Adaptive Computerized Instruction.

ERIC Educational Resources Information Center

Ray, Roger D.; And Others

1995-01-01

Describes an artificially intelligent multimedia computerized instruction system capable of developing a conceptual image of what a student is learning while the student is learning it. It focuses on principles of learning and adaptive behavioral control systems theory upon which the system is designed and demonstrates multiple user modes.…

The Curse of Planning: Dissecting multiple reinforcement learning systems by taxing the central executive

PubMed Central

Otto, A. Ross; Gershman, Samuel J.; Markman, Arthur B.; Daw, Nathaniel D.

2013-01-01

A number of accounts of human and animal behavior posit the operation of parallel and competing valuation systems in the control of choice behavior. Along these lines, a flexible but computationally expensive model-based reinforcement learning system has been contrasted with a less flexible but more efficient model-free reinforcement learning system. The factors governing which system controls behavior—and under what circumstances—are still unclear. Based on the hypothesis that model-based reinforcement learning requires cognitive resources, we demonstrate that having human decision-makers perform a demanding secondary task engenders increased reliance on a model-free reinforcement learning strategy. Further, we show that across trials, people negotiate this tradeoff dynamically as a function of concurrent executive function demands and their choice latencies reflect the computational expenses of the strategy employed. These results demonstrate that competition between multiple learning systems can be controlled on a trial-by-trial basis by modulating the availability of cognitive resources. PMID:23558545

Within- and across-trial dynamics of human EEG reveal cooperative interplay between reinforcement learning and working memory.

PubMed

Collins, Anne G E; Frank, Michael J

2018-03-06

Learning from rewards and punishments is essential to survival and facilitates flexible human behavior. It is widely appreciated that multiple cognitive and reinforcement learning systems contribute to decision-making, but the nature of their interactions is elusive. Here, we leverage methods for extracting trial-by-trial indices of reinforcement learning (RL) and working memory (WM) in human electro-encephalography to reveal single-trial computations beyond that afforded by behavior alone. Neural dynamics confirmed that increases in neural expectation were predictive of reduced neural surprise in the following feedback period, supporting central tenets of RL models. Within- and cross-trial dynamics revealed a cooperative interplay between systems for learning, in which WM contributes expectations to guide RL, despite competition between systems during choice. Together, these results provide a deeper understanding of how multiple neural systems interact for learning and decision-making and facilitate analysis of their disruption in clinical populations.

How Should Intelligent Tutoring Systems Sequence Multiple Graphical Representations of Fractions? A Multi-Methods Study

ERIC Educational Resources Information Center

Rau, M. A.; Aleven, V.; Rummel, N.; Pardos, Z.

2014-01-01

Providing learners with multiple representations of learning content has been shown to enhance learning outcomes. When multiple representations are presented across consecutive problems, we have to decide in what sequence to present them. Prior research has demonstrated that interleaving "tasks types" (as opposed to blocking them) can…

Multiple Views, Contexts, and Symbol Systems in Learning with Hypertext/Hypermedia: A Critical Review of Research.

ERIC Educational Resources Information Center

Tergan, Sigmar-Olaf

1997-01-01

Reviews research on the effectiveness of hypertext/hypermedia-based learning and concludes that presenting subject matter from different perspectives, in multiple contexts, and in multiple codes does not automatically contribute to higher performance but may when instructional scaffolding is provided. The additional cognitive load may actually…

Evaluation of Learning Group Approaches for Fostering Integrated Cropping Systems Management

ERIC Educational Resources Information Center

Blissett, Hana; Simmons, Steve; Jordan, Nicholas; Nelson, Kristen

2004-01-01

Cropping systems management requires integration of multiple forms of knowledge, practice, and learning by farmers, extension educators, and researchers. We evaluated the outcomes of participation in collaborative learning groups organized to address cropping systems and, specifically, challenges of integrated weed management. Groups were…

Procedural Learning during Declarative Control

ERIC Educational Resources Information Center

Crossley, Matthew J.; Ashby, F. Gregory

2015-01-01

There is now abundant evidence that human learning and memory are governed by multiple systems. As a result, research is now turning to the next question of how these putative systems interact. For instance, how is overall control of behavior coordinated, and does learning occur independently within systems regardless of what system is in control?…

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.

Adaptive learning and control for MIMO system based on adaptive dynamic programming.

PubMed

Fu, Jian; He, Haibo; Zhou, Xinmin

2011-07-01

Adaptive dynamic programming (ADP) is a promising research field for design of intelligent controllers, which can both learn on-the-fly and exhibit optimal behavior. Over the past decades, several generations of ADP design have been proposed in the literature, which have demonstrated many successful applications in various benchmarks and industrial applications. While many of the existing researches focus on multiple-inputs-single-output system with steepest descent search, in this paper we investigate a generalized multiple-input-multiple-output (GMIMO) ADP design for online learning and control, which is more applicable to a wide range of practical real-world applications. Furthermore, an improved weight-updating algorithm based on recursive Levenberg-Marquardt methods is presented and embodied in the GMIMO approach to improve its performance. Finally, we test the performance of this approach based on a practical complex system, namely, the learning and control of the tension and height of the looper system in a hot strip mill. Experimental results demonstrate that the proposed approach can achieve effective and robust performance.

Delayed Feedback Disrupts the Procedural-Learning System but Not the Hypothesis-Testing System in Perceptual Category Learning

ERIC Educational Resources Information Center

Maddox, W. Todd; Ing, A. David

2005-01-01

W. T. Maddox, F. G. Ashby, and C. J. Bohil (2003) found that delayed feedback adversely affects information-integration but not rule-based category learning in support of a multiple-systems approach to category learning. However, differences in the number of stimulus dimensions relevant to solving the task and perceptual similarity failed to rule…

Collective Machine Learning: Team Learning and Classification in Multi-Agent Systems

ERIC Educational Resources Information Center

Gifford, Christopher M.

2009-01-01

This dissertation focuses on the collaboration of multiple heterogeneous, intelligent agents (hardware or software) which collaborate to learn a task and are capable of sharing knowledge. The concept of collaborative learning in multi-agent and multi-robot systems is largely under studied, and represents an area where further research is needed to…

Does Supporting Multiple Student Strategies Lead to Greater Learning and Motivation? Investigating a Source of Complexity in the Architecture of Intelligent Tutoring Systems

ERIC Educational Resources Information Center

Waalkens, Maaike; Aleven, Vincent; Taatgen, Niels

2013-01-01

Intelligent tutoring systems (ITS) support students in learning a complex problem-solving skill. One feature that makes an ITS architecturally complex, and hard to build, is support for strategy freedom, that is, the ability to let students pursue multiple solution strategies within a given problem. But does greater freedom mean that students…

Paired-Associate and Feedback-Based Weather Prediction Tasks Support Multiple Category Learning Systems.

PubMed

Li, Kaiyun; Fu, Qiufang; Sun, Xunwei; Zhou, Xiaoyan; Fu, Xiaolan

2016-01-01

It remains unclear whether probabilistic category learning in the feedback-based weather prediction task (FB-WPT) can be mediated by a non-declarative or procedural learning system. To address this issue, we compared the effects of training time and verbal working memory, which influence the declarative learning system but not the non-declarative learning system, in the FB and paired-associate (PA) WPTs, as the PA task recruits a declarative learning system. The results of Experiment 1 showed that the optimal accuracy in the PA condition was significantly decreased when the training time was reduced from 7 to 3 s, but this did not occur in the FB condition, although shortened training time impaired the acquisition of explicit knowledge in both conditions. The results of Experiment 2 showed that the concurrent working memory task impaired the optimal accuracy and the acquisition of explicit knowledge in the PA condition but did not influence the optimal accuracy or the acquisition of self-insight knowledge in the FB condition. The apparent dissociation results between the FB and PA conditions suggested that a non-declarative or procedural learning system is involved in the FB-WPT and provided new evidence for the multiple-systems theory of human category learning.

Paired-Associate and Feedback-Based Weather Prediction Tasks Support Multiple Category Learning Systems

PubMed Central

Li, Kaiyun; Fu, Qiufang; Sun, Xunwei; Zhou, Xiaoyan; Fu, Xiaolan

2016-01-01

It remains unclear whether probabilistic category learning in the feedback-based weather prediction task (FB-WPT) can be mediated by a non-declarative or procedural learning system. To address this issue, we compared the effects of training time and verbal working memory, which influence the declarative learning system but not the non-declarative learning system, in the FB and paired-associate (PA) WPTs, as the PA task recruits a declarative learning system. The results of Experiment 1 showed that the optimal accuracy in the PA condition was significantly decreased when the training time was reduced from 7 to 3 s, but this did not occur in the FB condition, although shortened training time impaired the acquisition of explicit knowledge in both conditions. The results of Experiment 2 showed that the concurrent working memory task impaired the optimal accuracy and the acquisition of explicit knowledge in the PA condition but did not influence the optimal accuracy or the acquisition of self-insight knowledge in the FB condition. The apparent dissociation results between the FB and PA conditions suggested that a non-declarative or procedural learning system is involved in the FB-WPT and provided new evidence for the multiple-systems theory of human category learning. PMID:27445958

Learning classifier systems for single and multiple mobile robots in unstructured environments

NASA Astrophysics Data System (ADS)

Bay, John S.

1995-12-01

The learning classifier system (LCS) is a learning production system that generates behavioral rules via an underlying discovery mechanism. The LCS architecture operates similarly to a blackboard architecture; i.e., by posted-message communications. But in the LCS, the message board is wiped clean at every time interval, thereby requiring no persistent shared resource. In this paper, we adapt the LCS to the problem of mobile robot navigation in completely unstructured environments. We consider the model of the robot itself, including its sensor and actuator structures, to be part of this environment, in addition to the world-model that includes a goal and obstacles at unknown locations. This requires a robot to learn its own I/O characteristics in addition to solving its navigation problem, but results in a learning controller that is equally applicable, unaltered, in robots with a wide variety of kinematic structures and sensing capabilities. We show the effectiveness of this LCS-based controller through both simulation and experimental trials with a small robot. We then propose a new architecture, the Distributed Learning Classifier System (DLCS), which generalizes the message-passing behavior of the LCS from internal messages within a single agent to broadcast massages among multiple agents. This communications mode requires little bandwidth and is easily implemented with inexpensive, off-the-shelf hardware. The DLCS is shown to have potential application as a learning controller for multiple intelligent agents.

Validity and Realibility of Chemistry Systemic Multiple Choices Questions (CSMCQs)

ERIC Educational Resources Information Center

Priyambodo, Erfan; Marfuatun

2016-01-01

Nowadays, Rasch model analysis is used widely in social research, moreover in educational research. In this research, Rasch model is used to determine the validation and the reliability of systemic multiple choices question in chemistry teaching and learning. There were 30 multiple choices question with systemic approach for high school student…

Student and Staff Perceptions of a Learning Management System for Blended Learning in Teacher Education

ERIC Educational Resources Information Center

Holmes, Kathryn A.; Prieto-Rodriguez, Elena

2018-01-01

Higher education institutions routinely use Learning Management Systems (LMS) for multiple purposes; to organise coursework and assessment, to facilitate staff and student interactions, and to act as repositories of learning objects. The analysis reported here involves staff (n = 46) and student (n = 470) responses to surveys as well as data…

Motor imagery learning modulates functional connectivity of multiple brain systems in resting state.

PubMed

Zhang, Hang; Long, Zhiying; Ge, Ruiyang; Xu, Lele; Jin, Zhen; Yao, Li; Liu, Yijun

2014-01-01

Learning motor skills involves subsequent modulation of resting-state functional connectivity in the sensory-motor system. This idea was mostly derived from the investigations on motor execution learning which mainly recruits the processing of sensory-motor information. Behavioral evidences demonstrated that motor skills in our daily lives could be learned through imagery procedures. However, it remains unclear whether the modulation of resting-state functional connectivity also exists in the sensory-motor system after motor imagery learning. We performed a fMRI investigation on motor imagery learning from resting state. Based on previous studies, we identified eight sensory and cognitive resting-state networks (RSNs) corresponding to the brain systems and further explored the functional connectivity of these RSNs through the assessments, connectivity and network strengths before and after the two-week consecutive learning. Two intriguing results were revealed: (1) The sensory RSNs, specifically sensory-motor and lateral visual networks exhibited greater connectivity strengths in precuneus and fusiform gyrus after learning; (2) Decreased network strength induced by learning was proved in the default mode network, a cognitive RSN. These results indicated that resting-state functional connectivity could be modulated by motor imagery learning in multiple brain systems, and such modulation displayed in the sensory-motor, visual and default brain systems may be associated with the establishment of motor schema and the regulation of introspective thought. These findings further revealed the neural substrates underlying motor skill learning and potentially provided new insights into the therapeutic benefits of motor imagery learning.

Linear System Control Using Stochastic Learning Automata

NASA Technical Reports Server (NTRS)

Ziyad, Nigel; Cox, E. Lucien; Chouikha, Mohamed F.

1998-01-01

This paper explains the use of a Stochastic Learning Automata (SLA) to control switching between three systems to produce the desired output response. The SLA learns the optimal choice of the damping ratio for each system to achieve a desired result. We show that the SLA can learn these states for the control of an unknown system with the proper choice of the error criteria. The results of using a single automaton are compared to using multiple automata.

Adaptive versus Learner Control in a Multiple Intelligence Learning Environment

ERIC Educational Resources Information Center

Kelly, Declan

2008-01-01

Within the field of technology enhanced learning, adaptive educational systems offer an advanced form of learning environment that attempts to meet the needs of different students. Such systems capture and represent, for each student, various characteristics such as knowledge and traits in an individual learner model. Subsequently, using the…

Recommendation System Based On Association Rules For Distributed E-Learning Management Systems

NASA Astrophysics Data System (ADS)

Mihai, Gabroveanu

2015-09-01

Traditional Learning Management Systems are installed on a single server where learning materials and user data are kept. To increase its performance, the Learning Management System can be installed on multiple servers; learning materials and user data could be distributed across these servers obtaining a Distributed Learning Management System. In this paper is proposed the prototype of a recommendation system based on association rules for Distributed Learning Management System. Information from LMS databases is analyzed using distributed data mining algorithms in order to extract the association rules. Then the extracted rules are used as inference rules to provide personalized recommendations. The quality of provided recommendations is improved because the rules used to make the inferences are more accurate, since these rules aggregate knowledge from all e-Learning systems included in Distributed Learning Management System.

Critical Success Factors for E-Learning in Developing Countries: A Comparative Analysis between ICT Experts and Faculty

ERIC Educational Resources Information Center

Bhuasiri, Wannasiri; Xaymoungkhoun, Oudone; Zo, Hangjung; Rho, Jae Jeung; Ciganek, Andrew P.

2012-01-01

This study identifies the critical success factors that influence the acceptance of e-learning systems in developing countries. E-learning is a popular mode of delivering educational materials in higher education by universities throughout the world. This study identifies multiple factors that influence the success of e-learning systems from the…

Using a Learning Management System to Personalise Learning for Primary School Students

ERIC Educational Resources Information Center

Edmunds, Bronwyn; Hartnett, Maggie

2014-01-01

This paper reports on one aspect of a descriptive multiple-case study which set out to explore the role of a learning management system (LMS) in personalising learning for students from the perspective of three teachers in one primary school in New Zealand. The intention was to provide insight into the role the LMS could play in classrooms when…

A learning controller for nonrepetitive robotic operation

NASA Technical Reports Server (NTRS)

Miller, W. T., III

1987-01-01

A practical learning control system is described which is applicable to complex robotic and telerobotic systems involving multiple feedback sensors and multiple command variables. In the controller, the learning algorithm is used to learn to reproduce the nonlinear relationship between the sensor outputs and the system command variables over particular regions of the system state space, rather than learning the actuator commands required to perform a specific task. The learned information is used to predict the command signals required to produce desired changes in the sensor outputs. The desired sensor output changes may result from automatic trajectory planning or may be derived from interactive input from a human operator. The learning controller requires no a priori knowledge of the relationships between the sensor outputs and the command variables. The algorithm is well suited for real time implementation, requiring only fixed point addition and logical operations. The results of learning experiments using a General Electric P-5 manipulator interfaced to a VAX-11/730 computer are presented. These experiments involved interactive operator control, via joysticks, of the position and orientation of an object in the field of view of a video camera mounted on the end of the robot arm.

ICPL: Intelligent Cooperative Planning and Learning for Multi-agent Systems

DTIC Science & Technology

2012-02-29

objective was to develop a new planning approach for teams!of multiple UAVs that tightly integrates learning and cooperative!control algorithms at... algorithms at multiple levels of the planning architecture. The research results enabled a team of mobile agents to learn to adapt and react to uncertainty in...expressive representation that incorporates feature conjunctions. Our algorithm is simple to implement, fast to execute, and can be combined with any

Learning by Reading for Robust Reasoning in Intelligent Agents

DTIC Science & Technology

2018-04-24

SUPPLEMENTARY NOTES 14. ABSTRACT Our hypotheses are that analogical processing plays multiple roles in enabling machines to learn by reading, and that...systems). Our overall hypotheses are that analogical processing plays multiple roles in learning by reading, and that qualitative representations provide...from reading this text? Narrative function can be seen as a kind of communication act, but the idea goes a bit beyond that. Communication acts are

Hybrid Multiagent System for Automatic Object Learning Classification

NASA Astrophysics Data System (ADS)

Gil, Ana; de La Prieta, Fernando; López, Vivian F.

The rapid evolution within the context of e-learning is closely linked to international efforts on the standardization of learning object metadata, which provides learners in a web-based educational system with ubiquitous access to multiple distributed repositories. This article presents a hybrid agent-based architecture that enables the recovery of learning objects tagged in Learning Object Metadata (LOM) and provides individualized help with selecting learning materials to make the most suitable choice among many alternatives.

Motor Imagery Learning Modulates Functional Connectivity of Multiple Brain Systems in Resting State

PubMed Central

Zhang, Hang; Long, Zhiying; Ge, Ruiyang; Xu, Lele; Jin, Zhen; Yao, Li; Liu, Yijun

2014-01-01

Background Learning motor skills involves subsequent modulation of resting-state functional connectivity in the sensory-motor system. This idea was mostly derived from the investigations on motor execution learning which mainly recruits the processing of sensory-motor information. Behavioral evidences demonstrated that motor skills in our daily lives could be learned through imagery procedures. However, it remains unclear whether the modulation of resting-state functional connectivity also exists in the sensory-motor system after motor imagery learning. Methodology/Principal Findings We performed a fMRI investigation on motor imagery learning from resting state. Based on previous studies, we identified eight sensory and cognitive resting-state networks (RSNs) corresponding to the brain systems and further explored the functional connectivity of these RSNs through the assessments, connectivity and network strengths before and after the two-week consecutive learning. Two intriguing results were revealed: (1) The sensory RSNs, specifically sensory-motor and lateral visual networks exhibited greater connectivity strengths in precuneus and fusiform gyrus after learning; (2) Decreased network strength induced by learning was proved in the default mode network, a cognitive RSN. Conclusions/Significance These results indicated that resting-state functional connectivity could be modulated by motor imagery learning in multiple brain systems, and such modulation displayed in the sensory-motor, visual and default brain systems may be associated with the establishment of motor schema and the regulation of introspective thought. These findings further revealed the neural substrates underlying motor skill learning and potentially provided new insights into the therapeutic benefits of motor imagery learning. PMID:24465577

Multiple Criteria Evaluation of Quality and Optimisation of e-Learning System Components

ERIC Educational Resources Information Center

Kurilovas, Eugenijus; Dagiene, Valentina

2010-01-01

The main research object of the paper is investigation and proposal of the comprehensive Learning Object Repositories (LORs) quality evaluation tool suitable for their multiple criteria decision analysis, evaluation and optimisation. Both LORs "internal quality" and "quality in use" evaluation (decision making) criteria are analysed in the paper.…

Developing Web-Based Assessment Strategies for Facilitating Junior High School Students to Perform Self-Regulated Learning in an E-Learning Environment

ERIC Educational Resources Information Center

Wang, Tzu-Hua

2011-01-01

This research refers to the self-regulated learning strategies proposed by Pintrich (1999) in developing a multiple-choice Web-based assessment system, the Peer-Driven Assessment Module of the Web-based Assessment and Test Analysis system (PDA-WATA). The major purpose of PDA-WATA is to facilitate learner use of self-regulatory learning behaviors…

A Study of the Design and Implementation of the ASR-Based iCASL System with Corrective Feedback to Facilitate English Learning

ERIC Educational Resources Information Center

Wang, Yi-Hsuan; Young, Shelley Shwu-Ching

2014-01-01

The purpose of the study is to explore and describe how to implement a pedagogical ASR-based intelligent computer-assisted speaking learning (iCASL) system to support adult learners with a private, flexible and individual learning environment to practice English pronunciation. The iCASL system integrates multiple levels of corrective feedback and…

Cleared for Launch - Lessons Learned from the OSIRIS-REx System Requirements Verification Program

NASA Technical Reports Server (NTRS)

Stevens, Craig; Adams, Angela; Williams, Bradley; Goodloe, Colby

2017-01-01

Requirements verification of a large flight system is a challenge. It is especially challenging for engineers taking on their first role in space systems engineering. This paper describes our approach to verification of the Origins, Spectral Interpretation, Resource Identification, Security-Regolith Explorer (OSIRIS-REx) system requirements. It also captures lessons learned along the way from developing systems engineers embroiled in this process. We begin with an overview of the mission and science objectives as well as the project requirements verification program strategy. A description of the requirements flow down is presented including our implementation for managing the thousands of program and element level requirements and associated verification data. We discuss both successes and methods to improve the managing of this data across multiple organizational interfaces. Our approach to verifying system requirements at multiple levels of assembly is presented using examples from our work at instrument, spacecraft, and ground segment levels. We include a discussion of system end-to-end testing limitations and their impacts to the verification program. Finally, we describe lessons learned that are applicable to all emerging space systems engineers using our unique perspectives across multiple organizations of a large NASA program.

The Effects of Integrating Social Learning Environment with Online Learning

ERIC Educational Resources Information Center

Raspopovic, Miroslava; Cvetanovic, Svetlana; Medan, Ivana; Ljubojevic, Danijela

2017-01-01

The aim of this paper is to present the learning and teaching styles using the Social Learning Environment (SLE), which was developed based on the computer supported collaborative learning approach. To avoid burdening learners with multiple platforms and tools, SLE was designed and developed in order to integrate existing systems, institutional…

Learning of Rule Ensembles for Multiple Attribute Ranking Problems

NASA Astrophysics Data System (ADS)

Dembczyński, Krzysztof; Kotłowski, Wojciech; Słowiński, Roman; Szeląg, Marcin

In this paper, we consider the multiple attribute ranking problem from a Machine Learning perspective. We propose two approaches to statistical learning of an ensemble of decision rules from decision examples provided by the Decision Maker in terms of pairwise comparisons of some objects. The first approach consists in learning a preference function defining a binary preference relation for a pair of objects. The result of application of this function on all pairs of objects to be ranked is then exploited using the Net Flow Score procedure, giving a linear ranking of objects. The second approach consists in learning a utility function for single objects. The utility function also gives a linear ranking of objects. In both approaches, the learning is based on the boosting technique. The presented approaches to Preference Learning share good properties of the decision rule preference model and have good performance in the massive-data learning problems. As Preference Learning and Multiple Attribute Decision Aiding share many concepts and methodological issues, in the introduction, we review some aspects bridging these two fields. To illustrate the two approaches proposed in this paper, we solve with them a toy example concerning the ranking of a set of cars evaluated by multiple attributes. Then, we perform a large data experiment on real data sets. The first data set concerns credit rating. Since recent research in the field of Preference Learning is motivated by the increasing role of modeling preferences in recommender systems and information retrieval, we chose two other massive data sets from this area - one comes from movie recommender system MovieLens, and the other concerns ranking of text documents from 20 Newsgroups data set.

Touching Mercury in Community Media: Identifying Multiple Literacy Learning through Digital Arts Production

ERIC Educational Resources Information Center

Arndt, Angela E.

2011-01-01

Educational paradigm shifts call for 21st century learners to possess the knowledge, skills, abilities, values, and experiences associated with multiple forms of literacy in a participatory learning culture. Contemporary educational systems are slow to adapt. Outside of school, people have to be self-motivated and have access to resources in order…

A Conceptual Framework for Error Remediation with Multiple External Representations Applied to Learning Objects

ERIC Educational Resources Information Center

Leite, Maici Duarte; Marczal, Diego; Pimentel, Andrey Ricardo; Direne, Alexandre Ibrahim

2014-01-01

This paper presents the application of some concepts of Intelligent Tutoring Systems (ITS) to elaborate a conceptual framework that uses the remediation of errors with Multiple External Representations (MERs) in Learning Objects (LO). To this is demonstrated a development of LO for teaching the Pythagorean Theorem through this framework. This…

Improving Predictions of Multiple Binary Models in ILP

PubMed Central

2014-01-01

Despite the success of ILP systems in learning first-order rules from small number of examples and complexly structured data in various domains, they struggle in dealing with multiclass problems. In most cases they boil down a multiclass problem into multiple black-box binary problems following the one-versus-one or one-versus-rest binarisation techniques and learn a theory for each one. When evaluating the learned theories of multiple class problems in one-versus-rest paradigm particularly, there is a bias caused by the default rule toward the negative classes leading to an unrealistic high performance beside the lack of prediction integrity between the theories. Here we discuss the problem of using one-versus-rest binarisation technique when it comes to evaluating multiclass data and propose several methods to remedy this problem. We also illustrate the methods and highlight their link to binary tree and Formal Concept Analysis (FCA). Our methods allow learning of a simple, consistent, and reliable multiclass theory by combining the rules of the multiple one-versus-rest theories into one rule list or rule set theory. Empirical evaluation over a number of data sets shows that our proposed methods produce coherent and accurate rule models from the rules learned by the ILP system of Aleph. PMID:24696657

Ontology Mappings to Improve Learning Resource Search

ERIC Educational Resources Information Center

Gasevic, Dragan; Hatala, Marek

2006-01-01

This paper proposes an ontology mapping-based framework that allows searching for learning resources using multiple ontologies. The present applications of ontologies in e-learning use various ontologies (eg, domain, curriculum, context), but they do not give a solution on how to interoperate e-learning systems based on different ontologies. The…

Assessing the Application of Three-Dimensional Collaborative Technologies within an E-Learning Environment

ERIC Educational Resources Information Center

McArdle, Gavin; Bertolotto, Michela

2012-01-01

Today, the Internet plays a major role in distributing learning material within third level education. Multiple online facilities provide access to educational resources. While early systems relied on webpages, which acted as repositories for learning material, nowadays sophisticated online applications manage and deliver learning resources.…

Gaming the System: Helping Students Level up Their Learning

ERIC Educational Resources Information Center

Hill, David; Brunvan, Stein

2018-01-01

The use of gamified learning has increased within the educational community over the last decade in an attempt to enhance student learning in multiple ways. In particular, researchers have started to examine gamified learning and its impact on student motivation and engagement within educational settings. However, few have examined the…

Imaging Evidence for Disturbances in Multiple Learning and Memory Systems in Persons with Autism Spectrum Disorders

ERIC Educational Resources Information Center

Goh, Suzanne; Peterson, Bradley S.

2012-01-01

Aim: The aim of this article is to review neuroimaging studies of autism spectrum disorders (ASD) that examine declarative, socio-emotional, and procedural learning and memory systems. Method: We conducted a search of PubMed from 1996 to 2010 using the terms "autism,""learning,""memory," and "neuroimaging." We limited our review to studies…

ElectronixTutor: An Intelligent Tutoring System with Multiple Learning Resources for Electronics

ERIC Educational Resources Information Center

Graesser, Arthur C.; Hu, Xiangen; Nye, Benjamin D.; VanLehn, Kurt; Kumar, Rohit; Heffernan, Cristina; Heffernan, Neil; Woolf, Beverly; Olney, Andrew M.; Rus, Vasile; Andrasik, Frank; Pavlik, Philip; Cai, Zhiqiang; Wetzel, Jon; Morgan, Brent; Hampton, Andrew J.; Lippert, Anne M.; Wang, Lijia; Cheng, Qinyu; Vinson, Joseph E.; Kelly, Craig N.; McGlown, Cadarrius; Majmudar, Charvi A.; Morshed, Bashir; Baer, Whitney

2018-01-01

Background: The Office of Naval Research (ONR) organized a STEM Challenge initiative to explore how intelligent tutoring systems (ITSs) can be developed in a reasonable amount of time to help students learn STEM topics. This competitive initiative sponsored four teams that separately developed systems that covered topics in mathematics,…

A Research Context for Diagnostic and Prescriptive Mathematics.

ERIC Educational Resources Information Center

Engelhardt, Jon; Uprichard, A. Edward

1998-01-01

Argues that a position should be taken on which future research initiatives on learning and instruction will be most worthy if grounded in general systems theory and multiple research methods are employed. Presents an application of general systems theory to research on learning and instruction, including a system of research methods and…

The Effects of DI Flashcards and Math Racetrack on Multiplication Facts for Two Elementary Students with Learning Disabilities

ERIC Educational Resources Information Center

Lund, Kaitlyn; McLaughlin, T. F.; Neyman, Jen; Everson, Mary

2012-01-01

The purpose of this study was to evaluate the effects of a Direct Instruction (DI) flashcard system paired with a math racetrack to teach basic multiplication facts to two elementary students diagnosed with learning disabilities. The study was conducted in a resource room which served intermediate aged elementary students. The school was located…

Investigation Gender/Ethnicity Heterogeneity in Course Management System Use in Higher Education by Utilizing the MIMIC Model

ERIC Educational Resources Information Center

Li, Yi

2012-01-01

This study focuses on the issue of learning equity in colleges and universities where teaching and learning have come to depend heavily on computer technologies. The study uses the Multiple Indicators Multiple Causes (MIMIC) latent variable model to quantitatively investigate whether there is a gender /ethnicity difference in using computer based…

Enhanced Multisensory Integration and Motor Reactivation after Active Motor Learning of Audiovisual Associations

ERIC Educational Resources Information Center

Butler, Andrew J.; James, Thomas W.; James, Karin Harman

2011-01-01

Everyday experience affords us many opportunities to learn about objects through multiple senses using physical interaction. Previous work has shown that active motor learning of unisensory items enhances memory and leads to the involvement of motor systems during subsequent perception. However, the impact of active motor learning on subsequent…

Foundations for Modeling University Curricula in Terms of Multiple Learning Goal Sets

ERIC Educational Resources Information Center

Gluga, R.; Kay, J.; Lever, T.

