of dynamic online courseware authored by the foremost industry experts within the remote sensing and GIS industries. Virtual classrooms equipped with the most advanced instructions, computations, communications, course evaluation, and management facilities amplify these courses to enhance the learning environment and provide rapid feedback between instructors and students. The launch of this program included the objective development of the Model Curriculum by an independent consortium of remote sensing industry leaders. The Center's research and development focus on recruiting additional industry experts to develop the technical content of the courseware and then utilize state-of-the-art technology to enhance their material with visually stimulating animations, compelling audio clips and entertaining, interactive exercises intended to reach the broadest audience possible by targeting various learning styles. The courseware will be delivered via various media: Internet, CD-ROM, DVD, and compressed video, that translates into anywhere, anytime delivery of GeoSpatial Information Technology education.

Sparse Bayesian Learning for Nonstationary Data Sources

NASA Astrophysics Data System (ADS)

Fujimaki, Ryohei; Yairi, Takehisa; Machida, Kazuo

This paper proposes an online Sparse Bayesian Learning (SBL) algorithm for modeling nonstationary data sources. Although most learning algorithms implicitly assume that a data source does not change over time (stationary), one in the real world usually does due to such various factors as dynamically changing environments, device degradation, sudden failures, etc (nonstationary). The proposed algorithm can be made useable for stationary online SBL by setting time decay parameters to zero, and as such it can be interpreted as a single unified framework for online SBL for use with stationary and nonstationary data sources. Tests both on four types of benchmark problems and on actual stock price data have shown it to perform well.

Behavioural variation in 172 small-scale societies indicates that social learning is the main mode of human adaptation.

PubMed

Mathew, Sarah; Perreault, Charles

2015-07-07

The behavioural variation among human societies is vast and unmatched in the animal world. It is unclear whether this variation is due to variation in the ecological environment or to differences in cultural traditions. Underlying this debate is a more fundamental question: is the richness of humans' behavioural repertoire due to non-cultural mechanisms, such as causal reasoning, inventiveness, reaction norms, trial-and-error learning and evoked culture, or is it due to the population-level dynamics of cultural transmission? Here, we measure the relative contribution of environment and cultural history in explaining the behavioural variation of 172 Native American tribes at the time of European contact. We find that the effect of cultural history is typically larger than that of environment. Behaviours also persist over millennia within cultural lineages. This indicates that human behaviour is not predominantly determined by single-generation adaptive responses, contra theories that emphasize non-cultural mechanisms as determinants of human behaviour. Rather, the main mode of human adaptation is social learning mechanisms that operate over multiple generations. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

What Can Reinforcement Learning Teach Us About Non-Equilibrium Quantum Dynamics

NASA Astrophysics Data System (ADS)

Bukov, Marin; Day, Alexandre; Sels, Dries; Weinberg, Phillip; Polkovnikov, Anatoli; Mehta, Pankaj

Equilibrium thermodynamics and statistical physics are the building blocks of modern science and technology. Yet, our understanding of thermodynamic processes away from equilibrium is largely missing. In this talk, I will reveal the potential of what artificial intelligence can teach us about the complex behaviour of non-equilibrium systems. Specifically, I will discuss the problem of finding optimal drive protocols to prepare a desired target state in quantum mechanical systems by applying ideas from Reinforcement Learning [one can think of Reinforcement Learning as the study of how an agent (e.g. a robot) can learn and perfect a given policy through interactions with an environment.]. The driving protocols learnt by our agent suggest that the non-equilibrium world features possibilities easily defying intuition based on equilibrium physics.

Locally optimal control under unknown dynamics with learnt cost function: application to industrial robot positioning

NASA Astrophysics Data System (ADS)

Guérin, Joris; Gibaru, Olivier; Thiery, Stéphane; Nyiri, Eric

2017-01-01

Recent methods of Reinforcement Learning have enabled to solve difficult, high dimensional, robotic tasks under unknown dynamics using iterative Linear Quadratic Gaussian control theory. These algorithms are based on building a local time-varying linear model of the dynamics from data gathered through interaction with the environment. In such tasks, the cost function is often expressed directly in terms of the state and control variables so that it can be locally quadratized to run the algorithm. If the cost is expressed in terms of other variables, a model is required to compute the cost function from the variables manipulated. We propose a method to learn the cost function directly from the data, in the same way as for the dynamics. This way, the cost function can be defined in terms of any measurable quantity and thus can be chosen more appropriately for the task to be carried out. With our method, any sensor information can be used to design the cost function. We demonstrate the efficiency of this method through simulating, with the V-REP software, the learning of a Cartesian positioning task on several industrial robots with different characteristics. The robots are controlled in joint space and no model is provided a priori. Our results are compared with another model free technique, consisting in writing the cost function as a state variable.

Architecture for robot intelligence

NASA Technical Reports Server (NTRS)

Peters, II, Richard Alan (Inventor)

2004-01-01

An architecture for robot intelligence enables a robot to learn new behaviors and create new behavior sequences autonomously and interact with a dynamically changing environment. Sensory information is mapped onto a Sensory Ego-Sphere (SES) that rapidly identifies important changes in the environment and functions much like short term memory. Behaviors are stored in a DBAM that creates an active map from the robot's current state to a goal state and functions much like long term memory. A dream state converts recent activities stored in the SES and creates or modifies behaviors in the DBAM.

A Q-Learning Approach to Flocking With UAVs in a Stochastic Environment.

PubMed

Hung, Shao-Ming; Givigi, Sidney N

2017-01-01

In the past two decades, unmanned aerial vehicles (UAVs) have demonstrated their efficacy in supporting both military and civilian applications, where tasks can be dull, dirty, dangerous, or simply too costly with conventional methods. Many of the applications contain tasks that can be executed in parallel, hence the natural progression is to deploy multiple UAVs working together as a force multiplier. However, to do so requires autonomous coordination among the UAVs, similar to swarming behaviors seen in animals and insects. This paper looks at flocking with small fixed-wing UAVs in the context of a model-free reinforcement learning problem. In particular, Peng's Q(λ) with a variable learning rate is employed by the followers to learn a control policy that facilitates flocking in a leader-follower topology. The problem is structured as a Markov decision process, where the agents are modeled as small fixed-wing UAVs that experience stochasticity due to disturbances such as winds and control noises, as well as weight and balance issues. Learned policies are compared to ones solved using stochastic optimal control (i.e., dynamic programming) by evaluating the average cost incurred during flight according to a cost function. Simulation results demonstrate the feasibility of the proposed learning approach at enabling agents to learn how to flock in a leader-follower topology, while operating in a nonstationary stochastic environment.

Stochastic abstract policies: generalizing knowledge to improve reinforcement learning.

PubMed

Koga, Marcelo L; Freire, Valdinei; Costa, Anna H R

2015-01-01

Reinforcement learning (RL) enables an agent to learn behavior by acquiring experience through trial-and-error interactions with a dynamic environment. However, knowledge is usually built from scratch and learning to behave may take a long time. Here, we improve the learning performance by leveraging prior knowledge; that is, the learner shows proper behavior from the beginning of a target task, using the knowledge from a set of known, previously solved, source tasks. In this paper, we argue that building stochastic abstract policies that generalize over past experiences is an effective way to provide such improvement and this generalization outperforms the current practice of using a library of policies. We achieve that contributing with a new algorithm, AbsProb-PI-multiple and a framework for transferring knowledge represented as a stochastic abstract policy in new RL tasks. Stochastic abstract policies offer an effective way to encode knowledge because the abstraction they provide not only generalizes solutions but also facilitates extracting the similarities among tasks. We perform experiments in a robotic navigation environment and analyze the agent's behavior throughout the learning process and also assess the transfer ratio for different amounts of source tasks. We compare our method with the transfer of a library of policies, and experiments show that the use of a generalized policy produces better results by more effectively guiding the agent when learning a target task.

Work-engaged nurses for a better clinical learning environment: a ward-level analysis.

PubMed

Tomietto, Marco; Comparcini, Dania; Simonetti, Valentina; Pelusi, Gilda; Troiani, Silvano; Saarikoski, Mikko; Cicolini, Giancarlo

2016-05-01

To correlate workgroup engagement in nursing teams and the clinical learning experience of nursing students. Work engagement plays a pivotal role in explaining motivational dynamics. Nursing education is workplace-based and, through their clinical placements, nursing students develop both their clinical competences and their professional identity. However, there is currently a lack of evidence on the role of work engagement related to students' learning experiences. A total of 519 nurses and 519 nursing students were enrolled in hospital settings. The Utrecht Work Engagement Scale (UWES) was used to assess work engagement, and the Clinical Learning Environment and Supervision plus nurse Teacher (CLES+T) scale was used to assess students' learning experience. A multilevel linear regression analysis was performed. Group-level work engagement of nurses correlated with students' clinical learning experience (β = 0.11, P < 0.001). Specifically, the 'absorption' and 'dedication' factors mostly contributed to enhancing clinical learning (respectively, β = 0.37, P < 0.001 and β = 0.20, P < 0.001). Nursing teams' work engagement is an important motivational factor to enhance effective nursing education. Nursing education institutions and health-care settings need to conjointly work to build effective organisational climates. The results highlighted the importance of considering the group-level analysis to understand the most effective strategies of intervention for both organisations and nursing education. © 2015 John Wiley & Sons Ltd.

Metacognitive components in smart learning environment

NASA Astrophysics Data System (ADS)

Sumadyo, M.; Santoso, H. B.; Sensuse, D. I.

2018-03-01

Metacognitive ability in digital-based learning process helps students in achieving learning goals. So that digital-based learning environment should make the metacognitive component as a facility that must be equipped. Smart Learning Environment is the concept of a learning environment that certainly has more advanced components than just a digital learning environment. This study examines the metacognitive component of the smart learning environment to support the learning process. A review of the metacognitive literature was conducted to examine the components involved in metacognitive learning strategies. Review is also conducted on the results of study smart learning environment, ranging from design to context in building smart learning. Metacognitive learning strategies certainly require the support of adaptable, responsive and personalize learning environments in accordance with the principles of smart learning. The current study proposed the role of metacognitive component in smart learning environment, which is useful as the basis of research in building environment in smart learning.

Designing instruction to support mechanical reasoning: Three alternatives in the simple machines learning environment

NASA Astrophysics Data System (ADS)

McKenna, Ann Frances

2001-07-01

Creating a classroom environment that fosters a productive learning experience and engages students in the learning process is a complex endeavor. A classroom environment is dynamic and requires a unique synergy among students, teacher, classroom artifacts and events to achieve robust understanding and knowledge integration. This dissertation addresses this complex issue by developing, implementing, and investigating the simple machines learning environment (SIMALE) to support students' mechanical reasoning and understanding. SIMALE was designed to support reflection, collaborative learning, and to engage students in generative learning through multiple representations of concepts and successive experimentation and design activities. Two key components of SIMALE are an original web-based software tool and hands-on Lego activities. A research study consisting of three treatment groups was created to investigate the benefits of hands-on and web-based computer activities on students' analytic problem solving ability, drawing/modeling ability, and conceptual understanding. The study was conducted with two populations of students that represent a diverse group with respect to gender, ethnicity, academic achievement and social/economic status. One population of students in this dissertation study participated from the Mathematics, Engineering, and Science Achievement (MESA) program that serves minorities and under-represented groups in science and mathematics. The second group was recruited from the Academic Talent Development Program (ATDP) that is an academically competitive outreach program offered through the University of California at Berkeley. Results from this dissertation show success of the SIMALE along several dimensions. First, students in both populations achieved significant gains in analytic problem solving ability, drawing/modeling ability, and conceptual understanding. Second, significant differences that were found on pre-test measures were eliminated on post-test measures. Specifically, female students scored significantly lower than males on the overall pre-tests but scored as well as males on the same post-test measures. MESA students also scored significantly lower than ATDP students on pre-test measures but both populations scored equally well on the post-tests. This dissertation has therefore shown the SIMALE to support a collaborative, reflective, and generative learning environment. Furthermore, the SIMALE clearly contributes to students' mechanical reasoning and understanding of simple machines concepts for a diverse population of students.

Using Information Communication Technologies to Develop Dynamic Curriculum Frameworks for Diverse Cohorts: A Case Study from Event Management

ERIC Educational Resources Information Center

Hadley, Bree Jamila

2012-01-01

This article investigates the role of information communication technologies (ICTs) in establishing a well-aligned, authentic learning environment for a diverse cohort of non-cognate and cognate students studying event management in a higher education context. Based on a case study which examined the way ICTs assisted in accommodating diverse…

Short-Term Gains, Long-Term Pains: How Cues about State Aid Learning in Dynamic Environments

ERIC Educational Resources Information Center

Gureckis, Todd M.; Love, Bradley C.

2009-01-01

Successful investors seeking returns, animals foraging for food, and pilots controlling aircraft all must take into account how their current decisions will impact their future standing. One challenge facing decision makers is that options that appear attractive in the short-term may not turn out best in the long run. In this paper, we explore…

A Complexity Theory Approach to Sustainability: A Longitudinal Study in Two London NHS Hospitals

ERIC Educational Resources Information Center

Mitleton-Kelly, Eve

2011-01-01

Purpose: The purpose of this paper is to demonstrate that organisational sustainability is not a continuation of the status quo but, seen from a complexity theory perspective, is a continuous dynamic process of co-evolution with a changing environment. It is underpinned by learning, and it creates new structures and ways of working to adjust and…

The Effect on Prospective Teachers of the Learning Environment Supported by Dynamic Statistics Software

ERIC Educational Resources Information Center

Koparan, Timur

2016-01-01

In this study, the effect on the achievement and attitudes of prospective teachers is examined. With this aim ahead, achievement test, attitude scale for statistics and interviews were used as data collection tools. The achievement test comprises 8 problems based on statistical data, and the attitude scale comprises 13 Likert-type items. The study…

Nondegree Options for Expanding a Leadership Portfolio.

