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

PubMed

Gordon, Goren; Dorfman, Nimrod; Ahissar, Ehud

2013-01-01

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

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…

Creating an Optimal Language Learning Environment: A Focus on Family and Culture

ERIC Educational Resources Information Center

Cheng, Li-Rong Lilly

2009-01-01

Understanding the family systems and structures of our diverse populations is one of the most important tasks of professionals in education. Children learn from their family, school, and community. They learn from their experiences by observing, talking, and interacting with their environment. Parents play a pivotal role in the education of their…

What if Learning Analytics Were Based on Learning Science?

ERIC Educational Resources Information Center

Marzouk, Zahia; Rakovic, Mladen; Liaqat, Amna; Vytasek, Jovita; Samadi, Donya; Stewart-Alonso, Jason; Ram, Ilana; Woloshen, Sonya; Winne, Philip H.; Nesbit, John C.

2016-01-01

Learning analytics are often formatted as visualisations developed from traced data collected as students study in online learning environments. Optimal analytics inform and motivate students' decisions about adaptations that improve their learning. We observe that designs for learning often neglect theories and empirical findings in learning…

The Framework of Intervention Engine Based on Learning Analytics

ERIC Educational Resources Information Center

Sahin, Muhittin; Yurdugül, Halil

2017-01-01

Learning analytics primarily deals with the optimization of learning environments and the ultimate goal of learning analytics is to improve learning and teaching efficiency. Studies on learning analytics seem to have been made in the form of adaptation engine and intervention engine. Adaptation engine studies are quite widespread, but intervention…

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.

Cognitive state monitoring and the design of adaptive instruction in digital environments: lessons learned from cognitive workload assessment using a passive brain-computer interface approach

PubMed Central

Gerjets, Peter; Walter, Carina; Rosenstiel, Wolfgang; Bogdan, Martin; Zander, Thorsten O.

2014-01-01

According to Cognitive Load Theory (CLT), one of the crucial factors for successful learning is the type and amount of working-memory load (WML) learners experience while studying instructional materials. Optimal learning conditions are characterized by providing challenges for learners without inducing cognitive over- or underload. Thus, presenting instruction in a way that WML is constantly held within an optimal range with regard to learners' working-memory capacity might be a good method to provide these optimal conditions. The current paper elaborates how digital learning environments, which achieve this goal can be developed by combining approaches from Cognitive Psychology, Neuroscience, and Computer Science. One of the biggest obstacles that needs to be overcome is the lack of an unobtrusive method of continuously assessing learners' WML in real-time. We propose to solve this problem by applying passive Brain-Computer Interface (BCI) approaches to realistic learning scenarios in digital environments. In this paper we discuss the methodological and theoretical prospects and pitfalls of this approach based on results from the literature and from our own research. We present a strategy on how several inherent challenges of applying BCIs to WML and learning can be met by refining the psychological constructs behind WML, by exploring their neural signatures, by using these insights for sophisticated task designs, and by optimizing algorithms for analyzing electroencephalography (EEG) data. Based on this strategy we applied machine-learning algorithms for cross-task classifications of different levels of WML to tasks that involve studying realistic instructional materials. We obtained very promising results that yield several recommendations for future work. PMID:25538544

Mobile-Assisted Second Language Learning: Developing a Learner-Centered Framework

ERIC Educational Resources Information Center

Leow, Choy Khim; Yahaya, Wan Ahmad Jaafar Wan; Samsudin, Zarina

2014-01-01

The Mobile Assisted Language Learning concept has offered infinite language learning opportunities since its inception 20 years ago. Second Language Acquisition however embraces a considerably different body of knowledge from first language learning. While technological advances have optimized the psycholinguistic environment for language…

Comparing Two Types of Model Progression in an Inquiry Learning Environment with Modelling Facilities

ERIC Educational Resources Information Center

Mulder, Yvonne G.; Lazonder, Ard W.; de Jong, Ton

2011-01-01

The educational advantages of inquiry learning environments that incorporate modelling facilities are often challenged by students' poor inquiry skills. This study examined two types of model progression as means to compensate for these skill deficiencies. Model order progression (MOP), the predicted optimal variant, gradually increases the…

Attentive Facework during Instructional Feedback: Key to Perceiving Mentorship and an Optimal Learning Environment

ERIC Educational Resources Information Center

Kerssen-Griep, Jeff; Trees, April R.; Hess, Jon A.

2008-01-01

This study investigated how the face threat mitigation students received from their teachers during feedback influenced their judgments about mentored relationships with their teachers and the supportiveness of the classroom learning environment. Public speaking students (N=345) at three universities completed an online survey about the speech…

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

PubMed Central

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

2016-01-01

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

Statistically optimal perception and learning: from behavior to neural representations

PubMed Central

Fiser, József; Berkes, Pietro; Orbán, Gergő; Lengyel, Máté

2010-01-01

Human perception has recently been characterized as statistical inference based on noisy and ambiguous sensory inputs. Moreover, suitable neural representations of uncertainty have been identified that could underlie such probabilistic computations. In this review, we argue that learning an internal model of the sensory environment is another key aspect of the same statistical inference procedure and thus perception and learning need to be treated jointly. We review evidence for statistically optimal learning in humans and animals, and reevaluate possible neural representations of uncertainty based on their potential to support statistically optimal learning. We propose that spontaneous activity can have a functional role in such representations leading to a new, sampling-based, framework of how the cortex represents information and uncertainty. PMID:20153683

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…

Learning Analytics: Potential for Enhancing School Library Programs

ERIC Educational Resources Information Center

Boulden, Danielle Cadieux

2015-01-01

Learning analytics has been defined as the measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs. The potential use of data and learning analytics in educational contexts has caught the attention of educators and…

"SMALLab": Virtual Geology Studies Using Embodied Learning with Motion, Sound, and Graphics

ERIC Educational Resources Information Center

Johnson-Glenberg, Mina C.; Birchfield, David; Usyal, Sibel

2009-01-01

We present a new and innovative interface that allows the learner's body to move freely in a multimodal learning environment. The Situated Multimedia Arts Learning Laboratory ("SMALLab") uses 3D object tracking, real time graphics, and surround-sound to enhance embodied learning. Our hypothesis is that optimal learning and retention occur when…

A Heuristic Algorithm for Planning Personalized Learning Paths for Context-Aware Ubiquitous Learning

ERIC Educational Resources Information Center

Hwang, Gwo-Jen; Kuo, Fan-Ray; Yin, Peng-Yeng; Chuang, Kuo-Hsien

2010-01-01

In a context-aware ubiquitous learning environment, learning systems can detect students' learning behaviors in the real-world with the help of context-aware (sensor) technology; that is, students can be guided to observe or operate real-world objects with personalized support from the digital world. In this study, an optimization problem that…

Blended Learning Environments in Arab Universities: Probing Current Status and Projecting Future Directions

ERIC Educational Resources Information Center

AlFuqaha, Isam Najib

2013-01-01

This paper is a review of blended learning as a catalyst of optimizing the achievement of learning objectives. Blended learning forms an attempt to apply the right learning technologies to match the right personal learning styles to transfer the right skills to the right persons at the right times. The paper is about rethinking the teaching and…

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

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

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.

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.

Cost Optimization in E-Learning-Based Education Systems: Implementation and Learning Sequence

ERIC Educational Resources Information Center

Fazlollahtabar, Hamed; Yousefpoor, Narges

2009-01-01

Increasing the effectiveness of e-learning has become one of the most practically and theoretically important issues within both educational engineering and information system fields. The development of information technologies has contributed to growth in online training as an important education method. The online training environment enables…

Differences between Students' and Teachers' Perceptions of Education: Profiles to Describe Congruence and Friction

ERIC Educational Resources Information Center

Könings, Karen D.; Seidel, Tina; Brand-Gruwel, Saskia; Merriënboer, Jeroen J. G.

2014-01-01

Teachers and students have their own perceptions of education. Congruent perceptions contribute to optimal teaching-learning processes and help achieving best learning outcomes. This study investigated patterns in differences between students' and teachers' perceptions of their learning environment. Student profiles were identified taking into…

Overcoming Barriers to Educational Analytics: How Systems Thinking and Pragmatism Can Help

ERIC Educational Resources Information Center

Macfadyen, Leah P.

2017-01-01

Learning technologies are now commonplace in education, and generate large volumes of educational data. Scholars have argued that analytics can and should be employed to optimize learning and learning environments. This article explores what is really meant by "analytics", describes the current best-known examples of institutional…

Cocreating Collaborative Leadership Learning Environments: Using Adult Learning Principles and a Coach Approach

ERIC Educational Resources Information Center

Page, M. Beth; Margolis, Rhonda L.

2017-01-01

As educators, we seek to answer the following question: "What magic can happen when you believe that people are whole and resourceful and you hold the space for generative, collective wisdom?" This chapter explores collaborative leadership and learning with adult learners. We focus on creative ways to optimize learning and enhance…

The Job Is the Learning Environment: Performance-Centered Learning To Support Knowledge Worker Performance.

ERIC Educational Resources Information Center

Dickover, Noel T.

2002-01-01

Explains performance-centered learning (PCL), an approach to optimize support for performance on the job by making corporate assets available to knowledge workers so they can solve actual problems. Illustrates PCL with a Web site that provides just-in-time learning, collaboration, and performance support tools to improve performance at the…

Fast and Epsilon-Optimal Discretized Pursuit Learning Automata.

PubMed

Zhang, JunQi; Wang, Cheng; Zhou, MengChu

2015-10-01

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

Integration of Web 2.0 Tools in Learning a Programming Course

ERIC Educational Resources Information Center

Majid, Nazatul Aini Abd

2014-01-01

Web 2.0 tools are expected to assist students to acquire knowledge effectively in their university environment. However, the lack of effort from lecturers in planning the learning process can make it difficult for the students to optimize their learning experiences. The aim of this paper is to integrate Web 2.0 tools with learning strategy in…

A Wiki-Based Teaching Material Development Environment with Enhanced Particle Swarm Optimization

ERIC Educational Resources Information Center

Lin, Yen-Ting; Lin, Yi-Chun; Huang, Yueh-Min; Cheng, Shu-Chen

2013-01-01

One goal of e-learning is to enhance the interoperability and reusability of learning resources. However, current e-learning systems do little to adequately support this. In order to achieve this aim, the first step is to consider how to assist instructors in re-organizing the existing learning objects. However, when instructors are dealing with a…

Toward Effective Group Formation in Computer-Supported Collaborative Learning

ERIC Educational Resources Information Center

Sadeghi, Hamid; Kardan, Ahmad A.

2016-01-01

Group formation task as a starting point for computer-supported collaborative learning plays a key role in achieving pedagogical goals. Various approaches have been reported in the literature to address this problem, but none have offered an optimal solution. In this research, an online learning environment was modeled as a weighted undirected…

The Skinny on Big Data in Education: Learning Analytics Simplified

ERIC Educational Resources Information Center

Reyes, Jacqueleen A.

2015-01-01

This paper examines the current state of learning analytics (LA), its stakeholders and the benefits and challenges these stakeholders face. LA is a field of research that involves the gathering, analyzing and reporting of data related to learners and their environments with the purpose of optimizing the learning experience. Stakeholders in LA are…

Automated Scenario Generation: Toward Tailored and Optimized Military Training in Virtual Environments

DTIC Science & Technology

2012-01-01

us.army.mil ABSTRACT Scenario-based training exemplifies the learning-by-doing approach to human performance improvement. In this paper , we enumerate...through a narrative, mission, quest, or scenario. In this paper we argue for a combinatorial optimization search approach to selecting and ordering...the role of an expert for the purposes of practicing skills and knowledge in realistic situations in a learning-by-doing approach to performance

The Sense of Confidence during Probabilistic Learning: A Normative Account.

PubMed

Meyniel, Florent; Schlunegger, Daniel; Dehaene, Stanislas

2015-06-01

Learning in a stochastic environment consists of estimating a model from a limited amount of noisy data, and is therefore inherently uncertain. However, many classical models reduce the learning process to the updating of parameter estimates and neglect the fact that learning is also frequently accompanied by a variable "feeling of knowing" or confidence. The characteristics and the origin of these subjective confidence estimates thus remain largely unknown. Here we investigate whether, during learning, humans not only infer a model of their environment, but also derive an accurate sense of confidence from their inferences. In our experiment, humans estimated the transition probabilities between two visual or auditory stimuli in a changing environment, and reported their mean estimate and their confidence in this report. To formalize the link between both kinds of estimate and assess their accuracy in comparison to a normative reference, we derive the optimal inference strategy for our task. Our results indicate that subjects accurately track the likelihood that their inferences are correct. Learning and estimating confidence in what has been learned appear to be two intimately related abilities, suggesting that they arise from a single inference process. We show that human performance matches several properties of the optimal probabilistic inference. In particular, subjective confidence is impacted by environmental uncertainty, both at the first level (uncertainty in stimulus occurrence given the inferred stochastic characteristics) and at the second level (uncertainty due to unexpected changes in these stochastic characteristics). Confidence also increases appropriately with the number of observations within stable periods. Our results support the idea that humans possess a quantitative sense of confidence in their inferences about abstract non-sensory parameters of the environment. This ability cannot be reduced to simple heuristics, it seems instead a core property of the learning process.

The Sense of Confidence during Probabilistic Learning: A Normative Account

PubMed Central

Meyniel, Florent; Schlunegger, Daniel; Dehaene, Stanislas

2015-01-01

Learning in a stochastic environment consists of estimating a model from a limited amount of noisy data, and is therefore inherently uncertain. However, many classical models reduce the learning process to the updating of parameter estimates and neglect the fact that learning is also frequently accompanied by a variable “feeling of knowing” or confidence. The characteristics and the origin of these subjective confidence estimates thus remain largely unknown. Here we investigate whether, during learning, humans not only infer a model of their environment, but also derive an accurate sense of confidence from their inferences. In our experiment, humans estimated the transition probabilities between two visual or auditory stimuli in a changing environment, and reported their mean estimate and their confidence in this report. To formalize the link between both kinds of estimate and assess their accuracy in comparison to a normative reference, we derive the optimal inference strategy for our task. Our results indicate that subjects accurately track the likelihood that their inferences are correct. Learning and estimating confidence in what has been learned appear to be two intimately related abilities, suggesting that they arise from a single inference process. We show that human performance matches several properties of the optimal probabilistic inference. In particular, subjective confidence is impacted by environmental uncertainty, both at the first level (uncertainty in stimulus occurrence given the inferred stochastic characteristics) and at the second level (uncertainty due to unexpected changes in these stochastic characteristics). Confidence also increases appropriately with the number of observations within stable periods. Our results support the idea that humans possess a quantitative sense of confidence in their inferences about abstract non-sensory parameters of the environment. This ability cannot be reduced to simple heuristics, it seems instead a core property of the learning process. PMID:26076466

Teachers and Technology Use in Secondary Science Classrooms: Investigating the Experiences of Middle School Science Teachers Implementing the Web-based Inquiry Science Environment (WISE)

NASA Astrophysics Data System (ADS)

Schulz, Rachel Corinne

This study investigated the intended teacher use of a technology-enhanced learning tool, Web-based Inquiry Science Environment (WISE), and the first experiences of teachers new to using it and untrained in its use. The purpose of the study was to learn more about the factors embedded into the design of the technology that enabled it or hindered it from being used as intended. The qualitative research design applied grounded theory methods. Using theoretical sampling and a constant comparative analysis, a document review of WISE website led to a model of intended teacher use. The experiences of four middle school science teachers as they enacted WISE for the first time were investigated through ethnographic field observations, surveys and interviews using thematic analysis to construct narratives of each teachers use. These narratives were compared to the model of intended teacher use of WISE. This study found two levels of intended teacher uses for WISE. A basic intended use involved having student running the project to completion while the teacher provides feedback and assesses student learning. A more optimal description of intended use involved the supplementing the core curriculum with WISE as well as enhancing the core scope and sequence of instruction and aligning assessment with the goals of instruction through WISE. Moreover, WISE projects were optimally intended to be facilitated through student-centered teaching practices and inquiry-based instruction in a collaborative learning environment. It is also optimally intended for these projects to be shared with other colleagues for feedback and iterative development towards improving the Knowledge Integration of students. Of the four teachers who participated in this study, only one demonstrated the use of WISE as intended in the most basic way. This teacher also demonstrated the use of WISE in a number of optimal ways. Teacher confusion with certain tools available within WISE suggests that there may be a way to develop the user experience through these touch points and help teachers learn how to use the technology as they are selecting and setting up a project run. Further research may study whether improving these touch points can improve the teachers' use of WISE as intended both basically and optimally. It may also study whether or not teacher in basic and optimal ways directly impact student learning results.

A Constructionist Learning Environment for Teachers to Model Learning Designs

ERIC Educational Resources Information Center

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

2013-01-01

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

An Efficient Approach to Improve the Usability of e-Learning Resources: The Role of Heuristic Evaluation

ERIC Educational Resources Information Center

Davids, Mogamat Razeen; Chikte, Usuf M. E.; Halperin, Mitchell L.

2013-01-01

Optimizing the usability of e-learning materials is necessary to maximize their potential educational impact, but this is often neglected when time and other resources are limited, leading to the release of materials that cannot deliver the desired learning outcomes. As clinician-teachers in a resource-constrained environment, we investigated…

The learning environment of paediatric interns in South Africa.

PubMed

Naidoo, Kimesh L; Van Wyk, Jacqueline M; Adhikari, Miriam

2017-11-29

South African (SA) paediatric interns (recently qualified medical graduates) work in a high disease burdened and resource deficient environment for two years, prior to independent practice. Perceptions of this learning environment (LE) influences their approaches to training as well as the outcomes of this period of development. Obstacles to creating a supportive LE and supervisor interaction affects the quality of this training. Measuring perceptions of the LE with validated instruments can help inform improvements in learning during this crucial period of medical education. The aims of this study was to determine the psychometric qualities of the Postgraduate Hospital Educational Environment Measure (PHEEM) amongst paediatric interns across four hospital complexes in South Africa and to measure the LE as perceived by both interns and their supervisors. Construct validity was tested using factor analysis and internal consistency was measured with Cronbach's alpha. A total of 209 interns and 60 supervisors (69% intern response rate) responded to the questionnaire. The PHEEM was found to be very reliable with an overall Cronbach's alpha of 0.943 and 0.874 for intern and supervisors respectively. Factor analysis using a 3-factor solution accounted for 42% of the variance with the teaching subscale having the best fit compared with the other sub-scales of the original tool. Most interns perceived the learning environment as being more positive than negative however, their perceptions differed significantly from that of their supervisors. Poor infrastructural support from institutions, excessive workloads and inadequate supervision were factors preventing optimal training of paediatric interns. The SA version of the PHEEM tool used was found to be a reliable and valid instrument for use in interns amongst high disease burdened contexts. Various obstacles to creating an ideal learning environment for paediatric interns were identified to be in need of urgent review. Key differences in perceptions of an ideal learning environment between interns and their supervisors need to be fully explored as these may result in sub-optimal supervision and mentoring.

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.

Optimization through satisficing with prospects

NASA Astrophysics Data System (ADS)

Oyo, Kuratomo; Takahashi, Tatsuji

2017-07-01

As the broadening scope of reinforcement learning calls for a rational and more efficient heuristics, we test a satisficing strategy named RS, based on the theory of bounded rationality that considers the limited resources in agents. In K-armed bandit problems, despite its simpler form than the previous formalization of satisficing, RS shows better-than-optimal performances when the optimal aspiration level is given. We also show that RS shows a scalability for the number of actions, K, and an adaptability in the face of an infinite number of actions. It may be an efficient means for online learning in a complex or real environments.

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

Attention control learning in the decision space using state estimation

NASA Astrophysics Data System (ADS)

Gharaee, Zahra; Fatehi, Alireza; Mirian, Maryam S.; Nili Ahmadabadi, Majid

2016-05-01

The main goal of this paper is modelling attention while using it in efficient path planning of mobile robots. The key challenge in concurrently aiming these two goals is how to make an optimal, or near-optimal, decision in spite of time and processing power limitations, which inherently exist in a typical multi-sensor real-world robotic application. To efficiently recognise the environment under these two limitations, attention of an intelligent agent is controlled by employing the reinforcement learning framework. We propose an estimation method using estimated mixture-of-experts task and attention learning in perceptual space. An agent learns how to employ its sensory resources, and when to stop observing, by estimating its perceptual space. In this paper, static estimation of the state space in a learning task problem, which is examined in the WebotsTM simulator, is performed. Simulation results show that a robot learns how to achieve an optimal policy with a controlled cost by estimating the state space instead of continually updating sensory information.

Memristive device based learning for navigation in robots.

PubMed

Sarim, Mohammad; Kumar, Manish; Jha, Rashmi; Minai, Ali A

2017-11-08

Biomimetic robots have gained attention recently for various applications ranging from resource hunting to search and rescue operations during disasters. Biological species are known to intuitively learn from the environment, gather and process data, and make appropriate decisions. Such sophisticated computing capabilities in robots are difficult to achieve, especially if done in real-time with ultra-low energy consumption. Here, we present a novel memristive device based learning architecture for robots. Two terminal memristive devices with resistive switching of oxide layer are modeled in a crossbar array to develop a neuromorphic platform that can impart active real-time learning capabilities in a robot. This approach is validated by navigating a robot vehicle in an unknown environment with randomly placed obstacles. Further, the proposed scheme is compared with reinforcement learning based algorithms using local and global knowledge of the environment. The simulation as well as experimental results corroborate the validity and potential of the proposed learning scheme for robots. The results also show that our learning scheme approaches an optimal solution for some environment layouts in robot navigation.

Learning Multirobot Hose Transportation and Deployment by Distributed Round-Robin Q-Learning.

PubMed

Fernandez-Gauna, Borja; Etxeberria-Agiriano, Ismael; Graña, Manuel

2015-01-01

Multi-Agent Reinforcement Learning (MARL) algorithms face two main difficulties: the curse of dimensionality, and environment non-stationarity due to the independent learning processes carried out by the agents concurrently. In this paper we formalize and prove the convergence of a Distributed Round Robin Q-learning (D-RR-QL) algorithm for cooperative systems. The computational complexity of this algorithm increases linearly with the number of agents. Moreover, it eliminates environment non sta tionarity by carrying a round-robin scheduling of the action selection and execution. That this learning scheme allows the implementation of Modular State-Action Vetoes (MSAV) in cooperative multi-agent systems, which speeds up learning convergence in over-constrained systems by vetoing state-action pairs which lead to undesired termination states (UTS) in the relevant state-action subspace. Each agent's local state-action value function learning is an independent process, including the MSAV policies. Coordination of locally optimal policies to obtain the global optimal joint policy is achieved by a greedy selection procedure using message passing. We show that D-RR-QL improves over state-of-the-art approaches, such as Distributed Q-Learning, Team Q-Learning and Coordinated Reinforcement Learning in a paradigmatic Linked Multi-Component Robotic System (L-MCRS) control problem: the hose transportation task. L-MCRS are over-constrained systems with many UTS induced by the interaction of the passive linking element and the active mobile robots.

Toward an Optimal Pedagogy for Teamwork.

PubMed

Earnest, Mark A; Williams, Jason; Aagaard, Eva M

2017-10-01

Teamwork and collaboration are increasingly listed as core competencies for undergraduate health professions education. Despite the clear mandate for teamwork training, the optimal method for providing that training is much less certain. In this Perspective, the authors propose a three-level classification of pedagogical approaches to teamwork training based on the presence of two key learning factors: interdependent work and explicit training in teamwork. In this classification framework, level 1-minimal team learning-is where learners work in small groups but neither of the key learning factors is present. Level 2-implicit team learning-engages learners in interdependent learning activities but does not include an explicit focus on teamwork. Level 3-explicit team learning-creates environments where teams work interdependently toward common goals and are given explicit instruction and practice in teamwork. The authors provide examples that demonstrate each level. They then propose that the third level of team learning, explicit team learning, represents a best practice approach in teaching teamwork, highlighting their experience with an explicit team learning course at the University of Colorado Anschutz Medical Campus. Finally, they discuss several challenges to implementing explicit team-learning-based curricula: the lack of a common teamwork model on which to anchor such a curriculum; the question of whether the knowledge, skills, and attitudes acquired during training would be transferable to the authentic clinical environment; and effectively evaluating the impact of explicit team learning.

Melioration as rational choice: sequential decision making in uncertain environments.

PubMed

Sims, Chris R; Neth, Hansjörg; Jacobs, Robert A; Gray, Wayne D

2013-01-01

Melioration-defined as choosing a lesser, local gain over a greater longer term gain-is a behavioral tendency that people and pigeons share. As such, the empirical occurrence of meliorating behavior has frequently been interpreted as evidence that the mechanisms of human choice violate the norms of economic rationality. In some environments, the relationship between actions and outcomes is known. In this case, the rationality of choice behavior can be evaluated in terms of how successfully it maximizes utility given knowledge of the environmental contingencies. In most complex environments, however, the relationship between actions and future outcomes is uncertain and must be learned from experience. When the difficulty of this learning challenge is taken into account, it is not evident that melioration represents suboptimal choice behavior. In the present article, we examine human performance in a sequential decision-making experiment that is known to induce meliorating behavior. In keeping with previous results using this paradigm, we find that the majority of participants in the experiment fail to adopt the optimal decision strategy and instead demonstrate a significant bias toward melioration. To explore the origins of this behavior, we develop a rational analysis (Anderson, 1990) of the learning problem facing individuals in uncertain decision environments. Our analysis demonstrates that an unbiased learner would adopt melioration as the optimal response strategy for maximizing long-term gain. We suggest that many documented cases of melioration can be reinterpreted not as irrational choice but rather as globally optimal choice under uncertainty.

Online adaptation and over-trial learning in macaque visuomotor control.

PubMed

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

2011-01-01

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

Online Adaptation and Over-Trial Learning in Macaque Visuomotor Control

PubMed Central

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

2011-01-01

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

Finding the Optimal Guidance for Enhancing Anchored Instruction

ERIC Educational Resources Information Center

Zydney, Janet Mannheimer; Bathke, Arne; Hasselbring, Ted S.

2014-01-01

This study investigated the effect of different methods of guidance with anchored instruction on students' mathematical problem-solving performance. The purpose of this research was to iteratively design a learning environment to find the optimal level of guidance. Two iterations of the software were compared. The first iteration used explicit…

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.

A Systems Model Comparing Australian and Chinese HRM Education

ERIC Educational Resources Information Center

Davidson, Paul; Tsakissiris, Jane; Guo, Yuanyuan

2017-01-01

This paper explores the implications for learning design in HRM education in the 21st century. An open systems perspective is used to argue the importance of establishing productive relationships between academia, professional associations, regulators and industry (resource inputs) to support the creation of optimal learning environments (the…

Exploring Optimal Conditions of Instructional Guidance in an Algebra Tutor

ERIC Educational Resources Information Center

Lee, Hee Seung; Anderson, John R.; Berman, Susan R.; Ferris-Glick, Jennifer; Joshi, Ambarish; Nixon, Tristan; Ritter, Steve

2013-01-01

In designing learning environments that support student learning, there are many instructional design decisions. These include when and how to provide examples, verbal explanations, feedback, and other scaffolding features. In this paper, the authors investigate instructional guidance as it relates to Cognitive Tutor, an intelligent tutoring…

Using the "Zone" to Help Reach Every Learner

ERIC Educational Resources Information Center

Silver, Debbie

2011-01-01

Basically everything associated with maximizing student engagement, achievement, optimal learning environment, learning zone, and the like can be attributed to the work of Lev Vygotsky (1978). A Russian psychologist and social constructivist, Vygotsky (1896-1934) proposed a concept so fundamental to the theory of motivation that it undergirds…

Student Pedagogical Teams: Students as Course Consultants Engaged in Process of Teaching and Learning

ERIC Educational Resources Information Center

Hayward, Lorna; Ventura, Susan; Schuldt, Hilary; Donlan, Pamela

2018-01-01

Faculty engage in "pedagogical solitude," in which they plan, teach, and assess their work alone. To optimize teaching environments and learning outcomes, students can serve as "student pedagogical teams" (SPT) and provide feedback on instructor performance, course structure, and content. Using self-determination theory, this…

Learner Perceptions of Reliance on Captions in EFL Multimedia Listening Comprehension

ERIC Educational Resources Information Center

Leveridge, Aubrey Neil; Yang, Jie Chi

2014-01-01

Instructional support has been widely discussed as a strategy to optimize student-learning experiences. This study examines instructional support within the context of a multimedia language-learning environment, with the predominant focus on learners' perceptions of captioning support for listening comprehension. The study seeks to answer two…

Optimization of Educational Environment for Students

ERIC Educational Resources Information Center

Tausan, Liana

2015-01-01

The paradigm of adapting school to the learning necessities and possibilities of the student, characteristic for future systems of education and for contemporary type of educational system network requires a diversity of learning situations and experiences, built in accordance with the possibilities and the needs of all student categories, in…

Acoustics in Physical Education Settings: The Learning Roadblock

ERIC Educational Resources Information Center

Ryan, Stu; Mendel, Lisa Lucks

2010-01-01

Background: The audibility of teachers and peers is an essential factor in determining the academic performance of school children. However, acoustic conditions in most classrooms are less than optimal and have been viewed as "hostile listening environments" that undermine the learning of children in school. While research has shown that…

Medical Student Perceptions of the Learning Environment at the End of the First Year: A 28-Medical School Collaborative.

PubMed

Skochelak, Susan E; Stansfield, R Brent; Dunham, Lisette; Dekhtyar, Michael; Gruppen, Larry D; Christianson, Charles; Filstead, William; Quirk, Mark

2016-09-01

Accreditation and professional organizations have recognized the importance of measuring medical students' perceptions of the learning environment, which influences well-being and professional competency development, to optimize professional development. This study was conducted to explore interactions between students' perceptions of the medical school learning environment, student demographic variables, and students' professional attributes of empathy, coping, tolerance of ambiguity, and patient-centeredness to provide ideas for improving the learning environment. Twenty-eight medical schools at 38 campuses recruited 4,664 entering medical students to participate in the two-cohort longitudinal study (2010-2014 or 2011-2015). The authors employed chi-square tests and analysis of variance to examine the relationship between Medical School Learning Environment Survey (MSLES) scores and student characteristics. The authors used mixed-effects models with random school and campus effects to test the overall variances accounted for in MSLES scores at the end of the first year of medical school. Student attributes and demographic characteristics differed significantly across schools but accounted for only 2.2% of the total variance in MSLES scores. Medical school campus explained 15.6% of the variance in MSLES scores. At year's end, students' perceptions toward the learning environment, as reported on the MSLES, differed significantly according to the medical school campus where they trained. Further studies are needed to identify specific factors, such as grading policies, administrative support, and existence of learning communities, which may influence perceptions of the learning environment at various schools. Identifying such variables would assist schools in developing a positive learning environment.

The Developing Infant Creates a Curriculum for Statistical Learning.

PubMed

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

2018-04-01

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

Understanding the optimal learning environment in palliative care.

PubMed

Connell, Shirley E; Yates, Patsy; Barrett, Linda

2011-07-01

The learning experiences of student nurses undertaking clinical placement are reported widely, however little is known about the learning experiences of health professionals undertaking continuing professional development (CPD) in a clinical setting, especially in palliative care. The aim of this study, which was conducted as part of the national evaluation of a professional development program involving clinical attachments with palliative care services (The Program of Experience in the Palliative Approach [PEPA]), was to explore factors influencing the learning experiences of participants over time. Thirteen semi-structured, one-to-one telephone interviews were conducted with five participants throughout their PEPA experience. The analysis was informed by the traditions of adult, social and psychological learning theories and relevant literature. The participants' learning was enhanced by engaging interactively with host site staff and patients, and by the validation of their personal and professional life experiences together with the reciprocation of their knowledge with host site staff. Self-directed learning strategies maximised the participants' learning outcomes. Inclusion in team activities aided the participants to feel accepted within the host site. Personal interactions with host site staff and patients shaped this social/cultural environment of the host site. Optimal learning was promoted when participants were actively engaged, felt accepted and supported by, and experienced positive interpersonal interactions with, the host site staff. Copyright © 2010 Elsevier Ltd. All rights reserved.

From a Gloss to a Learning Tool: Does Visual Aids Enhance Better Sentence Comprehension?

ERIC Educational Resources Information Center

Sato, Takeshi; Suzuki, Akio

2012-01-01

The aim of this study is to optimize CALL environments as a learning tool rather than a gloss, focusing on the learning of polysemous words which refer to spatial relationship between objects. A lot of research has already been conducted to examine the efficacy of visual glosses while reading L2 texts and has reported that visual glosses can be…

The ALIVE Project: Astronomy Learning in Immersive Virtual Environments

NASA Astrophysics Data System (ADS)

Yu, K. C.; Sahami, K.; Denn, G.

2008-06-01

The Astronomy Learning in Immersive Virtual Environments (ALIVE) project seeks to discover learning modes and optimal teaching strategies using immersive virtual environments (VEs). VEs are computer-generated, three-dimensional environments that can be navigated to provide multiple perspectives. Immersive VEs provide the additional benefit of surrounding a viewer with the simulated reality. ALIVE evaluates the incorporation of an interactive, real-time ``virtual universe'' into formal college astronomy education. In the experiment, pre-course, post-course, and curriculum tests will be used to determine the efficacy of immersive visualizations presented in a digital planetarium versus the same visual simulations in the non-immersive setting of a normal classroom, as well as a control case using traditional classroom multimedia. To normalize for inter-instructor variability, each ALIVE instructor will teach at least one of each class in each of the three test groups.

Status of knowledge on student-learning environments in nursing homes: A mixed-method systematic review.

PubMed

Husebø, Anne Marie Lunde; Storm, Marianne; Våga, Bodil Bø; Rosenberg, Adriana; Akerjordet, Kristin

2018-04-01

To give an overview of empirical studies investigating nursing homes as a learning environment during nursing students' clinical practice. A supportive clinical learning environment is crucial to students' learning and for their development into reflective and capable practitioners. Nursing students' experience with clinical practice can be decisive in future workplace choices. A competent workforce is needed for the future care of older people. Opportunities for maximum learning among nursing students during clinical practice studies in nursing homes should therefore be explored. Mixed-method systematic review using PRISMA guidelines, on learning environments in nursing homes, published in English between 2005-2015. Search of CINAHL with Full Text, Academic Search Premier, MEDLINE and SocINDEX with Full Text, in combination with journal hand searches. Three hundred and thirty-six titles were identified. Twenty studies met the review inclusion criteria. Assessment of methodological quality was based on the Mixed Methods Appraisal Tool. Data were extracted and synthesised using a data analysis method for integrative reviews. Twenty articles were included. The majority of the studies showed moderately high methodological quality. Four main themes emerged from data synthesis: "Student characteristic and earlier experience"; "Nursing home ward environment"; "Quality of mentoring relationship and learning methods"; and "Students' achieved nursing competencies." Nursing home learning environments may be optimised by a well-prepared academic-clinical partnership, supervision by encouraging mentors and high-quality nursing care of older people. Positive learning experiences may increase students' professional development through achievement of basic nursing skills and competencies and motivate them to choose the nursing home as their future workplace. An optimal learning environment can be ensured by thorough preplacement preparations in academia and in nursing home wards, continuous supervision and facilitation of team learning. © 2018 John Wiley & Sons Ltd.

Optimize Knowledge Sharing, Team Effectiveness, and Individual Learning within the Flipped Team-Based Classroom

ERIC Educational Resources Information Center

Huang, Chung-Kai; Lin, Chun-Yu; Lin, Zih-Cin; Wang, Cui; Lin, Chia-Jung

2017-01-01

Due to the competitive and fast-changing nature of external business environments, university students should acquire knowledge of how to cooperate, share knowledge, and enhance team effectiveness and individual learning in the future workplace. Consequently, the redesign of business courses in higher education merits more discussion. Based on the…

Goals, Success Factors, and Barriers for Simulation-Based Learning: A Qualitative Interview Study in Health Care

ERIC Educational Resources Information Center

Dieckmann, Peter; Friis, Susanne Molin; Lippert, Anne; Ostergaard, Doris

2012-01-01

Introduction: This study describes (a) process goals, (b) success factors, and (c) barriers for optimizing simulation-based learning environments within the simulation setting model developed by Dieckmann. Methods: Seven simulation educators of different experience levels were interviewed using the Critical Incident Technique. Results: (a) The…

Connecting to Communities: Powerful Pedagogies for Leading for Social Change.

PubMed

Wagner, Wendy; Mathison, Patricia

2015-01-01

This chapter explores the use of powerful pedagogies such as service-learning, cultural immersion, and community-based research to enhance leadership development. Four key principles are presented that describe how leadership educators can facilitate community-based learning in a way that creates an optimal learning environment for students, while also engaging ethically with individuals and organizations in the community. © 2015 Wiley Periodicals, Inc., A Wiley Company.

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.

Learning optimal eye movements to unusual faces

PubMed Central

Peterson, Matthew F.; Eckstein, Miguel P.

2014-01-01

Eye movements, which guide the fovea’s high resolution and computational power to relevant areas of the visual scene, are integral to efficient, successful completion of many visual tasks. How humans modify their eye movements through experience with their perceptual environments, and its functional role in learning new tasks, has not been fully investigated. Here, we used a face identification task where only the mouth discriminated exemplars to assess if, how, and when eye movement modulation may mediate learning. By interleaving trials of unconstrained eye movements with trials of forced fixation, we attempted to separate the contributions of eye movements and covert mechanisms to performance improvements. Without instruction, a majority of observers substantially increased accuracy and learned to direct their initial eye movements towards the optimal fixation point. The proximity of an observer’s default face identification eye movement behavior to the new optimal fixation point and the observer’s peripheral processing ability were predictive of performance gains and eye movement learning. After practice in a subsequent condition in which observers were directed to fixate different locations along the face, including the relevant mouth region, all observers learned to make eye movements to the optimal fixation point. In this fully learned state, augmented fixation strategy accounted for 43% of total efficiency improvements while covert mechanisms accounted for the remaining 57%. The findings suggest a critical role for eye movement planning to perceptual learning, and elucidate factors that can predict when and how well an observer can learn a new task with unusual exemplars. PMID:24291712

Learning in neural networks based on a generalized fluctuation theorem

NASA Astrophysics Data System (ADS)

Hayakawa, Takashi; Aoyagi, Toshio

2015-11-01

Information maximization has been investigated as a possible mechanism of learning governing the self-organization that occurs within the neural systems of animals. Within the general context of models of neural systems bidirectionally interacting with environments, however, the role of information maximization remains to be elucidated. For bidirectionally interacting physical systems, universal laws describing the fluctuation they exhibit and the information they possess have recently been discovered. These laws are termed fluctuation theorems. In the present study, we formulate a theory of learning in neural networks bidirectionally interacting with environments based on the principle of information maximization. Our formulation begins with the introduction of a generalized fluctuation theorem, employing an interpretation appropriate for the present application, which differs from the original thermodynamic interpretation. We analytically and numerically demonstrate that the learning mechanism presented in our theory allows neural networks to efficiently explore their environments and optimally encode information about them.

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.

Nursing students' satisfaction of the clinical learning environment: a research study.

PubMed

Papastavrou, Evridiki; Dimitriadou, Maria; Tsangari, Haritini; Andreou, Christos

2016-01-01

The acquisition of quality clinical experience within a supportive and pedagogically adjusted clinical learning environment is a significant concern for educational institutions. The quality of clinical learning usually reflects the quality of the curriculum structure. The assessment of the clinical settings as learning environment is a significant concern within the contemporary nursing education. The nursing students' satisfaction is considered as an important factor of such assessment, contributing to any potential reforms in order to optimize the learning activities and achievements within clinical settings. The aim of the study was to investigate nursing students' satisfaction of the clinical settings as learning environments. A quantitative descriptive, correlational design was used. A sample of 463 undergraduate nursing students from the three universities in Cyprus were participated. Data were collected using the Clinical Learning Environment, Supervision and Nurse Teacher (CLES + T). Nursing students were highly satisfied with the clinical learning environment and their satisfaction has been positively related to all clinical learning environment constructs namely the pedagogical atmosphere, the Ward Manager's leadership style, the premises of Nursing in the ward, the supervisory relationship (mentor) and the role of the Nurse Teacher (p < 0.001). Students who had a named mentor reported more satisfied with the supervisory relationship. The frequency of meetings among the students and the mentors increased the students' satisfaction with the clinical learning environment. It was also revealed that 1st year students were found to be more satisfied than the students in other years. The supervisory relationship was evaluated by the students as the most influential factor in their satisfaction with the clinical learning environment. Student's acceptance within the nursing team and a well-documented individual nursing care is also related with students' satisfaction. The pedagogical atmosphere is considered pivotal, with reference to students' learning activities and competent development within the clinical setting. Therefore, satisfaction could be used as an important contributing factor towards the development of clinical learning environments in order to satisfy the needs and expectations of students. The value of the development of an organized mentorship system is illustrated in the study.

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

Fostering Self-Regulation in Training Complex Cognitive Tasks

ERIC Educational Resources Information Center

van Meeuwen, Ludo W.; Brand-Gruwel, Saskia; Kirschner, Paul A.; de Bock, Jeano J. P. R.; van Merriënboer, Jeroen J. G.

2018-01-01

In complex cognitive domains such as air traffic control, professionals must be able to adapt to and act upon continuing changes in a highly advanced technological work environment. To function optimally in such an environment, the controllers must be able to regulate their learning. Although these regulation skills should be part of their…

Effectiveness of Selected Teaching Strategies in Relation to the Learning Styles of Secondary School Students in India

ERIC Educational Resources Information Center

Kamboj, Pooja; Singh, Sushil Kumar

2015-01-01

Effective teaching in schools requires flexibility, energy and commitment. Successful teaching also requires that teachers are able to address learner's needs and understand the variations in learner's styles and approaches. Teachers can accomplish these requirements while creating an optimal teaching-learning environment by utilizing a variety of…

What Do Students Do in a F2F CSCL Classroom? The Optimization of Multiple Communications Modes

ERIC Educational Resources Information Center

Chen, Wenli; Looi, Chee-Kit; Tan, Sini

2010-01-01

This exploratory study analyzes how students use different communication modes to share information, negotiate meaning and construct knowledge in the process of doing a group learning activity in a Primary Grade 5 blended learning environment in Singapore. Small groups of students interacted face-to-face over a computer-mediated communication…

[Imprinting as a mechanism of information memorizing in the adult BALB/c mice].

PubMed

Nikol'skaia, K A; Berezhnoĭ, D S

2011-09-01

Study of spatial learning in adult BALB/c mice revealed that a short exposition to the environment (from 3 to 8 minutes) could be enough for spatial information to be fixed in the long-term memory, and affected subsequent learning process in the new environment. Control group, learning in the same maze, followed the "shortest path" principle during formation of the optimal food-obtaining habit. Experimental animals, learning in a slightly changed environment, were unable to apply this rule due to persistent coupling of the new spatial information with the old memory traces which led to constant errors. The obtained effect was observed during the whole learning period and depended neither on frequency nor on interval of repetition during the initial information acquisition. The obtained data testify that memorizing in adult state share the properties with the imprinting process inherent in the early ontogeny. The memory fixation on all development stages seems to be based on a universal mechanism.

Modeling the Player: Predictability of the Models of Bartle and Kolb Based on NEO-FFI (Big5) and the Implications for Game Based Learning

ERIC Educational Resources Information Center

Konert, Johannes; Gutjahr, Michael; Göbel, Stefan; Steinmetz, Ralf

2014-01-01

For adaptation and personalization of game play sophisticated player models and learner models are used in game-based learning environments. Thus, the game flow can be optimized to increase efficiency and effectiveness of gaming and learning in parallel. In the field of gaming still the Bartle model is commonly used due to its simplicity and good…

An instrument to characterize the environment for residents' evidence-based medicine learning and practice.

PubMed

Mi, Misa; Moseley, James L; Green, Michael L

2012-02-01

Many residency programs offer training in evidence-based medicine (EBM). However, these curricula often fail to achieve optimal learning outcomes, perhaps because they neglect various contextual factors in the learning environment. We developed and validated an instrument to characterize the environment for EBM learning and practice in residency programs. An EBM Environment Scale was developed following scale development principles. A survey was administered to residents across six programs in primary care specialties at four medical centers. Internal consistency reliability was analyzed with Cronbach's coefficient alpha. Validity was assessed by comparing predetermined subscales with the survey's internal structure as assessed via factor analysis. Scores were also compared for subgroups based on residency program affiliation and residency characteristics. Out of 262 eligible residents, 124 completed the survey (response rate 47%). The overall mean score was 3.89 (standard deviation=0.56). The initial reliability analysis of the 48-item scale had a high reliability coefficient (Cronbach α=.94). Factor analysis and further item analysis resulted in a shorter 36-item scale with a satisfactory reliability coefficient (Cronbach α=.86). Scores were higher for residents with prior EBM training in medical school (4.14 versus 3.62) and in residency (4.25 versus 3.69). If further testing confirms its properties, the EBM Environment Scale may be used to understand the influence of the learning environment on the effectiveness of EBM training. Additionally, it may detect changes in the EBM learning environment in response to programmatic or institutional interventions.

Career Success: The Effects of Personality.

ERIC Educational Resources Information Center

Lau, Victor P.; Shaffer, Margaret A.

1999-01-01

A model based on Bandura's Social Learning Theory proposes the following personality traits as determinants of career success: locus of control, self-monitoring, self-esteem, and optimism, along with job performance and person-to-environment fit. (SK)

Theoretically Grounded Guidelines for Assessing Learning Progress: Cognitive Changes in Ill-Structured Complex Problem-Solving Contexts

ERIC Educational Resources Information Center

Kim, Min Kyu

2012-01-01

It is generally accepted that the cognitive development for a wide range of students can be improved through adaptive instruction-learning environments optimized to suit individual needs (e.g., Cronbach, Am Psychol 12:671-684, 1957; Lee and Park, in Handbook of research for educational communications and technology, Taylor & Francis Group,…

Development and Validation of a Measurement Scale to Analyze the Environment for Evidence-Based Medicine Learning and Practice by Medical Residents

ERIC Educational Resources Information Center

Mi, Fangqiong

2010-01-01

A growing number of residency programs are instituting curricula to include the component of evidence-based medicine (EBM) principles and process. However, these curricula may not be able to achieve the optimal learning outcomes, perhaps because various contextual factors are often overlooked when EBM training is being designed, developed, and…

Evolutionary Fuzzy Control and Navigation for Two Wheeled Robots Cooperatively Carrying an Object in Unknown Environments.