2013-01-01

It is important, but very challenging, to design degree programs, so that the sequence of learning activities, topics, and assessments over three to five years give an effective progression in learning of generic skills, discipline-specific learning goals and accreditation competencies. Our CUSP (Course and Unit of Study Portal) system tackles…

Fostering the Development of Critical Thinking Skills, and Reading Comprehension of Undergraduates Using a Web 2.0 Tool Coupled with a Learning System

ERIC Educational Resources Information Center

Mendenhall, Anne; Johnson, Tristan E.

2010-01-01

A social annotation model learning system (SAM-LS) was created using multiple instructional strategies thereby supporting the student in improving in critical thinking, critical writing and related literacy. There are four mechanisms in which the SAM-LS methodology is believed to improve learning and performance. These mechanisms include providing…

Wide coverage biomedical event extraction using multiple partially overlapping corpora

PubMed Central

2013-01-01

Background Biomedical events are key to understanding physiological processes and disease, and wide coverage extraction is required for comprehensive automatic analysis of statements describing biomedical systems in the literature. In turn, the training and evaluation of extraction methods requires manually annotated corpora. However, as manual annotation is time-consuming and expensive, any single event-annotated corpus can only cover a limited number of semantic types. Although combined use of several such corpora could potentially allow an extraction system to achieve broad semantic coverage, there has been little research into learning from multiple corpora with partially overlapping semantic annotation scopes. Results We propose a method for learning from multiple corpora with partial semantic annotation overlap, and implement this method to improve our existing event extraction system, EventMine. An evaluation using seven event annotated corpora, including 65 event types in total, shows that learning from overlapping corpora can produce a single, corpus-independent, wide coverage extraction system that outperforms systems trained on single corpora and exceeds previously reported results on two established event extraction tasks from the BioNLP Shared Task 2011. Conclusions The proposed method allows the training of a wide-coverage, state-of-the-art event extraction system from multiple corpora with partial semantic annotation overlap. The resulting single model makes broad-coverage extraction straightforward in practice by removing the need to either select a subset of compatible corpora or semantic types, or to merge results from several models trained on different individual corpora. Multi-corpus learning also allows annotation efforts to focus on covering additional semantic types, rather than aiming for exhaustive coverage in any single annotation effort, or extending the coverage of semantic types annotated in existing corpora. PMID:23731785

ICCE/ICCAI 2000 Full & Short Papers (Creative Learning).

ERIC Educational Resources Information Center

2000

This document contains the following full and short papers on creative learning from ICCE/ICCAI 2000 (International Conference on Computers in Education/International Conference on Computer-Assisted Instruction): (1) "A Collaborative Learning Support System Based on Virtual Environment Server for Multiple Agents" (Takashi Ohno, Kenji…

Free Choice of Learning Management Systems: Do Student Habits Override Inherent System Quality?

ERIC Educational Resources Information Center

Porter, Gavin W.

2013-01-01

Purpose: Although multiple studies examine institutional transitions of learning management systems (LMS) or compare their merits, studies examining students' free choice of access on parallel LMSs for the same course are absent from the literature. In order to investigate usage in a free-choice situation, identical content was posted at the same…

Preserved learning of novel information in amnesia: evidence for multiple memory systems.

PubMed

Gordon, B

1988-06-01

Four of five patients with marked global amnesia, and others with new learning impairments, showed normal processing facilitation for novel stimuli (nonwords) and/or for familiar stimuli (words) on a word/nonword (lexical) decision task. The data are interpreted as a reflection of the learning capabilities of in-line neural processing stages with multiple, distinct, informational codes. These in-line learning processes are separate from the recognition/recall memory impaired by amygdalohippocampal/dosomedial thalamic damage, but probably supplement such memory in some tasks in normal individuals. Preserved learning of novel information seems incompatible with explanations of spared learning in amnesia that are based on the episodic/semantic or memory/habit distinctions, but is consistent with the procedural/declarative hypothesis.

Memory and cognitive control circuits in mathematical cognition and learning.

PubMed

Menon, V

2016-01-01

Numerical cognition relies on interactions within and between multiple functional brain systems, including those subserving quantity processing, working memory, declarative memory, and cognitive control. This chapter describes recent advances in our understanding of memory and control circuits in mathematical cognition and learning. The working memory system involves multiple parietal-frontal circuits which create short-term representations that allow manipulation of discrete quantities over several seconds. In contrast, hippocampal-frontal circuits underlying the declarative memory system play an important role in formation of associative memories and binding of new and old information, leading to the formation of long-term memories that allow generalization beyond individual problem attributes. The flow of information across these systems is regulated by flexible cognitive control systems which facilitate the integration and manipulation of quantity and mnemonic information. The implications of recent research for formulating a more comprehensive systems neuroscience view of the neural basis of mathematical learning and knowledge acquisition in both children and adults are discussed. © 2016 Elsevier B.V. All rights reserved.

Memory and cognitive control circuits in mathematical cognition and learning

PubMed Central

Menon, V.

2018-01-01

Numerical cognition relies on interactions within and between multiple functional brain systems, including those subserving quantity processing, working memory, declarative memory, and cognitive control. This chapter describes recent advances in our understanding of memory and control circuits in mathematical cognition and learning. The working memory system involves multiple parietal–frontal circuits which create short-term representations that allow manipulation of discrete quantities over several seconds. In contrast, hippocampal–frontal circuits underlying the declarative memory system play an important role in formation of associative memories and binding of new and old information, leading to the formation of long-term memories that allow generalization beyond individual problem attributes. The flow of information across these systems is regulated by flexible cognitive control systems which facilitate the integration and manipulation of quantity and mnemonic information. The implications of recent research for formulating a more comprehensive systems neuroscience view of the neural basis of mathematical learning and knowledge acquisition in both children and adults are discussed. PMID:27339012

Learning from Multiple Collaborating Intelligent Tutors: An Agent-based Approach.

ERIC Educational Resources Information Center

Solomos, Konstantinos; Avouris, Nikolaos

1999-01-01

Describes an open distributed multi-agent tutoring system (MATS) and discusses issues related to learning in such open environments. Topics include modeling a one student-many teachers approach in a computer-based learning context; distributed artificial intelligence; implementation issues; collaboration; and user interaction. (Author/LRW)

Multiple systems for motor skill learning.

PubMed

Clark, Dav; Ivry, Richard B

2010-07-01

Motor learning is a ubiquitous feature of human competence. This review focuses on two particular classes of model tasks for studying skill acquisition. The serial reaction time (SRT) task is used to probe how people learn sequences of actions, while adaptation in the context of visuomotor or force field perturbations serves to illustrate how preexisting movements are recalibrated in novel environments. These tasks highlight important issues regarding the representational changes that occur during the course of motor learning. One important theme is that distinct mechanisms vary in their information processing costs during learning and performance. Fast learning processes may require few trials to produce large changes in performance but impose demands on cognitive resources. Slower processes are limited in their ability to integrate complex information but minimally demanding in terms of attention or processing resources. The representations derived from fast systems may be accessible to conscious processing and provide a relatively greater measure of flexibility, while the representations derived from slower systems are more inflexible and automatic in their behavior. In exploring these issues, we focus on how multiple neural systems may interact and compete during the acquisition and consolidation of new behaviors. Copyright © 2010 John Wiley & Sons, Ltd. This article is categorized under: Psychology > Motor Skill and Performance. Copyright © 2010 John Wiley & Sons, Ltd.

Enriching Adaptation in E-Learning Systems through a Situation-Aware Ontology Network

ERIC Educational Resources Information Center

Pernas, Ana Marilza; Diaz, Alicia; Motz, Regina; de Oliveira, Jose Palazzo Moreira

2012-01-01

Purpose: The broader adoption of the internet along with web-based systems has defined a new way of exchanging information. That advance added by the multiplication of mobile devices has required systems to be even more flexible and personalized. Maybe because of that, the traditional teaching-controlled learning style has given up space to a new…

Prediction of Ionizing Radiation Resistance in Bacteria Using a Multiple Instance Learning Model.

PubMed

Aridhi, Sabeur; Sghaier, Haïtham; Zoghlami, Manel; Maddouri, Mondher; Nguifo, Engelbert Mephu

2016-01-01

Ionizing-radiation-resistant bacteria (IRRB) are important in biotechnology. In this context, in silico methods of phenotypic prediction and genotype-phenotype relationship discovery are limited. In this work, we analyzed basal DNA repair proteins of most known proteome sequences of IRRB and ionizing-radiation-sensitive bacteria (IRSB) in order to learn a classifier that correctly predicts this bacterial phenotype. We formulated the problem of predicting bacterial ionizing radiation resistance (IRR) as a multiple-instance learning (MIL) problem, and we proposed a novel approach for this purpose. We provide a MIL-based prediction system that classifies a bacterium to either IRRB or IRSB. The experimental results of the proposed system are satisfactory with 91.5% of successful predictions.

Stakeholders' Conceptions of Connecting Learning at Different Sites in Two National VET Systems

ERIC Educational Resources Information Center

Sappa, Viviana; Choy, Sarojni; Aprea, Carmela

2016-01-01

Learning through active participation and engagement in education and workplace settings is a prerequisite for effective professional competence development through Vocational Education and Training (VET). Equally important is that learning from multiple sites and sources needs to be purposefully connected and integrated to construct meaningful…

The Relationship of Neurogenesis and Growth of Brain Regions to Song Learning

ERIC Educational Resources Information Center

Kirn, John R.

2010-01-01

Song learning, maintenance and production require coordinated activity across multiple auditory, sensory-motor, and neuromuscular structures. Telencephalic components of the sensory-motor circuitry are unique to avian species that engage in song learning. The song system shows protracted development that begins prior to hatching but continues well…

Effects of Response-Driven Feedback in Computer Science Learning

ERIC Educational Resources Information Center

Fernandez Aleman, J. L.; Palmer-Brown, D.; Jayne, C.

2011-01-01

This paper presents the results of a project on generating diagnostic feedback for guided learning in a first-year course on programming and a Master's course on software quality. An online multiple-choice questions (MCQs) system is integrated with neural network-based data analysis. Findings about how students use the system suggest that the…

A single-layer network unsupervised feature learning method for white matter hyperintensity segmentation

NASA Astrophysics Data System (ADS)

Vijverberg, Koen; Ghafoorian, Mohsen; van Uden, Inge W. M.; de Leeuw, Frank-Erik; Platel, Bram; Heskes, Tom

2016-03-01

Cerebral small vessel disease (SVD) is a disorder frequently found among the old people and is associated with deterioration in cognitive performance, parkinsonism, motor and mood impairments. White matter hyperintensities (WMH) as well as lacunes, microbleeds and subcortical brain atrophy are part of the spectrum of image findings, related to SVD. Accurate segmentation of WMHs is important for prognosis and diagnosis of multiple neurological disorders such as MS and SVD. Almost all of the published (semi-)automated WMH detection models employ multiple complex hand-crafted features, which require in-depth domain knowledge. In this paper we propose to apply a single-layer network unsupervised feature learning (USFL) method to avoid hand-crafted features, but rather to automatically learn a more efficient set of features. Experimental results show that a computer aided detection system with a USFL system outperforms a hand-crafted approach. Moreover, since the two feature sets have complementary properties, a hybrid system that makes use of both hand-crafted and unsupervised learned features, shows a significant performance boost compared to each system separately, getting close to the performance of an independent human expert.

Multiple transient memories in sheared suspensions: Robustness, structure, and routes to plasticity

NASA Astrophysics Data System (ADS)

Keim, Nathan C.; Paulsen, Joseph D.; Nagel, Sidney R.

2013-09-01

Multiple transient memories, originally discovered in charge-density-wave conductors, are a remarkable and initially counterintuitive example of how a system can store information about its driving. In this class of memories, a system can learn multiple driving inputs, nearly all of which are eventually forgotten despite their continual input. If sufficient noise is present, the system regains plasticity so that it can continue to learn new memories indefinitely. Recently, Keim and Nagel [Phys. Rev. Lett.PRLTAO0031-900710.1103/PhysRevLett.107.010603 107, 010603 (2011)] showed how multiple transient memories could be generalized to a generic driven disordered system with noise, giving as an example simulations of a simple model of a sheared non-Brownian suspension. Here, we further explore simulation models of suspensions under cyclic shear, focusing on three main themes: robustness, structure, and overdriving. We show that multiple transient memories are a robust feature independent of many details of the model. The steady-state spatial distribution of the particles is sensitive to the driving algorithm; nonetheless, the memory formation is independent of such a change in particle correlations. Finally, we demonstrate that overdriving provides another means for controlling memory formation and retention.

Learning to learn causal models.

PubMed

Kemp, Charles; Goodman, Noah D; Tenenbaum, Joshua B

2010-09-01

Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical Bayesian framework that helps to explain how learning about several causal systems can accelerate learning about systems that are subsequently encountered. Given experience with a set of objects, our framework learns a causal model for each object and a causal schema that captures commonalities among these causal models. The schema organizes the objects into categories and specifies the causal powers and characteristic features of these categories and the characteristic causal interactions between categories. A schema of this kind allows causal models for subsequent objects to be rapidly learned, and we explore this accelerated learning in four experiments. Our results confirm that humans learn rapidly about the causal powers of novel objects, and we show that our framework accounts better for our data than alternative models of causal learning. Copyright © 2010 Cognitive Science Society, Inc.

A presentation system for just-in-time learning in radiology.

PubMed

Kahn, Charles E; Santos, Amadeu; Thao, Cheng; Rock, Jayson J; Nagy, Paul G; Ehlers, Kevin C

2007-03-01

There is growing interest in bringing medical educational materials to the point of care. We sought to develop a system for just-in-time learning in radiology. A database of 34 learning modules was derived from previously published journal articles. Learning objectives were specified for each module, and multiple-choice test items were created. A web-based system-called TEMPO-was developed to allow radiologists to select and view the learning modules. Web services were used to exchange clinical context information between TEMPO and the simulated radiology work station. Preliminary evaluation was conducted using the System Usability Scale (SUS) questionnaire. TEMPO identified learning modules that were relevant to the age, sex, imaging modality, and body part or organ system of the patient being viewed by the radiologist on the simulated clinical work station. Users expressed a high degree of satisfaction with the system's design and user interface. TEMPO enables just-in-time learning in radiology, and can be extended to create a fully functional learning management system for point-of-care learning in radiology.

Multiplicity in public health supply systems: a learning agenda.

PubMed

Bornbusch, Alan; Bates, James

2013-08-01

Supply chain integration-merging products for health programs into a single supply chain-tends to be the dominant model in health sector reform. However, multiplicity in a supply system may be justified as a risk management strategy that can better ensure product availability, advance specific health program objectives, and increase efficiency.

Comulang: towards a collaborative e-learning system that supports student group modeling.

PubMed

Troussas, Christos; Virvou, Maria; Alepis, Efthimios

2013-01-01

This paper describes an e-learning system that is expected to further enhance the educational process in computer-based tutoring systems by incorporating collaboration between students and work in groups. The resulting system is called "Comulang" while as a test bed for its effectiveness a multiple language learning system is used. Collaboration is supported by a user modeling module that is responsible for the initial creation of student clusters, where, as a next step, working groups of students are created. A machine learning clustering algorithm works towards group formatting, so that co-operations between students from different clusters are attained. One of the resulting system's basic aims is to provide efficient student groups whose limitations and capabilities are well balanced.

Anxiety, cognition, and habit: a multiple memory systems perspective.

PubMed

Packard, Mark G

2009-10-13

Consistent with a multiple systems approach to memory organization in the mammalian brain, numerous studies have differentiated the roles of the hippocampus and dorsal striatum in "cognitive" and "habit" learning and memory, respectively. Additional research indicates that activation of efferent projections of the basolateral amygdala (BLA), a brain region implicated in mammalian emotion, modulates memory processes occurring in other brain structures. The present brief review describes research designed to link these general concepts by examining the manner in which emotional state may influence the relative use of multiple memory systems. In a dual-solution plus-maze task that can be acquired using either hippocampus-dependent or dorsal striatal-dependent learning, acute pre-training or pre-retrieval emotional arousal (restraint stress/inescapable foot shock, exposure to the predator odor TMT, or peripheral injection of anixogenic drugs) biases rats towards the use of habit memory. Moreover, intra-BLA injection of anxiogenic drugs is sufficient to bias rats towards the use of dorsal striatal-dependent habit memory. In single-solution plus-maze tasks that require the use of either cognitive or habit learning, intra-BLA infusions of anxiogenic drugs result in a behavioral profile indicating an impairing effect on hippocampus-dependent memory that effectively produces enhanced habit learning by eliminating competitive interference between cognitive and habit memory systems. It is speculated that the predominant use of habit memory that can be produced by anxious and/or stressful emotional states may have implications for understanding the role of learning and memory processes in various human psychopathologies, including for example post-traumatic stress disorder and drug addiction.

Learning to improve iterative repair scheduling

NASA Technical Reports Server (NTRS)

Zweben, Monte; Davis, Eugene

1992-01-01

This paper presents a general learning method for dynamically selecting between repair heuristics in an iterative repair scheduling system. The system employs a version of explanation-based learning called Plausible Explanation-Based Learning (PEBL) that uses multiple examples to confirm conjectured explanations. The basic approach is to conjecture contradictions between a heuristic and statistics that measure the quality of the heuristic. When these contradictions are confirmed, a different heuristic is selected. To motivate the utility of this approach we present an empirical evaluation of the performance of a scheduling system with respect to two different repair strategies. We show that the scheduler that learns to choose between the heuristics outperforms the same scheduler with any one of two heuristics alone.

A neurocomputational theory of how explicit learning bootstraps early procedural learning.

PubMed

Paul, Erick J; Ashby, F Gregory

2013-01-01

It is widely accepted that human learning and memory is mediated by multiple memory systems that are each best suited to different requirements and demands. Within the domain of categorization, at least two systems are thought to facilitate learning: an explicit (declarative) system depending largely on the prefrontal cortex, and a procedural (non-declarative) system depending on the basal ganglia. Substantial evidence suggests that each system is optimally suited to learn particular categorization tasks. However, it remains unknown precisely how these systems interact to produce optimal learning and behavior. In order to investigate this issue, the present research evaluated the progression of learning through simulation of categorization tasks using COVIS, a well-known model of human category learning that includes both explicit and procedural learning systems. Specifically, the model's parameter space was thoroughly explored in procedurally learned categorization tasks across a variety of conditions and architectures to identify plausible interaction architectures. The simulation results support the hypothesis that one-way interaction between the systems occurs such that the explicit system "bootstraps" learning early on in the procedural system. Thus, the procedural system initially learns a suboptimal strategy employed by the explicit system and later refines its strategy. This bootstrapping could be from cortical-striatal projections that originate in premotor or motor regions of cortex, or possibly by the explicit system's control of motor responses through basal ganglia-mediated loops.

Intelligent multiagent coordination based on reinforcement hierarchical neuro-fuzzy models.

PubMed

Mendoza, Leonardo Forero; Vellasco, Marley; Figueiredo, Karla

2014-12-01

This paper presents the research and development of two hybrid neuro-fuzzy models for the hierarchical coordination of multiple intelligent agents. The main objective of the models is to have multiple agents interact intelligently with each other in complex systems. We developed two new models of coordination for intelligent multiagent systems, which integrates the Reinforcement Learning Hierarchical Neuro-Fuzzy model with two proposed coordination mechanisms: the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with a market-driven coordination mechanism (MA-RL-HNFP-MD) and the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with graph coordination (MA-RL-HNFP-CG). In order to evaluate the proposed models and verify the contribution of the proposed coordination mechanisms, two multiagent benchmark applications were developed: the pursuit game and the robot soccer simulation. The results obtained demonstrated that the proposed coordination mechanisms greatly improve the performance of the multiagent system when compared with other strategies.

Ontology-Driven Disability-Aware E-Learning Personalisation with ONTODAPS

ERIC Educational Resources Information Center

Nganji, Julius T.; Brayshaw, Mike; Tompsett, Brian

2013-01-01

Purpose: The purpose of this paper is to show how personalisation of learning resources and services can be achieved for students with and without disabilities, particularly responding to the needs of those with multiple disabilities in e-learning systems. The paper aims to introduce ONTODAPS, the Ontology-Driven Disability-Aware Personalised…

Designing for Discovery Learning of Complexity Principles of Congestion by Driving Together in the TrafficJams Simulation

ERIC Educational Resources Information Center

Levy, Sharona T.; Peleg, Ran; Ofeck, Eyal; Tabor, Naamit; Dubovi, Ilana; Bluestein, Shiri; Ben-Zur, Hadar

2018-01-01

We propose and evaluate a framework supporting collaborative discovery learning of complex systems. The framework blends five design principles: (1) individual action: amidst (2) social interactions; challenged with (3) multiple tasks; set in (4) a constrained interactive learning environment that draws attention to (5) highlighted target…

Student Usage of Instructional Technologies: Differences in Online Learning Styles

ERIC Educational Resources Information Center

Ballenger, Robert M.; Garvis, Dennis M.

2010-01-01

We contribute to the MIS education literature by empirically examining Web log server data generated by undergraduate students enrolled in multiple sections of a MIS course where an online Learning Management System (LMS) was used to complement a traditional classroom environment. We identify online learning styles by investigating differences in…

Automatic Visual Tracking and Social Behaviour Analysis with Multiple Mice

PubMed Central

Giancardo, Luca; Sona, Diego; Huang, Huiping; Sannino, Sara; Managò, Francesca; Scheggia, Diego; Papaleo, Francesco; Murino, Vittorio

2013-01-01

Social interactions are made of complex behavioural actions that might be found in all mammalians, including humans and rodents. Recently, mouse models are increasingly being used in preclinical research to understand the biological basis of social-related pathologies or abnormalities. However, reliable and flexible automatic systems able to precisely quantify social behavioural interactions of multiple mice are still missing. Here, we present a system built on two components. A module able to accurately track the position of multiple interacting mice from videos, regardless of their fur colour or light settings, and a module that automatically characterise social and non-social behaviours. The behavioural analysis is obtained by deriving a new set of specialised spatio-temporal features from the tracker output. These features are further employed by a learning-by-example classifier, which predicts for each frame and for each mouse in the cage one of the behaviours learnt from the examples given by the experimenters. The system is validated on an extensive set of experimental trials involving multiple mice in an open arena. In a first evaluation we compare the classifier output with the independent evaluation of two human graders, obtaining comparable results. Then, we show the applicability of our technique to multiple mice settings, using up to four interacting mice. The system is also compared with a solution recently proposed in the literature that, similarly to us, addresses the problem with a learning-by-examples approach. Finally, we further validated our automatic system to differentiate between C57B/6J (a commonly used reference inbred strain) and BTBR T+tf/J (a mouse model for autism spectrum disorders). Overall, these data demonstrate the validity and effectiveness of this new machine learning system in the detection of social and non-social behaviours in multiple (>2) interacting mice, and its versatility to deal with different experimental settings and scenarios. PMID:24066146

System and method for cognitive processing for data fusion

NASA Technical Reports Server (NTRS)

Duong, Tuan A. (Inventor); Duong, Vu A. (Inventor)

2012-01-01

A system and method for cognitive processing of sensor data. A processor array receiving analog sensor data and having programmable interconnects, multiplication weights, and filters provides for adaptive learning in real-time. A static random access memory contains the programmable data for the processor array and the stored data is modified to provide for adaptive learning.

There is No Free Lunch: Tradeoffs in the Utility of Learned Knowledge

NASA Technical Reports Server (NTRS)

Kedar, Smadar T.; McKusick, Kathleen B.

1992-01-01

With the recent introduction of learning in integrated systems, there is a need to measure the utility of learned knowledge for these more complex systems. A difficulty arrises when there are multiple, possibly conflicting, utility metrics to be measured. In this paper, we present schemes which trade off conflicting utility metrics in order to achieve some global performance objectives. In particular, we present a case study of a multi-strategy machine learning system, mutual theory refinement, which refines world models for an integrated reactive system, the Entropy Reduction Engine. We provide experimental results on the utility of learned knowledge in two conflicting metrics - improved accuracy and degraded efficiency. We then demonstrate two ways to trade off these metrics. In each, some learned knowledge is either approximated or dynamically 'forgotten' so as to improve efficiency while degrading accuracy only slightly.

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.

Supporting Scientific Experimentation and Reasoning in Young Elementary School Students

NASA Astrophysics Data System (ADS)

Varma, Keisha

2014-06-01

Researchers from multiple perspectives have shown that young students can engage in the scientific reasoning involved in science experimentation. However, there is little research on how well these young students learn in inquiry-based learning environments that focus on using scientific experimentation strategies to learn new scientific information. This work investigates young children's science concept learning via inquiry-based instruction on the thermodynamics system in a developmentally appropriate, technology-supported learning environment. First- and third-grade students participate in three sets of guided experimentation activities that involve using handheld computers to measure change in temperature given different types of insulation materials. Findings from pre- and post-comparisons show that students at both grade levels are able to learn about the thermodynamics system through engaging in the guided experiment activities. The instruction groups outperformed the control groups on multiple measures of thermodynamics knowledge, and the older children outperform the younger children. Knowledge gains are discussed in the context of mental models of the thermodynamics system that include the individual concepts mentioned above and the relationships between them. This work suggests that young students can benefit from science instruction centered on experimentation activities. It shows the benefits of presenting complex scientific information authentic contexts and the importance of providing the necessary scaffolding for meaningful scientific inquiry and experimentation.

Multiple Attempts for Online Assessments in an Operations Management Course: An Exploration

ERIC Educational Resources Information Center

Orchard, Ryan K.

2016-01-01

In learning management systems, tools for online homework assessments include a number of alternatives for the assessment settings, including the ability to permit students to attempt an assessment multiple times, with options for how the multiple attempts are administered. A specific implementation of online assessments in an introductory…

Multiple Serial List Learning with Two Mnemonic Techniques.

ERIC Educational Resources Information Center

Marston, Paul T.; Young, Robert K.

The classic mnemonic for learning serial lists, the method of loci, and its modern counterpart, the peg system, were compared by having subjects learn three 20-item serial lists. In addition to the type of mnemonic training, list imagery was either high (rated 6-7) or medium (rated 4-5), and instructions were either progressive elaboration (e.g.,…

Using Multiple Big Datasets and Machine Learning to Produce a New Global Particulate Dataset: A Technology Challenge Case Study

NASA Astrophysics Data System (ADS)

Lary, D. J.

2013-12-01

A BigData case study is described where multiple datasets from several satellites, high-resolution global meteorological data, social media and in-situ observations are combined using machine learning on a distributed cluster using an automated workflow. The global particulate dataset is relevant to global public health studies and would not be possible to produce without the use of the multiple big datasets, in-situ data and machine learning.To greatly reduce the development time and enhance the functionality a high level language capable of parallel processing has been used (Matlab). A key consideration for the system is high speed access due to the large data volume, persistence of the large data volumes and a precise process time scheduling capability.

The application of multiple intelligence approach to the learning of human circulatory system

NASA Astrophysics Data System (ADS)

Kumalasari, Lita; Yusuf Hilmi, A.; Priyandoko, Didik

2017-11-01

The purpose of this study is to offer an alternative teaching approach or strategies which able to accommodate students’ different ability, intelligence and learning style. Also can gives a new idea for the teacher as a facilitator for exploring how to teach the student in creative ways and more student-center activities, for a lesson such as circulatory system. This study was carried out at one private school in Bandung involved eight students to see their responses toward the lesson that delivered by using Multiple Intelligence approach which is include Linguistic, Logical-Mathematical, Visual-Spatial, Musical, Bodily-Kinesthetic, Interpersonal, Intrapersonal, and Naturalistic. Students were test by using MI test based on Howard Gardner’s MI model to see their dominant intelligence. The result showed the percentage of top three ranks of intelligence are Bodily-Kinesthetic (73%), Visual-Spatial (68%), and Logical-Mathematical (61%). The learning process is given by using some different multimedia and activities to engaged their learning style and intelligence such as mini experiment, short clip, and questions. Student response is given by using self-assessment and the result is all students said the lesson gives them a knowledge and skills that useful for their life, they are clear with the explanation given, they didn’t find difficulties to understand the lesson and can complete the assignment given. At the end of the study, it is reveal that the students who are learned by Multiple Intelligence instructional approach have more enhance to the lesson given. It’s also found out that the students participated in the learning process which Multiple Intelligence approach was applied enjoyed the activities and have great fun.

A participatory learning approach to biochemistry using student authored and evaluated multiple-choice questions.

PubMed

Bottomley, Steven; Denny, Paul

2011-01-01

A participatory learning approach, combined with both a traditional and a competitive assessment, was used to motivate students and promote a deep approach to learning biochemistry. Students were challenged to research, author, and explain their own multiple-choice questions (MCQs). They were also required to answer, evaluate, and discuss MCQs written by their peers. The technology used to support this activity was PeerWise--a freely available, innovative web-based system that supports students in the creation of an annotated question repository. In this case study, we describe students' contributions to, and perceptions of, the PeerWise system for a cohort of 107 second-year biomedical science students from three degree streams studying a core biochemistry subject. Our study suggests that the students are eager participants and produce a large repository of relevant, good quality MCQs. In addition, they rate the PeerWise system highly and use higher order thinking skills while taking an active role in their learning. We also discuss potential issues and future work using PeerWise for biomedical students. Copyright © 2011 Wiley Periodicals, Inc.

Pre-Exposure to Context Affects Learning Strategy Selection in Mice

ERIC Educational Resources Information Center

Tunur, Tumay; Dohanich, Gary P.; Schrader, Laura A.

2010-01-01

The multiple memory systems hypothesis proposes that different types of learning strategies are mediated by distinct neural systems in the brain. Male and female mice were tested on a water plus-maze task that could be solved by either a place or response strategy. One group of mice was pre-exposed to the same context as training and testing (PTC)…

Lessons Learned In Developing Multiple Distributed Planning Systems for the International Space Station

NASA Technical Reports Server (NTRS)

Maxwell, Theresa G.; McNair, Ann R. (Technical Monitor)

2002-01-01

The planning processes for the International Space Station (ISS) Program are quite complex. Detailed mission planning for ISS on-orbit operations is a distributed function. Pieces of the on-orbit plan are developed by multiple planning organizations, located around the world, based on their respective expertise and responsibilities. The "pieces" are then integrated to yield the final detailed plan that will be executed onboard the ISS. Previous space programs have not distributed the planning and scheduling functions to this extent. Major ISS planning organizations are currently located in the United States (at both the NASA Johnson Space Center (JSC) and NASA Marshall Space Flight Center (MSFC)), in Russia, in Europe, and in Japan. Software systems have been developed by each of these planning organizations to support their assigned planning and scheduling functions. Although there is some cooperative development and sharing of key software components, each planning system has been tailored to meet the unique requirements and operational environment of the facility in which it operates. However, all the systems must operate in a coordinated fashion in order to effectively and efficiently produce a single integrated plan of ISS operations, in accordance with the established planning processes. This paper addresses lessons learned during the development of these multiple distributed planning systems, from the perspective of the developer of one of the software systems. The lessons focus on the coordination required to allow the multiple systems to operate together, rather than on the problems associated with the development of any particular system. Included in the paper is a discussion of typical problems faced during the development and coordination process, such as incompatible development schedules, difficulties in defining system interfaces, technical coordination and funding for shared tools, continually evolving planning concepts/requirements, programmatic and budget issues, and external influences. Techniques that mitigated some of these problems will also be addressed, along with recommendations for any future programs involving the development of multiple planning and scheduling systems. Many of these lessons learned are not unique to the area of planning and scheduling systems, so may be applied to other distributed ground systems that must operate in concert to successfully support space mission operations.