PubMed

Bleich, Michael R; Jones-Schenk, Jan

2016-07-01

Organizational leaders are time-challenged to stay attuned with dynamic health care and business environments, leaving time for professional development at a premium. Beyond interorganizational leadership programs, learning options for nondegree-enhanced education are provided, referencing some of the high-quality, high-volume programs available at no or low cost. J Contin Educ Nurs. 2016;47(7):299-301. Copyright 2016, SLACK Incorporated.

Collaboration amidst Disagreement and Moral Judgment: The Dynamics of Jewish and Arab Students' Collaborative Inquiry of Their Joint Past

ERIC Educational Resources Information Center

Pollack, Sarah; Kolikant, Yifat Ben-David

2012-01-01

We present an instructional model involving a computer-supported collaborative learning environment, in which students from two conflicting groups collaboratively investigate an event relevant to their past using historical texts. We traced one enactment of the model by a group comprised of two Israeli Jewish and two Israeli Arab students. Our…

An Inquiry-Based Biochemistry Laboratory Structure Emphasizing Competency in the Scientific Process: A Guided Approach with an Electronic Notebook Format

ERIC Educational Resources Information Center

Hall, Mona L.; Vardar-Ulu, Didem

2014-01-01

The laboratory setting is an exciting and gratifying place to teach because you can actively engage the students in the learning process through hands-on activities; it is a dynamic environment amenable to collaborative work, critical thinking, problem-solving and discovery. The guided inquiry-based approach described here guides the students…

Recent CESAR (Center for Engineering Systems Advanced Research) research activities in sensor based reasoning for autonomous machines

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pin, F.G.; de Saussure, G.; Spelt, P.F.

1988-01-01

This paper describes recent research activities at the Center for Engineering Systems Advanced Research (CESAR) in the area of sensor based reasoning, with emphasis being given to their application and implementation on our HERMIES-IIB autonomous mobile vehicle. These activities, including navigation and exploration in a-priori unknown and dynamic environments, goal recognition, vision-guided manipulation and sensor-driven machine learning, are discussed within the framework of a scenario in which an autonomous robot is asked to navigate through an unknown dynamic environment, explore, find and dock at the panel, read and understand the status of the panel's meters and dials, learn the functioningmore » of a process control panel, and successfully manipulate the control devices of the panel to solve a maintenance emergency problems. A demonstration of the successful implementation of the algorithms on our HERMIES-IIB autonomous robot for resolution of this scenario is presented. Conclusions are drawn concerning the applicability of the methodologies to more general classes of problems and implications for future work on sensor-driven reasoning for autonomous robots are discussed. 8 refs., 3 figs.« less

A dynamic case-based planning system for space station application

NASA Technical Reports Server (NTRS)

Oppacher, F.; Deugo, D.

1988-01-01

We are currently investigating the use of a case-based reasoning approach to develop a dynamic planning system. The dynamic planning system (DPS) is designed to perform resource management, i.e., to efficiently schedule tasks both with and without failed components. This approach deviates from related work on scheduling and on planning in AI in several aspects. In particular, an attempt is made to equip the planner with an ability to cope with a changing environment by dynamic replanning, to handle resource constraints and feedback, and to achieve some robustness and autonomy through plan learning by dynamic memory techniques. We briefly describe the proposed architecture of DPS and its four major components: the PLANNER, the plan EXECUTOR, the dynamic REPLANNER, and the plan EVALUATOR. The planner, which is implemented in Smalltalk, is being evaluated for use in connection with the Space Station Mobile Service System (MSS).

Multidimensional Learner Model In Intelligent Learning System

NASA Astrophysics Data System (ADS)

Deliyska, B.; Rozeva, A.

2009-11-01

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

Design of a biochemical circuit motif for learning linear functions

PubMed Central

Lakin, Matthew R.; Minnich, Amanda; Lane, Terran; Stefanovic, Darko

2014-01-01

Learning and adaptive behaviour are fundamental biological processes. A key goal in the field of bioengineering is to develop biochemical circuit architectures with the ability to adapt to dynamic chemical environments. Here, we present a novel design for a biomolecular circuit capable of supervised learning of linear functions, using a model based on chemical reactions catalysed by DNAzymes. To achieve this, we propose a novel mechanism of maintaining and modifying internal state in biochemical systems, thereby advancing the state of the art in biomolecular circuit architecture. We use simulations to demonstrate that the circuit is capable of learning behaviour and assess its asymptotic learning performance, scalability and robustness to noise. Such circuits show great potential for building autonomous in vivo nanomedical devices. While such a biochemical system can tell us a great deal about the fundamentals of learning in living systems and may have broad applications in biomedicine (e.g. autonomous and adaptive drugs), it also offers some intriguing challenges and surprising behaviours from a machine learning perspective. PMID:25401175

Design of a biochemical circuit motif for learning linear functions.

PubMed

Lakin, Matthew R; Minnich, Amanda; Lane, Terran; Stefanovic, Darko

2014-12-06

Learning and adaptive behaviour are fundamental biological processes. A key goal in the field of bioengineering is to develop biochemical circuit architectures with the ability to adapt to dynamic chemical environments. Here, we present a novel design for a biomolecular circuit capable of supervised learning of linear functions, using a model based on chemical reactions catalysed by DNAzymes. To achieve this, we propose a novel mechanism of maintaining and modifying internal state in biochemical systems, thereby advancing the state of the art in biomolecular circuit architecture. We use simulations to demonstrate that the circuit is capable of learning behaviour and assess its asymptotic learning performance, scalability and robustness to noise. Such circuits show great potential for building autonomous in vivo nanomedical devices. While such a biochemical system can tell us a great deal about the fundamentals of learning in living systems and may have broad applications in biomedicine (e.g. autonomous and adaptive drugs), it also offers some intriguing challenges and surprising behaviours from a machine learning perspective.

Learning molecular energies using localized graph kernels.

PubMed

Ferré, Grégoire; Haut, Terry; Barros, Kipton

2017-03-21

Recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although simple to state, these symmetries are complicated to encode into machine learning algorithms. In this paper, we present a machine learning approach based on graph theory that naturally incorporates translation, rotation, and permutation symmetries. Specifically, we use a random walk graph kernel to measure the similarity of two adjacency matrices, each of which represents a local atomic environment. This Graph Approximated Energy (GRAPE) approach is flexible and admits many possible extensions. We benchmark a simple version of GRAPE by predicting atomization energies on a standard dataset of organic molecules.

Learning molecular energies using localized graph kernels

NASA Astrophysics Data System (ADS)

Ferré, Grégoire; Haut, Terry; Barros, Kipton

2017-03-01

Recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although simple to state, these symmetries are complicated to encode into machine learning algorithms. In this paper, we present a machine learning approach based on graph theory that naturally incorporates translation, rotation, and permutation symmetries. Specifically, we use a random walk graph kernel to measure the similarity of two adjacency matrices, each of which represents a local atomic environment. This Graph Approximated Energy (GRAPE) approach is flexible and admits many possible extensions. We benchmark a simple version of GRAPE by predicting atomization energies on a standard dataset of organic molecules.

Neural Basis of Reinforcement Learning and Decision Making

PubMed Central

Lee, Daeyeol; Seo, Hyojung; Jung, Min Whan

2012-01-01

Reinforcement learning is an adaptive process in which an animal utilizes its previous experience to improve the outcomes of future choices. Computational theories of reinforcement learning play a central role in the newly emerging areas of neuroeconomics and decision neuroscience. In this framework, actions are chosen according to their value functions, which describe how much future reward is expected from each action. Value functions can be adjusted not only through reward and penalty, but also by the animal’s knowledge of its current environment. Studies have revealed that a large proportion of the brain is involved in representing and updating value functions and using them to choose an action. However, how the nature of a behavioral task affects the neural mechanisms of reinforcement learning remains incompletely understood. Future studies should uncover the principles by which different computational elements of reinforcement learning are dynamically coordinated across the entire brain. PMID:22462543

ICT-Supported Education; Learning Styles for Individual Knowledge Building

NASA Astrophysics Data System (ADS)

Haugen, Harald; Ask, Bodil; Bjørke, Sven Åke

School surveys and reports on integration of ICT in teaching and learning indicate that the technology is mainly used in traditional learning environments. Furthermore, the most frequently used software in the classrooms are general tools like word processors, presentation tools and Internet browsers. Recent attention among youngsters on social software / web 2.0, contemporary pedagogical approaches like social constructivism and long time experiences with system dynamics and simulations, seem to have a hard time being accepted by teachers and curriculum designers. How can teachers be trained to understand and apply these possibilities optimally that are now available in the classroom and online, on broadband connections and with high capacity computers? Some views on practices with the above-mentioned alternative approaches to learning are presented in this paper, focusing particularly on the options for online work and learning programmes. Here we have first hand experience with adult and mature academics, but also some background with other target groups.

Neuromorphic Learning From Noisy Data

NASA Technical Reports Server (NTRS)

Merrill, Walter C.; Troudet, Terry

1993-01-01

Two reports present numerical study of performance of feedforward neural network trained by back-propagation algorithm in learning continuous-valued mappings from data corrupted by noise. Two types of noise considered: plant noise which affects dynamics of controlled process and data-processing noise, which occurs during analog processing and digital sampling of signals. Study performed with view toward use of neural networks as neurocontrollers to substitute for, or enhance, performances of human experts in controlling mechanical devices in presence of sensor and actuator noise and to enhance performances of more-conventional digital feedback electronic process controllers in noisy environments.

A Parallel and Incremental Approach for Data-Intensive Learning of Bayesian Networks.

PubMed

Yue, Kun; Fang, Qiyu; Wang, Xiaoling; Li, Jin; Liu, Weiyi

2015-12-01

Bayesian network (BN) has been adopted as the underlying model for representing and inferring uncertain knowledge. As the basis of realistic applications centered on probabilistic inferences, learning a BN from data is a critical subject of machine learning, artificial intelligence, and big data paradigms. Currently, it is necessary to extend the classical methods for learning BNs with respect to data-intensive computing or in cloud environments. In this paper, we propose a parallel and incremental approach for data-intensive learning of BNs from massive, distributed, and dynamically changing data by extending the classical scoring and search algorithm and using MapReduce. First, we adopt the minimum description length as the scoring metric and give the two-pass MapReduce-based algorithms for computing the required marginal probabilities and scoring the candidate graphical model from sample data. Then, we give the corresponding strategy for extending the classical hill-climbing algorithm to obtain the optimal structure, as well as that for storing a BN by pairs. Further, in view of the dynamic characteristics of the changing data, we give the concept of influence degree to measure the coincidence of the current BN with new data, and then propose the corresponding two-pass MapReduce-based algorithms for BNs incremental learning. Experimental results show the efficiency, scalability, and effectiveness of our methods.

The Software Engineering Laboratory: An operational software experience factory

NASA Technical Reports Server (NTRS)

Basili, Victor R.; Caldiera, Gianluigi; Mcgarry, Frank; Pajerski, Rose; Page, Gerald; Waligora, Sharon

1992-01-01

For 15 years, the Software Engineering Laboratory (SEL) has been carrying out studies and experiments for the purpose of understanding, assessing, and improving software and software processes within a production software development environment at NASA/GSFC. The SEL comprises three major organizations: (1) NASA/GSFC, Flight Dynamics Division; (2) University of Maryland, Department of Computer Science; and (3) Computer Sciences Corporation, Flight Dynamics Technology Group. These organizations have jointly carried out several hundred software studies, producing hundreds of reports, papers, and documents, all of which describe some aspect of the software engineering technology that was analyzed in the flight dynamics environment at NASA. The studies range from small, controlled experiments (such as analyzing the effectiveness of code reading versus that of functional testing) to large, multiple project studies (such as assessing the impacts of Ada on a production environment). The organization's driving goal is to improve the software process continually, so that sustained improvement may be observed in the resulting products. This paper discusses the SEL as a functioning example of an operational software experience factory and summarizes the characteristics of and major lessons learned from 15 years of SEL operations.

Visual analysis of fluid dynamics at NASA's numerical aerodynamic simulation facility

NASA Technical Reports Server (NTRS)

Watson, Velvin R.

1991-01-01

A study aimed at describing and illustrating visualization tools used in Computational Fluid Dynamics (CFD) and indicating how these tools are likely to change by showing a projected resolution of the human computer interface is presented. The following are outlined using a graphically based test format: the revolution of human computer environments for CFD research; comparison of current environments; current environments with the ideal; predictions for the future CFD environments; what can be done to accelerate the improvements. The following comments are given: when acquiring visualization tools, potential rapid changes must be considered; environmental changes over the next ten years due to human computer interface cannot be fathomed; data flow packages such as AVS, apE, Explorer and Data Explorer are easy to learn and use for small problems, excellent for prototyping, but not so efficient for large problems; the approximation techniques used in visualization software must be appropriate for the data; it has become more cost effective to move jobs that fit on workstations and run only memory intensive jobs on the supercomputer; use of three dimensional skills will be maximized when the three dimensional environment is built in from the start.

Remembering to learn: independent place and journey coding mechanisms contribute to memory transfer.