PubMed

Juang, Chia-Feng; Lai, Min-Ge; Zeng, Wan-Ting

2015-09-01

This paper presents a method that allows two wheeled, mobile robots to navigate unknown environments while cooperatively carrying an object. In the navigation method, a leader robot and a follower robot cooperatively perform either obstacle boundary following (OBF) or target seeking (TS) to reach a destination. The two robots are controlled by fuzzy controllers (FC) whose rules are learned through an adaptive fusion of continuous ant colony optimization and particle swarm optimization (AF-CACPSO), which avoids the time-consuming task of manually designing the controllers. The AF-CACPSO-based evolutionary fuzzy control approach is first applied to the control of a single robot to perform OBF. The learning approach is then applied to achieve cooperative OBF with two robots, where an auxiliary FC designed with the AF-CACPSO is used to control the follower robot. For cooperative TS, a rule for coordination of the two robots is developed. To navigate cooperatively, a cooperative behavior supervisor is introduced to select between cooperative OBF and cooperative TS. The performance of the AF-CACPSO is verified through comparisons with various population-based optimization algorithms for the OBF learning problem. Simulations and experiments verify the effectiveness of the approach for cooperative navigation of two robots.

Enhancing health leadership performance using neurotherapy.

PubMed

Swingle, Paul G; Hartney, Elizabeth

2018-05-01

The discovery of neuroplasticity means the brain can change, functionally, in response to the environment and to learning. While individuals can develop harmful patterns of brain activity in response to stressors, they can also learn to modify or control neurological conditions associated with specific behaviors. Neurotherapy is one way of changing brain functioning to modify troubling conditions which can impair leadership performance, through responding to feedback on their own brain activity, and enhancing optimal leadership functioning through learning to maximize such cognitive strengths as mental efficiency, focus, creativity, perseverance, and executive functioning. The present article outlines the application of the concept of optimal performance training to organizational leadership in a healthcare context, by describing approaches to neurotherapy and illustrating their application through a case study of a health leader learning to overcome the neurological and emotional sequelae of workplace stress and trauma.

Rejecting salient distractors: Generalization from experience.

PubMed

Vatterott, Daniel B; Mozer, Michael C; Vecera, Shaun P

2018-02-01

Distraction impairs performance of many important, everyday tasks. Attentional control limits distraction by preferentially selecting important items for limited-capacity cognitive operations. Research in attentional control has typically investigated the degree to which selection of items is stimulus-driven versus goal-driven. Recent work finds that when observers initially learn a task, the selection is based on stimulus-driven factors, but through experience, goal-driven factors have an increasing influence. The modulation of selection by goals has been studied within the paradigm of learned distractor rejection, in which experience over a sequence of trials enables individuals eventually to ignore a perceptually salient distractor. The experiments presented examine whether observers can generalize learned distractor rejection to novel distractors. Observers searched for a target and ignored a salient color-singleton distractor that appeared in half of the trials. In Experiment 1, observers who learned distractor rejection in a variable environment rejected a novel distractor more effectively than observers who learned distractor rejection in a less variable, homogeneous environment, demonstrating that variable, heterogeneous stimulus environments encourage generalizable learned distractor rejection. Experiments 2 and 3 investigated the time course of learned distractor rejection across the experiment and found that after experiencing four color-singleton distractors in different blocks, observers could effectively reject subsequent novel color-singleton distractors. These results suggest that the optimization of attentional control to the task environment can be interpreted as a form of learning, demonstrating experience's critical role in attentional control.

Learning and inference using complex generative models in a spatial localization task.

PubMed

Bejjanki, Vikranth R; Knill, David C; Aslin, Richard N

2016-01-01

A large body of research has established that, under relatively simple task conditions, human observers integrate uncertain sensory information with learned prior knowledge in an approximately Bayes-optimal manner. However, in many natural tasks, observers must perform this sensory-plus-prior integration when the underlying generative model of the environment consists of multiple causes. Here we ask if the Bayes-optimal integration seen with simple tasks also applies to such natural tasks when the generative model is more complex, or whether observers rely instead on a less efficient set of heuristics that approximate ideal performance. Participants localized a "hidden" target whose position on a touch screen was sampled from a location-contingent bimodal generative model with different variances around each mode. Over repeated exposure to this task, participants learned the a priori locations of the target (i.e., the bimodal generative model), and integrated this learned knowledge with uncertain sensory information on a trial-by-trial basis in a manner consistent with the predictions of Bayes-optimal behavior. In particular, participants rapidly learned the locations of the two modes of the generative model, but the relative variances of the modes were learned much more slowly. Taken together, our results suggest that human performance in a more complex localization task, which requires the integration of sensory information with learned knowledge of a bimodal generative model, is consistent with the predictions of Bayes-optimal behavior, but involves a much longer time-course than in simpler tasks.

Agent Reward Shaping for Alleviating Traffic Congestion

NASA Technical Reports Server (NTRS)

Tumer, Kagan; Agogino, Adrian

2006-01-01

Traffic congestion problems provide a unique environment to study how multi-agent systems promote desired system level behavior. What is particularly interesting in this class of problems is that no individual action is intrinsically "bad" for the system but that combinations of actions among agents lead to undesirable outcomes, As a consequence, agents need to learn how to coordinate their actions with those of other agents, rather than learn a particular set of "good" actions. This problem is ubiquitous in various traffic problems, including selecting departure times for commuters, routes for airlines, and paths for data routers. In this paper we present a multi-agent approach to two traffic problems, where far each driver, an agent selects the most suitable action using reinforcement learning. The agent rewards are based on concepts from collectives and aim to provide the agents with rewards that are both easy to learn and that if learned, lead to good system level behavior. In the first problem, we study how agents learn the best departure times of drivers in a daily commuting environment and how following those departure times alleviates congestion. In the second problem, we study how agents learn to select desirable routes to improve traffic flow and minimize delays for. all drivers.. In both sets of experiments,. agents using collective-based rewards produced near optimal performance (93-96% of optimal) whereas agents using system rewards (63-68%) barely outperformed random action selection (62-64%) and agents using local rewards (48-72%) performed worse than random in some instances.

iMidwife: midwifery students' use of smartphone technology as a mediated educational tool in clinical environments.

PubMed

DeLeo, Annemarie; Geraghty, Sadie

2017-12-18

The increasing use of smartphone technology in health care provides midwifery students with unprecedented access to online resources that facilitates the optimal care of women and supports ongoing learning. A small pilot study was conducted in Western Australia, with 29 undergraduate and postgraduate midwifery students to explore the use of smartphone technology whilst in clinical practice. This study aimed to define the impact of smartphones in clinical decision-making and learning whilst in clinical areas, by midwifery students at the point of care. An online survey was used to collect data. Five consistent themes were identified from the results. Smartphone technology encourages self-directed learning, consolidation of theory, engagement through blended learning, complements online education in clinical practice and is a trend in the future of midwifery curriculum. Smartphones enhance the learning and mobility of supportive resources that consolidate midwifery students' clinical experience in workplace environments.

a Fully Automated Pipeline for Classification Tasks with AN Application to Remote Sensing

NASA Astrophysics Data System (ADS)

Suzuki, K.; Claesen, M.; Takeda, H.; De Moor, B.

2016-06-01

Nowadays deep learning has been intensively in spotlight owing to its great victories at major competitions, which undeservedly pushed `shallow' machine learning methods, relatively naive/handy algorithms commonly used by industrial engineers, to the background in spite of their facilities such as small requisite amount of time/dataset for training. We, with a practical point of view, utilized shallow learning algorithms to construct a learning pipeline such that operators can utilize machine learning without any special knowledge, expensive computation environment, and a large amount of labelled data. The proposed pipeline automates a whole classification process, namely feature-selection, weighting features and the selection of the most suitable classifier with optimized hyperparameters. The configuration facilitates particle swarm optimization, one of well-known metaheuristic algorithms for the sake of generally fast and fine optimization, which enables us not only to optimize (hyper)parameters but also to determine appropriate features/classifier to the problem, which has conventionally been a priori based on domain knowledge and remained untouched or dealt with naïve algorithms such as grid search. Through experiments with the MNIST and CIFAR-10 datasets, common datasets in computer vision field for character recognition and object recognition problems respectively, our automated learning approach provides high performance considering its simple setting (i.e. non-specialized setting depending on dataset), small amount of training data, and practical learning time. Moreover, compared to deep learning the performance stays robust without almost any modification even with a remote sensing object recognition problem, which in turn indicates that there is a high possibility that our approach contributes to general classification problems.

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.

"Back to Bedside": Residents' and Fellows' Perspectives on Finding Meaning in Work.

PubMed

Hipp, Dustin M; Rialon, Kristy L; Nevel, Kathryn; Kothari, Anai N; Jardine, Lcdr Dinchen A

2017-04-01

Physician burnout is common and associated with significant consequences for physicians and patients. One mechanism to combat burnout is to enhance meaning in work. To provide a trainee perspective on how meaning in work can be enhanced in the clinical learning environment through individual, program, and institutional efforts. "Back to Bedside" resulted from an appreciative inquiry exercise by 37 resident and fellow members of the ACGME's Council of Review Committee Residents (CRCR), which was guided by the memoir When Breath Becomes Air by Paul Kalanithi. The exercise was designed to (1) discover current best practices in existing learning environments; (2) dream of ideal ways to enhance meaning in work; (3) design solutions that move toward this optimal environment; and (4) support trainees in operationalizing innovative solutions. Back to Bedside consists of 5 themes for how the learning environment can enhance meaning in daily work: (1) more time at the bedside, engaged in direct patient care, dialogue with patients and families, and bedside clinical teaching; (2) a shared sense of teamwork and respect among multidisciplinary health professionals and trainees; (3) decreasing the time spent on nonclinical and administrative responsibilities; (4) a supportive, collegial work environment; and (5) a learning environment conducive to developing clinical mastery and progressive autonomy. Participants identified actions to achieve these goals. A national, multispecialty group of trainees developed actionable recommendations for how clinical learning environments can be improved to combat physician burnout by fostering meaning in work. These improvements can be championed by trainees.

Impact of Indoor Physical Environment on Learning Efficiency in Different Types of Tasks: A 3 × 4 × 3 Full Factorial Design Analysis.

PubMed

Xiong, Lilin; Huang, Xiao; Li, Jie; Mao, Peng; Wang, Xiang; Wang, Rubing; Tang, Meng

2018-06-13

Indoor physical environments appear to influence learning efficiency nowadays. For improvement in learning efficiency, environmental scenarios need to be designed when occupants engage in different learning tasks. However, how learning efficiency is affected by indoor physical environment based on task types are still not well understood. The present study aims to explore the impacts of three physical environmental factors (i.e., temperature, noise, and illuminance) on learning efficiency according to different types of tasks, including perception, memory, problem-solving, and attention-oriented tasks. A 3 × 4 × 3 full factorial design experiment was employed in a university classroom with 10 subjects recruited. Environmental scenarios were generated based on different levels of temperature (17 °C, 22 °C, and 27 °C), noise (40 dB(A), 50 dB(A), 60 dB(A), and 70 dB(A)) and illuminance (60 lx, 300 lx, and 2200 lx). Accuracy rate (AC), reaction time (RT), and the final performance indicator (PI) were used to quantify learning efficiency. The results showed ambient temperature, noise, and illuminance exerted significant main effect on learning efficiency based on four task types. Significant concurrent effects of the three factors on final learning efficiency was found in all tasks except problem-solving-oriented task. The optimal environmental scenarios for top learning efficiency were further identified under different environmental interactions. The highest learning efficiency came in thermoneutral, relatively quiet, and bright conditions in perception-oriented task. Subjects performed best under warm, relatively quiet, and moderately light exposure when recalling images in the memory-oriented task. Learning efficiency peaked to maxima in thermoneutral, fairly quiet, and moderately light environment in problem-solving process while in cool, fairly quiet and bright environment with regard to attention-oriented task. The study provides guidance for building users to conduct effective environmental intervention with simultaneous controls of ambient temperature, noise, and illuminance. It contributes to creating the most suitable indoor physical environment for improving occupants learning efficiency according to different task types. The findings could further supplement the present indoor environment-related standards or norms with providing empirical reference on environmental interactions.

Changing viewer perspectives reveals constraints to implicit visual statistical learning.

PubMed

Jiang, Yuhong V; Swallow, Khena M

2014-10-07

Statistical learning-learning environmental regularities to guide behavior-likely plays an important role in natural human behavior. One potential use is in search for valuable items. Because visual statistical learning can be acquired quickly and without intention or awareness, it could optimize search and thereby conserve energy. For this to be true, however, visual statistical learning needs to be viewpoint invariant, facilitating search even when people walk around. To test whether implicit visual statistical learning of spatial information is viewpoint independent, we asked participants to perform a visual search task from variable locations around a monitor placed flat on a stand. Unbeknownst to participants, the target was more often in some locations than others. In contrast to previous research on stationary observers, visual statistical learning failed to produce a search advantage for targets in high-probable regions that were stable within the environment but variable relative to the viewer. This failure was observed even when conditions for spatial updating were optimized. However, learning was successful when the rich locations were referenced relative to the viewer. We conclude that changing viewer perspective disrupts implicit learning of the target's location probability. This form of learning shows limited integration with spatial updating or spatiotopic representations. © 2014 ARVO.

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

Online Pedagogical Tutorial Tactics Optimization Using Genetic-Based Reinforcement Learning

PubMed Central

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

2015-01-01

Tutorial tactics are policies for an Intelligent Tutoring System (ITS) to decide the next action when there are multiple actions available. Recent research has demonstrated that when the learning contents were controlled so as to be the same, different tutorial tactics would make difference in students' learning gains. However, the Reinforcement Learning (RL) techniques that were used in previous studies to induce tutorial tactics are insufficient when encountering large problems and hence were used in offline manners. Therefore, we introduced a Genetic-Based Reinforcement Learning (GBML) approach to induce tutorial tactics in an online-learning manner without basing on any preexisting dataset. The introduced method can learn a set of rules from the environment in a manner similar to RL. It includes a genetic-based optimizer for rule discovery task by generating new rules from the old ones. This increases the scalability of a RL learner for larger problems. The results support our hypothesis about the capability of the GBML method to induce tutorial tactics. This suggests that the GBML method should be favorable in developing real-world ITS applications in the domain of tutorial tactics induction. PMID:26065018

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

PubMed

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

2015-01-01

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

An Energy-Efficient Spectrum-Aware Reinforcement Learning-Based Clustering Algorithm for Cognitive Radio Sensor Networks

PubMed Central

Mustapha, Ibrahim; Ali, Borhanuddin Mohd; Rasid, Mohd Fadlee A.; Sali, Aduwati; Mohamad, Hafizal

2015-01-01

It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach. PMID:26287191

An Energy-Efficient Spectrum-Aware Reinforcement Learning-Based Clustering Algorithm for Cognitive Radio Sensor Networks.

PubMed

Mustapha, Ibrahim; Mohd Ali, Borhanuddin; Rasid, Mohd Fadlee A; Sali, Aduwati; Mohamad, Hafizal

2015-08-13

It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach.

Circuit mechanisms of sensorimotor learning

PubMed Central

Makino, Hiroshi; Hwang, Eun Jung; Hedrick, Nathan G.; Komiyama, Takaki

2016-01-01

SUMMARY The relationship between the brain and the environment is flexible, forming the foundation for our ability to learn. Here we review the current state of our understanding of the modifications in the sensorimotor pathway related to sensorimotor learning. We divide the process in three hierarchical levels with distinct goals: 1) sensory perceptual learning, 2) sensorimotor associative learning, and 3) motor skill learning. Perceptual learning optimizes the representations of important sensory stimuli. Associative learning and the initial phase of motor skill learning are ensured by feedback-based mechanisms that permit trial-and-error learning. The later phase of motor skill learning may primarily involve feedback-independent mechanisms operating under the classic Hebbian rule. With these changes under distinct constraints and mechanisms, sensorimotor learning establishes dedicated circuitry for the reproduction of stereotyped neural activity patterns and behavior. PMID:27883902

Operating Room of the Future: Advanced Technologies in Safe and Efficient Operating Rooms

DTIC Science & Technology

2008-10-01

fit” or compatibility with different tasks. Ideally, the optimal match between tasks and well-designed display alternatives will be self -apparent...hierarchical display environment. The FARO robot arm is used as an accurate and reliable tracker to control a virtual camera. The virtual camera pose is...in learning outcomes due to self -feedback, improvements in learning outcomes due to instructor feedback and synchronous versus asynchronous

Efficacy of navigation may be influenced by retrosplenial cortex-mediated learning of landmark stability.

PubMed

Auger, Stephen D; Zeidman, Peter; Maguire, Eleanor A

2017-09-01

Human beings differ considerably in their ability to orient and navigate within the environment, but it has been difficult to determine specific causes of these individual differences. Permanent, stable landmarks are thought to be crucial for building a mental representation of an environment. Poor, compared to good, navigators have been shown to have difficulty identifying permanent landmarks, with a concomitant reduction in functional MRI (fMRI) activity in the retrosplenial cortex. However, a clear association between navigation ability and the learning of permanent landmarks has not been established. Here we tested for such a link. We had participants learn a virtual reality environment by repeatedly moving through it during fMRI scanning. The environment contained landmarks of which participants had no prior experience, some of which remained fixed in their locations while others changed position each time they were seen. After the fMRI learning phase, we divided participants into good and poor navigators based on their ability to find their way in the environment. The groups were closely matched on a range of cognitive and structural brain measures. Examination of the learning phase during scanning revealed that, while good and poor navigators learned to recognise the environment's landmarks at a similar rate, poor navigators were impaired at registering whether landmarks were stable or transient, and this was associated with reduced engagement of the retrosplenial cortex. Moreover, a mediation analysis showed that there was a significant effect of landmark permanence learning on navigation performance mediated through retrosplenial cortex activity. We conclude that a diminished ability to process landmark permanence may be a contributory factor to sub-optimal navigation, and could be related to the level of retrosplenial cortex engagement. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

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

PubMed

Wakano, Joe Yuichiro; Miura, Chiaki

2014-02-01

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

Resident Wellness Matters: Optimizing Resident Education and Wellness Through the Learning Environment.

PubMed

Jennings, M L; Slavin, Stuart J

2015-09-01

The problem of poor mental health in residency is well established. Burnout, depression, and suicidal ideation are prevalent among resident physicians, and these problems appear to persist into practice. Leaders in graduate medical education such as policy makers at the Accreditation Council for Graduate Medical Education (ACGME) and directors of individual programs and institutions should acknowledge these important issues and take steps to address them. The ACGME's Clinical Learning Environment Review (CLER) Program currently outlines an expectation that institutions both educate residents about burnout and measure burnout annually. The CLER Program could go further by expecting institutions to create quality initiatives to enhance resident wellness and increase resident engagement. The ACGME should also call for and support research in this area. Leaders or directors of individual programs and institutions should consider wellness initiatives that both (1) identify and address suboptimal aspects of the learning environment and (2) train residents in resilience skills. Efforts to improve the residency learning environment could be guided by the work of Maslach and Leiter, who describe six categories of work stress that can contribute to burnout: (1) workload, (2) control, (3) balance between effort and reward, (4) community, (5) fairness, and (6) values.

“Back to Bedside”: Residents' and Fellows' Perspectives on Finding Meaning in Work

PubMed Central

Hipp, Dustin M.; Rialon, Kristy L.; Nevel, Kathryn; Kothari, Anai N.

2017-01-01

Background Physician burnout is common and associated with significant consequences for physicians and patients. One mechanism to combat burnout is to enhance meaning in work. Objective To provide a trainee perspective on how meaning in work can be enhanced in the clinical learning environment through individual, program, and institutional efforts. Methods “Back to Bedside” resulted from an appreciative inquiry exercise by 37 resident and fellow members of the ACGME's Council of Review Committee Residents (CRCR), which was guided by the memoir When Breath Becomes Air by Paul Kalanithi. The exercise was designed to (1) discover current best practices in existing learning environments; (2) dream of ideal ways to enhance meaning in work; (3) design solutions that move toward this optimal environment; and (4) support trainees in operationalizing innovative solutions. Results Back to Bedside consists of 5 themes for how the learning environment can enhance meaning in daily work: (1) more time at the bedside, engaged in direct patient care, dialogue with patients and families, and bedside clinical teaching; (2) a shared sense of teamwork and respect among multidisciplinary health professionals and trainees; (3) decreasing the time spent on nonclinical and administrative responsibilities; (4) a supportive, collegial work environment; and (5) a learning environment conducive to developing clinical mastery and progressive autonomy. Participants identified actions to achieve these goals. Conclusions A national, multispecialty group of trainees developed actionable recommendations for how clinical learning environments can be improved to combat physician burnout by fostering meaning in work. These improvements can be championed by trainees. PMID:28439376

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.

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

Query construction, entropy, and generalization in neural-network models

NASA Astrophysics Data System (ADS)

Sollich, Peter

1994-05-01

We study query construction algorithms, which aim at improving the generalization ability of systems that learn from examples by choosing optimal, nonredundant training sets. We set up a general probabilistic framework for deriving such algorithms from the requirement of optimizing a suitable objective function; specifically, we consider the objective functions entropy (or information gain) and generalization error. For two learning scenarios, the high-low game and the linear perceptron, we evaluate the generalization performance obtained by applying the corresponding query construction algorithms and compare it to training on random examples. We find qualitative differences between the two scenarios due to the different structure of the underlying rules (nonlinear and ``noninvertible'' versus linear); in particular, for the linear perceptron, random examples lead to the same generalization ability as a sequence of queries in the limit of an infinite number of examples. We also investigate learning algorithms which are ill matched to the learning environment and find that, in this case, minimum entropy queries can in fact yield a lower generalization ability than random examples. Finally, we study the efficiency of single queries and its dependence on the learning history, i.e., on whether the previous training examples were generated randomly or by querying, and the difference between globally and locally optimal query construction.

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.

TLBO based Voltage Stable Environment Friendly Economic Dispatch Considering Real and Reactive Power Constraints

NASA Astrophysics Data System (ADS)

Verma, H. K.; Mafidar, P.

2013-09-01

In view of growing concern towards environment, power system engineers are forced to generate quality green energy. Hence the economic dispatch (ED) aims at the power generation to meet the load demand at minimum fuel cost with environmental and voltage constraints along with essential constraints on real and reactive power. The emission control which reduces the negative impact on environment is achieved by including the additional constraints in ED problem. Presently, the power system mostly operates near its stability limits, therefore with increased demand the system faces voltage problem. The bus voltages are brought within limit in the present work by placement of static var compensator (SVC) at weak bus which is identified from bus participation factor. The optimal size of SVC is determined by univariate search method. This paper presents the use of Teaching Learning based Optimization (TLBO) algorithm for voltage stable environment friendly ED problem with real and reactive power constraints. The computational effectiveness of TLBO is established through test results over particle swarm optimization (PSO) and Big Bang-Big Crunch (BB-BC) algorithms for the ED problem.

Challenging High-Ability Students

ERIC Educational Resources Information Center

Scager, Karin; Akkerman, Sanne F.; Pilot, Albert; Wubbels, Theo

2014-01-01

The existing literature on indicators of an optimal learning environment for high-ability students frequently discusses the concept of challenge. It is, however, not clear what, precisely, constitutes appropriate challenge for these students. In this study, the authors examined an undergraduate honours course, Advanced Cell Biology, which has…

Growing a Nurturing Classroom

ERIC Educational Resources Information Center

Boorn, Clare; Dunn, Paula Hopkins; Page, Claire

2010-01-01

"Growing a nurturing classroom" is an awareness training programme presented by educational psychologists in Leicestershire for professionals working in primary schools with the aim of promoting an optimal environment for learning and emotional well-being. The training helps primary school staff to take a holistic approach to education;…

The brain ages optimally to model its environment: evidence from sensory learning over the adult lifespan.

PubMed

Moran, Rosalyn J; Symmonds, Mkael; Dolan, Raymond J; Friston, Karl J

2014-01-01

The aging brain shows a progressive loss of neuropil, which is accompanied by subtle changes in neuronal plasticity, sensory learning and memory. Neurophysiologically, aging attenuates evoked responses--including the mismatch negativity (MMN). This is accompanied by a shift in cortical responsivity from sensory (posterior) regions to executive (anterior) regions, which has been interpreted as a compensatory response for cognitive decline. Theoretical neurobiology offers a simpler explanation for all of these effects--from a Bayesian perspective, as the brain is progressively optimized to model its world, its complexity will decrease. A corollary of this complexity reduction is an attenuation of Bayesian updating or sensory learning. Here we confirmed this hypothesis using magnetoencephalographic recordings of the mismatch negativity elicited in a large cohort of human subjects, in their third to ninth decade. Employing dynamic causal modeling to assay the synaptic mechanisms underlying these non-invasive recordings, we found a selective age-related attenuation of synaptic connectivity changes that underpin rapid sensory learning. In contrast, baseline synaptic connectivity strengths were consistently strong over the decades. Our findings suggest that the lifetime accrual of sensory experience optimizes functional brain architectures to enable efficient and generalizable predictions of the world.

Asymptotically Optimal Motion Planning for Learned Tasks Using Time-Dependent Cost Maps

PubMed Central

Bowen, Chris; Ye, Gu; Alterovitz, Ron

2015-01-01

In unstructured environments in people’s homes and workspaces, robots executing a task may need to avoid obstacles while satisfying task motion constraints, e.g., keeping a plate of food level to avoid spills or properly orienting a finger to push a button. We introduce a sampling-based method for computing motion plans that are collision-free and minimize a cost metric that encodes task motion constraints. Our time-dependent cost metric, learned from a set of demonstrations, encodes features of a task’s motion that are consistent across the demonstrations and, hence, are likely required to successfully execute the task. Our sampling-based motion planner uses the learned cost metric to compute plans that simultaneously avoid obstacles and satisfy task constraints. The motion planner is asymptotically optimal and minimizes the Mahalanobis distance between the planned trajectory and the distribution of demonstrations in a feature space parameterized by the locations of task-relevant objects. The motion planner also leverages the distribution of the demonstrations to significantly reduce plan computation time. We demonstrate the method’s effectiveness and speed using a small humanoid robot performing tasks requiring both obstacle avoidance and satisfaction of learned task constraints. Note to Practitioners Motivated by the desire to enable robots to autonomously operate in cluttered home and workplace environments, this paper presents an approach for intuitively training a robot in a manner that enables it to repeat the task in novel scenarios and in the presence of unforeseen obstacles in the environment. Based on user-provided demonstrations of the task, our method learns features of the task that are consistent across the demonstrations and that we expect should be repeated by the robot when performing the task. We next present an efficient algorithm for planning robot motions to perform the task based on the learned features while avoiding obstacles. We demonstrate the effectiveness of our motion planner for scenarios requiring transferring a powder and pushing a button in environments with obstacles, and we plan to extend our results to more complex tasks in the future. PMID:26279642

Last-position elimination-based learning automata.

PubMed

Zhang, Junqi; Wang, Cheng; Zhou, MengChu

2014-12-01

An update scheme of the state probability vector of actions is critical for learning automata (LA). The most popular is the pursuit scheme that pursues the estimated optimal action and penalizes others. This paper proposes a reverse philosophy that leads to last-position elimination-based learning automata (LELA). The action graded last in terms of the estimated performance is penalized by decreasing its state probability and is eliminated when its state probability becomes zero. All active actions, that is, actions with nonzero state probability, equally share the penalized state probability from the last-position action at each iteration. The proposed LELA is characterized by the relaxed convergence condition for the optimal action, the accelerated step size of the state probability update scheme for the estimated optimal action, and the enriched sampling for the estimated nonoptimal actions. The proof of the ϵ-optimal property for the proposed algorithm is presented. Last-position elimination is a widespread philosophy in the real world and has proved to be also helpful for the update scheme of the learning automaton via the simulations of well-known benchmark environments. In the simulations, two versions of the LELA, using different selection strategies of the last action, are compared with the classical pursuit algorithms Discretized Pursuit Reward-Inaction (DP(RI)) and Discretized Generalized Pursuit Algorithm (DGPA). Simulation results show that the proposed schemes achieve significantly faster convergence and higher accuracy than the classical ones. Specifically, the proposed schemes reduce the interval to find the best parameter for a specific environment in the classical pursuit algorithms. Thus, they can have their parameter tuning easier to perform and can save much more time when applied to a practical case. Furthermore, the convergence curves and the corresponding variance coefficient curves of the contenders are illustrated to characterize their essential differences and verify the analysis results of the proposed algorithms.

Play Therapy: Role in Reading Improvement.

ERIC Educational Resources Information Center

Carmichael, Karla

1991-01-01

Reviews the literature concerning the role of play therapy (particularly sandplay and nondirected play therapy) in the improvement of reading. Suggests that the role of play therapy is to support the child, encourage the child, and build self-esteem thus creating the optimal learning environment for reading improvement. (RS)

Optimal ordering and production policy for a recoverable item inventory system with learning effect

NASA Astrophysics Data System (ADS)

Tsai, Deng-Maw

2012-02-01

This article presents two models for determining an optimal integrated economic order quantity and economic production quantity policy in a recoverable manufacturing environment. The models assume that the unit production time of the recovery process decreases with the increase in total units produced as a result of learning. A fixed proportion of used products are collected from customers and then recovered for reuse. The recovered products are assumed to be in good condition and acceptable to customers. Constant demand can be satisfied by utilising both newly purchased products and recovered products. The aim of this article is to show how to minimise total inventory-related cost. The total cost functions of the two models are derived and two simple search procedures are proposed to determine optimal policy parameters. Numerical examples are provided to illustrate the proposed models. In addition, sensitivity analyses have also been performed and are discussed.

A junction-tree based learning algorithm to optimize network wide traffic control: A coordinated multi-agent framework

DOE PAGES

Zhu, Feng; Aziz, H. M. Abdul; Qian, Xinwu; ...

2015-01-31

Our study develops a novel reinforcement learning algorithm for the challenging coordinated signal control problem. Traffic signals are modeled as intelligent agents interacting with the stochastic traffic environment. The model is built on the framework of coordinated reinforcement learning. The Junction Tree Algorithm (JTA) based reinforcement learning is proposed to obtain an exact inference of the best joint actions for all the coordinated intersections. Moreover, the algorithm is implemented and tested with a network containing 18 signalized intersections in VISSIM. Finally, our results show that the JTA based algorithm outperforms independent learning (Q-learning), real-time adaptive learning, and fixed timing plansmore » in terms of average delay, number of stops, and vehicular emissions at the network level.« less

Space stress and genome shock in developing plant cells

NASA Technical Reports Server (NTRS)

Krikorian, A. D.

1996-01-01

In the present paper I review symptoms of stress at the level of the nucleus in cells of plants grown in space under nonoptimized conditions. It remains to be disclosed to what extent gravity "unloading" in the space environment directly contributes to the low mitotic index and the chromosomal anomalies and damage that is frequently, but not invariably, demonstrable in space-grown plants. Evaluation of the available facts indicates that indirect effects play a major role and that there is a significant biological component to the susceptibility to stress damage equation as well. Much remains to be learned on how to provide strictly controlled, optimal environments for plant growth in space. Only after optimized controls become possible will one be able to attribute any observed space effects to lowered gravity or to other significant but more indirect effects of the space environment.

The Responsive Classroom Approach: A Caring, Respectful School Environment as a Context for Development.

ERIC Educational Resources Information Center

Horsch, Patricia; Chen, Jie-Qi; Wagner, Suzanne L.

2002-01-01

The Schools Project, a partnership between the Erickson Institute and low-income Chicago elementary schools, which optimized student learning through various school-based interventions, particularly developmentally appropriate curricula, tended to aggravate students' behavioral problems. The Responsive Classroom approach was implemented to support…

Potential of Audiographic Computerized Telelearning for Distance Extension Education.

ERIC Educational Resources Information Center

Verma, Satish; And Others

In the last 10 years, an approach to electronic distance education called audiographic computerized telelearning using standard telephone lines has come to the fore. Telelearning is a cost-effective system which optimizes existing computer facilities and creates a teaching-learning environment that is interactive, efficient, and adaptable to a…

What's Keeping Us? Some Thoughts on Moving Forward

ERIC Educational Resources Information Center

Kobet, Robert J.

2011-01-01

Recognize we still have a massive job of educating all the critical stakeholders, especially those with administrative decision-making and fiscal responsibility. Understand the potential for LEED and CHPS to enrich the educational delivery process. Ultimately, the goal is to provide the best learning environment possible. Work to optimize the…

Rational Approximations to Rational Models: Alternative Algorithms for Category Learning

ERIC Educational Resources Information Center

Sanborn, Adam N.; Griffiths, Thomas L.; Navarro, Daniel J.

2010-01-01

Rational models of cognition typically consider the abstract computational problems posed by the environment, assuming that people are capable of optimally solving those problems. This differs from more traditional formal models of cognition, which focus on the psychological processes responsible for behavior. A basic challenge for rational models…

The Application of Modeling and Simulation to the Behavioral Deficit of Autism

NASA Technical Reports Server (NTRS)

Anton, John J.

2010-01-01

This abstract describes a research effort to apply technological advances in virtual reality simulation and computer-based games to create behavioral modification programs for individuals with Autism Spectrum Disorder (ASD). The research investigates virtual social skills training within a 3D game environment to diminish the impact of ASD social impairments and to increase learning capacity for optimal intellectual capability. Individuals with autism will encounter prototypical social contexts via computer interface and will interact with 3D avatars with predefined roles within a game-like environment. Incremental learning objectives will combine to form a collaborative social environment. A secondary goal of the effort is to begin the research and development of virtual reality exercises aimed at triggering the release of neurotransmitters to promote critical aspects of synaptic maturation at an early age to change the course of the disease.

Behavior of Machine Learning Algorithms in Adversarial Environments

DTIC Science & Technology

2010-11-23

handwriting recog- nition [cf., Plamondon and Srihari, 2000], they also have potentially far-reaching utility for many applications in security, networking...cost of the largest ℓp cost ball that fits entirely within their convex hull; let’s say this cost is C† ≤ C+0 . To achieve ǫ-multiplicative optimality...optimal on Fconvex,’+’ for ℓ2 costs. The proof of this result is in Appendix C.4. This result says that there is no algorithm can generally achieve ǫ

Experimental Evaluation of Performance Feedback Using the Dismounted Infantry Virtual After Action Review System. Long Range Navy and Marine Corps Science and Technology Program

DTIC Science & Technology

2007-11-14

Artificial intelligence and 4 23 education , Volume 1: Learning environments and tutoring systems. Hillsdale, NJ: Erlbaum. Wickens, C.D. (1984). Processing...and how to use it to best optimize the learning process. Some researchers (see Loftin & Savely, 1991) have proposed adding intelligent systems to the...is experienced as the cognitive centers in an individual’s brain process visual, tactile, kinesthetic , olfactory, proprioceptive, and auditory

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.

Evolution of social learning when high expected payoffs are associated with high risk of failure.

PubMed

Arbilly, Michal; Motro, Uzi; Feldman, Marcus W; Lotem, Arnon

2011-11-07

In an environment where the availability of resources sought by a forager varies greatly, individual foraging is likely to be associated with a high risk of failure. Foragers that learn where the best sources of food are located are likely to develop risk aversion, causing them to avoid the patches that are in fact the best; the result is sub-optimal behaviour. Yet, foragers living in a group may not only learn by themselves, but also by observing others. Using evolutionary agent-based computer simulations of a social foraging game, we show that in an environment where the most productive resources occur with the lowest probability, socially acquired information is strongly favoured over individual experience. While social learning is usually regarded as beneficial because it filters out maladaptive behaviours, the advantage of social learning in a risky environment stems from the fact that it allows risk aversion to be circumvented and the best food source to be revisited despite repeated failures. Our results demonstrate that the consequences of individual risk aversion may be better understood within a social context and suggest one possible explanation for the strong preference for social information over individual experience often observed in both humans and animals.

Evolution of social learning when high expected payoffs are associated with high risk of failure

PubMed Central

Arbilly, Michal; Motro, Uzi; Feldman, Marcus W.; Lotem, Arnon

2011-01-01

In an environment where the availability of resources sought by a forager varies greatly, individual foraging is likely to be associated with a high risk of failure. Foragers that learn where the best sources of food are located are likely to develop risk aversion, causing them to avoid the patches that are in fact the best; the result is sub-optimal behaviour. Yet, foragers living in a group may not only learn by themselves, but also by observing others. Using evolutionary agent-based computer simulations of a social foraging game, we show that in an environment where the most productive resources occur with the lowest probability, socially acquired information is strongly favoured over individual experience. While social learning is usually regarded as beneficial because it filters out maladaptive behaviours, the advantage of social learning in a risky environment stems from the fact that it allows risk aversion to be circumvented and the best food source to be revisited despite repeated failures. Our results demonstrate that the consequences of individual risk aversion may be better understood within a social context and suggest one possible explanation for the strong preference for social information over individual experience often observed in both humans and animals. PMID:21508013

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

Scheduled power tracking control of the wind-storage hybrid system based on the reinforcement learning theory

NASA Astrophysics Data System (ADS)

Li, Ze

2017-09-01

In allusion to the intermittency and uncertainty of the wind electricity, energy storage and wind generator are combined into a hybrid system to improve the controllability of the output power. A scheduled power tracking control method is proposed based on the reinforcement learning theory and Q-learning algorithm. In this method, the state space of the environment is formed with two key factors, i.e. the state of charge of the energy storage and the difference value between the actual wind power and scheduled power, the feasible action is the output power of the energy storage, and the corresponding immediate rewarding function is designed to reflect the rationality of the control action. By interacting with the environment and learning from the immediate reward, the optimal control strategy is gradually formed. After that, it could be applied to the scheduled power tracking control of the hybrid system. Finally, the rationality and validity of the method are verified through simulation examples.

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.

Adaptive Critic Nonlinear Robust Control: A Survey.

PubMed

Wang, Ding; He, Haibo; Liu, Derong

2017-10-01

Adaptive dynamic programming (ADP) and reinforcement learning are quite relevant to each other when performing intelligent optimization. They are both regarded as promising methods involving important components of evaluation and improvement, at the background of information technology, such as artificial intelligence, big data, and deep learning. Although great progresses have been achieved and surveyed when addressing nonlinear optimal control problems, the research on robustness of ADP-based control strategies under uncertain environment has not been fully summarized. Hence, this survey reviews the recent main results of adaptive-critic-based robust control design of continuous-time nonlinear systems. The ADP-based nonlinear optimal regulation is reviewed, followed by robust stabilization of nonlinear systems with matched uncertainties, guaranteed cost control design of unmatched plants, and decentralized stabilization of interconnected systems. Additionally, further comprehensive discussions are presented, including event-based robust control design, improvement of the critic learning rule, nonlinear H ∞ control design, and several notes on future perspectives. By applying the ADP-based optimal and robust control methods to a practical power system and an overhead crane plant, two typical examples are provided to verify the effectiveness of theoretical results. Overall, this survey is beneficial to promote the development of adaptive critic control methods with robustness guarantee and the construction of higher level intelligent systems.

Bio-robots automatic navigation with graded electric reward stimulation based on Reinforcement Learning.

PubMed

Zhang, Chen; Sun, Chao; Gao, Liqiang; Zheng, Nenggan; Chen, Weidong; Zheng, Xiaoxiang

2013-01-01

Bio-robots based on brain computer interface (BCI) suffer from the lack of considering the characteristic of the animals in navigation. This paper proposed a new method for bio-robots' automatic navigation combining the reward generating algorithm base on Reinforcement Learning (RL) with the learning intelligence of animals together. Given the graded electrical reward, the animal e.g. the rat, intends to seek the maximum reward while exploring an unknown environment. Since the rat has excellent spatial recognition, the rat-robot and the RL algorithm can convergent to an optimal route by co-learning. This work has significant inspiration for the practical development of bio-robots' navigation with hybrid intelligence.

Beyond adaptive-critic creative learning for intelligent mobile robots

NASA Astrophysics Data System (ADS)

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

2001-10-01

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

(Ir)rationality in action: do soccer players and goalkeepers fail to learn how to best perform during a penalty kick?

PubMed

Bar-Eli, Michael; Azar, Ofer H; Lurie, Yotam

2009-01-01

This chapter discusses penalty kicks in soccer, interpreted within the framework of behavioral economics. We present two behaviors of professional soccer players during penalty kicks that seem nonoptimal, and possibly indicate biases in decision making. We ask whether, despite the huge incentives involved in professional soccer and the possibility of learning through feedback from the outcomes of previous penalty kicks, goalkeepers and penalty kickers are not optimizing their actions. We suggest that they do indeed learn to optimize, but have a different utility function; goalkeepers are not solely interested in minimizing the chances of the goal, and kickers are not solely interested in maximizing these chances. We believe that, in general, in cases where decision makers have the ability to learn through feedback about the outcome of their actions but exhibit behavior that seems nonoptimal, it is possible that they do optimize, but that their utility function is different from the one assumed. We propose that such decision makers' behavior be reconceived as "socially rational," in the sense that their social environment seems to be incorporated into their utility functions. Finally, the concept of "socio-emotional rationality" is suggested to account for possible implications in future studies of motion-cognition interactions.

Evaluating data distribution and drift vulnerabilities of machine learning algorithms in secure and adversarial environments

NASA Astrophysics Data System (ADS)

Nelson, Kevin; Corbin, George; Blowers, Misty

2014-05-01

Machine learning is continuing to gain popularity due to its ability to solve problems that are difficult to model using conventional computer programming logic. Much of the current and past work has focused on algorithm development, data processing, and optimization. Lately, a subset of research has emerged which explores issues related to security. This research is gaining traction as systems employing these methods are being applied to both secure and adversarial environments. One of machine learning's biggest benefits, its data-driven versus logic-driven approach, is also a weakness if the data on which the models rely are corrupted. Adversaries could maliciously influence systems which address drift and data distribution changes using re-training and online learning. Our work is focused on exploring the resilience of various machine learning algorithms to these data-driven attacks. In this paper, we present our initial findings using Monte Carlo simulations, and statistical analysis, to explore the maximal achievable shift to a classification model, as well as the required amount of control over the data.

How much to trust the senses: Likelihood learning

PubMed Central

Sato, Yoshiyuki; Kording, Konrad P.

2014-01-01

Our brain often needs to estimate unknown variables from imperfect information. Our knowledge about the statistical distributions of quantities in our environment (called priors) and currently available information from sensory inputs (called likelihood) are the basis of all Bayesian models of perception and action. While we know that priors are learned, most studies of prior-likelihood integration simply assume that subjects know about the likelihood. However, as the quality of sensory inputs change over time, we also need to learn about new likelihoods. Here, we show that human subjects readily learn the distribution of visual cues (likelihood function) in a way that can be predicted by models of statistically optimal learning. Using a likelihood that depended on color context, we found that a learned likelihood generalized to new priors. Thus, we conclude that subjects learn about likelihood. PMID:25398975

Noise levels in an urban Asian school environment

PubMed Central

Chan, Karen M.K.; Li, Chi Mei; Ma, Estella P.M.; Yiu, Edwin M.L.; McPherson, Bradley

2015-01-01

Background noise is known to adversely affect speech perception and speech recognition. High levels of background noise in school classrooms may affect student learning, especially for those pupils who are learning in a second language. The current study aimed to determine the noise level and teacher speech-to-noise ratio (SNR) in Hong Kong classrooms. Noise level was measured in 146 occupied classrooms in 37 schools, including kindergartens, primary schools, secondary schools and special schools, in Hong Kong. The mean noise levels in occupied kindergarten, primary school, secondary school and special school classrooms all exceeded recommended maximum noise levels, and noise reduction measures were seldom used in classrooms. The measured SNRs were not optimal and could have adverse implications for student learning and teachers’ vocal health. Schools in urban Asian environments are advised to consider noise reduction measures in classrooms to better comply with recommended maximum noise levels for classrooms. PMID:25599758

Noise levels in an urban Asian school environment.

PubMed

Chan, Karen M K; Li, Chi Mei; Ma, Estella P M; Yiu, Edwin M L; McPherson, Bradley

2015-01-01

Background noise is known to adversely affect speech perception and speech recognition. High levels of background noise in school classrooms may affect student learning, especially for those pupils who are learning in a second language. The current study aimed to determine the noise level and teacher speech-to-noise ratio (SNR) in Hong Kong classrooms. Noise level was measured in 146 occupied classrooms in 37 schools, including kindergartens, primary schools, secondary schools and special schools, in Hong Kong. The mean noise levels in occupied kindergarten, primary school, secondary school and special school classrooms all exceeded recommended maximum noise levels, and noise reduction measures were seldom used in classrooms. The measured SNRs were not optimal and could have adverse implications for student learning and teachers' vocal health. Schools in urban Asian environments are advised to consider noise reduction measures in classrooms to better comply with recommended maximum noise levels for classrooms.

Using Machine Learning in Adversarial Environments.

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

Warren Leon Davis

Intrusion/anomaly detection systems are among the first lines of cyber defense. Commonly, they either use signatures or machine learning (ML) to identify threats, but fail to account for sophisticated attackers trying to circumvent them. We propose to embed machine learning within a game theoretic framework that performs adversarial modeling, develops methods for optimizing operational response based on ML, and integrates the resulting optimization codebase into the existing ML infrastructure developed by the Hybrid LDRD. Our approach addresses three key shortcomings of ML in adversarial settings: 1) resulting classifiers are typically deterministic and, therefore, easy to reverse engineer; 2) ML approachesmore » only address the prediction problem, but do not prescribe how one should operationalize predictions, nor account for operational costs and constraints; and 3) ML approaches do not model attackers’ response and can be circumvented by sophisticated adversaries. The principal novelty of our approach is to construct an optimization framework that blends ML, operational considerations, and a model predicting attackers reaction, with the goal of computing optimal moving target defense. One important challenge is to construct a realistic model of an adversary that is tractable, yet realistic. We aim to advance the science of attacker modeling by considering game-theoretic methods, and by engaging experimental subjects with red teaming experience in trying to actively circumvent an intrusion detection system, and learning a predictive model of such circumvention activities. In addition, we will generate metrics to test that a particular model of an adversary is consistent with available data.« less

Center for the Built Environment: Research on Building HVAC Systems

Science.gov Websites

, and lessons learned. Underfloor Air Distribution (UFAD) Cooling Airflow Design Tool Developing simplified design tools for optimization of underfloor systems. Underfloor Air Distribution (UFAD) Cost Near-ZNE Buildings Setpoint Energy Savings Calculator UFAD Case Studies UFAD Cooling Design Tool UFAD

Modifying Parents' Perceptions of Their Child's Developmental Progress: An Approach to Creating Optimal Learning Environments.