Lessons Learned in Developing Multiple Distributed Planning Systems for the International Space Station

NASA Technical Reports Server (NTRS)

Maxwell, Theresa G.

2002-01-01

The planning processes for the International Space Station (ISS) Program are quite complex. Detailed mission planning for ISS on-orbit operations is a distributed function. Pieces of the on-orbit plan are developed by multiple planning organizations, located around the world, based on their respective expertise and responsibilities. The pieces are then integrated to yield the final detailed plan that will be executed onboard the ISS. Previous space programs have not distributed the planning and scheduling functions to this extent. Major ISS planning organizations are currently located in the United States (at both the NASA Johnson Space Center (JSC) and NASA Marshall Space Flight Center (MSFC)), in Russia, in Europe, and in Japan. Software systems have been developed by each of these planning organizations to support their assigned planning and scheduling functions. Although there is some cooperative development and sharing of key software components, each planning system has been tailored to meet the unique requirements and operational environment of the facility in which it operates. However, all the systems must operate in a coordinated fashion in order to effectively and efficiently produce a single integrated plan of ISS operations, in accordance with the established planning processes. This paper addresses lessons learned during the development of these multiple distributed planning systems, from the perspective of the developer of one of the software systems. The lessons focus on the coordination required to allow the multiple systems to operate together, rather than on the problems associated with the development of any particular system. Included in the paper is a discussion of typical problems faced during the development and coordination process, such as incompatible development schedules, difficulties in defining system interfaces, technical coordination and funding for shared tools, continually evolving planning concepts/requirements, programmatic and budget issues, and external influences. Techniques that mitigated some of these problems will also be addressed, along with recommendations for any future programs involving the development of multiple planning and scheduling systems. Many of these lessons learned are not unique to the area of planning and scheduling systems, so may be applied to other distributed ground systems that must operate in concert to successfully support space mission operations.

Dual-learning systems during speech category learning

PubMed Central

Chandrasekaran, Bharath; Yi, Han-Gyol; Maddox, W. Todd

2013-01-01

Dual-systems models of visual category learning posit the existence of an explicit, hypothesis-testing ‘reflective’ system, as well as an implicit, procedural-based ‘reflexive’ system. The reflective and reflexive learning systems are competitive and neurally dissociable. Relatively little is known about the role of these domain-general learning systems in speech category learning. Given the multidimensional, redundant, and variable nature of acoustic cues in speech categories, our working hypothesis is that speech categories are learned reflexively. To this end, we examined the relative contribution of these learning systems to speech learning in adults. Native English speakers learned to categorize Mandarin tone categories over 480 trials. The training protocol involved trial-by-trial feedback and multiple talkers. Experiment 1 and 2 examined the effect of manipulating the timing (immediate vs. delayed) and information content (full vs. minimal) of feedback. Dual-systems models of visual category learning predict that delayed feedback and providing rich, informational feedback enhance reflective learning, while immediate and minimally informative feedback enhance reflexive learning. Across the two experiments, our results show feedback manipulations that targeted reflexive learning enhanced category learning success. In Experiment 3, we examined the role of trial-to-trial talker information (mixed vs. blocked presentation) on speech category learning success. We hypothesized that the mixed condition would enhance reflexive learning by not allowing an association between talker-related acoustic cues and speech categories. Our results show that the mixed talker condition led to relatively greater accuracies. Our experiments demonstrate that speech categories are optimally learned by training methods that target the reflexive learning system. PMID:24002965

Information Retrieval: A Sequential Learning Process.

ERIC Educational Resources Information Center

Bookstein, Abraham

1983-01-01

Presents decision-theoretic models which intrinsically include retrieval of multiple documents whereby system responds to request by presenting documents to patron in sequence, gathering feedback, and using information to modify future retrievals. Document independence model, set retrieval model, sequential retrieval model, learning model,…

Successful Clicker Standardization

ERIC Educational Resources Information Center

Twetten, Jim; Smith, M. K.; Julius, Jim; Murphy-Boyer, Linda

2007-01-01

Student response systems, commonly referred to as "clickers," have become an important learning tool in higher education. With a growing number of faculty using the technology to promote active learning, student engagement, and assessment, most campuses have seen increasing clicker use. And with faculty bombarded by multiple,…

Deep learning of support vector machines with class probability output networks.

PubMed

Kim, Sangwook; Yu, Zhibin; Kil, Rhee Man; Lee, Minho

2015-04-01

Deep learning methods endeavor to learn features automatically at multiple levels and allow systems to learn complex functions mapping from the input space to the output space for the given data. The ability to learn powerful features automatically is increasingly important as the volume of data and range of applications of machine learning methods continues to grow. This paper proposes a new deep architecture that uses support vector machines (SVMs) with class probability output networks (CPONs) to provide better generalization power for pattern classification problems. As a result, deep features are extracted without additional feature engineering steps, using multiple layers of the SVM classifiers with CPONs. The proposed structure closely approaches the ideal Bayes classifier as the number of layers increases. Using a simulation of classification problems, the effectiveness of the proposed method is demonstrated. Copyright © 2014 Elsevier Ltd. All rights reserved.

Multiple Memory Systems Are Unnecessary to Account for Infant Memory Development: An Ecological Model

PubMed Central

Rovee-Collier, Carolyn; Cuevas, Kimberly

2009-01-01

How the memory of adults evolves from the memory abilities of infants is a central problem in cognitive development. The popular solution holds that the multiple memory systems of adults mature at different rates during infancy. The early-maturing system (implicit or nondeclarative memory) functions automatically from birth, whereas the late-maturing system (explicit or declarative memory) functions intentionally, with awareness, from late in the first year. Data are presented from research on deferred imitation, sensory preconditioning, potentiation, and context for which this solution cannot account and present an alternative model that eschews the need for multiple memory systems. The ecological model of infant memory development (N. E. Spear, 1984) holds that members of all species are perfectly adapted to their niche at each point in ontogeny and exhibit effective, evolutionarily selected solutions to whatever challenges each new niche poses. Because adults and infants occupy different niches, what they perceive, learn, and remember about the same event differs, but their raw capacity to learn and remember does not. PMID:19209999

Student Cognitive Difficulties and Mental Model Development of Complex Earth and Environmental Systems

NASA Astrophysics Data System (ADS)

Sell, K.; Herbert, B.; Schielack, J.

2004-05-01

Students organize scientific knowledge and reason about environmental issues through manipulation of mental models. The nature of the environmental sciences, which are focused on the study of complex, dynamic systems, may present cognitive difficulties to students in their development of authentic, accurate mental models of environmental systems. The inquiry project seeks to develop and assess the coupling of information technology (IT)-based learning with physical models in order to foster rich mental model development of environmental systems in geoscience undergraduate students. The manipulation of multiple representations, the development and testing of conceptual models based on available evidence, and exposure to authentic, complex and ill-constrained problems were the components of investigation utilized to reach the learning goals. Upper-level undergraduate students enrolled in an environmental geology course at Texas A&M University participated in this research which served as a pilot study. Data based on rubric evaluations interpreted by principal component analyses suggest students' understanding of the nature of scientific inquiry is limited and the ability to cross scales and link systems proved problematic. Results categorized into content knowledge and cognition processes where reasoning, critical thinking and cognitive load were driving factors behind difficulties in student learning. Student mental model development revealed multiple misconceptions and lacked complexity and completeness to represent the studied systems. Further, the positive learning impacts of the implemented modules favored the physical model over the IT-based learning projects, likely due to cognitive load issues. This study illustrates the need to better understand student difficulties in solving complex problems when using IT, where the appropriate scaffolding can then be implemented to enhance student learning of the earth system sciences.

Eliciting design patterns for e-learning systems

NASA Astrophysics Data System (ADS)

Retalis, Symeon; Georgiakakis, Petros; Dimitriadis, Yannis

2006-06-01

Design pattern creation, especially in the e-learning domain, is a highly complex process that has not been sufficiently studied and formalized. In this paper, we propose a systematic pattern development cycle, whose most important aspects focus on reverse engineering of existing systems in order to elicit features that are cross-validated through the use of appropriate, authentic scenarios. However, an iterative pattern process is proposed that takes advantage of multiple data sources, thus emphasizing a holistic view of the teaching learning processes. The proposed schema of pattern mining has been extensively validated for Asynchronous Network Supported Collaborative Learning (ANSCL) systems, as well as for other types of tools in a variety of scenarios, with promising results.

Mobile and Accessible Learning for MOOCs

ERIC Educational Resources Information Center

Sharples, Mike; Kloos, Carlos Delgado; Dimitriadis, Yannis; Garlatti, Serge; Specht, Marcus

2015-01-01

Many modern web-based systems provide a "responsive" design that allows material and services to be accessed on mobile and desktop devices, with the aim of providing "ubiquitous access." Besides offering access to learning materials such as podcasts and videos across multiple locations, mobile, wearable and ubiquitous…

Integrating Case Topics in Medical School Curriculum to Enhance Multiple Skill Learning: Using Fetal Alcohol Spectrum Disorders as an Exemplary Case

ERIC Educational Resources Information Center

Paley, Blair; O'Connor, Mary J.; Baillie, Susan J.; Guiton, Gretchen; Stuber, Margaret L.

2009-01-01

Objectives: This article describes the use of fetal alcohol spectrum disorders (FASDs) as a theme to connect the learning of basic neurosciences with clinical applications across the age span within a systems-based, integrated curricular structure that emphasizes problem-based learning. Methods: In collaboration with the Centers for Disease…

A Framework System for Intelligent Support in Open Distributed Learning Environments--A Look Back from 16 Years Later

ERIC Educational Resources Information Center

Hoppe, H. Ulrich

2016-01-01

The 1998 paper by Martin Mühlenbrock, Frank Tewissen, and myself introduced a multi-agent architecture and a component engineering approach for building open distributed learning environments to support group learning in different types of classroom settings. It took up prior work on "multiple student modeling" as a method to configure…

Using Software Testing Techniques for Efficient Handling of Programming Exercises in an e-Learning Platform

ERIC Educational Resources Information Center

Schwieren, Joachim; Vossen, Gottfried; Westerkamp, Peter

2006-01-01

e-Learning has become a major field of interest in recent years, and multiple approaches and solutions have been developed. A typical form of e-learning application comprises exercise submission and assessment systems that allow students to work on assignments whenever and where they want (i.e., dislocated, asynchronous work). In basic computer…

An Examination on the Effect of Prior Knowledge, Personal Goals, and Incentive in an Online Employee Training Program

ERIC Educational Resources Information Center

Zha, Shenghua; Adams, Andrea Harpine; Calcagno-Roach, Jamie Marie; Stringham, David A.

2017-01-01

This study explored factors that predicted learners' transformative learning in an online employee training program in a higher education institution in the U.S. A multivariate multiple regression analysis was conducted with a sample of 74 adult learners on their learning of a new learning management system. Four types of participants' behaviors…

An Evaluation of Pedagogical Tutorial Tactics for a Natural Language Tutoring System: A Reinforcement Learning Approach

ERIC Educational Resources Information Center

Chi, Min; VanLehn, Kurt; Litman, Diane; Jordan, Pamela

2011-01-01

Pedagogical strategies are policies for a tutor to decide the next action when there are multiple actions available. When the content is controlled to be the same across experimental conditions, there has been little evidence that tutorial decisions have an impact on students' learning. In this paper, we applied Reinforcement Learning (RL) to…

Predicting Student Grades in Learning Management Systems with Multiple Instance Genetic Programming

ERIC Educational Resources Information Center

Zafra, Amelia; Ventura, Sebastian

2009-01-01

The ability to predict a student's performance could be useful in a great number of different ways associated with university-level learning. In this paper, a grammar guided genetic programming algorithm, G3P-MI, has been applied to predict if the student will fail or pass a certain course and identifies activities to promote learning in a…

Quantitative Predictive Models for Systemic Toxicity (SOT)

EPA Science Inventory

Models to identify systemic and specific target organ toxicity were developed to help transition the field of toxicology towards computational models. By leveraging multiple data sources to incorporate read-across and machine learning approaches, a quantitative model of systemic ...

Combining Machine Learning Systems and Multiple Docking Simulation Packages to Improve Docking Prediction Reliability for Network Pharmacology

PubMed Central

Hsin, Kun-Yi; Ghosh, Samik; Kitano, Hiroaki

2013-01-01

Increased availability of bioinformatics resources is creating opportunities for the application of network pharmacology to predict drug effects and toxicity resulting from multi-target interactions. Here we present a high-precision computational prediction approach that combines two elaborately built machine learning systems and multiple molecular docking tools to assess binding potentials of a test compound against proteins involved in a complex molecular network. One of the two machine learning systems is a re-scoring function to evaluate binding modes generated by docking tools. The second is a binding mode selection function to identify the most predictive binding mode. Results from a series of benchmark validations and a case study show that this approach surpasses the prediction reliability of other techniques and that it also identifies either primary or off-targets of kinase inhibitors. Integrating this approach with molecular network maps makes it possible to address drug safety issues by comprehensively investigating network-dependent effects of a drug or drug candidate. PMID:24391846

Serious Use of a Serious Game for Language Learning

ERIC Educational Resources Information Center

Johnson, W. Lewis

2010-01-01

The Tactical Language and Culture Training System (TLCTS) helps learners acquire basic communicative skills in foreign languages and cultures. Learners acquire communication skills through a combination of interactive lessons and serious games. Artificial intelligence plays multiple roles in this learning environment: to process the learner's…

Using Technology to Enhance Collaborative Learning

ERIC Educational Resources Information Center

Wasonga, Teresa A.

2007-01-01

Purpose: The purpose of this research project is to explore the use of technology in enhancing and creating opportunities for collaborative learning by connecting prospective school leaders and practicing principals from multiple settings. Design/methodology/approach: This was a research project in which an internet-based network system was…

Formation Learning Control of Multiple Autonomous Underwater Vehicles With Heterogeneous Nonlinear Uncertain Dynamics.

PubMed

Yuan, Chengzhi; Licht, Stephen; He, Haibo

2017-09-26

In this paper, a new concept of formation learning control is introduced to the field of formation control of multiple autonomous underwater vehicles (AUVs), which specifies a joint objective of distributed formation tracking control and learning/identification of nonlinear uncertain AUV dynamics. A novel two-layer distributed formation learning control scheme is proposed, which consists of an upper-layer distributed adaptive observer and a lower-layer decentralized deterministic learning controller. This new formation learning control scheme advances existing techniques in three important ways: 1) the multi-AUV system under consideration has heterogeneous nonlinear uncertain dynamics; 2) the formation learning control protocol can be designed and implemented by each local AUV agent in a fully distributed fashion without using any global information; and 3) in addition to the formation control performance, the distributed control protocol is also capable of accurately identifying the AUVs' heterogeneous nonlinear uncertain dynamics and utilizing experiences to improve formation control performance. Extensive simulations have been conducted to demonstrate the effectiveness of the proposed results.

Exposure to predator odor influences the relative use of multiple memory systems: role of basolateral amygdala.

PubMed

Leong, Kah-Chung; Packard, Mark G

2014-03-01

In a dual-solution plus-maze task in which both hippocampus-dependent place learning and dorsolateral striatal-dependent response learning provide an adequate solution, the relative use of multiple memory systems can be influenced by emotional state. Specifically, pre-training peripheral or intra-basolateral (BLA) administration of anxiogenic drugs result in the predominant use of response learning. The present experiments were designed to extend these findings by examining whether exposure to a putatively ethologically valid stressor would also produce a predominant use of response learning. In experiment 1, adult male Long-Evans rats were exposed to either a predator odor (trimethylthiazoline [TMT], a component of fox feces) or distilled water prior to training in a dual-solution water plus maze task. On a probe trial 24h following task acquisition, rats previously exposed to TMT predominantly displayed response learning relative to control animals. In experiment 2, rats trained on a single-solution plus maze task that required the use of response learning displayed enhanced acquisition following pre-training TMT exposure. In experiment 3, rats exposed to TMT or distilled water were trained in the dual-solution task and received post-training intra-BLA injections of the sodium channel blocker bupivacaine (1.0% solution, 0.5 μl) or saline. Relative to control animals, rats exposed to TMT predominantly displayed response learning on the probe trial, and this effect was blocked by neural inactivation of the BLA. The findings indicate that (1) the use of dorsal striatal-dependent habit memory produced by emotional arousal generalizes from anxiogenic drug administration to a putatively ecologically valid stressor (i.e. predator odor), and (2) the BLA mediates the modulatory effect of exposure to predator odor on the relative use of multiple memory systems. Copyright © 2013 Elsevier Inc. All rights reserved.

Stimulating Multiple-Demand Cortex Enhances Vocabulary Learning

PubMed Central

Wise, Richard J.S.; Geranmayeh, Fatemeh; Hampshire, Adam

2017-01-01

It is well established that networks within multiple-demand cortex (MDC) become active when diverse skills and behaviors are being learnt. However, their causal role in learning remains to be established. In the present study, we first performed functional magnetic resonance imaging on healthy female and male human participants to confirm that MDC was most active in the initial stages of learning a novel vocabulary, consisting of pronounceable nonwords (pseudowords), each associated with a picture of a real object. We then examined, in healthy female and male human participants, whether repetitive transcranial magnetic stimulation of a frontal midline node of the cingulo-opercular MDC affected learning rates specifically during the initial stages of learning. We report that stimulation of this node, but not a control brain region, substantially improved both accuracy and response times during the earliest stage of learning pseudoword–object associations. This stimulation had no effect on the processing of established vocabulary, tested by the accuracy and response times when participants decided whether a real word was accurately paired with a picture of an object. These results provide evidence that noninvasive stimulation to MDC nodes can enhance learning rates, thereby demonstrating their causal role in the learning process. We propose that this causal role makes MDC candidate target for experimental therapeutics; for example, in stroke patients with aphasia attempting to reacquire a vocabulary. SIGNIFICANCE STATEMENT Learning a task involves the brain system within which that specific task becomes established. Therefore, successfully learning a new vocabulary establishes the novel words in the language system. However, there is evidence that in the early stages of learning, networks within multiple-demand cortex (MDC), which control higher cognitive functions, such as working memory, attention, and monitoring of performance, become active. This activity declines once the task is learnt. The present study demonstrated that a node within MDC, located in midline frontal cortex, becomes active during the early stage of learning a novel vocabulary. Importantly, noninvasive brain stimulation of this node improved performance during this stage of learning. This observation demonstrated that MDC activity is important for learning. PMID:28676576

A New Mathematical Framework for Design Under Uncertainty

DTIC Science & Technology

2016-05-05

blending multiple information sources via auto-regressive stochastic modeling. A computationally efficient machine learning framework is developed based on...sion and machine learning approaches; see Fig. 1. This will lead to a comprehensive description of system performance with less uncertainty than in the...Bayesian optimization of super-cavitating hy- drofoils The goal of this study is to demonstrate the capabilities of statistical learning and

Learn the Lagrangian: A Vector-Valued RKHS Approach to Identifying Lagrangian Systems.

PubMed

Cheng, Ching-An; Huang, Han-Pang

2016-12-01

We study the modeling of Lagrangian systems with multiple degrees of freedom. Based on system dynamics, canonical parametric models require ad hoc derivations and sometimes simplification for a computable solution; on the other hand, due to the lack of prior knowledge in the system's structure, modern nonparametric models in machine learning face the curse of dimensionality, especially in learning large systems. In this paper, we bridge this gap by unifying the theories of Lagrangian systems and vector-valued reproducing kernel Hilbert space. We reformulate Lagrangian systems with kernels that embed the governing Euler-Lagrange equation-the Lagrangian kernels-and show that these kernels span a subspace capturing the Lagrangian's projection as inverse dynamics. By such property, our model uses only inputs and outputs as in machine learning and inherits the structured form as in system dynamics, thereby removing the need for the mundane derivations for new systems as well as the generalization problem in learning from scratches. In effect, it learns the system's Lagrangian, a simpler task than directly learning the dynamics. To demonstrate, we applied the proposed kernel to identify the robot inverse dynamics in simulations and experiments. Our results present a competitive novel approach to identifying Lagrangian systems, despite using only inputs and outputs.

Transferring Learning from the Workshop to the Classroom

ERIC Educational Resources Information Center

Johnson, Kimberly A.

2009-01-01

As coordinator of the ABE Teaching and Learning Advancement System (ATLAS) based in the Hamline University School of Education in St. Paul, the author does many workshops or conference sessions in Minnesota's nine professional development regions each year. Typical single-day events offer multiple 90-minute workshops. She often questions the…

E-Assessment as a Service

ERIC Educational Resources Information Center

Amelung, M.; Krieger, K.; Rosner, D.

2011-01-01

Assessment is an essential element in learning processes. It is therefore not unsurprising that almost all learning management systems (LMSs) offer support for assessment, e.g., for the creation, execution, and evaluation of multiple choice tests. We have designed and implemented generic support for assessment that is based on assignments that…

Feedback for Thought: Examining the Influence of Feedback Constituents on Learning Experience

ERIC Educational Resources Information Center

Aoun, Chadi; Vatanasakdakul, Savanid; Ang, Karyne

2018-01-01

Reflective teaching practice is often heralded as a pillar of effective tuition. However, the perceptions of multiple forms of feedback among learners and their contributions to reflective learning is yet to attract significant attention, particularly in the Information Systems (IS) context. This research investigates the antecedent constituents…

Stress Modulates the Use of Spatial versus Stimulus-Response Learning Strategies in Humans

ERIC Educational Resources Information Center

Philippsen, Christine; Richter, Steffen; Bohringer, Andreas; Wippich, Werner; Schachinger, Hartmut; Schwabe, Lars; Oitzl, Melly S.

2007-01-01

Animal studies provided evidence that stress modulates multiple memory systems, favoring caudate nucleus-based "habit" memory over hippocampus-based "cognitive" memory. However, effects of stress on learning strategy and memory consolidation were not differentiated. We specifically address the effects of psychosocial stress on the applied learning…

Learning Is the Journey: From Process Reengineering to Systemic Customer-Service Design at the United States Department of Veterans Affairs, Veterans Benefits Administration

DTIC Science & Technology

2013-05-23

This monograph borrows from multiple disciplines to argue for an organizational shift from process reengineering to system design to improve...government customer-service delivery. Specifically, the monograph proposes a transformation in claims processing within the Veterans Benefits Administration...required. The proposed system design is an attempt to place the disability claims process within a larger environment encompassing multiple dimensions of customers.

Multi-sensor physical activity recognition in free-living.

PubMed

Ellis, Katherine; Godbole, Suneeta; Kerr, Jacqueline; Lanckriet, Gert

Physical activity monitoring in free-living populations has many applications for public health research, weight-loss interventions, context-aware recommendation systems and assistive technologies. We present a system for physical activity recognition that is learned from a free-living dataset of 40 women who wore multiple sensors for seven days. The multi-level classification system first learns low-level codebook representations for each sensor and uses a random forest classifier to produce minute-level probabilities for each activity class. Then a higher-level HMM layer learns patterns of transitions and durations of activities over time to smooth the minute-level predictions. [Formula: see text].

Active learning for ontological event extraction incorporating named entity recognition and unknown word handling.

PubMed

Han, Xu; Kim, Jung-jae; Kwoh, Chee Keong

2016-01-01

Biomedical text mining may target various kinds of valuable information embedded in the literature, but a critical obstacle to the extension of the mining targets is the cost of manual construction of labeled data, which are required for state-of-the-art supervised learning systems. Active learning is to choose the most informative documents for the supervised learning in order to reduce the amount of required manual annotations. Previous works of active learning, however, focused on the tasks of entity recognition and protein-protein interactions, but not on event extraction tasks for multiple event types. They also did not consider the evidence of event participants, which might be a clue for the presence of events in unlabeled documents. Moreover, the confidence scores of events produced by event extraction systems are not reliable for ranking documents in terms of informativity for supervised learning. We here propose a novel committee-based active learning method that supports multi-event extraction tasks and employs a new statistical method for informativity estimation instead of using the confidence scores from event extraction systems. Our method is based on a committee of two systems as follows: We first employ an event extraction system to filter potential false negatives among unlabeled documents, from which the system does not extract any event. We then develop a statistical method to rank the potential false negatives of unlabeled documents 1) by using a language model that measures the probabilities of the expression of multiple events in documents and 2) by using a named entity recognition system that locates the named entities that can be event arguments (e.g. proteins). The proposed method further deals with unknown words in test data by using word similarity measures. We also apply our active learning method for the task of named entity recognition. We evaluate the proposed method against the BioNLP Shared Tasks datasets, and show that our method can achieve better performance than such previous methods as entropy and Gibbs error based methods and a conventional committee-based method. We also show that the incorporation of named entity recognition into the active learning for event extraction and the unknown word handling further improve the active learning method. In addition, the adaptation of the active learning method into named entity recognition tasks also improves the document selection for manual annotation of named entities.

What is an Objective Structured Practical Examination in Anatomy?

ERIC Educational Resources Information Center

Yaqinuddin, Ahmed; Zafar, Muhammad; Ikram, Muhammad Faisal; Ganguly, Paul

2013-01-01

Assessing teaching-learning outcomes in anatomical knowledge is a complex task that requires the evaluation of multiple domains: theoretical, practical, and clinical knowledge. In general, theoretical knowledge is tested by a written examination system constituted by multiple choice questions (MCQs) and/or short answer questions (SAQ). The…

A system for diagnosis, referral, and rehabilitation of persons convicted of driving while intoxicated : a special rehabilitation program for multiple offenders

DOT National Transportation Integrated Search

1977-11-01

A rehabilitation program is presented for multiple DWI offenders. The program includes education related to alcohol use and abuse and therapeutic activities to help a client learn new techniques for living and alternatives to alcohol abuse. /Abstract...

Patient source of learning about health technologies and ratings of trust in technologies used in their care

PubMed Central

Montague, Enid

2011-01-01

In order to design effective health technologies and systems, it is important to understand how patients learn and make decisions about health technologies used in their care. The objective of this study was to examine patients' source of learning about technologies used in their care and how the source related to their trust in the technology used. Individual face-to-face and telephone interviews were conducted with 24 patients. Thirteen unique sources of information about technology were identified and three major themes emerged; outside of the work system versus inside the work system, when the health information was provided, and the medium used. Patients used multiple sources outside of the health care work system to learn about technologies that will be used in their care. Results showed a relationship between learning about technologies from web sources and trust in technologies but no relationship between learning about technologies from health care providers and trust in technologies. PMID:20967654

Learner's Satisfaction: A Case Study on IGNOU's Engineering Diploma Program

ERIC Educational Resources Information Center

Venkateshwarlu, Neelam; Agarwal, Ashish

2016-01-01

Open and Distance Learning (ODL) system is different from conventional education system. ODL system imparts education through multiple media and techniques to equalize the class room education. Unlike the conventional system, the distant learners (students, adults, employed persons, etc.) may face some problems during their course of study. In…

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

DTIC Science & Technology

2016-05-01

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

Expanding Teachers' Technological Pedagogical Reasoning with a Systems Pedagogical Approach

ERIC Educational Resources Information Center

Niess, Margaret L.; Gillow-Wiles, Henry

2017-01-01

A systems approach provides insight for expanding teachers' pedagogical reasoning for integrating multiple technologies in inquiry, communication, and collaboration. An online learning trajectory supports the integration of a systems pedagogical approach for guiding teachers in developing their technological pedagogical thinking and reasoning so…

Cerebellar and prefrontal cortex contributions to adaptation, strategies, and reinforcement learning.

PubMed

Taylor, Jordan A; Ivry, Richard B

2014-01-01

Traditionally, motor learning has been studied as an implicit learning process, one in which movement errors are used to improve performance in a continuous, gradual manner. The cerebellum figures prominently in this literature given well-established ideas about the role of this system in error-based learning and the production of automatized skills. Recent developments have brought into focus the relevance of multiple learning mechanisms for sensorimotor learning. These include processes involving repetition, reinforcement learning, and strategy utilization. We examine these developments, considering their implications for understanding cerebellar function and how this structure interacts with other neural systems to support motor learning. Converging lines of evidence from behavioral, computational, and neuropsychological studies suggest a fundamental distinction between processes that use error information to improve action execution or action selection. While the cerebellum is clearly linked to the former, its role in the latter remains an open question. © 2014 Elsevier B.V. All rights reserved.

Comparison of performance due to guided hyperlearning, unguided hyperlearning, and conventional learning in mathematics: an empirical study

NASA Astrophysics Data System (ADS)

Fathurrohman, Maman; Porter, Anne; Worthy, Annette L.

2014-07-01

In this paper, the use of guided hyperlearning, unguided hyperlearning, and conventional learning methods in mathematics are compared. The design of the research involved a quasi-experiment with a modified single-factor multiple treatment design comparing the three learning methods, guided hyperlearning, unguided hyperlearning, and conventional learning. The participants were from three first-year university classes, numbering 115 students in total. Each group received guided, unguided, or conventional learning methods in one of the three different topics, namely number systems, functions, and graphing. The students' academic performance differed according to the type of learning. Evaluation of the three methods revealed that only guided hyperlearning and conventional learning were appropriate methods for the psychomotor aspects of drawing in the graphing topic. There was no significant difference between the methods when learning the cognitive aspects involved in the number systems topic and the functions topic.

Cerebellar and Prefrontal Cortex Contributions to Adaptation, Strategies, and Reinforcement Learning

PubMed Central

Taylor, Jordan A.; Ivry, Richard B.