PubMed

Bahar, Amir S; Shapiro, Matthew L

2012-02-08

The neural mechanisms that integrate new episodes with established memories are unknown. When rats explore an environment, CA1 cells fire in place fields that indicate locations. In goal-directed spatial memory tasks, some place fields differentiate behavioral histories ("journey-dependent" place fields) while others do not ("journey-independent" place fields). To investigate how these signals inform learning and memory for new and familiar episodes, we recorded CA1 and CA3 activity in rats trained to perform a "standard" spatial memory task in a plus maze and in two new task variants. A "switch" task exchanged the start and goal locations in the same environment; an "altered environment" task contained unfamiliar local and distal cues. In the switch task, performance was mildly impaired, new firing maps were stable, but the proportion and stability of journey-dependent place fields declined. In the altered environment, overall performance was strongly impaired, new firing maps were unstable, and stable proportions of journey-dependent place fields were maintained. In both tasks, memory errors were accompanied by a decline in journey codes. The different dynamics of place and journey coding suggest that they reflect separate mechanisms and contribute to distinct memory computations. Stable place fields may represent familiar relationships among environmental features that are required for consistent memory performance. Journey-dependent activity may correspond with goal-directed behavioral sequences that reflect expectancies that generalize across environments. The complementary signals could help link current events with established memories, so that familiarity with either a behavioral strategy or an environment can inform goal-directed learning.

REMEMBERING TO LEARN: INDEPENDENT PLACE AND JOURNEY CODING MECHANISMS CONTRIBUTE TO MEMORY TRANSFER

PubMed Central

Bahar, Amir S.; Shapiro, Matthew L.

2012-01-01

The neural mechanisms that integrate new episodes with established memories are unknown. When rats explore an environment, CA1 cells fire in place fields that indicate locations. In goal-directed spatial memory tasks, some place fields differentiate behavioral histories (journey-dependent place fields) while others do not (journey-independent place fields). To investigate how these signals inform learning and memory for new and familiar episodes, we recorded CA1 and CA3 activity in rats trained to perform a standard spatial memory task in a plus maze and in two new task variants. A switch task exchanged the start and goal locations in the same environment; an altered environment task contained unfamiliar local and distal cues. In the switch task, performance was mildly impaired, new firing maps were stable, but the proportion and stability of journey-dependent place fields declined. In the altered environment, overall performance was strongly impaired, new firing maps were unstable, and stable proportions of journey-dependent place fields were maintained. In both tasks, memory errors were accompanied by a decline in journey codes. The different dynamics of place and journey coding suggest that they reflect separate mechanisms and contribute to distinct memory computations. Stable place fields may represent familiar relationships among environmental features that are required for consistent memory performance. Journey-dependent activity may correspond with goal directed behavioral sequences that reflect expectancies that generalize across environments. The complementary signals could help link current events with established memories, so that familiarity with either a behavioral strategy or an environment can inform goal-directed learning. PMID:22323731

The Relationship of Learning Traits, Motivation and Performance-Learning Response Dynamics

ERIC Educational Resources Information Center

Hwang, Wu-Yuin; Chang, Chen-Bin; Chen, Gan-Jung

2004-01-01

This paper proposes a model of learning dynamics and learning energy, one that analyzes learning systems scientifically. This model makes response to the learner action by means of some equations relating to learning dynamics, learning energy, learning speed, learning force, and learning acceleration, which is analogous to the notion of Newtonian…

Research Into the Role of Students’ Affective Domain While Learning Geology in Field Environments

NASA Astrophysics Data System (ADS)

Elkins, J.

2009-12-01

Existing research programs in field-based geocognition include assessment of cognitive, psychomotor, and affective domains. Assessment of the affective domain often involves the use of instruments and techniques uncommon to the geosciences. Research regarding the affective domain also commonly results in the collection and production of qualitative data that is difficult for geoscientists to analyze due to their lack of familiarity with these data sets. However, important information about students’ affective responses to learning in field environments can be obtained by using these methods. My research program focuses on data produced by students’ affective responses to field-based learning environments, primarily among students at the introductory level. For this research I developed a Likert-scale Novelty Space Survey, which presents student ‘novelty space’ (Orion and Hofstien, 1993) as a polygon; the larger the polygons, the more novelty students are experiencing. The axises for these polygons correspond to novelty domains involving geographic, social, cognitive, and psychological factors. In addition to the Novelty Space Survey, data which I have collected/generated includes focus group interviews on the role of recreational experiences in geology field programs. I have also collected data concerning the motivating factors that cause students to take photographs on field trips. The results of these studies give insight to the emotional responses students have to learning in the field and are important considerations for practitioners of teaching in these environments. Collaborative investigations among research programs that cross university departments and include multiple institutions is critical at this point in development of geocognition as a field due to unfamiliarity with cognitive science methodology by practitioners teaching geosciences and the dynamic nature of field work by cognitive scientists. However, combining the efforts of cognitive scientists and practitioners of geoscience teaching into research teams is a recommended strategy for understanding the role of the affective domain in student learning in field environments.

A Discussion of Possibility of Reinforcement Learning Using Event-Related Potential in BCI

NASA Astrophysics Data System (ADS)

Yamagishi, Yuya; Tsubone, Tadashi; Wada, Yasuhiro

Recently, Brain computer interface (BCI) which is a direct connecting pathway an external device such as a computer or a robot and a human brain have gotten a lot of attention. Since BCI can control the machines as robots by using the brain activity without using the voluntary muscle, the BCI may become a useful communication tool for handicapped persons, for instance, amyotrophic lateral sclerosis patients. However, in order to realize the BCI system which can perform precise tasks on various environments, it is necessary to design the control rules to adapt to the dynamic environments. Reinforcement learning is one approach of the design of the control rule. If this reinforcement leaning can be performed by the brain activity, it leads to the attainment of BCI that has general versatility. In this research, we paid attention to P300 of event-related potential as an alternative signal of the reward of reinforcement learning. We discriminated between the success and the failure trials from P300 of the EEG of the single trial by using the proposed discrimination algorithm based on Support vector machine. The possibility of reinforcement learning was examined from the viewpoint of the number of discriminated trials. It was shown that there was a possibility to be able to learn in most subjects.

Reinforcement Learning Multi-Agent Modeling of Decision-Making Agents for the Study of Transboundary Surface Water Conflicts with Application to the Syr Darya River Basin

NASA Astrophysics Data System (ADS)

Riegels, N.; Siegfried, T.; Pereira Cardenal, S. J.; Jensen, R. A.; Bauer-Gottwein, P.

2008-12-01

In most economics--driven approaches to optimizing water use at the river basin scale, the system is modelled deterministically with the goal of maximizing overall benefits. However, actual operation and allocation decisions must be made under hydrologic and economic uncertainty. In addition, river basins often cross political boundaries, and different states may not be motivated to cooperate so as to maximize basin- scale benefits. Even within states, competing agents such as irrigation districts, municipal water agencies, and large industrial users may not have incentives to cooperate to realize efficiency gains identified in basin- level studies. More traditional simulation--optimization approaches assume pre-commitment by individual agents and stakeholders and unconditional compliance on each side. While this can help determine attainable gains and tradeoffs from efficient management, such hardwired policies do not account for dynamic feedback between agents themselves or between agents and their environments (e.g. due to climate change etc.). In reality however, we are dealing with an out-of-equilibrium multi-agent system, where there is neither global knowledge nor global control, but rather continuous strategic interaction between decision making agents. Based on the theory of stochastic games, we present a computational framework that allows for studying the dynamic feedback between decision--making agents themselves and an inherently uncertain environment in a spatially and temporally distributed manner. Agents with decision-making control over water allocation such as countries, irrigation districts, and municipalities are represented by reinforcement learning agents and coupled to a detailed hydrologic--economic model. This approach emphasizes learning by agents from their continuous interaction with other agents and the environment. It provides a convenient framework for the solution of the problem of dynamic decision-making in a mixed cooperative / non-cooperative environment with which different institutional setups and incentive systems can be studied so to identify reasonable ways to reach desirable, Pareto--optimal allocation outcomes. Preliminary results from an application to the Syr Darya river basin in Central Asia will be presented and discussed. The Syr Darya River is a classic example of a transboundary river basin in which basin-wide efficiency gains identified in optimization studies have not been sufficient to induce cooperative management of the river by the riparian states.

A Sarsa(λ)-based control model for real-time traffic light coordination.

PubMed

Zhou, Xiaoke; Zhu, Fei; Liu, Quan; Fu, Yuchen; Huang, Wei

2014-01-01

Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors that can affect the traffic control model, it is difficult to find the optimal solution. The disability of traffic light controllers to learn from past experiences caused them to be unable to adaptively fit dynamic changes of traffic flow. Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. The proposed Sarsa(λ)-based real-time traffic control optimization model can maintain the traffic signal timing policy more effectively. The Sarsa(λ)-based model gains traffic cost of the vehicle, which considers delay time, the number of waiting vehicles, and the integrated saturation from its experiences to learn and determine the optimal actions. The experiment results show an inspiring improvement in traffic control, indicating the proposed model is capable of facilitating real-time dynamic traffic control.

Determining sociability, social space, and social presence in (a)synchronous collaborative groups.

PubMed

Kreijns, Karel; Kirschner, Paul A; Jochems, Wim; Van Buuren, Hans

2004-04-01

The effectiveness of group learning in asynchronous distributed learning groups depends on the social interaction that takes place. This social interaction affects both cognitive and socioemotional processes that take place during learning, group forming, establishment of group structures, and group dynamics. Though now known to be important, this aspect is often ignored, denied or forgotten by educators and researchers who tend to concentrate on cognitive processes and on-task contexts. This "one-sided" educational focus largely determines the set of requirements in the design of computer-supported collaborative learning (CSCL) environments resulting in functional CSCL environments. In contrast, our research is aimed at the design and implementation of sociable CSCL environments which may increase the likelihood that a sound social space will emerge. We use a theoretical framework that is based upon an ecological approach to social interaction, centering on the concept of social affordances, the concept of the sociability of CSCL environments, and social presence theory. The hypothesis is that the higher the sociability, the more likely that social interaction will take place or will increase, and the more likely that this will result in an emerging sound social space. In the present research, the variables of interest are sociability, social space, and social presence. This study deals with the construction and validation of three instruments to determine sociability, social space, and social presence in (a)synchronous collaborating groups. The findings suggest that the instruments have potential to be useful as measures for the respective variables. However, it must be realized that these measures are "first steps."

Effects of age on associating virtual and embodied toys.

PubMed

Okita, Sandra Y

2004-08-01

Technologies such as videos, toys, and video games are used as tools in delivering education to young children. Do children spontaneously transfer between virtual and real-world mediums as they learn? Fifty-six children learned facts about a toy dog presented through varying levels of technology and interactivity (e.g., video game, stuffed animal, picture books). They then met a similar dog character in a new embodiment (e.g., as a stuffed animal if first met the dog as video character). Would children spontaneously generalize the facts they learned about the dog character across mediums (dynamic and static environments)? Results indicate that younger children were more likely to generalize facts across mediums. Specific aspects of the level of technology and interactivity had little effect.

Robotic Mission to Mars: Hands-on, minds-on, web-based learning

NASA Astrophysics Data System (ADS)

Mathers, Naomi; Goktogen, Ali; Rankin, John; Anderson, Marion

2012-11-01

Problem-based learning has been demonstrated as an effective methodology for developing analytical skills and critical thinking. The use of scenario-based learning incorporates problem-based learning whilst encouraging students to collaborate with their colleagues and dynamically adapt to their environment. This increased interaction stimulates a deeper understanding and the generation of new knowledge. The Victorian Space Science Education Centre (VSSEC) uses scenario-based learning in its Mission to Mars, Mission to the Orbiting Space Laboratory and Primary Expedition to the M.A.R.S. Base programs. These programs utilize methodologies such as hands-on applications, immersive-learning, integrated technologies, critical thinking and mentoring to engage students in Science, Technology, Engineering and Mathematics (STEM) and highlight potential career paths in science and engineering. The immersive nature of the programs demands specialist environments such as a simulated Mars environment, Mission Control and Space Laboratory, thus restricting these programs to a physical location and limiting student access to the programs. To move beyond these limitations, VSSEC worked with its university partners to develop a web-based mission that delivered the benefits of scenario-based learning within a school environment. The Robotic Mission to Mars allows students to remotely control a real rover, developed by the Australian Centre for Field Robotics (ACFR), on the VSSEC Mars surface. After completing a pre-mission training program and site selection activity, students take on the roles of scientists and engineers in Mission Control to complete a mission and collect data for further analysis. Mission Control is established using software developed by the ACRI Games Technology Lab at La Trobe University using the principles of serious gaming. The software allows students to control the rover, monitor its systems and collect scientific data for analysis. This program encourages students to work scientifically and explores the interaction between scientists and engineers. This paper presents the development of the program, including the involvement of university students in the development of the rover, the software, and the collation of the scientific data. It also presents the results of the trial phase of this program including the impact on student engagement and learning outcomes.

Envisioning engineering education and practice in the coming intelligence convergence era — a complex adaptive systems approach

NASA Astrophysics Data System (ADS)

Noor, Ahmed K.

2013-12-01

Some of the recent attempts for improving and transforming engineering education are reviewed. The attempts aim at providing the entry level engineers with the skills needed to address the challenges of future large-scale complex systems and projects. Some of the frontier sectors and future challenges for engineers are outlined. The major characteristics of the coming intelligence convergence era (the post-information age) are identified. These include the prevalence of smart devices and environments, the widespread applications of anticipatory computing and predictive / prescriptive analytics, as well as a symbiotic relationship between humans and machines. Devices and machines will be able to learn from, and with, humans in a natural collaborative way. The recent game changers in learnscapes (learning paradigms, technologies, platforms, spaces, and environments) that can significantly impact engineering education in the coming era are identified. Among these are open educational resources, knowledge-rich classrooms, immersive interactive 3D learning, augmented reality, reverse instruction / flipped classroom, gamification, robots in the classroom, and adaptive personalized learning. Significant transformative changes in, and mass customization of, learning are envisioned to emerge from the synergistic combination of the game changers and other technologies. The realization of the aforementioned vision requires the development of a new multidisciplinary framework of emergent engineering for relating innovation, complexity and cybernetics, within the future learning environments. The framework can be used to treat engineering education as a complex adaptive system, with dynamically interacting and communicating components (instructors, individual, small, and large groups of learners). The emergent behavior resulting from the interactions can produce progressively better, and continuously improving, learning environment. As a first step towards the realization of the vision, intelligent adaptive cyber-physical ecosystems need to be developed to facilitate collaboration between the various stakeholders of engineering education, and to accelerate the development of a skilled engineering workforce. The major components of the ecosystems include integrated knowledge discovery and exploitation facilities, blended learning and research spaces, novel ultra-intelligent software agents, multimodal and autonomous interfaces, and networked cognitive and tele-presence robots.