ERIC Educational Resources Information Center

Diamond, Karen E.; Reed, Deborah J.

A program to help parents understand their child's developmental level was evaluated with 13 handicapped infants and their mothers. The intervention sought to increase parents' overall understanding of child development, improve their observational skills, and help them adjust their interactions by taking cues from the child's responses. Mental…

Social Capital by Design: Normative Systems and Social Structures in Six High Schools.

ERIC Educational Resources Information Center

Marks, Helen M.

This paper focuses on investigating the purposive design of learning environments to counter the erosion of social capital in communities and schools in contemporary society. Can schools intentionally replenish stocks of social capital by creating normative systems conducive to the optimal academic and social development of students, and by…

Outreach Science Education: Evidence-Based Studies in a Gene Technology Lab

ERIC Educational Resources Information Center

Scharfenberg, Franz-Josef; Bogner, Franz X.

2014-01-01

Nowadays, outreach labs are important informal learning environments in science education. After summarizing research to goals outreach labs focus on, we describe our evidence-based gene technology lab as a model of a research-driven outreach program. Evaluation-based optimizations of hands-on teaching based on cognitive load theory (additional…

SciJourn Is Magic: Construction of a Science Journalism Community of Practice

ERIC Educational Resources Information Center

Nicholas, Celeste R.

2017-01-01

This article is the first to describe the discoursal construction of an adolescent community of practice (CoP) in a non-school setting. CoPs can provide optimal learning environments. The adolescent community centered around science journalism and positioned itself dichotomously in relationship to school literacy practices. The analysis focuses on…

Healthy eating design guidelines for school architecture.

PubMed

Huang, Terry T-K; Sorensen, Dina; Davis, Steven; Frerichs, Leah; Brittin, Jeri; Celentano, Joseph; Callahan, Kelly; Trowbridge, Matthew J

2013-01-01

We developed a new tool, Healthy Eating Design Guidelines for School Architecture, to provide practitioners in architecture and public health with a practical set of spatially organized and theory-based strategies for making school environments more conducive to learning about and practicing healthy eating by optimizing physical resources and learning spaces. The design guidelines, developed through multidisciplinary collaboration, cover 10 domains of the school food environment (eg, cafeteria, kitchen, garden) and 5 core healthy eating design principles. A school redesign project in Dillwyn, Virginia, used the tool to improve the schools' ability to adopt a healthy nutrition curriculum and promote healthy eating. The new tool, now in a pilot version, is expected to evolve as its components are tested and evaluated through public health and design research.

Effects of repeated walking in a perturbing environment: a 4-day locomotor learning study.

PubMed

Blanchette, Andreanne; Moffet, Helene; Roy, Jean-Sébastien; Bouyer, Laurent J

2012-07-01

Previous studies have shown that when subjects repeatedly walk in a perturbing environment, initial movement error becomes smaller, suggesting that retention of the adapted locomotor program occurred (learning). It has been proposed that the newly learned locomotor program may be stored separately from the baseline program. However, how locomotor performance evolves with repeated sessions of walking with the perturbation is not yet known. To address this question, 10 healthy subjects walked on a treadmill on 4 consecutive days. Each day, locomotor performance was measured using kinematics and surface electromyography (EMGs), before, during, and after exposure to a perturbation, produced by an elastic tubing that pulled the foot forward and up during swing, inducing a foot velocity error in the first strides. Initial movement error decreased significantly between days 1 and 2 and then remained stable. Associated changes in medial hamstring EMG activity stabilized only on day 3, however. Aftereffects were present after perturbation removal, suggesting that daily adaptation involved central command recalibration of the baseline program. Aftereffects gradually decreased across days but were still visible on day 4. Separation between the newly learned and baseline programs may take longer than suggested by the daily improvement in initial performance in the perturbing environment or may never be complete. These results therefore suggest that reaching optimal performance in a perturbing environment should not be used as the main indicator of a completed learning process, as central reorganization of the motor commands continues days after initial performance has stabilized.

A Study To Determine Instructors Self-Reported Instructional Strategies Which Foster Science Literacy In An EFL (English as a Foreign Language) Environment

NASA Astrophysics Data System (ADS)

Noseworthy, Mark Joseph

2011-12-01

This research titled 'A Study to Determine Instructors Self-Reported Instructional Strategies Which Foster Science Literacy in an EFL (English as a Foreign Language) Environment' is an ethnographic study based on grounded theory principles and research design. The essence of the research was to answer five research questions that would ultimately create a foundation for instructional strategies allowing science instructors to foster science literacy in an EFL environment. The research attempts to conceptualize the research participants' instructional strategies that promote strong science literacy skills. Further to this, consider the complexities that this learning environment inherently offers, where the learning event is occurring in an English environment that is a second language for the learner. The research was designed to generate personal truths that produced common themes as it relates to the five research questions posed in this thesis; what instructional strategies do current post secondary science instructors at one College in Qatar believe foster science literacy in an EFL environment? As well, do science instructors believe that total immersion is the best approach to science literacy in an EFL environment? Is the North American model of teaching/learning science appropriate in this Middle Eastern environment? Are the current modes of teaching/instruction optimizing student's chances of success for science literacy? What do you feel are the greatest challenges for the EFL learner as it relates to science?

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.

Developing a reading concentration monitoring system by applying an artificial bee colony algorithm to e-books in an intelligent classroom.

PubMed

Hsu, Chia-Cheng; Chen, Hsin-Chin; Su, Yen-Ning; Huang, Kuo-Kuang; Huang, Yueh-Min

2012-10-22

A growing number of educational studies apply sensors to improve student learning in real classroom settings. However, how can sensors be integrated into classrooms to help instructors find out students' reading concentration rates and thus better increase learning effectiveness? The aim of the current study was to develop a reading concentration monitoring system for use with e-books in an intelligent classroom and to help instructors find out the students' reading concentration rates. The proposed system uses three types of sensor technologies, namely a webcam, heartbeat sensor, and blood oxygen sensor to detect the learning behaviors of students by capturing various physiological signals. An artificial bee colony (ABC) optimization approach is applied to the data gathered from these sensors to help instructors understand their students' reading concentration rates in a classroom learning environment. The results show that the use of the ABC algorithm in the proposed system can effectively obtain near-optimal solutions. The system has a user-friendly graphical interface, making it easy for instructors to clearly understand the reading status of their students.

Developing a Reading Concentration Monitoring System by Applying an Artificial Bee Colony Algorithm to E-Books in an Intelligent Classroom

PubMed Central

Hsu, Chia-Cheng; Chen, Hsin-Chin; Su, Yen-Ning; Huang, Kuo-Kuang; Huang, Yueh-Min

2012-01-01

A growing number of educational studies apply sensors to improve student learning in real classroom settings. However, how can sensors be integrated into classrooms to help instructors find out students' reading concentration rates and thus better increase learning effectiveness? The aim of the current study was to develop a reading concentration monitoring system for use with e-books in an intelligent classroom and to help instructors find out the students' reading concentration rates. The proposed system uses three types of sensor technologies, namely a webcam, heartbeat sensor, and blood oxygen sensor to detect the learning behaviors of students by capturing various physiological signals. An artificial bee colony (ABC) optimization approach is applied to the data gathered from these sensors to help instructors understand their students' reading concentration rates in a classroom learning environment. The results show that the use of the ABC algorithm in the proposed system can effectively obtain near-optimal solutions. The system has a user-friendly graphical interface, making it easy for instructors to clearly understand the reading status of their students. PMID:23202042

The effect of haptic guidance and visual feedback on learning a complex tennis task.

PubMed

Marchal-Crespo, Laura; van Raai, Mark; Rauter, Georg; Wolf, Peter; Riener, Robert

2013-11-01

While haptic guidance can improve ongoing performance of a motor task, several studies have found that it ultimately impairs motor learning. However, some recent studies suggest that the haptic demonstration of optimal timing, rather than movement magnitude, enhances learning in subjects trained with haptic guidance. Timing of an action plays a crucial role in the proper accomplishment of many motor skills, such as hitting a moving object (discrete timing task) or learning a velocity profile (time-critical tracking task). The aim of the present study is to evaluate which feedback conditions-visual or haptic guidance-optimize learning of the discrete and continuous elements of a timing task. The experiment consisted in performing a fast tennis forehand stroke in a virtual environment. A tendon-based parallel robot connected to the end of a racket was used to apply haptic guidance during training. In two different experiments, we evaluated which feedback condition was more adequate for learning: (1) a time-dependent discrete task-learning to start a tennis stroke and (2) a tracking task-learning to follow a velocity profile. The effect that the task difficulty and subject's initial skill level have on the selection of the optimal training condition was further evaluated. Results showed that the training condition that maximizes learning of the discrete time-dependent motor task depends on the subjects' initial skill level. Haptic guidance was especially suitable for less-skilled subjects and in especially difficult discrete tasks, while visual feedback seems to benefit more skilled subjects. Additionally, haptic guidance seemed to promote learning in a time-critical tracking task, while visual feedback tended to deteriorate the performance independently of the task difficulty and subjects' initial skill level. Haptic guidance outperformed visual feedback, although additional studies are needed to further analyze the effect of other types of feedback visualization on motor learning of time-critical tasks.

Mentor experiences of international healthcare students' learning in a clinical environment: A systematic review.

PubMed

Mikkonen, Kristina; Elo, Satu; Tuomikoski, Anna-Maria; Kääriäinen, Maria

2016-05-01

Globalisation has brought new possibilities for international growth in education and professional mobility among healthcare professionals. There has been a noticeable increase of international degree programmes in non-English speaking countries in Europe, creating clinical learning challenges for healthcare students. The aim of this systematic review was to describe mentors' experiences of international healthcare students' learning in a clinical environment. The objective of the review was to identify what influences the success or failure of mentoring international healthcare students when learning in the clinical environment, with the ultimate aim being to promote optimal mentoring practice. A systematic review was conducted according to the guidelines of the Centre for Reviews and Dissemination. Seven electronic databases were used to search for the published results of previous research: CINAHL, Medline Ovid, Scopus, the Web of Science, Academic Search Premiere, Eric, and the Cochrane Library. Search inclusion criteria were planned in the PICOS review format by including peer-reviewed articles published in any language between 2000 and 2014. Five peer-reviewed articles remained after the screening process. The results of the original studies were analysed using a thematic synthesis. The results indicate that a positive intercultural mentor enhanced reciprocal learning by improving the experience of international healthcare students and reducing stress in the clinical environment. Integrating international healthcare students into work with domestic students was seen to be important for reciprocal learning and the avoidance of discrimination. Many healthcare students were found to share similar experiences of mentoring and learning irrespective of their cultural background. However, the role of a positive intercultural mentor was found to make a significant difference for international students: such mentors advocated and mediated cultural differences and created a welcoming environment for international students by helping to minimise feelings of social isolation. Copyright © 2016 Elsevier Ltd. All rights reserved.

Influences of early child nutritional status and home learning environment on child development in Vietnam.

PubMed

Nguyen, Phuong H; DiGirolamo, Ann M; Gonzalez-Casanova, Ines; Young, Melissa; Kim, Nicole; Nguyen, Son; Martorell, Reynaldo; Ramakrishnan, Usha

2018-01-01

Early childhood development plays a key role in a child's future health, educational success, and economic status. However, suboptimal early development remains a global challenge. This study examines the influences of quality of the home learning environment (HOME) and child stunting in the first year of life on child development. We used data collected from a randomized controlled trial of preconceptional micronutrient supplementation in Vietnam (n = 1,458). The Bayley Scales of Infant Development-III were used to assess cognition, language, and motor development domains at 2 years. At 1 year, 14% of children were stunted, and 15%, 58%, and 28% of children lived in poor, medium, and high HOME environments, respectively. In multivariate generalized linear regression models, living in a high HOME environment was significantly associated with higher scores (0.10 to 0.13 SD) in each of the developmental domains. Stunted children scored significantly lower for cognitive, language, and motor development (-0.11 to -0.18), compared to nonstunted children. The negative associations between stunting on development were modified by HOME; the associations were strong among children living in homes with a poor learning environment whereas they were nonsignificant for those living in high-quality learning environments. In conclusion, child stunting the first year of life was negatively associated with child development at 2 years among children in Vietnam, but a high-quality HOME appeared to attenuate these associations. Early interventions aimed at improving early child growth as well as providing a stimulating home environment are critical to ensure optimal child development. © 2017 John Wiley & Sons Ltd.

Assessing predation risk: optimal behaviour and rules of thumb.

PubMed

Welton, Nicky J; McNamara, John M; Houston, Alasdair I

2003-12-01

We look at a simple model in which an animal makes behavioural decisions over time in an environment in which all parameters are known to the animal except predation risk. In the model there is a trade-off between gaining information about predation risk and anti-predator behaviour. All predator attacks lead to death for the prey, so that the prey learns about predation risk by virtue of the fact that it is still alive. We show that it is not usually optimal to behave as if the current unbiased estimate of the predation risk is its true value. We consider two different ways to model reproduction; in the first scenario the animal reproduces throughout its life until it dies, and in the second scenario expected reproductive success depends on the level of energy reserves the animal has gained by some point in time. For both of these scenarios we find results on the form of the optimal strategy and give numerical examples which compare optimal behaviour with behaviour under simple rules of thumb. The numerical examples suggest that the value of the optimal strategy over the rules of thumb is greatest when there is little current information about predation risk, learning is not too costly in terms of predation, and it is energetically advantageous to learn about predation. We find that for the model and parameters investigated, a very simple rule of thumb such as 'use the best constant control' performs well.

"Notice of Violation of IEEE Publication Principles" Multiobjective Reinforcement Learning: A Comprehensive Overview.

PubMed

Liu, Chunming; Xu, Xin; Hu, Dewen

2013-04-29

Reinforcement learning is a powerful mechanism for enabling agents to learn in an unknown environment, and most reinforcement learning algorithms aim to maximize some numerical value, which represents only one long-term objective. However, multiple long-term objectives are exhibited in many real-world decision and control problems; therefore, recently, there has been growing interest in solving multiobjective reinforcement learning (MORL) problems with multiple conflicting objectives. The aim of this paper is to present a comprehensive overview of MORL. In this paper, the basic architecture, research topics, and naive solutions of MORL are introduced at first. Then, several representative MORL approaches and some important directions of recent research are reviewed. The relationships between MORL and other related research are also discussed, which include multiobjective optimization, hierarchical reinforcement learning, and multi-agent reinforcement learning. Finally, research challenges and open problems of MORL techniques are highlighted.

Neural signatures of experience-based improvements in deterministic decision-making.

PubMed

Tremel, Joshua J; Laurent, Patryk A; Wolk, David A; Wheeler, Mark E; Fiez, Julie A

2016-12-15

Feedback about our choices is a crucial part of how we gather information and learn from our environment. It provides key information about decision experiences that can be used to optimize future choices. However, our understanding of the processes through which feedback translates into improved decision-making is lacking. Using neuroimaging (fMRI) and cognitive models of decision-making and learning, we examined the influence of feedback on multiple aspects of decision processes across learning. Subjects learned correct choices to a set of 50 word pairs across eight repetitions of a concurrent discrimination task. Behavioral measures were then analyzed with both a drift-diffusion model and a reinforcement learning model. Parameter values from each were then used as fMRI regressors to identify regions whose activity fluctuates with specific cognitive processes described by the models. The patterns of intersecting neural effects across models support two main inferences about the influence of feedback on decision-making. First, frontal, anterior insular, fusiform, and caudate nucleus regions behave like performance monitors, reflecting errors in performance predictions that signal the need for changes in control over decision-making. Second, temporoparietal, supplementary motor, and putamen regions behave like mnemonic storage sites, reflecting differences in learned item values that inform optimal decision choices. As information about optimal choices is accrued, these neural systems dynamically adjust, likely shifting the burden of decision processing from controlled performance monitoring to bottom-up, stimulus-driven choice selection. Collectively, the results provide a detailed perspective on the fundamental ability to use past experiences to improve future decisions. Copyright © 2016 Elsevier B.V. All rights reserved.

Neural signatures of experience-based improvements in deterministic decision-making

PubMed Central

Tremel, Joshua J.; Laurent, Patryk A.; Wolk, David A.; Wheeler, Mark E.; Fiez, Julie A.

2016-01-01

Feedback about our choices is a crucial part of how we gather information and learn from our environment. It provides key information about decision experiences that can be used to optimize future choices. However, our understanding of the processes through which feedback translates into improved decision-making is lacking. Using neuroimaging (fMRI) and cognitive models of decision-making and learning, we examined the influence of feedback on multiple aspects of decision processes across learning. Subjects learned correct choices to a set of 50 word pairs across eight repetitions of a concurrent discrimination task. Behavioral measures were then analyzed with both a drift-diffusion model and a reinforcement learning model. Parameter values from each were then used as fMRI regressors to identify regions whose activity fluctuates with specific cognitive processes described by the models. The patterns of intersecting neural effects across models support two main inferences about the influence of feedback on decision-making. First, frontal, anterior insular, fusiform, and caudate nucleus regions behave like performance monitors, reflecting errors in performance predictions that signal the need for changes in control over decision-making. Second, temporoparietal, supplementary motor, and putamen regions behave like mnemonic storage sites, reflecting differences in learned item values that inform optimal decision choices. As information about optimal choices is accrued, these neural systems dynamically adjust, likely shifting the burden of decision processing from controlled performance monitoring to bottom-up, stimulus-driven choice selection. Collectively, the results provide a detailed perspective on the fundamental ability to use past experiences to improve future decisions. PMID:27523644

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

PubMed

Winkler, David A

2017-01-01

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

Biomimetic molecular design tools that learn, evolve, and adapt

PubMed Central

2017-01-01

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

Learning stochastic reward distributions in a speeded pointing task.

PubMed

Seydell, Anna; McCann, Brian C; Trommershäuser, Julia; Knill, David C

2008-04-23

Recent studies have shown that humans effectively take into account task variance caused by intrinsic motor noise when planning fast hand movements. However, previous evidence suggests that humans have greater difficulty accounting for arbitrary forms of stochasticity in their environment, both in economic decision making and sensorimotor tasks. We hypothesized that humans can learn to optimize movement strategies when environmental randomness can be experienced and thus implicitly learned over several trials, especially if it mimics the kinds of randomness for which subjects might have generative models. We tested the hypothesis using a task in which subjects had to rapidly point at a target region partly covered by three stochastic penalty regions introduced as "defenders." At movement completion, each defender jumped to a new position drawn randomly from fixed probability distributions. Subjects earned points when they hit the target, unblocked by a defender, and lost points otherwise. Results indicate that after approximately 600 trials, subjects approached optimal behavior. We further tested whether subjects simply learned a set of stimulus-contingent motor plans or the statistics of defenders' movements by training subjects with one penalty distribution and then testing them on a new penalty distribution. Subjects immediately changed their strategy to achieve the same average reward as subjects who had trained with the second penalty distribution. These results indicate that subjects learned the parameters of the defenders' jump distributions and used this knowledge to optimally plan their hand movements under conditions involving stochastic rewards and penalties.

Implementation effect of productive 4-stage field orientation on the student technopreneur skill in vocational schools

NASA Astrophysics Data System (ADS)

Ismail, Edy; Samsudi, Widjanarko, Dwi; Joyce, Peter; Stearns, Roman

2018-03-01

This model integrates project base learning by creating a product based on environmental needs. The Produktif Orientasi Lapangan 4 Tahap (POL4T) combines technical skills and entrepreneurial elements together in the learning process. This study is to implement the result of technopreneurship learning model development which is environment-oriented by combining technology and entrepreneurship components on Machining Skill Program. This study applies research and development design by optimizing experimental subject. Data were obtained from questionnaires, learning material validation, interpersonal, intrapersonal observation forms, skills, product, teachers and students' responses, and cognitive tasks. Expert validation and t-test calculation are applied to see how effective POL4T learning model. The result of the study is in the form of 4 steps learning model to enhance interpersonal and intrapersonal attitudes, develop practical products which orient to society and appropriate technology so that the products can have high selling value. The model is effective based on the students' post test result, which is better than the pre-test. The product obtained from POL4T model is proven to be better than the productive learning. POL4T model is recommended to be implemented for XI grade students. This is can develop entrepreneurial attitudes that are environment oriented, community needs and technical competencies students.

Examining the Potential for Response to Intervention (RTI) Delivery Models in Secondary Education: Emerging Research and Opportunities

ERIC Educational Resources Information Center

Epler, Pam

2017-01-01

To provide the highest quality of education to students, school administrators must adopt new frameworks to meet learners' needs. This allows teaching practices to be optimized to create a meaningful learning environment. "Examining the Potential for Response to Intervention (RTI) Delivery Models in Secondary Education: Emerging Research and…

Digital Curation as a Core Competency in Current Learning and Literacy: A Higher Education Perspective

ERIC Educational Resources Information Center

Ungerer, Leona M.

2016-01-01

Digital curation may be regarded as a core competency in higher education since it contributes to establishing a sense of metaliteracy (an essential requirement for optimally functioning in a modern media environment) among students. Digital curation is gradually finding its way into higher education curricula aimed at fostering social media…

Using a Recommendation System to Support Problem Solving and Case-Based Reasoning Retrieval

ERIC Educational Resources Information Center

Tawfik, Andrew A.; Alhoori, Hamed; Keene, Charles Wayne; Bailey, Christian; Hogan, Maureen

2018-01-01

In case library learning environments, learners are presented with an array of narratives that can be used to guide their problem solving. However, according to theorists, learners struggle to identify and retrieve the optimal case to solve a new problem. Given the challenges novice face during case retrieval, recommender systems can be embedded…

Hybrid Motion Planning with Multiple Destinations

NASA Technical Reports Server (NTRS)

Clouse, Jeffery

1998-01-01

In our initial proposal, we laid plans for developing a hybrid motion planning system that combines the concepts of visibility-based motion planning, artificial potential field based motion planning, evolutionary constrained optimization, and reinforcement learning. Our goal was, and still is, to produce a hybrid motion planning system that outperforms the best traditional motion planning systems on problems with dynamic environments. The proposed hybrid system will be in two parts the first is a global motion planning system and the second is a local motion planning system. The global system will take global information about the environment, such as the placement of the obstacles and goals, and produce feasible paths through those obstacles. We envision a system that combines the evolutionary-based optimization and visibility-based motion planning to achieve this end.

Globalization of problem-based learning (PBL): cross-cultural implications.

PubMed

Gwee, Matthew Choon-Eng

2008-03-01

Problem-based learning (PBL) is essentially a learning system design that incorporates several educational strategies to optimize student-centered learning outcomes beyond just knowledge acquisition. PBL was implemented almost four decades ago as an innovative and alternative pathway to learning in medical education in McMaster University Medical School. Since then, PBL has spread widely across the world and has now been adopted globally, including in much of Asia. The globalization of PBL has important cross-cultural implications. Delivery of instruction in PBL involves active peer teaching-learning in an open communication style. Consequently, this may pose an apparent serious conflict with the Asian communication style generally dominated by a cultural reticence. However, evidence available, especially from the PBL experience of some senior Korean medical students doing an elective in the University of Toronto Medical School and the cross-cultural PBL experience initiated by Kaohsiung Medical University, strongly suggests creating a conducive and supportive learning environment for students learning in a PBL setting can overcome the perceived cultural barriers; that is, nurture matters more than culture in the learning environment. Karaoke is very much an Asian initiative. The Karaoke culture and philosophy provide a useful lesson on how to create a conducive and supportive environment to encourage, enhance and motivate group activity. Some key attributes associated with Asian culture are in fact consistent with, and aligned to, some of the basic tenets of PBL, including the congruence between the Asian emphasis on group before individual interest, and the collaborative small group learning design used in PBL. Although there are great expectations of the educational outcomes students can acquire from PBL, the available evidence supports the contention the actual educational outcomes acquired from PBL do not really match the expected educational outcomes commonly intended and specified for a PBL program. Proficiency in the English language can pose serious problems for some Asian medical schools, which choose to use English as the language for discussion in PBL tutorials. A novel approach that can be applied to overcome this problem is to allow students to engage in discussions using both their native language as well as English, a highly successful practice implemented by the University of Airlangga, Surabaya, Indonesia. As PBL is a highly resource-intensive pedagogy, Asian medical educators need to have a clear understanding of the PBL process, philosophy and practice in order to be able to optimize the educational outcomes that can be derived from a PBL curriculum.

Optimal speech level for speech transmission in a noisy environment for young adults and aged persons

NASA Astrophysics Data System (ADS)

Sato, Hayato; Ota, Ryo; Morimoto, Masayuki; Sato, Hiroshi

2005-04-01

Assessing sound environment of classrooms for the aged is a very important issue, because classrooms can be used by the aged for their lifelong learning, especially in the aged society. Hence hearing loss due to aging is a considerable factor for classrooms. In this study, the optimal speech level in noisy fields for both young adults and aged persons was investigated. Listening difficulty ratings and word intelligibility scores for familiar words were used to evaluate speech transmission performance. The results of the tests demonstrated that the optimal speech level for moderate background noise (i.e., less than around 60 dBA) was fairly constant. Meanwhile, the optimal speech level depended on the speech-to-noise ratio when the background noise level exceeded around 60 dBA. The minimum required speech level to minimize difficulty ratings for the aged was higher than that for the young. However, the minimum difficulty ratings for both the young and the aged were given in the range of speech level of 70 to 80 dBA of speech level.

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

Healthy Eating Design Guidelines for School Architecture

PubMed Central

Huang, Terry T-K; Sorensen, Dina; Davis, Steven; Frerichs, Leah; Brittin, Jeri; Celentano, Joseph; Callahan, Kelly

2013-01-01

We developed a new tool, Healthy Eating Design Guidelines for School Architecture, to provide practitioners in architecture and public health with a practical set of spatially organized and theory-based strategies for making school environments more conducive to learning about and practicing healthy eating by optimizing physical resources and learning spaces. The design guidelines, developed through multidisciplinary collaboration, cover 10 domains of the school food environment (eg, cafeteria, kitchen, garden) and 5 core healthy eating design principles. A school redesign project in Dillwyn, Virginia, used the tool to improve the schools’ ability to adopt a healthy nutrition curriculum and promote healthy eating. The new tool, now in a pilot version, is expected to evolve as its components are tested and evaluated through public health and design research. PMID:23449281

Hierarchical extreme learning machine based reinforcement learning for goal localization

NASA Astrophysics Data System (ADS)

AlDahoul, Nouar; Zaw Htike, Zaw; Akmeliawati, Rini

2017-03-01

The objective of goal localization is to find the location of goals in noisy environments. Simple actions are performed to move the agent towards the goal. The goal detector should be capable of minimizing the error between the predicted locations and the true ones. Few regions need to be processed by the agent to reduce the computational effort and increase the speed of convergence. In this paper, reinforcement learning (RL) method was utilized to find optimal series of actions to localize the goal region. The visual data, a set of images, is high dimensional unstructured data and needs to be represented efficiently to get a robust detector. Different deep Reinforcement models have already been used to localize a goal but most of them take long time to learn the model. This long learning time results from the weights fine tuning stage that is applied iteratively to find an accurate model. Hierarchical Extreme Learning Machine (H-ELM) was used as a fast deep model that doesn’t fine tune the weights. In other words, hidden weights are generated randomly and output weights are calculated analytically. H-ELM algorithm was used in this work to find good features for effective representation. This paper proposes a combination of Hierarchical Extreme learning machine and Reinforcement learning to find an optimal policy directly from visual input. This combination outperforms other methods in terms of accuracy and learning speed. The simulations and results were analysed by using MATLAB.

Cognitive maps and attention.

PubMed

Hardt, Oliver; Nadel, Lynn

2009-01-01

Cognitive map theory suggested that exploring an environment and attending to a stimulus should lead to its integration into an allocentric environmental representation. We here report that directed attention in the form of exploration serves to gather information needed to determine an optimal spatial strategy, given task demands and characteristics of the environment. Attended environmental features may integrate into spatial representations if they meet the requirements of the optimal spatial strategy: when learning involves a cognitive mapping strategy, cues with high codability (e.g., concrete objects) will be incorporated into a map, but cues with low codability (e.g., abstract paintings) will not. However, instructions encouraging map learning can lead to the incorporation of cues with low codability. On the other hand, if spatial learning is not map-based, abstract cues can and will be used to encode locations. Since exploration appears to determine what strategy to apply and whether or not to encode a cue, recognition memory for environmental features is independent of whether or not a cue is part of a spatial representation. In fact, when abstract cues were used in a way that was not map-based, or when they were not used for spatial navigation at all, they were nevertheless recognized as familiar. Thus, the relation between exploratory activity on the one hand and spatial strategy and memory on the other appears more complex than initially suggested by cognitive map theory.

EPQ model with learning consideration, imperfect production and partial backlogging in fuzzy random environment

NASA Astrophysics Data System (ADS)

Shankar Kumar, Ravi; Goswami, A.

2015-06-01

The article scrutinises the learning effect of the unit production time on optimal lot size for the uncertain and imprecise imperfect production process, wherein shortages are permissible and partially backlogged. Contextually, we contemplate the fuzzy chance of production process shifting from an 'in-control' state to an 'out-of-control' state and re-work facility of imperfect quality of produced items. The elapsed time until the process shifts is considered as a fuzzy random variable, and consequently, fuzzy random total cost per unit time is derived. Fuzzy expectation and signed distance method are used to transform the fuzzy random cost function into an equivalent crisp function. The results are illustrated with the help of numerical example. Finally, sensitivity analysis of the optimal solution with respect to major parameters is carried out.

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.

Efficiency of goal-oriented communicating agents in different graph topologies: A study with Internet crawlers

NASA Astrophysics Data System (ADS)

Lőrincz, András; Lázár, Katalin A.; Palotai, Zsolt

2007-05-01

To what extent does the communication make a goal-oriented community efficient in different topologies? In order to gain insight into this problem, we study the influence of learning method as well as that of the topology of the environment on the communication efficiency of crawlers in quest of novel information in different topics on the Internet. Individual crawlers employ selective learning, function approximation-based reinforcement learning (RL), and their combination. Selective learning, in effect, modifies the starting URL lists of the crawlers, whilst RL alters the URL orderings. Real data have been collected from the web and scale-free worlds, scale-free small world (SFSW), and random world environments (RWEs) have been created by link reorganization. In our previous experiments [ Zs. Palotai, Cs. Farkas, A. Lőrincz, Is selection optimal in scale-free small worlds?, ComPlexUs 3 (2006) 158-168], the crawlers searched for novel, genuine documents and direct communication was not possible. Herein, our finding is reproduced: selective learning performs the best and RL the worst in SFSW, whereas the combined, i.e., selective learning coupled with RL is the best-by a slight margin-in scale-free worlds. This effect is demonstrated to be more pronounced when the crawlers search for different topic-specific documents: the relative performance of the combined learning algorithm improves in all worlds, i.e., in SFSW, in SFW, and in RWE. If the tasks are more complex and the work sharing is enforced by the environment then the combined learning algorithm becomes at least equal, even superior to both the selective and the RL algorithms in most cases, irrespective of the efficiency of communication. Furthermore, communication improves the performance by a large margin and adaptive communication is advantageous in the majority of the cases.

How do early emotional experiences in the operating theatre influence medical student learning in this environment?

PubMed

Bowrey, David J; Kidd, Jane M

2014-01-01

The emotions experienced by medical students on first exposure to the operating theatre are unknown. It is also unclear what influence these emotions have on the learning process. To understand the emotions experienced by students when in the operating theatre for the first time and the impact of these emotions on learning. Nine 3rd-year medical students participated in semistructured interviews to explore these themes. A qualitative approach was used; interviews were transcribed and coded thematically. All participants reported initial negative emotions (apprehension, anxiety, fear, shame, overwhelmed), with excitement being reported by 3. Six participants considered that their anxiety was so overwhelming that it was detrimental to their learning. Participants described a period of familiarization to the environment, after which learning was facilitated. Early learning experiences centered around adjustment to the physical environment of the operating theatre. Factors driving initial negative feelings were loss of familiarity, organizational issues, concerns about violating protocol, and a fear of syncope. Participants considered that it took a median of 1 week (range = 1 day-3 weeks) or 5 visits to the operating theatre (range = 1-10) before feeling comfortable in the new setting. Emotions experienced on subsequent visits to the operating theatre were predominantly positive (enjoyment, happiness, confident, involved, pride). Two participants reported negative feelings related to social exclusion. Being included in the team was a powerful determinant of enjoyment. These findings indicate that for learning in the operating theatre to be effective, addressing the negative emotions of the students might be beneficial. This could be achieved by a formal orientation program for both learners and tutors in advance of attendance in the operating theatre. For learning to be optimized, students must feel a sense of inclusion in the theatre community of practice.

Emergence of Virtual Reality as a Tool for Upper Limb Rehabilitation: Incorporation of Motor Control and Motor Learning Principles

PubMed Central

Weiss, Patrice L.; Keshner, Emily A.

2015-01-01

The primary focus of rehabilitation for individuals with loss of upper limb movement as a result of acquired brain injury is the relearning of specific motor skills and daily tasks. This relearning is essential because the loss of upper limb movement often results in a reduced quality of life. Although rehabilitation strives to take advantage of neuroplastic processes during recovery, results of traditional approaches to upper limb rehabilitation have not entirely met this goal. In contrast, enriched training tasks, simulated with a wide range of low- to high-end virtual reality–based simulations, can be used to provide meaningful, repetitive practice together with salient feedback, thereby maximizing neuroplastic processes via motor learning and motor recovery. Such enriched virtual environments have the potential to optimize motor learning by manipulating practice conditions that explicitly engage motivational, cognitive, motor control, and sensory feedback–based learning mechanisms. The objectives of this article are to review motor control and motor learning principles, to discuss how they can be exploited by virtual reality training environments, and to provide evidence concerning current applications for upper limb motor recovery. The limitations of the current technologies with respect to their effectiveness and transfer of learning to daily life tasks also are discussed. PMID:25212522

Can a Teaching Assistant Experience in a Surgical Anatomy Course Influence the Learning Curve for Nontechnical Skill Development for Surgical Residents?

ERIC Educational Resources Information Center

Heidenreich, Mark J.; Musonza, Tashinga; Pawlina, Wojciech; Lachman, Nirusha

2016-01-01

The foundation upon which surgical residents are trained to work comprises more than just critical cognitive, clinical, and technical skill. In an environment where the synchronous application of expertise is vital to patient outcomes, the expectation for optimal functioning within a multidisciplinary team is extremely high. Studies have shown…

The Effect of Nursing Faculty Presence on Students' Level of Anxiety, Self-Confidence, and Clinical Performance during a Clinical Simulation Experience

ERIC Educational Resources Information Center

Horsley, Trisha Leann

2012-01-01

Nursing schools design their clinical simulation labs based upon faculty's perception of the optimal environment to meet the students' learning needs, other programs' success with integrating high-tech clinical simulation, and the funds available. No research has been conducted on nursing faculty presence during a summative evaluation. The…

Revisiting Expansive Learning for Knowledge Production and Capability Development at Postgraduate Level in Higher Education Studies

ERIC Educational Resources Information Center

Niemann, Rita

2013-01-01

Higher education in South Africa is challenged by academic and social demands. Universities, therefore, have to produce graduates who will be able to function optimally within their field of study, as well as act as agents of change in their social environment. The main purpose of this article is to theorise about applying Engestrom's expansive…

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.

Social cognitive theory, metacognition, and simulation learning in nursing education.

PubMed

Burke, Helen; Mancuso, Lorraine

2012-10-01

Simulation learning encompasses simple, introductory scenarios requiring response to patients' needs during basic hygienic care and during situations demanding complex decision making. Simulation integrates principles of social cognitive theory (SCT) into an interactive approach to learning that encompasses the core principles of intentionality, forethought, self-reactiveness, and self-reflectiveness. Effective simulation requires an environment conducive to learning and introduces activities that foster symbolic coding operations and mastery of new skills; debriefing builds self-efficacy and supports self-regulation of behavior. Tailoring the level of difficulty to students' mastery level supports successful outcomes and motivation to set higher standards. Mindful selection of simulation complexity and structure matches course learning objectives and supports progressive development of metacognition. Theory-based facilitation of simulated learning optimizes efficacy of this learning method to foster maturation of cognitive processes of SCT, metacognition, and self-directedness. Examples of metacognition that are supported through mindful, theory-based implementation of simulation learning are provided. Copyright 2012, SLACK Incorporated.

Maternal intraguild predation risk affects offspring anti-predator behavior and learning in mites.

PubMed

Seiter, Michael; Schausberger, Peter

2015-10-09

Predation risk is a strong selective force shaping prey morphology, life history and behavior. Anti-predator behaviors may be innate, learned or both but little is known about the transgenerational behavioral effects of maternally experienced predation risk. We examined intraguild predation (IGP) risk-induced maternal effects on offspring anti-predator behavior, including learning, in the predatory mite Phytoseiulus persimilis. We exposed predatory mite mothers during egg production to presence or absence of the IG predator Amblyseius andersoni and assessed whether maternal stress affects the anti-predator behavior, including larval learning ability, of their offspring as protonymphs. Protonymphs emerging from stressed or unstressed mothers, and having experienced IGP risk as larvae or not, were subjected to choice situations with and without IG predator traces. Predator-experienced protonymphs from stressed mothers were the least active and acted the boldest in site choice towards predator cues. We argue that the attenuated response of the protonymphs to predator traces alone represents optimized risk management because no immediate risk existed. Such behavioral adjustment could reduce the inherent fitness costs of anti-predator behaviors. Overall, our study suggests that P. persimilis mothers experiencing IGP risk may prime their offspring to behave more optimally in IGP environments.

Maternal intraguild predation risk affects offspring anti-predator behavior and learning in mites

PubMed Central

Seiter, Michael; Schausberger, Peter

2015-01-01

Predation risk is a strong selective force shaping prey morphology, life history and behavior. Anti-predator behaviors may be innate, learned or both but little is known about the transgenerational behavioral effects of maternally experienced predation risk. We examined intraguild predation (IGP) risk-induced maternal effects on offspring anti-predator behavior, including learning, in the predatory mite Phytoseiulus persimilis. We exposed predatory mite mothers during egg production to presence or absence of the IG predator Amblyseius andersoni and assessed whether maternal stress affects the anti-predator behavior, including larval learning ability, of their offspring as protonymphs. Protonymphs emerging from stressed or unstressed mothers, and having experienced IGP risk as larvae or not, were subjected to choice situations with and without IG predator traces. Predator-experienced protonymphs from stressed mothers were the least active and acted the boldest in site choice towards predator cues. We argue that the attenuated response of the protonymphs to predator traces alone represents optimized risk management because no immediate risk existed. Such behavioral adjustment could reduce the inherent fitness costs of anti-predator behaviors. Overall, our study suggests that P. persimilis mothers experiencing IGP risk may prime their offspring to behave more optimally in IGP environments. PMID:26449645

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.

Overtaking method based on sand-sifter mechanism: Why do optimistic value functions find optimal solutions in multi-armed bandit problems?

PubMed

Ochi, Kento; Kamiura, Moto

2015-09-01

A multi-armed bandit problem is a search problem on which a learning agent must select the optimal arm among multiple slot machines generating random rewards. UCB algorithm is one of the most popular methods to solve multi-armed bandit problems. It achieves logarithmic regret performance by coordinating balance between exploration and exploitation. Since UCB algorithms, researchers have empirically known that optimistic value functions exhibit good performance in multi-armed bandit problems. The terms optimistic or optimism might suggest that the value function is sufficiently larger than the sample mean of rewards. The first definition of UCB algorithm is focused on the optimization of regret, and it is not directly based on the optimism of a value function. We need to think the reason why the optimism derives good performance in multi-armed bandit problems. In the present article, we propose a new method, which is called Overtaking method, to solve multi-armed bandit problems. The value function of the proposed method is defined as an upper bound of a confidence interval with respect to an estimator of expected value of reward: the value function asymptotically approaches to the expected value of reward from the upper bound. If the value function is larger than the expected value under the asymptote, then the learning agent is almost sure to be able to obtain the optimal arm. This structure is called sand-sifter mechanism, which has no regrowth of value function of suboptimal arms. It means that the learning agent can play only the current best arm in each time step. Consequently the proposed method achieves high accuracy rate and low regret and some value functions of it can outperform UCB algorithms. This study suggests the advantage of optimism of agents in uncertain environment by one of the simplest frameworks. Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Medical student burnout: interdisciplinary exploration and analysis.

PubMed

Jennings, M L

2009-12-01

Burnout--a stress-related syndrome characterized by exhaustion, depersonalization, and a diminished sense of accomplishment--is a common phenomenon among medical students with significant potential consequences for student health, professionalism, and patient care. This essay proposes that the epidemic of medical student burnout can be attributed to a technocratic paradigm that fails to value medical students as persons with human needs and limitations. After briefly reviewing the literature on medical student burnout, the author uses two theories to elucidate potential causes: unsatisfactory aspects of the learning environment and a feeling one's efforts are meaningless or irrelevant. Cultural factors also facilitate burnout in medical students immersed in a clinical environment that cultivates excessive detachment from patient and self, impairing self-care, damaging a sense of self, and impeding the development of a mature, well-integrated professional identity. The ethical implications of medical student burnout are also addressed. Finally, this paper suggests possible preventive and remediative strategies such as optimizing the learning environment as well as narrative approaches that promise enhancement of both individual and institutional well-being.

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.

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.

A novel approach to enhance ACL injury prevention programs.

PubMed

Gokeler, Alli; Seil, Romain; Kerkhoffs, Gino; Verhagen, Evert

2018-06-18

Efficacy studies have demonstrated decreased anterior cruciate ligament (ACL) injury rates for athletes participating in injury prevention programs. Typically, ACL injury prevention programs entail a combination of plyometrics, strength training, agility and balance exercises. Unfortunately, improvements of movement patterns are not sustained over time. The reason may be related to the type of instructions given during training. Encouraging athletes to consciously control knee movements during exercises may not be optimal for the acquisition of complex motor skills as needed in complex sports environments. In the motor learning domain, these types of instructions are defined as an internal attentional focus. An internal focus, on one's own movements results in a more conscious type of control that may hamper motor learning. It has been established in numerous studies that an external focus of attention facilitates motor learning more effectively due to the utilization of automatic motor control. Subsequently, the athlete has more recourses available to anticipate on situations on the field and take appropriate feed forward directed actions. The purpose of this manuscript was to present methods to optimize motor skill acquisition of athletes and elaborate on athletes' behavior.

Improving the Critic Learning for Event-Based Nonlinear $H_{\\infty }$ Control Design.

PubMed

Wang, Ding; He, Haibo; Liu, Derong

2017-10-01

In this paper, we aim at improving the critic learning criterion to cope with the event-based nonlinear H ∞ state feedback control design. First of all, the H ∞ control problem is regarded as a two-player zero-sum game and the adaptive critic mechanism is used to achieve the minimax optimization under event-based environment. Then, based on an improved updating rule, the event-based optimal control law and the time-based worst-case disturbance law are obtained approximately by training a single critic neural network. The initial stabilizing control is no longer required during the implementation process of the new algorithm. Next, the closed-loop system is formulated as an impulsive model and its stability issue is handled by incorporating the improved learning criterion. The infamous Zeno behavior of the present event-based design is also avoided through theoretical analysis on the lower bound of the minimal intersample time. Finally, the applications to an aircraft dynamics and a robot arm plant are carried out to verify the efficient performance of the present novel design method.

Creating Sustainable Learning Environments in Schools by Means of Strategic Planning: The Experience of Engagement by a Comparative Education Team at a University

ERIC Educational Resources Information Center

Steyn, H.; Wolhuter, C.

2010-01-01

Many schools in South Africa are dysfunctional, or at least do not function optimally. This statement could be substantiated by just citing statistics about failure rates, school drop-out rates, school violence, matric pass rates, learner absenteeism, educator absenteeism or the incidence of discipline problems and the effect thereof on educators.…

Knowledge-Based Reinforcement Learning for Data Mining

NASA Astrophysics Data System (ADS)

Kudenko, Daniel; Grzes, Marek

Data Mining is the process of extracting patterns from data. Two general avenues of research in the intersecting areas of agents and data mining can be distinguished. The first approach is concerned with mining an agent’s observation data in order to extract patterns, categorize environment states, and/or make predictions of future states. In this setting, data is normally available as a batch, and the agent’s actions and goals are often independent of the data mining task. The data collection is mainly considered as a side effect of the agent’s activities. Machine learning techniques applied in such situations fall into the class of supervised learning. In contrast, the second scenario occurs where an agent is actively performing the data mining, and is responsible for the data collection itself. For example, a mobile network agent is acquiring and processing data (where the acquisition may incur a certain cost), or a mobile sensor agent is moving in a (perhaps hostile) environment, collecting and processing sensor readings. In these settings, the tasks of the agent and the data mining are highly intertwined and interdependent (or even identical). Supervised learning is not a suitable technique for these cases. Reinforcement Learning (RL) enables an agent to learn from experience (in form of reward and punishment for explorative actions) and adapt to new situations, without a teacher. RL is an ideal learning technique for these data mining scenarios, because it fits the agent paradigm of continuous sensing and acting, and the RL agent is able to learn to make decisions on the sampling of the environment which provides the data. Nevertheless, RL still suffers from scalability problems, which have prevented its successful use in many complex real-world domains. The more complex the tasks, the longer it takes a reinforcement learning algorithm to converge to a good solution. For many real-world tasks, human expert knowledge is available. For example, human experts have developed heuristics that help them in planning and scheduling resources in their work place. However, this domain knowledge is often rough and incomplete. When the domain knowledge is used directly by an automated expert system, the solutions are often sub-optimal, due to the incompleteness of the knowledge, the uncertainty of environments, and the possibility to encounter unexpected situations. RL, on the other hand, can overcome the weaknesses of the heuristic domain knowledge and produce optimal solutions. In the talk we propose two techniques, which represent first steps in the area of knowledge-based RL (KBRL). The first technique [1] uses high-level STRIPS operator knowledge in reward shaping to focus the search for the optimal policy. Empirical results show that the plan-based reward shaping approach outperforms other RL techniques, including alternative manual and MDP-based reward shaping when it is used in its basic form. We showed that MDP-based reward shaping may fail and successful experiments with STRIPS-based shaping suggest modifications which can overcome encountered problems. The STRIPSbased method we propose allows expressing the same domain knowledge in a different way and the domain expert can choose whether to define an MDP or STRIPS planning task. We also evaluated the robustness of the proposed STRIPS-based technique to errors in the plan knowledge. In case that STRIPS knowledge is not available, we propose a second technique [2] that shapes the reward with hierarchical tile coding. Where the Q-function is represented with low-level tile coding, a V-function with coarser tile coding can be learned in parallel and used to approximate the potential for ground states. In the context of data mining, our KBRL approaches can also be used for any data collection task where the acquisition of data may incur considerable cost. In addition, observing the data collection agent in specific scenarios may lead to new insights into optimal data collection behaviour in the respective domains. In future work, we intend to demonstrate and evaluate our techniques on concrete real-world data mining applications.