2014-01-01

Traditionally, motor learning has been studied as an implicit learning process, one in which movement errors are used to improve performance in a continuous, gradual manner. The cerebellum figures prominently in this literature given well-established ideas about the role of this system in error-based learning and the production of automatized skills. Recent developments have brought into focus the relevance of multiple learning mechanisms for sensorimotor learning. These include processes involving repetition, reinforcement learning, and strategy utilization. We examine these developments, considering their implications for understanding cerebellar function and how this structure interacts with other neural systems to support motor learning. Converging lines of evidence from behavioral, computational, and neuropsychological studies suggest a fundamental distinction between processes that use error information to improve action execution or action selection. While the cerebellum is clearly linked to the former, its role in the latter remains an open question. PMID:24916295

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

ERIC Educational Resources Information Center

Li, Na; Black, John B.

2016-01-01

Chemistry knowledge can be represented at macro-, micro- and symbolic levels, and learning a chemistry topic requires students to engage in multiple representational activities. This study focused on scaffolding for inter-level connection-making in learning chemistry knowledge with graphical simulations. We also tested whether different sequences…

Research on the Correlations among Mobile Learning Perception, Study Habits, and Continuous Learning

ERIC Educational Resources Information Center

Wu, Wen-Chun; Perng, Yeng-Hong

2016-01-01

In addition to the rapidly developed Internet information technology, the admission to secondary schools has changed from single entry to multiple entries, among which the performance of Basic Competence Test and at schools have replaced Joint University Programs Admissions System. Traditional instruction therefore could no longer cope with such…

A Study of the Predictive Relationships between Faculty Engagement, Learner Satisfaction and Outcomes in Multiple Learning Delivery Modes

ERIC Educational Resources Information Center

Yen, Cherng-Jyh; Abdous, M'hammed

2011-01-01

The confluence of technology convergence, market forces, and student demand for greater access is reshaping higher education institutions. Indeed, the convergence of technological innovations in hardware, software, and telecommunications, combined with the ubiquity of learning management systems, is reconfiguring and strengthening traditional…

Multiple Measures of Teaching Effectiveness: Classroom Observations and Student Surveys as Predictors of Student Learning

ERIC Educational Resources Information Center

Muñoz, Marco A.; Dossett, Dena H.

2016-01-01

This study advances our understanding of the relationships among the different elements of a teacher evaluation model and its usefulness in predicting student learning. Important questions arise about teacher evaluation systems, including (a) the magnitude of correlations among the sources of evidence used for identifying teacher effectiveness and…

A learning setup for a post-coma adolescent with profound multiple disabilities involving small forehead movements and new microswitch technology.

PubMed

Lancioni, Giulio E; Singh, Nirbhay N; O'Reilly, Mark F; Sigafoos, Jeff; Didden, Robert; Oliva, Doretta; Calzolari, Cinzia; Montironi, Gianluigi

2007-09-01

A learning setup was arranged for an adolescent with profound multiple disabilities and a diagnosis of vegetative state. Signs of learning by the adolescent would underline an improvement in his immediate situation with potential implications for his general prospect, and could help revise his diagnosis. The response adopted in the learning setup was forehead skin movement. The microswitch technology used for detecting such a response consisted of (a) an optic sensor (i.e., barcode reader), (b) a small tag with horizontal bars attached to the participant's forehead, and (c) an electronic control system that activated stimuli in relation to the participant's forehead responses. The study followed an ABABACAB sequence, in which A represented baseline periods, B intervention periods with stimuli contingent on the response, and C a control condition with stimuli presented non-contingently. Data showed that the level of responding during the B phases was significantly higher than the levels observed during the A phases as well as the C phase, indicating clear signs of learning. Intervention strategies based on a learning format and suitable technology might be useful to improve the situation and prospect of persons with profound multiple disabilities and a diagnosis of vegetative state.

Basal ganglia and Dopamine Contributions to Probabilistic Category Learning

PubMed Central

Shohamy, D.; Myers, C.E.; Kalanithi, J.; Gluck, M.A.

2009-01-01

Studies of the medial temporal lobe and basal ganglia memory systems have recently been extended towards understanding the neural systems contributing to category learning. The basal ganglia, in particular, have been linked to probabilistic category learning in humans. A separate parallel literature in systems neuroscience has emerged, indicating a role for the basal ganglia and related dopamine inputs in reward prediction and feedback processing. Here, we review behavioral, neuropsychological, functional neuroimaging, and computational studies of basal ganglia and dopamine contributions to learning in humans. Collectively, these studies implicate the basal ganglia in incremental, feedback-based learning that involves integrating information across multiple experiences. The medial temporal lobes, by contrast, contribute to rapid encoding of relations between stimuli and support flexible generalization of learning to novel contexts and stimuli. By breaking down our understanding of the cognitive and neural mechanisms contributing to different aspects of learning, recent studies are providing insight into how, and when, these different processes support learning, how they may interact with each other, and the consequence of different forms of learning for the representation of knowledge. PMID:18061261

A digital protection system incorporating knowledge based learning

NASA Astrophysics Data System (ADS)

Watson, Karan; Russell, B. Don; McCall, Kurt

A digital system architecture used to diagnoses the operating state and health of electric distribution lines and to generate actions for line protection is presented. The architecture is described functionally and to a limited extent at the hardware level. This architecture incorporates multiple analysis and fault-detection techniques utilizing a variety of parameters. In addition, a knowledge-based decision maker, a long-term memory retention and recall scheme, and a learning environment are described. Preliminary laboratory implementations of the system elements have been completed. Enhanced protection for electric distribution feeders is provided by this system. Advantages of the system are enumerated.

A framework for exploring integrated learning systems for the governance and management of public protected areas.

PubMed

Nkhata, Bimo Abraham; Breen, Charles

2010-02-01

This article discusses how the concept of integrated learning systems provides a useful means of exploring the functional linkages between the governance and management of public protected areas. It presents a conceptual framework of an integrated learning system that explicitly incorporates learning processes in governance and management subsystems. The framework is premised on the assumption that an understanding of an integrated learning system is essential if we are to successfully promote learning across multiple scales as a fundamental component of adaptability in the governance and management of protected areas. The framework is used to illustrate real-world situations that reflect the nature and substance of the linkages between governance and management. Drawing on lessons from North America and Africa, the article demonstrates that the establishment and maintenance of an integrated learning system take place in a complex context which links elements of governance learning and management learning subsystems. The degree to which the two subsystems are coupled influences the performance of an integrated learning system and ultimately adaptability. Such performance is largely determined by how integrated learning processes allow for the systematic testing of societal assumptions (beliefs, values, and public interest) to enable society and protected area agencies to adapt and learn in the face of social and ecological change. It is argued that an integrated perspective provides a potentially useful framework for explaining and improving shared understanding around which the concept of adaptability is structured and implemented.

An Intelligent System for Document Retrieval in Distributed Office Environments.

ERIC Educational Resources Information Center

Mukhopadhyay, Uttam; And Others

1986-01-01

MINDS (Multiple Intelligent Node Document Servers) is a distributed system of knowledge-based query engines for efficiently retrieving multimedia documents in an office environment of distributed workstations. By learning document distribution patterns and user interests and preferences during system usage, it customizes document retrievals for…

Multiple Possibilities: The Multi-Literate Lives of Three Children

ERIC Educational Resources Information Center

Wood, Jeffrey

2015-01-01

This paper presents findings from an eleven-year ethnographic study which describes how three children used different sign systems to become literate, to define who they are and to construct their literate identity. They each engaged with literacies in powerful and life transforming ways. Each child used multiple literacies to learn, understand…

Multiple Intelligence and Digital Learning Awareness of Prospective B.Ed Teachers

ERIC Educational Resources Information Center

Gracious, F. L. Antony; Shyla, F. L. Jasmine Anne

2012-01-01

The present study Multiple Intelligence and Digital Learning Awareness of prospective B.Ed teachers was probed to find the relationship between Multiple Intelligence and Digital Learning Awareness of Prospective B.Ed Teachers. Data for the study were collected using self made Multiple Intelligence Inventory and Digital Learning Awareness Scale.…

The implementation of multiple intelligences based teaching model to improve mathematical problem solving ability for student of junior high school

NASA Astrophysics Data System (ADS)

Fasni, Nurli; Fatimah, Siti; Yulanda, Syerli

2017-05-01

This research aims to achieve some purposes such as: to know whether mathematical problem solving ability of students who have learned mathematics using Multiple Intelligences based teaching model is higher than the student who have learned mathematics using cooperative learning; to know the improvement of the mathematical problem solving ability of the student who have learned mathematics using Multiple Intelligences based teaching model., to know the improvement of the mathematical problem solving ability of the student who have learned mathematics using cooperative learning; to know the attitude of the students to Multiple Intelligences based teaching model. The method employed here is quasi-experiment which is controlled by pre-test and post-test. The population of this research is all of VII grade in SMP Negeri 14 Bandung even-term 2013/2014, later on two classes of it were taken for the samples of this research. A class was taught using Multiple Intelligences based teaching model and the other one was taught using cooperative learning. The data of this research were gotten from the test in mathematical problem solving, scale questionnaire of the student attitudes, and observation. The results show the mathematical problem solving of the students who have learned mathematics using Multiple Intelligences based teaching model learning is higher than the student who have learned mathematics using cooperative learning, the mathematical problem solving ability of the student who have learned mathematics using cooperative learning and Multiple Intelligences based teaching model are in intermediate level, and the students showed the positive attitude in learning mathematics using Multiple Intelligences based teaching model. As for the recommendation for next author, Multiple Intelligences based teaching model can be tested on other subject and other ability.

Colour processing in complex environments: insights from the visual system of bees

PubMed Central

Dyer, Adrian G.; Paulk, Angelique C.; Reser, David H.

2011-01-01

Colour vision enables animals to detect and discriminate differences in chromatic cues independent of brightness. How the bee visual system manages this task is of interest for understanding information processing in miniaturized systems, as well as the relationship between bee pollinators and flowering plants. Bees can quickly discriminate dissimilar colours, but can also slowly learn to discriminate very similar colours, raising the question as to how the visual system can support this, or whether it is simply a learning and memory operation. We discuss the detailed neuroanatomical layout of the brain, identify probable brain areas for colour processing, and suggest that there may be multiple systems in the bee brain that mediate either coarse or fine colour discrimination ability in a manner dependent upon individual experience. These multiple colour pathways have been identified along both functional and anatomical lines in the bee brain, providing us with some insights into how the brain may operate to support complex colour discrimination behaviours. PMID:21147796

Abortion training at multiple sites: an unexpected curriculum for teaching systems-based practice.

PubMed

Herbitter, Cara; Kumar, Vanita; Karasz, Alison; Gold, Marji

2010-04-01

In 1999, the Accreditation Council for Graduate Medical Education endorsed systems-based practice as one of six general competencies. The objective is to explore the paradigm of teaching residents systems-based practice during a women's health rotation that included abortion training in multiple settings. During a routine women's health rotation, residents from two urban family medicine residency programs received early abortion training at a high-volume abortion clinic and their continuity clinic. Thirty-min semistructured interviews were conducted with all 26 residents who rotated between July 2005 and August 2006. Transcripts were analyzed using thematic codes. Through exposure to different healthcare delivery systems, residents learned about systems-based practice, including understanding the failure of the larger system to meet patients' reproductive healthcare needs, differences between two systems, and potential systems barriers they might face as providers. Abortion training in multiple settings may serve as a paradigm for teaching systems-based practice during other rotations that include training in multiple sites.

Interleaved Practice in Multi-Dimensional Learning Tasks: Which Dimension Should We Interleave?

ERIC Educational Resources Information Center

Rau, Martina A.; Aleven, Vincent; Rummel, Nikol

2013-01-01

Research shows that multiple representations can enhance student learning. Many curricula use multiple representations across multiple task types. The temporal sequence of representations and task types is likely to impact student learning. Research on contextual interference shows that interleaving learning tasks leads to better learning results…

The relationship of neurogenesis and growth of brain regions to song learning

PubMed Central

Kirn, John R.

2009-01-01

Song learning, maintenance and production require coordinated activity across multiple auditory, sensory-motor, and neuromuscular structures. Telencephalic components of the sensory-motor circuitry are unique to avian species that engage in song learning. The song system shows protracted development that begins prior to hatching but continues well into adulthood. The staggered developmental timetable for construction of the song system provides clues of subsystems involved in specific stages of song learning and maintenance. Progressive events, including neurogenesis and song system growth, as well as regressive events such as apoptosis and synapse elimination, occur during periods of song learning and the transitions between stereotyped and variable song during both development and adulthood. There is clear evidence that gonadal steroids influence the development of song attributes and shape the underlying neural circuitry. Some aspects of song system development are influenced by sensory, motor and social experience, while other aspects of neural development appear to be experience-independent. Although there are species differences in the extent to which song learning continues into adulthood, growing evidence suggests that despite differences in learning trajectories, adult refinement of song motor control and song maintenance can require remarkable behavioral and neural flexibility reminiscent of sensory-motor learning. PMID:19853905

The use of Multiple Representations to Enhance Student Mental Model Development of a Complex Earth System in an Introductory Geoscience Course

NASA Astrophysics Data System (ADS)

Sell, K. S.; Heather, M. R.; Herbert, B. E.

2004-12-01

Exposing earth system science (ESS) concepts into introductory geoscience courses may present new and unique cognitive learning issues for students including understanding the role of positive and negative feedbacks in system responses to perturbations, spatial heterogeneity, and temporal dynamics, especially when systems exhibit complex behavior. Implicit learning goals of typical introductory undergraduate geoscience courses are more focused on building skill-sets and didactic knowledge in learners than developing a deeper understanding of the dynamics and processes of complex earth systems through authentic inquiry. Didactic teaching coupled with summative assessment of factual knowledge tends to limit student¡¦s understanding of the nature of science, their belief in the relevancy of science to their lives, and encourages memorization and regurgitation; this is especially true among the non-science majors who compose the majority of students in introductory courses within the large university setting. Students organize scientific knowledge and reason about earth systems by manipulating internally constructed mental models. This pilot study focuses on characterizing the impact of inquiry-based learning with multiple representations to foster critical thinking and mental model development about authentic environmental issues of coastal systems in an introductory geoscience course. The research was conducted in nine introductory physical geology laboratory sections (N ˜ 150) at Texas A&M University as part of research connected with the Information Technology in Science (ITS) Center. Participants were randomly placed into experimental and control groups. Experimental groups were exposed to multiple representations including both web-based learning materials (i.e. technology-supported visualizations and analysis of multiple datasets) and physical models, whereas control groups were provided with the traditional ¡workbook style¡" laboratory assignments. Assessment of pre- and post-test results was performed to provide indications of content knowledge and mental model expression improvements between groups. A rubric was used as the assessment instrument to evaluate student products (Cronbach alpha: 0.84 ¡V 0.98). Characterization of student performance based on a Student¡¦s t-test indicates that significant differences (p < 0.05) in pre-post achievement occurred primarily within the experimental group; this illustrates that the use of multiple representations had an impact on student learning of ESS concepts, particularly in regard to mental model constructions. Analysis of variance also suggests that student mental model constructions were significantly different (p < 0.10) between test groups. Factor analysis extracted three principle components (eigenvalue > 1) which show similar clustering of variables that influence cognition, indicating that the cognitive processes driving student understanding of geoscience do not vary among student test groups. Categories of cognition include critical thinking skills (percent variance = 22.16%), understanding of the nature of science (percent variance = 25.16%), and ability to interpret results (percent variance = 28.89%). Lower numbers of students completed all of the required assignments of this research than expected (65.3%), restricting the quality of the results and therefore the ability to make more significant interpretations; this was likely due to the non-supportive learning environment in which the research was implemented.

LDA merging and splitting with applications to multiagent cooperative learning and system alteration.

PubMed

Pang, Shaoning; Ban, Tao; Kadobayashi, Youki; Kasabov, Nikola K

2012-04-01

To adapt linear discriminant analysis (LDA) to real-world applications, there is a pressing need to equip it with an incremental learning ability to integrate knowledge presented by one-pass data streams, a functionality to join multiple LDA models to make the knowledge sharing between independent learning agents more efficient, and a forgetting functionality to avoid reconstruction of the overall discriminant eigenspace caused by some irregular changes. To this end, we introduce two adaptive LDA learning methods: LDA merging and LDA splitting. These provide the benefits of ability of online learning with one-pass data streams, retained class separability identical to the batch learning method, high efficiency for knowledge sharing due to condensed knowledge representation by the eigenspace model, and more preferable time and storage costs than traditional approaches under common application conditions. These properties are validated by experiments on a benchmark face image data set. By a case study on the application of the proposed method to multiagent cooperative learning and system alternation of a face recognition system, we further clarified the adaptability of the proposed methods to complex dynamic learning tasks.

The Process of Becoming an Embedded Curriculum Librarian in Multiple Health Sciences Programs.

PubMed

Wilson, Gwen

2015-01-01

Higher education is moving to offer more fully online programs, and the health science fields are no different. These programs are either hybrid or completely online. It is up to the health sciences librarian to adapt services offered by the academic library to these types of courses. This column discusses the multiple ways a librarian can be an embedded librarian in a course using a learning management system (LMS). The process of creating a customized embedded librarian program, results, and lessons learned from the different embedded librarian roles are also discussed.

Ask Systems: Interrogative Access to Multiple Ways of Thinking

ERIC Educational Resources Information Center

Jonassen, David H.

2011-01-01

The purpose of this paper is to familiarize instructional designers and researchers with a useful design and research paradigm known as "Ask Systems." Ask Systems are interrogative interfaces to information and learning environments that model conversations with a skilled, reflective practitioner (Schon, The reflective practitioner, "1983") or…

Changing Course Management Systems: Lessons Learned

ERIC Educational Resources Information Center

Smart, Kathy A.; Meyer, Katrina A.

2005-01-01

During 2003, the North Dakota University System began to be concerned about the cost of supporting multiple course management systems. Since 1997, the 11 NDUS institutions had used 9 different course management packages, including one homegrown product (HTMLeZ) and such proprietary products as Blackboard, WebCT, and e-College. The University of…

Developing Adaptive and Intelligent Tutoring Systems (AITS): A General Framework and Its Implementations

ERIC Educational Resources Information Center

Hafidi, Mohamed; Bensebaa, Tahar

2014-01-01

Several adaptive and intelligent tutoring systems (AITS) have been developed with different variables. These variables were the cognitive traits, cognitive styles, and learning behavior. However, these systems neglect the importance of the learner's multiple intelligences, the learner's skill level and the learner's feedback when implementing…

Response to "Redesigning Systems of School Accountability": Addressing Underlying Inequities

ERIC Educational Resources Information Center

Gil, Elizabeth; Kim, Taeyon

2018-01-01

As Bae (2018) suggests, one way to fill gaps between a holistic view of student learning and accountability policy implementation is to use multiple measures that reflect diverse perspectives of learning. The purpose of this commentary is to provide a discussion of issues, which need to be considered in order to achieve the desired outcomes of…

The Use of Multiple Tools for Teaching Medical Biochemistry

ERIC Educational Resources Information Center

Se, Alexandre B.; Passos, Renato M.; Ono, Andre H.; Hermes-Lima, Marcelo

2008-01-01

In this work, we describe the use of several strategies employing the philosophies of active learning and problem-based learning (PBL) that may be used to improve the teaching of metabolic biochemistry to medical and nutritional undergraduate students. The main activities are as follows: 1) a seminar/poster system in a mini-congress format (using…

Problems of Implementing SCORM in an Enterprise Distance Learning Architecture: SCORM Incompatibility across Multiple Web Domains.

ERIC Educational Resources Information Center

Engelbrecht, Jeffrey C.

2003-01-01

Delivering content to distant users located in dispersed networks, separated by firewalls and different web domains requires extensive customization and integration. This article outlines some of the problems of implementing the Sharable Content Object Reference Model (SCORM) in the Marine Corps' Distance Learning System (MarineNet) and extends…

Web-Based Dynamic Assessment: Taking Assessment as Teaching and Learning Strategy for Improving Students e-Learning Effectiveness

ERIC Educational Resources Information Center

Wang, Tzu-Hua

2010-01-01

This research combines the idea of cake format dynamic assessment defined by Sternberg and Grigorenko (2001) and the "graduated prompt approach" proposed by (Campione and Brown, 1985) and (Campione and Brown, 1987) to develop a multiple-choice Web-based dynamic assessment system. This research adopts a quasi-experimental design to…

Multiple Learning Strategies Project. Small Engine Repair Service. Regular Vocational. [Vol. 1.

ERIC Educational Resources Information Center

Pitts, Jim; And Others

This instructional package is one of two designed for use by regular vocational students in the vocational area of small engine repair service. Contained in this document are forty-four learning modules organized into ten units: engine block; air cleaner; starters; fuel tanks; lines, filters, and pumps; carburetors; electrical; magneto systems;…

Guiding Principles for a Research Schools Network: Successes and Challenges

ERIC Educational Resources Information Center

Schwartz, Marc S.; Gerlach, Jeanne

2011-01-01

Building on J. Dewey's (1907) original work with the laboratory school, the College of Education and Health Professions at the University of Texas-Arlington is expanding the original concept to include partners throughout a school system and the community in order to support and advance learning in multiple learning environments. The goal is to…

Blockade of Dopamine Activity in the Nucleus Accumbens Impairs Learning Extinction of Conditioned Fear

ERIC Educational Resources Information Center

Holtzman-Assif, Orit; Laurent, Vincent; Westbrook, R. Frederick

2010-01-01

Three experiments used rats to investigate the role of dopamine activity in learning to inhibit conditioned fear responses (freezing) in extinction. In Experiment 1, rats systemically injected with the D2 dopamine antagonist, haloperidol, froze more across multiple extinction sessions and on a drug-free retention test than control rats. In…

How to Schedule Multiple Graphical Representations? A Classroom Experiment with an Intelligent Tutoring System for Fractions

ERIC Educational Resources Information Center

Rau, M. A.; Aleven, V.; Rummel, N.

2011-01-01

Graphical representations (GRs) of the learning content are often used for instruction (Ainsworth, 2006). When used in learning technology, GRs can be especially useful since they allow for interactions across representations that are physically impossible, for instance by dragging and dropping symbolic statements into a chart that automatically…

On Learning to Write Those **** References

ERIC Educational Resources Information Center

Hartley, James

2014-01-01

In this article, the author discusses how difficult it is for psychology college students to learn to write multiple disciplines of references. It is hard for students to understand why all details have to be written in the right order and the right type-style--depending upon which reference system is used. In this article, the author proposes…

Students' Perceived Learning and Anticipated Future Behaviors as a Result of Participation in the Student Judicial Process

ERIC Educational Resources Information Center

Howell, Martin T.

2005-01-01

This qualitative study was undertaken to explore the meaning that students make of their interactions with campus judicial systems. Using a multiple case study approach, 10 students from 3 institutions in the Southeastern United States were observed and interviewed. The findings presented here relate to students' perceived learning and anticipated…

Multi-Objective Reinforcement Learning for Cognitive Radio-Based Satellite Communications

NASA Technical Reports Server (NTRS)

Ferreira, Paulo Victor R.; Paffenroth, Randy; Wyglinski, Alexander M.; Hackett, Timothy M.; Bilen, Sven G.; Reinhart, Richard C.; Mortensen, Dale J.

2016-01-01

Previous research on cognitive radios has addressed the performance of various machine-learning and optimization techniques for decision making of terrestrial link properties. In this paper, we present our recent investigations with respect to reinforcement learning that potentially can be employed by future cognitive radios installed onboard satellite communications systems specifically tasked with radio resource management. This work analyzes the performance of learning, reasoning, and decision making while considering multiple objectives for time-varying communications channels, as well as different cross-layer requirements. Based on the urgent demand for increased bandwidth, which is being addressed by the next generation of high-throughput satellites, the performance of cognitive radio is assessed considering links between a geostationary satellite and a fixed ground station operating at Ka-band (26 GHz). Simulation results show multiple objective performance improvements of more than 3.5 times for clear sky conditions and 6.8 times for rain conditions.

Early comprehension of the Spanish plural*

PubMed Central

Arias-Trejo, Natalia; Cantrell, Lisa M.; Smith, Linda B.; Alva Canto, Elda A.

2015-01-01

Understanding how linguistic cues map to the environment is crucial for early language comprehension and may provide a way for bootstrapping and learning words. Research has suggested that learning how plural syntax maps to the perceptual environment may show a trajectory in which children first learn surrounding cues (verbs, modifiers) before a full mastery of the noun morpheme alone. The Spanish plural system of simple codas, dominated by one allomorph -s, and with redundant agreement markers, may facilitate early understanding of how plural linguistic cues map to novel referents. Two-year-old Mexican children correctly identified multiple novel object referents when multiple verbal cues in a phrase indicated plurality as well as in instances when the noun morphology in novel nouns was the ONLY indicator of plurality. These results demonstrate Spanish-speaking children’s ability to use plural noun inflectional morphology to infer novel word referents which may have implications for their word learning. PMID:24560441

Multi-Objective Reinforcement Learning for Cognitive Radio Based Satellite Communications

NASA Technical Reports Server (NTRS)

Ferreira, Paulo; Paffenroth, Randy; Wyglinski, Alexander; Hackett, Timothy; Bilen, Sven; Reinhart, Richard; Mortensen, Dale John

2016-01-01

Previous research on cognitive radios has addressed the performance of various machine learning and optimization techniques for decision making of terrestrial link properties. In this paper, we present our recent investigations with respect to reinforcement learning that potentially can be employed by future cognitive radios installed onboard satellite communications systems specifically tasked with radio resource management. This work analyzes the performance of learning, reasoning, and decision making while considering multiple objectives for time-varying communications channels, as well as different crosslayer requirements. Based on the urgent demand for increased bandwidth, which is being addressed by the next generation of high-throughput satellites, the performance of cognitive radio is assessed considering links between a geostationary satellite and a fixed ground station operating at Ka-band (26 GHz). Simulation results show multiple objective performance improvements of more than 3:5 times for clear sky conditions and 6:8 times for rain conditions.

Teaching "with" Rather than "about" Geographic Information Systems

ERIC Educational Resources Information Center

Hammond, Thomas C.; Bodzin, Alec M.

2009-01-01

Both "teaching" and "teaching" with Geographic Information Systems (GIS) are "wicked problems," in the sense that they involve multiple variables that interact with one another. Effective teaching calls for both learning with understanding and transfer. The authors' own experience implementing a geography and…

Imaging evidence for disturbances in multiple learning and memory systems in persons with autism spectrum disorders.

PubMed

Goh, Suzanne; Peterson, Bradley S

2012-03-01

The aim of this article is to review neuroimaging studies of autism spectrum disorders (ASD) that examine declarative, socio-emotional, and procedural learning and memory systems. We conducted a search of PubMed from 1996 to 2010 using the terms 'autism,''learning,''memory,' and 'neuroimaging.' We limited our review to studies correlating learning and memory function with neuroimaging features of the brain. The early literature supports the following preliminary hypotheses: (1) abnormalities of hippocampal subregions may contribute to autistic deficits in episodic and relational memory; (2) disturbances to an amygdala-based network (which may include the fusiform gyrus, superior temporal cortex, and mirror neuron system) may contribute to autistic deficits in socio-emotional learning and memory; and (3) abnormalities of the striatum may contribute to developmental dyspraxia in individuals with ASD. Characterizing the disturbances to learning and memory systems in ASD can inform our understanding of the neural bases of autistic behaviors and the phenotypic heterogeneity of ASD. © The Authors. Developmental Medicine & Child Neurology © 2012 Mac Keith Press.

Stress Effects on Multiple Memory System Interactions

PubMed Central

Ness, Deborah; Calabrese, Pasquale

2016-01-01

Extensive behavioural, pharmacological, and neurological research reports stress effects on mammalian memory processes. While stress effects on memory quantity have been known for decades, the influence of stress on multiple memory systems and their distinct contributions to the learning process have only recently been described. In this paper, after summarizing the fundamental biological aspects of stress/emotional arousal and recapitulating functionally and anatomically distinct memory systems, we review recent animal and human studies exploring the effects of stress on multiple memory systems. Apart from discussing the interaction between distinct memory systems in stressful situations, we will also outline the fundamental role of the amygdala in mediating such stress effects. Additionally, based on the methods applied in the herein discussed studies, we will discuss how memory translates into behaviour. PMID:27034845

Supporting inquiry learning by promoting normative understanding of multivariable causality

NASA Astrophysics Data System (ADS)

Keselman, Alla

2003-11-01

Early adolescents may lack the cognitive and metacognitive skills necessary for effective inquiry learning. In particular, they are likely to have a nonnormative mental model of multivariable causality in which effects of individual variables are neither additive nor consistent. Described here is a software-based intervention designed to facilitate students' metalevel and performance-level inquiry skills by enhancing their understanding of multivariable causality. Relative to an exploration-only group, sixth graders who practiced predicting an outcome (earthquake risk) based on multiple factors demonstrated increased attention to evidence, improved metalevel appreciation of effective strategies, and a trend toward consistent use of a controlled comparison strategy. Sixth graders who also received explicit instruction in making predictions based on multiple factors showed additional improvement in their ability to compare multiple instances as a basis for inferences and constructed the most accurate knowledge of the system. Gains were maintained in transfer tasks. The cognitive skills and metalevel understanding examined here are essential to inquiry learning.

Detecting Parkinsons' symptoms in uncontrolled home environments: a multiple instance learning approach.

PubMed

Das, Samarjit; Amoedo, Breogan; De la Torre, Fernando; Hodgins, Jessica

2012-01-01

In this paper, we propose to use a weakly supervised machine learning framework for automatic detection of Parkinson's Disease motor symptoms in daily living environments. Our primary goal is to develop a monitoring system capable of being used outside of controlled laboratory settings. Such a system would enable us to track medication cycles at home and provide valuable clinical feedback. Most of the relevant prior works involve supervised learning frameworks (e.g., Support Vector Machines). However, in-home monitoring provides only coarse ground truth information about symptom occurrences, making it very hard to adapt and train supervised learning classifiers for symptom detection. We address this challenge by formulating symptom detection under incomplete ground truth information as a multiple instance learning (MIL) problem. MIL is a weakly supervised learning framework that does not require exact instances of symptom occurrences for training; rather, it learns from approximate time intervals within which a symptom might or might not have occurred on a given day. Once trained, the MIL detector was able to spot symptom-prone time windows on other days and approximately localize the symptom instances. We monitored two Parkinson's disease (PD) patients, each for four days with a set of five triaxial accelerometers and utilized a MIL algorithm based on axis parallel rectangle (APR) fitting in the feature space. We were able to detect subject specific symptoms (e.g. dyskinesia) that conformed with a daily log maintained by the patients.