Synaptic State Matching: A Dynamical Architecture for Predictive Internal Representation and Feature Detection

PubMed Central

Tavazoie, Saeed

2013-01-01

Here we explore the possibility that a core function of sensory cortex is the generation of an internal simulation of sensory environment in real-time. A logical elaboration of this idea leads to a dynamical neural architecture that oscillates between two fundamental network states, one driven by external input, and the other by recurrent synaptic drive in the absence of sensory input. Synaptic strength is modified by a proposed synaptic state matching (SSM) process that ensures equivalence of spike statistics between the two network states. Remarkably, SSM, operating locally at individual synapses, generates accurate and stable network-level predictive internal representations, enabling pattern completion and unsupervised feature detection from noisy sensory input. SSM is a biologically plausible substrate for learning and memory because it brings together sequence learning, feature detection, synaptic homeostasis, and network oscillations under a single unifying computational framework. PMID:23991161

Evaluation of a conceptual framework for predicting navigation performance in virtual reality.

PubMed

Grübel, Jascha; Thrash, Tyler; Hölscher, Christoph; Schinazi, Victor R

2017-01-01

Previous research in spatial cognition has often relied on simple spatial tasks in static environments in order to draw inferences regarding navigation performance. These tasks are typically divided into categories (e.g., egocentric or allocentric) that reflect different two-systems theories. Unfortunately, this two-systems approach has been insufficient for reliably predicting navigation performance in virtual reality (VR). In the present experiment, participants were asked to learn and navigate towards goal locations in a virtual city and then perform eight simple spatial tasks in a separate environment. These eight tasks were organised along four orthogonal dimensions (static/dynamic, perceived/remembered, egocentric/allocentric, and distance/direction). We employed confirmatory and exploratory analyses in order to assess the relationship between navigation performance and performances on these simple tasks. We provide evidence that a dynamic task (i.e., intercepting a moving object) is capable of predicting navigation performance in a familiar virtual environment better than several categories of static tasks. These results have important implications for studies on navigation in VR that tend to over-emphasise the role of spatial memory. Given that our dynamic tasks required efficient interaction with the human interface device (HID), they were more closely aligned with the perceptuomotor processes associated with locomotion than wayfinding. In the future, researchers should consider training participants on HIDs using a dynamic task prior to conducting a navigation experiment. Performances on dynamic tasks should also be assessed in order to avoid confounding skill with an HID and spatial knowledge acquisition.

Evaluation of a conceptual framework for predicting navigation performance in virtual reality

PubMed Central

Thrash, Tyler; Hölscher, Christoph; Schinazi, Victor R.

2017-01-01

Previous research in spatial cognition has often relied on simple spatial tasks in static environments in order to draw inferences regarding navigation performance. These tasks are typically divided into categories (e.g., egocentric or allocentric) that reflect different two-systems theories. Unfortunately, this two-systems approach has been insufficient for reliably predicting navigation performance in virtual reality (VR). In the present experiment, participants were asked to learn and navigate towards goal locations in a virtual city and then perform eight simple spatial tasks in a separate environment. These eight tasks were organised along four orthogonal dimensions (static/dynamic, perceived/remembered, egocentric/allocentric, and distance/direction). We employed confirmatory and exploratory analyses in order to assess the relationship between navigation performance and performances on these simple tasks. We provide evidence that a dynamic task (i.e., intercepting a moving object) is capable of predicting navigation performance in a familiar virtual environment better than several categories of static tasks. These results have important implications for studies on navigation in VR that tend to over-emphasise the role of spatial memory. Given that our dynamic tasks required efficient interaction with the human interface device (HID), they were more closely aligned with the perceptuomotor processes associated with locomotion than wayfinding. In the future, researchers should consider training participants on HIDs using a dynamic task prior to conducting a navigation experiment. Performances on dynamic tasks should also be assessed in order to avoid confounding skill with an HID and spatial knowledge acquisition. PMID:28915266

Studying primate learning in group contexts: Tests of social foraging, response to novelty, and cooperative problem solving.

PubMed

Drea, Christine M

2006-03-01

Learning commonly refers to the modification of behavior through experience, whereby an animal gains information about stimulus-response contingencies from interacting with its physical environment. Social learning, on the other hand, occurs when the same information originates, not from the animal's personal experience, but from the actions of others. Socially biased learning is the 'collective outcome of interacting physical, social, and individual factors' [D. Fragaszy, E. Visalberghi, Learn. Behav. 32 (2004) 24-35.] (see p. 24). Mounting interest in animal social learning has brought with it certain innovations in animal testing procedures. Variants of the observer-demonstrator and cooperation paradigms, for instance, have been used widely in captive settings to examine the transmission or coordination of behavior, respectively, between two animals. Relatively few studies, however, have examined social learning in more complex group settings and even fewer have manipulated the social environment to empirically test the effect of group dynamics on problem solving. The present paper outlines procedures for group testing captive non-human primates, in spacious arenas, to evaluate the social modulation of learning and performance. These methods are illustrated in the context of (1) naturalistic social foraging problems, modeled after traditional visual discrimination paradigms, (2) response to novel objects and novel extractive foraging tasks, and (3) cooperative problem solving. Each example showcases the benefits of experimentally manipulating social context to compare an animal's performance in intact groups (or even pairs) against its performance under different social circumstances. Broader application of group testing procedures and manipulation of group composition promise to provide meaningful insight into socially biased learning.

Incorporation of perception-based information in robot learning using fuzzy reinforcement learning agents

NASA Astrophysics Data System (ADS)

Zhou, Changjiu; Meng, Qingchun; Guo, Zhongwen; Qu, Wiefen; Yin, Bo

2002-04-01

Robot learning in unstructured environments has been proved to be an extremely challenging problem, mainly because of many uncertainties always present in the real world. Human beings, on the other hand, seem to cope very well with uncertain and unpredictable environments, often relying on perception-based information. Furthermore, humans beings can also utilize perceptions to guide their learning on those parts of the perception-action space that are actually relevant to the task. Therefore, we conduct a research aimed at improving robot learning through the incorporation of both perception-based and measurement-based information. For this reason, a fuzzy reinforcement learning (FRL) agent is proposed in this paper. Based on a neural-fuzzy architecture, different kinds of information can be incorporated into the FRL agent to initialise its action network, critic network and evaluation feedback module so as to accelerate its learning. By making use of the global optimisation capability of GAs (genetic algorithms), a GA-based FRL (GAFRL) agent is presented to solve the local minima problem in traditional actor-critic reinforcement learning. On the other hand, with the prediction capability of the critic network, GAs can perform a more effective global search. Different GAFRL agents are constructed and verified by using the simulation model of a physical biped robot. The simulation analysis shows that the biped learning rate for dynamic balance can be improved by incorporating perception-based information on biped balancing and walking evaluation. The biped robot can find its application in ocean exploration, detection or sea rescue activity, as well as military maritime activity.

Early Childhood Transitions Research: A Review of Concepts, Theory, and Practice. Working Papers in Early Childhood Development, No. 48

ERIC Educational Resources Information Center

Vogler, Pia; Crivello, Gina; Woodhead, Martin

2008-01-01

Children face many important changes in the first eight years of life, including different learning centres, social groups, roles and expectations. Their ability to adapt to such a dynamic and evolving environment directly affects their sense of identity and status within their community over the short and long term. In particular, the key turning…

Organization and Administration of Adult Education Programs: A Guide for Practitioners. Adult Education Special Topics: Theory, Research and Practice in LifeLong Learning

ERIC Educational Resources Information Center

Schmidt, Steven W.; Biniecki, Susan M. Yelich

2016-01-01

Administrators of adult education programs work in dynamic and ever-changing environments. They are continually challenged with a myriad of issues related to program budgeting, marketing, strategic planning, funding, human resources, and other topics. With decades of real world experience in the field, Steven Schmidt and Susan Yelich Biniecki have…

Control Grouped Pedagogical Experiment to Test the Performance of Second-Generation Web Maps and the Traditional Maps at the University of Debrecen

ERIC Educational Resources Information Center

Balla, Dániel; Zichar, Marianna; Boda, Judit; Novák, Tibor József

2015-01-01

Almost every component of the information society is influenced by elements built on communication technology. Learning also tends to be related to the dynamic usage of computers. Nowadays, a number of applications (online or offline) are also available that engage large groups of potential users and simultaneously provide a virtual environment to…

Learning comunication strategies for distributed artificial intelligence

NASA Astrophysics Data System (ADS)

Kinney, Michael; Tsatsoulis, Costas

1992-08-01

We present a methodology that allows collections of intelligent system to automatically learn communication strategies, so that they can exchange information and coordinate their problem solving activity. In our methodology communication between agents is determined by the agents themselves, which consider the progress of their individual problem solving activities compared to the communication needs of their surrounding agents. Through learning, communication lines between agents might be established or disconnected, communication frequencies modified, and the system can also react to dynamic changes in the environment that might force agents to cease to exist or to be added. We have established dynamic, quantitative measures of the usefulness of a fact, the cost of a fact, the work load of an agent, and the selfishness of an agent (a measure indicating an agent's preference between transmitting information versus performing individual problem solving), and use these values to adapt the communication between intelligent agents. In this paper we present the theoretical foundations of our work together with experimental results and performance statistics of networks of agents involved in cooperative problem solving activities.

Effects of congruence between preferred and perceived learning environments in nursing education in Taiwan: a cross-sectional study.

PubMed

Yeh, Ting-Kuang; Huang, Hsiu-Mei; Chan, Wing P; Chang, Chun-Yen

2016-05-20

To investigate the effects of congruence between preferred and perceived learning environments on learning outcomes of nursing students. A nursing course at a university in central Taiwan. 124 Taiwanese nursing students enrolled in a 13-week problem-based Fundamental Nursing curriculum. Students' preferred learning environment, perceptions about the learning environment and learning outcomes (knowledge, self-efficacy and attitudes) were assessed. On the basis of test scores measuring their preferred and perceived learning environments, students were assigned to one of two groups: a 'preferred environment aligned with perceived learning environment' group and a 'preferred environment discordant with perceived learning environment' group. Learning outcomes were analysed by group. Most participants preferred learning in a classroom environment that combined problem-based and lecture-based instruction. However, a mismatch of problem-based instruction with students' perceptions occurred. Learning outcomes were significantly better when students' perceptions of their instructional activities were congruent with their preferred learning environment. As problem-based learning becomes a focus of educational reform in nursing, teachers need to be aware of students' preferences and perceptions of the learning environment. Teachers may also need to improve the match between an individual student's perception and a teacher's intention in the learning environment, and between the student's preferred and actual perceptions of the learning environment. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Transitions in sensitive period attachment learning in infancy: the role of corticosterone.

PubMed

Sullivan, Regina M; Holman, Parker J

2010-05-01

Survival of altricial infants, including humans and rats, depends on attachment to the caregiver - a process that requires infants to recognize, learn, and remember their attachment figure. The demands of a dynamic environment combined with a maturing organism require frequent neurobehavioral reorganization. This restructuring of behavior and its supporting neural circuitry can be viewed through the unique lens of attachment learning in rats in which preference learning is enhanced and aversion learning is attenuated. Behavioral restructuring is well adapted to securing the crucial infant-caregiver relationship regardless of the quality of care. With maturation and the end of the infant-caregiver attachment learning period, the complex interplay of neural structures, hormones, and social behavior coordinates the developing rat's eventual transition to life outside of the nest. Nevertheless, early-life environmental and physiological stressors can alter the resilient nature of this system, particularly with respect to the amygdala, and these changes may provide important clues to understanding the lasting effects of early stress. (c) 2009 Elsevier Ltd. All rights reserved.

Neural networks involved in learning lexical-semantic and syntactic information in a second language.

PubMed

Mueller, Jutta L; Rueschemeyer, Shirley-Ann; Ono, Kentaro; Sugiura, Motoaki; Sadato, Norihiro; Nakamura, Akinori

2014-01-01

The present study used functional magnetic resonance imaging (fMRI) to investigate the neural correlates of language acquisition in a realistic learning environment. Japanese native speakers were trained in a miniature version of German prior to fMRI scanning. During scanning they listened to (1) familiar sentences, (2) sentences including a novel sentence structure, and (3) sentences containing a novel word while visual context provided referential information. Learning-related decreases of brain activation over time were found in a mainly left-hemispheric network comprising classical frontal and temporal language areas as well as parietal and subcortical regions and were largely overlapping for novel words and the novel sentence structure in initial stages of learning. Differences occurred at later stages of learning during which content-specific activation patterns in prefrontal, parietal and temporal cortices emerged. The results are taken as evidence for a domain-general network supporting the initial stages of language learning which dynamically adapts as learners become proficient.

Evolution of learning and levels of selection: A lesson from avian parent-offspring communication.