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

Online interprofessional health sciences education: From theory to practice.

PubMed

Luke, Robert; Solomon, Patty; Baptiste, Sue; Hall, Pippa; Orchard, Carole; Rukholm, Ellen; Carter, Lorraine

2009-01-01

Online learning (e-learning) has a nascent but established history. Its application to interprofessional education (IPE), however, is relatively new. Over the past 2 decades the Internet has been used increasingly to mediate education. We have come past the point of "should we use the Internet for education" to "how should we use the Internet for education." Research has begun on the optimal development of online learning environments to support IPE. Developing online IPE should follow best practices in e-learning generally, though there are some special considerations for acknowledging the interprofessional context and clinical environments that online IPE is designed to support. The design, development, and deployment of effective online IPE must therefore pay special attention to the particular constraints of the health care worker educational matrix, both pre- and postlicensure. In this article we outline the design of online, interprofessional health sciences education. Our work has involved 4 educational and 4 clinical service institutions. We establish the context in which we situate our development activities that created learning modules designed to support IPE and its transfer into new interprofessional health care practices. We illustrate some best practices for the design of effective online IPE, and show how this design can create effective learning for IPE. Challenges exist regarding the full implementation of interprofessional clinical practice that are beginning to be met by coordinated efforts of multiple health care education silos.

Using design to drive organizational performance and innovation in the corporate workplace: implications for interprofessional environments.

PubMed

Laing, Andrew; Bacevice, Peter Anthony

2013-09-01

Learning and working are increasingly inseparable social processes characterized by a mix of routine and non-routine activities, which are meant to sustain an optimal balance of creative risk taking, idea exploration and development of professional mastery. Learning and working are embedded in broader social institutions such as universities, academic medical centers, professional organizations and business firms. The future of learning and working is witnessing a blurring of these institutional boundaries, and consequently, a spanning of disciplines and professions that have traditionally assimilated and oriented people around knowledge domains. Learning and working practices are increasingly less defined by bureaucratic controls and are, instead, more collaborative, fluid and interdisciplinary. One of the most tangible manifestations of this shift is in the spaces and places where learning and working activities happen and where people interact and organize. This article explores these learning and working paradigm shifts by discussing recent developments in the corporate workplace and exploring how such changes inform the future of interprofessional education.

Flow Navigation by Smart Microswimmers via Reinforcement Learning

NASA Astrophysics Data System (ADS)

Colabrese, Simona; Gustavsson, Kristian; Celani, Antonio; Biferale, Luca

2017-04-01

Smart active particles can acquire some limited knowledge of the fluid environment from simple mechanical cues and exert a control on their preferred steering direction. Their goal is to learn the best way to navigate by exploiting the underlying flow whenever possible. As an example, we focus our attention on smart gravitactic swimmers. These are active particles whose task is to reach the highest altitude within some time horizon, given the constraints enforced by fluid mechanics. By means of numerical experiments, we show that swimmers indeed learn nearly optimal strategies just by experience. A reinforcement learning algorithm allows particles to learn effective strategies even in difficult situations when, in the absence of control, they would end up being trapped by flow structures. These strategies are highly nontrivial and cannot be easily guessed in advance. This Letter illustrates the potential of reinforcement learning algorithms to model adaptive behavior in complex flows and paves the way towards the engineering of smart microswimmers that solve difficult navigation problems.

An Exploratory Analysis of the Relationship between Media Richness and Managerial Information Processing

DTIC Science & Technology

1984-07-01

engineers use technology to design optimal information systems that will provide clear, correct data to help managers solve current problems (Keen...is in a changing environment (Hedberg and Jonsson, 1978; Mowshowitz, 1976; Hedberg, 1981; Hedberg, Nystrom, and Starbuck , 1976). Technology based...organizations learn and unlearn." In P. C. Nystrom, and W. H. Starbuck (eds.), Handbook of Organizational Design, Vol. 1. Amsterdam: Elsevier Scientific

Task Difficulty and Prior Videogame Experience: Their Role in Performance and Motivation in Instructional Videogames

DTIC Science & Technology

2007-06-01

Video game -based environments are an increasingly popular medium for training Soldiers. This research investigated how various strategies for...modifying task difficulty over the progression of an instructional video game impact learner performance and motivation. Further, the influence of prior... video game experience on these learning outcomes was examined, as well as the role prior experience played in determining the optimal approach for

Intelligent Tutoring Methods for Optimizing Learning Outcomes with Embedded Training

DTIC Science & Technology

2009-10-01

after action review. Particularly with free - play virtual environments, it is important to constrain the development task for constructing an...evaluation approach. Attempts to model all possible variations of correct performance can be prohibitive in free - play scenarios, and so for such conditions...member R for proper execution during free - play execution. In the first tier, the evaluation must know when it applies, or more specifically, when

Designing an Optimally Educational Anesthesia Clerkship for Medical Students - Survey Results of a New Curriculum.

PubMed

Galway, Ursula A

2010-01-01

The field of anesthesia continues to be poorly understood and underestimated as a career choice for graduating medical students. The anesthesia clerkship is an important educational experience in which students learn a wealth of medical knowledge. Our aim was to develop an anesthesia clerkship which exposed the students to many aspects of anesthesiology in a well structured supervised environment. Based on this, we hoped that a positive learning experience would attract medical students to choose anesthesiology as a career. We structured a four week anesthesia clerkship for third and fourth year medical students, which comprised of time in operating room, intensive care unit, pain and perioperative environments. The students completed a survey anonymously at the conclusion of their clerkship. We gathered 25 medical students' opinion of their newly revised 4 week anesthesia clerkship and analyzed their comments in the hope of creating an optimal educational experience for future students. Students reported an overall satisfaction with the new curriculum. Ninety-six percent of students stated that the clerkship increased their desire to pursue a career in anesthesia. The response to our survey showed that a structured educational four week anesthesia clerkship was highly satisfactory and increased medical students desire to pursue a career in anesthesia.

The Educational Kanban: promoting effective self-directed adult learning in medical education.

PubMed

Goldman, Stuart

2009-07-01

The author reviews the many forces that have driven contemporary medical education approaches to evaluation and places them in an adult learning theory context. After noting their strengths and limitations, the author looks to lessons learned from manufacturing on both efficacy and efficiency and explores how these can be applied to the process of trainee assessment in medical education.Building on this, the author describes the rationale for and development of the Educational Kanban (EK) at Children's Hospital Boston--specifically, how it was designed to integrate adult learning theory, Japanese manufacturing models, and educator observations into a unique form of teacher-student collaboration that allows for continuous improvement. It is a formative tool, built on the Accreditation Council for Graduate Medical Education's six core competencies, that guides educational efforts to optimize teaching and learning, promotes adult learner responsibility and efficacy, and takes advantage of the labor-intensive clinical educational setting. The author discusses how this model, which will be implemented in July 2009, will lead to training that is highly individualized, optimizes faculty and student educational efforts, and ultimately conserves faculty resources. A model EK is provided for general reference.The EK represents a novel approach to adult learning that will enhance educational effectiveness and efficiency and complement existing evaluative models. Described here in a specific graduate medical setting, it can readily be adapted and integrated into a wide range of undergraduate and graduate clinical educational environments.

Using virtual reality environment to facilitate training with advanced upper-limb prosthesis.

PubMed

Resnik, Linda; Etter, Katherine; Klinger, Shana Lieberman; Kambe, Charles

2011-01-01

Technological advances in upper-limb prosthetic design offer dramatically increased possibilities for powered movement. The DEKA Arm system allows users 10 powered degrees of movement. Learning to control these movements by utilizing a set of motions that, in most instances, differ from those used to obtain the desired action prior to amputation is a challenge for users. In the Department of Veterans Affairs "Study to Optimize the DEKA Arm," we attempted to facilitate motor learning by using a virtual reality environment (VRE) program. This VRE program allows users to practice controlling an avatar using the controls designed to operate the DEKA Arm in the real world. In this article, we provide highlights from our experiences implementing VRE in training amputees to use the full DEKA Arm. This article discusses the use of VRE in amputee rehabilitation, describes the VRE system used with the DEKA Arm, describes VRE training, provides qualitative data from a case study of a subject, and provides recommendations for future research and implementation of VRE in amputee rehabilitation. Our experience has led us to believe that training with VRE is particularly valuable for upper-limb amputees who must master a large number of controls and for those amputees who need a structured learning environment because of cognitive deficits.

Adult neurogenesis: optimizing hippocampal function to suit the environment.

PubMed

Glasper, Erica R; Schoenfeld, Timothy J; Gould, Elizabeth

2012-02-14

Numerous studies have attempted to determine the function of adult neurogenesis in the hippocampus using methods to deplete new neurons and examine changes in behaviors associated with this brain region. This approach has produced a set of findings that, although not entirely consistent, suggest new neurons are associated with improved learning and reduced anxiety. This paper attempts to synthesize some of these findings into a model that proposes adaptive significance to experience-dependent alterations in new neuron formation. We suggest that the modulation of adult neurogenesis, as well as of the microcircuitry associated with new neurons, by experience prepares the hippocampus to meet the specific demands of an environment that is predictably similar to one that existed previously. Reduced neurogenesis that occurs with persistent exposure to a high threat environment produces a hippocampus that is more likely to respond with behavior that maximizes the chance of survival. Conversely, enhanced neurogenesis that occurs with continual exposure to a rewarding environment leads to behavior that optimizes the chances of successful reproduction. The persistence of this form of plasticity throughout adulthood may provide the neural substrate for adaptive responding to both stable and dynamic environmental conditions. Copyright © 2011. Published by Elsevier B.V.

Learning to Rapidly Re-Contact the Lost Plume in Chemical Plume Tracing

PubMed Central

Cao, Meng-Li; Meng, Qing-Hao; Wang, Jia-Ying; Luo, Bing; Jing, Ya-Qi; Ma, Shu-Gen

2015-01-01

Maintaining contact between the robot and plume is significant in chemical plume tracing (CPT). In the time immediately following the loss of chemical detection during the process of CPT, Track-Out activities bias the robot heading relative to the upwind direction, expecting to rapidly re-contact the plume. To determine the bias angle used in the Track-Out activity, we propose an online instance-based reinforcement learning method, namely virtual trail following (VTF). In VTF, action-value is generalized from recently stored instances of successful Track-Out activities. We also propose a collaborative VTF (cVTF) method, in which multiple robots store their own instances, and learn from the stored instances, in the same database. The proposed VTF and cVTF methods are compared with biased upwind surge (BUS) method, in which all Track-Out activities utilize an offline optimized universal bias angle, in an indoor environment with three different airflow fields. With respect to our experimental conditions, VTF and cVTF show stronger adaptability to different airflow environments than BUS, and furthermore, cVTF yields higher success rates and time-efficiencies than VTF. PMID:25825974

Using Simulation in Interprofessional Education.

PubMed

Paige, John T; Garbee, Deborah D; Brown, Kimberly M; Rojas, Jose D

2015-08-01

Simulation-based training (SBT) is a powerful educational tool permitting the acquisition of surgical knowledge, skills, and attitudes at both the individual- and team-based level in a safe, nonthreatening learning environment at no risk to a patient. Interprofessional education (IPE), in which participants from 2 or more health or social care professions learn interactively, can help improve patient care through the promotion of efficient coordination, dissemination of advances in care across specialties and professions, and optimization of individual- and team-based function. Nonetheless, conducting SBT IPE sessions poses several tactical and strategic challenges that must be effectively overcome to reap IPE's benefits. Copyright © 2015 Elsevier Inc. All rights reserved.

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.

Perceptual learning through optimization of attentional weighting: human versus optimal Bayesian learner

NASA Technical Reports Server (NTRS)

Eckstein, Miguel P.; Abbey, Craig K.; Pham, Binh T.; Shimozaki, Steven S.

2004-01-01

Human performance in visual detection, discrimination, identification, and search tasks typically improves with practice. Psychophysical studies suggest that perceptual learning is mediated by an enhancement in the coding of the signal, and physiological studies suggest that it might be related to the plasticity in the weighting or selection of sensory units coding task relevant information (learning through attention optimization). We propose an experimental paradigm (optimal perceptual learning paradigm) to systematically study the dynamics of perceptual learning in humans by allowing comparisons to that of an optimal Bayesian algorithm and a number of suboptimal learning models. We measured improvement in human localization (eight-alternative forced-choice with feedback) performance of a target randomly sampled from four elongated Gaussian targets with different orientations and polarities and kept as a target for a block of four trials. The results suggest that the human perceptual learning can occur within a lapse of four trials (<1 min) but that human learning is slower and incomplete with respect to the optimal algorithm (23.3% reduction in human efficiency from the 1st-to-4th learning trials). The greatest improvement in human performance, occurring from the 1st-to-2nd learning trial, was also present in the optimal observer, and, thus reflects a property inherent to the visual task and not a property particular to the human perceptual learning mechanism. One notable source of human inefficiency is that, unlike the ideal observer, human learning relies more heavily on previous decisions than on the provided feedback, resulting in no human learning on trials following a previous incorrect localization decision. Finally, the proposed theory and paradigm provide a flexible framework for future studies to evaluate the optimality of human learning of other visual cues and/or sensory modalities.

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 Optimization

NASA Technical Reports Server (NTRS)

Laird, Philip

1992-01-01

We distinguish static and dynamic optimization of programs: whereas static optimization modifies a program before runtime and is based only on its syntactical structure, dynamic optimization is based on the statistical properties of the input source and examples of program execution. Explanation-based generalization is a commonly used dynamic optimization method, but its effectiveness as a speedup-learning method is limited, in part because it fails to separate the learning process from the program transformation process. This paper describes a dynamic optimization technique called a learn-optimize cycle that first uses a learning element to uncover predictable patterns in the program execution and then uses an optimization algorithm to map these patterns into beneficial transformations. The technique has been used successfully for dynamic optimization of pure Prolog.

Improving physics instruction by analyzing video games

NASA Astrophysics Data System (ADS)

Beatty, Ian D.

2013-01-01

Video games can be very powerful teaching systems, and game designers have become adept at optimizing player engagement while scaffolding development of complex skills and situated knowledge. One implication is that we might create games to teach physics. Another, which I explore here, is that we might learn to improve classroom physics instruction by studying effective games. James Gee, in his book What Video Games Have to Teach Us About Learning and Literacy (2007), articulates 36 principles that make good video games highly effective as learning environments. In this theoretical work, I identify 16 themes running through Gee's principles, and explore how these themes and Gee's principles could be applied to the design of an on-campus physics course. I argue that the process pushes us to confront aspects of learning that physics instructors and even physics education researchers generally neglect, and suggest some novel ideas for course design.

Medical school clinical placements - the optimal method for assessing the clinical educational environment from a graduate entry perspective.

PubMed

Hyde, Sarah; Hannigan, Ailish; Dornan, Tim; McGrath, Deirdre

2018-01-05

Educational environment is a strong determinant of student satisfaction and achievement. The learning environments of medical students on clinical placements are busy workplaces, composed of many variables. There is no universally accepted method of evaluating the clinical learning environment, nor is there consensus on what concepts or aspects should be measured. The aims of this study were to compare the Dundee ready educational environment measure (DREEM - the current de facto standard) and the more recently developed Manchester clinical placement index (MCPI) for the assessment of the clinical learning environment in a graduate entry medical student cohort by correlating the scores of each and analysing free text comments. This study also explored student perceptionof how the clinical educational environment is assessed. An online, anonymous survey comprising of both the DREEM and MCPI instruments was delivered to students on clinical placement in a graduate entry medical school. Additional questions explored students' perceptions of instruments for giving feedback. Numeric variables (DREEM score, MCPI score, ratings) were tested for normality and summarised. Pearson's correlation coefficient was used to measure the strength of the association between total DREEM score and total MCPI scores. Thematic analysis was used to analyse the free text comments. The overall response rate to the questionnaire was 67% (n = 180), with a completed response rate for the MCPI of 60% (n = 161) and for the DREEM of 58% (n = 154). There was a strong, positive correlation between total DREEM and MCPI scores (r = 0.71, p < 0.001). On a scale of 0 to 7, the mean rating for how worthwhile students found completing the DREEM was 3.27 (SD 1.41) and for the MCPI was 3.49 (SD 1.57). 'Finding balance' and 'learning at work' were among the themes to emerge from analysis of free text comments. The present study confirms that DREEM and MCPI total scores are strongly correlated. Graduate entry students tended to favour this method of evaluation over the DREEM with the MCPI prompting rich description of the clinical learning environment. Further study is warranted to determine if this finding is transferable to all clinical medical student cohorts.

A Simple Artificial Life Model Explains Irrational Behavior in Human Decision-Making

PubMed Central

Feher da Silva, Carolina; Baldo, Marcus Vinícius Chrysóstomo

2012-01-01

Although praised for their rationality, humans often make poor decisions, even in simple situations. In the repeated binary choice experiment, an individual has to choose repeatedly between the same two alternatives, where a reward is assigned to one of them with fixed probability. The optimal strategy is to perseverate with choosing the alternative with the best expected return. Whereas many species perseverate, humans tend to match the frequencies of their choices to the frequencies of the alternatives, a sub-optimal strategy known as probability matching. Our goal was to find the primary cognitive constraints under which a set of simple evolutionary rules can lead to such contrasting behaviors. We simulated the evolution of artificial populations, wherein the fitness of each animat (artificial animal) depended on its ability to predict the next element of a sequence made up of a repeating binary string of varying size. When the string was short relative to the animats’ neural capacity, they could learn it and correctly predict the next element of the sequence. When it was long, they could not learn it, turning to the next best option: to perseverate. Animats from the last generation then performed the task of predicting the next element of a non-periodical binary sequence. We found that, whereas animats with smaller neural capacity kept perseverating with the best alternative as before, animats with larger neural capacity, which had previously been able to learn the pattern of repeating strings, adopted probability matching, being outperformed by the perseverating animats. Our results demonstrate how the ability to make predictions in an environment endowed with regular patterns may lead to probability matching under less structured conditions. They point to probability matching as a likely by-product of adaptive cognitive strategies that were crucial in human evolution, but may lead to sub-optimal performances in other environments. PMID:22563454

A simple artificial life model explains irrational behavior in human decision-making.

PubMed

Feher da Silva, Carolina; Baldo, Marcus Vinícius Chrysóstomo

2012-01-01

Although praised for their rationality, humans often make poor decisions, even in simple situations. In the repeated binary choice experiment, an individual has to choose repeatedly between the same two alternatives, where a reward is assigned to one of them with fixed probability. The optimal strategy is to perseverate with choosing the alternative with the best expected return. Whereas many species perseverate, humans tend to match the frequencies of their choices to the frequencies of the alternatives, a sub-optimal strategy known as probability matching. Our goal was to find the primary cognitive constraints under which a set of simple evolutionary rules can lead to such contrasting behaviors. We simulated the evolution of artificial populations, wherein the fitness of each animat (artificial animal) depended on its ability to predict the next element of a sequence made up of a repeating binary string of varying size. When the string was short relative to the animats' neural capacity, they could learn it and correctly predict the next element of the sequence. When it was long, they could not learn it, turning to the next best option: to perseverate. Animats from the last generation then performed the task of predicting the next element of a non-periodical binary sequence. We found that, whereas animats with smaller neural capacity kept perseverating with the best alternative as before, animats with larger neural capacity, which had previously been able to learn the pattern of repeating strings, adopted probability matching, being outperformed by the perseverating animats. Our results demonstrate how the ability to make predictions in an environment endowed with regular patterns may lead to probability matching under less structured conditions. They point to probability matching as a likely by-product of adaptive cognitive strategies that were crucial in human evolution, but may lead to sub-optimal performances in other environments.

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.

Becoming an expert: a review of adult learning theory and implications for vocational training in ophthalmology.

PubMed

Roberts, Timothy V; Gustavs, Julie; Mack, Heather G

2012-07-01

One of the key responsibilities of professional bodies, such as the Royal Australian and New Zealand College of Ophthalmologists, is to determine, teach and assess the competencies required for trainees to reach an expert level. Vocational training programs (VTP) need to incorporate advances in educational research and reflect changes in generational thinking and learning styles to provide the most optimal learning environment to meet the desired educational outcomes. This paper seeks to introduce some of the important concepts of adult educational theory and to explain how they connect to four strategic areas in the development and implementation of the VTP: 1 What are the learning needs of trainees? 2 What educational methods best address these needs? 3 What assessment methods best test the acquisition of the desired learning outcomes? 4 What are the needs of supervisors and teachers? © 2011 The Authors. Clinical and Experimental Ophthalmology © 2011 Royal Australian and New Zealand College of Ophthalmologists.

The effect of brain based learning with contextual approach viewed from adversity quotient

NASA Astrophysics Data System (ADS)

Kartikaningtyas, V.; Kusmayadi, T. A.; Riyadi, R.

2018-05-01

The aim of this research was to find out the effect of Brain Based Learning (BBL) with contextual approach viewed from adversity quotient (AQ) on mathematics achievement. BBL-contextual is the model to optimize the brain in the new concept learning and real life problem solving by making the good environment. Adversity Quotient is the ability to response and faces the problems. In addition, it is also about how to turn the difficulties into chances. This AQ classified into quitters, campers, and climbers. The research method used in this research was quasi experiment by using 2x3 factorial designs. The sample was chosen by using stratified cluster random sampling. The instruments were test and questionnaire for the data of AQ. The results showed that (1) BBL-contextual is better than direct learning on mathematics achievement, (2) there is no significant difference between each types of AQ on mathematics achievement, and (3) there is no interaction between learning model and AQ on mathematics achievement.

The World Wide Web as a Medium of Instruction: What Works and What Doesn't

NASA Technical Reports Server (NTRS)

McCarthy, Marianne; Grabowski, Barbara; Hernandez, Angel; Koszalka, Tiffany; Duke, Lee

1997-01-01

A conference was held on March 18-20, 1997 to investigate the lessons learned by the Aeronautics Cooperative Agreement Projects with regard to the most effective strategies for developing instruction for the World Wide Web. The conference was a collaboration among the NASA Aeronautics and Space Transportation Technology Centers (Ames, Dryden, Langley, and Lewis), NASA Headquarters, the University of Idaho and The Pennsylvania State University. The conference consisted of presentations by the Aeronautics Cooperative Agreement Teams, the University of Idaho, and working sessions in which the participants addressed teacher training and support, technology, evaluation and pedagogy. The conference was also undertaken as part of the Dryden Learning Technologies Project which is a collaboration between the Dryden Education Office and The Pennsylvania State University. The DFRC Learning Technology Project goals relevant to the conference are as follows: conducting an analysis of current teacher needs, classroom infrastructure and exemplary instructional World Wide Web sites, and developing models for Web-enhanced learning environments that optimize teaching practices and student learning.

An Optimization of the Basic School Military Occupational Skill Assignment Process

DTIC Science & Technology

2003-06-01

Corps Intranet (NMCI)23 supports it. We evaluated the use of Microsoft’s SQL Server, but dismissed this after learning that TBS did not possess a SQL ...Server license or a qualified SQL Server administrator.24 SQL Server would have provided for additional security measures not available in MS...administrator. Although not has powerful as SQL Server, MS Access can handle the multi-user environment necessary for this system.25 The training

Improving Memory for Optimization and Learning in Dynamic Environments

DTIC Science & Technology

2011-07-01

algorithm uses simple, in- cremental clustering to separate solutions into memory entries. The cluster centers are used as the models in the memory. This is...entire days of traffic with realistic traffic de - mands and turning ratios on a 32 intersection network modeled on downtown Pittsburgh, Pennsyl- vania...early/tardy problem. Management Science, 35(2):177–191, 1989. [78] Daniel Parrott and Xiaodong Li. A particle swarm model for tracking multiple peaks in

Nature vs Nurture: Effects of Learning on Evolution

NASA Astrophysics Data System (ADS)

Nagrani, Nagina

In the field of Evolutionary Robotics, the design, development and application of artificial neural networks as controllers have derived their inspiration from biology. Biologists and artificial intelligence researchers are trying to understand the effects of neural network learning during the lifetime of the individuals on evolution of these individuals by qualitative and quantitative analyses. The conclusion of these analyses can help develop optimized artificial neural networks to perform any given task. The purpose of this thesis is to study the effects of learning on evolution. This has been done by applying Temporal Difference Reinforcement Learning methods to the evolution of Artificial Neural Tissue controller. The controller has been assigned the task to collect resources in a designated area in a simulated environment. The performance of the individuals is measured by the amount of resources collected. A comparison has been made between the results obtained by incorporating learning in evolution and evolution alone. The effects of learning parameters: learning rate, training period, discount rate, and policy on evolution have also been studied. It was observed that learning delays the performance of the evolving individuals over the generations. However, the non zero learning rate throughout the evolution process signifies natural selection preferring individuals possessing plasticity.

Artificial intelligence framework for simulating clinical decision-making: a Markov decision process approach.

PubMed

Bennett, Casey C; Hauser, Kris

2013-01-01

In the modern healthcare system, rapidly expanding costs/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general purpose (non-disease-specific) computational/artificial intelligence (AI) framework to address these challenges. This framework serves two potential functions: (1) a simulation environment for exploring various healthcare policies, payment methodologies, etc., and (2) the basis for clinical artificial intelligence - an AI that can "think like a doctor". This approach combines Markov decision processes and dynamic decision networks to learn from clinical data and develop complex plans via simulation of alternative sequential decision paths while capturing the sometimes conflicting, sometimes synergistic interactions of various components in the healthcare system. It can operate in partially observable environments (in the case of missing observations or data) by maintaining belief states about patient health status and functions as an online agent that plans and re-plans as actions are performed and new observations are obtained. This framework was evaluated using real patient data from an electronic health record. The results demonstrate the feasibility of this approach; such an AI framework easily outperforms the current treatment-as-usual (TAU) case-rate/fee-for-service models of healthcare. The cost per unit of outcome change (CPUC) was $189 vs. $497 for AI vs. TAU (where lower is considered optimal) - while at the same time the AI approach could obtain a 30-35% increase in patient outcomes. Tweaking certain AI model parameters could further enhance this advantage, obtaining approximately 50% more improvement (outcome change) for roughly half the costs. Given careful design and problem formulation, an AI simulation framework can approximate optimal decisions even in complex and uncertain environments. Future work is described that outlines potential lines of research and integration of machine learning algorithms for personalized medicine. Copyright © 2012 Elsevier B.V. All rights reserved.

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.

Integration of the e-Learning into the medical university curricula.

PubMed

Rusnakova, V; Bacharova, L; Simo, J; Krcmeryova, T; Finka, M; Kovac, R

2012-01-01

The aim of this contribution was to present the e-Learning introduction in the Slovak Medical University (SMU) with a focus on the implementation phase of the two blended courses - Healthcare Quality and Healthcare Professionals' Ethics. The introduction of the e-Learning was realized during the period 2008-2009 in the partnership of SMU and IBM Company, following strictly the project management approach. The development of the e-module beta-versions was evaluated by the modules' authors using a structured interview. In a consequent pilot testing, the blended courses were evaluated by 23 students of the bachelor program in Rescue health care, and by 61 public health students at the master level program, respectively, using the standardized questionnaires. The tangible results included the documented SMU strategy for the e-Learning integration, six e-Learning modules and evaluation results. The authors' evaluation showed high scores for the experience in collaboration with IBM, as well as for the experience with the LMS environment. The students' evaluation showed a high acceptance of the e-Learning by both part-time and full-time students. The access to Internet was not recognized as a serious barrier. The first experience with the integration of the e-Learning into the curricula of the Slovak Medical University showed the advantage of the systematic approach. The experience with developing the strategy in an interdisciplinary/ intercultural team, the knowledge about specific characteristics of distance learning by the involved SMU staff, and the know-how and skills represented the important benefits. It was demonstrated that the blended learning is recommended as optimal for the education in medical environment (Tab. 4, Fig. 1, Ref. 22).

Human-level control through deep reinforcement learning.

PubMed

Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Rusu, Andrei A; Veness, Joel; Bellemare, Marc G; Graves, Alex; Riedmiller, Martin; Fidjeland, Andreas K; Ostrovski, Georg; Petersen, Stig; Beattie, Charles; Sadik, Amir; Antonoglou, Ioannis; King, Helen; Kumaran, Dharshan; Wierstra, Daan; Legg, Shane; Hassabis, Demis

2015-02-26

The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

Human-level control through deep reinforcement learning

NASA Astrophysics Data System (ADS)

Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Rusu, Andrei A.; Veness, Joel; Bellemare, Marc G.; Graves, Alex; Riedmiller, Martin; Fidjeland, Andreas K.; Ostrovski, Georg; Petersen, Stig; Beattie, Charles; Sadik, Amir; Antonoglou, Ioannis; King, Helen; Kumaran, Dharshan; Wierstra, Daan; Legg, Shane; Hassabis, Demis

2015-02-01

The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

Derivative Free Optimization of Complex Systems with the Use of Statistical Machine Learning Models

DTIC Science & Technology

2015-09-12

AFRL-AFOSR-VA-TR-2015-0278 DERIVATIVE FREE OPTIMIZATION OF COMPLEX SYSTEMS WITH THE USE OF STATISTICAL MACHINE LEARNING MODELS Katya Scheinberg...COMPLEX SYSTEMS WITH THE USE OF STATISTICAL MACHINE LEARNING MODELS 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER FA9550-11-1-0239 5c.  PROGRAM ELEMENT...developed, which has been the focus of our research. 15. SUBJECT TERMS optimization, Derivative-Free Optimization, Statistical Machine Learning 16. SECURITY

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/

Machine Learning, deep learning and optimization in computer vision

NASA Astrophysics Data System (ADS)

Canu, Stéphane

2017-03-01

As quoted in the Large Scale Computer Vision Systems NIPS workshop, computer vision is a mature field with a long tradition of research, but recent advances in machine learning, deep learning, representation learning and optimization have provided models with new capabilities to better understand visual content. The presentation will go through these new developments in machine learning covering basic motivations, ideas, models and optimization in deep learning for computer vision, identifying challenges and opportunities. It will focus on issues related with large scale learning that is: high dimensional features, large variety of visual classes, and large number of examples.

C-learning: A new classification framework to estimate optimal dynamic treatment regimes.

PubMed

Zhang, Baqun; Zhang, Min

2017-12-11

A dynamic treatment regime is a sequence of decision rules, each corresponding to a decision point, that determine that next treatment based on each individual's own available characteristics and treatment history up to that point. We show that identifying the optimal dynamic treatment regime can be recast as a sequential optimization problem and propose a direct sequential optimization method to estimate the optimal treatment regimes. In particular, at each decision point, the optimization is equivalent to sequentially minimizing a weighted expected misclassification error. Based on this classification perspective, we propose a powerful and flexible C-learning algorithm to learn the optimal dynamic treatment regimes backward sequentially from the last stage until the first stage. C-learning is a direct optimization method that directly targets optimizing decision rules by exploiting powerful optimization/classification techniques and it allows incorporation of patient's characteristics and treatment history to improve performance, hence enjoying advantages of both the traditional outcome regression-based methods (Q- and A-learning) and the more recent direct optimization methods. The superior performance and flexibility of the proposed methods are illustrated through extensive simulation studies. © 2017, The International Biometric Society.

Bare-Bones Teaching-Learning-Based Optimization

PubMed Central

Zou, Feng; Wang, Lei; Hei, Xinhong; Chen, Debao; Jiang, Qiaoyong; Li, Hongye

2014-01-01

Teaching-learning-based optimization (TLBO) algorithm which simulates the teaching-learning process of the class room is one of the recently proposed swarm intelligent (SI) algorithms. In this paper, a new TLBO variant called bare-bones teaching-learning-based optimization (BBTLBO) is presented to solve the global optimization problems. In this method, each learner of teacher phase employs an interactive learning strategy, which is the hybridization of the learning strategy of teacher phase in the standard TLBO and Gaussian sampling learning based on neighborhood search, and each learner of learner phase employs the learning strategy of learner phase in the standard TLBO or the new neighborhood search strategy. To verify the performance of our approaches, 20 benchmark functions and two real-world problems are utilized. Conducted experiments can been observed that the BBTLBO performs significantly better than, or at least comparable to, TLBO and some existing bare-bones algorithms. The results indicate that the proposed algorithm is competitive to some other optimization algorithms. PMID:25013844

Bare-bones teaching-learning-based optimization.

PubMed

Zou, Feng; Wang, Lei; Hei, Xinhong; Chen, Debao; Jiang, Qiaoyong; Li, Hongye

2014-01-01

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

Simulation as a surgical teaching model.

PubMed

Ruiz-Gómez, José Luis; Martín-Parra, José Ignacio; González-Noriega, Mónica; Redondo-Figuero, Carlos Godofredo; Manuel-Palazuelos, José Carlos

2018-01-01

Teaching of surgery has been affected by many factors over the last years, such as the reduction of working hours, the optimization of the use of the operating room or patient safety. Traditional teaching methodology fails to reduce the impact of these factors on surgeońs training. Simulation as a teaching model minimizes such impact, and is more effective than traditional teaching methods for integrating knowledge and clinical-surgical skills. Simulation complements clinical assistance with training, creating a safe learning environment where patient safety is not affected, and ethical or legal conflicts are avoided. Simulation uses learning methodologies that allow teaching individualization, adapting it to the learning needs of each student. It also allows training of all kinds of technical, cognitive or behavioural skills. Copyright © 2017 AEC. Publicado por Elsevier España, S.L.U. All rights reserved.

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

Enhancing students' higher order thinking skills through computer-based scaffolding in problem-based learning

NASA Astrophysics Data System (ADS)

Kim, Nam Ju

This multiple paper dissertation addressed several issues in Problem-based learning (PBL) through conceptual analysis, meta-analysis, and empirical research. PBL is characterized by ill-structured tasks, self-directed learning process, and a combination of individual and cooperative learning activities. Students who lack content knowledge and problem-solving skills may struggle to address associated tasks that are beyond their current ability levels in PBL. This dissertation addressed a) scaffolding characteristics (i.e., scaffolding types, delivery method, customization) and their effects on students' perception of optimal challenge in PBL, b) the possibility of virtual learning environments for PBL, and c) the importance of information literacy for successful PBL learning. Specifically, this dissertation demonstrated the effectiveness of scaffolding customization (i.e., fading, adding, and fading/adding) to enhance students' self-directed learning in PBL. Moreover, the effectiveness of scaffolding was greatest when scaffolding customization is self-selected than based on fixed-time interval and their performance. This suggests that it might be important for students to take responsibility for their learning in PBL and individualized and just-in-time scaffolding can be one of the solutions to address K-12 students' difficulties in improving problem-solving skills and adjusting to PBL.

SU-E-E-03: Shared Space Fosters Didactic and Professional Learning Across Professions for Medical and Physics Residents

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

Dieterich, S; Perks, J; Fragoso, R

Purpose: Medical Physicists and Radiation Oncologists are two professions who should be working as a team for optimal patient care, yet lack of mutual understanding about each others respective role and work environment creates barriers To improve collaboration and learning, we designed a shared didactic and work space for physics and radiation oncology residents to maximize interaction throughout their professional training. Methods: Physician and Physics residents are required to take the same didactic classes, including journal clubs and respective seminars. The residents also share an office environment among the seven physician and two physic residents. Results: By maximizing didactic overlapmore » and sharing office space, the two resident groups have developed a close professional relationship and supportive work environment. Several joint research projects have been initiated by the residents. Awareness of physics tasks in the clinic has led to a request by the physician residents to change physics didactics, converting the physics short course into a lab-oriented course for the medical residents which is in part taught by the physics residents. The physics seminar is given by both residency groups; increased motivation and interest in learning about physics has led to several medical resident-initiated topic selections which generated lively discussion. The physics long course has changed toward including more discussion among residents to delve deeper into topics and study beyond what passing the boards would require. A supportive work environment has developed, embedding the two physics residents into a larger residents group, allowing them to find mentor and peers more easily. Conclusion: By creating a shared work and didactic environment, physician and physics residents have improved their understanding of respective professional practice. Resident-initiated changes in didactic practice have led to improved learning and joint research. A strong social support system has developed, embedding physics residents into a larger peer group.« less

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.

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.

Neuro Inspired Adaptive Perception and Control for Agile Mobility of Autonomous Vehicles in Uncertain and Hostile Environments

DTIC Science & Technology

2017-02-08

Georgia Tech Research Corporation 505 Tenth Street NW Atlanta, GA 30332 -0420 ABSTRACT Final Report: MURI: Neuro-Inspired Adaptive Perception and...Conquer Strategy for Optimal Trajectory Planning via Mixed-Integer Programming, IEEE Transactions on Robotics, (12 2015): 0. doi: 10.1109/TRO...Learning Day, Microsoft Corporation , Cambridge, MA, May 18, 2015. (c) Presentations 09/06/2015 09/08/2015 125 131 Ali Borji, Dicky N. Sihite, Laurent Itti

A global bioheat model with self-tuning optimal regulation of body temperature using Hebbian feedback covariance learning.

PubMed

Ong, M L; Ng, E Y K

2005-12-01

In the lower brain, body temperature is continually being regulated almost flawlessly despite huge fluctuations in ambient and physiological conditions that constantly threaten the well-being of the body. The underlying control problem defining thermal homeostasis is one of great enormity: Many systems and sub-systems are involved in temperature regulation and physiological processes are intrinsically complex and intertwined. Thus the defining control system has to take into account the complications of nonlinearities, system uncertainties, delayed feedback loops as well as internal and external disturbances. In this paper, we propose a self-tuning adaptive thermal controller based upon Hebbian feedback covariance learning where the system is to be regulated continually to best suit its environment. This hypothesis is supported in part by postulations of the presence of adaptive optimization behavior in biological systems of certain organisms which face limited resources vital for survival. We demonstrate the use of Hebbian feedback covariance learning as a possible self-adaptive controller in body temperature regulation. The model postulates an important role of Hebbian covariance adaptation as a means of reinforcement learning in the thermal controller. The passive system is based on a simplified 2-node core and shell representation of the body, where global responses are captured. Model predictions are consistent with observed thermoregulatory responses to conditions of exercise and rest, and heat and cold stress. An important implication of the model is that optimal physiological behaviors arising from self-tuning adaptive regulation in the thermal controller may be responsible for the departure from homeostasis in abnormal states, e.g., fever. This was previously unexplained using the conventional "set-point" control theory.

A review on data mining and continuous optimization applications in computational biology and medicine.

PubMed

Weber, Gerhard-Wilhelm; Ozöğür-Akyüz, Süreyya; Kropat, Erik

2009-06-01

An emerging research area in computational biology and biotechnology is devoted to mathematical modeling and prediction of gene-expression patterns; it nowadays requests mathematics to deeply understand its foundations. This article surveys data mining and machine learning methods for an analysis of complex systems in computational biology. It mathematically deepens recent advances in modeling and prediction by rigorously introducing the environment and aspects of errors and uncertainty into the genetic context within the framework of matrix and interval arithmetics. Given the data from DNA microarray experiments and environmental measurements, we extract nonlinear ordinary differential equations which contain parameters that are to be determined. This is done by a generalized Chebychev approximation and generalized semi-infinite optimization. Then, time-discretized dynamical systems are studied. By a combinatorial algorithm which constructs and follows polyhedra sequences, the region of parametric stability is detected. In addition, we analyze the topological landscape of gene-environment networks in terms of structural stability. As a second strategy, we will review recent model selection and kernel learning methods for binary classification which can be used to classify microarray data for cancerous cells or for discrimination of other kind of diseases. This review is practically motivated and theoretically elaborated; it is devoted to a contribution to better health care, progress in medicine, a better education, and more healthy living conditions.

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

DTIC Science & Technology

2016-09-09

prenticeship Scheduling (COVAS), which performs ma- chine learning using human expert demonstration, in conjunction with optimization, to automatically and ef...ficiently produce optimal solutions to challenging real- world scheduling problems. COVAS first learns a policy from human scheduling demonstration via...apprentice- ship learning , then uses this initial solution to provide a tight bound on the value of the optimal solution, thereby substantially

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.

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.

The Influence of Parental Self-Efficacy and Perceived Control on the Home Learning Environment of Young Children.

PubMed

Peacock-Chambers, Elizabeth; Martin, Justin T; Necastro, Kelly A; Cabral, Howard J; Bair-Merritt, Megan

2017-03-01

To: 1) examine sociodemographic factors associated with high parental self-efficacy and perceived control, and 2) determine how self-efficacy and control relate to the home learning environment (HLE), including whether they mediate the relationship between sociodemographic characteristics and HLE, among low-income parents of young children. Cross-sectional survey of English- and Spanish-speaking parents, 18 years of age and older, with children 15 to 36 months old, to assess parental self-efficacy, perceived control, HLE, and sociodemographic characteristics. Bivariate analysis identified sociodemographic predictors of high self-efficacy and control. Separate multivariate linear regression models were used to examine associations between self-efficacy, control, and the HLE. Formal path analysis was used to assess whether self-efficacy and control mediate the relationship between sociodemographic characteristics and HLE. Of 144 participants, 25% were white, 65% were immigrants, and 35% completed the survey in Spanish. US-born subjects, those who completed English surveys, or who had higher educational levels had significantly higher mean self-efficacy and perceived control scores (P < .05). Higher self-efficacy and perceived control were associated with a positive change in HLE score in separate multivariate models (self-efficacy β = .7 [95% confidence interval (CI), 0.5-0.9]; control β = .5 [95% CI, 0.2-0.8]). Self-efficacy acted as a mediator such that low self-efficacy explained part of the association between parental depressive symptoms, immigrant status, and less optimal HLE (P = .04 and < .001, respectively). High parental self-efficacy and perceived control positively influence HLEs of young children. Self-efficacy alone mediates the relationship between parental depressive symptoms, immigrant status, and less optimal early home learning. Copyright © 2016 Academic Pediatric Association. Published by Elsevier Inc. All rights reserved.

Lessons about Virtual-Environment Software Systems from 20 years of VE building

PubMed Central

Taylor, Russell M.; Jerald, Jason; VanderKnyff, Chris; Wendt, Jeremy; Borland, David; Marshburn, David; Sherman, William R.; Whitton, Mary C.

2010-01-01

What are desirable and undesirable features of virtual-environment (VE) software architectures? What should be present (and absent) from such systems if they are to be optimally useful? How should they be structured? To help answer these questions we present experience from application designers, toolkit designers, and VE system architects along with examples of useful features from existing systems. Topics are organized under the major headings of: 3D space management, supporting display hardware, interaction, event management, time management, computation, portability, and the observation that less can be better. Lessons learned are presented as discussion of the issues, field experiences, nuggets of knowledge, and case studies. PMID:20567602

Team Formation in Partially Observable Multi-Agent Systems

NASA Technical Reports Server (NTRS)

Agogino, Adrian K.; Tumer, Kagan

2004-01-01

Sets of multi-agent teams often need to maximize a global utility rating the performance of the entire system where a team cannot fully observe other teams agents. Such limited observability hinders team-members trying to pursue their team utilities to take actions that also help maximize the global utility. In this article, we show how team utilities can be used in partially observable systems. Furthermore, we show how team sizes can be manipulated to provide the best compromise between having easy to learn team utilities and having them aligned with the global utility, The results show that optimally sized teams in a partially observable environments outperform one team in a fully observable environment, by up to 30%.

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 remapping of space in motor learning and human-machine interfaces

PubMed Central

Mussa-Ivaldi, F.A.; Danziger, Z.

2009-01-01

Studies of motor adaptation to patterns of deterministic forces have revealed the ability of the motor control system to form and use predictive representations of the environment. One of the most fundamental elements of our environment is space itself. This article focuses on the notion of Euclidean space as it applies to common sensory motor experiences. Starting from the assumption that we interact with the world through a system of neural signals, we observe that these signals are not inherently endowed with metric properties of the ordinary Euclidean space. The ability of the nervous system to represent these properties depends on adaptive mechanisms that reconstruct the Euclidean metric from signals that are not Euclidean. Gaining access to these mechanisms will reveal the process by which the nervous system handles novel sophisticated coordinate transformation tasks, thus highlighting possible avenues to create functional human-machine interfaces that can make that task much easier. A set of experiments is presented that demonstrate the ability of the sensory-motor system to reorganize coordination in novel geometrical environments. In these environments multiple degrees of freedom of body motions are used to control the coordinates of a point in a two-dimensional Euclidean space. We discuss how practice leads to the acquisition of the metric properties of the controlled space. Methods of machine learning based on the reduction of reaching errors are tested as a means to facilitate learning by adaptively changing he map from body motions to controlled device. We discuss the relevance of the results to the development of adaptive human machine interfaces and optimal control. PMID:19665553

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…

Learning rational temporal eye movement strategies.

PubMed

Hoppe, David; Rothkopf, Constantin A

2016-07-19

During active behavior humans redirect their gaze several times every second within the visual environment. Where we look within static images is highly efficient, as quantified by computational models of human gaze shifts in visual search and face recognition tasks. However, when we shift gaze is mostly unknown despite its fundamental importance for survival in a dynamic world. It has been suggested that during naturalistic visuomotor behavior gaze deployment is coordinated with task-relevant events, often predictive of future events, and studies in sportsmen suggest that timing of eye movements is learned. Here we establish that humans efficiently learn to adjust the timing of eye movements in response to environmental regularities when monitoring locations in the visual scene to detect probabilistically occurring events. To detect the events humans adopt strategies that can be understood through a computational model that includes perceptual and acting uncertainties, a minimal processing time, and, crucially, the intrinsic costs of gaze behavior. Thus, subjects traded off event detection rate with behavioral costs of carrying out eye movements. Remarkably, based on this rational bounded actor model the time course of learning the gaze strategies is fully explained by an optimal Bayesian learner with humans' characteristic uncertainty in time estimation, the well-known scalar law of biological timing. Taken together, these findings establish that the human visual system is highly efficient in learning temporal regularities in the environment and that it can use these regularities to control the timing of eye movements to detect behaviorally relevant events.