Learning Multiple Band-Pass Filters for Sleep Stage Estimation: Towards Care Support for Aged Persons

NASA Astrophysics Data System (ADS)

Takadama, Keiki; Hirose, Kazuyuki; Matsushima, Hiroyasu; Hattori, Kiyohiko; Nakajima, Nobuo

This paper proposes the sleep stage estimation method that can provide an accurate estimation for each person without connecting any devices to human's body. In particular, our method learns the appropriate multiple band-pass filters to extract the specific wave pattern of heartbeat, which is required to estimate the sleep stage. For an accurate estimation, this paper employs Learning Classifier System (LCS) as the data-mining techniques and extends it to estimate the sleep stage. Extensive experiments on five subjects in mixed health confirm the following implications: (1) the proposed method can provide more accurate sleep stage estimation than the conventional method, and (2) the sleep stage estimation calculated by the proposed method is robust regardless of the physical condition of the subject.

Cavity approach to noisy learning in nonlinear perceptrons.

PubMed

Luo, P; Michael Wong, K Y

2001-12-01

We analyze the learning of noisy teacher-generated examples by nonlinear and differentiable student perceptrons using the cavity method. The generic activation of an example is a function of the cavity activation of the example, which is its activation in the perceptron that learns without the example. Mean-field equations for the macroscopic parameters and the stability condition yield results consistent with the replica method. When a single value of the cavity activation maps to multiple values of the generic activation, there is a competition in learning strategy between preferentially learning an example and sacrificing it in favor of the background adjustment. We find parameter regimes in which examples are learned preferentially or sacrificially, leading to a gap in the activation distribution. Full phase diagrams of this complex system are presented, and the theory predicts the existence of a phase transition from poor to good generalization states in the system. Simulation results confirm the theoretical predictions.

Neural priming in human frontal cortex: multiple forms of learning reduce demands on the prefrontal executive system.

PubMed

Race, Elizabeth A; Shanker, Shanti; Wagner, Anthony D

2009-09-01

Past experience is hypothesized to reduce computational demands in PFC by providing bottom-up predictive information that informs subsequent stimulus-action mapping. The present fMRI study measured cortical activity reductions ("neural priming"/"repetition suppression") during repeated stimulus classification to investigate the mechanisms through which learning from the past decreases demands on the prefrontal executive system. Manipulation of learning at three levels of representation-stimulus, decision, and response-revealed dissociable neural priming effects in distinct frontotemporal regions, supporting a multiprocess model of neural priming. Critically, three distinct patterns of neural priming were identified in lateral frontal cortex, indicating that frontal computational demands are reduced by three forms of learning: (a) cortical tuning of stimulus-specific representations, (b) retrieval of learned stimulus-decision mappings, and (c) retrieval of learned stimulus-response mappings. The topographic distribution of these neural priming effects suggests a rostrocaudal organization of executive function in lateral frontal cortex.

Category Specificity in Normal Episodic Learning: Applications to Object Recognition and Category-Specific Agnosia

ERIC Educational Resources Information Center

Bukach, Cindy M.; Bub, Daniel N.; Masson, Michael E. J.; Lindsay, D. Stephen

2004-01-01

Studies of patients with category-specific agnosia (CSA) have given rise to multiple theories of object recognition, most of which assume the existence of a stable, abstract semantic memory system. We applied an episodic view of memory to questions raised by CSA in a series of studies examining normal observers' recall of newly learned attributes…

A Dynamic Systems Account of Learning a Word: From Ecology to Form Relations

ERIC Educational Resources Information Center

Churchill, Eton

2008-01-01

This paper responds to calls for studies that investigate multiple types of word knowledge and the processes of word learning. Focusing on a single word, this three-month diary study describes the micro-development of an adult male's Japanese L2 lexical knowledge. In contrast to most L2 vocabulary acquisition studies, this study posits a dynamic…

On the impact of approximate computation in an analog DeSTIN architecture.

PubMed

Young, Steven; Lu, Junjie; Holleman, Jeremy; Arel, Itamar

2014-05-01

Deep machine learning (DML) holds the potential to revolutionize machine learning by automating rich feature extraction, which has become the primary bottleneck of human engineering in pattern recognition systems. However, the heavy computational burden renders DML systems implemented on conventional digital processors impractical for large-scale problems. The highly parallel computations required to implement large-scale deep learning systems are well suited to custom hardware. Analog computation has demonstrated power efficiency advantages of multiple orders of magnitude relative to digital systems while performing nonideal computations. In this paper, we investigate typical error sources introduced by analog computational elements and their impact on system-level performance in DeSTIN--a compositional deep learning architecture. These inaccuracies are evaluated on a pattern classification benchmark, clearly demonstrating the robustness of the underlying algorithm to the errors introduced by analog computational elements. A clear understanding of the impacts of nonideal computations is necessary to fully exploit the efficiency of analog circuits.

A Multimedia Adaptive Tutoring System for Mathematics That Addresses Cognition, Metacognition and Affect

ERIC Educational Resources Information Center

Arroyo, Ivon; Woolf, Beverly Park; Burelson, Winslow; Muldner, Kasia; Rai, Dovan; Tai, Minghui

2014-01-01

This article describes research results based on multiple years of experimentation and real-world experience with an adaptive tutoring system named Wayang Outpost. The system represents a novel adaptive learning technology that has shown successful outcomes with thousands of students, and provided teachers with valuable information about students'…

Self, System, Synergy: A Career-Life Development Framework for Individuals and Organizations.

ERIC Educational Resources Information Center

Gelatt, H. B.

1998-01-01

The Self-System-Synergy model provides the philosophical framework for the concept of career resiliency, which has become the basis for many organizational initiatives. The three elements are self-reliance (the power of personal beliefs), interdependence (the connectedness of multiple systems), and self-renewal through continuous learning. (JOW)

Facilitating Multiple Intelligences through Multimodal Learning Analytics

ERIC Educational Resources Information Center

Perveen, Ayesha

2018-01-01

This paper develops a theoretical framework for employing learning analytics in online education to trace multiple learning variations of online students by considering their potential of being multiple intelligences based on Howard Gardner's 1983 theory of multiple intelligences. The study first emphasizes the need to facilitate students as…

Bayesian analysis of energy and count rate data for detection of low count rate radioactive sources

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

Klumpp, John

We propose a radiation detection system which generates its own discrete sampling distribution based on past measurements of background. The advantage to this approach is that it can take into account variations in background with respect to time, location, energy spectra, detector-specific characteristics (i.e. different efficiencies at different count rates and energies), etc. This would therefore be a 'machine learning' approach, in which the algorithm updates and improves its characterization of background over time. The system would have a 'learning mode,' in which it measures and analyzes background count rates, and a 'detection mode,' in which it compares measurements frommore » an unknown source against its unique background distribution. By characterizing and accounting for variations in the background, general purpose radiation detectors can be improved with little or no increase in cost. The statistical and computational techniques to perform this kind of analysis have already been developed. The necessary signal analysis can be accomplished using existing Bayesian algorithms which account for multiple channels, multiple detectors, and multiple time intervals. Furthermore, Bayesian machine-learning techniques have already been developed which, with trivial modifications, can generate appropriate decision thresholds based on the comparison of new measurements against a nonparametric sampling distribution. (authors)« less

Music mnemonics aid Verbal Memory and Induce Learning – Related Brain Plasticity in Multiple Sclerosis

PubMed Central

Thaut, Michael H.; Peterson, David A.; McIntosh, Gerald C.; Hoemberg, Volker

2014-01-01

Recent research on music and brain function has suggested that the temporal pattern structure in music and rhythm can enhance cognitive functions. To further elucidate this question specifically for memory, we investigated if a musical template can enhance verbal learning in patients with multiple sclerosis (MS) and if music-assisted learning will also influence short-term, system-level brain plasticity. We measured systems-level brain activity with oscillatory network synchronization during music-assisted learning. Specifically, we measured the spectral power of 128-channel electroencephalogram (EEG) in alpha and beta frequency bands in 54 patients with MS. The study sample was randomly divided into two groups, either hearing a spoken or a musical (sung) presentation of Rey’s auditory verbal learning test. We defined the “learning-related synchronization” (LRS) as the percent change in EEG spectral power from the first time the word was presented to the average of the subsequent word encoding trials. LRS differed significantly between the music and the spoken conditions in low alpha and upper beta bands. Patients in the music condition showed overall better word memory and better word order memory and stronger bilateral frontal alpha LRS than patients in the spoken condition. The evidence suggests that a musical mnemonic recruits stronger oscillatory network synchronization in prefrontal areas in MS patients during word learning. It is suggested that the temporal structure implicit in musical stimuli enhances “deep encoding” during verbal learning and sharpens the timing of neural dynamics in brain networks degraded by demyelination in MS. PMID:24982626

Learning procedures from interactive natural language instructions

NASA Technical Reports Server (NTRS)

Huffman, Scott B.; Laird, John E.

1994-01-01

Despite its ubiquity in human learning, very little work has been done in artificial intelligence on agents that learn from interactive natural language instructions. In this paper, the problem of learning procedures from interactive, situated instruction is examined in which the student is attempting to perform tasks within the instructional domain, and asks for instruction when it is needed. Presented is Instructo-Soar, a system that behaves and learns in response to interactive natural language instructions. Instructo-Soar learns completely new procedures from sequences of instruction, and also learns how to extend its knowledge of previously known procedures to new situations. These learning tasks require both inductive and analytic learning. Instructo-Soar exhibits a multiple execution learning process in which initial learning has a rote, episodic flavor, and later executions allow the initially learned knowledge to be generalized properly.

Implementation of Multiple Intelligences Supported Project-Based Learning in EFL/ESL Classrooms

ERIC Educational Resources Information Center

Bas, Gokhan

2008-01-01

This article deals with the implementation of Multiple Intelligences supported Project-Based learning in EFL/ESL Classrooms. In this study, after Multiple Intelligences supported Project-based learning was presented shortly, the implementation of this learning method into English classrooms. Implementation process of MI supported Project-based…

An Investigation between Multiple Intelligences and Learning Styles

ERIC Educational Resources Information Center

Sener, Sabriye; Çokçaliskan, Ayten

2018-01-01

Exploring learning style and multiple intelligence type of learners can enable the students to identify their strengths and weaknesses and learn from them. It is also very important for teachers to understand their learners' learning styles and multiple intelligences since they can carefully identify their goals and design activities that can…

Hierarchical control of procedural and declarative category-learning systems

PubMed Central

Turner, Benjamin O.; Crossley, Matthew J.; Ashby, F. Gregory

2017-01-01

Substantial evidence suggests that human category learning is governed by the interaction of multiple qualitatively distinct neural systems. In this view, procedural memory is used to learn stimulus-response associations, and declarative memory is used to apply explicit rules and test hypotheses about category membership. However, much less is known about the interaction between these systems: how is control passed between systems as they interact to influence motor resources? Here, we used fMRI to elucidate the neural correlates of switching between procedural and declarative categorization systems. We identified a key region of the cerebellum (left Crus I) whose activity was bidirectionally modulated depending on switch direction. We also identified regions of the default mode network (DMN) that were selectively connected to left Crus I during switching. We propose that the cerebellum—in coordination with the DMN—serves a critical role in passing control between procedural and declarative memory systems. PMID:28213114

Exploring the potential of a multi-level approach to improve capability for continuous organizational improvement and learning in a Swedish healthcare region.

PubMed

Nyström, M E; Höög, E; Garvare, R; Andersson Bäck, M; Terris, D D; Hansson, J

2018-05-24

Eldercare and care of people with functional impairments is organized by the municipalities in Sweden. Improving care in these areas is complex, with multiple stakeholders and organizations. Appropriate strategies to develop capability for continuing organizational improvement and learning (COIL) are needed. The purpose of our study was to develop and pilot-test a flexible, multilevel approach for COIL capability building and to identify what it takes to achieve changes in key actors' approaches to COIL. The approach, named "Sustainable Improvement and Development through Strategic and Systematic Approaches" (SIDSSA), was applied through an action-research and action-learning intervention. The SIDSSA approach was tested in a regional research and development (R&D) unit, and in two municipalities handling care of the elderly and people with functional impairments. Our approach included a multilevel strategy, development loops of five flexible phases, and an action-learning loop. The approach was designed to support systems understanding, strategic focus, methodological practices, and change process knowledge - all of which required double-loop learning. Multiple qualitative methods, i.e., repeated interviews, process diaries, and documents, provided data for conventional content analyses. The new approach was successfully tested on all cases and adopted and sustained by the R&D unit. Participants reported new insights and skills. The development loop facilitated a sense of coherence and control during uncertainty, improved planning and problem analysis, enhanced mapping of context and conditions, and supported problem-solving at both the individual and unit levels. The systems-level view and structured approach helped participants to explain, motivate, and implement change initiatives, especially after working more systematically with mapping, analyses, and goal setting. An easily understood and generalizable model internalized by key organizational actors is an important step before more complex development models can be implemented. SIDSSA facilitated individual and group learning through action-learning and supported systems-level views and structured approaches across multiple organizational levels. Active involvement of diverse organizational functions and levels in the learning process was facilitated. However, the time frame was too short to fully test all aspects of the approach, specifically in reaching beyond the involved managers to front-line staff and patients.

Onboard planning for geological investigations using a rover team

NASA Technical Reports Server (NTRS)

Estlin, Tara; Gaines, Daniel; Fisher, Forest; Castano, Rebecca

2004-01-01

This paper describes an integrated system for coordinating multiple rover behavior with the overall goal of collecting planetary surface data. The Multi-Rover Integrated Science Understanding System (MISUS) combines techniques from planning and scheduling with machine learning to perform autonomous scientific exploration with cooperating rovers.

The relationship of neurogenesis and growth of brain regions to song learning.

PubMed

Kirn, John R

2010-10-01

Song learning, maintenance and production require coordinated activity across multiple auditory, sensory-motor, and neuromuscular structures. Telencephalic components of the sensory-motor circuitry are unique to avian species that engage in song learning. The song system shows protracted development that begins prior to hatching but continues well into adulthood. The staggered developmental timetable for construction of the song system provides clues of subsystems involved in specific stages of song learning and maintenance. Progressive events, including neurogenesis and song system growth, as well as regressive events such as apoptosis and synapse elimination, occur during periods of song learning and the transitions between variable and stereotyped song during both development and adulthood. There is clear evidence that gonadal steroids influence the development of song attributes and shape the underlying neural circuitry. Some aspects of song system development are influenced by sensory, motor and social experience, while other aspects of neural development appear to be experience-independent. Although there are species differences in the extent to which song learning continues into adulthood, growing evidence suggests that despite differences in learning trajectories, adult refinement of song motor control and song maintenance can require remarkable behavioral and neural flexibility reminiscent of sensory-motor learning. Copyright © 2009 Elsevier Inc. All rights reserved.

Designing flexible instructional space for teaching introductory physics with emphasis on inquiry and collaborative active learning

NASA Astrophysics Data System (ADS)

Bykov, Tikhon

2010-03-01

In recent years McMurry University's introductory physics curriculum has gone through a series of significant changes to achieve better integration of traditional course components (lecture/lab/discussion) by means of instructional design and technology. A system of flexible curriculum modules with emphasis on inquiry-based teaching and collaborative active learning has been introduced. To unify module elements, a technology suite has been used that consists of Tablet PC's and software applications including Physlets, tablet-adapted personal response system, PASCO data acquisition systems, and MS One-note collaborative writing software. Adoption of the new teaching model resulted in reevaluation of existing instructional spaces. The new teaching space will be created during the renovation of the McMurry Science Building. This space will allow for easy transitions between lecture and laboratory modes. Movable partitions will be used to accommodate student groups of different sizes. The space will be supportive of small peer-group activities with easy-to-reconfigure furniture, multiple white and black board surfaces and multiple projection screens. The new space will be highly flexible to account for different teaching functions, different teaching modes and learning styles.

Two men with multiple disabilities carry out an assembly work activity with the support of a technology system.

PubMed

Lancioni, Giulio E; Singh, Nirbhay N; O'Reilly, Mark F; Green, Vanessa A; Oliva, Doretta; Campodonico, Francesca

2013-10-01

To assess whether two persons with multiple disabilities could learn a work activity (i.e., assembling trolley wheels) with the support of a technology system. After an initial baseline, the study compared the effects of intervention sessions relying on the technology system (which called the participants to the different workstations and provided feedback and final stimulation) with the effects of intervention sessions carried out without technology. The two types of intervention sessions were conducted according to an alternating treatments design. Eventually, only intervention sessions relying on the technology system were used. Both participants managed to assemble wheels independently during intervention sessions relying on the technology system while they failed during sessions without the system. Their performance was strengthened during the final part of the study, in which only sessions with the system occurred. Technology may be critical in helping persons with multiple disabilities manage multi-step work activities.

A Field Study of a Standardized Tangible Symbol System for Learners Who Are Visually Impaired and Have Multiple Disabilities

ERIC Educational Resources Information Center

Trief, Ellen; Cascella, Paul W.; Bruce, Susan M.

2013-01-01

Introduction: The study reported in this article tracked the learning rate of 43 children with multiple disabilities and visual impairments who had limited to no verbal language across seven months of classroom-based intervention using a standardized set of tangible symbols. Methods: The participants were introduced to tangible symbols on a daily…

Choline acetyltransferase in the hippocampus is associated with learning strategy preference in adult male rats.

PubMed

Hawley, Wayne R; Witty, Christine F; Daniel, Jill M; Dohanich, Gary P

2015-08-01

One principle of the multiple memory systems hypothesis posits that the hippocampus-based and striatum-based memory systems compete for control over learning. Consistent with this notion, previous research indicates that the cholinergic system of the hippocampus plays a role in modulating the preference for a hippocampus-based place learning strategy over a striatum-based stimulus--response learning strategy. Interestingly, in the hippocampus, greater activity and higher protein levels of choline acetyltransferase (ChAT), the enzyme that synthesizes acetylcholine, are associated with better performance on hippocampus-based learning and memory tasks. With this in mind, the primary aim of the current study was to determine if higher levels of ChAT and the high-affinity choline uptake transporter (CHT) in the hippocampus were associated with a preference for a hippocampus-based place learning strategy on a task that also could be solved by relying on a striatum-based stimulus--response learning strategy. Results confirmed that levels of ChAT in the dorsal region of the hippocampus were associated with a preference for a place learning strategy on a water maze task that could also be solved by adopting a stimulus-response learning strategy. Consistent with previous studies, the current results support the hypothesis that the cholinergic system of the hippocampus plays a role in balancing competition between memory systems that modulate learning strategy preference. Copyright © 2015 Elsevier B.V. All rights reserved.

Integrated defense system overlaps as a disease model: with examples for multiple chemical sensitivity.

PubMed Central

Rowat, S C

1998-01-01

The central nervous, immune, and endocrine systems communicate through multiple common messengers. Over evolutionary time, what may be termed integrated defense system(s) (IDS) have developed to coordinate these communications for specific contexts; these include the stress response, acute-phase response, nonspecific immune response, immune response to antigen, kindling, tolerance, time-dependent sensitization, neurogenic switching, and traumatic dissociation (TD). These IDSs are described and their overlap is examined. Three models of disease production are generated: damage, in which IDSs function incorrectly; inadequate/inappropriate, in which IDS response is outstripped by a changing context; and evolving/learning, in which the IDS learned response to a context is deemed pathologic. Mechanisms of multiple chemical sensitivity (MCS) are developed from several IDS disease models. Model 1A is pesticide damage to the central nervous system, overlapping with body chemical burdens, TD, and chronic zinc deficiency; model 1B is benzene disruption of interleukin-1, overlapping with childhood developmental windows and hapten-antigenic spreading; and model 1C is autoimmunity to immunoglobulin-G (IgG), overlapping with spreading to other IgG-inducers, sudden spreading of inciters, and food-contaminating chemicals. Model 2A is chemical and stress overload, including comparison with the susceptibility/sensitization/triggering/spreading model; model 2B is genetic mercury allergy, overlapping with: heavy metals/zinc displacement and childhood/gestational mercury exposures; and model 3 is MCS as evolution and learning. Remarks are offered on current MCS research. Problems with clinical measurement are suggested on the basis of IDS models. Large-sample patient self-report epidemiology is described as an alternative or addition to clinical biomarker and animal testing. Images Figure 1 Figure 2 Figure 3 Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 PMID:9539008

Neural correlates of reward-based spatial learning in persons with cocaine dependence.

PubMed

Tau, Gregory Z; Marsh, Rachel; Wang, Zhishun; Torres-Sanchez, Tania; Graniello, Barbara; Hao, Xuejun; Xu, Dongrong; Packard, Mark G; Duan, Yunsuo; Kangarlu, Alayar; Martinez, Diana; Peterson, Bradley S

2014-02-01

Dysfunctional learning systems are thought to be central to the pathogenesis of and impair recovery from addictions. The functioning of the brain circuits for episodic memory or learning that support goal-directed behavior has not been studied previously in persons with cocaine dependence (CD). Thirteen abstinent CD and 13 healthy participants underwent MRI scanning while performing a task that requires the use of spatial cues to navigate a virtual-reality environment and find monetary rewards, allowing the functional assessment of the brain systems for spatial learning, a form of episodic memory. Whereas both groups performed similarly on the reward-based spatial learning task, we identified disturbances in brain regions involved in learning and reward in CD participants. In particular, CD was associated with impaired functioning of medial temporal lobe (MTL), a brain region that is crucial for spatial learning (and episodic memory) with concomitant recruitment of striatum (which normally participates in stimulus-response, or habit, learning), and prefrontal cortex. CD was also associated with enhanced sensitivity of the ventral striatum to unexpected rewards but not to expected rewards earned during spatial learning. We provide evidence that spatial learning in CD is characterized by disturbances in functioning of an MTL-based system for episodic memory and a striatum-based system for stimulus-response learning and reward. We have found additional abnormalities in distributed cortical regions. Consistent with findings from animal studies, we provide the first evidence in humans describing the disruptive effects of cocaine on the coordinated functioning of multiple neural systems for learning and memory.

Specialized Motor-Driven dusp1 Expression in the Song Systems of Multiple Lineages of Vocal Learning Birds

PubMed Central

Horita, Haruhito; Kobayashi, Masahiko; Liu, Wan-chun; Oka, Kotaro; Jarvis, Erich D.; Wada, Kazuhiro

2012-01-01

Mechanisms for the evolution of convergent behavioral traits are largely unknown. Vocal learning is one such trait that evolved multiple times and is necessary in humans for the acquisition of spoken language. Among birds, vocal learning is evolved in songbirds, parrots, and hummingbirds. Each time similar forebrain song nuclei specialized for vocal learning and production have evolved. This finding led to the hypothesis that the behavioral and neuroanatomical convergences for vocal learning could be associated with molecular convergence. We previously found that the neural activity-induced gene dual specificity phosphatase 1 (dusp1) was up-regulated in non-vocal circuits, specifically in sensory-input neurons of the thalamus and telencephalon; however, dusp1 was not up-regulated in higher order sensory neurons or motor circuits. Here we show that song motor nuclei are an exception to this pattern. The song nuclei of species from all known vocal learning avian lineages showed motor-driven up-regulation of dusp1 expression induced by singing. There was no detectable motor-driven dusp1 expression throughout the rest of the forebrain after non-vocal motor performance. This pattern contrasts with expression of the commonly studied activity-induced gene egr1, which shows motor-driven expression in song nuclei induced by singing, but also motor-driven expression in adjacent brain regions after non-vocal motor behaviors. In the vocal non-learning avian species, we found no detectable vocalizing-driven dusp1 expression in the forebrain. These findings suggest that independent evolutions of neural systems for vocal learning were accompanied by selection for specialized motor-driven expression of the dusp1 gene in those circuits. This specialized expression of dusp1 could potentially lead to differential regulation of dusp1-modulated molecular cascades in vocal learning circuits. PMID:22876306

Learning to navigate the healthcare system in a new country: a qualitative study.

PubMed

Straiton, Melanie L; Myhre, Sonja

2017-12-01

Learning to navigate a healthcare system in a new country is a barrier to health care. Understanding more about the specific navigation challenges immigrants experience may be the first step towards improving health information and thus access to care. This study considers the challenges that Thai and Filipino immigrant women encounter when learning to navigate the Norwegian primary healthcare system and the strategies they use. A qualitative interview study using thematic analysis. Norway. Fifteen Thai and 15 Filipino immigrant women over the age of 18 who had been living in Norway at least one year. The women took time to understand the role of the general practitioner and some were unaware of their right to an interpreter during consultations. In addition to reliance on family members and friends in their social networks, voluntary and cultural organisations provided valuable tips and advice on how to navigate the Norwegian health system. While some women actively engaged in learning more about the system, they noted a lack of information available in multiple languages. Informal sources play an important role in learning about the health care system. Formal information should be available in different languages in order to better empower immigrant women.

Relationships of Attention and Executive Functions to Oral Language, Reading, and Writing Skills and Systems in Middle Childhood and Early Adolescence.

PubMed

Berninger, Virginia; Abbott, Robert; Cook, Clayton R; Nagy, William

Relationships between attention/executive functions and language learning were investigated in students in Grades 4 to 9 ( N = 88) with and without specific learning disabilities (SLDs) in multiword syntax in oral and written language (OWL LD), word reading and spelling (dyslexia), and subword letter writing (dysgraphia). Prior attention-deficit/hyperactivity disorder (ADHD) diagnosis was correlated only with impaired handwriting. Parental ratings of inattention, but not hyperactivity, correlated with measures of written language but not oral language. Sustaining switching attention correlated with writing the alphabet from memory in manuscript or by keyboard and fast copying of a sentence with all the letters of the alphabet. Multiple regressions based on a principal component for composites of multiple levels of language (subword, word, and syntax/text) showed that measures of attention and executive function involving language processing rather than ratings of attention and executive function not specifically related to language accounted for more variance and identified more unique predictors in the composite outcomes for oral language, reading, and writing systems. Inhibition related to focused attention uniquely predicted outcomes for the oral language system. Findings are discussed in reference to implications for assessing and teaching students who are still learning to pay attention to heard and written language and self-regulate their language learning during middle childhood and adolescence.

Creating a learning organization to help meet the needs of multihospital health systems.

PubMed

Ward, Angela; Berensen, Nannette; Daniels, Rowell

2018-04-01

The considerations that leaders of multihospital health systems must take into account in developing and implementing initiatives to build and maintain an exceptional pharmacy workforce are described. Significant changes that require constant individual and organizational learning are occurring throughout healthcare and within the profession of pharmacy. These considerations include understanding why it is important to have a succession plan and determining what types of education and training are important to support that plan. Other considerations include strategies for leveraging learners, dealing with a large geographic footprint, adjusting training opportunities to accommodate the ever-evolving demands on pharmacy staffs in terms of skill mix, and determining ways to either budget for or internally develop content for staff development. All of these methods are critically important to ensuring an optimized workforce. Especially for large health systems operating multiple sites across large distances, the use of technology-enabled solutions to provide effective delivery of programming to multiple sites is critical. Commonly used tools include live webinars, live "telepresence" programs, prerecorded programming that is available through an on-demand repository, and computer-based training modules. A learning management system is helpful to assign and document completion of educational requirements, especially those related to regulatory requirements (e.g., controlled substances management, sterile and nonsterile compounding, competency assessment). Creating and sustaining an environment where all pharmacy caregivers feel invested in and connected to ongoing learning is a powerful motivator for performance, engagement, and retention. Copyright © 2018 by the American Society of Health-System Pharmacists, Inc. All rights reserved.

Differential impact of relevant and irrelevant dimension primes on rule-based and information-integration category learning.

PubMed

Grimm, Lisa R; Maddox, W Todd

2013-11-01

Research has identified multiple category-learning systems with each being "tuned" for learning categories with different task demands and each governed by different neurobiological systems. Rule-based (RB) classification involves testing verbalizable rules for category membership while information-integration (II) classification requires the implicit learning of stimulus-response mappings. In the first study to directly test rule priming with RB and II category learning, we investigated the influence of the availability of information presented at the beginning of the task. Participants viewed lines that varied in length, orientation, and position on the screen, and were primed to focus on stimulus dimensions that were relevant or irrelevant to the correct classification rule. In Experiment 1, we used an RB category structure, and in Experiment 2, we used an II category structure. Accuracy and model-based analyses suggested that a focus on relevant dimensions improves RB task performance later in learning while a focus on an irrelevant dimension improves II task performance early in learning. © 2013.

Robust sensorimotor representation to physical interaction changes in humanoid motion learning.

PubMed

Shimizu, Toshihiko; Saegusa, Ryo; Ikemoto, Shuhei; Ishiguro, Hiroshi; Metta, Giorgio

2015-05-01

This paper proposes a learning from demonstration system based on a motion feature, called phase transfer sequence. The system aims to synthesize the knowledge on humanoid whole body motions learned during teacher-supported interactions, and apply this knowledge during different physical interactions between a robot and its surroundings. The phase transfer sequence represents the temporal order of the changing points in multiple time sequences. It encodes the dynamical aspects of the sequences so as to absorb the gaps in timing and amplitude derived from interaction changes. The phase transfer sequence was evaluated in reinforcement learning of sitting-up and walking motions conducted by a real humanoid robot and compatible simulator. In both tasks, the robotic motions were less dependent on physical interactions when learned by the proposed feature than by conventional similarity measurements. Phase transfer sequence also enhanced the convergence speed of motion learning. Our proposed feature is original primarily because it absorbs the gaps caused by changes of the originally acquired physical interactions, thereby enhancing the learning speed in subsequent interactions.

[Systemic learning planification for medical students during oncology clinical rotation].

PubMed

Gonçalves, Anthony; Viens, Patrice; Gilabert, Marine; Turrini, Olivier; Lambaudie, Eric; Prebet, Thomas; Farnault, Bertrand; Eisinger, François; Gorincour, Guillaume; Bertucci, François

2011-12-01

The expected increase in cancer incidence emphasizes the need for specific training in this area, including either family physician or specialized oncologists. In France, the fourth to sixth years of medical teaching include both theoretical classes at the university and daily actual practice at the hospital. Thus, clinical rotations are thought to play a major role in the training of medical students and also largely participate to the choice of the student of his/her final specialty. Pedagogic quality of these rotations is dependent on multiple parameters, including a rigorous planification of the expected learning. Here, we reported a systemic planification of learning activities for medical students during an oncology rotation at the Paoli-Calmettes Institute in Marseille, France, a regional comprehensive cancer center. This planification includes an evaluation of learning requirements, definition of learning objectives, selection of learning methods and choice of methods of assessment of the students' achievement of these objectives as well as the learning activity itself.

Improved Academic Performance and Student Perceptions of Learning through Use of a Cell Phone-Based Personal Response System

ERIC Educational Resources Information Center

Ma, Sihui; Steger, Daniel G.; Doolittle, Peter E.; Stewart, Amanda C.