PubMed

Lotem, Arnon; Biran-Yoeli, Inbar

2013-09-20

In recent years, it has become increasingly clear that the evolution of behavior may be better understood as the evolution of the learning mechanisms that produce it, and that such mechanisms should be modeled and tested explicitly. However, this approach, which has recently been applied to animal foraging and decision-making, has rarely been applied to the social and communicative behaviors that are likely to operate in complex social environments and be subject to multi-level selection. Here we use genetic, agent-based evolutionary simulations to explore how learning mechanisms may evolve to adjust the level of nestling begging (offspring signaling of need), and to examine the possible consequences of this process for parent-offspring conflict and communication. In doing so, we also provide the first step-by-step dynamic model of parent-offspring communication. The results confirm several previous theoretical predictions and demonstrate three novel phenomena. First, negatively frequency-dependent group-level selection can generate a stable polymorphism of learning strategies and parental responses. Second, while conventional reinforcement learning models fail to cope successfully with family dynamics at the nest, a newly developed learning model (incorporating behaviors that are consistent with recent experimental results on learning in nestling begging) produced effective learning, which evolved successfully. Third, while kin-selection affects the frequency of the different learning genes, its impact on begging slope and intensity was unexpectedly negligible, demonstrating that evolution is a complex process, and showing that the effect of kin-selection on behaviors that are shaped by learning may not be predicted by simple application of Hamilton's rule. Copyright © 2013 Elsevier Inc. All rights reserved.

Evolution of learning and levels of selection: a lesson from avian parent-offspring communication.

PubMed

Lotem, Arnon; Biran-Yoeli, Inbar

2014-02-01

In recent years, it has become increasingly clear that the evolution of behavior may be better understood as the evolution of the learning mechanisms that produce it, and that such mechanisms should be modeled and tested explicitly. However, this approach, which has recently been applied to animal foraging and decision-making, has rarely been applied to the social and communicative behaviors that are likely to operate in complex social environments and be subject to multi-level selection. Here we use genetic, agent-based evolutionary simulations to explore how learning mechanisms may evolve to adjust the level of nestling begging (offspring signaling of need), and to examine the possible consequences of this process for parent-offspring conflict and communication. In doing so, we also provide the first step-by-step dynamic model of parent-offspring communication. The results confirm several previous theoretical predictions and demonstrate three novel phenomena. First, negatively frequency-dependent group-level selection can generate a stable polymorphism of learning strategies and parental responses. Second, while conventional reinforcement learning models fail to cope successfully with family dynamics at the nest, a newly developed learning model (incorporating behaviors that are consistent with recent experimental results on learning in nestling begging) produced effective learning, which evolved successfully. Third, while kin-selection affects the frequency of the different learning genes, its impact on begging slope and intensity was unexpectedly negligible, demonstrating that evolution is a complex process, and showing that the effect of kin-selection on behaviors that are shaped by learning may not be predicted by simple application of Hamilton's rule. Copyright © 2013 Elsevier Inc. All rights reserved.

Apparatus for multiprocessor-based control of a multiagent robot

NASA Technical Reports Server (NTRS)

Peters, II, Richard Alan (Inventor)

2009-01-01

An architecture for robot intelligence enables a robot to learn new behaviors and create new behavior sequences autonomously and interact with a dynamically changing environment. Sensory information is mapped onto a Sensory Ego-Sphere (SES) that rapidly identifies important changes in the environment and functions much like short term memory. Behaviors are stored in a DBAM that creates an active map from the robot's current state to a goal state and functions much like long term memory. A dream state converts recent activities stored in the SES and creates or modifies behaviors in the DBAM.

Architecture for Multiple Interacting Robot Intelligences

NASA Technical Reports Server (NTRS)

Peters, Richard Alan, II (Inventor)

2008-01-01

An architecture for robot intelligence enables a robot to learn new behaviors and create new behavior sequences autonomously and interact with a dynamically changing environment. Sensory information is mapped onto a Sensory Ego-Sphere (SES) that rapidly identifies important changes in the environment and functions much like short term memory. Behaviors are stored in a database associative memory (DBAM) that creates an active map from the robot's current state to a goal state and functions much like long term memory. A dream state converts recent activities stored in the SES and creates or modifies behaviors in the DBAM.

Effects of congruence between preferred and perceived learning environments in nursing education in Taiwan: a cross-sectional study

PubMed Central

Yeh, Ting-Kuang; Huang, Hsiu-Mei; Chan, Wing P; Chang, Chun-Yen

2016-01-01

Objective To investigate the effects of congruence between preferred and perceived learning environments on learning outcomes of nursing students. Setting A nursing course at a university in central Taiwan. Participants 124 Taiwanese nursing students enrolled in a 13-week problem-based Fundamental Nursing curriculum. Design and methods Students' preferred learning environment, perceptions about the learning environment and learning outcomes (knowledge, self-efficacy and attitudes) were assessed. On the basis of test scores measuring their preferred and perceived learning environments, students were assigned to one of two groups: a ‘preferred environment aligned with perceived learning environment’ group and a ‘preferred environment discordant with perceived learning environment’ group. Learning outcomes were analysed by group. Outcome measures Most participants preferred learning in a classroom environment that combined problem-based and lecture-based instruction. However, a mismatch of problem-based instruction with students' perceptions occurred. Learning outcomes were significantly better when students' perceptions of their instructional activities were congruent with their preferred learning environment. Conclusions As problem-based learning becomes a focus of educational reform in nursing, teachers need to be aware of students' preferences and perceptions of the learning environment. Teachers may also need to improve the match between an individual student's perception and a teacher's intention in the learning environment, and between the student's preferred and actual perceptions of the learning environment. PMID:27207620

Brief Report: Preliminary Proposal of a Conceptual Model of a Digital Environment for Developing Mathematical Reasoning in Students with Autism Spectrum Disorders.

PubMed

Santos, Maria Isabel; Breda, Ana; Almeida, Ana Margarida

2015-08-01

There is clear evidence that in typically developing children reasoning and sense-making are essential in all mathematical learning and understanding processes. In children with autism spectrum disorders (ASD), however, these become much more significant, considering their importance to successful independent living. This paper presents a preliminary proposal of a digital environment, specifically targeted to promote the development of mathematical reasoning in students with ASD. Given the diversity of ASD, the prototyping of this environment requires the study of dynamic adaptation processes and the development of activities adjusted to each user's profile. We present the results obtained during the first phase of this ongoing research, describing a conceptual model of the proposed digital environment. Guidelines for future research are also discussed.

Science Learning Outcomes in Alignment with Learning Environment Preferences

NASA Astrophysics Data System (ADS)

Chang, Chun-Yen; Hsiao, Chien-Hua; Chang, Yueh-Hsia

2011-04-01

This study investigated students' learning environment preferences and compared the relative effectiveness of instructional approaches on students' learning outcomes in achievement and attitude among 10th grade earth science classes in Taiwan. Data collection instruments include the Earth Science Classroom Learning Environment Inventory and Earth Science Learning Outcomes Inventory. The results showed that most students preferred learning in a classroom environment where student-centered and teacher-centered instructional approaches coexisted over a teacher-centered learning environment. A multivariate analysis of covariance also revealed that the STBIM students' cognitive achievement and attitude toward earth science were enhanced when the learning environment was congruent with their learning environment preference.

Expanding nursing education through e-learning: A case study in Malaysia.

PubMed

Syed-Mohamad, Sharifah-Mastura; Pardi, Kasmah-Wati; Zainal, Nor-Azmi; Ismail, Zalina

2006-01-01

The School of Health Sciences, Universiti Sains Malaysia (SHS) is planning to expand its contribution to produce more graduate nurses by offering a nursing degree through e-learning. After three years of using e-learning by four lecturers in seven nursing courses, we conducted a study to get the lecturers feedback and to compare the students' preference and their actual experiences in e-learning. Lecturers' feedback were collected based on six open-ended questions. Feedback from all the 36 final year nursing students were collected using Constructivist On-line Learning Environment Survey (COLLES)--the Student Experience/Preferred Form. Results show that lecturers and students have positive perception on e-learning. They perceive e-learning as a powerful and effective tool for expanding nursing education to meet the demand for a labour force that is knowledgeable, highly skilled and equipped with positive values. We believe blended learning is the most suitable approach to implement e-learning and social constructivism theory provides the dynamic view of learning. To increase success in e-learning implementation for the nursing programme, lecturers should be educated regarding proper instructional design so that their content delivery blends well with the technology and pedagogy.

Learning and choosing in an uncertain world: An investigation of the explore-exploit dilemma in static and dynamic environments.

PubMed

Navarro, Daniel J; Newell, Ben R; Schulze, Christin

2016-03-01

How do people solve the explore-exploit trade-off in a changing environment? In this paper we present experimental evidence from an "observe or bet" task, in which people have to determine when to engage in information-seeking behavior and when to switch to reward-taking actions. In particular we focus on the comparison between people's behavior in a changing environment and their behavior in an unchanging one. Our experimental work is motivated by rational analysis of the problem that makes strong predictions about information search and reward seeking in static and changeable environments. Our results show a striking agreement between human behavior and the optimal policy, but also highlight a number of systematic differences. In particular, we find that while people often employ suboptimal strategies the first time they encounter the learning problem, most people are able to approximate the correct strategy after minimal experience. In order to describe both the manner in which people's choices are similar to but slightly different from an optimal standard, we introduce four process models for the observe or bet task and evaluate them as potential theories of human behavior. Copyright © 2016 Elsevier Inc. All rights reserved.

Grounded and embodied mathematical cognition: Promoting mathematical insight and proof using action and language.

PubMed

Nathan, Mitchell J; Walkington, Candace

2017-01-01

We develop a theory of grounded and embodied mathematical cognition (GEMC) that draws on action-cognition transduction for advancing understanding of how the body can support mathematical reasoning. GEMC proposes that participants' actions serve as inputs capable of driving the cognition-action system toward associated cognitive states. This occurs through a process of transduction that promotes valuable mathematical insights by eliciting dynamic depictive gestures that enact spatio-temporal properties of mathematical entities. Our focus here is on pre-college geometry proof production. GEMC suggests that action alone can foster insight but is insufficient for valid proof production if action is not coordinated with language systems for propositionalizing general properties of objects and space. GEMC guides the design of a video game-based learning environment intended to promote students' mathematical insights and informal proofs by eliciting dynamic gestures through in-game directed actions. GEMC generates several hypotheses that contribute to theories of embodied cognition and to the design of science, technology, engineering, and mathematics (STEM) education interventions. Pilot study results with a prototype video game tentatively support theory-based predictions regarding the role of dynamic gestures for fostering insight and proof-with-insight, and for the role of action coupled with language to promote proof-with-insight. But the pilot yields mixed results for deriving in-game interventions intended to elicit dynamic gesture production. Although our central purpose is an explication of GEMC theory and the role of action-cognition transduction, the theory-based video game design reveals the potential of GEMC to improve STEM education, and highlights the complex challenges of connecting embodiment research to education practices and learning environment design.

Resonant spatiotemporal learning in large random recurrent networks.

PubMed

Daucé, Emmanuel; Quoy, Mathias; Doyon, Bernard

2002-09-01

Taking a global analogy with the structure of perceptual biological systems, we present a system composed of two layers of real-valued sigmoidal neurons. The primary layer receives stimulating spatiotemporal signals, and the secondary layer is a fully connected random recurrent network. This secondary layer spontaneously displays complex chaotic dynamics. All connections have a constant time delay. We use for our experiments a Hebbian (covariance) learning rule. This rule slowly modifies the weights under the influence of a periodic stimulus. The effect of learning is twofold: (i) it simplifies the secondary-layer dynamics, which eventually stabilizes to a periodic orbit; and (ii) it connects the secondary layer to the primary layer, and realizes a feedback from the secondary to the primary layer. This feedback signal is added to the incoming signal, and matches it (i.e., the secondary layer performs a one-step prediction of the forthcoming stimulus). After learning, a resonant behavior can be observed: the system resonates with familiar stimuli, which activates a feedback signal. In particular, this resonance allows the recognition and retrieval of partial signals, and dynamic maintenance of the memory of past stimuli. This resonance is highly sensitive to the temporal relationships and to the periodicity of the presented stimuli. When we present stimuli which do not match in time or space, the feedback remains silent. The number of different stimuli for which resonant behavior can be learned is analyzed. As with Hopfield networks, the capacity is proportional to the size of the second, recurrent layer. Moreover, the high capacity displayed allows the implementation of our model on real-time systems interacting with their environment. Such an implementation is reported in the case of a simple behavior-based recognition task on a mobile robot. Finally, we present some functional analogies with biological systems in terms of autonomy and dynamic binding, and present some hypotheses on the computational role of feedback connections.

Evaluation of Learning Associated with Multiple Exposures to Computerized Dynamic Posturography

NASA Technical Reports Server (NTRS)

Dean, S. Lance; Paloski, William H.; Taylor, Laura C.; Vanya, Robert D.; Feiveson, Alan H.; Wood, Scott J.