Learning what matters: A neural explanation for the sparsity bias.

PubMed

Hassall, Cameron D; Connor, Patrick C; Trappenberg, Thomas P; McDonald, John J; Krigolson, Olave E

2018-05-01

The visual environment is filled with complex, multi-dimensional objects that vary in their value to an observer's current goals. When faced with multi-dimensional stimuli, humans may rely on biases to learn to select those objects that are most valuable to the task at hand. Here, we show that decision making in a complex task is guided by the sparsity bias: the focusing of attention on a subset of available features. Participants completed a gambling task in which they selected complex stimuli that varied randomly along three dimensions: shape, color, and texture. Each dimension comprised three features (e.g., color: red, green, yellow). Only one dimension was relevant in each block (e.g., color), and a randomly-chosen value ranking determined outcome probabilities (e.g., green > yellow > red). Participants were faster to respond to infrequent probe stimuli that appeared unexpectedly within stimuli that possessed a more valuable feature than to probes appearing within stimuli possessing a less valuable feature. Event-related brain potentials recorded during the task provided a neurophysiological explanation for sparsity as a learning-dependent increase in optimal attentional performance (as measured by the N2pc component of the human event-related potential) and a concomitant learning-dependent decrease in prediction errors (as measured by the feedback-elicited reward positivity). Together, our results suggest that the sparsity bias guides human reinforcement learning in complex environments. Copyright © 2018 Elsevier B.V. All rights reserved.

Genetic algorithm learning in a New Keynesian macroeconomic setup.

PubMed

Hommes, Cars; Makarewicz, Tomasz; Massaro, Domenico; Smits, Tom

2017-01-01

In order to understand heterogeneous behavior amongst agents, empirical data from Learning-to-Forecast (LtF) experiments can be used to construct learning models. This paper follows up on Assenza et al. (2013) by using a Genetic Algorithms (GA) model to replicate the results from their LtF experiment. In this GA model, individuals optimize an adaptive, a trend following and an anchor coefficient in a population of general prediction heuristics. We replicate experimental treatments in a New-Keynesian environment with increasing complexity and use Monte Carlo simulations to investigate how well the model explains the experimental data. We find that the evolutionary learning model is able to replicate the three different types of behavior, i.e. convergence to steady state, stable oscillations and dampened oscillations in the treatments using one GA model. Heterogeneous behavior can thus be explained by an adaptive, anchor and trend extrapolating component and the GA model can be used to explain heterogeneous behavior in LtF experiments with different types of complexity.

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…

Decision theory, reinforcement learning, and the brain.

PubMed

Dayan, Peter; Daw, Nathaniel D

2008-12-01

Decision making is a core competence for animals and humans acting and surviving in environments they only partially comprehend, gaining rewards and punishments for their troubles. Decision-theoretic concepts permeate experiments and computational models in ethology, psychology, and neuroscience. Here, we review a well-known, coherent Bayesian approach to decision making, showing how it unifies issues in Markovian decision problems, signal detection psychophysics, sequential sampling, and optimal exploration and discuss paradigmatic psychological and neural examples of each problem. We discuss computational issues concerning what subjects know about their task and how ambitious they are in seeking optimal solutions; we address algorithmic topics concerning model-based and model-free methods for making choices; and we highlight key aspects of the neural implementation of decision making.

Planning Mars Memory: Learning from the Mer Mission

NASA Technical Reports Server (NTRS)

Linde, Charlotte

2004-01-01

Knowledge management for space exploration is part of a multi-generational effort at recognizing, preserving and transmitting learning. Each mission should be built on the learning, of both successes and failures, derived from previous missions. Knowledge management begins with learning, and the recognition that this learning has produced knowledge. The Mars Exploration Rover mission provides us with an opportunity to track how learning occurs, how it is recorded, and whether the representations of this learning will be optimally useful for subsequent missions. This paper focuses on the MER science and engineering teams during Rover operations. A NASA team conducted an observational study of the ongoing work and learning of the these teams. Learning occurred in a wide variety of areas: how to run two teams on Mars time for three months; how to use the instruments within the constraints of the martian environment, the deep space network and the mission requirements; how to plan science strategy; how best to use the available software tools. This learning is preserved in many ways. Primarily it resides in peoples memories, to be carried on to the next mission. It is also encoded in stones, in programming sequences, in published reports, and in lessons learned activities, Studying learning and knowledge development as it happens allows us to suggest proactive ways of capturing and using it across multiple missions and generations.

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

NASA Astrophysics Data System (ADS)

Zuel, Brian

The purpose of this dissertation was to examine the effectiveness of matching learners' optimal learning styles to their overall knowledge retention. The study attempted to determine if learners who are placed in an online learning environment that matches their optimal learning styles will retain the information at a higher rate than those learners who are not in an adapted learning environment. There were 56 participants that took one of two lessons; the first lesson was textual based, had no hypertext, and was not influenced heavily by the coherence principle, while the second lesson was multimedia based utilizing hypermedia guided by the coherence principle. Each participant took Felder and Soloman's (1991, 2000) Index of Learning Styles (ILS) questionnaire and was classified using the Felder-Silverman Learning Style Model (FSLSM; 1998) into four individual categories. Groups were separated using the Visual/Verbal section of the FSLSM with 55% (n = 31) of participants going to the adapted group, and 45% (n =25) of participants going to the non-adapted group. Each participant completed an immediate posttest directly after the lesson and a retention posttest a week later. Several repeated measures MANOVA tests were conducted to measure the significance of differences in the tests between groups and within groups. Repeated measures MANOVA tests were conducted to determine if significance existed between the immediate posttest results and the retention posttest results. Also, participants were asked their perspectives if the lesson type they received was beneficial to their perceived learning of the material. Of the 56 students who took part in this study, 31 students were placed in the adapted group and 25 in the non-adapted group based on outcomes of the ILS and the FLSSM. No significant differences were found between groups taking the multimedia lesson and the textual lesson in the immediate posttest. No significant differences were found between the adapted and the non-adapted groups on the immediate posttest. No significant difference was found between the adapted and the non-adapted groups on the retention posttest. However, results also revealed that the adapted group scored significantly higher on the retention posttest when compared with the immediate posttest. Interestingly, the non-adapted group scored significantly higher on the immediate posttest when compared with the retention posttest. When queried about the perception of benefit of the lesson style, 42% of the adapted group replied in the affirmative following the immediate posttest, yet that percentage grew to 81% following the retention posttest. The non-adapted group had 28% reply in the affirmative following the immediate posttest, and that percentage grew to 48% following the retention posttest. Both groups found benefit, yet the numbers associated with the adapted group were higher. Overall perceptions of benefit corresponded to higher test scores as opposed to those who did not find benefit, who had a lower score.

Machine learning based Intelligent cognitive network using fog computing

NASA Astrophysics Data System (ADS)

Lu, Jingyang; Li, Lun; Chen, Genshe; Shen, Dan; Pham, Khanh; Blasch, Erik

2017-05-01

In this paper, a Cognitive Radio Network (CRN) based on artificial intelligence is proposed to distribute the limited radio spectrum resources more efficiently. The CRN framework can analyze the time-sensitive signal data close to the signal source using fog computing with different types of machine learning techniques. Depending on the computational capabilities of the fog nodes, different features and machine learning techniques are chosen to optimize spectrum allocation. Also, the computing nodes send the periodic signal summary which is much smaller than the original signal to the cloud so that the overall system spectrum source allocation strategies are dynamically updated. Applying fog computing, the system is more adaptive to the local environment and robust to spectrum changes. As most of the signal data is processed at the fog level, it further strengthens the system security by reducing the communication burden of the communications network.

Differential sensitivity to the environment: contribution of cognitive biases and genes to psychological wellbeing

PubMed Central

Fox, E; Beevers, C G

2016-01-01

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. PMID:27431291

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…

Factors Influencing Learning Environments in an Integrated Experiential Program

NASA Astrophysics Data System (ADS)

Koci, Peter

The research conducted for this dissertation examined the learning environment of a specific high school program that delivered the explicit curriculum through an integrated experiential manner, which utilized field and outdoor experiences. The program ran over one semester (five months) and it integrated the grade 10 British Columbian curriculum in five subjects. A mixed methods approach was employed to identify the students' perceptions and provide richer descriptions of their experiences related to their unique learning environment. Quantitative instruments were used to assess changes in students' perspectives of their learning environment, as well as other supporting factors including students' mindfulness, and behaviours towards the environment. Qualitative data collection included observations, open-ended questions, and impromptu interviews with the teacher. The qualitative data describe the factors and processes that influenced the learning environment and give a richer, deeper interpretation which complements the quantitative findings. The research results showed positive scores on all the quantitative measures conducted, and the qualitative data provided further insight into descriptions of learning environment constructs that the students perceived as most important. A major finding was that the group cohesion measure was perceived by students as the most important attribute of their preferred learning environment. A flow chart was developed to help the researcher conceptualize how the learning environment, learning process, and outcomes relate to one another in the studied program. This research attempts to explain through the consideration of this case study: how learning environments can influence behavioural change and how an interconnectedness among several factors in the learning process is influenced by the type of learning environment facilitated. Considerably more research is needed in this area to understand fully the complexity learning environments and how they influence learning and behaviour. Keywords: learning environments; integrated experiential programs; environmental education.

The Internet as an informal learning environment: Assessing knowledge acquisition of science and engineering students using constructivist and objectivist formats

NASA Astrophysics Data System (ADS)

Hargis, Jace

This study examined the effects of two different instructional formats on Internet WebPages in an informal learning environment. The purpose of this study is to (a) identify optimal instructional formats for on-line learning; (b) identify the relationship between post-assessment scores and the student's gender, age or racial identity; (c) examine the effects of verbal aptitudes on learning in different formats; (d) identify relationships between computer attitudes and achievement; and (e) identify the potential power for self-regulated learning and self-efficacy on Internet WebPages. Two learning strategy modules were developed; a constructivist and an objectivist instruction module. The study program consisted of an on-line consent form; a computer attitude survey; a Motivated Strategies for Learning Questionnaire; a verbal aptitude test; a pre-assessment; instructional directions followed by the instructional module and a post-assessment. The study tested 145 post-secondary science and engineering participants from the University of Florida. Participants were randomly assigned to one of two treatment groups or a control in a pretest/posttest design. An analysis of covariance with general linear models was used to account for effects of individual difference variables and aptitude treatment interaction (ATI). This statistical procedure was used to determine the relationships among the dependent variable, the achievement on each of the formats and the independent variables, attitudes, gender, racial identity, verbal aptitudes, and self-regulated learning/self-efficacy. Significant results at alpha = .05 were found for none of these variables. However, a linear prediction of age shows that older participants scored higher on the post-assessment after completing the objectivist module. Although there were no significant differences between the learning format and the variables, there was a difference between the modules and the control. Therefore, it is possible that regardless of characteristics, science and engineering students can learn on-line technical material.

Large-Scale Linear Optimization through Machine Learning: From Theory to Practical System Design and Implementation

DTIC Science & Technology

2016-08-10

AFRL-AFOSR-JP-TR-2016-0073 Large-scale Linear Optimization through Machine Learning: From Theory to Practical System Design and Implementation ...2016 4.  TITLE AND SUBTITLE Large-scale Linear Optimization through Machine Learning: From Theory to Practical System Design and Implementation 5a...performances on various machine learning tasks and it naturally lends itself to fast parallel implementations . Despite this, very little work has been

The role of dopamine in positive and negative prediction error utilization during incidental learning - Insights from Positron Emission Tomography, Parkinson's disease and Huntington's disease.

PubMed

Mathar, David; Wilkinson, Leonora; Holl, Anna K; Neumann, Jane; Deserno, Lorenz; Villringer, Arno; Jahanshahi, Marjan; Horstmann, Annette

2017-05-01

Incidental learning of appropriate stimulus-response associations is crucial for optimal functioning within our complex environment. Positive and negative prediction errors (PEs) serve as neural teaching signals within distinct ('direct'/'indirect') dopaminergic pathways to update associations and optimize subsequent behavior. Using a computational reinforcement learning model, we assessed learning from positive and negative PEs on a probabilistic task (Weather Prediction Task - WPT) in three populations that allow different inferences on the role of dopamine (DA) signals: (1) Healthy volunteers that repeatedly underwent [ 11 C]raclopride Positron Emission Tomography (PET), allowing for assessment of striatal DA release during learning, (2) Parkinson's disease (PD) patients tested both on and off L-DOPA medication, (3) early Huntington's disease (HD) patients, a disease that is associated with hyper-activation of the 'direct' pathway. Our results show that learning from positive and negative feedback on the WPT is intimately linked to different aspects of dopaminergic transmission. In healthy individuals, the difference in [ 11 C]raclopride binding potential (BP) as a measure for striatal DA release was linearly associated with the positive learning rate. Further, asymmetry between baseline DA tone in the left and right ventral striatum was negatively associated with learning from positive PEs. Female patients with early HD exhibited exaggerated learning rates from positive feedback. In contrast, dopaminergic tone predicted learning from negative feedback, as indicated by an inverted u-shaped association observed with baseline [ 11 C]raclopride BP in healthy controls and the difference between PD patients' learning rate on and off dopaminergic medication. Thus, the ability to learn from positive and negative feedback is a sensitive marker for the integrity of dopaminergic signal transmission in the 'direct' and 'indirect' dopaminergic pathways. The present data are interesting beyond clinical context in that imbalances of dopaminergic signaling have not only been observed for neurological and psychiatric conditions but also been proposed for obesity and adolescence. Copyright © 2016 Elsevier Ltd. All rights reserved.

Workplace culture and the practice experience of midwifery students: A meta-synthesis.

PubMed

Arundell, Fiona; Mannix, Judy; Sheehan, Athena; Peters, Kath

2018-04-01

To describe midwifery students' practice experience and to explore facilitators and barriers to positive clinical learning experiences. Practice experience is a vital component of every midwifery course. Course dissatisfaction and attrition of midwifery students has been attributed to sub-optimal practice experiences. Events or actions experienced by midwifery students that trigger dissatisfaction and attrition need to be identified. A meta-synthesis was based on that developed by Noblit and Hare. Students perceive workplaces as poorly prepared for their arrival and subsequent support. Students' experience in the practice setting is influenced by the existing workplace culture. Workplace culture influences institutional functioning and individuals within the culture. Enculturation of students into the midwifery culture and subsequent learning is affected by the support received. The practice experience of midwifery students was profoundly influenced by workplace culture. Students tended to have polarized accounts of their experience that were predominantly negative. To provide an optimal environment for midwifery students; midwifery managers and individual midwives need to be aware of the facilitators and barriers to midwifery student development in the practice setting. © 2017 John Wiley & Sons Ltd.

Hypermedia in Vocational Learning: A Hypermedia Learning Environment for Training Management Skills

ERIC Educational Resources Information Center

Konradt, Udo

2004-01-01

A learning environment is defined as an arrangement of issues, methods, techniques, and media in a given domain. Besides temporal and spatial features a learning environment considers the social situation in which learning takes place. In (hypermedia) learning environments the concept of exploration and the active role of the learner is…

Using Wikis as a Support and Assessment Tool in Collaborative Digital Game-Based Learning Environments

ERIC Educational Resources Information Center

Samur, Yavuz

2011-01-01

In computer-supported collaborative learning (CSCL) environments, there are many researches done on collaborative learning activities; however, in game-based learning environments, more research and literature on collaborative learning activities are required. Actually, both game-based learning environments and wikis enable us to use new chances…

Assessing culturally sensitive factors in the learning environment of science classrooms

NASA Astrophysics Data System (ADS)

Fisher, Darrell L.; Waldrip, Bruce G.

1997-03-01

As schools are becoming increasingly diverse in their scope and clientele, any examination of the interaction of culturally sensitive factors of students' learning environments with learning science assumes critical importance. The purpose of this exploratory study was to develop an instrument to assess learning environment factors that are culturally sensitive, to provide initial validation information on the instrument and to examine associations between students' perceptions of their learning environments and their attitudes towards science and achievement of enquiry skills. A measure of these factors of science student's learning environment, namely the Cultural Learning Environment Questionnaire (CLEQ), was developed from past learning environment instruments and influenced by Hofstede's four dimensions of culture (Power Distance, Uncertainty Avoidance, Individualism, and Masculinity/Femininity). The reliability and discriminant validity for each scale were obtained and associations between learning environment, attitude to science and enquiry skills achievement were found.

Lifelong Optimization

DTIC Science & Technology

2015-04-13

cope with dynamic, online optimisation problems with uncertainty, we developed some powerful and sophisticated techniques for learning heuristics...NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) National ICT Australia United NICTA, Locked Bag 6016 Kensington...ABSTRACT Optimization solvers should learn to improve their performance over time. By learning both during the course of solving an optimization

Improved Modeling of Intelligent Tutoring Systems Using Ant Colony Optimization

ERIC Educational Resources Information Center

Rastegarmoghadam, Mahin; Ziarati, Koorush

2017-01-01

Swarm intelligence approaches, such as ant colony optimization (ACO), are used in adaptive e-learning systems and provide an effective method for finding optimal learning paths based on self-organization. The aim of this paper is to develop an improved modeling of adaptive tutoring systems using ACO. In this model, the learning object is…

Generalized SMO algorithm for SVM-based multitask learning.

PubMed

Cai, Feng; Cherkassky, Vladimir

2012-06-01

Exploiting additional information to improve traditional inductive learning is an active research area in machine learning. In many supervised-learning applications, training data can be naturally separated into several groups, and incorporating this group information into learning may improve generalization. Recently, Vapnik proposed a general approach to formalizing such problems, known as "learning with structured data" and its support vector machine (SVM) based optimization formulation called SVM+. Liang and Cherkassky showed the connection between SVM+ and multitask learning (MTL) approaches in machine learning, and proposed an SVM-based formulation for MTL called SVM+MTL for classification. Training the SVM+MTL classifier requires the solution of a large quadratic programming optimization problem which scales as O(n(3)) with sample size n. So there is a need to develop computationally efficient algorithms for implementing SVM+MTL. This brief generalizes Platt's sequential minimal optimization (SMO) algorithm to the SVM+MTL setting. Empirical results show that, for typical SVM+MTL problems, the proposed generalized SMO achieves over 100 times speed-up, in comparison with general-purpose optimization routines.

Spiking neuron network Helmholtz machine.

PubMed

Sountsov, Pavel; Miller, Paul

2015-01-01

An increasing amount of behavioral and neurophysiological data suggests that the brain performs optimal (or near-optimal) probabilistic inference and learning during perception and other tasks. Although many machine learning algorithms exist that perform inference and learning in an optimal way, the complete description of how one of those algorithms (or a novel algorithm) can be implemented in the brain is currently incomplete. There have been many proposed solutions that address how neurons can perform optimal inference but the question of how synaptic plasticity can implement optimal learning is rarely addressed. This paper aims to unify the two fields of probabilistic inference and synaptic plasticity by using a neuronal network of realistic model spiking neurons to implement a well-studied computational model called the Helmholtz Machine. The Helmholtz Machine is amenable to neural implementation as the algorithm it uses to learn its parameters, called the wake-sleep algorithm, uses a local delta learning rule. Our spiking-neuron network implements both the delta rule and a small example of a Helmholtz machine. This neuronal network can learn an internal model of continuous-valued training data sets without supervision. The network can also perform inference on the learned internal models. We show how various biophysical features of the neural implementation constrain the parameters of the wake-sleep algorithm, such as the duration of the wake and sleep phases of learning and the minimal sample duration. We examine the deviations from optimal performance and tie them to the properties of the synaptic plasticity rule.

Spiking neuron network Helmholtz machine

PubMed Central

Sountsov, Pavel; Miller, Paul

2015-01-01

An increasing amount of behavioral and neurophysiological data suggests that the brain performs optimal (or near-optimal) probabilistic inference and learning during perception and other tasks. Although many machine learning algorithms exist that perform inference and learning in an optimal way, the complete description of how one of those algorithms (or a novel algorithm) can be implemented in the brain is currently incomplete. There have been many proposed solutions that address how neurons can perform optimal inference but the question of how synaptic plasticity can implement optimal learning is rarely addressed. This paper aims to unify the two fields of probabilistic inference and synaptic plasticity by using a neuronal network of realistic model spiking neurons to implement a well-studied computational model called the Helmholtz Machine. The Helmholtz Machine is amenable to neural implementation as the algorithm it uses to learn its parameters, called the wake-sleep algorithm, uses a local delta learning rule. Our spiking-neuron network implements both the delta rule and a small example of a Helmholtz machine. This neuronal network can learn an internal model of continuous-valued training data sets without supervision. The network can also perform inference on the learned internal models. We show how various biophysical features of the neural implementation constrain the parameters of the wake-sleep algorithm, such as the duration of the wake and sleep phases of learning and the minimal sample duration. We examine the deviations from optimal performance and tie them to the properties of the synaptic plasticity rule. PMID:25954191

A Learning System for Discriminating Variants of Malicious Network Traffic

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

Beaver, Justin M; Symons, Christopher T; Gillen, Rob

Modern computer network defense systems rely primarily on signature-based intrusion detection tools, which generate alerts when patterns that are pre-determined to be malicious are encountered in network data streams. Signatures are created reactively, and only after in-depth manual analysis of a network intrusion. There is little ability for signature-based detectors to identify intrusions that are new or even variants of an existing attack, and little ability to adapt the detectors to the patterns unique to a network environment. Due to these limitations, the need exists for network intrusion detection techniques that can more comprehensively address both known unknown networkbased attacksmore » and can be optimized for the target environment. This work describes a system that leverages machine learning to provide a network intrusion detection capability that analyzes behaviors in channels of communication between individual computers. Using examples of malicious and non-malicious traffic in the target environment, the system can be trained to discriminate between traffic types. The machine learning provides insight that would be difficult for a human to explicitly code as a signature because it evaluates many interdependent metrics simultaneously. With this approach, zero day detection is possible by focusing on similarity to known traffic types rather than mining for specific bit patterns or conditions. This also reduces the burden on organizations to account for all possible attack variant combinations through signatures. The approach is presented along with results from a third-party evaluation of its performance.« less

A predictive model for turfgrass color and quality evaluation using deep learning and UAV imageries

NASA Astrophysics Data System (ADS)

Phan, Claude; Raheja, Amar; Bhandari, Subodh; Green, Robert L.; Do, Dat

2017-05-01

Millions of Americans come into contact with turfgrass on a daily basis. Often undervalued and seen as visual support stimulus for a larger entity, millions of acres of turfgrass can be found on residential lawns (which also provides an area for recreation), commercial landscape, parks, athletic fields, and golf courses. Besides these uses, turfgrass provides many functional benefits to the environment, such as reducing soil erosion, cooling its surrounding area, and soil carbon sequestration. However, rapidly expanding uses of turfgrass have also raised alarm for natural resources conservation and environmental quality, the largest impact being water consumption. This paper presents a machine learning approach that can assist growers and researchers in determining the overall quality and color rating of turfgrass, thereby assisting in turfgrass management including optimized irrigation water scheduling. Tools from Google and NVIDIA enable models to be trained using deep learning techniques on personal computers or on small form factor processors that can be used aboard small unmanned aerial vehicles (UAVs). The typical evaluation process is a long, laborious process, which is subjective by nature, and thus often exposed to criticism and concern. A computational approach to quality and color assessment will provide faster, accurate, and more consistent ratings, which in turn will help increase irrigation water use efficiency. The overall goal of the ongoing research is to use deep learning techniques and UAV imageries for the turfgrass quality and color assessment and help all the stakeholders to optimize water conservation.

Relationship between learning environment characteristics and academic engagement.

PubMed

Opdenakker, Marie-Christine; Minnaert, Alexander

2011-08-01

The relationship between learning environment characteristics and academic engagement of 777 Grade 6 children located in 41 learning environments was explored. Questionnaires were used to tap learning environment perceptions of children, their academic engagement, and their ethnic-cultural background. The basis of the learning environment questionnaire was the International System for Teacher Observation and Feedback (ISTOF). Factor analysis indicated three factors: the teacher as a helpful and good instructor (having good instructional skills, clear instruction), the teacher as promoter of active learning and differentiation, and the teacher as manager and organizer of classroom activities. Multilevel analysis indicated that about 12% of the differences in engagement between children was related to the learning environment. All the mentioned learning environment characteristics mattered, but the teacher as a helpful, good instructor was most important followed by the teacher as promoter of active learning and differentiation.

Nigerian Physiotherapy Clinical Students' Perception of Their Learning Environment Measured by the Dundee Ready Education Environment Measure Inventory

ERIC Educational Resources Information Center

Odole, Adesola C.; Oyewole, Olufemi O.; Ogunmola, Oluwasolape T.

2014-01-01

The identification of the learning environment and the understanding of how students learn will help teacher to facilitate learning and plan a curriculum to achieve the learning outcomes. The purpose of this study was to investigate undergraduate physiotherapy clinical students' perception of University of Ibadan's learning environment. Using the…

Supporting cognitive engagement in a learning-by-doing learning environment: Case studies of participant engagement and social configurations in Kitchen Science Investigators

NASA Astrophysics Data System (ADS)

Gardner, Christina M.

Learning-by-doing learning environments support a wealth of physical engagement in activities. However, there is also a lot of variability in what participants learn in each enactment of these types of environments. Therefore, it is not always clear how participants are learning in these environments. In order to design technologies to support learning in these environments, we must have a greater understanding of how participants engage in learning activities, their goals for their engagement, and the types of help they need to cognitively engage in learning activities. To gain a greater understanding of participant engagement and factors and circumstances that promote and inhibit engagement, this dissertation explores and answers several questions: What are the types of interactions and experiences that promote and /or inhibit learning and engagement in learning-by-doing learning environments? What are the types of configurations that afford or inhibit these interactions and experiences in learning-by-doing learning environments? I explore answers to these questions through the context of two enactments of Kitchen Science Investigators (KSI), a learning-by-doing learning environment where middle-school aged children learn science through cooking from customizing recipes to their own taste and texture preferences. In small groups, they investigate effects of ingredients through the design of cooking and science experiments, through which they experience and learn about chemical, biological, and physical science phenomena and concepts (Clegg, Gardner, Williams, & Kolodner, 2006). The research reported in this dissertation sheds light on the different ways participant engagement promotes and/or inhibits cognitive engagement in by learning-by-doing learning environments through two case studies. It also provides detailed descriptions of the circumstances (social, material, and physical configurations) that promote and/or inhibit participant engagement in these learning environments through cross-case analyses of these cases. Finally, it offers suggestions about structuring activities, selecting materials and resources, and designing facilitation and software-realized scaffolding in the design of these types of learning environments. These design implications focus on affording participant engagement in science content and practices learning. Overall, the case studies, cross-case analyses, and empirically-based design implications begin to bridge the gap between theory and practice in the design and implementation of these learning environments. This is demonstrated by providing detailed and explanatory examples and factors that affect how participants take up the affordances of the learning opportunities designed into these learning environments.

How People Learn in an Asynchronous Online Learning Environment: The Relationships between Graduate Students' Learning Strategies and Learning Satisfaction

ERIC Educational Resources Information Center

Choi, Beomkyu

2016-01-01

The purpose of this study was to examine the relationships between learners' learning strategies and learning satisfaction in an asynchronous online learning environment. In an attempt to shed some light on how people learn in an online learning environment, one hundred and sixteen graduate students who were taking online learning courses…

Optimal Learning for Efficient Experimentation in Nanotechnology and Biochemistry

DTIC Science & Technology

2015-12-22

AFRL-AFOSR-VA-TR-2016-0018 Optimal Learning for Efficient Experimentation in Nanotechnology , Biochemistry Warren Powell TRUSTEES OF PRINCETON...3. DATES COVERED (From - To) 01-07-2012 to 30-09-2015 4. TITLE AND SUBTITLE Optimal Learning for Efficient Experimentation in Nanotechnology and...in Nanotechnology and Biochemistry Principal Investigators: Warren B. Powell Princeton University Department of Operations Research and

Optimal and Autonomous Control Using Reinforcement Learning: A Survey.

PubMed

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

2018-06-01

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

Web-based newborn screening system for metabolic diseases: machine learning versus clinicians.

PubMed

Chen, Wei-Hsin; Hsieh, Sheau-Ling; Hsu, Kai-Ping; Chen, Han-Ping; Su, Xing-Yu; Tseng, Yi-Ju; Chien, Yin-Hsiu; Hwu, Wuh-Liang; Lai, Feipei

2013-05-23

A hospital information system (HIS) that integrates screening data and interpretation of the data is routinely requested by hospitals and parents. However, the accuracy of disease classification may be low because of the disease characteristics and the analytes used for classification. The objective of this study is to describe a system that enhanced the neonatal screening system of the Newborn Screening Center at the National Taiwan University Hospital. The system was designed and deployed according to a service-oriented architecture (SOA) framework under the Web services .NET environment. The system consists of sample collection, testing, diagnosis, evaluation, treatment, and follow-up services among collaborating hospitals. To improve the accuracy of newborn screening, machine learning and optimal feature selection mechanisms were investigated for screening newborns for inborn errors of metabolism. The framework of the Newborn Screening Hospital Information System (NSHIS) used the embedded Health Level Seven (HL7) standards for data exchanges among heterogeneous platforms integrated by Web services in the C# language. In this study, machine learning classification was used to predict phenylketonuria (PKU), hypermethioninemia, and 3-methylcrotonyl-CoA-carboxylase (3-MCC) deficiency. The classification methods used 347,312 newborn dried blood samples collected at the Center between 2006 and 2011. Of these, 220 newborns had values over the diagnostic cutoffs (positive cases) and 1557 had values that were over the screening cutoffs but did not meet the diagnostic cutoffs (suspected cases). The original 35 analytes and the manifested features were ranked based on F score, then combinations of the top 20 ranked features were selected as input features to support vector machine (SVM) classifiers to obtain optimal feature sets. These feature sets were tested using 5-fold cross-validation and optimal models were generated. The datasets collected in year 2011 were used as predicting cases. The feature selection strategies were implemented and the optimal markers for PKU, hypermethioninemia, and 3-MCC deficiency were obtained. The results of the machine learning approach were compared with the cutoff scheme. The number of the false positive cases were reduced from 21 to 2 for PKU, from 30 to 10 for hypermethioninemia, and 209 to 46 for 3-MCC deficiency. This SOA Web service-based newborn screening system can accelerate screening procedures effectively and efficiently. An SVM learning methodology for PKU, hypermethioninemia, and 3-MCC deficiency metabolic diseases classification, including optimal feature selection strategies, is presented. By adopting the results of this study, the number of suspected cases could be reduced dramatically.

Web-Based Newborn Screening System for Metabolic Diseases: Machine Learning Versus Clinicians

PubMed Central

Chen, Wei-Hsin; Hsu, Kai-Ping; Chen, Han-Ping; Su, Xing-Yu; Tseng, Yi-Ju; Chien, Yin-Hsiu; Hwu, Wuh-Liang; Lai, Feipei

2013-01-01

Background A hospital information system (HIS) that integrates screening data and interpretation of the data is routinely requested by hospitals and parents. However, the accuracy of disease classification may be low because of the disease characteristics and the analytes used for classification. Objective The objective of this study is to describe a system that enhanced the neonatal screening system of the Newborn Screening Center at the National Taiwan University Hospital. The system was designed and deployed according to a service-oriented architecture (SOA) framework under the Web services .NET environment. The system consists of sample collection, testing, diagnosis, evaluation, treatment, and follow-up services among collaborating hospitals. To improve the accuracy of newborn screening, machine learning and optimal feature selection mechanisms were investigated for screening newborns for inborn errors of metabolism. Methods The framework of the Newborn Screening Hospital Information System (NSHIS) used the embedded Health Level Seven (HL7) standards for data exchanges among heterogeneous platforms integrated by Web services in the C# language. In this study, machine learning classification was used to predict phenylketonuria (PKU), hypermethioninemia, and 3-methylcrotonyl-CoA-carboxylase (3-MCC) deficiency. The classification methods used 347,312 newborn dried blood samples collected at the Center between 2006 and 2011. Of these, 220 newborns had values over the diagnostic cutoffs (positive cases) and 1557 had values that were over the screening cutoffs but did not meet the diagnostic cutoffs (suspected cases). The original 35 analytes and the manifested features were ranked based on F score, then combinations of the top 20 ranked features were selected as input features to support vector machine (SVM) classifiers to obtain optimal feature sets. These feature sets were tested using 5-fold cross-validation and optimal models were generated. The datasets collected in year 2011 were used as predicting cases. Results The feature selection strategies were implemented and the optimal markers for PKU, hypermethioninemia, and 3-MCC deficiency were obtained. The results of the machine learning approach were compared with the cutoff scheme. The number of the false positive cases were reduced from 21 to 2 for PKU, from 30 to 10 for hypermethioninemia, and 209 to 46 for 3-MCC deficiency. Conclusions This SOA Web service–based newborn screening system can accelerate screening procedures effectively and efficiently. An SVM learning methodology for PKU, hypermethioninemia, and 3-MCC deficiency metabolic diseases classification, including optimal feature selection strategies, is presented. By adopting the results of this study, the number of suspected cases could be reduced dramatically. PMID:23702487

Fantastic Learning Moments and Where to Find Them

PubMed Central

Sheng, Alexander Y.; Sullivan, Ryan; Kleber, Kara; Mitchell, Patricia M.; Liu, James H.; McGreevy, Jolion; McCabe, Kerry; Atema, Annemieke; Schneider, Jeffrey I.

2018-01-01

Introduction Experiential learning is crucial for the development of all learners. Literature exploring how and where experiential learning happens in the modern clinical learning environment is sparse. We created a novel, web-based educational tool called “Learning Moment” (LM) to foster experiential learning among our learners. We used data captured by LM as a research database to determine where learning experiences were occuring within our emergency department (ED). We hypothesized that these moments would occur more frequently at the physician workstations as opposed to the bedside. Methods We implemented LM at a single ED’s medical student clerkship. The platform captured demographic data including the student’s intended specialty and year of training as well as “learning moments,” defined as logs of learner self-selected learning experiences that included the clinical “pearl,” clinical scenario, and location where the “learning moment” occurred. We presented data using descriptive statistics with frequencies and percentages. Locations of learning experiences were stratified by specialty and training level. Results A total of 323 “learning moments” were logged by 42 registered medical students (29 fourth-year medical students (MS 4) and 13 MS 3 over a six-month period. Over half (52.4%) intended to enter the field of emergency medicine (EM). Of these “learning moments,” 266 included optional location data. The most frequently reported location was patient rooms (135 “learning moments”, 50.8%). Physician workstations hosted the second most frequent “learning moments” (67, 25.2%). EM-bound students reported 43.7% of “learning moments” happening in patient rooms, followed by workstations (32.8%). On the other hand, non EM-bound students reported that 66.3% of “learning moments” occurred in patient rooms and only 8.4% at workstations (p<0.001). Conclusion LM was implemented within our ED as an innovative, web-based tool to fulfill and optimize the experiential learning cycle for our learners. In our environment, patient rooms represented the most frequent location of “learning moments,” followed by physician workstations. EM-bound students were considerably more likely to document “learning moments” occurring at the workstation and less likely in patient rooms than their non EM-bound colleagues. PMID:29383057

Scaffolding in Connectivist Mobile Learning Environment

ERIC Educational Resources Information Center

Ozan, Ozlem

2013-01-01

Social networks and mobile technologies are transforming learning ecology. In this changing learning environment, we find a variety of new learner needs. The aim of this study is to investigate how to provide scaffolding to the learners in connectivist mobile learning environment: (1) to learn in a networked environment; (2) to manage their…

Online Resource-Based Learning Environment: Case Studies in Primary Classrooms

ERIC Educational Resources Information Center

So, Winnie Wing Mui; Ching, Fiona Ngai Ying

2012-01-01

This paper discusses the creation of learning environments with online resources by three primary school teachers for pupil's learning of science-related topics with reference to the resource-based e-learning environments (RBeLEs) framework. Teachers' choice of contexts, resources, tools, and scaffolds in designing the learning environments are…

The Predicaments of Language Learners in Traditional Learning Environments

ERIC Educational Resources Information Center

Shafie, Latisha Asmaak; Mansor, Mahani

2009-01-01

Some public universities in developing countries have traditional language learning environments such as classrooms with only blackboards and furniture which do not provide conducive learning environments. These traditional environments are unable to cater for digital learners who need to learn with learning technologies. In order to create…

The Integration of Personal Learning Environments & Open Network Learning Environments

ERIC Educational Resources Information Center

Tu, Chih-Hsiung; Sujo-Montes, Laura; Yen, Cherng-Jyh; Chan, Junn-Yih; Blocher, Michael

2012-01-01

Learning management systems traditionally provide structures to guide online learners to achieve their learning goals. Web 2.0 technology empowers learners to create, share, and organize their personal learning environments in open network environments; and allows learners to engage in social networking and collaborating activities. Advanced…

Experiential Learning and Learning Environments: The Case of Active Listening Skills

ERIC Educational Resources Information Center

Huerta-Wong, Juan Enrique; Schoech, Richard

2010-01-01

Social work education research frequently has suggested an interaction between teaching techniques and learning environments. However, this interaction has never been tested. This study compared virtual and face-to-face learning environments and included active listening concepts to test whether the effectiveness of learning environments depends…

Proposing an Optimal Learning Architecture for the Digital Enterprise.

ERIC Educational Resources Information Center

O'Driscoll, Tony

2003-01-01

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

Barriers to optimizing investments in the built environment to reduce youth obesity: policy-maker perspectives.

PubMed

Grant, Jill L; MacKay, Kathryn C; Manuel, Patricia M; McHugh, Tara-Leigh F

2010-01-01

To identify factors which limit the ability of local governments to make appropriate investments in the built environment to promote youth health and reduce obesity outcomes in Atlantic Canada. Policy-makers and professionals participated in focus groups to discuss the receptiveness of local governments to introducing health considerations into decision-making. Seven facilitated focus groups involved 44 participants from Atlantic Canada. Thematic discourse analysis of the meeting transcripts identified systemic barriers to creating a built environment that fosters health for youth aged 12-15 years. Participants consistently identified four categories of barriers. Financial barriers limit the capacities of local government to build, maintain and operate appropriate facilities. Legacy issues mean that communities inherit a built environment designed to facilitate car use, with inadequate zoning authority to control fast food outlets, and without the means to determine where schools are built or how they are used. Governance barriers derive from government departments with distinct and competing mandates, with a professional structure that privileges engineering, and with funding programs that encourage competition between municipalities. Cultural factors and values affect outcomes: people have adapted to car-oriented living; poverty reduces options for many families; parental fears limit children's mobility; youth receive limited priority in built environment investments. Participants indicated that health issues have increasing profile within local government, making this an opportune time to discuss strategies for optimizing investments in the built environment. The focus group method can foster mutual learning among professionals within government in ways that could advance health promotion.

The Role of the Constructivist Learning Theory and Collaborative Learning Environment on Wiki Classroom, and the Relationship between Them

ERIC Educational Resources Information Center

Alzahrani, Ibraheem; Woollard, John

2013-01-01

This paper seeks to discover the relationship between both the social constructivist learning theory and the collaborative learning environment. This relationship can be identified by giving an example of the learning environment. Due to wiki characteristics, Wiki technology is one of the most famous learning environments that can show the…

Practice education learning environments: the mismatch between perceived and preferred expectations of undergraduate health science students.

PubMed

Brown, Ted; Williams, Brett; McKenna, Lisa; Palermo, Claire; McCall, Louise; Roller, Louis; Hewitt, Lesley; Molloy, Liz; Baird, Marilyn; Aldabah, Ligal

2011-11-01

Practical hands-on learning opportunities are viewed as a vital component of the education of health science students, but there is a critical shortage of fieldwork placement experiences. It is therefore important that these clinical learning environments are well suited to students' perceptions and expectations. To investigate how undergraduate students enrolled in health-related education programs view their clinical learning environments and specifically to compare students' perception of their 'actual' clinical learning environment to that of their 'preferred/ideal' clinical learning environment. The Clinical Learning Environment Inventory (CLEI) was used to collect data from 548 undergraduate students (55% response rate) enrolled in all year levels of paramedics, midwifery, radiography and medical imaging, occupational therapy, pharmacy, nutrition and dietetics, physiotherapy and social work at Monash University via convenience sampling. Students were asked to rate their perception of the clinical learning environment at the completion of their placements using the CLEI. Satisfaction of the students enrolled in the health-related disciplines was closely linked with the five constructs measured by the CLEI: Personalization, Student Involvement, Task Orientation, Innovation, and Individualization. Significant differences were found between the student's perception of their 'actual' clinical learning environment and their 'ideal' clinical learning environment. The study highlights the importance of a supportive clinical learning environment that places emphasis on effective two-way communication. A thorough understanding of students' perceptions of their clinical learning environments is essential. Copyright © 2010 Elsevier Ltd. All rights reserved.

Motor System Development Depends on Experience: A Microgravity Study of Rats

NASA Technical Reports Server (NTRS)

Walton, Kerry D.; Llinas, Rodolfo R.; Kalb, Robert; Hillman, Dean; DeFelipe, Javier; Garcia-Segura, Luis Miguel

2003-01-01

Animals move about their environment by sensing their surroundings and making adjustments according to need. All animals take the force of gravity into account when the brain and spinal cord undertake the planning and execution of movements. To what extent must animals learn to factor in the force of gravity when making neural calculations about movement? Are animals born knowing how to respond to gravity, or must the young nervous system learn to enter gravity into the equation? To study this issue, young rats were reared in two different gravitational environments (the one-G of Earth and the microgravity of low Earth orbit) that necessitated two different types of motor operations (movements) for optimal behavior. We inquired whether those portions of the young nervous system involved in movement, the motor system, can adapt to different gravitational levels and, if so, the cellular basis for this phenomenon. We studied two groups of rats that had been raised for 16 days in microgravity (eight or 14 days old at launch) and compared their walking and righting (ability to go from upside down to upright) and brain structure to those of control rats that developed on Earth. Flight rats were easily distinguished from the age-matched ground control rats in terms of both motor function and central nervous system structure. Mature surface righting predominated in control rats on the day of landing (R+O), while immature righting predominated in the flight rats on landing day and 30 days after landing. Some of these changes appear to be permanent. Several conclusions can be drawn from these studies: (1) Many aspects of motor behavior are preprogrammed into the young nervous system. In addition, several aspects of motor behavior are acquired as a function of the interaction of the developing organism and the rearing environment; (2) Widespread neuroanatomical differences between one-G- and microgravity-reared rats indicate that there is a structural basis for the adaptation to the rearing environment. These observations provide support for the idea that an animal's motor system adapts for optimal function within the environment experienced during a critical period in early postnatal life.

Concern for Others Leads to Vicarious Optimism

PubMed Central

Kappes, Andreas; Faber, Nadira S.; Kahane, Guy; Savulescu, Julian; Crockett, Molly J.

2018-01-01

An optimistic learning bias leads people to update their beliefs in response to better-than-expected good news but neglect worse-than-expected bad news. Because evidence suggests that this bias arises from self-concern, we hypothesized that a similar bias may affect beliefs about other people’s futures, to the extent that people care about others. Here, we demonstrated the phenomenon of vicarious optimism and showed that it arises from concern for others. Participants predicted the likelihood of unpleasant future events that could happen to either themselves or others. In addition to showing an optimistic learning bias for events affecting themselves, people showed vicarious optimism when learning about events affecting friends and strangers. Vicarious optimism for strangers correlated with generosity toward strangers, and experimentally increasing concern for strangers amplified vicarious optimism for them. These findings suggest that concern for others can bias beliefs about their future welfare and that optimism in learning is not restricted to oneself. PMID:29381448

Concern for Others Leads to Vicarious Optimism.

PubMed

Kappes, Andreas; Faber, Nadira S; Kahane, Guy; Savulescu, Julian; Crockett, Molly J

2018-03-01

An optimistic learning bias leads people to update their beliefs in response to better-than-expected good news but neglect worse-than-expected bad news. Because evidence suggests that this bias arises from self-concern, we hypothesized that a similar bias may affect beliefs about other people's futures, to the extent that people care about others. Here, we demonstrated the phenomenon of vicarious optimism and showed that it arises from concern for others. Participants predicted the likelihood of unpleasant future events that could happen to either themselves or others. In addition to showing an optimistic learning bias for events affecting themselves, people showed vicarious optimism when learning about events affecting friends and strangers. Vicarious optimism for strangers correlated with generosity toward strangers, and experimentally increasing concern for strangers amplified vicarious optimism for them. These findings suggest that concern for others can bias beliefs about their future welfare and that optimism in learning is not restricted to oneself.

Applying a Framework for Student Modeling in Exploratory Learning Environments: Comparing Data Representation Granularity to Handle Environment Complexity

ERIC Educational Resources Information Center

Fratamico, Lauren; Conati, Cristina; Kardan, Samad; Roll, Ido

2017-01-01

Interactive simulations can facilitate inquiry learning. However, similarly to other Exploratory Learning Environments, students may not always learn effectively in these unstructured environments. Thus, providing adaptive support has great potential to help improve student learning with these rich activities. Providing adaptive support requires a…

A Simultaneous Mobile E-Learning Environment and Application

ERIC Educational Resources Information Center

Karal, Hasan; Bahcekapili, Ekrem; Yildiz, Adil

2010-01-01

The purpose of the present study was to design a mobile learning environment that enables the use of a teleconference application used in simultaneous e-learning with mobile devices and to evaluate this mobile learning environment based on students' views. With the mobile learning environment developed in the study, the students are able to follow…

Using Scenarios to Design Complex Technology-Enhanced Learning Environments

ERIC Educational Resources Information Center

de Jong, Ton; Weinberger, Armin; Girault, Isabelle; Kluge, Anders; Lazonder, Ard W.; Pedaste, Margus; Ludvigsen, Sten; Ney, Muriel; Wasson, Barbara; Wichmann, Astrid; Geraedts, Caspar; Giemza, Adam; Hovardas, Tasos; Julien, Rachel; van Joolingen, Wouter R.; Lejeune, Anne; Manoli, Constantinos C.; Matteman, Yuri; Sarapuu, Tago; Verkade, Alex; Vold, Vibeke; Zacharia, Zacharias C.