2018-01-01

Personal response systems, such as clickers, have been widely used to improve the effectiveness of teaching in various classroom settings. Although hand-held clicker response systems have been the subject of multiple prior studies, few studies have focused on the use of cell phone-based personal response system (CPPRS) specifically. This study…

Fundamental Change: Innovation in America's Schools Under Race to the Top. Executive Summary

ERIC Educational Resources Information Center

US Department of Education, 2015

2015-01-01

Race to the Top's success ultimately must be measured by its long-term impact on student learning. Because simultaneous change in multiple systems takes time, it is too early to make that determination of success now. However, it is not too early to learn from the positive achievements of and challenges faced by Race to the Top states. This…

A Method for Writing Open-Ended Curved Arrow Notation Questions for Multiple-Choice Exams and Electronic-Response Systems

ERIC Educational Resources Information Center

Ruder, Suzanne M.; Straumanis, Andrei R.

2009-01-01

A critical stage in the process of developing a conceptual understanding of organic chemistry is learning to use curved arrow notation. From this stems the ability to predict reaction products and mechanisms beyond the realm of memorization. Since evaluation (i.e., testing) is known to be a key driver of student learning, it follows that a new…

Dorsolateral Striatal Lesions Impair Navigation Based on Landmark-Goal Vectors but Facilitate Spatial Learning Based on a "Cognitive Map"

ERIC Educational Resources Information Center

Kosaki, Yutaka; Poulter, Steven L.; Austen, Joe M.; McGregor, Anthony

2015-01-01

In three experiments, the nature of the interaction between multiple memory systems in rats solving a variation of a spatial task in the water maze was investigated. Throughout training rats were able to find a submerged platform at a fixed distance and direction from an intramaze landmark by learning a landmark-goal vector. Extramaze cues were…

Fundamental Change: Innovation in America's Schools Under Race to the Top

ERIC Educational Resources Information Center

US Department of Education, 2015

2015-01-01

Race to the Top's success ultimately must be measured by its long-term impact on student learning. Because simultaneous change in multiple systems takes time, it is too early to make that determination of success now. However, it is not too early to learn from the positive achievements of and challenges faced by Race to the Top states. This report…

Generalizing on Multiple Grounds: Performance Learning in Model-Based Troubleshooting

DTIC Science & Technology

1989-02-01

Aritificial Intelligence , 24, 1984. [Ble88] Guy E. Blelloch. Scan Primitives and Parallel Vector Models. PhD thesis, Artificial Intelligence Laboratory...Diagnostic reasoning based on strcture and behavior. Aritificial Intelligence , 24, 1984. [dK86] J. de Kleer. An assumption-based truth maintenance system...diagnosis. Aritificial Intelligence , 24. �. )3 94 BIBLIOGRAPHY [Ham87] Kristian J. Hammond. Learning to anticipate and avoid planning prob- lems

The Stability of Observational and Student Survey Measures of Teaching Effectiveness

ERIC Educational Resources Information Center

Polikoff, Morgan S.

2015-01-01

Responding to federal policy and recent research, states and districts have developed and begun implementing multiple-measure teacher evaluation systems. These systems generally include observational and/or student survey measures of instructional quality alongside measures of teachers' contributions to student learning (e.g., value-added models…

Multi-instance multi-label distance metric learning for genome-wide protein function prediction.

PubMed

Xu, Yonghui; Min, Huaqing; Song, Hengjie; Wu, Qingyao

2016-08-01

Multi-instance multi-label (MIML) learning has been proven to be effective for the genome-wide protein function prediction problems where each training example is associated with not only multiple instances but also multiple class labels. To find an appropriate MIML learning method for genome-wide protein function prediction, many studies in the literature attempted to optimize objective functions in which dissimilarity between instances is measured using the Euclidean distance. But in many real applications, Euclidean distance may be unable to capture the intrinsic similarity/dissimilarity in feature space and label space. Unlike other previous approaches, in this paper, we propose to learn a multi-instance multi-label distance metric learning framework (MIMLDML) for genome-wide protein function prediction. Specifically, we learn a Mahalanobis distance to preserve and utilize the intrinsic geometric information of both feature space and label space for MIML learning. In addition, we try to deal with the sparsely labeled data by giving weight to the labeled data. Extensive experiments on seven real-world organisms covering the biological three-domain system (i.e., archaea, bacteria, and eukaryote; Woese et al., 1990) show that the MIMLDML algorithm is superior to most state-of-the-art MIML learning algorithms. Copyright © 2016 Elsevier Ltd. All rights reserved.

A Multimodal Dialog System for Language Assessment: Current State and Future Directions. Research Report. ETS RR-17-21

ERIC Educational Resources Information Center

Suendermann-Oeft, David; Ramanarayanan, Vikram; Yu, Zhou; Qian, Yao; Evanini, Keelan; Lange, Patrick; Wang, Xinhao; Zechner, Klaus

2017-01-01

We present work in progress on a multimodal dialog system for English language assessment using a modular cloud-based architecture adhering to open industry standards. Among the modules being developed for the system, multiple modules heavily exploit machine learning techniques, including speech recognition, spoken language proficiency rating,…

Designing a Web-Based Science Learning Environment for Model-Based Collaborative Inquiry

NASA Astrophysics Data System (ADS)

Sun, Daner; Looi, Chee-Kit

2013-02-01

The paper traces a research process in the design and development of a science learning environment called WiMVT (web-based inquirer with modeling and visualization technology). The WiMVT system is designed to help secondary school students build a sophisticated understanding of scientific conceptions, and the science inquiry process, as well as develop critical learning skills through model-based collaborative inquiry approach. It is intended to support collaborative inquiry, real-time social interaction, progressive modeling, and to provide multiple sources of scaffolding for students. We first discuss the theoretical underpinnings for synthesizing the WiMVT design framework, introduce the components and features of the system, and describe the proposed work flow of WiMVT instruction. We also elucidate our research approach that supports the development of the system. Finally, the findings of a pilot study are briefly presented to demonstrate of the potential for learning efficacy of the WiMVT implementation in science learning. Implications are drawn on how to improve the existing system, refine teaching strategies and provide feedback to researchers, designers and teachers. This pilot study informs designers like us on how to narrow the gap between the learning environment's intended design and its actual usage in the classroom.

Working Memory Contributions to Reinforcement Learning Impairments in Schizophrenia

PubMed Central

Brown, Jaime K.; Gold, James M.; Waltz, James A.; Frank, Michael J.

2014-01-01

Previous research has shown that patients with schizophrenia are impaired in reinforcement learning tasks. However, behavioral learning curves in such tasks originate from the interaction of multiple neural processes, including the basal ganglia- and dopamine-dependent reinforcement learning (RL) system, but also prefrontal cortex-dependent cognitive strategies involving working memory (WM). Thus, it is unclear which specific system induces impairments in schizophrenia. We recently developed a task and computational model allowing us to separately assess the roles of RL (slow, cumulative learning) mechanisms versus WM (fast but capacity-limited) mechanisms in healthy adult human subjects. Here, we used this task to assess patients' specific sources of impairments in learning. In 15 separate blocks, subjects learned to pick one of three actions for stimuli. The number of stimuli to learn in each block varied from two to six, allowing us to separate influences of capacity-limited WM from the incremental RL system. As expected, both patients (n = 49) and healthy controls (n = 36) showed effects of set size and delay between stimulus repetitions, confirming the presence of working memory effects. Patients performed significantly worse than controls overall, but computational model fits and behavioral analyses indicate that these deficits could be entirely accounted for by changes in WM parameters (capacity and reliability), whereas RL processes were spared. These results suggest that the working memory system contributes strongly to learning impairments in schizophrenia. PMID:25297101

The Answering Process for Multiple-Choice Questions in Collaborative Learning: A Mathematical Learning Model Analysis

ERIC Educational Resources Information Center

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

2014-01-01

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

Machine learning algorithms for the creation of clinical healthcare enterprise systems

NASA Astrophysics Data System (ADS)

Mandal, Indrajit

2017-10-01

Clinical recommender systems are increasingly becoming popular for improving modern healthcare systems. Enterprise systems are persuasively used for creating effective nurse care plans to provide nurse training, clinical recommendations and clinical quality control. A novel design of a reliable clinical recommender system based on multiple classifier system (MCS) is implemented. A hybrid machine learning (ML) ensemble based on random subspace method and random forest is presented. The performance accuracy and robustness of proposed enterprise architecture are quantitatively estimated to be above 99% and 97%, respectively (above 95% confidence interval). The study then extends to experimental analysis of the clinical recommender system with respect to the noisy data environment. The ranking of items in nurse care plan is demonstrated using machine learning algorithms (MLAs) to overcome the drawback of the traditional association rule method. The promising experimental results are compared against the sate-of-the-art approaches to highlight the advancement in recommendation technology. The proposed recommender system is experimentally validated using five benchmark clinical data to reinforce the research findings.

Alert Triage v 0.1 beta

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

Doak, Justin E.; Ingram, Joe; Johnson, Josh

2016-01-06

In the cyber security operations of a typical organization, data from multiple sources are monitored, and when certain conditions in the data are met, an alert is generated in an alert management system. Analysts inspect these alerts to decide if any deserve promotion to an event requiring further scrutiny. This triage process is manual, time-consuming, and detracts from the in-depth investigation of events. We have created a software system that uses supervised machine learning to automatically prioritize these alerts. In particular we utilize active learning to make efficient use of the pool of unlabeled alerts, thereby improving the performance ofmore » our ranking models over passive learning. We have demonstrated the effectiveness of our system on a large, real-world dataset of cyber security alerts.« less

Parent Perception of Two Eye-Gaze Control Technology Systems in Young Children with Cerebral Palsy: Pilot Study.

PubMed

Karlsson, Petra; Wallen, Margaret

2017-01-01

Eye-gaze control technology enables people with significant physical disability to access computers for communication, play, learning and environmental control. This pilot study used a multiple case study design with repeated baseline assessment and parents' evaluations to compare two eye-gaze control technology systems to identify any differences in factors such as ease of use and impact of the systems for their young children. Five children, aged 3 to 5 years, with dyskinetic cerebral palsy, and their families participated. Overall, families were satisfied with both the Tobii PCEye Go and myGaze® eye tracker, found them easy to position and use, and children learned to operate them quickly. This technology provides young children with important opportunities for learning, play, leisure, and developing communication.

Patients with Parkinson's disease learn to control complex systems-an indication for intact implicit cognitive skill learning.

PubMed

Witt, Karsten; Daniels, Christine; Daniel, Victoria; Schmitt-Eliassen, Julia; Volkmann, Jens; Deuschl, Günther

2006-01-01

Implicit memory and learning mechanisms are composed of multiple processes and systems. Previous studies demonstrated a basal ganglia involvement in purely cognitive tasks that form stimulus response habits by reinforcement learning such as implicit classification learning. We will test the basal ganglia influence on two cognitive implicit tasks previously described by Berry and Broadbent, the sugar production task and the personal interaction task. Furthermore, we will investigate the relationship between certain aspects of an executive dysfunction and implicit learning. To this end, we have tested 22 Parkinsonian patients and 22 age-matched controls on two implicit cognitive tasks, in which participants learned to control a complex system. They interacted with the system by choosing an input value and obtaining an output that was related in a complex manner to the input. The objective was to reach and maintain a specific target value across trials (dynamic system learning). The two tasks followed the same underlying complex rule but had different surface appearances. Subsequently, participants performed an executive test battery including the Stroop test, verbal fluency and the Wisconsin card sorting test (WCST). The results demonstrate intact implicit learning in patients, despite an executive dysfunction in the Parkinsonian group. They lead to the conclusion that the basal ganglia system affected in Parkinson's disease does not contribute to the implicit acquisition of a new cognitive skill. Furthermore, the Parkinsonian patients were able to reach a specific goal in an implicit learning context despite impaired goal directed behaviour in the WCST, a classic test of executive functions. These results demonstrate a functional independence of implicit cognitive skill learning and certain aspects of executive functions.

Enhanced multisensory integration and motor reactivation after active motor learning of audiovisual associations.

PubMed

Butler, Andrew J; James, Thomas W; James, Karin Harman

2011-11-01

Everyday experience affords us many opportunities to learn about objects through multiple senses using physical interaction. Previous work has shown that active motor learning of unisensory items enhances memory and leads to the involvement of motor systems during subsequent perception. However, the impact of active motor learning on subsequent perception and recognition of associations among multiple senses has not been investigated. Twenty participants were included in an fMRI study that explored the impact of active motor learning on subsequent processing of unisensory and multisensory stimuli. Participants were exposed to visuo-motor associations between novel objects and novel sounds either through self-generated actions on the objects or by observing an experimenter produce the actions. Immediately after exposure, accuracy, RT, and BOLD fMRI measures were collected with unisensory and multisensory stimuli in associative perception and recognition tasks. Response times during audiovisual associative and unisensory recognition were enhanced by active learning, as was accuracy during audiovisual associative recognition. The difference in motor cortex activation between old and new associations was greater for the active than the passive group. Furthermore, functional connectivity between visual and motor cortices was stronger after active learning than passive learning. Active learning also led to greater activation of the fusiform gyrus during subsequent unisensory visual perception. Finally, brain regions implicated in audiovisual integration (e.g., STS) showed greater multisensory gain after active learning than after passive learning. Overall, the results show that active motor learning modulates the processing of multisensory associations.

Supporting Instruction By Defining Conceptual Relevance Of Materials: Alignment Of Resources To An Earth Systems Framework

NASA Astrophysics Data System (ADS)

Menicucci, A. J.; Bean, J. R.

2017-12-01

Environmental, geological, and climatological sciences are important facets of physical science education. However, it is often difficult for educators to acquire the necessary resources to facilitate content explanations, and demonstration of the conceptual links between individual lessons. The Understanding Global Change (UGC) Project at the University of California Museum of Paleontology (UCMP) at UC Berkeley is aligning new and existing Earth systems educational resources that are high-quality, interactive and inquiry based. Learning resources are organized by the UGC framework topics (Causes of Change, How the Earth System Works, and Measurable Changes), and focus on exploring topic relationships. Resources are currently aligned with both the UGC framework and the Next Generation Science Standards (NGSS), facilitating broad utility among K-16 educators. The overarching goal of the UGC Project is to provide the necessary resources that guide the construction of coherent, interdisciplinary instructional units. These units can be reinforced through system models, providing visual learning scaffolds for assessments of student content knowledge. Utilizing the central framework of UGC alleviates the long-standing problem of creating coherent instructional units from multiple learning resources, each organized and categorized independently across multiple platforms that may not provide explicit connections among Earth science subjects UGC topic cross listing of learning modules establishes conceptual links. Each resource is linked across several Earth system components, facilitating exploration of relationships and feedbacks between processes. Cross listed topics are therefore useful for development of broad picture learning goals via targeted instructional units. We also anticipate cultivating summaries of the explicit conceptual links explored in each resource from both current teachers and content specialists. Insructional units currated and aligned under the UGC framework therefore have the potential for users to develop and impliment inderdisciplinary lesson plans, including multi-segmented units designed to function as independent educational segments, that combine to provide broader subject exploration and deeper understanding of Earth system relationships.

A review on machine learning principles for multi-view biological data integration.

PubMed

Li, Yifeng; Wu, Fang-Xiang; Ngom, Alioune

2018-03-01

Driven by high-throughput sequencing techniques, modern genomic and clinical studies are in a strong need of integrative machine learning models for better use of vast volumes of heterogeneous information in the deep understanding of biological systems and the development of predictive models. How data from multiple sources (called multi-view data) are incorporated in a learning system is a key step for successful analysis. In this article, we provide a comprehensive review on omics and clinical data integration techniques, from a machine learning perspective, for various analyses such as prediction, clustering, dimension reduction and association. We shall show that Bayesian models are able to use prior information and model measurements with various distributions; tree-based methods can either build a tree with all features or collectively make a final decision based on trees learned from each view; kernel methods fuse the similarity matrices learned from individual views together for a final similarity matrix or learning model; network-based fusion methods are capable of inferring direct and indirect associations in a heterogeneous network; matrix factorization models have potential to learn interactions among features from different views; and a range of deep neural networks can be integrated in multi-modal learning for capturing the complex mechanism of biological systems.

Learning With E-books and Project-based Strategy in a Community Health Nursing Course.

PubMed

Sung, Tien-Wen; Wu, Ting-Ting

2018-03-01

With advances in information technology, "information-assisted instruction" has been gradually introduced to nursing education curricula. Specifically, the integration of an e-book system can effectively enhance nursing students' attention and interest. Most studies on nursing education that incorporated e-books have focused on the advantages of convenience and assistance provided by e-books. Few studies have addressed community health nursing and off-campus practice activities in relation to suitable teaching strategies for learning activities. This study involved designing and planning a multimedia e-book learning system with a project-based learning activity that conforms to the curriculum and practical requirements of a community health nursing course. The purpose was to reduce the gap between theory and practice and realize an effective learning process. For learning evaluations, a final examination analysis with an independent sample t test; a scoring scheme with intrateam, interteam, and expert ratings; and Bloom's taxonomy-based analysis were conducted. The evaluation results indicated that the comprehension and learning abilities of the experimental group using the e-book system with a mobile device were effectively improved. In addition, the exploratory process involved in project-based learning can develop multiple cognitive skills and problem-solving ability, thereby realizing effective learning.

Mapping groundwater contamination risk of multiple aquifers using multi-model ensemble of machine learning algorithms.

PubMed

Barzegar, Rahim; Moghaddam, Asghar Asghari; Deo, Ravinesh; Fijani, Elham; Tziritis, Evangelos

2018-04-15

Constructing accurate and reliable groundwater risk maps provide scientifically prudent and strategic measures for the protection and management of groundwater. The objectives of this paper are to design and validate machine learning based-risk maps using ensemble-based modelling with an integrative approach. We employ the extreme learning machines (ELM), multivariate regression splines (MARS), M5 Tree and support vector regression (SVR) applied in multiple aquifer systems (e.g. unconfined, semi-confined and confined) in the Marand plain, North West Iran, to encapsulate the merits of individual learning algorithms in a final committee-based ANN model. The DRASTIC Vulnerability Index (VI) ranged from 56.7 to 128.1, categorized with no risk, low and moderate vulnerability thresholds. The correlation coefficient (r) and Willmott's Index (d) between NO 3 concentrations and VI were 0.64 and 0.314, respectively. To introduce improvements in the original DRASTIC method, the vulnerability indices were adjusted by NO 3 concentrations, termed as the groundwater contamination risk (GCR). Seven DRASTIC parameters utilized as the model inputs and GCR values utilized as the outputs of individual machine learning models were served in the fully optimized committee-based ANN-predictive model. The correlation indicators demonstrated that the ELM and SVR models outperformed the MARS and M5 Tree models, by virtue of a larger d and r value. Subsequently, the r and d metrics for the ANN-committee based multi-model in the testing phase were 0.8889 and 0.7913, respectively; revealing the superiority of the integrated (or ensemble) machine learning models when compared with the original DRASTIC approach. The newly designed multi-model ensemble-based approach can be considered as a pragmatic step for mapping groundwater contamination risks of multiple aquifer systems with multi-model techniques, yielding the high accuracy of the ANN committee-based model. Copyright © 2017 Elsevier B.V. All rights reserved.

Explicit pre-training instruction does not improve implicit perceptual-motor sequence learning

PubMed Central

Sanchez, Daniel J.; Reber, Paul J.

2012-01-01

Memory systems theory argues for separate neural systems supporting implicit and explicit memory in the human brain. Neuropsychological studies support this dissociation, but empirical studies of cognitively healthy participants generally observe that both kinds of memory are acquired to at least some extent, even in implicit learning tasks. A key question is whether this observation reflects parallel intact memory systems or an integrated representation of memory in healthy participants. Learning of complex tasks in which both explicit instruction and practice is used depends on both kinds of memory, and how these systems interact will be an important component of the learning process. Theories that posit an integrated, or single, memory system for both types of memory predict that explicit instruction should contribute directly to strengthening task knowledge. In contrast, if the two types of memory are independent and acquired in parallel, explicit knowledge should have no direct impact and may serve in a “scaffolding” role in complex learning. Using an implicit perceptual-motor sequence learning task, the effect of explicit pre-training instruction on skill learning and performance was assessed. Explicit pre-training instruction led to robust explicit knowledge, but sequence learning did not benefit from the contribution of pre-training sequence memorization. The lack of an instruction benefit suggests that during skill learning, implicit and explicit memory operate independently. While healthy participants will generally accrue parallel implicit and explicit knowledge in complex tasks, these types of information appear to be separately represented in the human brain consistent with multiple memory systems theory. PMID:23280147

Multiple Learning Approaches in the Professional Development of School Leaders -- Theoretical Perspectives and Empirical Findings on Self-assessment and Feedback

ERIC Educational Resources Information Center

Huber, Stephan Gerhard

2013-01-01

This article investigates the use of multiple learning approaches and different modes and types of learning in the (continuous) professional development (PD) of school leaders, particularly the use of self-assessment and feedback. First, formats and multiple approaches to professional learning are described. Second, a possible approach to…

The effect of multiple intelligence-based learning towards students’ concept mastery and interest in learning matter

NASA Astrophysics Data System (ADS)

Pratiwi, W. N.; Rochintaniawati, D.; Agustin, R. R.

2018-05-01

This research was focused on investigating the effect of multiple intelligence -based learning as a learning approach towards students’ concept mastery and interest in learning matter. The one-group pre-test - post-test design was used in this research towards a sample which was according to the suitable situation of the research sample, n = 13 students of the 7th grade in a private school in Bandar Seri Begawan. The students’ concept mastery was measured using achievement test and given at the pre-test and post-test, meanwhile the students’ interest level was measured using a Likert Scale for interest. Based on the analysis of the data, the result shows that the normalized gain was .61, which was considered as a medium improvement. in other words, students’ concept mastery in matter increased after being taught using multiple intelligence-based learning. The Likert scale of interest shows that most students have a high interest in learning matter after being taught by multiple intelligence-based learning. Therefore, it is concluded that multiple intelligence – based learning helped in improving students’ concept mastery and gain students’ interest in learning matter.

Learning and motivation in the human striatum.

PubMed

Shohamy, Daphna

2011-06-01

The past decade has seen a dramatic change in our understanding of the role of the striatum in behavior. Early perspectives emphasized a role for the striatum in habitual learning of stimulus-response associations and sequences of actions. Recent advances from human neuroimaging research suggest a broader role for the striatum in motivated learning. New findings demonstrate that the striatum represents multiple learning signals and highlight the contribution of the striatum across many cognitive domains and contexts. Recent findings also emphasize interactions between the striatum and other specialized brain systems for learning. Together, these findings suggest that the striatum contributes to a distributed network that learns to select actions based on their predicted value in order to optimize behavior. Copyright © 2011 Elsevier Ltd. All rights reserved.

Successful Learning with Multiple Graphical Representations and Self-Explanation Prompts

ERIC Educational Resources Information Center

Rau, Martina A.; Aleven, Vincent; Rummel, Nikol

2015-01-01

Research shows that multiple external representations can significantly enhance students' learning. Most of this research has focused on learning with text and 1 additional graphical representation. However, real instructional materials often employ multiple "graphical" representations (MGRs) in addition to text. An important open…

Constrained Deep Weak Supervision for Histopathology Image Segmentation.

PubMed

Jia, Zhipeng; Huang, Xingyi; Chang, Eric I-Chao; Xu, Yan

2017-11-01

In this paper, we develop a new weakly supervised learning algorithm to learn to segment cancerous regions in histopathology images. This paper is under a multiple instance learning (MIL) framework with a new formulation, deep weak supervision (DWS); we also propose an effective way to introduce constraints to our neural networks to assist the learning process. The contributions of our algorithm are threefold: 1) we build an end-to-end learning system that segments cancerous regions with fully convolutional networks (FCNs) in which image-to-image weakly-supervised learning is performed; 2) we develop a DWS formulation to exploit multi-scale learning under weak supervision within FCNs; and 3) constraints about positive instances are introduced in our approach to effectively explore additional weakly supervised information that is easy to obtain and enjoy a significant boost to the learning process. The proposed algorithm, abbreviated as DWS-MIL, is easy to implement and can be trained efficiently. Our system demonstrates the state-of-the-art results on large-scale histopathology image data sets and can be applied to various applications in medical imaging beyond histopathology images, such as MRI, CT, and ultrasound images.

One Giant Leap for Categorizers: One Small Step for Categorization Theory

PubMed Central

Smith, J. David; Ell, Shawn W.

2015-01-01

We explore humans’ rule-based category learning using analytic approaches that highlight their psychological transitions during learning. These approaches confirm that humans show qualitatively sudden psychological transitions during rule learning. These transitions contribute to the theoretical literature contrasting single vs. multiple category-learning systems, because they seem to reveal a distinctive learning process of explicit rule discovery. A complete psychology of categorization must describe this learning process, too. Yet extensive formal-modeling analyses confirm that a wide range of current (gradient-descent) models cannot reproduce these transitions, including influential rule-based models (e.g., COVIS) and exemplar models (e.g., ALCOVE). It is an important theoretical conclusion that existing models cannot explain humans’ rule-based category learning. The problem these models have is the incremental algorithm by which learning is simulated. Humans descend no gradient in rule-based tasks. Very different formal-modeling systems will be required to explain humans’ psychology in these tasks. An important next step will be to build a new generation of models that can do so. PMID:26332587

A History and Overview of the Behavioral Neuroscience of Learning and Memory.

PubMed

Clark, Robert E

2018-01-01

Here, I provide a basic history of important milestones in the development of theories for how the brain accomplishes the phenomenon of learning and memory. Included are the ideas of Plato, René Descartes, Théodule Ribot, William James, Ivan Pavlov, John Watson, Karl Lashley, and others. The modern era of learning and memory research begins with the description of H.M. by Brenda Milner and the gradual discovery that the brain contains multiple learning and memory systems that are supported by anatomically discrete brain structures. Finally, a brief overview is provided for the chapters that are included in current topics in Behavioral Neuroscience-Learning and Memory.

A History and Overview of the Behavioral Neuroscience of Learning and Memory.

PubMed

Clark, Robert E

2018-01-05

Here, I provide a basic history of important milestones in the development of theories for how the brain accomplishes the phenomenon of learning and memory. Included are the ideas of Plato, René Descartes, Théodule Ribot, William James, Ivan Pavlov, John Watson, Karl Lashley, and others. The modern era of learning and memory research begins with the description of H.M. by Brenda Milner and the gradual discovery that the brain contains multiple learning and memory systems that are supported by anatomically discrete brain structures. Finally, a brief overview is provided for the chapters that are included in current topics in Behavioral Neuroscience-Learning and Memory.

Teaching English as a Foreign Language to Students with Learning Disabilities at the Intermediate and Advanced Levels: A Multiple-Strategies Approach

ERIC Educational Resources Information Center

El-Koumy, Abdel Salam A.

2016-01-01

The idea of this book arose out of an awareness that students with language learning disabilities are completely ignored in the Egyptian school system and there are no special programs that cater to these students. They are placed in normal schools that are not prepared to deal with their unique difficulties. This book, therefore, is an attempt to…

Six to Ten Digits Multiplication Fun Learning Using Puppet Prototype

NASA Astrophysics Data System (ADS)

Islamiah Rosli, D.'oria; Ali, Azita; Peng, Lim Soo; Sujardi, Imam; Usodo, Budi; Adie Perdana, Fengky

2017-01-01

Logic and technical subjects require students to understand basic knowledge in mathematic. For instance, addition, minus, division and multiplication operations need to be mastered by students due to mathematic complexity as the learning mathematic grows higher. Weak foundation in mathematic also contribute to high failure rate in mathematic subjects in schools. In fact, students in primary schools are struggling to learn mathematic because they need to memorize formulas, multiplication or division operations. To date, this study will develop a puppet prototyping for learning mathematic for six to ten digits multiplication. Ten participants involved in the process of developing the prototype in this study. Students involved in the study were those from the intermediate class students whilst teachers were selected based on their vast knowledge and experiences and have more than five years of experience in teaching mathematic. Close participatory analysis will be used in the prototyping process as to fulfil the requirements of the students and teachers whom will use the puppet in learning six to ten digit multiplication in mathematic. Findings showed that, the students had a great time and fun learning experience in learning multiplication and they able to understand the concept of multiplication using puppet. Colour and materials of the puppet also help to attract student attention during learning. Additionally, students able to visualized and able to calculate accurate multiplication value and the puppet help them to recall in multiplying and adding the digits accordingly.

Quiz Making Activities Using the Multi-Mouse Quiz System in an Elementary School

ERIC Educational Resources Information Center

Zhou, Juan; Mori, Mikihiko; Ueda, Hiroshi; Kita, Hajime

2013-01-01

The Multi-Mouse Quiz System is an application used to treat quizzes in a classroom or other learning environment. The system comprises the Multi Mouse Quiz (MMQ) and MMQEditor. The MMQ is an application of Single Display Groupware (SDG), which enables multiple users to answer quizzes by connecting several mice to an ordinary computer. The…

A Multiple Case Study of Preservice Science Teachers' TPACK: Embedded in a Comprehensive Belief System

ERIC Educational Resources Information Center

Günes, Erhan; Bahçivan, Eralp

2016-01-01

Integrating technology into science education provides opportunities to foster students' meaningful learning. This study focused on technological pedagogical content knowledge (TPACK) and its connections to belief system in a science teaching context. The purpose of this study was to investigate the effects of preservice science teachers' (PST)…

Integration of e-Management, e-Development and e-Learning Technologies for Blended Course Delivery

ERIC Educational Resources Information Center

Johnson, Lynn E.; Tang, Michael

2005-01-01

This paper describes and assesses a pre-engineering curriculum development project called Foundations of Engineering, Science and Technology (FEST). FEST integrates web-based technologies into an inter-connected system to enable delivery of a blended program at multiple institutions. Tools and systems described include 1) technologies to deliver…

Promoting Conceptual Change for Complex Systems Understanding: Outcomes of an Agent-Based Participatory Simulation

ERIC Educational Resources Information Center

Rates, Christopher A.; Mulvey, Bridget K.; Feldon, David F.

2016-01-01

Components of complex systems apply across multiple subject areas, and teaching these components may help students build unifying conceptual links. Students, however, often have difficulty learning these components, and limited research exists to understand what types of interventions may best help improve understanding. We investigated 32 high…

Measuring students' self-regulated learning in professional education: bridging the gap between event and aptitude measurements.