2009-01-01

Computerized dynamic posturography has been used to quantitatively assess the time course of functional sensorimotor recovery after exposure to spaceflight or to groundbased analogs such as head-down bed rest. An assessment of balance recovery may be confounded as subjects develop new strategies through repeated exposures to test paradigms. The purpose of this control study was to characterize the learning effects of sensory organization and motor control tests across multiple sessions. METHODS: Twenty-eight healthy subjects were tested over four sessions. To examine the effects of between-session interval, subjects were assigned to one of four groups in which the interval between the 1 st and 2nd sessions was 7 (+/- 1) days, 14 (+/-1) days, 28 (+/-2) days, or 56 (+/-3) days. The interval between remaining sessions was 28 (+/-4) days. Peak-to-peak anterior-posterior sway was measured during standard Sensory Organization Tests (SOTs) using either fixed or unstable sway-referenced support with eyes open, eyes closed, or sway-referenced vision. Sway was also measured during modified SOTs using eyes-closed conditions with either static or dynamic head tilts. Postural recovery to unexpected support surface perturbations (translations or rotations) was measured during Motor Control Tests. The test order was block randomized across subjects. RESULTS: The learning effects varied with test condition. There were no measurable differences with a stable support surface. The more challenging conditions (unstable support surface with and without head tilts) led to greater differences and took more trials to stabilize. The effect of time interval between the first two sessions was negligible across conditions. Evidence suggested that learning carried across similar conditions (such as unstable support SOTs). DISCUSSION: Familiarization session and/or trials are recommended to minimize learning effects when characterizing functional recovery after exposure to altered sensory environments. The number of practice trials required depends on task difficulty and similarity across conditions. Learning statement: This presentation will review the learning effects of computerized d

Cooperation enhanced by indirect reciprocity in spatial prisoner's dilemma games for social P2P systems

NASA Astrophysics Data System (ADS)

Tian, Lin-Lin; Li, Ming-Chu; Wang, Zhen

2016-11-01

With the growing interest in social Peer-to-Peer (P2P) applications, relationships of individuals are further exploited to improve the performances of reputation systems. It is an on-going challenge to investigate how spatial reciprocity aids indirect reciprocity in sustaining cooperation in practical P2P environments. This paper describes the construction of an extended prisoner's dilemma game on square lattice networks with three strategies, i.e., defection, unconditional cooperation, and reciprocal cooperation. Reciprocators discriminate partners according to their reputations based on image scoring, where mistakes in judgment of reputations may occur. The independent structures of interaction and learning neighborhood are discussed, with respect to the situation in which learning environments differ from interaction networks. The simulation results have indicated that the incentive mechanism enhances cooperation better in structured peers than among a well-mixed population. Given the realistic condition of inaccurate reputation scores, defection is still successfully held down when the players interact and learn within the unified neighborhoods. Extensive simulations have further confirmed the positive impact of spatial structure on cooperation with different sizes of lattice neighborhoods. And similar conclusions can also be drawn on regular random networks and scale-free networks. Moreover, for the separated structures of the neighborhoods, the interaction network has a critical effect on the evolution dynamics of cooperation and learning environments only have weaker impacts on the process. Our findings further provide some insights concerning the evolution of collective behaviors in social systems.

Agent-Based Models in Social Physics

NASA Astrophysics Data System (ADS)

Quang, Le Anh; Jung, Nam; Cho, Eun Sung; Choi, Jae Han; Lee, Jae Woo

2018-06-01

We review the agent-based models (ABM) on social physics including econophysics. The ABM consists of agent, system space, and external environment. The agent is autonomous and decides his/her behavior by interacting with the neighbors or the external environment with the rules of behavior. Agents are irrational because they have only limited information when they make decisions. They adapt using learning from past memories. Agents have various attributes and are heterogeneous. ABM is a non-equilibrium complex system that exhibits various emergence phenomena. The social complexity ABM describes human behavioral characteristics. In ABMs of econophysics, we introduce the Sugarscape model and the artificial market models. We review minority games and majority games in ABMs of game theory. Social flow ABM introduces crowding, evacuation, traffic congestion, and pedestrian dynamics. We also review ABM for opinion dynamics and voter model. We discuss features and advantages and disadvantages of Netlogo, Repast, Swarm, and Mason, which are representative platforms for implementing ABM.

Dynamic detection of window starting positions and its implementation within an activity recognition framework.

PubMed

Ni, Qin; Patterson, Timothy; Cleland, Ian; Nugent, Chris

2016-08-01

Activity recognition is an intrinsic component of many pervasive computing and ambient intelligent solutions. This has been facilitated by an explosion of technological developments in the area of wireless sensor network, wearable and mobile computing. Yet, delivering robust activity recognition, which could be deployed at scale in a real world environment, still remains an active research challenge. Much of the existing literature to date has focused on applying machine learning techniques to pre-segmented data collected in controlled laboratory environments. Whilst this approach can provide valuable ground truth information from which to build recognition models, these techniques often do not function well when implemented in near real time applications. This paper presents the application of a multivariate online change detection algorithm to dynamically detect the starting position of windows for the purposes of activity recognition. Copyright © 2016 Elsevier Inc. All rights reserved.

Tracking fluid-borne odors in diverse and dynamic environments using multiple sensory mechanisms

NASA Astrophysics Data System (ADS)

Taylor, Brian Kyle

The ability to locate odor sources in different types of environments (i.e. diverse) and environments that change radically during the mission (i.e., dynamic) is essential. While many engineered odor tracking systems have been developed, they appear to be designed for a particular environment (e.g., strong or low flow). In field conditions, agents may encounter both. Insect olfactory orientation studies show that several animals can locate odor sources in both high and low flow environments, and environments where the wind vanishes during tracking behavior. Furthermore, animals use multi-modal sensing, including olfaction, vision and touch to localize a source. This work uses simulated and hardware environments to explore how engineered systems can maintain wind-driven tracking behavior in diverse and dynamic environments. The simulation uses olfaction, vision and tactile attributes to track and localize a source in the following environments: high flow, low flow, and transition from high to low flow (i.e., Wind Stop). The hardware platform tests two disparate tracking strategies (including the simulated strategy) in an environment that transitions from strong to low flow. Results indicate that using a remembered wind direction post wind-shutoff is a viable way to maintain wind-driven tracking behavior in a wind stop environment, which can help bridge the gap between high flow and low flow strategies. Also, multi-modal sensing with tactile attributes, vision and olfaction helps a vehicle to localize a source. In addition to engineered systems, the moth Manduca sexta is challenged to track in the following environments: Wind and Odor, Wind Stop, Odor and No Wind, No Odor and No Wind to gain a better understanding of animal behavior in these environments. Results show that contrary to previous studies of different moth species, M. sexta does not generally maintain its wind-driven tracking behavior post-wind shutoff, but instead executes a stereotyped sequence of maneuvers followed by odor-modulated undirected exploration of its environment. In the Odor and No Wind environment, animals become biased towards the area of the arena where odor is located compared to the No Odor and No Wind environment. Robot and animal results are compared to learn more about both.

Dynamic Interactive Learning Systems

ERIC Educational Resources Information Center

Sabry, Khaled; Barker, Jeff

2009-01-01

This paper reviews and discusses the notions of interactivity and dynamicity of learning systems in relation to information technologies and design principles that can contribute to interactive and dynamic learning. It explores the concept of dynamic interactive learning systems based on the emerging generation of information as part of a…

Toward a critical approach to the study of learning environments in science classrooms

NASA Astrophysics Data System (ADS)

Lorsbach, Anthony; Tobin, Kenneth

1995-03-01

Traditional learning environment research in science classrooms has been built on survey methods meant to measure students' and teachers' perceptions of variables used to define the learning environment. This research has led mainly to descriptions of learning environments. We argue that learning environment research should play a transformative role in science classrooms; that learning environment research should take into account contemporary post-positivist ways of thinking about learning and teaching to assist students and teachers to construct a more emancipatory learning environment. In particular, we argue that a critical perspective could lead to research playing a larger role in the transformation of science classroom learning environments. This argument is supplemented with an example from a middle school science classroom.

Sports Coaching through the Ages with an Empirical Study of Predictors of Rowing Coaching Effectiveness

ERIC Educational Resources Information Center

Kiosoglous, Cameron Michael

2013-01-01

Coaching effectiveness is a result of a coach getting the best out of the people and resources in their environment. For coaches, learning from experience is vital in a role that is a complex, dynamic and multifaceted process of balancing fun and winning where one cannot be sure if results will go according to plan. At the Olympic level, due to…

Building adaptive connectionist-based controllers: review of experiments in human-robot interaction, collective robotics, and computational neuroscience

NASA Astrophysics Data System (ADS)

Billard, Aude

2000-10-01

This paper summarizes a number of experiments in biologically inspired robotics. The common feature to all experiments is the use of artificial neural networks as the building blocks for the controllers. The experiments speak in favor of using a connectionist approach for designing adaptive and flexible robot controllers, and for modeling neurological processes. I present 1) DRAMA, a novel connectionist architecture, which has general property for learning time series and extracting spatio-temporal regularities in multi-modal and highly noisy data; 2) Robota, a doll-shaped robot, which imitates and learns a proto-language; 3) an experiment in collective robotics, where a group of 4 to 15 Khepera robots learn dynamically the topography of an environment whose features change frequently; 4) an abstract, computational model of primate ability to learn by imitation; 5) a model for the control of locomotor gaits in a quadruped legged robot.

A Fly-Inspired Mushroom Bodies Model for Sensory-Motor Control Through Sequence and Subsequence Learning.

PubMed

Arena, Paolo; Calí, Marco; Patané, Luca; Portera, Agnese; Strauss, Roland

2016-09-01

Classification and sequence learning are relevant capabilities used by living beings to extract complex information from the environment for behavioral control. The insect world is full of examples where the presentation time of specific stimuli shapes the behavioral response. On the basis of previously developed neural models, inspired by Drosophila melanogaster, a new architecture for classification and sequence learning is here presented under the perspective of the Neural Reuse theory. Classification of relevant input stimuli is performed through resonant neurons, activated by the complex dynamics generated in a lattice of recurrent spiking neurons modeling the insect Mushroom Bodies neuropile. The network devoted to context formation is able to reconstruct the learned sequence and also to trace the subsequences present in the provided input. A sensitivity analysis to parameter variation and noise is reported. Experiments on a roving robot are reported to show the capabilities of the architecture used as a neural controller.

Intelligent control and cooperation for mobile robots

NASA Astrophysics Data System (ADS)

Stingu, Petru Emanuel

The topic discussed in this work addresses the current research being conducted at the Automation & Robotics Research Institute in the areas of UAV quadrotor control and heterogenous multi-vehicle cooperation. Autonomy can be successfully achieved by a robot under the following conditions: the robot has to be able to acquire knowledge about the environment and itself, and it also has to be able to reason under uncertainty. The control system must react quickly to immediate challenges, but also has to slowly adapt and improve based on accumulated knowledge. The major contribution of this work is the transfer of the ADP algorithms from the purely theoretical environment to the complex real-world robotic platforms that work in real-time and in uncontrolled environments. Many solutions are adopted from those present in nature because they have been proven to be close to optimal in very different settings. For the control of a single platform, reinforcement learning algorithms are used to design suboptimal controllers for a class of complex systems that can be conceptually split in local loops with simpler dynamics and relatively weak coupling to the rest of the system. Optimality is enforced by having a global critic but the curse of dimensionality is avoided by using local actors and intelligent pre-processing of the information used for learning the optimal controllers. The system model is used for constructing the structure of the control system, but on top of that the adaptive neural networks that form the actors use the knowledge acquired during normal operation to get closer to optimal control. In real-world experiments, efficient learning is a strong requirement for success. This is accomplished by using an approximation of the system model to focus the learning for equivalent configurations of the state space. Due to the availability of only local data for training, neural networks with local activation functions are implemented. For the control of a formation of robots subjected to dynamic communication constraints, game theory is used in addition to reinforcement learning. The nodes maintain an extra set of state variables about all the other nodes that they can communicate to. The more important are trust and predictability. They are a way to incorporate knowledge acquired in the past into the control decisions taken by each node. The trust variable provides a simple mechanism for the implementation of reinforcement learning. For robot formations, potential field based control algorithms are used to generate the control commands. The formation structure changes due to the environment and due to the decisions of the nodes. It is a problem of building a graph and coalitions by having distributed decisions but still reaching an optimal behavior globally.

A Proposed Intelligent Policy-Based Interface for a Mobile eHealth Environment

NASA Astrophysics Data System (ADS)

Tavasoli, Amir; Archer, Norm

Users of mobile eHealth systems are often novices, and the learning process for them may be very time consuming. In order for systems to be attractive to potential adopters, it is important that the interface should be very convenient and easy to learn. However, the community of potential users of a mobile eHealth system may be quite varied in their requirements, so the system must be able to adapt easily to suit user preferences. One way to accomplish this is to have the interface driven by intelligent policies. These policies can be refined gradually, using inputs from potential users, through intelligent agents. This paper develops a framework for policy refinement for eHealth mobile interfaces, based on dynamic learning from user interactions.

Guidelines for developing distributed virtual environment applications

NASA Astrophysics Data System (ADS)

Stytz, Martin R.; Banks, Sheila B.

1998-08-01

We have conducted a variety of projects that served to investigate the limits of virtual environments and distributed virtual environment (DVE) technology for the military and medical professions. The projects include an application that allows the user to interactively explore a high-fidelity, dynamic scale model of the Solar System and a high-fidelity, photorealistic, rapidly reconfigurable aircraft simulator. Additional projects are a project for observing, analyzing, and understanding the activity in a military distributed virtual environment, a project to develop a distributed threat simulator for training Air Force pilots, a virtual spaceplane to determine user interface requirements for a planned military spaceplane system, and an automated wingman for use in supplementing or replacing human-controlled systems in a DVE. The last two projects are a virtual environment user interface framework; and a project for training hospital emergency department personnel. In the process of designing and assembling the DVE applications in support of these projects, we have developed rules of thumb and insights into assembling DVE applications and the environment itself. In this paper, we open with a brief review of the applications that were the source for our insights and then present the lessons learned as a result of these projects. The lessons we have learned fall primarily into five areas. These areas are requirements development, software architecture, human-computer interaction, graphical database modeling, and construction of computer-generated forces.

An intelligent agent for optimal river-reservoir system management

NASA Astrophysics Data System (ADS)

Rieker, Jeffrey D.; Labadie, John W.