2012-01-01

Science Created by You (SCY) learning environments are computer-based environments in which students learn about science topics in the context of addressing a socio-scientific problem. Along their way to a solution for this problem students produce many types of intermediate products or learning objects. SCY learning environments center the entire…

Stochastic Averaging for Constrained Optimization With Application to Online Resource Allocation

NASA Astrophysics Data System (ADS)

Chen, Tianyi; Mokhtari, Aryan; Wang, Xin; Ribeiro, Alejandro; Giannakis, Georgios B.

2017-06-01

Existing approaches to resource allocation for nowadays stochastic networks are challenged to meet fast convergence and tolerable delay requirements. The present paper leverages online learning advances to facilitate stochastic resource allocation tasks. By recognizing the central role of Lagrange multipliers, the underlying constrained optimization problem is formulated as a machine learning task involving both training and operational modes, with the goal of learning the sought multipliers in a fast and efficient manner. To this end, an order-optimal offline learning approach is developed first for batch training, and it is then generalized to the online setting with a procedure termed learn-and-adapt. The novel resource allocation protocol permeates benefits of stochastic approximation and statistical learning to obtain low-complexity online updates with learning errors close to the statistical accuracy limits, while still preserving adaptation performance, which in the stochastic network optimization context guarantees queue stability. Analysis and simulated tests demonstrate that the proposed data-driven approach improves the delay and convergence performance of existing resource allocation schemes.

Learning Environments Designed According to Learning Styles and Its Effects on Mathematics Achievement

ERIC Educational Resources Information Center

Özerem, Aysen; Akkoyunlu, Buket

2015-01-01

Problem Statement: While designing a learning environment it is vital to think about learner characteristics (learning styles, approaches, motivation, interests… etc.) in order to promote effective learning. The learning environment and learning process should be designed not to enable students to learn in the same manner and at the same level,…

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

NASA Astrophysics Data System (ADS)

Cao, Jia; Yan, Zheng; He, Guangyu

2016-06-01

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

Web-Based Learning Environment Based on Students’ Needs

NASA Astrophysics Data System (ADS)

Hamzah, N.; Ariffin, A.; Hamid, H.

2017-08-01

Traditional learning needs to be improved since it does not involve active learning among students. Therefore, in the twenty-first century, the development of internet technology in the learning environment has become the main needs of each student. One of the learning environments to meet the needs of the teaching and learning process is a web-based learning environment. This study aims to identify the characteristics of a web-based learning environment that supports students’ learning needs. The study involved 542 students from fifteen faculties in a public higher education institution in Malaysia. A quantitative method was used to collect the data via a questionnaire survey by randomly. The findings indicate that the characteristics of a web-based learning environment that support students’ needs in the process of learning are online discussion forum, lecture notes, assignments, portfolio, and chat. In conclusion, the students overwhelmingly agreed that online discussion forum is the highest requirement because the tool can provide a space for students and teachers to share knowledge and experiences related to teaching and learning.

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

ERIC Educational Resources Information Center

Hamza, Lamia; Tlili, Guiassa Yamina

2018-01-01

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

Accelerating atomic structure search with cluster regularization

NASA Astrophysics Data System (ADS)

Sørensen, K. H.; Jørgensen, M. S.; Bruix, A.; Hammer, B.

2018-06-01

We present a method for accelerating the global structure optimization of atomic compounds. The method is demonstrated to speed up the finding of the anatase TiO2(001)-(1 × 4) surface reconstruction within a density functional tight-binding theory framework using an evolutionary algorithm. As a key element of the method, we use unsupervised machine learning techniques to categorize atoms present in a diverse set of partially disordered surface structures into clusters of atoms having similar local atomic environments. Analysis of more than 1000 different structures shows that the total energy of the structures correlates with the summed distances of the atomic environments to their respective cluster centers in feature space, where the sum runs over all atoms in each structure. Our method is formulated as a gradient based minimization of this summed cluster distance for a given structure and alternates with a standard gradient based energy minimization. While the latter minimization ensures local relaxation within a given energy basin, the former enables escapes from meta-stable basins and hence increases the overall performance of the global optimization.

Distributing vs. Blocking Learning Questions in a Web-Based Learning Environment

ERIC Educational Resources Information Center

Kapp, Felix; Proske, Antje; Narciss, Susanne; Körndle, Hermann

2015-01-01

Effective studying in web-based learning environments (web-LEs) requires cognitive engagement and demands learners to regulate their learning activities. One way to support learners in web-LEs is to provide interactive learning questions within the learning environment. Even though research on learning questions has a long tradition, there are…

Learning with Collaborative Inquiry: A Science Learning Environment for Secondary Students

ERIC Educational Resources Information Center

Sun, Daner; Looi, Chee-Kit; Xie, Wenting

2017-01-01

When inquiry-based learning is designed for a collaborative context, the interactions that arise in the learning environment can become fairly complex. While the learning effectiveness of such learning environments has been reported in the literature, there have been fewer studies on the students' learning processes. To address this, the article…

Learning in a u-Museum: Developing a Context-Aware Ubiquitous Learning Environment

ERIC Educational Resources Information Center

Chen, Chia-Chen; Huang, Tien-Chi

2012-01-01

Context-awareness techniques can support learners in learning without time or location constraints by using mobile devices and associated learning activities in a real learning environment. Enrichment of context-aware technologies has enabled students to learn in an environment that integrates learning resources from both the real world and the…

Optimizing Classroom Instruction through Self-Paced Learning Prototype

ERIC Educational Resources Information Center

Bautista, Romiro G.

2015-01-01

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

Brain mapping in cognitive disorders: a multidisciplinary approach to learning the tools and applications of functional neuroimaging

PubMed Central

Kelley, Daniel J; Johnson, Sterling C

2007-01-01

Background With rapid advances in functional imaging methods, human studies that feature functional neuroimaging techniques are increasing exponentially and have opened a vast arena of new possibilities for understanding brain function and improving the care of patients with cognitive disorders in the clinical setting. There is a growing need for medical centers to offer clinically relevant functional neuroimaging courses that emphasize the multifaceted and multidisciplinary nature of this field. In this paper, we describe the implementation of a functional neuroimaging course focusing on cognitive disorders that might serve as a model for other medical centers. We identify key components of an active learning course design that impact student learning gains in methods and issues pertaining to functional neuroimaging that deserve consideration when optimizing the medical neuroimaging curriculum. Methods Learning gains associated with the course were assessed using polychoric correlation analysis of responses to the SALG (Student Assessment of Learning Gains) instrument. Results Student gains in the functional neuroimaging of cognition as assessed by the SALG instrument were strongly associated with several aspects of the course design. Conclusion Our implementation of a multidisciplinary and active learning functional neuroimaging course produced positive learning outcomes. Inquiry-based learning activities and an online learning environment contributed positively to reported gains. This functional neuroimaging course design may serve as a useful model for other medical centers. PMID:17953758

Assessing the Impact of Student Learning Style Preferences

NASA Astrophysics Data System (ADS)

Davis, Stacey M.; Franklin, Scott V.

2004-09-01

Students express a wide range of preferences for learning environments. We are trying to measure the manifestation of learning styles in various learning environments. In particular, we are interested in performance in an environment that disagrees with the expressed learning style preference, paying close attention to social (group vs. individual) and auditory (those who prefer to learn by listening) environments. These are particularly relevant to activity-based curricula which typically emphasize group-work and de-emphasize lectures. Our methods include multiple-choice assessments, individual student interviews, and a study in which we attempt to isolate the learning environment.

Construction of a Digital Learning Environment Based on Cloud Computing

ERIC Educational Resources Information Center

Ding, Jihong; Xiong, Caiping; Liu, Huazhong

2015-01-01

Constructing the digital learning environment for ubiquitous learning and asynchronous distributed learning has opened up immense amounts of concrete research. However, current digital learning environments do not fully fulfill the expectations on supporting interactive group learning, shared understanding and social construction of knowledge.…

Evidence Accumulation and Change Rate Inference in Dynamic Environments.

PubMed

Radillo, Adrian E; Veliz-Cuba, Alan; Josić, Krešimir; Kilpatrick, Zachary P

2017-06-01

In a constantly changing world, animals must account for environmental volatility when making decisions. To appropriately discount older, irrelevant information, they need to learn the rate at which the environment changes. We develop an ideal observer model capable of inferring the present state of the environment along with its rate of change. Key to this computation is an update of the posterior probability of all possible change point counts. This computation can be challenging, as the number of possibilities grows rapidly with time. However, we show how the computations can be simplified in the continuum limit by a moment closure approximation. The resulting low-dimensional system can be used to infer the environmental state and change rate with accuracy comparable to the ideal observer. The approximate computations can be performed by a neural network model via a rate-correlation-based plasticity rule. We thus show how optimal observers accumulate evidence in changing environments and map this computation to reduced models that perform inference using plausible neural mechanisms.

Students' perception of the learning environment in a distributed medical programme.

PubMed

Veerapen, Kiran; McAleer, Sean

2010-09-24

The learning environment of a medical school has a significant impact on students' achievements and learning outcomes. The importance of equitable learning environments across programme sites is implicit in distributed undergraduate medical programmes being developed and implemented. To study the learning environment and its equity across two classes and three geographically separate sites of a distributed medical programme at the University of British Columbia Medical School that commenced in 2004. The validated Dundee Ready Educational Environment Survey was sent to all students in their 2nd and 3rd year (classes graduating in 2009 and 2008) of the programme. The domains of the learning environment surveyed were: students' perceptions of learning, students' perceptions of teachers, students' academic self-perceptions, students' perceptions of the atmosphere, and students' social self-perceptions. Mean scores, frequency distribution of responses, and inter- and intrasite differences were calculated. The perception of the global learning environment at all sites was more positive than negative. It was characterised by a strongly positive perception of teachers. The work load and emphasis on factual learning were perceived negatively. Intersite differences within domains of the learning environment were more evident in the pioneer class (2008) of the programme. Intersite differences consistent across classes were largely related to on-site support for students. Shared strengths and weaknesses in the learning environment at UBC sites were evident in areas that were managed by the parent institution, such as the attributes of shared faculty and curriculum. A greater divergence in the perception of the learning environment was found in domains dependent on local arrangements and social factors that are less amenable to central regulation. This study underlines the need for ongoing comparative evaluation of the learning environment at the distributed sites and interaction between leaders of these sites.

Personal Learning Environments: A Solution for Self-Directed Learners

ERIC Educational Resources Information Center

Haworth, Ryan

2016-01-01

In this paper I discuss "personal learning environments" and their diverse benefits, uses, and implications for life-long learning. Personal Learning Environments (PLEs) are Web 2.0 and social media technologies that enable individual learners the ability to manage their own learning. Self-directed learning is explored as a foundation…

Ubiquitous Learning Environments in Higher Education: A Scoping Literature Review

ERIC Educational Resources Information Center

Virtanen, Mari Aulikki; Haavisto, Elina; Liikanen, Eeva; Kääriäinen, Maria

2018-01-01

Ubiquitous learning and the use of ubiquitous learning environments heralds a new era in higher education. Ubiquitous learning environments enhance context-aware and seamless learning experiences available from any location at any time. They support smooth interaction between authentic and digital learning resources and provide personalized…

Co-Regulation of Learning in Computer-Supported Collaborative Learning Environments: A Discussion

ERIC Educational Resources Information Center

Chan, Carol K. K.

2012-01-01

This discussion paper for this special issue examines co-regulation of learning in computer-supported collaborative learning (CSCL) environments extending research on self-regulated learning in computer-based environments. The discussion employs a socio-cognitive perspective focusing on social and collective views of learning to examine how…

Quantitative learning strategies based on word networks

NASA Astrophysics Data System (ADS)

Zhao, Yue-Tian-Yi; Jia, Zi-Yang; Tang, Yong; Xiong, Jason Jie; Zhang, Yi-Cheng

2018-02-01

Learning English requires a considerable effort, but the way that vocabulary is introduced in textbooks is not optimized for learning efficiency. With the increasing population of English learners, learning process optimization will have significant impact and improvement towards English learning and teaching. The recent developments of big data analysis and complex network science provide additional opportunities to design and further investigate the strategies in English learning. In this paper, quantitative English learning strategies based on word network and word usage information are proposed. The strategies integrate the words frequency with topological structural information. By analyzing the influence of connected learned words, the learning weights for the unlearned words and dynamically updating of the network are studied and analyzed. The results suggest that quantitative strategies significantly improve learning efficiency while maintaining effectiveness. Especially, the optimized-weight-first strategy and segmented strategies outperform other strategies. The results provide opportunities for researchers and practitioners to reconsider the way of English teaching and designing vocabularies quantitatively by balancing the efficiency and learning costs based on the word network.

Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments.

PubMed

Baldominos, Alejandro; Saez, Yago; Isasi, Pedro

2018-04-23

Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures.

Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments

PubMed Central

2018-01-01

Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures. PMID:29690587

Trajectories of the home learning environment across the first 5 years: associations with children's vocabulary and literacy skills at prekindergarten.

PubMed

Rodriguez, Eileen T; Tamis-LeMonda, Catherine S

2011-01-01

Children's home learning environments were examined in a low-income sample of 1,852 children and families when children were 15, 25, 37, and 63 months. During home visits, children's participation in literacy activities, the quality of mothers' engagements with their children, and the availability of learning materials were assessed, yielding a total learning environment score at each age. At 63 months, children's vocabulary and literacy skills were assessed. Six learning environment trajectories were identified, including environments that were consistently low, environments that were consistently high, and environments characterized by varying patterns of change. The skills of children at the extremes of learning environment trajectories differed by more than 1 SD and the timing of learning experiences related to specific emerging skills. © 2011 The Authors. Child Development © 2011 Society for Research in Child Development, Inc.

Leveraging Trauma Lessons from War to Win in a Complex Global Environment.

PubMed

Remick, Kyle N

2016-01-01

The US military has made great strides in combat casualty care since 2001. As the Army concludes combat operations in Iraq and Afghanistan, it faces new operational challenges in trauma care. The military medical community must stay ahead of the curve through sustaining current investments in combat casualty care research. This article describes lessons learned at war from a Joint Trauma System perspective in order to place in context how we should proceed in order to provide optimal care for our Warfighters in the future.

The Interplay of Perceptions of the Learning Environment, Personality and Learning Strategies: A Study amongst International Business Studies Students

ERIC Educational Resources Information Center

Nijhuis, Jan; Segers, Mien; Gijselaers, Wim

2007-01-01

Previous research on students' learning strategies has examined the relationships between either perceptions of the learning environment or personality and learning strategies. The focus of this study was on the joint relationships between the students' perceptions of the learning environment, their personality, and the learning strategies they…

Fuzzy Adaptive Control for Intelligent Autonomous Space Exploration Problems

NASA Technical Reports Server (NTRS)

Esogbue, Augustine O.

1998-01-01

The principal objective of the research reported here is the re-design, analysis and optimization of our newly developed neural network fuzzy adaptive controller model for complex processes capable of learning fuzzy control rules using process data and improving its control through on-line adaption. The learned improvement is according to a performance objective function that provides evaluative feedback; this performance objective is broadly defined to meet long-range goals over time. Although fuzzy control had proven effective for complex, nonlinear, imprecisely-defined processes for which standard models and controls are either inefficient, impractical or cannot be derived, the state of the art prior to our work showed that procedures for deriving fuzzy control, however, were mostly ad hoc heuristics. The learning ability of neural networks was exploited to systematically derive fuzzy control and permit on-line adaption and in the process optimize control. The operation of neural networks integrates very naturally with fuzzy logic. The neural networks which were designed and tested using simulation software and simulated data, followed by realistic industrial data were reconfigured for application on several platforms as well as for the employment of improved algorithms. The statistical procedures of the learning process were investigated and evaluated with standard statistical procedures (such as ANOVA, graphical analysis of residuals, etc.). The computational advantage of dynamic programming-like methods of optimal control was used to permit on-line fuzzy adaptive control. Tests for the consistency, completeness and interaction of the control rules were applied. Comparisons to other methods and controllers were made so as to identify the major advantages of the resulting controller model. Several specific modifications and extensions were made to the original controller. Additional modifications and explorations have been proposed for further study. Some of these are in progress in our laboratory while others await additional support. All of these enhancements will improve the attractiveness of the controller as an effective tool for the on line control of an array of complex process environments.

CLEW: A Cooperative Learning Environment for the Web.

ERIC Educational Resources Information Center

Ribeiro, Marcelo Blois; Noya, Ricardo Choren; Fuks, Hugo

This paper outlines CLEW (collaborative learning environment for the Web). The project combines MUD (Multi-User Dimension), workflow, VRML (Virtual Reality Modeling Language) and educational concepts like constructivism in a learning environment where students actively participate in the learning process. The MUD shapes the environment structure.…

Evaluating and Implementing Learning Environments: A United Kingdom Experience.

ERIC Educational Resources Information Center

Ingraham, Bruce; Watson, Barbara; McDowell, Liz; Brockett, Adrian; Fitzpatrick, Simon

2002-01-01

Reports on ongoing work at five universities in northeastern England that have been evaluating and implementing online learning environments known as virtual learning environments (VLEs) or managed learning environments (MLEs). Discusses do-it-yourself versus commercial systems; transferability; Web-based versus client-server; integration with…

Precision Oncology beyond Targeted Therapy: Combining Omics Data with Machine Learning Matches the Majority of Cancer Cells to Effective Therapeutics.

PubMed

Ding, Michael Q; Chen, Lujia; Cooper, Gregory F; Young, Jonathan D; Lu, Xinghua

2018-02-01

Precision oncology involves identifying drugs that will effectively treat a tumor and then prescribing an optimal clinical treatment regimen. However, most first-line chemotherapy drugs do not have biomarkers to guide their application. For molecularly targeted drugs, using the genomic status of a drug target as a therapeutic indicator has limitations. In this study, machine learning methods (e.g., deep learning) were used to identify informative features from genome-scale omics data and to train classifiers for predicting the effectiveness of drugs in cancer cell lines. The methodology introduced here can accurately predict the efficacy of drugs, regardless of whether they are molecularly targeted or nonspecific chemotherapy drugs. This approach, on a per-drug basis, can identify sensitive cancer cells with an average sensitivity of 0.82 and specificity of 0.82; on a per-cell line basis, it can identify effective drugs with an average sensitivity of 0.80 and specificity of 0.82. This report describes a data-driven precision medicine approach that is not only generalizable but also optimizes therapeutic efficacy. The framework detailed herein, when successfully translated to clinical environments, could significantly broaden the scope of precision oncology beyond targeted therapies, benefiting an expanded proportion of cancer patients. Mol Cancer Res; 16(2); 269-78. ©2017 AACR . ©2017 American Association for Cancer Research.

Distributed cerebellar plasticity implements generalized multiple-scale memory components in real-robot sensorimotor tasks.

PubMed

Casellato, Claudia; Antonietti, Alberto; Garrido, Jesus A; Ferrigno, Giancarlo; D'Angelo, Egidio; Pedrocchi, Alessandra

2015-01-01

The cerebellum plays a crucial role in motor learning and it acts as a predictive controller. Modeling it and embedding it into sensorimotor tasks allows us to create functional links between plasticity mechanisms, neural circuits and behavioral learning. Moreover, if applied to real-time control of a neurorobot, the cerebellar model has to deal with a real noisy and changing environment, thus showing its robustness and effectiveness in learning. A biologically inspired cerebellar model with distributed plasticity, both at cortical and nuclear sites, has been used. Two cerebellum-mediated paradigms have been designed: an associative Pavlovian task and a vestibulo-ocular reflex, with multiple sessions of acquisition and extinction and with different stimuli and perturbation patterns. The cerebellar controller succeeded to generate conditioned responses and finely tuned eye movement compensation, thus reproducing human-like behaviors. Through a productive plasticity transfer from cortical to nuclear sites, the distributed cerebellar controller showed in both tasks the capability to optimize learning on multiple time-scales, to store motor memory and to effectively adapt to dynamic ranges of stimuli.

Toward the optimization of normalized graph Laplacian.

PubMed

Xie, Bo; Wang, Meng; Tao, Dacheng

2011-04-01

Normalized graph Laplacian has been widely used in many practical machine learning algorithms, e.g., spectral clustering and semisupervised learning. However, all of them use the Euclidean distance to construct the graph Laplacian, which does not necessarily reflect the inherent distribution of the data. In this brief, we propose a method to directly optimize the normalized graph Laplacian by using pairwise constraints. The learned graph is consistent with equivalence and nonequivalence pairwise relationships, and thus it can better represent similarity between samples. Meanwhile, our approach, unlike metric learning, automatically determines the scale factor during the optimization. The learned normalized Laplacian matrix can be directly applied in spectral clustering and semisupervised learning algorithms. Comprehensive experiments demonstrate the effectiveness of the proposed approach.

Group Modeling in Social Learning Environments

ERIC Educational Resources Information Center

Stankov, Slavomir; Glavinic, Vlado; Krpan, Divna

2012-01-01

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

The clinical learning environment in nursing education: a concept analysis.

PubMed

Flott, Elizabeth A; Linden, Lois

2016-03-01

The aim of this study was to report an analysis of the clinical learning environment concept. Nursing students are evaluated in clinical learning environments where skills and knowledge are applied to patient care. These environments affect achievement of learning outcomes, and have an impact on preparation for practice and student satisfaction with the nursing profession. Providing clarity of this concept for nursing education will assist in identifying antecedents, attributes and consequences affecting student transition to practice. The clinical learning environment was investigated using Walker and Avant's concept analysis method. A literature search was conducted using WorldCat, MEDLINE and CINAHL databases using the keywords clinical learning environment, clinical environment and clinical education. Articles reviewed were written in English and published in peer-reviewed journals between 1995-2014. All data were analysed for recurring themes and terms to determine possible antecedents, attributes and consequences of this concept. The clinical learning environment contains four attribute characteristics affecting student learning experiences. These include: (1) the physical space; (2) psychosocial and interaction factors; (3) the organizational culture and (4) teaching and learning components. These attributes often determine achievement of learning outcomes and student self-confidence. With better understanding of attributes comprising the clinical learning environment, nursing education programmes and healthcare agencies can collaborate to create meaningful clinical experiences and enhance student preparation for the professional nurse role. © 2015 John Wiley & Sons Ltd.

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.

Educational environment and approaches to learning of undergraduate nursing students in an Indonesian school of nursing.

PubMed

Rochmawati, Erna; Rahayu, Gandes Retno; Kumara, Amitya

2014-11-01

The aims of this study were to assess students' perceptions of their educational environment and approaches to learning, and determine if perceptions of learning environment associates with approaches to learning. A survey was conducted to collect data from a regional private university in Indonesia. A total of 232 nursing students completed two questionnaires that measured their perceptions of educational environment and approaches to learning. The measurement was based on Dundee Ready Education Environment Measurement (DREEM) and Approaches and Study Skills Inventory for Students (ASSIST). Five learning environments dimensions and three learning approaches dimensions from two measures were measured. The overall score of DREEM was 131.03/200 (SD 17.04), it was in the range considered to be favourable. The overall score is different significantly between years of study (p value = 0.01). This study indicated that the majority of undergraduate nursing students' adopt strategic approach (n = 139. 59.9%). The finding showed that perceived educational environment significantly associated with approaches to learning. This study implicated the need to maintain conducive learning environment. There is also a need to improve the management of learning activities that reflect the use of student-centered learning. Copyright © 2014 Elsevier Ltd. All rights reserved.

Optimal teaching strategy in periodic impulsive knowledge dissemination system.

PubMed

Liu, Dan-Qing; Wu, Zhen-Qiang; Wang, Yu-Xin; Guo, Qiang; Liu, Jian-Guo

2017-01-01

Accurately describing the knowledge dissemination process is significant to enhance the performance of personalized education. In this study, considering the effect of periodic teaching activities on the learning process, we propose a periodic impulsive knowledge dissemination system to regenerate the knowledge dissemination process. Meanwhile, we put forward learning effectiveness which is an outcome of a trade-off between the benefits and costs raised by knowledge dissemination as objective function. Further, we investigate the optimal teaching strategy which can maximize learning effectiveness, to obtain the optimal effect of knowledge dissemination affected by the teaching activities. We solve this dynamic optimization problem by optimal control theory and get the optimization system. At last we numerically solve this system in several practical examples to make the conclusions intuitive and specific. The optimal teaching strategy proposed in this paper can be applied widely in the optimization problem of personal education and beneficial for enhancing the effect of knowledge dissemination.

Optimal teaching strategy in periodic impulsive knowledge dissemination system

PubMed Central

Liu, Dan-Qing; Wu, Zhen-Qiang; Wang, Yu-Xin; Guo, Qiang

2017-01-01

Accurately describing the knowledge dissemination process is significant to enhance the performance of personalized education. In this study, considering the effect of periodic teaching activities on the learning process, we propose a periodic impulsive knowledge dissemination system to regenerate the knowledge dissemination process. Meanwhile, we put forward learning effectiveness which is an outcome of a trade-off between the benefits and costs raised by knowledge dissemination as objective function. Further, we investigate the optimal teaching strategy which can maximize learning effectiveness, to obtain the optimal effect of knowledge dissemination affected by the teaching activities. We solve this dynamic optimization problem by optimal control theory and get the optimization system. At last we numerically solve this system in several practical examples to make the conclusions intuitive and specific. The optimal teaching strategy proposed in this paper can be applied widely in the optimization problem of personal education and beneficial for enhancing the effect of knowledge dissemination. PMID:28665961

Investigating 6th graders' use of a tablet-based app supporting synchronous use of multiple tools designed to promote collaborative knowledge building in science

NASA Astrophysics Data System (ADS)

Sherwood, Carrie-Anne

At this pivotal moment in time, when the proliferation of mobile technologies in our daily lives is influencing the relatively fast integration of these technologies into classrooms, there is little known about the process of student learning, and the role of collaboration, with app-based learning environments on mobile devices. To address this gap, this dissertation, comprised of three manuscripts, investigated three pairs of sixth grade students' synchronous collaborative use of a tablet-based science app called WeInvestigate . The first paper illustrated the methodological decisions necessary to conduct the study of student synchronous and face-to-face collaboration and knowledge building within the complex WeInvestigate and classroom learning environments. The second paper provided the theory of collaboration that guided the design of supports in WeInvestigate, and described its subsequent development. The third paper detailed the interactions between pairs of students as they engaged collaboratively in model construction and explanation tasks using WeInvestigate, hypothesizing connections between these interactions and the designed supports for collaboration. Together, these manuscripts provide encouraging evidence regarding the potential of teaching and learning with WeInvestigate. Findings demonstrated that the students in this study learned science through WeInvestigate , and were supported by the app - particularly the collabrification - to engage in collaborative modeling of phenomena. The findings also highlight the potential of the multiple methods used in this study to understand students' face-to-face and technology-based interactions within the "messy" context of an app-based learning environment and a traditional K-12 classroom. However, as the third manuscript most clearly illustrates, there are still a number of modifications to be made to the WeInvestigate technology before it can be optimally used in classrooms to support students' collaborative science endeavors. The findings presented in this dissertation contribute in theoretical, methodological, and applied ways to the fields of science education, educational technology, and the learning sciences, and point to exciting possibilities for future research on students' collaborations using future iterations of WeInvestigate with more embedded supports; comparative studies of students' use of synchronous collaboration; and studies focused on elucidating the role of the teacher using WeInvestigate - and similar mobile platforms - for teaching and learning.

The Good, the Bad, and the Irrelevant: Neural Mechanisms of Learning Real and Hypothetical Rewards and Effort

PubMed Central

Kolling, Nils; Nelissen, Natalie; Wittmann, Marco K.; Harmer, Catherine J.; Rushworth, Matthew F. S.

2015-01-01

Natural environments are complex, and a single choice can lead to multiple outcomes. Agents should learn which outcomes are due to their choices and therefore relevant for future decisions and which are stochastic in ways common to all choices and therefore irrelevant for future decisions between options. We designed an experiment in which human participants learned the varying reward and effort magnitudes of two options and repeatedly chose between them. The reward associated with a choice was randomly real or hypothetical (i.e., participants only sometimes received the reward magnitude associated with the chosen option). The real/hypothetical nature of the reward on any one trial was, however, irrelevant for learning the longer-term values of the choices, and participants ought to have only focused on the informational content of the outcome and disregarded whether it was a real or hypothetical reward. However, we found that participants showed an irrational choice bias, preferring choices that had previously led, by chance, to a real reward in the last trial. Amygdala and ventromedial prefrontal activity was related to the way in which participants' choices were biased by real reward receipt. By contrast, activity in dorsal anterior cingulate cortex, frontal operculum/anterior insula, and especially lateral anterior prefrontal cortex was related to the degree to which participants resisted this bias and chose effectively in a manner guided by aspects of outcomes that had real and more sustained relationships with particular choices, suppressing irrelevant reward information for more optimal learning and decision making. SIGNIFICANCE STATEMENT In complex natural environments, a single choice can lead to multiple outcomes. Human agents should only learn from outcomes that are due to their choices, not from outcomes without such a relationship. We designed an experiment to measure learning about reward and effort magnitudes in an environment in which other features of the outcome were random and had no relationship with choice. We found that, although people could learn about reward magnitudes, they nevertheless were irrationally biased toward repeating certain choices as a function of the presence or absence of random reward features. Activity in different brain regions in the prefrontal cortex either reflected the bias or reflected resistance to the bias. PMID:26269633

Machine Learning to Discover and Optimize Materials

NASA Astrophysics Data System (ADS)

Rosenbrock, Conrad Waldhar

For centuries, scientists have dreamed of creating materials by design. Rather than discovery by accident, bespoke materials could be tailored to fulfill specific technological needs. Quantum theory and computational methods are essentially equal to the task, and computational power is the new bottleneck. Machine learning has the potential to solve that problem by approximating material behavior at multiple length scales. A full end-to-end solution must allow us to approximate the quantum mechanics, microstructure and engineering tasks well enough to be predictive in the real world. In this dissertation, I present algorithms and methodology to address some of these problems at various length scales. In the realm of enumeration, systems with many degrees of freedom such as high-entropy alloys may contain prohibitively many unique possibilities so that enumerating all of them would exhaust available compute memory. One possible way to address this problem is to know in advance how many possibilities there are so that the user can reduce their search space by restricting the occupation of certain lattice sites. Although tools to calculate this number were available, none performed well for very large systems and none could easily be integrated into low-level languages for use in existing scientific codes. I present an algorithm to solve these problems. Testing the robustness of machine-learned models is an essential component in any materials discovery or optimization application. While it is customary to perform a small number of system-specific tests to validate an approach, this may be insufficient in many cases. In particular, for Cluster Expansion models, the expansion may not converge quickly enough to be useful and reliable. Although the method has been used for decades, a rigorous investigation across many systems to determine when CE "breaks" was still lacking. This dissertation includes this investigation along with heuristics that use only a small training database to predict whether a model is worth pursuing in detail. To be useful, computational materials discovery must lead to experimental validation. However, experiments are difficult due to sample purity, environmental effects and a host of other considerations. In many cases, it is difficult to connect theory to experiment because computation is deterministic. By combining advanced group theory with machine learning, we created a new tool that bridges the gap between experiment and theory so that experimental and computed phase diagrams can be harmonized. Grain boundaries in real materials control many important material properties such as corrosion, thermal conductivity, and creep. Because of their high dimensionality, learning the underlying physics to optimizing grain boundaries is extremely complex. By leveraging a mathematically rigorous representation for local atomic environments, machine learning becomes a powerful tool to approximate properties for grain boundaries. But it also goes beyond predicting properties by highlighting those atomic environments that are most important for influencing the boundary properties. This provides an immense dimensionality reduction that empowers grain boundary scientists to know where to look for deeper physical insights.

Learning with Hypertext Learning Environments: Theory, Design, and Research.

ERIC Educational Resources Information Center

Jacobson, Michael J.; And Others

1996-01-01

Studied 69 undergraduates who used conceptually-indexed hypertext learning environments with differently structured thematic criss-crossing (TCC) treatments: guided and learner selected. Found that students need explicit modeling and scaffolding support to learn complex knowledge from these learning environments, and considers implications for…

A QoS Optimization Approach in Cognitive Body Area Networks for Healthcare Applications.

PubMed

Ahmed, Tauseef; Le Moullec, Yannick

2017-04-06

Wireless body area networks are increasingly featuring cognitive capabilities. This work deals with the emerging concept of cognitive body area networks. In particular, the paper addresses two important issues, namely spectrum sharing and interferences. We propose methods for channel and power allocation. The former builds upon a reinforcement learning mechanism, whereas the latter is based on convex optimization. Furthermore, we also propose a mathematical channel model for off-body communication links in line with the IEEE 802.15.6 standard. Simulation results for a nursing home scenario show that the proposed approach yields the best performance in terms of throughput and QoS for dynamic environments. For example, in a highly demanding scenario our approach can provide throughput up to 7 Mbps, while giving an average of 97.2% of time QoS satisfaction in terms of throughput. Simulation results also show that the power optimization algorithm enables reducing transmission power by approximately 4.5 dBm, thereby sensibly and significantly reducing interference.

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

PubMed

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

2005-06-01

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

Learning automata-based solutions to the nonlinear fractional knapsack problem with applications to optimal resource allocation.

PubMed

Granmo, Ole-Christoffer; Oommen, B John; Myrer, Svein Arild; Olsen, Morten Goodwin

2007-02-01

This paper considers the nonlinear fractional knapsack problem and demonstrates how its solution can be effectively applied to two resource allocation problems dealing with the World Wide Web. The novel solution involves a "team" of deterministic learning automata (LA). The first real-life problem relates to resource allocation in web monitoring so as to "optimize" information discovery when the polling capacity is constrained. The disadvantages of the currently reported solutions are explained in this paper. The second problem concerns allocating limited sampling resources in a "real-time" manner with the purpose of estimating multiple binomial proportions. This is the scenario encountered when the user has to evaluate multiple web sites by accessing a limited number of web pages, and the proportions of interest are the fraction of each web site that is successfully validated by an HTML validator. Using the general LA paradigm to tackle both of the real-life problems, the proposed scheme improves a current solution in an online manner through a series of informed guesses that move toward the optimal solution. At the heart of the scheme, a team of deterministic LA performs a controlled random walk on a discretized solution space. Comprehensive experimental results demonstrate that the discretization resolution determines the precision of the scheme, and that for a given precision, the current solution (to both problems) is consistently improved until a nearly optimal solution is found--even for switching environments. Thus, the scheme, while being novel to the entire field of LA, also efficiently handles a class of resource allocation problems previously not addressed in the literature.

Facilitative and obstructive factors in the clinical learning environment: Experiences of pupil enrolled nurses.

PubMed

Lekalakala-Mokgele, Eucebious; Caka, Ernestine M

2015-03-31

The clinical learning environment is a complex social entity that influences student learning outcomes in the clinical setting. Students can experience the clinical learning environment as being both facilitative and obstructive to their learning. The clinical environment may be a source of stress, creating feelings of fear and anxiety which in turn affect the students' responses to learning. Equally, the environment can enhance learning if experienced positively. This study described pupil enrolled nurses' experiences of facilitative and obstructive factors in military and public health clinical learning settings. Using a qualitative, contextual, exploratory descriptive design, three focus group interviews were conducted until data saturation was reached amongst pupil enrolled nurses in a military School of Nursing. Data analysed provided evidence that acceptance by clinical staff and affordance of self-directed learning facilitated learning. Students felt safe to practise when they were supported by the clinical staff. They felt a sense of belonging when the staff showed an interest in and welcomed them. Learning was obstructed when students were met with condescending comments. Wearing of a military uniform in the public hospital and horizontal violence obstructed learning in the clinical learning environment. Students cannot have effective clinical preparation if the environment is not conducive to and supportive of clinical learning, The study shows that military nursing students experience unique challenges as they are trained in two professions that are hierarchical in nature. The students experienced both facilitating and obstructing factors to their learning during their clinical practice. Clinical staff should be made aware of factors which can impact on students' learning. Policies need to be developed for supporting students in the clinical learning environment.

Patient Safety Learning Systems: A Systematic Review and Qualitative Synthesis.

PubMed

2017-01-01

A patient safety learning system (sometimes called a critical incident reporting system) refers to structured reporting, collation, and analysis of critical incidents. To inform a provincial working group's recommendations for an Ontario Patient Safety Event Learning System, a systematic review was undertaken to determine design features that would optimize its adoption into the health care system and would inform implementation strategies. The objective of this review was to address two research questions: (a) what are the barriers to and facilitators of successful adoption of a patient safety learning system reported by health professionals and (b) what design components maximize successful adoption and implementation? To answer the first question, we used a published systematic review. To answer the second question, we used scoping study methodology. Common barriers reported in the literature by health care professionals included fear of blame, legal penalties, the perception that incident reporting does not improve patient safety, lack of organizational support, inadequate feedback, lack of knowledge about incident reporting systems, and lack of understanding about what constitutes an error. Common facilitators included a non-accusatory environment, the perception that incident reporting improves safety, clarification of the route of reporting and of how the system uses reports, enhanced feedback, role models (such as managers) using and promoting reporting, legislated protection of those who report, ability to report anonymously, education and training opportunities, and clear guidelines on what to report. Components of a patient safety learning system that increased successful adoption and implementation were emphasis on a blame-free culture that encourages reporting and learning, clear guidelines on how and what to report, making sure the system is user-friendly, organizational development support for data analysis to generate meaningful learning outcomes, and multiple mechanisms to provide feedback through routes to reporters and the wider community (local meetings, email alerts, bulletins, paper contributions, etc.). The design of a patient safety learning system can be optimized by an awareness of the barriers to and facilitators of successful adoption and implementation identified by health care professionals. Evaluation of the effectiveness of a patient safety learning system is needed to refine its design.

Automatic plankton image classification combining multiple view features via multiple kernel learning.

PubMed

Zheng, Haiyong; Wang, Ruchen; Yu, Zhibin; Wang, Nan; Gu, Zhaorui; Zheng, Bing

2017-12-28

Plankton, including phytoplankton and zooplankton, are the main source of food for organisms in the ocean and form the base of marine food chain. As the fundamental components of marine ecosystems, plankton is very sensitive to environment changes, and the study of plankton abundance and distribution is crucial, in order to understand environment changes and protect marine ecosystems. This study was carried out to develop an extensive applicable plankton classification system with high accuracy for the increasing number of various imaging devices. Literature shows that most plankton image classification systems were limited to only one specific imaging device and a relatively narrow taxonomic scope. The real practical system for automatic plankton classification is even non-existent and this study is partly to fill this gap. Inspired by the analysis of literature and development of technology, we focused on the requirements of practical application and proposed an automatic system for plankton image classification combining multiple view features via multiple kernel learning (MKL). For one thing, in order to describe the biomorphic characteristics of plankton more completely and comprehensively, we combined general features with robust features, especially by adding features like Inner-Distance Shape Context for morphological representation. For another, we divided all the features into different types from multiple views and feed them to multiple classifiers instead of only one by combining different kernel matrices computed from different types of features optimally via multiple kernel learning. Moreover, we also applied feature selection method to choose the optimal feature subsets from redundant features for satisfying different datasets from different imaging devices. We implemented our proposed classification system on three different datasets across more than 20 categories from phytoplankton to zooplankton. The experimental results validated that our system outperforms state-of-the-art plankton image classification systems in terms of accuracy and robustness. This study demonstrated automatic plankton image classification system combining multiple view features using multiple kernel learning. The results indicated that multiple view features combined by NLMKL using three kernel functions (linear, polynomial and Gaussian kernel functions) can describe and use information of features better so that achieve a higher classification accuracy.

Science Learning Outcomes in Alignment with Learning Environment Preferences

ERIC Educational Resources Information Center

Chang, Chun-Yen; Hsiao, Chien-Hua; Chang, Yueh-Hsia

2011-01-01

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…

Exploring Collaborative Learning Effect in Blended Learning Environments

ERIC Educational Resources Information Center

Sun, Z.; Liu, R.; Luo, L.; Wu, M.; Shi, C.

2017-01-01

The use of new technology encouraged exploration of the effectiveness and difference of collaborative learning in blended learning environments. This study investigated the social interactive network of students, level of knowledge building and perception level on usefulness in online and mobile collaborative learning environments in higher…

Encoder-Decoder Optimization for Brain-Computer Interfaces

PubMed Central

Merel, Josh; Pianto, Donald M.; Cunningham, John P.; Paninski, Liam

2015-01-01

Neuroprosthetic brain-computer interfaces are systems that decode neural activity into useful control signals for effectors, such as a cursor on a computer screen. It has long been recognized that both the user and decoding system can adapt to increase the accuracy of the end effector. Co-adaptation is the process whereby a user learns to control the system in conjunction with the decoder adapting to learn the user's neural patterns. We provide a mathematical framework for co-adaptation and relate co-adaptation to the joint optimization of the user's control scheme ("encoding model") and the decoding algorithm's parameters. When the assumptions of that framework are respected, co-adaptation cannot yield better performance than that obtainable by an optimal initial choice of fixed decoder, coupled with optimal user learning. For a specific case, we provide numerical methods to obtain such an optimized decoder. We demonstrate our approach in a model brain-computer interface system using an online prosthesis simulator, a simple human-in-the-loop pyschophysics setup which provides a non-invasive simulation of the BCI setting. These experiments support two claims: that users can learn encoders matched to fixed, optimal decoders and that, once learned, our approach yields expected performance advantages. PMID:26029919

Encoder-decoder optimization for brain-computer interfaces.

PubMed

Merel, Josh; Pianto, Donald M; Cunningham, John P; Paninski, Liam

2015-06-01

Neuroprosthetic brain-computer interfaces are systems that decode neural activity into useful control signals for effectors, such as a cursor on a computer screen. It has long been recognized that both the user and decoding system can adapt to increase the accuracy of the end effector. Co-adaptation is the process whereby a user learns to control the system in conjunction with the decoder adapting to learn the user's neural patterns. We provide a mathematical framework for co-adaptation and relate co-adaptation to the joint optimization of the user's control scheme ("encoding model") and the decoding algorithm's parameters. When the assumptions of that framework are respected, co-adaptation cannot yield better performance than that obtainable by an optimal initial choice of fixed decoder, coupled with optimal user learning. For a specific case, we provide numerical methods to obtain such an optimized decoder. We demonstrate our approach in a model brain-computer interface system using an online prosthesis simulator, a simple human-in-the-loop pyschophysics setup which provides a non-invasive simulation of the BCI setting. These experiments support two claims: that users can learn encoders matched to fixed, optimal decoders and that, once learned, our approach yields expected performance advantages.

Optimal structure of metaplasticity for adaptive learning

PubMed Central

2017-01-01

Learning from reward feedback in a changing environment requires a high degree of adaptability, yet the precise estimation of reward information demands slow updates. In the framework of estimating reward probability, here we investigated how this tradeoff between adaptability and precision can be mitigated via metaplasticity, i.e. synaptic changes that do not always alter synaptic efficacy. Using the mean-field and Monte Carlo simulations we identified ‘superior’ metaplastic models that can substantially overcome the adaptability-precision tradeoff. These models can achieve both adaptability and precision by forming two separate sets of meta-states: reservoirs and buffers. Synapses in reservoir meta-states do not change their efficacy upon reward feedback, whereas those in buffer meta-states can change their efficacy. Rapid changes in efficacy are limited to synapses occupying buffers, creating a bottleneck that reduces noise without significantly decreasing adaptability. In contrast, more-populated reservoirs can generate a strong signal without manifesting any observable plasticity. By comparing the behavior of our model and a few competing models during a dynamic probability estimation task, we found that superior metaplastic models perform close to optimally for a wider range of model parameters. Finally, we found that metaplastic models are robust to changes in model parameters and that metaplastic transitions are crucial for adaptive learning since replacing them with graded plastic transitions (transitions that change synaptic efficacy) reduces the ability to overcome the adaptability-precision tradeoff. Overall, our results suggest that ubiquitous unreliability of synaptic changes evinces metaplasticity that can provide a robust mechanism for mitigating the tradeoff between adaptability and precision and thus adaptive learning. PMID:28658247

GLOBAL SOLUTIONS TO FOLDED CONCAVE PENALIZED NONCONVEX LEARNING

PubMed Central

Liu, Hongcheng; Yao, Tao; Li, Runze

2015-01-01

This paper is concerned with solving nonconvex learning problems with folded concave penalty. Despite that their global solutions entail desirable statistical properties, there lack optimization techniques that guarantee global optimality in a general setting. In this paper, we show that a class of nonconvex learning problems are equivalent to general quadratic programs. This equivalence facilitates us in developing mixed integer linear programming reformulations, which admit finite algorithms that find a provably global optimal solution. We refer to this reformulation-based technique as the mixed integer programming-based global optimization (MIPGO). To our knowledge, this is the first global optimization scheme with a theoretical guarantee for folded concave penalized nonconvex learning with the SCAD penalty (Fan and Li, 2001) and the MCP penalty (Zhang, 2010). Numerical results indicate a significant outperformance of MIPGO over the state-of-the-art solution scheme, local linear approximation, and other alternative solution techniques in literature in terms of solution quality. PMID:27141126

A Preliminary Investigation of Self-Directed Learning Activities in a Non-Formal Blended Learning Environment

ERIC Educational Resources Information Center

Schwier, Richard A.; Morrison, Dirk; Daniel, Ben K.

2009-01-01

This research considers how professional participants in a non-formal self-directed learning environment (NFSDL) made use of self-directed learning activities in a blended face-to-face and on line learning professional development course. The learning environment for the study was a professional development seminar on teaching in higher education…

Students' Reflections on the Relationships between Safe Learning Environments, Learning Challenge and Positive Experiences of Learning in a Simulated GP Clinic

ERIC Educational Resources Information Center

Young, J. E.; Williamson, M. I.; Egan, T. G.

2016-01-01

Learning environments are a significant determinant of student behaviour, achievement and satisfaction. In this article we use students' reflective essays to identify key features of the learning environment that contributed to positive and transformative learning experiences. We explore the relationships between these features, the students'…

Personal Learning Environments in the Workplace: An Exploratory Study into the Key Business Decision Factors

ERIC Educational Resources Information Center

Chatterjee, Arunangsu; Law, Effie Lai-Chong; Mikroyannidis, Alexander; Owen, Glyn; Velasco, Karen

2013-01-01

Personal Learning Environments (PLEs) have emerged as a solution to the need of learners for open and easily customisable learning environments. PLEs essentially hand complete control over the learning process to the learner. However, this learning model is not fully compatible with learning in the workplace, which is influenced by certain…

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

NASA Astrophysics Data System (ADS)

Li, Jun-Bao; Liu, Jing; Pan, Jeng-Shyang; Yao, Hongxun

2017-06-01

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

Construction of Multimedia Courseware and Web-based E-Learning Courses of "Biomedical Materials".