PubMed

Endedijk, Maaike D; Brekelmans, Mieke; Sleegers, Peter; Vermunt, Jan D

Self-regulated learning has benefits for students' academic performance in school, but also for expertise development during their professional career. This study examined the validity of an instrument to measure student teachers' regulation of their learning to teach across multiple and different kinds of learning events in the context of a postgraduate professional teacher education programme. Based on an analysis of the literature, we developed a log with structured questions that could be used as a multiple-event instrument to determine the quality of student teachers' regulation of learning by combining data from multiple learning experiences. The findings showed that this structured version of the instrument measured student teachers' regulation of their learning in a valid and reliable way. Furthermore, with the aid of the Structured Learning Report individual differences in student teachers' regulation of learning could be discerned. Together the findings indicate that a multiple-event instrument can be used to measure regulation of learning in multiple contexts for various learning experiences at the same time, without the necessity of relying on students' ability to rate themselves across all these different experiences. In this way, this instrument can make an important contribution to bridging the gap between two dominant approaches to measure SRL, the traditional aptitude and event measurement approach.

Design and Control of Large Collections of Learning Agents

NASA Technical Reports Server (NTRS)

Agogino, Adrian

2001-01-01

The intelligent control of multiple autonomous agents is an important yet difficult task. Previous methods used to address this problem have proved to be either too brittle, too hard to use, or not scalable to large systems. The 'Collective Intelligence' project at NASA/Ames provides an elegant, machine-learning approach to address these problems. This approach mathematically defines some essential properties that a reward system should have to promote coordinated behavior among reinforcement learners. This work has focused on creating additional key properties and algorithms within the mathematics of the Collective Intelligence framework. One of the additions will allow agents to learn more quickly, in a more coordinated manner. The other will let agents learn with less knowledge of their environment. These additions will allow the framework to be applied more easily, to a much larger domain of multi-agent problems.

Learning Multiplication Using Indonesian Traditional Game in Third Grade

ERIC Educational Resources Information Center

Prahmana, Rully Charitas Indra; Zulkardi; Hartono, Yusuf

2012-01-01

Several previous researches showed that students had difficulty in understanding the basic concept of multiplication. Students are more likely to be introduced by using formula without involving the concept itself. This underlies the researcher to design a learning trajectory of learning multiplication using Permainan Tradisional Tepuk Bergambar…

The Community as Classroom: Multiple Perspectives on Student Learning.

ERIC Educational Resources Information Center

Kerrigan, Seanna; Gelmon, Sherrill; Spring, Amy

2003-01-01

Reports on the multiple perspectives of students, community members, and faculty to document the affect of student participation in service-learning courses. The study examined in this article used a large sample size and multiple qualitative and quantitative methods over several years. The results indicate that service learning affects students…

Multiple Intelligences in Virtual and Traditional Skill Instructional Learning Environments

ERIC Educational Resources Information Center

McKethan, Robert; Rabinowitz, Erik; Kernodle, Michael W.

2010-01-01

The purpose of this investigation was to examine (a) how Multiple Intelligence (MI) strengths correlate to learning in virtual and traditional environments and (b) the effectiveness of learning with and without an authority figure in attendance. Participants (N=69) were randomly assigned to four groups, administered the Multiple Intelligences…

Quicker Q-Learning in Multi-Agent Systems

NASA Technical Reports Server (NTRS)

Agogino, Adrian K.; Tumer, Kagan

2005-01-01

Multi-agent learning in Markov Decisions Problems is challenging because of the presence ot two credit assignment problems: 1) How to credit an action taken at time step t for rewards received at t' greater than t; and 2) How to credit an action taken by agent i considering the system reward is a function of the actions of all the agents. The first credit assignment problem is typically addressed with temporal difference methods such as Q-learning OK TD(lambda) The second credit assi,onment problem is typically addressed either by hand-crafting reward functions that assign proper credit to an agent, or by making certain independence assumptions about an agent's state-space and reward function. To address both credit assignment problems simultaneously, we propose the Q Updates with Immediate Counterfactual Rewards-learning (QUICR-learning) designed to improve both the convergence properties and performance of Q-learning in large multi-agent problems. Instead of assuming that an agent s value function can be made independent of other agents, this method suppresses the impact of other agents using counterfactual rewards. Results on multi-agent grid-world problems over multiple topologies show that QUICR-learning can achieve up to thirty fold improvements in performance over both conventional and local Q-learning in the largest tested systems.

Learned helplessness in the multiple sclerosis population.

PubMed

McGuinness, S

1996-06-01

The purpose of this cross-sectional, descriptive study was to describe the relationships between learned helplessness and disease status, functional and social disability, and disease activity in the multiple sclerosis population. Additionally, the relationships between learned helplessness and age, disease duration, education and marital and employment status were evaluated. Self-report instruments with established validity and reliability in the multiple sclerosis population were used to collect the data. Learned helplessness was significantly positively correlated with social and functional disability. Although not significant at the .05 level, disease status and disease activity were also positively correlated with learned helplessness. Additionally, unemployed individuals were more likely to be helpless than employed individuals. Overall, the results suggest that learned helplessness is related to negative health indicators in the multiple sclerosis population. Nursing interventions to decrease or prevent learned helplessness may be appropriate in this population.

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…

Assessment of item-writing flaws in multiple-choice questions.

PubMed

Nedeau-Cayo, Rosemarie; Laughlin, Deborah; Rus, Linda; Hall, John

2013-01-01

This study evaluated the quality of multiple-choice questions used in a hospital's e-learning system. Constructing well-written questions is fraught with difficulty, and item-writing flaws are common. Study results revealed that most items contained flaws and were written at the knowledge/comprehension level. Few items had linked objectives, and no association was found between the presence of objectives and flaws. Recommendations include education for writing test questions.

Strategy, Theory, Tactical Possibilities and the Design of Amphibious Concepts

DTIC Science & Technology

2012-05-17

the process of learning and pierce the veil of uncertainty that lies between the protagonists, a gambit must be made—sufficient energy must be...non-military means was again challenging amphibious operations advocates.34 From 1990 – 2010, the Marine Corps conducted approximately 104...power for political ends by using multiple means and approaches to attack multiple centers of gravity and thus collapse an adversary’s system. The

Cultural influences on science museum practices: A case study

NASA Astrophysics Data System (ADS)

Duensing, Sally Jeanne

This dissertation looks at how informal science museums and centers both reflect and create the cultural contexts in which they are embedded. Specifically, it explores the multiple cultural perspectives held by the staff of the Yapollo Science Center in Trinidad, West Indies. This study focuses on how these perspectives impact the science center's sense of mission, design of educational programs, and development of exhibits. The findings in this case study have implications for other science museums and learning environments. Through the conduct and analysis of interviews, group meetings and on-site observations, this study found that there are several cultural domains in which staff perspectives of museum practice are situated. These include the local popular Trinidadian culture, the formal school system, and international science center community practices. For example, learning in the science center is seen by Yapollo staff as a social endeavor, more than an individual act. There is an emphasis on group engagement and social learning processes in exhibit design and teaching programs. The impact of local culture is further evidenced by Trinidadian practices of social learning and social competition in steel pan learning and calypso competition. These practices inform images of learning at Yapollo. The study highlights the role of formal educational systems by discussing how staff's informal educational approaches have resulted in a dialectic with the local formal British based school system practices. The study also explores the ways staff have adapted exhibit and program ideas from the international science museum. The synthesis of these cultures creates its own cultural ways of thinking and practice about exhibits and pedagogy that form the shared common wisdom at Yapollo. Museum practice, in this context, is viewed as a culture shaping enterprise that is itself shaped by culture. It demonstrates that teaching and learning practices occur in, and can be reflected upon, in multiple cultural contexts. The findings of this study have implications for many other areas of sociocultural and educational research.

Aviation Safety Risk Modeling: Lessons Learned From Multiple Knowledge Elicitation Sessions

NASA Technical Reports Server (NTRS)

Luxhoj, J. T.; Ancel, E.; Green, L. L.; Shih, A. T.; Jones, S. M.; Reveley, M. S.

2014-01-01

Aviation safety risk modeling has elements of both art and science. In a complex domain, such as the National Airspace System (NAS), it is essential that knowledge elicitation (KE) sessions with domain experts be performed to facilitate the making of plausible inferences about the possible impacts of future technologies and procedures. This study discusses lessons learned throughout the multiple KE sessions held with domain experts to construct probabilistic safety risk models for a Loss of Control Accident Framework (LOCAF), FLightdeck Automation Problems (FLAP), and Runway Incursion (RI) mishap scenarios. The intent of these safety risk models is to support a portfolio analysis of NASA's Aviation Safety Program (AvSP). These models use the flexible, probabilistic approach of Bayesian Belief Networks (BBNs) and influence diagrams to model the complex interactions of aviation system risk factors. Each KE session had a different set of experts with diverse expertise, such as pilot, air traffic controller, certification, and/or human factors knowledge that was elicited to construct a composite, systems-level risk model. There were numerous "lessons learned" from these KE sessions that deal with behavioral aggregation, conditional probability modeling, object-oriented construction, interpretation of the safety risk results, and model verification/validation that are presented in this paper.

Cross-platform learning: on the nature of children's learning from multiple media platforms.

PubMed

Fisch, Shalom M

2013-01-01

It is increasingly common for an educational media project to span several media platforms (e.g., TV, Web, hands-on materials), assuming that the benefits of learning from multiple media extend beyond those gained from one medium alone. Yet research typically has investigated learning from a single medium in isolation. This paper reviews several recent studies to explore cross-platform learning (i.e., learning from combined use of multiple media platforms) and how such learning compares to learning from one medium. The paper discusses unique benefits of cross-platform learning, a theoretical mechanism to explain how these benefits might arise, and questions for future research in this emerging field. Copyright © 2013 Wiley Periodicals, Inc., A Wiley Company.

Declarative and nondeclarative memory: multiple brain systems supporting learning and memory.

PubMed

Squire, L R

1992-01-01

Abstract The topic of multiple forms of memory is considered from a biological point of view. Fact-and-event (declarative, explicit) memory is contrasted with a collection of non conscious (non-declarative, implicit) memory abilities including skills and habits, priming, and simple conditioning. Recent evidence is reviewed indicating that declarative and non declarative forms of memory have different operating characteristics and depend on separate brain systems. A brain-systems framework for understanding memory phenomena is developed in light of lesion studies involving rats, monkeys, and humans, as well as recent studies with normal humans using the divided visual field technique, event-related potentials, and positron emission tomography (PET).

The Impact of Team-Based Learning on Nervous System Examination Knowledge of Nursing Students.

PubMed

Hemmati Maslakpak, Masomeh; Parizad, Naser; Zareie, Farzad

2015-12-01

Team-based learning is one of the active learning approaches in which independent learning is combined with small group discussion in the class. This study aimed to determine the impact of team-based learning in nervous system examination knowledge of nursing students. This quasi-experimental study was conducted on 3(rd) grade nursing students, including 5th semester (intervention group) and 6(th) semester (control group). The traditional lecture method and the team-based learning method were used for educating the examination of the nervous system for intervention and control groups, respectively. The data were collected by a test covering 40-questions (multiple choice, matching, gap-filling and descriptive questions) before and after intervention in both groups. Individual Readiness Assurance Test (RAT) and Group Readiness Assurance Test (GRAT) used to collect data in the intervention group. In the end, the collected data were analyzed by SPSS ver. 13 using descriptive and inferential statistical tests. In team-based learning group, mean and standard deviation was 13.39 (4.52) before the intervention, which had been increased to 31.07 (3.20) after the intervention and this increase was statistically significant. Also, there was a statistically significant difference between the scores of RAT and GRAT in team-based learning group. Using team-based learning approach resulted in much better improvement and stability in the nervous system examination knowledge of nursing students compared to traditional lecture method; therefore, this method could be efficiently used as an effective educational approach in nursing education.

Working memory contributions to reinforcement learning impairments in schizophrenia.

PubMed

Collins, Anne G E; Brown, Jaime K; Gold, James M; Waltz, James A; Frank, Michael J

2014-10-08

Previous research has shown that patients with schizophrenia are impaired in reinforcement learning tasks. However, behavioral learning curves in such tasks originate from the interaction of multiple neural processes, including the basal ganglia- and dopamine-dependent reinforcement learning (RL) system, but also prefrontal cortex-dependent cognitive strategies involving working memory (WM). Thus, it is unclear which specific system induces impairments in schizophrenia. We recently developed a task and computational model allowing us to separately assess the roles of RL (slow, cumulative learning) mechanisms versus WM (fast but capacity-limited) mechanisms in healthy adult human subjects. Here, we used this task to assess patients' specific sources of impairments in learning. In 15 separate blocks, subjects learned to pick one of three actions for stimuli. The number of stimuli to learn in each block varied from two to six, allowing us to separate influences of capacity-limited WM from the incremental RL system. As expected, both patients (n = 49) and healthy controls (n = 36) showed effects of set size and delay between stimulus repetitions, confirming the presence of working memory effects. Patients performed significantly worse than controls overall, but computational model fits and behavioral analyses indicate that these deficits could be entirely accounted for by changes in WM parameters (capacity and reliability), whereas RL processes were spared. These results suggest that the working memory system contributes strongly to learning impairments in schizophrenia. Copyright © 2014 the authors 0270-6474/14/3413747-10$15.00/0.

Detecting Visually Observable Disease Symptoms from Faces.

PubMed

Wang, Kuan; Luo, Jiebo

2016-12-01

Recent years have witnessed an increasing interest in the application of machine learning to clinical informatics and healthcare systems. A significant amount of research has been done on healthcare systems based on supervised learning. In this study, we present a generalized solution to detect visually observable symptoms on faces using semi-supervised anomaly detection combined with machine vision algorithms. We rely on the disease-related statistical facts to detect abnormalities and classify them into multiple categories to narrow down the possible medical reasons of detecting. Our method is in contrast with most existing approaches, which are limited by the availability of labeled training data required for supervised learning, and therefore offers the major advantage of flagging any unusual and visually observable symptoms.

Multiple-choice pretesting potentiates learning of related information.

PubMed

Little, Jeri L; Bjork, Elizabeth Ligon

2016-10-01

Although the testing effect has received a substantial amount of empirical attention, such research has largely focused on the effects of tests given after study. The present research examines the effect of using tests prior to study (i.e., as pretests), focusing particularly on how pretesting influences the subsequent learning of information that is not itself pretested but that is related to the pretested information. In Experiment 1, we found that multiple-choice pretesting was better for the learning of such related information than was cued-recall pretesting or a pre-fact-study control condition. In Experiment 2, we found that the increased learning of non-pretested related information following multiple-choice testing could not be attributed to increased time allocated to that information during subsequent study. Last, in Experiment 3, we showed that the benefits of multiple-choice pretesting over cued-recall pretesting for the learning of related information persist over 48 hours, thus demonstrating the promise of multiple-choice pretesting to potentiate learning in educational contexts. A possible explanation for the observed benefits of multiple-choice pretesting for enhancing the effectiveness with which related nontested information is learned during subsequent study is discussed.

Learning to Control Advanced Life Support Systems

NASA Technical Reports Server (NTRS)

Subramanian, Devika

2004-01-01

Advanced life support systems have many interacting processes and limited resources. Controlling and optimizing advanced life support systems presents unique challenges. In particular, advanced life support systems are nonlinear coupled dynamical systems and it is difficult for humans to take all interactions into account to design an effective control strategy. In this project. we developed several reinforcement learning controllers that actively explore the space of possible control strategies, guided by rewards from a user specified long term objective function. We evaluated these controllers using a discrete event simulation of an advanced life support system. This simulation, called BioSim, designed by Nasa scientists David Kortenkamp and Scott Bell has multiple, interacting life support modules including crew, food production, air revitalization, water recovery, solid waste incineration and power. They are implemented in a consumer/producer relationship in which certain modules produce resources that are consumed by other modules. Stores hold resources between modules. Control of this simulation is via adjusting flows of resources between modules and into/out of stores. We developed adaptive algorithms that control the flow of resources in BioSim. Our learning algorithms discovered several ingenious strategies for maximizing mission length by controlling the air and water recycling systems as well as crop planting schedules. By exploiting non-linearities in the overall system dynamics, the learned controllers easily out- performed controllers written by human experts. In sum, we accomplished three goals. We (1) developed foundations for learning models of coupled dynamical systems by active exploration of the state space, (2) developed and tested algorithms that learn to efficiently control air and water recycling processes as well as crop scheduling in Biosim, and (3) developed an understanding of the role machine learning in designing control systems for advanced life support.

Distributed reinforcement learning for adaptive and robust network intrusion response

NASA Astrophysics Data System (ADS)

Malialis, Kleanthis; Devlin, Sam; Kudenko, Daniel

2015-07-01

Distributed denial of service (DDoS) attacks constitute a rapidly evolving threat in the current Internet. Multiagent Router Throttling is a novel approach to defend against DDoS attacks where multiple reinforcement learning agents are installed on a set of routers and learn to rate-limit or throttle traffic towards a victim server. The focus of this paper is on online learning and scalability. We propose an approach that incorporates task decomposition, team rewards and a form of reward shaping called difference rewards. One of the novel characteristics of the proposed system is that it provides a decentralised coordinated response to the DDoS problem, thus being resilient to DDoS attacks themselves. The proposed system learns remarkably fast, thus being suitable for online learning. Furthermore, its scalability is successfully demonstrated in experiments involving 1000 learning agents. We compare our approach against a baseline and a popular state-of-the-art throttling technique from the network security literature and show that the proposed approach is more effective, adaptive to sophisticated attack rate dynamics and robust to agent failures.

Towards automatic pulmonary nodule management in lung cancer screening with deep learning

NASA Astrophysics Data System (ADS)

Ciompi, Francesco; Chung, Kaman; van Riel, Sarah J.; Setio, Arnaud Arindra Adiyoso; Gerke, Paul K.; Jacobs, Colin; Th. Scholten, Ernst; Schaefer-Prokop, Cornelia; Wille, Mathilde M. W.; Marchianò, Alfonso; Pastorino, Ugo; Prokop, Mathias; van Ginneken, Bram

2017-04-01

The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy. According to the current guidelines, the workup of screen-detected nodules strongly relies on nodule size and nodule type. In this paper, we present a deep learning system based on multi-stream multi-scale convolutional networks, which automatically classifies all nodule types relevant for nodule workup. The system processes raw CT data containing a nodule without the need for any additional information such as nodule segmentation or nodule size and learns a representation of 3D data by analyzing an arbitrary number of 2D views of a given nodule. The deep learning system was trained with data from the Italian MILD screening trial and validated on an independent set of data from the Danish DLCST screening trial. We analyze the advantage of processing nodules at multiple scales with a multi-stream convolutional network architecture, and we show that the proposed deep learning system achieves performance at classifying nodule type that surpasses the one of classical machine learning approaches and is within the inter-observer variability among four experienced human observers.

Towards automatic pulmonary nodule management in lung cancer screening with deep learning.

PubMed

Ciompi, Francesco; Chung, Kaman; van Riel, Sarah J; Setio, Arnaud Arindra Adiyoso; Gerke, Paul K; Jacobs, Colin; Scholten, Ernst Th; Schaefer-Prokop, Cornelia; Wille, Mathilde M W; Marchianò, Alfonso; Pastorino, Ugo; Prokop, Mathias; van Ginneken, Bram

2017-04-19

The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy. According to the current guidelines, the workup of screen-detected nodules strongly relies on nodule size and nodule type. In this paper, we present a deep learning system based on multi-stream multi-scale convolutional networks, which automatically classifies all nodule types relevant for nodule workup. The system processes raw CT data containing a nodule without the need for any additional information such as nodule segmentation or nodule size and learns a representation of 3D data by analyzing an arbitrary number of 2D views of a given nodule. The deep learning system was trained with data from the Italian MILD screening trial and validated on an independent set of data from the Danish DLCST screening trial. We analyze the advantage of processing nodules at multiple scales with a multi-stream convolutional network architecture, and we show that the proposed deep learning system achieves performance at classifying nodule type that surpasses the one of classical machine learning approaches and is within the inter-observer variability among four experienced human observers.

Towards automatic pulmonary nodule management in lung cancer screening with deep learning

PubMed Central

Ciompi, Francesco; Chung, Kaman; van Riel, Sarah J.; Setio, Arnaud Arindra Adiyoso; Gerke, Paul K.; Jacobs, Colin; Th. Scholten, Ernst; Schaefer-Prokop, Cornelia; Wille, Mathilde M. W.; Marchianò, Alfonso; Pastorino, Ugo; Prokop, Mathias; van Ginneken, Bram

2017-01-01

The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy. According to the current guidelines, the workup of screen-detected nodules strongly relies on nodule size and nodule type. In this paper, we present a deep learning system based on multi-stream multi-scale convolutional networks, which automatically classifies all nodule types relevant for nodule workup. The system processes raw CT data containing a nodule without the need for any additional information such as nodule segmentation or nodule size and learns a representation of 3D data by analyzing an arbitrary number of 2D views of a given nodule. The deep learning system was trained with data from the Italian MILD screening trial and validated on an independent set of data from the Danish DLCST screening trial. We analyze the advantage of processing nodules at multiple scales with a multi-stream convolutional network architecture, and we show that the proposed deep learning system achieves performance at classifying nodule type that surpasses the one of classical machine learning approaches and is within the inter-observer variability among four experienced human observers. PMID:28422152

Effectiveness of an audience response system in teaching pharmacology to baccalaureate nursing students.

PubMed

Vana, Kimberly D; Silva, Graciela E; Muzyka, Diann; Hirani, Lorraine M

2011-06-01

It has been proposed that students' use of an audience response system, commonly called clickers, may promote comprehension and retention of didactic material. Whether this method actually improves students' grades, however, is still not determined. The purpose of this study was to evaluate whether a lecture format utilizing multiple-choice PowerPoint slides and an audience response system was more effective than a lecture format using only multiple-choice PowerPoint slides in the comprehension and retention of pharmacological knowledge in baccalaureate nursing students. The study also assessed whether the additional use of clickers positively affected students' satisfaction with their learning. Results from 78 students who attended lecture classes with multiple-choice PowerPoint slides plus clickers were compared with those of 55 students who utilized multiple-choice PowerPoint slides only. Test scores between these two groups were not significantly different. A satisfaction questionnaire showed that 72.2% of the control students did not desire the opportunity to use clickers. Of the group utilizing the clickers, 92.3% recommend the use of this system in future courses. The use of multiple-choice PowerPoint slides and an audience response system did not seem to improve the students' comprehension or retention of pharmacological knowledge as compared with those who used solely multiple-choice PowerPoint slides.

Active Learning in the Middle Grades Classroom: Overcoming the Barriers to Implementation

ERIC Educational Resources Information Center

Edwards, Susan

2015-01-01

The Association for Middle Level Education advocates for instruction that incorporates active learning and multiple learning approaches in middle grades classrooms. The aim of this qualitative study was to examine middle level teachers who are able to implement active learning and multiple learning approaches within the standardized testing and…

Child first language and adult second language are both tied to general-purpose learning systems.

PubMed

Hamrick, Phillip; Lum, Jarrad A G; Ullman, Michael T

2018-02-13

Do the mechanisms underlying language in fact serve general-purpose functions that preexist this uniquely human capacity? To address this contentious and empirically challenging issue, we systematically tested the predictions of a well-studied neurocognitive theory of language motivated by evolutionary principles. Multiple metaanalyses were performed to examine predicted links between language and two general-purpose learning systems, declarative and procedural memory. The results tied lexical abilities to learning only in declarative memory, while grammar was linked to learning in both systems in both child first language and adult second language, in specific ways. In second language learners, grammar was associated with only declarative memory at lower language experience, but with only procedural memory at higher experience. The findings yielded large effect sizes and held consistently across languages, language families, linguistic structures, and tasks, underscoring their reliability and validity. The results, which met the predicted pattern, provide comprehensive evidence that language is tied to general-purpose systems both in children acquiring their native language and adults learning an additional language. Crucially, if language learning relies on these systems, then our extensive knowledge of the systems from animal and human studies may also apply to this domain, leading to predictions that might be unwarranted in the more circumscribed study of language. Thus, by demonstrating a role for these systems in language, the findings simultaneously lay a foundation for potentially important advances in the study of this critical domain.

Developing a Diagnosis System of Work-Related Capabilities for Students: A Computer-Assisted Assessment

ERIC Educational Resources Information Center

Liao, C. H.; Yang, M. H.; Yang, B. C.

2013-01-01

A gap exists between students' employment needs and higher education offerings. Thus, developing the capability to meet the learning needs of students in supporting their future aspirations should be facilitated. To bridge this gap in practice, this study uses multiple methods (i.e., nominal group technique and instructional systems development)…

AutoTutor and Family: A Review of 17 Years of Natural Language Tutoring

ERIC Educational Resources Information Center

Nye, Benjamin D.; Graesser, Arthur C.; Hu, Xiangen

2014-01-01

AutoTutor is a natural language tutoring system that has produced learning gains across multiple domains (e.g., computer literacy, physics, critical thinking). In this paper, we review the development, key research findings, and systems that have evolved from AutoTutor. First, the rationale for developing AutoTutor is outlined and the advantages…

Alcoa North American Extrusions Implements Energy Use Assessments at Multiple Facilities

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

None

2001-08-01

This case study is the latest in a series on industrial firms who are implementing energy efficient technologies and system improvements into their manufacturing processes. The case studies document the activities, savings, and lessons learned on these projects.

Roundhouse Diagrams.

ERIC Educational Resources Information Center

Ward, Robin E.; Wandersee, James

2000-01-01

Students must understand key concepts through reasoning, searching out related concepts, and making connections within multiple systems to learn science. The Roundhouse diagram was developed to be a concise, holistic, graphic representation of a science topic, process, or activity. Includes sample Roundhouse diagrams, a diagram checklist, and…

NREL and IBM Improve Solar Forecasting with Big Data | Energy Systems

Science.gov Websites

forecasting model using deep-machine-learning technology. The multi-scale, multi-model tool, named Watt-sun the first standard suite of metrics for this purpose. Validating Watt-sun at multiple sites across the

A neural framework for organization and flexible utilization of episodic memory in cumulatively learning baby humanoids.

PubMed

Mohan, Vishwanathan; Sandini, Giulio; Morasso, Pietro

2014-12-01

Cumulatively developing robots offer a unique opportunity to reenact the constant interplay between neural mechanisms related to learning, memory, prospection, and abstraction from the perspective of an integrated system that acts, learns, remembers, reasons, and makes mistakes. Situated within such interplay lie some of the computationally elusive and fundamental aspects of cognitive behavior: the ability to recall and flexibly exploit diverse experiences of one's past in the context of the present to realize goals, simulate the future, and keep learning further. This article is an adventurous exploration in this direction using a simple engaging scenario of how the humanoid iCub learns to construct the tallest possible stack given an arbitrary set of objects to play with. The learning takes place cumulatively, with the robot interacting with different objects (some previously experienced, some novel) in an open-ended fashion. Since the solution itself depends on what objects are available in the "now," multiple episodes of past experiences have to be remembered and creatively integrated in the context of the present to be successful. Starting from zero, where the robot knows nothing, we explore the computational basis of organization episodic memory in a cumulatively learning humanoid and address (1) how relevant past experiences can be reconstructed based on the present context, (2) how multiple stored episodic memories compete to survive in the neural space and not be forgotten, (3) how remembered past experiences can be combined with explorative actions to learn something new, and (4) how multiple remembered experiences can be recombined to generate novel behaviors (without exploration). Through the resulting behaviors of the robot as it builds, breaks, learns, and remembers, we emphasize that mechanisms of episodic memory are fundamental design features necessary to enable the survival of autonomous robots in a real world where neither everything can be known nor can everything be experienced.

The effectiveness of music as a mnemonic device on recognition memory for people with multiple sclerosis.

PubMed

Moore, Kimberly Sena; Peterson, David A; O'Shea, Geoffrey; McIntosh, Gerald C; Thaut, Michael H

2008-01-01

Research shows that people with multiple sclerosis exhibit learning and memory difficulties and that music can be used successfully as a mnemonic device to aid in learning and memory. However, there is currently no research investigating the effectiveness of music mnemonics as a compensatory learning strategy for people with multiple sclerosis. Participants with clinically definitive multiple sclerosis (N = 38) were given a verbal learning and memory test. Results from a recognition memory task were analyzed that compared learning through music (n = 20) versus learning through speech (n = 18). Preliminary baseline neuropsychological data were collected that measured executive functioning skills, learning and memory abilities, sustained attention, and level of disability. An independent samples t test showed no significant difference between groups on baseline neuropsychological functioning or on recognition task measures. Correlation analyses suggest that music mnemonics may facilitate learning for people who are less impaired by the disease. Implications for future research are discussed.

Applying reinforcement learning techniques to detect hepatocellular carcinoma under limited screening capacity.

PubMed

Lee, Elliot; Lavieri, Mariel S; Volk, Michael L; Xu, Yongcai

2015-09-01

We investigate the problem faced by a healthcare system wishing to allocate its constrained screening resources across a population at risk for developing a disease. A patient's risk of developing the disease depends on his/her biomedical dynamics. However, knowledge of these dynamics must be learned by the system over time. Three classes of reinforcement learning policies are designed to address this problem of simultaneously gathering and utilizing information across multiple patients. We investigate a case study based upon the screening for Hepatocellular Carcinoma (HCC), and optimize each of the three classes of policies using the indifference zone method. A simulation is built to gauge the performance of these policies, and their performance is compared to current practice. We then demonstrate how the benefits of learning-based screening policies differ across various levels of resource scarcity and provide metrics of policy performance.

The achievement of spacecraft autonomy through the thematic application of multiple cooperating intelligent agents

NASA Technical Reports Server (NTRS)

Rossomando, Philip J.

1992-01-01

A description is given of UNICORN, a prototype system developed for the purpose of investigating artificial intelligence (AI) concepts supporting spacecraft autonomy. UNICORN employs thematic reasoning, of the type first described by Rodger Schank of Northwestern University, to allow the context-sensitive control of multiple intelligent agents within a blackboard based environment. In its domain of application, UNICORN demonstrates the ability to reason teleologically with focused knowledge. Also presented are some of the lessons learned as a result of this effort. These lessons apply to any effort wherein system level autonomy is the objective.

MOLECULAR MECHANISMS OF FEAR LEARNING AND MEMORY

PubMed Central

Johansen, Joshua P.; Cain, Christopher K.; Ostroff, Linnaea E.; LeDoux, Joseph E.