2012-09-01

A generalized software package is presented for developing an intelligent agent for stochastic optimization of complex river-reservoir system management and operations. Reinforcement learning is an approach to artificial intelligence for developing a decision-making agent that learns the best operational policies without the need for explicit probabilistic models of hydrologic system behavior. The agent learns these strategies experientially in a Markov decision process through observational interaction with the environment and simulation of the river-reservoir system using well-calibrated models. The graphical user interface for the reinforcement learning process controller includes numerous learning method options and dynamic displays for visualizing the adaptive behavior of the agent. As a case study, the generalized reinforcement learning software is applied to developing an intelligent agent for optimal management of water stored in the Truckee river-reservoir system of California and Nevada for the purpose of streamflow augmentation for water quality enhancement. The intelligent agent successfully learns long-term reservoir operational policies that specifically focus on mitigating water temperature extremes during persistent drought periods that jeopardize the survival of threatened and endangered fish species.

Toward Agent Programs with Circuit Semantics

NASA Technical Reports Server (NTRS)

Nilsson, Nils J.

1992-01-01

New ideas are presented for computing and organizing actions for autonomous agents in dynamic environments-environments in which the agent's current situation cannot always be accurately discerned and in which the effects of actions cannot always be reliably predicted. The notion of 'circuit semantics' for programs based on 'teleo-reactive trees' is introduced. Program execution builds a combinational circuit which receives sensory inputs and controls actions. These formalisms embody a high degree of inherent conditionality and thus yield programs that are suitably reactive to their environments. At the same time, the actions computed by the programs are guided by the overall goals of the agent. The paper also speculates about how programs using these ideas could be automatically generated by artificial intelligence planning systems and adapted by learning methods.

An Examination through Conjoint Analysis of the Preferences of Students Concerning Online Learning Environments According to Their Learning Styles

ERIC Educational Resources Information Center

Daghan, Gökhan; Akkoyunlu, Buket

2012-01-01

This study examines learning styles of students receiving education via online learning environments, and their preferences concerning the online learning environment. Maggie McVay Lynch Learning Style Inventory was used to determine learning styles of the students. The preferences of students concerning online learning environments were detected…

Dynamic neural networks based on-line identification and control of high performance motor drives

NASA Technical Reports Server (NTRS)

Rubaai, Ahmed; Kotaru, Raj

1995-01-01

In the automated and high-tech industries of the future, there wil be a need for high performance motor drives both in the low-power range and in the high-power range. To meet very straight demands of tracking and regulation in the two quadrants of operation, advanced control technologies are of a considerable interest and need to be developed. In response a dynamics learning control architecture is developed with simultaneous on-line identification and control. the feature of the proposed approach, to efficiently combine the dual task of system identification (learning) and adaptive control of nonlinear motor drives into a single operation is presented. This approach, therefore, not only adapts to uncertainties of the dynamic parameters of the motor drives but also learns about their inherent nonlinearities. In fact, most of the neural networks based adaptive control approaches in use have an identification phase entirely separate from the control phase. Because these approaches separate the identification and control modes, it is not possible to cope with dynamic changes in a controlled process. Extensive simulation studies have been conducted and good performance was observed. The robustness characteristics of neuro-controllers to perform efficiently in a noisy environment is also demonstrated. With this initial success, the principal investigator believes that the proposed approach with the suggested neural structure can be used successfully for the control of high performance motor drives. Two identification and control topologies based on the model reference adaptive control technique are used in this present analysis. No prior knowledge of load dynamics is assumed in either topology while the second topology also assumes no knowledge of the motor parameters.

THE DEFINITION AND INTERPRETATION OF TERRESTRIAL ENVIRONMENT DESIGN INPUTS FOR VEHICLE DESIGN CONSIDERATIONS

NASA Technical Reports Server (NTRS)

Johnson, Dale L.; Keller, Vernon W.; Vaughan, William W.

2005-01-01

The description and interpretation of the terrestrial environment (0-90 km altitude) is an important driver of aerospace vehicle structural, control, and thermal system design. NASA is currently in the process of reviewing the meteorological information acquired over the past decade and producing an update to the 1993 Terrestrial Environment Guidelines for Aerospace Vehicle Design and Development handbook. This paper addresses the contents of this updated handbook, with special emphasis on new material being included in the areas of atmospheric thermodynamic models, wind dynamics, atmospheric composition, atmospheric electricity, cloud phenomena, atmospheric extremes, sea state, etc. In addition, the respective engineering design elements will be discussed relative to the importance and influence of terrestrial environment inputs that require consideration and interpretation for design applications. Specific lessons learned that have contributed to the advancements made in the acquisition, interpretation, application and awareness of terrestrial environment inputs for aerospace engineering applications are discussed.

Differential sensitivity to the environment: contribution of cognitive biases and genes to psychological wellbeing.

PubMed

Fox, E; Beevers, C G

2016-12-01

Negative cognitive biases and genetic variation have been associated with risk of psychopathology in largely independent lines of research. Here, we discuss ways in which these dynamic fields of research might be fruitfully combined. We propose that gene by environment (G × E) interactions may be mediated by selective cognitive biases and that certain forms of genetic 'reactivity' or 'sensitivity' may represent heightened sensitivity to the learning environment in a 'for better and for worse' manner. To progress knowledge in this field, we recommend including assessments of cognitive processing biases; examining G × E interactions in 'both' negative and positive environments; experimentally manipulating the environment when possible; and moving beyond single-gene effects to assess polygenic sensitivity scores. We formulate a new methodological framework encapsulating cognitive and genetic factors in the development of both psychopathology and optimal wellbeing that holds long-term promise for the development of new personalized therapies.

The application of Markov decision process in restaurant delivery robot

NASA Astrophysics Data System (ADS)

Wang, Yong; Hu, Zhen; Wang, Ying

2017-05-01

As the restaurant delivery robot is often in a dynamic and complex environment, including the chairs inadvertently moved to the channel and customers coming and going. The traditional path planning algorithm is not very ideal. To solve this problem, this paper proposes the Markov dynamic state immediate reward (MDR) path planning algorithm according to the traditional Markov decision process. First of all, it uses MDR to plan a global path, then navigates along this path. When the sensor detects there is no obstructions in front state, increase its immediate state reward value; when the sensor detects there is an obstacle in front, plan a global path that can avoid obstacle with the current position as the new starting point and reduce its state immediate reward value. This continues until the target is reached. When the robot learns for a period of time, it can avoid those places where obstacles are often present when planning the path. By analyzing the simulation experiment, the algorithm has achieved good results in the global path planning under the dynamic environment.

Efficacy of a Meiosis Learning Module Developed for the Virtual Cell Animation Collection.

PubMed

Goff, Eric E; Reindl, Katie M; Johnson, Christina; McClean, Phillip; Offerdahl, Erika G; Schroeder, Noah L; White, Alan R

2017-01-01

Recent reports calling for change in undergraduate biology education have resulted in the redesign of many introductory biology courses. Reports on one common change to course structure, the active-learning environment, have placed an emphasis on student preparation, noting that the positive outcomes of active learning in the classroom depend greatly on how well the student prepares before class. As a possible preparatory resource, we test the efficacy of a learning module developed for the Virtual Cell Animation Collection. This module presents the concepts of meiosis in an interactive, dynamic environment that has previously been shown to facilitate learning in introductory biology students. Participants ( n = 534) were enrolled in an introductory biology course and were presented the concepts of meiosis in one of two treatments: the interactive-learning module or a traditional lecture session. Analysis of student achievement shows that students who viewed the learning module as their only means of conceptual presentation scored significantly higher ( d = 0.40, p < 0.001) than students who only attended a traditional lecture on the topic. Our results show the animation-based learning module effectively conveyed meiosis conceptual understanding, which suggests that it may facilitate student learning outside the classroom. Moreover, these results have implications for instructors seeking to expand their arsenal of tools for "flipping" undergraduate biology courses. © 2017 E. E. Goff et al. CBE—Life Sciences Education © 2017 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Pre-Service English Teachers in Blended Learning Environment in Respect to Their Learning Approaches

ERIC Educational Resources Information Center

Yilmaz, M. Betul; Orhan, Feza

2010-01-01

Blended learning environment (BLE) is increasingly used in the world, especially in university degrees and it is based on integrating web-based learning and face-to-face (FTF) learning environments. Besides integrating different learning environments, BLE also addresses to students with different learning approaches. The "learning…

Space Operations Learning Center

NASA Technical Reports Server (NTRS)

Lui, Ben; Milner, Barbara; Binebrink, Dan; Kuok, Heng

2012-01-01

The Space Operations Learning Center (SOLC) is a tool that provides an online learning environment where students can learn science, technology, engineering, and mathematics (STEM) through a series of training modules. SOLC is also an effective media for NASA to showcase its contributions to the general public. SOLC is a Web-based environment with a learning platform for students to understand STEM through interactive modules in various engineering topics. SOLC is unique in its approach to develop learning materials to teach schoolaged students the basic concepts of space operations. SOLC utilizes the latest Web and software technologies to present this educational content in a fun and engaging way for all grade levels. SOLC uses animations, streaming video, cartoon characters, audio narration, interactive games and more to deliver educational concepts. The Web portal organizes all of these training modules in an easily accessible way for visitors worldwide. SOLC provides multiple training modules on various topics. At the time of this reporting, seven modules have been developed: Space Communication, Flight Dynamics, Information Processing, Mission Operations, Kids Zone 1, Kids Zone 2, and Save The Forest. For the first four modules, each contains three components: Flight Training, Flight License, and Fly It! Kids Zone 1 and 2 include a number of educational videos and games designed specifically for grades K-6. Save The Forest is a space operations mission with four simulations and activities to complete, optimized for new touch screen technology. The Kids Zone 1 module has recently been ported to Facebook to attract wider audience.

Trans-algorithmic nature of learning in biological systems.

PubMed

Shimansky, Yury P

2018-05-02

Learning ability is a vitally important, distinctive property of biological systems, which provides dynamic stability in non-stationary environments. Although several different types of learning have been successfully modeled using a universal computer, in general, learning cannot be described by an algorithm. In other words, algorithmic approach to describing the functioning of biological systems is not sufficient for adequate grasping of what is life. Since biosystems are parts of the physical world, one might hope that adding some physical mechanisms and principles to the concept of algorithm could provide extra possibilities for describing learning in its full generality. However, a straightforward approach to that through the so-called physical hypercomputation so far has not been successful. Here an alternative approach is proposed. Biosystems are described as achieving enumeration of possible physical compositions though random incremental modifications inflicted on them by active operating resources (AORs) in the environment. Biosystems learn through algorithmic regulation of the intensity of the above modifications according to a specific optimality criterion. From the perspective of external observers, biosystems move in the space of different algorithms driven by random modifications imposed by the environmental AORs. A particular algorithm is only a snapshot of that motion, while the motion itself is essentially trans-algorithmic. In this conceptual framework, death of unfit members of a population, for example, is viewed as a trans-algorithmic modification made in the population as a biosystem by environmental AORs. Numerous examples of AOR utilization in biosystems of different complexity, from viruses to multicellular organisms, are provided.

All-memristive neuromorphic computing with level-tuned neurons

NASA Astrophysics Data System (ADS)

Pantazi, Angeliki; Woźniak, Stanisław; Tuma, Tomas; Eleftheriou, Evangelos

2016-09-01

In the new era of cognitive computing, systems will be able to learn and interact with the environment in ways that will drastically enhance the capabilities of current processors, especially in extracting knowledge from vast amount of data obtained from many sources. Brain-inspired neuromorphic computing systems increasingly attract research interest as an alternative to the classical von Neumann processor architecture, mainly because of the coexistence of memory and processing units. In these systems, the basic components are neurons interconnected by synapses. The neurons, based on their nonlinear dynamics, generate spikes that provide the main communication mechanism. The computational tasks are distributed across the neural network, where synapses implement both the memory and the computational units, by means of learning mechanisms such as spike-timing-dependent plasticity. In this work, we present an all-memristive neuromorphic architecture comprising neurons and synapses realized by using the physical properties and state dynamics of phase-change memristors. The architecture employs a novel concept of interconnecting the neurons in the same layer, resulting in level-tuned neuronal characteristics that preferentially process input information. We demonstrate the proposed architecture in the tasks of unsupervised learning and detection of multiple temporal correlations in parallel input streams. The efficiency of the neuromorphic architecture along with the homogenous neuro-synaptic dynamics implemented with nanoscale phase-change memristors represent a significant step towards the development of ultrahigh-density neuromorphic co-processors.

All-memristive neuromorphic computing with level-tuned neurons.

PubMed

Pantazi, Angeliki; Woźniak, Stanisław; Tuma, Tomas; Eleftheriou, Evangelos

2016-09-02

In the new era of cognitive computing, systems will be able to learn and interact with the environment in ways that will drastically enhance the capabilities of current processors, especially in extracting knowledge from vast amount of data obtained from many sources. Brain-inspired neuromorphic computing systems increasingly attract research interest as an alternative to the classical von Neumann processor architecture, mainly because of the coexistence of memory and processing units. In these systems, the basic components are neurons interconnected by synapses. The neurons, based on their nonlinear dynamics, generate spikes that provide the main communication mechanism. The computational tasks are distributed across the neural network, where synapses implement both the memory and the computational units, by means of learning mechanisms such as spike-timing-dependent plasticity. In this work, we present an all-memristive neuromorphic architecture comprising neurons and synapses realized by using the physical properties and state dynamics of phase-change memristors. The architecture employs a novel concept of interconnecting the neurons in the same layer, resulting in level-tuned neuronal characteristics that preferentially process input information. We demonstrate the proposed architecture in the tasks of unsupervised learning and detection of multiple temporal correlations in parallel input streams. The efficiency of the neuromorphic architecture along with the homogenous neuro-synaptic dynamics implemented with nanoscale phase-change memristors represent a significant step towards the development of ultrahigh-density neuromorphic co-processors.