PubMed

Xiaoying, Lu; Jian, He; Tian, Qin; Dongxu, Jiang; Wei, Chen

2005-01-01

In order to reform the traditional teaching methodology and to improve the teaching effect, we developed new teaching system for course "Biomedical Materials" in our university by the support of the computer technique and Internet. The new teaching system includes the construction of the multimedia courseware and web-based e-learning courses. More than 2000 PowerPoint slides have been designed and optimized and flash movies for several capitals are included. On the basis of this multimedia courseware, a web-based educational environment has been established further, which includes course contents, introduction of the teacher, courseware download, study forum, sitemap of the web, and relative link. The multimedia courseware has been introduced in the class teaching for "Biomedical Materials" for 6 years and a good teaching effect has been obtained. The web-based e-learning courses have been constructed for two years and proved that they are helpful for the students by their preparing and reviewing the teaching contents before and after the class teaching.

From Metacognition to Practice Cognition: The DNP e-Portfolio to Promote Integrated Learning.

PubMed

Anderson, Kelley M; DesLauriers, Patricia; Horvath, Catherine H; Slota, Margaret; Farley, Jean Nelson

2017-08-01

Educating Doctor of Nursing Practice (DNP) students for an increasingly complex health care environment requires novel applications of learning concepts and technology. A deliberate and thoughtful process is required to integrate concepts of the DNP program into practice paradigm changes to subsequently improve students' abilities to innovate solutions to complex practice problems. The authors constructed or participated in electronic portfolio development inspired by theories of metacognition and integrated learning. The objective was to develop DNP student's reflection, integration of concepts, and technological capabilities to foster the deliberative competencies related to the DNP Essentials and the foundations of the DNP program. The pedagogical process demonstrates how e-portfolios adapted into the doctoral-level curriculum for DNP students can address the Essentials and foster the development of metacognitive capabilities, which translates into practice changes. The authors suggest that this pedagogical approach has the potential to optimize reflective and deliberative competencies among DNP students. [J Nurs Educ. 2017;56(8):497-500.]. Copyright 2017, SLACK Incorporated.

An insula-frontostriatal network mediates flexible cognitive control by adaptively predicting changing control demands

PubMed Central

Jiang, Jiefeng; Beck, Jeffrey; Heller, Katherine; Egner, Tobias

2015-01-01

The anterior cingulate and lateral prefrontal cortices have been implicated in implementing context-appropriate attentional control, but the learning mechanisms underlying our ability to flexibly adapt the control settings to changing environments remain poorly understood. Here we show that human adjustments to varying control demands are captured by a reinforcement learner with a flexible, volatility-driven learning rate. Using model-based functional magnetic resonance imaging, we demonstrate that volatility of control demand is estimated by the anterior insula, which in turn optimizes the prediction of forthcoming demand in the caudate nucleus. The caudate's prediction of control demand subsequently guides the implementation of proactive and reactive attentional control in dorsal anterior cingulate and dorsolateral prefrontal cortices. These data enhance our understanding of the neuro-computational mechanisms of adaptive behaviour by connecting the classic cingulate-prefrontal cognitive control network to a subcortical control-learning mechanism that infers future demands by flexibly integrating remote and recent past experiences. PMID:26391305

Argumentative Knowledge Construction in Online Learning Environments in and across Different Cultures: A Collaboration Script Perspective

ERIC Educational Resources Information Center

Weinberger, A.; Clark, D. B.; Haekkinen, P.; Tamura, Y.; Fischer, F.

2007-01-01

In recent years, information and communication technology has established new opportunities to participate in online learning environments around the globe. These opportunities include the dissemination of specific online learning environments as well as opportunities for learners to connect to online learning environments in distant locations.…

Turkish High School Student's Perceptions of Learning Environment in Biology Classrooms and Their Attitudes toward Biology.

ERIC Educational Resources Information Center

Cakiroglu, Jale; Telli, Sibel; Cakiroglu, Erdinc

The purpose of this study was to examine Turkish high school students' perceptions of learning environment in biology classrooms and to investigate relationships between learning environment and students' attitudes toward biology. Secondly, the study aimed to investigate the differences in students' perceptions of learning environments in biology…

Data-driven model reference control of MIMO vertical tank systems with model-free VRFT and Q-Learning.

PubMed

Radac, Mircea-Bogdan; Precup, Radu-Emil; Roman, Raul-Cristian

2018-02-01

This paper proposes a combined Virtual Reference Feedback Tuning-Q-learning model-free control approach, which tunes nonlinear static state feedback controllers to achieve output model reference tracking in an optimal control framework. The novel iterative Batch Fitted Q-learning strategy uses two neural networks to represent the value function (critic) and the controller (actor), and it is referred to as a mixed Virtual Reference Feedback Tuning-Batch Fitted Q-learning approach. Learning convergence of the Q-learning schemes generally depends, among other settings, on the efficient exploration of the state-action space. Handcrafting test signals for efficient exploration is difficult even for input-output stable unknown processes. Virtual Reference Feedback Tuning can ensure an initial stabilizing controller to be learned from few input-output data and it can be next used to collect substantially more input-state data in a controlled mode, in a constrained environment, by compensating the process dynamics. This data is used to learn significantly superior nonlinear state feedback neural networks controllers for model reference tracking, using the proposed Batch Fitted Q-learning iterative tuning strategy, motivating the original combination of the two techniques. The mixed Virtual Reference Feedback Tuning-Batch Fitted Q-learning approach is experimentally validated for water level control of a multi input-multi output nonlinear constrained coupled two-tank system. Discussions on the observed control behavior are offered. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

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

ERIC Educational Resources Information Center

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

2016-01-01

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

Neighboring Optimal Aircraft Guidance in a General Wind Environment

NASA Technical Reports Server (NTRS)

Jardin, Matthew R. (Inventor)

2003-01-01

Method and system for determining an optimal route for an aircraft moving between first and second waypoints in a general wind environment. A selected first wind environment is analyzed for which a nominal solution can be determined. A second wind environment is then incorporated; and a neighboring optimal control (NOC) analysis is performed to estimate an optimal route for the second wind environment. In particular examples with flight distances of 2500 and 6000 nautical miles in the presence of constant or piecewise linearly varying winds, the difference in flight time between a nominal solution and an optimal solution is 3.4 to 5 percent. Constant or variable winds and aircraft speeds can be used. Updated second wind environment information can be provided and used to obtain an updated optimal route.

Q-learning residual analysis: application to the effectiveness of sequences of antipsychotic medications for patients with schizophrenia.

PubMed

Ertefaie, Ashkan; Shortreed, Susan; Chakraborty, Bibhas

2016-06-15

Q-learning is a regression-based approach that uses longitudinal data to construct dynamic treatment regimes, which are sequences of decision rules that use patient information to inform future treatment decisions. An optimal dynamic treatment regime is composed of a sequence of decision rules that indicate how to optimally individualize treatment using the patients' baseline and time-varying characteristics to optimize the final outcome. Constructing optimal dynamic regimes using Q-learning depends heavily on the assumption that regression models at each decision point are correctly specified; yet model checking in the context of Q-learning has been largely overlooked in the current literature. In this article, we show that residual plots obtained from standard Q-learning models may fail to adequately check the quality of the model fit. We present a modified Q-learning procedure that accommodates residual analyses using standard tools. We present simulation studies showing the advantage of the proposed modification over standard Q-learning. We illustrate this new Q-learning approach using data collected from a sequential multiple assignment randomized trial of patients with schizophrenia. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Does academic performance or personal growth share a stronger association with learning environment perception?

PubMed

Colbert-Getz, Jorie M; Tackett, Sean; Wright, Scott M; Shochet, Robert S

2016-08-28

This study was conducted to characterize the relative strength of associations of learning environment perception with academic performance and with personal growth. In 2012-2014 second and third year students at Johns Hopkins University School of Medicine completed a learning environment survey and personal growth scale. Hierarchical linear regression analysis was employed to determine if the proportion of variance in learning environment scores accounted for by personal growth was significantly larger than the proportion accounted for by academic performance (course/clerkship grades). The proportion of variance in learning environment scores accounted for by personal growth was larger than the proportion accounted for by academic performance in year 2 [R(2)Δ of 0.09, F(1,175) = 14.99,  p < .001] and year 3 [R(2)Δ of 0.28, F(1,169) = 76.80, p < .001]. Learning environment scores shared a small amount of variance with academic performance in years 2 and 3.  The amount of variance between learning environment scores and personal growth was small in year 2 and large in year 3. Since supportive learning environments are essential for medical education, future work must determine if enhancing personal growth prior to and during the clerkship year will increase learning environment perception.

Does academic performance or personal growth share a stronger association with learning environment perception?

PubMed Central

Tackett, Sean; Wright, Scott M.; Shochet, Robert S.

2016-01-01

Objectives This study was conducted to characterize the relative strength of associations of learning environment perception with academic performance and with personal growth. Methods In 2012-2014 second and third year students at Johns Hopkins University School of Medicine completed a learning environment survey and personal growth scale. Hierarchical linear regression analysis was employed to determine if the proportion of variance in learning environment scores accounted for by personal growth was significantly larger than the proportion accounted for by academic performance (course/clerkship grades). Results The proportion of variance in learning environment scores accounted for by personal growth was larger than the proportion accounted for by academic performance in year 2 [R2Δ of 0.09, F(1,175) = 14.99,  p < .001] and year 3 [R2Δ of 0.28, F(1,169) = 76.80, p < .001]. Learning environment scores shared a small amount of variance with academic performance in years 2 and 3.  The amount of variance between learning environment scores and personal growth was small in year 2 and large in year 3. Conclusions Since supportive learning environments are essential for medical education, future work must determine if enhancing personal growth prior to and during the clerkship year will increase learning environment perception. PMID:27570912

Developing Learning Theory by Refining Conjectures Embodied in Educational Designs

ERIC Educational Resources Information Center

Sandoval, William A.

2004-01-01

Designed learning environments embody conjectures about learning and instruction, and the empirical study of learning environments allows such conjectures to be refined over time. The construct of embodied conjecture is introduced as a way to demonstrate the theoretical nature of learning environment design and to frame methodological issues in…

Virtual Learning Environment for Interactive Engagement with Advanced Quantum Mechanics

ERIC Educational Resources Information Center

Pedersen, Mads Kock; Skyum, Birk; Heck, Robert; Müller, Romain; Bason, Mark; Lieberoth, Andreas; Sherson, Jacob F.

2016-01-01

A virtual learning environment can engage university students in the learning process in ways that the traditional lectures and lab formats cannot. We present our virtual learning environment "StudentResearcher," which incorporates simulations, multiple-choice quizzes, video lectures, and gamification into a learning path for quantum…

Issues of Learning Games: From Virtual to Real

ERIC Educational Resources Information Center

Carron, Thibault; Pernelle, Philippe; Talbot, Stéphane

2013-01-01

Our research work deals with the development of new learning environments, and we are particularly interested in studying the different aspects linked to users' collaboration in these environments. We believe that Game-based Learning can significantly enhance learning. That is why we have developed learning environments grounded on graphical…

Agent-Based Learning Environments as a Research Tool for Investigating Teaching and Learning.

ERIC Educational Resources Information Center

Baylor, Amy L.

2002-01-01

Discusses intelligent learning environments for computer-based learning, such as agent-based learning environments, and their advantages over human-based instruction. Considers the effects of multiple agents; agents and research design; the use of Multiple Intelligent Mentors Instructing Collaboratively (MIMIC) for instructional design for…

Student-Centred Learning Environments: An Investigation into Student Teachers' Instructional Preferences and Approaches to Learning

ERIC Educational Resources Information Center

Baeten, Marlies; Dochy, Filip; Struyven, Katrien; Parmentier, Emmeline; Vanderbruggen, Anne

2016-01-01

The use of student-centred learning environments in education has increased. This study investigated student teachers' instructional preferences for these learning environments and how these preferences are related to their approaches to learning. Participants were professional Bachelor students in teacher education. Instructional preferences and…

Active Learning Environment with Lenses in Geometric Optics

ERIC Educational Resources Information Center

Tural, Güner

2015-01-01

Geometric optics is one of the difficult topics for students within physics discipline. Students learn better via student-centered active learning environments than the teacher-centered learning environments. So this study aimed to present a guide for middle school teachers to teach lenses in geometric optics via active learning environment…

Practical Applications and Experiences in K-20 Blended Learning Environments

ERIC Educational Resources Information Center

Kyei-Blankson, Lydia, Ed.; Ntuli, Esther, Ed.

2014-01-01

Learning environments continue to change considerably and is no longer confined to the face-to-face classroom setting. As learning options have evolved, educators must adopt a variety of pedagogical strategies and innovative technologies to enable learning. "Practical Applications and Experiences in K-20 Blended Learning Environments"…

Modelling and Optimizing Mathematics Learning in Children

ERIC Educational Resources Information Center

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

2013-01-01

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

Formal and Informal Learning and First-Year Psychology Students’ Development of Scientific Thinking: A Two-Wave Panel Study

PubMed Central

Soyyılmaz, Demet; Griffin, Laura M.; Martín, Miguel H.; Kucharský, Šimon; Peycheva, Ekaterina D.; Vaupotič, Nina; Edelsbrunner, Peter A.

2017-01-01

Scientific thinking is a predicate for scientific inquiry, and thus important to develop early in psychology students as potential future researchers. The present research is aimed at fathoming the contributions of formal and informal learning experiences to psychology students’ development of scientific thinking during their 1st-year of study. We hypothesize that informal experiences are relevant beyond formal experiences. First-year psychology student cohorts from various European countries will be assessed at the beginning and again at the end of the second semester. Assessments of scientific thinking will include scientific reasoning skills, the understanding of basic statistics concepts, and epistemic cognition. Formal learning experiences will include engagement in academic activities which are guided by university authorities. Informal learning experiences will include non-compulsory, self-guided learning experiences. Formal and informal experiences will be assessed with a newly developed survey. As dispositional predictors, students’ need for cognition and self-efficacy in psychological science will be assessed. In a structural equation model, students’ learning experiences and personal dispositions will be examined as predictors of their development of scientific thinking. Commonalities and differences in predictive weights across universities will be tested. The project is aimed at contributing information for designing university environments to optimize the development of students’ scientific thinking. PMID:28239363

Improving Group Work Practices in Teaching Life Sciences: Trialogical Learning

NASA Astrophysics Data System (ADS)

Tammeorg, Priit; Mykkänen, Anna; Rantamäki, Tomi; Lakkala, Minna; Muukkonen, Hanni

2017-08-01

Trialogical learning, a collaborative and iterative knowledge creation process using real-life artefacts or problems, familiarizes students with working life environments and aims to teach skills required in the professional world. We target one of the major limitation factors for optimal trialogical learning in university settings, inefficient group work. We propose a course design combining effective group working practices with trialogical learning principles in life sciences. We assess the usability of our design in (a) a case study on crop science education and (b) a questionnaire for university teachers in life science fields. Our approach was considered useful and supportive of the learning process by all the participants in the case study: the students, the stakeholders and the facilitator. Correspondingly, a group of university teachers expressed that the trialogical approach and the involvement of stakeholders could promote efficient learning. In our case in life sciences, we identified the key issues in facilitating effective group work to be the design of meaningful tasks and the allowance of sufficient time to take action based on formative feedback. Even though trialogical courses can be time consuming, the experience of applying knowledge in real-life cases justifies using the approach, particularly for students just about to enter their professional careers.

Quantum reinforcement learning.

PubMed

Dong, Daoyi; Chen, Chunlin; Li, Hanxiong; Tarn, Tzyh-Jong

2008-10-01

The key approaches for machine learning, particularly learning in unknown probabilistic environments, are new representations and computation mechanisms. In this paper, a novel quantum reinforcement learning (QRL) method is proposed by combining quantum theory and reinforcement learning (RL). Inspired by the state superposition principle and quantum parallelism, a framework of a value-updating algorithm is introduced. The state (action) in traditional RL is identified as the eigen state (eigen action) in QRL. The state (action) set can be represented with a quantum superposition state, and the eigen state (eigen action) can be obtained by randomly observing the simulated quantum state according to the collapse postulate of quantum measurement. The probability of the eigen action is determined by the probability amplitude, which is updated in parallel according to rewards. Some related characteristics of QRL such as convergence, optimality, and balancing between exploration and exploitation are also analyzed, which shows that this approach makes a good tradeoff between exploration and exploitation using the probability amplitude and can speedup learning through the quantum parallelism. To evaluate the performance and practicability of QRL, several simulated experiments are given, and the results demonstrate the effectiveness and superiority of the QRL algorithm for some complex problems. This paper is also an effective exploration on the application of quantum computation to artificial intelligence.

Formal and Informal Learning and First-Year Psychology Students' Development of Scientific Thinking: A Two-Wave Panel Study.

PubMed

Soyyılmaz, Demet; Griffin, Laura M; Martín, Miguel H; Kucharský, Šimon; Peycheva, Ekaterina D; Vaupotič, Nina; Edelsbrunner, Peter A

2017-01-01

Scientific thinking is a predicate for scientific inquiry, and thus important to develop early in psychology students as potential future researchers. The present research is aimed at fathoming the contributions of formal and informal learning experiences to psychology students' development of scientific thinking during their 1st-year of study. We hypothesize that informal experiences are relevant beyond formal experiences. First-year psychology student cohorts from various European countries will be assessed at the beginning and again at the end of the second semester. Assessments of scientific thinking will include scientific reasoning skills, the understanding of basic statistics concepts, and epistemic cognition. Formal learning experiences will include engagement in academic activities which are guided by university authorities. Informal learning experiences will include non-compulsory, self-guided learning experiences. Formal and informal experiences will be assessed with a newly developed survey. As dispositional predictors, students' need for cognition and self-efficacy in psychological science will be assessed. In a structural equation model, students' learning experiences and personal dispositions will be examined as predictors of their development of scientific thinking. Commonalities and differences in predictive weights across universities will be tested. The project is aimed at contributing information for designing university environments to optimize the development of students' scientific thinking.

Intrinsic interactive reinforcement learning - Using error-related potentials for real world human-robot interaction.

PubMed

Kim, Su Kyoung; Kirchner, Elsa Andrea; Stefes, Arne; Kirchner, Frank

2017-12-14

Reinforcement learning (RL) enables robots to learn its optimal behavioral strategy in dynamic environments based on feedback. Explicit human feedback during robot RL is advantageous, since an explicit reward function can be easily adapted. However, it is very demanding and tiresome for a human to continuously and explicitly generate feedback. Therefore, the development of implicit approaches is of high relevance. In this paper, we used an error-related potential (ErrP), an event-related activity in the human electroencephalogram (EEG), as an intrinsically generated implicit feedback (rewards) for RL. Initially we validated our approach with seven subjects in a simulated robot learning scenario. ErrPs were detected online in single trial with a balanced accuracy (bACC) of 91%, which was sufficient to learn to recognize gestures and the correct mapping between human gestures and robot actions in parallel. Finally, we validated our approach in a real robot scenario, in which seven subjects freely chose gestures and the real robot correctly learned the mapping between gestures and actions (ErrP detection (90% bACC)). In this paper, we demonstrated that intrinsically generated EEG-based human feedback in RL can successfully be used to implicitly improve gesture-based robot control during human-robot interaction. We call our approach intrinsic interactive RL.

Learning about and from others' prudence, impatience or laziness: The computational bases of attitude alignment.

PubMed

Devaine, Marie; Daunizeau, Jean

2017-03-01

Peoples' subjective attitude towards costs such as, e.g., risk, delay or effort are key determinants of inter-individual differences in goal-directed behaviour. Thus, the ability to learn about others' prudent, impatient or lazy attitudes is likely to be critical for social interactions. Conversely, how adaptive such attitudes are in a given environment is highly uncertain. Thus, the brain may be tuned to garner information about how such costs ought to be arbitrated. In particular, observing others' attitude may change one's uncertain belief about how to best behave in related difficult decision contexts. In turn, learning from others' attitudes is determined by one's ability to learn about others' attitudes. We first derive, from basic optimality principles, the computational properties of such a learning mechanism. In particular, we predict two apparent cognitive biases that would arise when individuals are learning about others' attitudes: (i) people should overestimate the degree to which they resemble others (false-consensus bias), and (ii) they should align their own attitudes with others' (social influence bias). We show how these two biases non-trivially interact with each other. We then validate these predictions experimentally by profiling people's attitudes both before and after guessing a series of cost-benefit arbitrages performed by calibrated artificial agents (which are impersonating human individuals).

Learning about and from others' prudence, impatience or laziness: The computational bases of attitude alignment

PubMed Central

Devaine, Marie

2017-01-01

Peoples' subjective attitude towards costs such as, e.g., risk, delay or effort are key determinants of inter-individual differences in goal-directed behaviour. Thus, the ability to learn about others' prudent, impatient or lazy attitudes is likely to be critical for social interactions. Conversely, how adaptive such attitudes are in a given environment is highly uncertain. Thus, the brain may be tuned to garner information about how such costs ought to be arbitrated. In particular, observing others' attitude may change one's uncertain belief about how to best behave in related difficult decision contexts. In turn, learning from others' attitudes is determined by one's ability to learn about others' attitudes. We first derive, from basic optimality principles, the computational properties of such a learning mechanism. In particular, we predict two apparent cognitive biases that would arise when individuals are learning about others’ attitudes: (i) people should overestimate the degree to which they resemble others (false-consensus bias), and (ii) they should align their own attitudes with others’ (social influence bias). We show how these two biases non-trivially interact with each other. We then validate these predictions experimentally by profiling people's attitudes both before and after guessing a series of cost-benefit arbitrages performed by calibrated artificial agents (which are impersonating human individuals). PMID:28358869

Students' perception of the learning environment in a distributed medical programme

PubMed Central

Veerapen, Kiran; McAleer, Sean

2010-01-01

Background The learning environment of a medical school has a significant impact on students' achievements and learning outcomes. The importance of equitable learning environments across programme sites is implicit in distributed undergraduate medical programmes being developed and implemented. Purpose To study the learning environment and its equity across two classes and three geographically separate sites of a distributed medical programme at the University of British Columbia Medical School that commenced in 2004. Method The validated Dundee Ready Educational Environment Survey was sent to all students in their 2nd and 3rd year (classes graduating in 2009 and 2008) of the programme. The domains of the learning environment surveyed were: students' perceptions of learning, students' perceptions of teachers, students' academic self-perceptions, students' perceptions of the atmosphere, and students' social self-perceptions. Mean scores, frequency distribution of responses, and inter- and intrasite differences were calculated. Results The perception of the global learning environment at all sites was more positive than negative. It was characterised by a strongly positive perception of teachers. The work load and emphasis on factual learning were perceived negatively. Intersite differences within domains of the learning environment were more evident in the pioneer class (2008) of the programme. Intersite differences consistent across classes were largely related to on-site support for students. Conclusions Shared strengths and weaknesses in the learning environment at UBC sites were evident in areas that were managed by the parent institution, such as the attributes of shared faculty and curriculum. A greater divergence in the perception of the learning environment was found in domains dependent on local arrangements and social factors that are less amenable to central regulation. This study underlines the need for ongoing comparative evaluation of the learning environment at the distributed sites and interaction between leaders of these sites. PMID:20922033

The effects of different learning environments on students' motivation for learning and their achievement.

PubMed

Baeten, Marlies; Dochy, Filip; Struyven, Katrien

2013-09-01

Research in higher education on the effects of student-centred versus lecture-based learning environments generally does not take into account the psychological need support provided in these learning environments. From a self-determination theory perspective, need support is important to study because it has been associated with benefits such as autonomous motivation and achievement. The purpose of the study is to investigate the effects of different learning environments on students' motivation for learning and achievement, while taking into account the perceived need support. First-year student teachers (N= 1,098) studying a child development course completed questionnaires assessing motivation and perceived need support. In addition, a prior knowledge test and case-based assessment were administered. A quasi-experimental pre-test/post-test design was set up consisting of four learning environments: (1) lectures, (2) case-based learning (CBL), (3) alternation of lectures and CBL, and (4) gradual implementation with lectures making way for CBL. Autonomous motivation and achievement were higher in the gradually implemented CBL environment, compared to the CBL environment. Concerning achievement, two additional effects were found; students in the lecture-based learning environment scored higher than students in the CBL environment, and students in the gradually implemented CBL environment scored higher than students in the alternated learning environment. Additionally, perceived need support was positively related to autonomous motivation, and negatively to controlled motivation. The study shows the importance of gradually introducing students to CBL, in terms of their autonomous motivation and achievement. Moreover, the study emphasizes the importance of perceived need support for students' motivation. © 2012 The British Psychological Society.

Proposed learning strategies of medical students in a clinical rotation in obstetrics and gynecology: a descriptive study.

PubMed

Deane, Richard P; Murphy, Deirdre J

2016-01-01

Medical students face many challenges when learning within clinical environments. How students plan to use their time and engage with learning opportunities is therefore critical, as it may be possible to highlight strategies that optimize the learning experience at an early stage in the rotation. The aim of the study was to describe the learning drivers and proposed learning strategies of medical students for a clinical rotation in obstetrics and gynecology. A descriptive study of personal learning plans completed by students at the start of their clinical rotation in obstetrics and gynecology was undertaken. Data relating to students' learning strategies were obtained from the personal learning plans completed by students. Quantitative and qualitative analyses were used. The desire to obtain a good examination result was the most significant reason why the rotation was important to students (n=67/71, 94%). Students struggled to create a specific and practical learning outcome relevant to their career interest. Target scores of students were significantly higher than their reported typical scores (P<0.01). Textbooks were rated as likely to be the most helpful learning resource during the rotation. Bedside tutorials were rated as likely to be the most useful learning activity and small group learning activities were rated as likely to be more useful than lectures. Most students intended to study the course material linked to their clinical program rather than the classroom-based tutorial program. The main learning driver for medical students was academic achievement, and the proposed learning strategy favored by medical students was linking their study plans to clinical activities. Medical educators should consider strategies that foster more intrinsic drivers of student learning and more student-oriented learning resources and activities.

Motor Priming in Neurorehabilitation

PubMed Central

Stoykov, Mary Ellen; Madhavan, Sangeetha

2014-01-01

Priming is a type of implicit learning wherein a stimulus prompts a change in behavior. Priming has been long studied in the field of psychology. More recently, rehabilitation researchers have studied motor priming as a possible way to facilitate motor learning. For example, priming of the motor cortex is associated with changes in neuroplasticity that are associated with improvements in motor performance. Of the numerous motor priming paradigms under investigation, only a few are practical for the current clinical environment, and the optimal priming modalities for specific clinical presentations are not known. Accordingly, developing an understanding of the various types of motor priming paradigms and their underlying neural mechanisms is an important step for therapists in neurorehabilitation. Most importantly, an understanding of the methods and their underlying mechanisms is essential for optimizing rehabilitation outcomes. The future of neurorehabilitation is likely to include these priming methods, which are delivered prior to or in conjunction with primary neurorehabilitation therapies. In this Special Interest article we discuss those priming paradigms that are supported by the greatest amount of evidence including: (i) stimulation-based priming, (ii) motor imagery and action observation, (iii) sensory priming, (iv) movement-based priming, and (v) pharmacological priming. PMID:25415551

Understanding teacher responses to constructivist learning environments: Challenges and resolutions

NASA Astrophysics Data System (ADS)

Rosenfeld, Melodie; Rosenfeld, Sherman

2006-05-01

The research literature is just beginning to uncover factors involved in sustaining constructivist learning environments, such as Project-Based Learning (PBL). Our case study investigates teacher responses to the challenges of constructivist environments, since teachers can play strong roles in supporting or undermining even the best constructivist environments or materials. We were invited to work as mediators with a middle-school science staff that was experiencing conflicts regarding two learning environments, PBL (which was the school's politically correc learning environment) and traditional. With mediated group workshops, teachers were sensitized to their own and colleagues' individual learning differences (ILDs), as measured by two styles inventories (the LSI - Kolb, 1976; and the LCI - Johnston & Dainton, 1997). Using these inventories, a learning-environment questionnaire, field notes, and delayed interviews a year later, we found that there was a relationship between teachers' preferred styles, epistemological beliefs, and their preferred teaching environment. Moreover, when the participating teachers, including early-adopters and nonvolunteers to PBL, became more sensitive to their colleagues' preferences, many staff conflicts were resolved and some mismatched teachers expressed more openness to PBL. We argue that having teachers understand their own ILDs and related responses to constructivist learning environments can contribute to resolving staff conflicts and sustaining such environments. We present a cognitive model and a strategy which illustrate this argument.

Correctional nursing: a study protocol to develop an educational intervention to optimize nursing practice in a unique context.

PubMed

Almost, Joan; Gifford, Wendy A; Doran, Diane; Ogilvie, Linda; Miller, Crystal; Rose, Don N; Squires, Mae

2013-06-21

Nurses are the primary healthcare providers in correctional facilities. A solid knowledge and expertise that includes the use of research evidence in clinical decision making is needed to optimize nursing practice and promote positive health outcomes within these settings. The institutional emphasis on custodial care within a heavily secured, regulated, and punitive environment presents unique contextual challenges for nursing practice. Subsequently, correctional nurses are not always able to obtain training or ongoing education that is required for broad scopes of practice. The purpose of the proposed study is to develop an educational intervention for correctional nurses to support the provision of evidence-informed care. A two-phase mixed methods research design will be used. The setting will be three provincial correctional facilities. Phase one will focus on identifying nurses' scope of practice and practice needs, describing work environment characteristics that support evidence-informed practice and developing the intervention. Semi-structured interviews will be completed with nurses and nurse managers. To facilitate priorities for the intervention, a Delphi process will be used to rank the learning needs identified by participants. Based on findings, an online intervention will be developed. Phase two will involve evaluating the acceptability and feasibility of the intervention to inform a future experimental design. The context of provincial correctional facilities presents unique challenges for nurses' provision of care. This study will generate information to address practice and learning needs specific to correctional nurses. Interventions tailored to barriers and supports within specific contexts are important to enable nurses to provide evidence-informed care.

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

PubMed Central

Hayashibe, Mitsuhiro; Shimoda, Shingo

2014-01-01

A human motor system can improve its behavior toward optimal movement. The skeletal system has more degrees of freedom than the task dimensions, which incurs an ill-posed problem. The multijoint system involves complex interaction torques between joints. To produce optimal motion in terms of energy consumption, the so-called cost function based optimization has been commonly used in previous works.Even if it is a fact that an optimal motor pattern is employed phenomenologically, there is no evidence that shows the existence of a physiological process that is similar to such a mathematical optimization in our central nervous system.In this study, we aim to find a more primitive computational mechanism with a modular configuration to realize adaptability and optimality without prior knowledge of system dynamics.We propose a novel motor control paradigm based on tacit learning with task space feedback. The motor command accumulation during repetitive environmental interactions, play a major role in the learning process. It is applied to a vertical cyclic reaching which involves complex interaction torques.We evaluated whether the proposed paradigm can learn how to optimize solutions with a 3-joint, planar biomechanical model. The results demonstrate that the proposed method was valid for acquiring motor synergy and resulted in energy efficient solutions for different load conditions. The case in feedback control is largely affected by the interaction torques. In contrast, the trajectory is corrected over time with tacit learning toward optimal solutions.Energy efficient solutions were obtained by the emergence of motor synergy. During learning, the contribution from feedforward controller is augmented and the one from the feedback controller is significantly minimized down to 12% for no load at hand, 16% for a 0.5 kg load condition.The proposed paradigm could provide an optimization process in redundant system with dynamic-model-free and cost-function-free approach. PMID:24616695

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

PubMed

Hayashibe, Mitsuhiro; Shimoda, Shingo

2014-01-01

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

Use of Heuristics to Facilitate Scientific Discovery Learning in a Simulation Learning Environment in a Physics Domain

ERIC Educational Resources Information Center

Veermans, Koen; van Joolingen, Wouter; de Jong, Ton

2006-01-01

This article describes a study into the role of heuristic support in facilitating discovery learning through simulation-based learning. The study compares the use of two such learning environments in the physics domain of collisions. In one learning environment (implicit heuristics) heuristics are only used to provide the learner with guidance…

The Impacts of Network Centrality and Self-Regulation on an E-Learning Environment with the Support of Social Network Awareness

ERIC Educational Resources Information Center

Lin, Jian-Wei; Huang, Hsieh-Hong; Chuang, Yuh-Shy

2015-01-01

An e-learning environment that supports social network awareness (SNA) is a highly effective means of increasing peer interaction and assisting student learning by raising awareness of social and learning contexts of peers. Network centrality profoundly impacts student learning in an SNA-related e-learning environment. Additionally,…

Students' Conception of Learning Environment and Their Approach to Learning and Its Implication on Quality Education

ERIC Educational Resources Information Center

Belaineh, Matheas Shemelis

2017-01-01

Quality of education in higher institutions can be affected by different factors. It partly rests on the learning environment created by teachers and the learning approach students are employing during their learning. The main purpose of this study is to examine the learning environment at Mizan Tepi University from students' perspective and their…

Review of Opinions of Math Teachers Concerning the Learning Environment That They Design

ERIC Educational Resources Information Center

Aydin, Bünyamin; Yavuz, Ayse

2016-01-01

Design of appropriate learning environment has a significant importance in creation of aims of the math teaching. In the design of learning environments, teachers play a significant role. The aim of this study is determination of opinions of the math teachers concerning the learning environment that they design. In accordance with this aim, an…

An Effect of the Learning Environment Management System toward Student Quality of Thai Secondary School

ERIC Educational Resources Information Center

Wirussawa, Seatuch; Tesaputa, Kowat; Duangpaeng, Amporn

2016-01-01

This study aimed at 1) investigating the element of the learning environment management system in the secondary schools, 2) exploring the current states and problems of the system on the learning environment management in the secondary schools, 3) designing the learning environment management system for the secondary schools, and 4) identifying…

Authoring Adaptive 3D Virtual Learning Environments

ERIC Educational Resources Information Center

Ewais, Ahmed; De Troyer, Olga

2014-01-01

The use of 3D and Virtual Reality is gaining interest in the context of academic discussions on E-learning technologies. However, the use of 3D for learning environments also has drawbacks. One way to overcome these drawbacks is by having an adaptive learning environment, i.e., an environment that dynamically adapts to the learner and the…

Assessing the Quality of Learning Environments in Swedish Schools: Development and Analysis of a Theory-Based Instrument

ERIC Educational Resources Information Center

Westling Allodi, Mara

2007-01-01

The Goals, Attitudes and Values in School (GAVIS) questionnaire was developed on the basis of theoretical frameworks concerning learning environments, universal human values and studies of students' experience of learning environments. The theory hypothesises that learning environments can be described and structured in a circumplex model using…

Students' Perceptions of Computer-Based Learning Environments, Their Attitude towards Business Statistics, and Their Academic Achievement: Implications from a UK University

ERIC Educational Resources Information Center

Nguyen, ThuyUyen H.; Charity, Ian; Robson, Andrew

2016-01-01

This study investigates students' perceptions of computer-based learning environments, their attitude towards business statistics, and their academic achievement in higher education. Guided by learning environments concepts and attitudinal theory, a theoretical model was proposed with two instruments, one for measuring the learning environment and…

Blackboard as an Online Learning Environment: What Do Teacher Education Students and Staff Think?

ERIC Educational Resources Information Center

Heirdsfield, Ann; Walker, Susan; Tambyah, Mallihai; Beutel, Denise

2011-01-01

As online learning environments now have an established presence in higher education we need to ask the question: How effective are these environments for student learning? Online environments can provide a different type of learning experience than traditional face-to-face contexts (for on-campus students) or print-based materials (for distance…

Effects of the Physical Environment on Cognitive Load and Learning: Towards a New Model of Cognitive Load

ERIC Educational Resources Information Center

Choi, Hwan-Hee; van Merriënboer, Jeroen J. G.; Paas, Fred

2014-01-01

Although the theoretical framework of cognitive load theory has acknowledged a role for the learning environment, the specific characteristics of the physical learning environment that could affect cognitive load have never been considered, neither theoretically nor empirically. In this article, we argue that the physical learning environment, and…

Context Aware Ubiquitous Learning Environments for Peer-to-Peer Collaborative Learning

ERIC Educational Resources Information Center

Yang, Stephen J. H.

2006-01-01

A ubiquitous learning environment provides an interoperable, pervasive, and seamless learning architecture to connect, integrate, and share three major dimensions of learning resources: learning collaborators, learning contents, and learning services. Ubiquitous learning is characterized by providing intuitive ways for identifying right learning…

Smile: Student Modification in Learning Environments. Establishing Congruence between Actual and Preferred Classroom Learning Environment.

ERIC Educational Resources Information Center

Yarrow, Allan; Millwater, Jan

1995-01-01

This study investigated whether classroom psychosocial environment, as perceived by student teachers, could be improved to their preferred level. Students completed the College and University Classroom Environment Inventory, discussed interventions, then completed it again. Significant deficiencies surfaced in the learning environment early in the…

Integrating Model-Driven and Data-Driven Techniques for Analyzing Learning Behaviors in Open-Ended Learning Environments

ERIC Educational Resources Information Center

Kinnebrew, John S.; Segedy, James R.; Biswas, Gautam

2017-01-01

Research in computer-based learning environments has long recognized the vital role of adaptivity in promoting effective, individualized learning among students. Adaptive scaffolding capabilities are particularly important in open-ended learning environments, which provide students with opportunities for solving authentic and complex problems, and…

A Comparison of Participation Patterns in Selected Formal, Non-Formal, and Informal Online Learning Environments

ERIC Educational Resources Information Center

Schwier, Richard A.; Seaton, J. X.

2013-01-01

Does learner participation vary depending on the learning context? Are there characteristic features of participation evident in formal, non-formal, and informal online learning environments? Six online learning environments were chosen as epitomes of formal, non-formal, and informal learning contexts and compared. Transcripts of online…

Student-Teachers' Approaches to Learning, Academic Performance and Teaching Efficacy

ERIC Educational Resources Information Center

Swee-Choo, Pauline Goh; Kung-Teck, Wong; Osman, Rosma

2012-01-01

Purpose: It is argued that the approaches to learning of students undergoing teacher training are likely to be related to their teaching and learning environment, especially as they move from a more regimented, structured learning environment in school to a tertiary learning environment that encourages more independent thinking and perhaps…

Visits to Cultural Learning Places in the Early Childhood

ERIC Educational Resources Information Center

Mudiappa, Michael; Kluczniok, Katharina

2015-01-01

Studies show the important role of the home learning environment in early childhood for later school success. This article focuses on a particular aspect of the home learning environment: visits to cultural learning places (e.g. museums) as a component of the quality of the home learning environment. Therefore the educational concept of…

Investigating Learning through Work: Learning Environment Scale & User Guide to the Provider. Support Document

ERIC Educational Resources Information Center

Hawke, Geof; Chappell, Clive

2008-01-01

This Support Document was produced by the authors based on their research for the report, "Investigating Learning through Work: The Development of the 'Provider Learning Environment Scale'" (ED503392). It provides readers with a complete copy of the "Provider Learning Environment Scale" (version 2.0); and an accompanying user…

Assessing and Monitoring Student Progress in an E-Learning Personnel Preparation Environment.

ERIC Educational Resources Information Center

Meyen, Edward L.; Aust, Ronald J.; Bui, Yvonne N.; Isaacson, Robert

2002-01-01

Discussion of e-learning in special education personnel preparation focuses on student assessment in e-learning environments. It includes a review of the literature, lessons learned by the authors from assessing student performance in e-learning environments, a literature perspective on electronic portfolios in monitoring student progress, and the…

A Context-Adaptive Teacher Training Model in a Ubiquitous Learning Environment

ERIC Educational Resources Information Center

Chen, Min; Chiang, Feng Kuang; Jiang, Ya Na; Yu, Sheng Quan

2017-01-01

In view of the discrepancies in teacher training and teaching practice, this paper put forward a context-adaptive teacher training model in a ubiquitous learning (u-learning) environment. The innovative model provides teachers of different subjects with adaptive and personalized learning content in a u-learning environment, implements intra- and…

The importance of knowledge-based technology.

PubMed

Cipriano, Pamela F

2012-01-01

Nurse executives are responsible for a workforce that can provide safer and more efficient care in a complex sociotechnical environment. National quality priorities rely on technologies to provide data collection, share information, and leverage analytic capabilities to interpret findings and inform approaches to care that will achieve better outcomes. As a key steward for quality, the nurse executive exercises leadership to provide the infrastructure to build and manage nursing knowledge and instill accountability for following evidence-based practices. These actions contribute to a learning health system where new knowledge is captured as a by-product of care delivery enabled by knowledge-based electronic systems. The learning health system also relies on rigorous scientific evidence embedded into practice at the point of care. The nurse executive optimizes use of knowledge-based technologies, integrated throughout the organization, that have the capacity to help transform health care.

Developing a grounded theory for interprofessional collaboration acquisition using facilitator and actor perspectives in simulated wilderness medical emergencies.

PubMed

Smith, Heather A; Reade, Maurianne; Marr, Marion; Jeeves, Nicholas

2017-01-01

Interprofessional collaboration is a complex process that has the potential to transform patient care for the better in urban, rural and remote healthcare settings. Simulation has been found to improve participants' interprofessional competencies, but the mechanisms by which interprofessionalism is learned have yet to be understood. A rural wilderness medicine conference (WildER Med) in northern Ontario, Canada with simulated medical scenarios has been demonstrated to be effective in improving participants' collaboration without formal interprofessional education (IPE) curriculum. Interprofessionalism may be taught through rural and remote medical simulation, as done in WildER Med where participants' interprofessional competencies improved without any formal IPE curriculum. This learning may be attributed to the informal and hidden curriculum. Understanding the mechanism by which this rural educational experience contributed to participants' learning to collaborate requires insight into the events before, during and after the simulations. The authors drew upon feedback from facilitators and patient actors in one-on-one interviews to develop a grounded theory for how collaboration is taught and learned. Sharing emerged as the core concept of a grounded theory to explain how team members acquired interprofessional collaboration competencies. Sharing was enacted through the strategies of developing common goals, sharing leadership, and developing mutual respect and understanding. Further analysis of the data and literature suggests that the social wilderness environment was foundational in enabling sharing to occur. Medical simulations in other rural and remote settings may offer an environment conducive to collaboration and be effective in teaching collaboration. When designing interprofessional education, health educators should consider using emergency response teams or rural community health teams to optimize the informal and hidden curriculum contributing to interprofessional learning.

Medical Student Perceptions of the Learning Environment in Medical School Change as Students Transition to Clinical Training in Undergraduate Medical School.

PubMed

Dunham, Lisette; Dekhtyar, Michael; Gruener, Gregory; CichoskiKelly, Eileen; Deitz, Jennifer; Elliott, Donna; Stuber, Margaret L; Skochelak, Susan E

2017-01-01

Phenomenon: The learning environment is the physical, social, and psychological context in which a student learns. A supportive learning environment contributes to student well-being and enhances student empathy, professionalism, and academic success, whereas an unsupportive learning environment may lead to burnout, exhaustion, and cynicism. Student perceptions of the medical school learning environment may change over time and be associated with students' year of training and may differ significantly depending on the student's gender or race/ethnicity. Understanding the changes in perceptions of the learning environment related to student characteristics and year of training could inform interventions that facilitate positive experiences in undergraduate medical education. The Medical School Learning Environment Survey (MSLES) was administered to 4,262 students who matriculated at one of 23 U.S. and Canadian medical schools in 2010 and 2011. Students completed the survey at the end of each year of medical school as part of a battery of surveys in the Learning Environment Study. A mixed-effects longitudinal model, t tests, Cohen's d effect size, and analysis of variance assessed the relationship between MSLES score, year of training, and demographic variables. After controlling for gender, race/ethnicity, and school, students reported worsening perceptions toward the medical school learning environment, with the worst perceptions in the 3rd year of medical school as students begin their clinical experiences, and some recovery in the 4th year after Match Day. The drop in MSLES scores associated with the transition to the clinical learning environment (-0.26 point drop in addition to yearly change, effect size = 0.52, p < .0001) is more than 3 times greater than the drop between the 1st and 2nd year (0.07 points, effect size = 0.14, p < .0001). The largest declines were from items related to work-life balance and informal student relationships. There was some, but not complete, recovery in perceptions of the medical school learning environment in the 4th year. Insights: Perceptions of the medical school learning environment worsen as students continue through medical school, with a stronger decline in perception scores as students' transition to the clinical learning environment. Students reported the greatest drop in finding time for outside activities and students helping one another in the 3rd year. Perceptions differed based on gender and race/ethnicity. Future studies should investigate the specific features of medical schools that contribute most significantly to student perceptions of the medical school learning environment, both positive and negative, to pinpoint potential interventions and improvements.

The Use of Deep and Surface Learning Strategies among Students Learning English as a Foreign Language in an Internet Environment

ERIC Educational Resources Information Center

Aharony, Noa

2006-01-01

Background: The learning context is learning English in an Internet environment. The examination of this learning process was based on the Biggs and Moore's teaching-learning model (Biggs & Moore, 1993). Aim: The research aims to explore the use of the deep and surface strategies in an Internet environment among EFL students who come from…

Virtual Workshop Environment (VWE): A Taxonomy and Service Oriented Architecture (SOA) Framework for Modularized Virtual Learning Environments (VLE)--Applying the Learning Object Concept to the VLE

ERIC Educational Resources Information Center

Paulsson, Fredrik; Naeve, Ambjorn

2006-01-01

Based on existing Learning Object taxonomies, this article suggests an alternative Learning Object taxonomy, combined with a general Service Oriented Architecture (SOA) framework, aiming to transfer the modularized concept of Learning Objects to modularized Virtual Learning Environments. The taxonomy and SOA-framework exposes a need for a clearer…

Pre-registration student nurses perception of the hospital-learning environment during clinical placements.