A Sarsa(λ)-Based Control Model for Real-Time Traffic Light Coordination

PubMed Central

Zhu, Fei; Liu, Quan; Fu, Yuchen; Huang, Wei

2014-01-01

Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors that can affect the traffic control model, it is difficult to find the optimal solution. The disability of traffic light controllers to learn from past experiences caused them to be unable to adaptively fit dynamic changes of traffic flow. Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. The proposed Sarsa(λ)-based real-time traffic control optimization model can maintain the traffic signal timing policy more effectively. The Sarsa(λ)-based model gains traffic cost of the vehicle, which considers delay time, the number of waiting vehicles, and the integrated saturation from its experiences to learn and determine the optimal actions. The experiment results show an inspiring improvement in traffic control, indicating the proposed model is capable of facilitating real-time dynamic traffic control. PMID:24592183

Natural image sequences constrain dynamic receptive fields and imply a sparse code.

PubMed

Häusler, Chris; Susemihl, Alex; Nawrot, Martin P

2013-11-06

In their natural environment, animals experience a complex and dynamic visual scenery. Under such natural stimulus conditions, neurons in the visual cortex employ a spatially and temporally sparse code. For the input scenario of natural still images, previous work demonstrated that unsupervised feature learning combined with the constraint of sparse coding can predict physiologically measured receptive fields of simple cells in the primary visual cortex. This convincingly indicated that the mammalian visual system is adapted to the natural spatial input statistics. Here, we extend this approach to the time domain in order to predict dynamic receptive fields that can account for both spatial and temporal sparse activation in biological neurons. We rely on temporal restricted Boltzmann machines and suggest a novel temporal autoencoding training procedure. When tested on a dynamic multi-variate benchmark dataset this method outperformed existing models of this class. Learning features on a large dataset of natural movies allowed us to model spatio-temporal receptive fields for single neurons. They resemble temporally smooth transformations of previously obtained static receptive fields and are thus consistent with existing theories. A neuronal spike response model demonstrates how the dynamic receptive field facilitates temporal and population sparseness. We discuss the potential mechanisms and benefits of a spatially and temporally sparse representation of natural visual input. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

Learning molecular energies using localized graph kernels

DOE PAGES

Ferré, Grégoire; Haut, Terry Scot; Barros, Kipton Marcos

2017-03-21

We report that recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although simple to state, these symmetries are complicated to encode into machine learning algorithms. In this paper, we present a machine learning approach based on graph theory that naturallymore » incorporates translation, rotation, and permutation symmetries. Specifically, we use a random walk graph kernel to measure the similarity of two adjacency matrices, each of which represents a local atomic environment. This Graph Approximated Energy (GRAPE) approach is flexible and admits many possible extensions. Finally, we benchmark a simple version of GRAPE by predicting atomization energies on a standard dataset of organic molecules.« less

Learning molecular energies using localized graph kernels

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ferré, Grégoire; Haut, Terry Scot; Barros, Kipton Marcos

We report that recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although simple to state, these symmetries are complicated to encode into machine learning algorithms. In this paper, we present a machine learning approach based on graph theory that naturallymore » incorporates translation, rotation, and permutation symmetries. Specifically, we use a random walk graph kernel to measure the similarity of two adjacency matrices, each of which represents a local atomic environment. This Graph Approximated Energy (GRAPE) approach is flexible and admits many possible extensions. Finally, we benchmark a simple version of GRAPE by predicting atomization energies on a standard dataset of organic molecules.« less

Context transfer in reinforcement learning using action-value functions.

PubMed

Mousavi, Amin; Nadjar Araabi, Babak; Nili Ahmadabadi, Majid

2014-01-01

This paper discusses the notion of context transfer in reinforcement learning tasks. Context transfer, as defined in this paper, implies knowledge transfer between source and target tasks that share the same environment dynamics and reward function but have different states or action spaces. In other words, the agents learn the same task while using different sensors and actuators. This requires the existence of an underlying common Markov decision process (MDP) to which all the agents' MDPs can be mapped. This is formulated in terms of the notion of MDP homomorphism. The learning framework is Q-learning. To transfer the knowledge between these tasks, the feature space is used as a translator and is expressed as a partial mapping between the state-action spaces of different tasks. The Q-values learned during the learning process of the source tasks are mapped to the sets of Q-values for the target task. These transferred Q-values are merged together and used to initialize the learning process of the target task. An interval-based approach is used to represent and merge the knowledge of the source tasks. Empirical results show that the transferred initialization can be beneficial to the learning process of the target task.

Context Transfer in Reinforcement Learning Using Action-Value Functions

PubMed Central

Mousavi, Amin; Nadjar Araabi, Babak; Nili Ahmadabadi, Majid

2014-01-01

This paper discusses the notion of context transfer in reinforcement learning tasks. Context transfer, as defined in this paper, implies knowledge transfer between source and target tasks that share the same environment dynamics and reward function but have different states or action spaces. In other words, the agents learn the same task while using different sensors and actuators. This requires the existence of an underlying common Markov decision process (MDP) to which all the agents' MDPs can be mapped. This is formulated in terms of the notion of MDP homomorphism. The learning framework is Q-learning. To transfer the knowledge between these tasks, the feature space is used as a translator and is expressed as a partial mapping between the state-action spaces of different tasks. The Q-values learned during the learning process of the source tasks are mapped to the sets of Q-values for the target task. These transferred Q-values are merged together and used to initialize the learning process of the target task. An interval-based approach is used to represent and merge the knowledge of the source tasks. Empirical results show that the transferred initialization can be beneficial to the learning process of the target task. PMID:25610457

Professional judgement and decision-making in adventure sports coaching: the role of interaction.

PubMed

Collins, Loel; Collins, Dave

2016-01-01

This qualitative study presents the view that coaching practice places demands on the coach's adaptability and flexibility. These requirements for being adaptive and flexible are met through a careful process of professional judgement and decision-making based on context-appropriate bodies of knowledge. Adventure sports coaches were selected for study on the basis that adventure sports create a hyper-dynamic environment in which these features can be examined. Thematic analysis revealed that coaches were generally well informed and practised with respect to the technical aspects of their sporting disciplines. Less positively, however, they often relied on ad hoc contextualisation of generalised theories of coaching practice to respond to the hyper-dynamic environments encountered in adventure sports. We propose that coaching practice reflects the demands of the environment, individual learning needs of the students and the task at hand. Together, these factors outwardly resemble a constraints-led approach but, we suggest, actually reflect manipulation of these parameters from a cognitive rather than an ecological perspective. This process is facilitated by a refined judgement and decision-making process, sophisticated epistemology and an explicit interaction of coaching components.

Information-theoretic decomposition of embodied and situated systems.

PubMed

Da Rold, Federico

2018-07-01

The embodied and situated view of cognition stresses the importance of real-time and nonlinear bodily interaction with the environment for developing concepts and structuring knowledge. In this article, populations of robots controlled by an artificial neural network learn a wall-following task through artificial evolution. At the end of the evolutionary process, time series are recorded from perceptual and motor neurons of selected robots. Information-theoretic measures are estimated on pairings of variables to unveil nonlinear interactions that structure the agent-environment system. Specifically, the mutual information is utilized to quantify the degree of dependence and the transfer entropy to detect the direction of the information flow. Furthermore, the system is analyzed with the local form of such measures, thus capturing the underlying dynamics of information. Results show that different measures are interdependent and complementary in uncovering aspects of the robots' interaction with the environment, as well as characteristics of the functional neural structure. Therefore, the set of information-theoretic measures provides a decomposition of the system, capturing the intricacy of nonlinear relationships that characterize robots' behavior and neural dynamics. Copyright © 2018 Elsevier Ltd. All rights reserved.

Early prediction of student goals and affect in narrative-centered learning environments

NASA Astrophysics Data System (ADS)

Lee, Sunyoung

Recent years have seen a growing recognition of the role of goal and affect recognition in intelligent tutoring systems. Goal recognition is the task of inferring users' goals from a sequence of observations of their actions. Because of the uncertainty inherent in every facet of human computer interaction, goal recognition is challenging, particularly in contexts in which users can perform many actions in any order, as is the case with intelligent tutoring systems. Affect recognition is the task of identifying the emotional state of a user from a variety of physical cues, which are produced in response to affective changes in the individual. Accurately recognizing student goals and affect states could contribute to more effective and motivating interactions in intelligent tutoring systems. By exploiting knowledge of student goals and affect states, intelligent tutoring systems can dynamically modify their behavior to better support individual students. To create effective interactions in intelligent tutoring systems, goal and affect recognition models should satisfy two key requirements. First, because incorrectly predicted goals and affect states could significantly diminish the effectiveness of interactive systems, goal and affect recognition models should provide accurate predictions of user goals and affect states. When observations of users' activities become available, recognizers should make accurate early" predictions. Second, goal and affect recognition models should be highly efficient so they can operate in real time. To address key issues, we present an inductive approach to recognizing student goals and affect states in intelligent tutoring systems by learning goals and affect recognition models. Our work focuses on goal and affect recognition in an important new class of intelligent tutoring systems, narrative-centered learning environments. We report the results of empirical studies of induced recognition models from observations of students' interactions in narrative-centered learning environments. Experimental results suggest that induced models can make accurate early predictions of student goals and affect states, and they are sufficiently efficient to meet the real-time performance requirements of interactive learning environments.

Development of force adaptation during childhood.

PubMed

Konczak, Jürgen; Jansen-Osmann, Petra; Kalveram, Karl-Theodor

2003-03-01

Humans learn to make reaching movements in novel dynamic environments by acquiring an internal motor model of their limb dynamics. Here, the authors investigated how 4- to 11-year-old children (N = 39) and adults (N = 7) adapted to changes in arm dynamics, and they examined whether those data support the view that the human brain acquires inverse dynamics models (IDM) during development. While external damping forces were applied, the children learned to perform goal-directed forearm flexion movements. After changes in damping, all children showed kinematic aftereffects indicative of a neural controller that still attempted to compensate the no longer existing damping force. With increasing age, the number of trials toward complete adaptation decreased. When damping was present, forearm paths were most perturbed and most variable in the youngest children but were improved in the older children. The findings indicate that the neural representations of limb dynamics are less precise in children and less stable in time than those of adults. Such controller instability might be a primary cause of the high kinematic variability observed in many motor tasks during childhood. Finally, the young children were not able to update those models at the same rate as the older children, who, in turn, adapted more slowly than adults. In conclusion, the ability to adapt to unknown forces is a developmental achievement. The present results are consistent with the view that the acquisition and modification of internal models of the limb dynamics form the basis of that adaptive process.

Integrating Learning, Problem Solving, and Engagement in Narrative-Centered Learning Environments

ERIC Educational Resources Information Center

Rowe, Jonathan P.; Shores, Lucy R.; Mott, Bradford W.; Lester, James C.

2011-01-01

A key promise of narrative-centered learning environments is the ability to make learning engaging. However, there is concern that learning and engagement may be at odds in these game-based learning environments. This view suggests that, on the one hand, students interacting with a game-based learning environment may be engaged but unlikely to…

The Structural Consequences of Big Data-Driven Education.

PubMed

Zeide, Elana

2017-06-01

Educators and commenters who evaluate big data-driven learning environments focus on specific questions: whether automated education platforms improve learning outcomes, invade student privacy, and promote equality. This article puts aside separate unresolved-and perhaps unresolvable-issues regarding the concrete effects of specific technologies. It instead examines how big data-driven tools alter the structure of schools' pedagogical decision-making, and, in doing so, change fundamental aspects of America's education enterprise. Technological mediation and data-driven decision-making have a particularly significant impact in learning environments because the education process primarily consists of dynamic information exchange. In this overview, I highlight three significant structural shifts that accompany school reliance on data-driven instructional platforms that perform core school functions: teaching, assessment, and credentialing. First, virtual learning environments create information technology infrastructures featuring constant data collection, continuous algorithmic assessment, and possibly infinite record retention. This undermines the traditional intellectual privacy and safety of classrooms. Second, these systems displace pedagogical decision-making from educators serving public interests to private, often for-profit, technology providers. They constrain teachers' academic autonomy, obscure student evaluation, and reduce parents' and students' ability to participate or challenge education decision-making. Third, big data-driven tools define what "counts" as education by mapping the concepts, creating the content, determining the metrics, and setting desired learning outcomes of instruction. These shifts cede important decision-making to private entities without public scrutiny or pedagogical examination. In contrast to the public and heated debates that accompany textbook choices, schools often adopt education technologies ad hoc. Given education's crucial impact on individual and collective success, educators and policymakers must consider the implications of data-driven education proactively and explicitly.

Student self-assessment in an interactive learning environment: Technological tools for scaffolding and understanding self-assessment practices

NASA Astrophysics Data System (ADS)

Eslinger, Eric Martin

Metacognitive skills are a crucial component of a successful learning career. We define metacognition as the ability to plan, monitor progress toward a goal, reflect on the quality of work and process, and revise the work or plan accordingly. By explicitly addressing certain metacognitive practices in classrooms, researchers have observed improved learning outcomes in both science and mathematical problem solving. Although these efforts were successful, they were also limited in the range of skills that could be addressed at one time and the methods used to address them due to the static nature inherent in traditional pencil-and-paper format. We wished to address these skills in a more dynamic, continuous representation such as that afforded by a computerized learning environment. This paper outlines such an environment and describes pedagogical activities afforded by the system. The ThinkerTools group developed and tested a software scaffold for inquiry projects in a middle-school classroom. By analyzing student use of the software tool, three forms of self-assessment activity were noted: integrated, task and project self-assessment. Each assessment form was related to the degree of interleaving between assessment and work the students engaged in as they developed their inquiry products. I argue that the integrated forms of assessment are more beneficial to student learning, and show that there is a significant relationship between active self-assessment forms and measures of student achievement and product quality. Through the use of case studies including video analysis, I address specific student self-assessment activity that utilized the software as well as self-assessment that took place outside of the software. A model of student self-assessment activity was created, highlighting aspects of activity that afford more productive self-assessment episodes.