PubMed

Midgley, Kirsten

2006-05-01

If we subscribe to the notion that nursing is an action profession, that nurses learn by doing [Neary, M., 2000. Responsive assessment: assessing student nurses' clinical competence. Nurse Education Today 21, 3-17], then the mastery of fundamental clinical skills must be a key component of courses leading to registration. The last two decades have seen widespread changes to nurse education but the clinical field remains an invaluable resource in preparing students for the reality of their professional role supporting the integration of theory and practice and linking the 'knowing what' with the 'knowing how'. The clinical-learning environment represents an essential element of nurse education that needs to be measurable and warrants further investigation. This exploratory cohort study (n = 67) examined pre-registration student nurses' perception of the hospital-learning environment during clinical placements together with the key characteristics of the students' preferred learning environment utilising an established tool, the clinical-learning environment inventory (CLEI) tool [Chan, D., 2001a. Development of an innovative tool to assess hospital-learning environments. Nurse Education Today 21, 624-631; Chan, D., 2001b. Combining qualitative and quantitative methods in assessing hospital-learning environments. International Journal of Nursing Studies 3, 447-459]. The results demonstrated that in comparison with the actual hospital environment, students would prefer an environment with higher levels of individualisation, innovation in teaching and learning strategies, student involvement, personalisation and task orientation.

Integrated Method for Personal Thermal Comfort Assessment and Optimization through Users' Feedback, IoT and Machine Learning: A Case Study †.

PubMed

Salamone, Francesco; Belussi, Lorenzo; Currò, Cristian; Danza, Ludovico; Ghellere, Matteo; Guazzi, Giulia; Lenzi, Bruno; Megale, Valentino; Meroni, Italo

2018-05-17

Thermal comfort has become a topic issue in building performance assessment as well as energy efficiency. Three methods are mainly recognized for its assessment. Two of them based on standardized methodologies, face the problem by considering the indoor environment in steady-state conditions (PMV and PPD) and users as active subjects whose thermal perception is influenced by outdoor climatic conditions (adaptive approach). The latter method is the starting point to investigate thermal comfort from an overall perspective by considering endogenous variables besides the traditional physical and environmental ones. Following this perspective, the paper describes the results of an in-field investigation of thermal conditions through the use of nearable and wearable solutions, parametric models and machine learning techniques. The aim of the research is the exploration of the reliability of IoT-based solutions combined with advanced algorithms, in order to create a replicable framework for the assessment and improvement of user thermal satisfaction. For this purpose, an experimental test in real offices was carried out involving eight workers. Parametric models are applied for the assessment of thermal comfort; IoT solutions are used to monitor the environmental variables and the users' parameters; the machine learning CART method allows to predict the users' profile and the thermal comfort perception respect to the indoor environment.

Integrated Method for Personal Thermal Comfort Assessment and Optimization through Users’ Feedback, IoT and Machine Learning: A Case Study †

PubMed Central

Currò, Cristian; Danza, Ludovico; Ghellere, Matteo; Guazzi, Giulia; Lenzi, Bruno; Megale, Valentino; Meroni, Italo

2018-01-01

Thermal comfort has become a topic issue in building performance assessment as well as energy efficiency. Three methods are mainly recognized for its assessment. Two of them based on standardized methodologies, face the problem by considering the indoor environment in steady-state conditions (PMV and PPD) and users as active subjects whose thermal perception is influenced by outdoor climatic conditions (adaptive approach). The latter method is the starting point to investigate thermal comfort from an overall perspective by considering endogenous variables besides the traditional physical and environmental ones. Following this perspective, the paper describes the results of an in-field investigation of thermal conditions through the use of nearable and wearable solutions, parametric models and machine learning techniques. The aim of the research is the exploration of the reliability of IoT-based solutions combined with advanced algorithms, in order to create a replicable framework for the assessment and improvement of user thermal satisfaction. For this purpose, an experimental test in real offices was carried out involving eight workers. Parametric models are applied for the assessment of thermal comfort; IoT solutions are used to monitor the environmental variables and the users’ parameters; the machine learning CART method allows to predict the users’ profile and the thermal comfort perception respect to the indoor environment. PMID:29772818

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.

Using a Split-belt Treadmill to Evaluate Generalization of Human Locomotor Adaptation.

PubMed

Vasudevan, Erin V L; Hamzey, Rami J; Kirk, Eileen M

2017-08-23

Understanding the mechanisms underlying locomotor learning helps researchers and clinicians optimize gait retraining as part of motor rehabilitation. However, studying human locomotor learning can be challenging. During infancy and childhood, the neuromuscular system is quite immature, and it is unlikely that locomotor learning during early stages of development is governed by the same mechanisms as in adulthood. By the time humans reach maturity, they are so proficient at walking that it is difficult to come up with a sufficiently novel task to study de novo locomotor learning. The split-belt treadmill, which has two belts that can drive each leg at a different speed, enables the study of both short- (i.e., immediate) and long-term (i.e., over minutes-days; a form of motor learning) gait modifications in response to a novel change in the walking environment. Individuals can easily be screened for previous exposure to the split-belt treadmill, thus ensuring that all experimental participants have no (or equivalent) prior experience. This paper describes a typical split-belt treadmill adaptation protocol that incorporates testing methods to quantify locomotor learning and generalization of this learning to other walking contexts. A discussion of important considerations for designing split-belt treadmill experiments follows, including factors like treadmill belt speeds, rest breaks, and distractors. Additionally, potential but understudied confounding variables (e.g., arm movements, prior experience) are considered in the discussion.

Nursing students' perceptions of factors influencing their learning environment in a clinical skills laboratory: A qualitative study.

PubMed

Haraldseid, Cecilie; Friberg, Febe; Aase, Karina

2015-09-01

The mastery of clinical skills learning is required to become a trained nurse. Due to limited opportunities for clinical skills training in clinical practice, undergraduate training at clinical skills laboratories (CSLs) is an essential part of nursing education. In a sociocultural learning perspective learning is situated in an environment. Growing student cohorts, rapid introduction of technology-based teaching methods and a shift from a teaching- to a learning-centered education all influence the environment of the students. These changes also affect CSLs and therefore compel nursing faculties to adapt to the changing learning environment. This study aimed to explore students' perceptions of their learning environment in a clinical skills laboratory, and to increase the knowledge base for improving CSL learning conditions identifying the most important environmental factors according to the students. An exploratory qualitative methodology was used. Nineteen second-year students enrolled in an undergraduate nursing program in Norway participated in the study. They took the same clinical skills course. Eight were part-time students (group A) and 11 were full-time students (group B). Focus group interviews and content analysis were conducted to capture the students' perception of the CSL learning environment. The study documents students' experience of the physical (facilities, material equipment, learning tools, standard procedures), psychosocial (expectations, feedback, relations) and organizational (faculty resources, course structure) factors that affect the CSL learning environment. Creating an authentic environment, facilitating motivation, and providing resources for multiple methods and repetitions within clinical skills training are all important for improving CSL learning environments from the student perspective. Copyright © 2015 Elsevier Ltd. All rights reserved.

Validation of the Clinical Learning Environment Inventory.

PubMed

Chan, Dominic S

2003-08-01

One hundred eight preregistration nursing students took part in this survey study, which assessed their perceptions of the clinical learning environment. Statistical data based on the sample confirmed the reliability and validity of the Clinical Learning Environment Inventory (CLEI), which was developed using the concept of classroom learning environment studies. The study also found that there were significant differences between students' actual and preferred perceptions of the clinical learning environments. In terms of the CLEI scales, students preferred a more positive and favorable clinical environment than they perceived as being actually present. The achievement of certain outcomes of clinical field placements might be enhanced by attempting to change the actual clinical environment in ways that make it more congruent with that preferred by the students.

Improving Learning Performance Through Rational Resource Allocation

NASA Technical Reports Server (NTRS)

Gratch, J.; Chien, S.; DeJong, G.

1994-01-01

This article shows how rational analysis can be used to minimize learning cost for a general class of statistical learning problems. We discuss the factors that influence learning cost and show that the problem of efficient learning can be cast as a resource optimization problem. Solutions found in this way can be significantly more efficient than the best solutions that do not account for these factors. We introduce a heuristic learning algorithm that approximately solves this optimization problem and document its performance improvements on synthetic and real-world problems.

Nursing students' perceptions of learning in practice environments: a review.

PubMed

Henderson, Amanda; Cooke, Marie; Creedy, Debra K; Walker, Rachel

2012-04-01

Effective clinical learning requires integration of nursing students into ward activities, staff engagement to address individual student learning needs, and innovative teaching approaches. Assessing characteristics of practice environments can provide useful insights for development. This study identified predominant features of clinical learning environments from nursing students' perspectives across studies using the same measure in different countries over the last decade. Six studies, from three different countries, using the Clinical Leaning Environment Inventory (CLEI) were reviewed. Studies explored consistent trends about learning environment. Students rated sense of task accomplishment high. Affiliation also rated highly though was influenced by models of care. Feedback measuring whether students' individual needs and views were accommodated consistently rated lower. Across different countries students report similar perceptions about learning environments. Clinical learning environments are most effective in promoting safe practice and are inclusive of student learners, but not readily open to innovation and challenges to routine practices. Crown Copyright © 2011. Published by Elsevier Ltd. All rights reserved.

Influences of Formal Learning, Personal Learning Orientation, and Supportive Learning Environment on Informal Learning

ERIC Educational Resources Information Center

Choi, Woojae; Jacobs, Ronald L.

2011-01-01

While workplace learning includes formal and informal learning, the relationship between the two has been overlooked, because they have been viewed as separate entities. This study investigated the effects of formal learning, personal learning orientation, and supportive learning environment on informal learning among 203 middle managers in Korean…

A particle swarm optimization variant with an inner variable learning strategy.

PubMed

Wu, Guohua; Pedrycz, Witold; Ma, Manhao; Qiu, Dishan; Li, Haifeng; Liu, Jin

2014-01-01

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

Effects of Presence, Copresence, and Flow on Learning Outcomes in 3D Learning Spaces

ERIC Educational Resources Information Center

Hassell, Martin D.; Goyal, Sandeep; Limayem, Moez; Boughzala, Imed

2012-01-01

The level of satisfaction and effectiveness of 3D virtual learning environments were examined. Additionally, 3D virtual learning environments were compared with face-to-face learning environments. Students that experienced higher levels of flow and presence also experienced more satisfaction but not necessarily more effectiveness with 3D virtual…

Student Experiences on Interaction in an Online Learning Environment as Part of a Blended Learning Implementation: What Is Essential?

ERIC Educational Resources Information Center

Salmi, Laura

2013-01-01

Interaction and community building are essential elements of a well functioning online learning environment, especially in learning environments based on investigative learning with a strong emphasis on teamwork. In this paper, practical solutions covering quality criteria for interaction in online education are presented for a simple…

Investigating Learners' Attitudes toward Virtual Reality Learning Environments: Based on a Constructivist Approach

ERIC Educational Resources Information Center

Huang, Hsiu-Mei; Rauch, Ulrich; Liaw, Shu-Sheng

2010-01-01

The use of animation and multimedia for learning is now further extended by the provision of entire Virtual Reality Learning Environments (VRLE). This highlights a shift in Web-based learning from a conventional multimedia to a more immersive, interactive, intuitive and exciting VR learning environment. VRLEs simulate the real world through the…

Factors of Learner-Instructor Interaction Which Predict Perceived Learning Outcomes in Online Learning Environment

ERIC Educational Resources Information Center

Kang, M.; Im, T.

2013-01-01

Interaction in the online learning environment has been regarded as one of the most critical elements that affect learning outcomes. This study examined what factors in learner-instructor interaction can predict the learner's outcomes in the online learning environment. Learners in K Online University participated by answering the survey, and data…

Self-Regulated Learning in Technology Enhanced Learning Environments: An Investigation with University Students

ERIC Educational Resources Information Center

Lenne, Dominique; Abel, Marie-Helene; Trigano, Philippe; Leblanc, Adeline

2008-01-01

In Technology Enhanced Learning Environments, self-regulated learning (SRL) partly relies on the features of the technological tools. The authors present two environments they designed in order to facilitate SRL: the first one (e-Dalgo) is a website dedicated to the learning of algorithms and computer programming. It is structured as a classical…

Using Student-Centred Learning Environments to Stimulate Deep Approaches to Learning: Factors Encouraging or Discouraging Their Effectiveness

ERIC Educational Resources Information Center

Baeten, Marlies; Kyndt, Eva; Struyven, Katrien; Dochy, Filip

2010-01-01

This review outlines encouraging and discouraging factors in stimulating the adoption of deep approaches to learning in student-centred learning environments. Both encouraging and discouraging factors can be situated in the context of the learning environment, in students' perceptions of that context and in characteristics of the students…

Perceived Satisfaction, Perceived Usefulness and Interactive Learning Environments as Predictors to Self-Regulation in e-Learning Environments

ERIC Educational Resources Information Center

Liaw, Shu-Sheng; Huang, Hsiu-Mei

2013-01-01

The research purpose is to investigate learner self-regulation in e-learning environments. In order to better understand learner attitudes toward e-learning, 196 university students answer a questionnaire survey after use an e-learning system few months. The statistical results showed that perceived satisfaction, perceived usefulness, and…

Distance Metric Learning via Iterated Support Vector Machines.

PubMed

Zuo, Wangmeng; Wang, Faqiang; Zhang, David; Lin, Liang; Huang, Yuchi; Meng, Deyu; Zhang, Lei

2017-07-11

Distance metric learning aims to learn from the given training data a valid distance metric, with which the similarity between data samples can be more effectively evaluated for classification. Metric learning is often formulated as a convex or nonconvex optimization problem, while most existing methods are based on customized optimizers and become inefficient for large scale problems. In this paper, we formulate metric learning as a kernel classification problem with the positive semi-definite constraint, and solve it by iterated training of support vector machines (SVMs). The new formulation is easy to implement and efficient in training with the off-the-shelf SVM solvers. Two novel metric learning models, namely Positive-semidefinite Constrained Metric Learning (PCML) and Nonnegative-coefficient Constrained Metric Learning (NCML), are developed. Both PCML and NCML can guarantee the global optimality of their solutions. Experiments are conducted on general classification, face verification and person re-identification to evaluate our methods. Compared with the state-of-the-art approaches, our methods can achieve comparable classification accuracy and are efficient in training.

Learning in a Changing Environment

ERIC Educational Resources Information Center

Speekenbrink, Maarten; Shanks, David R.

2010-01-01

Multiple cue probability learning studies have typically focused on stationary environments. We present 3 experiments investigating learning in changing environments. A fine-grained analysis of the learning dynamics shows that participants were responsive to both abrupt and gradual changes in cue-outcome relations. We found no evidence that…

Student-Teacher Interaction in Online Learning Environments

ERIC Educational Resources Information Center

Wright, Robert D., Ed.

2015-01-01

As face-to-face interaction between student and instructor is not present in online learning environments, it is increasingly important to understand how to establish and maintain social presence in online learning. "Student-Teacher Interaction in Online Learning Environments" provides successful strategies and procedures for developing…

Preferred-Actual Learning Environment "Spaces" and Earth Science Outcomes in Taiwan

ERIC Educational Resources Information Center

Chang, Chun-Yen; Hsiao, Chien-Hua; Barufaldi, James P.

2006-01-01

This study examines the possibilities of differential impacts on students' earth science learning outcomes between different preferred-actual learning environment spaces by using a newly developed ESCLEI (Earth Science Classroom Learning Environment Instrument). The instrument emphasizes three simultaneously important classroom components:…

Learning With Mixed Hard/Soft Pointwise Constraints.

PubMed

Gnecco, Giorgio; Gori, Marco; Melacci, Stefano; Sanguineti, Marcello

2015-09-01

A learning paradigm is proposed and investigated, in which the classical framework of learning from examples is enhanced by the introduction of hard pointwise constraints, i.e., constraints imposed on a finite set of examples that cannot be violated. Such constraints arise, e.g., when requiring coherent decisions of classifiers acting on different views of the same pattern. The classical examples of supervised learning, which can be violated at the cost of some penalization (quantified by the choice of a suitable loss function) play the role of soft pointwise constraints. Constrained variational calculus is exploited to derive a representer theorem that provides a description of the functional structure of the optimal solution to the proposed learning paradigm. It is shown that such an optimal solution can be represented in terms of a set of support constraints, which generalize the concept of support vectors and open the doors to a novel learning paradigm, called support constraint machines. The general theory is applied to derive the representation of the optimal solution to the problem of learning from hard linear pointwise constraints combined with soft pointwise constraints induced by supervised examples. In some cases, closed-form optimal solutions are obtained.

Causal Model Progressions as a Foundation for Intelligent Learning Environments.

DTIC Science & Technology

1987-11-01

Foundation for Intelligent Learning Environments 3Barbara Y. White and John R. Frederiksen ~DTIC Novemr1987 ELECTE November1987 JUNO 9 88 Approved I )’I...Learning Environments 12. PERSONAL AUTHOR(S? Barbara Y. White and John R. Frederiksen 13a. TYPE OF REPORT 13b TIME COVERED 14. DATE OF REPORT (Year...architecture of a new type of learning environment that incorporates features of microworlds and of intelligent tutorng systems. The environment is based on

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

ERIC Educational Resources Information Center

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

2018-01-01

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

An analysis dictionary learning algorithm under a noisy data model with orthogonality constraint.

PubMed

Zhang, Ye; Yu, Tenglong; Wang, Wenwu

2014-01-01

Two common problems are often encountered in analysis dictionary learning (ADL) algorithms. The first one is that the original clean signals for learning the dictionary are assumed to be known, which otherwise need to be estimated from noisy measurements. This, however, renders a computationally slow optimization process and potentially unreliable estimation (if the noise level is high), as represented by the Analysis K-SVD (AK-SVD) algorithm. The other problem is the trivial solution to the dictionary, for example, the null dictionary matrix that may be given by a dictionary learning algorithm, as discussed in the learning overcomplete sparsifying transform (LOST) algorithm. Here we propose a novel optimization model and an iterative algorithm to learn the analysis dictionary, where we directly employ the observed data to compute the approximate analysis sparse representation of the original signals (leading to a fast optimization procedure) and enforce an orthogonality constraint on the optimization criterion to avoid the trivial solutions. Experiments demonstrate the competitive performance of the proposed algorithm as compared with three baselines, namely, the AK-SVD, LOST, and NAAOLA algorithms.

A Machine Learning and Optimization Toolkit for the Swarm

DTIC Science & Technology

2014-11-17

Machine   Learning  and  Op0miza0on   Toolkit  for  the  Swarm   Ilge  Akkaya,  Shuhei  Emoto...3. DATES COVERED 00-00-2014 to 00-00-2014 4. TITLE AND SUBTITLE A Machine Learning and Optimization Toolkit for the Swarm 5a. CONTRACT NUMBER... machine   learning   methodologies  by  providing  the  right  interfaces  between   machine   learning  tools  and

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling

PubMed Central

Cuperlovic-Culf, Miroslava

2018-01-01

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies. PMID:29324649

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling.

PubMed

Cuperlovic-Culf, Miroslava

2018-01-11

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies.

The use of deep and surface learning strategies among students learning English as a foreign language in an Internet environment.

PubMed

Aharony, Noa

2006-12-01

The learning context is learning English in an Internet environment. The examination of this learning process was based on the Biggs and Moore's teaching-learning model (Biggs & Moore, 1993). The research aims to explore the use of the deep and surface strategies in an Internet environment among EFL students who come from different socio-economic backgrounds. The results of the research may add an additional level to the understanding of students' functioning in the Internet environment. One hundred fourty-eight Israeli junior and high school students participated in this research. The methodology was based on special computer software: Screen Cam, which recorded the students' learning process. In addition, expert judges completed a questionnaire which examined and categorized the students' learning strategies. The research findings show a clear preference of participants from all socio-economic backgrounds towards the surface learning strategy. The findings also showed that students from the medium to high socio-economic background used both learning strategies more frequently than low socio-economic students. The results reflect the habits that students acquire during their adjustment process throughout their education careers. A brief encounter with the Internet learning environment apparently cannot change norms or habits, which were acquired in the non-Internet learning environment.

Designing new collaborative learning spaces in clinical environments: experiences from a children's hospital in Australia.

PubMed

Bines, Julie E; Jamieson, Peter

2013-09-01

Hospitals are complex places that provide a rich learning environment for students, staff, patients and their families, professional groups and the community. The "new" Royal Children's Hospital opened in late 2011. Its mission is focused on improving health and well-being of children and adolescents through leadership in healthcare, research and education. Addressing the need to create "responsive learning environments" aligned with the shift to student-centred pedagogy, two distinct learning environments were developed within the new Royal Children's Hospital; (i) a dedicated education precinct providing a suite of physical environments to promote a more active, collaborative and social learning experience for education and training programs conducted on the Royal Children's Hospital campus and (ii) a suite of learning spaces embedded within clinical areas so that learning becomes an integral part of the daily activities of this busy Hospital environment. The aim of this article is to present the overarching educational principles that lead the design of these learning spaces and describe the opportunities and obstacles encountered in the development of collaborative learning spaces within a large hospital development.

Perceptions of Pre-Service Teachers on the Design of a Learning Environment Based on the Seven Principles of Good Practice

ERIC Educational Resources Information Center

Al-Furaih, Suad Abdul Aziz

2017-01-01

This study explored the perceptions of 88 pre-service teachers on the design of a learning environment using the Seven Principles of Good Practice and its effect on participants' abilities to create their Cloud Learning Environment (CLE). In designing the learning environment, a conceptual model under the name 7 Principles and Integrated Learning…

A Case Study of the Experiences of Instructors and Students in a Virtual Learning Environment (VLE) with Different Cultural Backgrounds

ERIC Educational Resources Information Center

Lim, Keol; Kim, Mi Hwa

2015-01-01

The use of virtual learning environments (VLEs) has become more common and educators recognized the potential of VLEs as educational environments. The learning community in VLEs can be a mixture of people from all over the world with different cultural backgrounds. However, despite many studies about the use of virtual environments for learning,…

Virtual Representations in 3D Learning Environments

ERIC Educational Resources Information Center

Shonfeld, Miri; Kritz, Miki

2013-01-01

This research explores the extent to which virtual worlds can serve as online collaborative learning environments for students by increasing social presence and engagement. 3D environments enable learning, which simulates face-to-face encounters while retaining the advantages of online learning. Students in Education departments created avatars…

Model-Free Primitive-Based Iterative Learning Control Approach to Trajectory Tracking of MIMO Systems With Experimental Validation.

PubMed

Radac, Mircea-Bogdan; Precup, Radu-Emil; Petriu, Emil M

2015-11-01

This paper proposes a novel model-free trajectory tracking of multiple-input multiple-output (MIMO) systems by the combination of iterative learning control (ILC) and primitives. The optimal trajectory tracking solution is obtained in terms of previously learned solutions to simple tasks called primitives. The library of primitives that are stored in memory consists of pairs of reference input/controlled output signals. The reference input primitives are optimized in a model-free ILC framework without using knowledge of the controlled process. The guaranteed convergence of the learning scheme is built upon a model-free virtual reference feedback tuning design of the feedback decoupling controller. Each new complex trajectory to be tracked is decomposed into the output primitives regarded as basis functions. The optimal reference input for the control system to track the desired trajectory is next recomposed from the reference input primitives. This is advantageous because the optimal reference input is computed straightforward without the need to learn from repeated executions of the tracking task. In addition, the optimization problem specific to trajectory tracking of square MIMO systems is decomposed in a set of optimization problems assigned to each separate single-input single-output control channel that ensures a convenient model-free decoupling. The new model-free primitive-based ILC approach is capable of planning, reasoning, and learning. A case study dealing with the model-free control tuning for a nonlinear aerodynamic system is included to validate the new approach. The experimental results are given.

A stepwise approach for introducing numerical modeling in Environmental Engineering MSc unit: The impact of clear assessment criteria and detailed feedback

NASA Astrophysics Data System (ADS)

Rosolem, R.; Pritchard, J.

2017-12-01

An important aspect for the new generation of hydrologists and water resources managers is the understanding of hydrological processes through the application of numerical environmental models. Despite its importance, teaching numerical modeling subjects to young students in our MSc Water and Environment Management programme has been difficult, for instance, due to the wide range of student background and lack or poor contact with numerical modeling tools in the past. In previous years, this numerical skills concept has been introduced as a project assignment in our Terrestrial Hydrometeorology unit. However, previous efforts have shown non-optimal engagement by students with often signs of lack of interest or anxiety. Given our initial experience with this unit, we decided to make substantial changes to the coursework format with the aim to introduce a more efficient learning environment to the students. The proposed changes include: (1) a clear presentation and discussion of the assessment criteria at the beginning of the unit, (2) a stepwise approach in which students use our learning environment to acquire knowledge for individual components of the model step-by-step, and (3) access to timely and detailed feedback allowing for particular steps to be retraced or retested. In order to understand the overall impact on assessment and feedback, we carried out two surveys at the beginning and end of the module. Our results indicate a positive impact to student learning experience, as the students have clearly benefited from the early discussion on assignment criteria and appeared to have correctly identified the skills and knowledge required to carry out the assignment. In addition, we have observed a substantial increase in the quality of the reports. Our results results support that student engagement has increased since changes to the format of the coursework were introduced. Interestingly, we also observed a positive impact on the assignment to the final exam marks, even for students who did not particularly performed well in the coursework. This indicates that despite not reaching ideal marks, students were able to use this new learning environment to acquire their knowledge of key concepts which are needed for their final exam.

Elevated depressive symptoms enhance reflexive but not reflective auditory category learning.

PubMed

Maddox, W Todd; Chandrasekaran, Bharath; Smayda, Kirsten; Yi, Han-Gyol; Koslov, Seth; Beevers, Christopher G

2014-09-01

In vision an extensive literature supports the existence of competitive dual-processing systems of category learning that are grounded in neuroscience and are partially-dissociable. The reflective system is prefrontally-mediated and uses working memory and executive attention to develop and test rules for classifying in an explicit fashion. The reflexive system is striatally-mediated and operates by implicitly associating perception with actions that lead to reinforcement. Although categorization is fundamental to auditory processing, little is known about the learning systems that mediate auditory categorization and even less is known about the effects of individual difference in the relative efficiency of the two learning systems. Previous studies have shown that individuals with elevated depressive symptoms show deficits in reflective processing. We exploit this finding to test critical predictions of the dual-learning systems model in audition. Specifically, we examine the extent to which the two systems are dissociable and competitive. We predicted that elevated depressive symptoms would lead to reflective-optimal learning deficits but reflexive-optimal learning advantages. Because natural speech category learning is reflexive in nature, we made the prediction that elevated depressive symptoms would lead to superior speech learning. In support of our predictions, individuals with elevated depressive symptoms showed a deficit in reflective-optimal auditory category learning, but an advantage in reflexive-optimal auditory category learning. In addition, individuals with elevated depressive symptoms showed an advantage in learning a non-native speech category structure. Computational modeling suggested that the elevated depressive symptom advantage was due to faster, more accurate, and more frequent use of reflexive category learning strategies in individuals with elevated depressive symptoms. The implications of this work for dual-process approach to auditory learning and depression are discussed. Copyright © 2014 Elsevier Ltd. All rights reserved.

Elevated Depressive Symptoms Enhance Reflexive but not Reflective Auditory Category Learning

PubMed Central

Maddox, W. Todd; Chandrasekaran, Bharath; Smayda, Kirsten; Yi, Han-Gyol; Koslov, Seth; Beevers, Christopher G.

2014-01-01

In vision an extensive literature supports the existence of competitive dual-processing systems of category learning that are grounded in neuroscience and are partially-dissociable. The reflective system is prefrontally-mediated and uses working memory and executive attention to develop and test rules for classifying in an explicit fashion. The reflexive system is striatally-mediated and operates by implicitly associating perception with actions that lead to reinforcement. Although categorization is fundamental to auditory processing, little is known about the learning systems that mediate auditory categorization and even less is known about the effects of individual difference in the relative efficiency of the two learning systems. Previous studies have shown that individuals with elevated depressive symptoms show deficits in reflective processing. We exploit this finding to test critical predictions of the dual-learning systems model in audition. Specifically, we examine the extent to which the two systems are dissociable and competitive. We predicted that elevated depressive symptoms would lead to reflective-optimal learning deficits but reflexive-optimal learning advantages. Because natural speech category learning is reflexive in nature, we made the prediction that elevated depressive symptoms would lead to superior speech learning. In support of our predictions, individuals with elevated depressive symptoms showed a deficit in reflective-optimal auditory category learning, but an advantage in reflexive-optimal auditory category learning. In addition, individuals with elevated depressive symptoms showed an advantage in learning a non-native speech category structure. Computational modeling suggested that the elevated depressive symptom advantage was due to faster, more accurate, and more frequent use of reflexive category learning strategies in individuals with elevated depressive symptoms. The implications of this work for dual-process approach to auditory learning and depression are discussed. PMID:25041936

FSMRank: feature selection algorithm for learning to rank.

PubMed

Lai, Han-Jiang; Pan, Yan; Tang, Yong; Yu, Rong

2013-06-01

In recent years, there has been growing interest in learning to rank. The introduction of feature selection into different learning problems has been proven effective. These facts motivate us to investigate the problem of feature selection for learning to rank. We propose a joint convex optimization formulation which minimizes ranking errors while simultaneously conducting feature selection. This optimization formulation provides a flexible framework in which we can easily incorporate various importance measures and similarity measures of the features. To solve this optimization problem, we use the Nesterov's approach to derive an accelerated gradient algorithm with a fast convergence rate O(1/T(2)). We further develop a generalization bound for the proposed optimization problem using the Rademacher complexities. Extensive experimental evaluations are conducted on the public LETOR benchmark datasets. The results demonstrate that the proposed method shows: 1) significant ranking performance gain compared to several feature selection baselines for ranking, and 2) very competitive performance compared to several state-of-the-art learning-to-rank algorithms.

Analysis of Means for Building Context-Aware Recommendation System for Mobile Learning

ERIC Educational Resources Information Center

Shcherbachenko, Larysa; Nowakowski, Samuel

2017-01-01

One of the rapidly developing tools for online learning is learning through a mobile environment. Therefore, developing and improving mobile learning environments is an active topic now. One of the ways to adapt the learning environment to the user's needs is to use his context. Context of the user consists of the current context in online…

Design Patterns for Learning and Assessment: Facilitating the Introduction of a Complex Simulation-Based Learning Environment into a Community of Instructors

ERIC Educational Resources Information Center

Frezzo, Dennis C.; Behrens, John T.; Mislevy, Robert J.

2010-01-01

Simulation environments make it possible for science and engineering students to learn to interact with complex systems. Putting these capabilities to effective use for learning, and assessing learning, requires more than a simulation environment alone. It requires a conceptual framework for the knowledge, skills, and ways of thinking that are…

Environmental optimal control strategies based on plant canopy photosynthesis responses and greenhouse climate model

NASA Astrophysics Data System (ADS)

Deng, Lujuan; Xie, Songhe; Cui, Jiantao; Liu, Tao

2006-11-01

It is the essential goal of intelligent greenhouse environment optimal control to enhance income of cropper and energy save. There were some characteristics such as uncertainty, imprecision, nonlinear, strong coupling, bigger inertia and different time scale in greenhouse environment control system. So greenhouse environment optimal control was not easy and especially model-based optimal control method was more difficult. So the optimal control problem of plant environment in intelligent greenhouse was researched. Hierarchical greenhouse environment control system was constructed. In the first level data measuring was carried out and executive machine was controlled. Optimal setting points of climate controlled variable in greenhouse was calculated and chosen in the second level. Market analysis and planning were completed in third level. The problem of the optimal setting point was discussed in this paper. Firstly the model of plant canopy photosynthesis responses and the model of greenhouse climate model were constructed. Afterwards according to experience of the planting expert, in daytime the optimal goals were decided according to the most maximal photosynthesis rate principle. In nighttime on plant better growth conditions the optimal goals were decided by energy saving principle. Whereafter environment optimal control setting points were computed by GA. Compared the optimal result and recording data in real system, the method is reasonable and can achieve energy saving and the maximal photosynthesis rate in intelligent greenhouse

Gene expression profiling gut microbiota in different races of humans

NASA Astrophysics Data System (ADS)

Chen, Lei; Zhang, Yu-Hang; Huang, Tao; Cai, Yu-Dong

2016-03-01

The gut microbiome is shaped and modified by the polymorphisms of microorganisms in the intestinal tract. Its composition shows strong individual specificity and may play a crucial role in the human digestive system and metabolism. Several factors can affect the composition of the gut microbiome, such as eating habits, living environment, and antibiotic usage. Thus, various races are characterized by different gut microbiome characteristics. In this present study, we studied the gut microbiomes of three different races, including individuals of Asian, European and American races. The gut microbiome and the expression levels of gut microbiome genes were analyzed in these individuals. Advanced feature selection methods (minimum redundancy maximum relevance and incremental feature selection) and four machine-learning algorithms (random forest, nearest neighbor algorithm, sequential minimal optimization, Dagging) were employed to capture key differentially expressed genes. As a result, sequential minimal optimization was found to yield the best performance using the 454 genes, which could effectively distinguish the gut microbiomes of different races. Our analyses of extracted genes support the widely accepted hypotheses that eating habits, living environments and metabolic levels in different races can influence the characteristics of the gut microbiome.

Gene expression profiling gut microbiota in different races of humans

PubMed Central

Chen, Lei; Zhang, Yu-Hang; Huang, Tao; Cai, Yu-Dong

2016-01-01

The gut microbiome is shaped and modified by the polymorphisms of microorganisms in the intestinal tract. Its composition shows strong individual specificity and may play a crucial role in the human digestive system and metabolism. Several factors can affect the composition of the gut microbiome, such as eating habits, living environment, and antibiotic usage. Thus, various races are characterized by different gut microbiome characteristics. In this present study, we studied the gut microbiomes of three different races, including individuals of Asian, European and American races. The gut microbiome and the expression levels of gut microbiome genes were analyzed in these individuals. Advanced feature selection methods (minimum redundancy maximum relevance and incremental feature selection) and four machine-learning algorithms (random forest, nearest neighbor algorithm, sequential minimal optimization, Dagging) were employed to capture key differentially expressed genes. As a result, sequential minimal optimization was found to yield the best performance using the 454 genes, which could effectively distinguish the gut microbiomes of different races. Our analyses of extracted genes support the widely accepted hypotheses that eating habits, living environments and metabolic levels in different races can influence the characteristics of the gut microbiome. PMID:26975620

Sustaining Teacher Control in a Blog-Based Personal Learning Environment

ERIC Educational Resources Information Center

Tomberg, Vladimir; Laanpere, Mart; Ley, Tobias; Normak, Peeter

2013-01-01

Various tools and services based on Web 2.0 (mainly blogs, wikis, social networking tools) are increasingly used in formal education to create personal learning environments, providing self-directed learners with more freedom, choice, and control over their learning. In such distributed and personalized learning environments, the traditional role…

Supporting the Transition of Learning Disabled Students to the Postsecondary Environment

ERIC Educational Resources Information Center

Gray, Patricia Jean

2012-01-01

Students with learning disabilities present a diverse spectrum of learning needs; research suggest they may have difficulty making the transition to the postsecondary environment. Learning disabled students at the subject high school were not successfully making the transition from the secondary to the postsecondary environment. This study was…

Flipped Education: Transitioning to the Homeschool Environment

ERIC Educational Resources Information Center

Alamry, Adel; karaali, Abeer

2016-01-01

This paper seeks to introduce flipped learning as a viable learning method that can be used in the homeschool environment. Flipped learning can become a valuable aspect of homeschooling when the learning environment is conducive to the application of self-taught knowledge. In fact, the sessions evidently act as clarification bridges and…

Visualising Learning Design in LAMS: A Historical View

ERIC Educational Resources Information Center

Dalziel, James

2011-01-01

The Learning Activity Management System (LAMS) provides a web-based environment for the creation, sharing, running and monitoring of Learning Designs. A central feature of LAMS is the visual authoring environment, where educators use a drag-and-drop environment to create sequences of learning activities. The visualisation is based on boxes…

Determination of Science Teachers' Opinions about Outdoor Education

ERIC Educational Resources Information Center

Kubat, Ulas

2017-01-01

The aim of this research is to discover what science teachers' opinions about outdoor education learning environments are. Outdoor education learning environments contribute to problem-solving, critical and creative thinking skills of students. For this reason, outdoor education learning environments are very important for students to learn by…

Offering a Framework for Value Co-Creation in Virtual Academic Learning Environments

ERIC Educational Resources Information Center

Ranjbarfard, Mina; Heidari Sureshjani, Mahboobeh

2018-01-01

Purpose: This research aims to convert the traditional teacher-student models, in which teachers determine the learning resources, into a flexible structure and an active learning environment so that students can participate in the educational processes and value co-creation in virtual academic learning environments (VALEs).…

Constructivist Learning Environment among Palestinian Science Students

ERIC Educational Resources Information Center

Zeidan, Afif

2015-01-01

The purpose of this study was to investigate the constructivist learning environment among Palestinian science students. The study also aimed to investigate the effects of gender and learning level of these students on their perceptions of the constructivist learning environment. Data were collected from 125 male and 101 female students from the…

Disrupting a Learning Environment for Promotion of Geometry Teaching

ERIC Educational Resources Information Center

Jojo, Zingiswa

2017-01-01

Creating a classroom learning environment that is suitably designed for promotion of learners' performance in geometry, a branch of mathematics that addresses spatial sense and geometric reasoning, is a daunting task. This article focuses on how grade 8 teachers' action learning changed the learning environment for the promotion of geometry…

Criteria, Strategies and Research Issues of Context-Aware Ubiquitous Learning

ERIC Educational Resources Information Center

Hwang, Gwo-Jen; Tsai, Chin-Chung; Yang, Stephen J. H.

2008-01-01

Recent progress in wireless and sensor technologies has lead to a new development of learning environments, called context-aware ubiquitous learning environment, which is able to sense the situation of learners and provide adaptive supports. Many researchers have been investigating the development of such new learning environments; nevertheless,…

Kitchen Science Investigators: Promoting Identity Development as Scientific Reasoners and Thinkers

ERIC Educational Resources Information Center

Clegg, Tamara Lynnette

2010-01-01

My research centers upon designing transformative learning environments and supporting technologies. Kitchen Science Investigators (KSI) is an out-of-school transformative learning environment we designed to help young people learn science through cooking. My dissertation considers the question, "How can we design a learning environment in which…

Improving Collaborative Learning by Supporting Casual Encounters in Distance Learning.

ERIC Educational Resources Information Center

Contreras, Juan; Llamas, Rafael; Vizcaino, Aurora; Vavela, Jesus

Casual encounters in a learning environment are very useful in reinforcing previous knowledge and acquiring new knowledge. Such encounters are very common in traditional learning environments and can be used successfully in social environments in which students can discover and construct knowledge through a process of dialogue, negotiation, or…

Citizen Science as a REAL Environment for Authentic Scientific Inquiry

ERIC Educational Resources Information Center

Meyer, Nathan J.; Scott, Siri; Strauss, Andrea Lorek; Nippolt, Pamela L.; Oberhauser, Karen S.; Blair, Robert B.

2014-01-01

Citizen science projects can serve as constructivist learning environments for programming focused on science, technology, engineering, and math (STEM) for youth. Attributes of "rich environments for active learning" (REALs) provide a framework for design of Extension STEM learning environments. Guiding principles and design strategies…

Model predictive and reallocation problem for CubeSat fault recovery and attitude control

NASA Astrophysics Data System (ADS)

Franchi, Loris; Feruglio, Lorenzo; Mozzillo, Raffaele; Corpino, Sabrina

2018-01-01

In recent years, thanks to the increase of the know-how on machine-learning techniques and the advance of the computational capabilities of on-board processing, expensive computing algorithms, such as Model Predictive Control, have begun to spread in space applications even on small on-board processor. The paper presents an algorithm for an optimal fault recovery of a 3U CubeSat, developed in MathWorks Matlab & Simulink environment. This algorithm involves optimization techniques aiming at obtaining the optimal recovery solution, and involves a Model Predictive Control approach for the attitude control. The simulated system is a CubeSat in Low Earth Orbit: the attitude control is performed with three magnetic torquers and a single reaction wheel. The simulation neglects the errors in the attitude determination of the satellite, and focuses on the recovery approach and control method. The optimal recovery approach takes advantage of the properties of magnetic actuation, which gives the possibility of the redistribution of the control action when a fault occurs on a single magnetic torquer, even in absence of redundant actuators. In addition, the paper presents the results of the implementation of Model Predictive approach to control the attitude of the satellite.

Virtual reality simulator training of laparoscopic cholecystectomies - a systematic review.

PubMed

Ikonen, T S; Antikainen, T; Silvennoinen, M; Isojärvi, J; Mäkinen, E; Scheinin, T M

2012-01-01

Simulators are widely used in occupations where practice in authentic environments would involve high human or economic risks. Surgical procedures can be simulated by increasingly complex and expensive techniques. This review gives an update on computer-based virtual reality (VR) simulators in training for laparoscopic cholecystectomies. From leading databases (Medline, Cochrane, Embase), randomised or controlled trials and the latest systematic reviews were systematically searched and reviewed. Twelve randomised trials involving simulators were identified and analysed, as well as four controlled studies. Furthermore, seven studies comparing black boxes and simulators were included. The results indicated any kind of simulator training (black box, VR) to be beneficial at novice level. After VR training, novice surgeons seemed to be able to perform their first live cholecystectomies with fewer errors, and in one trial the positive effect remained during the first ten cholecystectomies. No clinical follow-up data were found. Optimal learning requires skills training to be conducted as part of a systematic training program. No data on the cost-benefit of simulators were found, the price of a VR simulator begins at EUR 60 000. Theoretical background to learning and limited research data support the use of simulators in the early phases of surgical training. The cost of buying and using simulators is justified if the risk of injuries and complications to patients can be reduced. Developing surgical skills requires repeated training. In order to achieve optimal learning a validated training program is needed.

A biologically inspired meta-control navigation system for the Psikharpax rat robot.

PubMed

Caluwaerts, K; Staffa, M; N'Guyen, S; Grand, C; Dollé, L; Favre-Félix, A; Girard, B; Khamassi, M

2012-06-01

A biologically inspired navigation system for the mobile rat-like robot named Psikharpax is presented, allowing for self-localization and autonomous navigation in an initially unknown environment. The ability of parts of the model (e.g. the strategy selection mechanism) to reproduce rat behavioral data in various maze tasks has been validated before in simulations. But the capacity of the model to work on a real robot platform had not been tested. This paper presents our work on the implementation on the Psikharpax robot of two independent navigation strategies (a place-based planning strategy and a cue-guided taxon strategy) and a strategy selection meta-controller. We show how our robot can memorize which was the optimal strategy in each situation, by means of a reinforcement learning algorithm. Moreover, a context detector enables the controller to quickly adapt to changes in the environment-recognized as new contexts-and to restore previously acquired strategy preferences when a previously experienced context is recognized. This produces adaptivity closer to rat behavioral performance and constitutes a computational proposition of the role of the rat prefrontal cortex in strategy shifting. Moreover, such a brain-inspired meta-controller may provide an advancement for learning architectures in robotics.

Experiential effects on mirror systems and social learning: implications for social intelligence.

PubMed

Reader, Simon M

2014-04-01

Investigations of biases and experiential effects on social learning, social information use, and mirror systems can usefully inform one another. Unconstrained learning is predicted to shape mirror systems when the optimal response to an observed act varies, but constraints may emerge when immediate error-free responses are required and evolutionary or developmental history reliably predicts the optimal response. Given the power of associative learning, such constraints may be rare.

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

PubMed

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

2017-09-21

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

Aversive learning of odor-heat associations in ants.

PubMed

Desmedt, Lucie; Baracchi, David; Devaud, Jean-Marc; Giurfa, Martin; d'Ettorre, Patrizia

2017-12-15

Ants have recently emerged as useful models for the study of olfactory learning. In this framework, the development of a protocol for the appetitive conditioning of the maxilla-labium extension response (MaLER) provided the possibility of studying Pavlovian odor-food learning in a controlled environment. Here we extend these studies by introducing the first Pavlovian aversive learning protocol for harnessed ants in the laboratory. We worked with carpenter ants Camponotus aethiops and first determined the capacity of different temperatures applied to the body surface to elicit the typical aversive mandible opening response (MOR). We determined that 75°C is the optimal temperature to induce MOR and chose the hind legs as the stimulated body region because of their high sensitivity. We then studied the ability of ants to learn and remember odor-heat associations using 75°C as the unconditioned stimulus. We studied learning and short-term retention after absolute (one odor paired with heat) and differential conditioning (a punished odor versus an unpunished odor). Our results show that ants successfully learn the odor-heat association under a differential-conditioning regime and thus exhibit a conditioned MOR to the punished odor. Yet, their performance under an absolute-conditioning regime is poor. These results demonstrate that ants are capable of aversive learning and confirm previous findings about the different attentional resources solicited by differential and absolute conditioning in general. © 2017. Published by The Company of Biologists Ltd.

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

ERIC Educational Resources Information Center

Lappas, Pantelis Z.; Kritikos, Manolis N.

2018-01-01

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

Nursing Students' Qualitative Experiences in the Medical-Surgical Clinical Learning Environment: A Cross-Cultural Integrative Review.

PubMed

Hooven, Katie

2015-08-01

The nature of the clinical learning environment has a huge impact on student learning. For instance, research has supported the idea that a positive learning environment increases student learning. Therefore, the ability to gain information from the student perspective about the learning environment is essential to nursing education. This article reviews qualitative research on nursing students' experiences of the clinical learning environment. The significance of the issue, the purpose of the integrative review, the methods used in the literature search, and the results of the review are presented. Seventeen studies from 12 countries are identified for review, and six common themes are discussed. An exhaustive literature review revealed that among the 17 articles evaluated, six themes were common. The findings indicate the need to continue quality improvement to advance clinical education. Copyright 2015, SLACK Incorporated.

The Relationship among Self-Regulated Learning, Procrastination, and Learning Behaviors in Blended Learning Environment

ERIC Educational Resources Information Center

Yamada, Masanori; Goda, Yoshiko; Matsuda, Takeshi; Kato, Hiroshi; Miyagawa, Hiroyuki

2015-01-01

This research aims to investigate the relationship among the awareness of self-regulated learning (SRL), procrastination, and learning behaviors in blended learning environment. One hundred seventy nine freshmen participated in this research, conducted in the blended learning style class using learning management system. Data collection was…

Behavioral Feature Extraction to Determine Learning Styles in e-Learning Environments

ERIC Educational Resources Information Center

Fatahi, Somayeh; Moradi, Hadi; Farmad, Elaheh

2015-01-01

Learning Style (LS) is an important parameter in the learning process. Therefore, learning styles should be considered in the design, development, and implementation of e-learning environments. Consequently, an important capability of an e-learning system could be the automatic determination of a student's learning style. In this paper, a set of…