2011-01-01

Pavlovian fear conditioning is a useful behavioral paradigm for exploring the molecular mechanisms of learning and memory because a well-defined response to a specific environmental stimulus is produced through associative learning processes. Synaptic plasticity in the lateral nucleus of the amygdala (LA) underlies this form of associative learning. Here we summarize the molecular mechanisms that contribute to this synaptic plasticity in the context of auditory fear conditioning, the form of fear conditioning best understood at the molecular level. We discuss the neurotransmitter systems and signaling cascades that contribute to three phases of auditory fear conditioning: acquisition, consolidation, and reconsolidation. These studies suggest that multiple intracellular signaling pathways, including those triggered by activation of Hebbian processes and neuromodulatory receptors, interact to produce neural plasticity in the LA and behavioral fear conditioning. Together, this research illustrates the power of fear conditioning as a model system for characterizing the mechanisms of learning and memory in mammals, and potentially for understanding fear related disorders, such as PTSD and phobias. PMID:22036561

Long-term memory: A review and meta-analysis of studies of declarative and procedural memory in specific language impairment

PubMed Central

Lum, Jarrad A. G.; Conti-Ramsden, Gina

2014-01-01

This review examined the status of long-term memory systems in specific language impairment (SLI), in particular declarative memory and aspects of procedural memory. Studies included in the review were identified following a systematic search of the literature and findings combined using meta-analysis. This review showed individuals with SLI are poorer than age matched controls in the learning and retrieval of verbal information from the declarative memory. However, there is evidence to suggest that the problems with declarative learning and memory for verbal information in SLI might be due to difficulties with verbal working memory and language. The learning and retrieval of non-verbal information from declarative memory appears relatively intact. In relation to procedural learning and memory, evidence indicates poor implicit learning of verbal information. Findings pertaining to nonverbal information have been mixed. This review of the literature indicates there are now substantial grounds for suspecting that multiple memory systems may be implicated in the impairment. PMID:24748707

Why do lesions in the rodent anterior thalamic nuclei cause such severe spatial deficits?

PubMed Central

Aggleton, John P.; Nelson, Andrew J.D.

2015-01-01

Lesions of the rodent anterior thalamic nuclei cause severe deficits to multiple spatial learning tasks. Possible explanations for these effects are examined, with particular reference to T-maze alternation. Anterior thalamic lesions not only impair allocentric place learning but also disrupt other spatial processes, including direction learning, path integration, and relative length discriminations, as well as aspects of nonspatial learning, e.g., temporal discriminations. Working memory tasks, such as T-maze alternation, appear particularly sensitive as they combine an array of these spatial and nonspatial demands. This sensitivity partly reflects the different functions supported by individual anterior thalamic nuclei, though it is argued that anterior thalamic lesion effects also arise from covert pathology in sites distal to the thalamus, most critically in the retrosplenial cortex and hippocampus. This two-level account, involving both local and distal lesion effects, explains the range and severity of the spatial deficits following anterior thalamic lesions. These findings highlight how the anterior thalamic nuclei form a key component in a series of interdependent systems that support multiple spatial functions. PMID:25195980

Prognostic Physiology: Modeling Patient Severity in Intensive Care Units Using Radial Domain Folding

PubMed Central

Joshi, Rohit; Szolovits, Peter

2012-01-01

Real-time scalable predictive algorithms that can mine big health data as the care is happening can become the new “medical tests” in critical care. This work describes a new unsupervised learning approach, radial domain folding, to scale and summarize the enormous amount of data collected and to visualize the degradations or improvements in multiple organ systems in real time. Our proposed system is based on learning multi-layer lower dimensional abstractions from routinely generated patient data in modern Intensive Care Units (ICUs), and is dramatically different from most of the current work being done in ICU data mining that rely on building supervised predictive models using commonly measured clinical observations. We demonstrate that our system discovers abstract patient states that summarize a patient’s physiology. Further, we show that a logistic regression model trained exclusively on our learned layer outperforms a customized SAPS II score on the mortality prediction task. PMID:23304406

Using Functional Electrical Stimulation Mediated by Iterative Learning Control and Robotics to Improve Arm Movement for People With Multiple Sclerosis.

PubMed

Sampson, Patrica; Freeman, Chris; Coote, Susan; Demain, Sara; Feys, Peter; Meadmore, Katie; Hughes, Ann-Marie

2016-02-01

Few interventions address multiple sclerosis (MS) arm dysfunction but robotics and functional electrical stimulation (FES) appear promising. This paper investigates the feasibility of combining FES with passive robotic support during virtual reality (VR) training tasks to improve upper limb function in people with multiple sclerosis (pwMS). The system assists patients in following a specified trajectory path, employing an advanced model-based paradigm termed iterative learning control (ILC) to adjust the FES to improve accuracy and maximise voluntary effort. Reaching tasks were repeated six times with ILC learning the optimum control action from previous attempts. A convenience sample of five pwMS was recruited from local MS societies, and the intervention comprised 18 one-hour training sessions over 10 weeks. The accuracy of tracking performance without FES and the amount of FES delivered during training were analyzed using regression analysis. Clinical functioning of the arm was documented before and after treatment with standard tests. Statistically significant results following training included: improved accuracy of tracking performance both when assisted and unassisted by FES; reduction in maximum amount of FES needed to assist tracking; and less impairment in the proximal arm that was trained. The system was well tolerated by all participants with no increase in muscle fatigue reported. This study confirms the feasibility of FES combined with passive robot assistance as a potentially effective intervention to improve arm movement and control in pwMS and provides the basis for a follow-up study.

A Systemic Approach to Elevating Teacher Leadership

ERIC Educational Resources Information Center

Killion, Joellen; Harrison, Cindy; Colton, Amy; Bryan, Chris; Delehant, Ann; Cooke, Debbie

2016-01-01

Teacher leadership is often defined as a "set of practices that enhance the teaching profession." States and districts are leveraging teacher leadership in multiple ways to professionalize teaching, create opportunities for teacher career advancement, facilitate school improvement, and facilitate professional learning for educator and…

AGE-RELATED, MULTIPLE-SYSTEM EFFECTS FROM ENVIRONMENTAL EXPOSURE TO AIRBORNE MANGANESE (MN)

EPA Science Inventory

Past research has tentatively associated excessive manganese (Mn) exposure with Parkinson-like effects in older adults, violent and aggressive behavior in young adults, and learning and neurobehavioral deficits in elementary school children. Our recent EPA/University of Quebec at...

Learning with Multiple Representations: Extending Multimedia Learning beyond the Lab

ERIC Educational Resources Information Center

Eilam, Billie; Poyas, Yael

2008-01-01

The present study extended multimedia learning principles beyond the lab to an ecologically valid setting (homework). Eighteen information cards were used to perform three homework tasks. The control group students learned from single representation (SR) cards that presented all information as printed text. The multiple representation (MR) group…

Effects of Example Variability and Prior Knowledge in How Students Learn to Solve Equations

ERIC Educational Resources Information Center

Guo, Jian-Peng; Yang, Ling-Yan; Ding, Yi

2014-01-01

Researchers have consistently demonstrated that multiple examples are better than one example in facilitating learning because the comparison evoked by multiple examples supports learning and transfer. However, research outcomes are unclear regarding the effects of example variability and prior knowledge on learning from comparing multiple…

Predicting Robust Vocabulary Growth from Measures of Incremental Learning

ERIC Educational Resources Information Center

Frishkoff, Gwen A.; Perfetti, Charles A.; Collins-Thompson, Kevyn

2011-01-01

We report a study of incremental learning of new word meanings over multiple episodes. A new method called MESA (Markov Estimation of Semantic Association) tracked this learning through the automated assessment of learner-generated definitions. The multiple word learning episodes varied in the strength of contextual constraint provided by…

Connecting Instances to Promote Children's Relational Reasoning

ERIC Educational Resources Information Center

Son, Ji Y.; Smith, Linda B.; Goldstone, Robert L.

2011-01-01

The practice of learning from multiple instances seems to allow children to learn about relational structure. The experiments reported here focused on two issues regarding relational learning from multiple instances: (a) what kind of perceptual situations foster such learning and (b) how particular object properties, such as complexity and…

Classification techniques on computerized systems to predict and/or to detect Apnea: A systematic review.

PubMed

Pombo, Nuno; Garcia, Nuno; Bousson, Kouamana

2017-03-01

Sleep apnea syndrome (SAS), which can significantly decrease the quality of life is associated with a major risk factor of health implications such as increased cardiovascular disease, sudden death, depression, irritability, hypertension, and learning difficulties. Thus, it is relevant and timely to present a systematic review describing significant applications in the framework of computational intelligence-based SAS, including its performance, beneficial and challenging effects, and modeling for the decision-making on multiple scenarios. This study aims to systematically review the literature on systems for the detection and/or prediction of apnea events using a classification model. Forty-five included studies revealed a combination of classification techniques for the diagnosis of apnea, such as threshold-based (14.75%) and machine learning (ML) models (85.25%). In addition, the ML models, were clustered in a mind map, include neural networks (44.26%), regression (4.91%), instance-based (11.47%), Bayesian algorithms (1.63%), reinforcement learning (4.91%), dimensionality reduction (8.19%), ensemble learning (6.55%), and decision trees (3.27%). A classification model should provide an auto-adaptive and no external-human action dependency. In addition, the accuracy of the classification models is related with the effective features selection. New high-quality studies based on randomized controlled trials and validation of models using a large and multiple sample of data are recommended. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

QUICR-learning for Multi-Agent Coordination

NASA Technical Reports Server (NTRS)

Agogino, Adrian K.; Tumer, Kagan

2006-01-01

Coordinating multiple agents that need to perform a sequence of actions to maximize a system level reward requires solving two distinct credit assignment problems. First, credit must be assigned for an action taken at time step t that results in a reward at time step t > t. Second, credit must be assigned for the contribution of agent i to the overall system performance. The first credit assignment problem is typically addressed with temporal difference methods such as Q-learning. The second credit assignment problem is typically addressed by creating custom reward functions. To address both credit assignment problems simultaneously, we propose the "Q Updates with Immediate Counterfactual Rewards-learning" (QUICR-learning) designed to improve both the convergence properties and performance of Q-learning in large multi-agent problems. QUICR-learning is based on previous work on single-time-step counterfactual rewards described by the collectives framework. Results on a traffic congestion problem shows that QUICR-learning is significantly better than a Q-learner using collectives-based (single-time-step counterfactual) rewards. In addition QUICR-learning provides significant gains over conventional and local Q-learning. Additional results on a multi-agent grid-world problem show that the improvements due to QUICR-learning are not domain specific and can provide up to a ten fold increase in performance over existing methods.

Pathways of Learning: Teaching Students and Parents about Multiple Intelligences.

ERIC Educational Resources Information Center

Lazear, David

This book is concerned with reinventing the learning process from a multiple intelligences perspective and urges explicitly teaching students about multiple intelligences to further their metacognitive understanding. The multiple-intelligence-based curriculum is intended to interface with the regular academic curriculum. An introductory chapter…

Teaching Multiplication of Numbers from 1 to 10 to STKIP Surya Students Using Matematika GASING

ERIC Educational Resources Information Center

Kusuma, Josephine; Sulistiawati

2014-01-01

Multiplication of numbers from 1 to 10 is very important as it provides the basis for learning multiplication of other larger numbers as well as other related mathematical operations. How do students learn multiplication? Usually students just memorize the results of multiplication. This is often performed without a complete comprehension of the…

The Relationship between Multiplication Fact Speed-Recall and Fluency and Higher Level Mathematics Learning with Eighth Grade Middle School Students

ERIC Educational Resources Information Center

Curry, Steven James

2012-01-01

This quantitative study investigated relationships between higher level mathematics learning and multiplication fact fluency, multiplication fact speed-recall, and reading grade equivalency of eighth grade students in Algebra I and Pre-Algebra. Higher level mathematics learning was indicated by an average score of 80% or higher on first and second…

Small-Group Technology-Assisted Instruction: Virtual Teacher and Robot Peer for Individuals with Autism Spectrum Disorder.

PubMed

Saadatzi, Mohammad Nasser; Pennington, Robert C; Welch, Karla C; Graham, James H

2018-06-20

The authors combined virtual reality technology and social robotics to develop a tutoring system that resembled a small-group arrangement. This tutoring system featured a virtual teacher instructing sight words, and included a humanoid robot emulating a peer. The authors used a multiple-probe design across word sets to evaluate the effects of the instructional package on the explicit acquisition and vicarious learning of sight words instructed to three children with autism spectrum disorder (ASD) and the robot peer. Results indicated that participants acquired, maintained, and generalized 100% of the words explicitly instructed to them, made fewer errors while learning the words common between them and the robot peer, and vicariously learned 94% of the words solely instructed to the robot.

Systems thinking.

PubMed

Cabrera, Derek; Colosi, Laura; Lobdell, Claire

2008-08-01

Evaluation is one of many fields where "systems thinking" is popular and is said to hold great promise. However, there is disagreement about what constitutes systems thinking. Its meaning is ambiguous, and systems scholars have made diverse and divergent attempts to describe it. Alternative origins include: von Bertalanffy, Aristotle, Lao Tsu or multiple aperiodic "waves." Some scholars describe it as synonymous with systems sciences (i.e., nonlinear dynamics, complexity, chaos). Others view it as taxonomy-a laundry list of systems approaches. Within so much noise, it is often difficult for evaluators to find the systems thinking signal. Recent work in systems thinking describes it as an emergent property of four simple conceptual patterns (rules). For an evaluator to become a "systems thinker", he or she need not spend years learning many methods or nonlinear sciences. Instead, with some practice, one can learn to apply these four simple rules to existing evaluation knowledge with transformative results.

Dual-Processes in Learning and Judgment: Evidence from the Multiple Cue Probability Learning Paradigm

ERIC Educational Resources Information Center

Rolison, Jonathan J.; Evans, Jonathan St. B. T.; Dennis, Ian; Walsh, Clare R.

2012-01-01

Multiple cue probability learning (MCPL) involves learning to predict a criterion based on a set of novel cues when feedback is provided in response to each judgment made. But to what extent does MCPL require controlled attention and explicit hypothesis testing? The results of two experiments show that this depends on cue polarity. Learning about…

Concept Mapping to Assess Learning and Understanding of Complexity in Courses on Global Climate Change

NASA Astrophysics Data System (ADS)

Rebich-Hespanha, S.; Gautier, C.

2010-12-01

The complex nature of climate change science poses special challenges for educators wishing to broaden and deepen student understanding of the climate system and its sensitivity to and impacts upon human activity. Learners have prior knowledge that may limit their perception and processing of the multiple relationships between processes (e.g., feedbacks) that arise in global change science, and these existing mental models serve as the scaffold for all future learning. Because adoption of complex scientific concepts is not likely if instruction includes presentation of information or concepts that are not compatible with the learners’ prior knowledge, providing effective instruction on this complex topic requires learning opportunities that are anchored upon an evaluation of the limitations and inaccuracies of the learners’ existing understandings of the climate system. The formative evaluation that serves as the basis for planning such instruction can also be useful as a baseline against which to evaluate subsequent learning. We will present concept-mapping activities that we have used to assess students’ knowledge and understanding about global climate change in courses that utilized multiple assessment methods including presentations, writings, discussions, and concept maps. The courses in which these activities were completed use a variety of instructional approaches (including standard lectures and lab assignments and a mock summit) to help students understand the inherently interdisciplinary topic of global climate change, its interwoven human and natural causes, and the connections it has with society through a complex range of political, social, technological and economic factors. Two instances of concept map assessment will be presented: one focused on evaluating student understanding of the major components of the climate system and their interconnections, and the other focused on student understanding of the connections between climate change and the global food system. We will discuss how concept mapping can be used to demonstrate evidence of learning and conceptual change, and also how it can be used to provide information about gaps in knowledge and misconceptions students have about the topic.

Diagnosis - the limiting focus of taxonomy.

PubMed

Sturmberg, Joachim P; Martin, Carmel M

2016-02-01

The focus on the diagnosis is a pivotal aspect of medical practice since antiquity. Diagnostic taxonomy helped to categorize ailments to improve medical care, and in its social sense resulted in validation of the sick role for some, but marginalization or stigmatization for others. In the medical industrial complex, diagnostic taxonomy structured health care financing, management and practitioner remuneration. However, with increasing demands from multiple agencies, there are increasing unintended and unwarranted consequences of our current taxonomies and diagnostic processes resulting from the conglomeration of underpinning concepts, theories, information and motivations. We argue that the increasing focus on the diagnosis resulted in excessive compartmentalization - 'partialism' - of medical practice, diminishing medical care and being naively simplistic in light of the emerging understanding of the interconnected nature of the diseasome. The human is a complex organic system of interconnecting dynamics and feedback loops responding to internal and external forces including genetic, epigenetic and environmental attractors, rather than the sum of multiple discrete organs which can develop isolated diseases or multiple morbidities. Solutions to these unintended consequences of many contemporary health system processes involve revisiting the nature of diagnostic taxonomies and the processes of their construction. A dynamic taxonomic framework would shift to more relevant attractors at personal, clinical and health system levels recognizing the non-linear nature of health and disease. Human health at an individual, group and population level is the ability to adapt to internal and external stressors with resilience throughout the life course, yet diagnostic taxonomies are increasingly constructed around fixed anchors. Understanding diagnosis as dissecting, pigeonholing or bean counting (learning by dividing) is no longer useful, the challenge for the future is to understand the big picture (learning by connecting). Diagnostic categorization needs to embrace a meta-learning approach open to human variability. © 2014 John Wiley & Sons, Ltd.

Design of multiple representations e-learning resources based on a contextual approach for the basic physics course

NASA Astrophysics Data System (ADS)

Bakri, F.; Muliyati, D.

2018-05-01

This research aims to design e-learning resources with multiple representations based on a contextual approach for the Basic Physics Course. The research uses the research and development methods accordance Dick & Carey strategy. The development carried out in the digital laboratory of Physics Education Department, Mathematics and Science Faculty, Universitas Negeri Jakarta. The result of the process of product development with Dick & Carey strategy, have produced e-learning design of the Basic Physics Course is presented in multiple representations in contextual learning syntax. The appropriate of representation used in the design of learning basic physics include: concept map, video, figures, data tables of experiment results, charts of data tables, the verbal explanations, mathematical equations, problem and solutions example, and exercise. Multiple representations are presented in the form of contextual learning by stages: relating, experiencing, applying, transferring, and cooperating.

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.

Do Recognition and Priming Index a Unitary Knowledge Base? Comment on Shanks et al. (2003)

ERIC Educational Resources Information Center

Runger, Dennis; Nagy, Gabriel; Frensch, Peter A.

2009-01-01

Whether sequence learning entails a single or multiple memory systems is a moot issue. Recently, D. R. Shanks, L. Wilkinson, and S. Channon advanced a single-system model that predicts a perfect correlation between true (i.e., error free) response time priming and recognition. The Shanks model is contrasted with a dual-process model that…

Lessons Learned. Multiple Launch Rocket System

DTIC Science & Technology

1980-07-01

should be cognizant of the five- year planning cycle for NATO programs and/or the military construction funding cycle ix o There is a definite need to...FY 86, a rocket buy will be awarded following a multiple- year buy-out competition. To satisfy US needs, this award is efexpected to bc in excess of...support facilities, should be cognizant of the five- year planning cycle for NATO programs and/or the military construction funding, as well as the long

Connecting Multiple Intelligences through Open and Distance Learning: Going towards a Collective Intelligence?

ERIC Educational Resources Information Center

Medeiros Vieira, Leandro Mauricio; Ferasso, Marcos; Schröeder, Christine da Silva

2014-01-01

This theoretical essay is a learning approach reflexion on Howard Gardner's Theory of Multiple Intelligences and the possibilities provided by the education model known as open and distance learning. Open and distance learning can revolutionize traditional pedagogical practice, meeting the needs of those who have different forms of cognitive…

Learning by Understanding: The Role of Multiple Representations in Learning Algebra.

ERIC Educational Resources Information Center

Brenner, Mary E.; Mayer, Richard E.; Moseley, Bryan; Brar, Theresa; Duran, Richard; Reed, Barbara Smith; Webb, David

1997-01-01

In posttest results, 76 prealgebra students who learned about functions in a unit emphasizing multiple formats, anchoring learning in a thematic context, and problem solving in cooperative groups were more successful at problem solving and problem representation than were 56 comparison students conventionally taught. Similar results were found for…

Gavagai Is as Gavagai Does: Learning Nouns and Verbs from Cross-Situational Statistics

ERIC Educational Resources Information Center

Monaghan, Padraic; Mattock, Karen; Davies, Robert A. I.; Smith, Alastair C.

2015-01-01

Learning to map words onto their referents is difficult, because there are multiple possibilities for forming these mappings. Cross-situational learning studies have shown that word-object mappings can be learned across multiple situations, as can verbs when presented in a syntactic context. However, these previous studies have presented either…

Lesion classification using clinical and visual data fusion by multiple kernel learning

NASA Astrophysics Data System (ADS)

Kisilev, Pavel; Hashoul, Sharbell; Walach, Eugene; Tzadok, Asaf

2014-03-01

To overcome operator dependency and to increase diagnosis accuracy in breast ultrasound (US), a lot of effort has been devoted to developing computer-aided diagnosis (CAD) systems for breast cancer detection and classification. Unfortunately, the efficacy of such CAD systems is limited since they rely on correct automatic lesions detection and localization, and on robustness of features computed based on the detected areas. In this paper we propose a new approach to boost the performance of a Machine Learning based CAD system, by combining visual and clinical data from patient files. We compute a set of visual features from breast ultrasound images, and construct the textual descriptor of patients by extracting relevant keywords from patients' clinical data files. We then use the Multiple Kernel Learning (MKL) framework to train SVM based classifier to discriminate between benign and malignant cases. We investigate different types of data fusion methods, namely, early, late, and intermediate (MKL-based) fusion. Our database consists of 408 patient cases, each containing US images, textual description of complaints and symptoms filled by physicians, and confirmed diagnoses. We show experimentally that the proposed MKL-based approach is superior to other classification methods. Even though the clinical data is very sparse and noisy, its MKL-based fusion with visual features yields significant improvement of the classification accuracy, as compared to the image features only based classifier.

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

PubMed

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

2015-01-01

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

How does a specific learning and memory system in the mammalian brain gain control of behavior?

PubMed

McDonald, Robert J; Hong, Nancy S

2013-11-01

This review addresses a fundamental, yet poorly understood set of issues in systems neuroscience. The issues revolve around conceptualizations of the organization of learning and memory in the mammalian brain. One intriguing, and somewhat popular, conceptualization is the idea that there are multiple learning and memory systems in the mammalian brain and they interact in different ways to influence and/or control behavior. This approach has generated interesting empirical and theoretical work supporting this view. One issue that needs to be addressed is how these systems influence or gain control of voluntary behavior. To address this issue, we clearly specify what we mean by a learning and memory system. We then review two types of processes that might influence which memory system gains control of behavior. One set of processes are external factors that can affect which system controls behavior in a given situation including task parameters like the kind of information available to the subject, types of training experience, and amount of training. The second set of processes are brain mechanisms that might influence what memory system controls behavior in a given situation including executive functions mediated by the prefrontal cortex; switching mechanisms mediated by ascending neurotransmitter systems, the unique role of the hippocampus during learning. The issue of trait differences in control of different learning and memory systems will also be considered in which trait differences in learning and memory function are thought to potentially emerge from differences in level of prefrontal influence, differences in plasticity processes, differences in ascending neurotransmitter control, differential access to effector systems like motivational and motor systems. Finally, we present scenarios in which different mechanisms might interact. This review was conceived to become a jumping off point for new work directed at understanding these issues. The outcome of this work, in combination with other approaches, might improve understanding of the mechanisms of volition in human and non-human animals. Copyright © 2013 Wiley Periodicals, Inc.

Data Science in the Research Domain Criteria Era: Relevance of Machine Learning to the Study of Stress Pathology, Recovery, and Resilience

PubMed Central

Galatzer-Levy, Isaac R.; Ruggles, Kelly; Chen, Zhe

2017-01-01

Diverse environmental and biological systems interact to influence individual differences in response to environmental stress. Understanding the nature of these complex relationships can enhance the development of methods to: (1) identify risk, (2) classify individuals as healthy or ill, (3) understand mechanisms of change, and (4) develop effective treatments. The Research Domain Criteria (RDoC) initiative provides a theoretical framework to understand health and illness as the product of multiple inter-related systems but does not provide a framework to characterize or statistically evaluate such complex relationships. Characterizing and statistically evaluating models that integrate multiple levels (e.g. synapses, genes, environmental factors) as they relate to outcomes that a free from prior diagnostic benchmarks represents a challenge requiring new computational tools that are capable to capture complex relationships and identify clinically relevant populations. In the current review, we will summarize machine learning methods that can achieve these goals. PMID:29527592

A Self-Synthesis Approach to Perceptual Learning for Multisensory Fusion in Robotics

PubMed Central

Axenie, Cristian; Richter, Christoph; Conradt, Jörg

2016-01-01

Biological and technical systems operate in a rich multimodal environment. Due to the diversity of incoming sensory streams a system perceives and the variety of motor capabilities a system exhibits there is no single representation and no singular unambiguous interpretation of such a complex scene. In this work we propose a novel sensory processing architecture, inspired by the distributed macro-architecture of the mammalian cortex. The underlying computation is performed by a network of computational maps, each representing a different sensory quantity. All the different sensory streams enter the system through multiple parallel channels. The system autonomously associates and combines them into a coherent representation, given incoming observations. These processes are adaptive and involve learning. The proposed framework introduces mechanisms for self-creation and learning of the functional relations between the computational maps, encoding sensorimotor streams, directly from the data. Its intrinsic scalability, parallelisation, and automatic adaptation to unforeseen sensory perturbations make our approach a promising candidate for robust multisensory fusion in robotic systems. We demonstrate this by applying our model to a 3D motion estimation on a quadrotor. PMID:27775621

Discrete Serotonin Systems Mediate Memory Enhancement and Escape Latencies after Unpredicted Aversive Experience in Drosophila Place Memory

PubMed Central

Sitaraman, Divya; Kramer, Elizabeth F.; Kahsai, Lily; Ostrowski, Daniela; Zars, Troy

2017-01-01

Feedback mechanisms in operant learning are critical for animals to increase reward or reduce punishment. However, not all conditions have a behavior that can readily resolve an event. Animals must then try out different behaviors to better their situation through outcome learning. This form of learning allows for novel solutions and with positive experience can lead to unexpected behavioral routines. Learned helplessness, as a type of outcome learning, manifests in part as increases in escape latency in the face of repeated unpredicted shocks. Little is known about the mechanisms of outcome learning. When fruit fly Drosophila melanogaster are exposed to unpredicted high temperatures in a place learning paradigm, flies both increase escape latencies and have a higher memory when given control of a place/temperature contingency. Here we describe discrete serotonin neuronal circuits that mediate aversive reinforcement, escape latencies, and memory levels after place learning in the presence and absence of unexpected aversive events. The results show that two features of learned helplessness depend on the same modulatory system as aversive reinforcement. Moreover, changes in aversive reinforcement and escape latency depend on local neural circuit modulation, while memory enhancement requires larger modulation of multiple behavioral control circuits. PMID:29321732

Peer assisted learning in the clinical setting: an activity systems analysis.

PubMed

Bennett, Deirdre; O'Flynn, Siun; Kelly, Martina

2015-08-01

Peer assisted learning (PAL) is a common feature of medical education. Understanding of PAL has been based on processes and outcomes in controlled settings, such as clinical skills labs. PAL in the clinical setting, a complex learning environment, requires fresh evaluation. Socio-cultural theory is proposed as a means to understand educational interventions in ways that are practical and meaningful. We describe the evaluation of a PAL intervention, introduced to support students' transition into full time clinical attachments, using activity theory and activity systems analysis (ASA). Our research question was How does PAL transfer to the clinical environment? Junior students on their first clinical attachments undertook a weekly same-level, reciprocal PAL activity. Qualitative data was collected after each session, and focus groups (n = 3) were held on completion. Data was analysed using ASA. ASA revealed two competing activity systems on clinical attachment; Learning from Experts, which students saw as the primary function of the attachment and Learning with Peers, the PAL intervention. The latter took time from the first and was in tension with it. Tensions arose from student beliefs about how learning takes place in clinical settings, and the importance of social relationships, leading to variable engagement with PAL. Differing perspectives within the group were opportunities for expansive learning. PAL in the clinical environment presents challenges specific to that context. Using ASA helped to describe student activity on clinical attachment and to highlight tensions and contradictions relating PAL in that setting. Planning learning opportunities on clinical placements, must take account of how students learn in workplaces, and the complexity of the multiple competing activity systems related to learning and social activities.

Predicate calculus for an architecture of multiple neural networks

NASA Astrophysics Data System (ADS)

Consoli, Robert H.

1990-08-01

Future projects with neural networks will require multiple individual network components. Current efforts along these lines are ad hoc. This paper relates the neural network to a classical device and derives a multi-part architecture from that model. Further it provides a Predicate Calculus variant for describing the location and nature of the trainings and suggests Resolution Refutation as a method for determining the performance of the system as well as the location of needed trainings for specific proofs. 2. THE NEURAL NETWORK AND A CLASSICAL DEVICE Recently investigators have been making reports about architectures of multiple neural networksL234. These efforts are appearing at an early stage in neural network investigations they are characterized by architectures suggested directly by the problem space. Touretzky and Hinton suggest an architecture for processing logical statements1 the design of this architecture arises from the syntax of a restricted class of logical expressions and exhibits syntactic limitations. In similar fashion a multiple neural netword arises out of a control problem2 from the sequence learning problem3 and from the domain of machine learning. 4 But a general theory of multiple neural devices is missing. More general attempts to relate single or multiple neural networks to classical computing devices are not common although an attempt is made to relate single neural devices to a Turing machines and Sun et a!. develop a multiple neural architecture that performs pattern classification.

Learning Sequences of Actions in Collectives of Autonomous Agents

NASA Technical Reports Server (NTRS)

Turner, Kagan; Agogino, Adrian K.; Wolpert, David H.; Clancy, Daniel (Technical Monitor)

2001-01-01

In this paper we focus on the problem of designing a collective of autonomous agents that individually learn sequences of actions such that the resultant sequence of joint actions achieves a predetermined global objective. We are particularly interested in instances of this problem where centralized control is either impossible or impractical. For single agent systems in similar domains, machine learning methods (e.g., reinforcement learners) have been successfully used. However, applying such solutions directly to multi-agent systems often proves problematic, as agents may work at cross-purposes, or have difficulty in evaluating their contribution to achievement of the global objective, or both. Accordingly, the crucial design step in multiagent systems centers on determining the private objectives of each agent so that as the agents strive for those objectives, the system reaches a good global solution. In this work we consider a version of this problem involving multiple autonomous agents in a grid world. We use concepts from collective intelligence to design goals for the agents that are 'aligned' with the global goal, and are 'learnable' in that agents can readily see how their behavior affects their utility. We show that reinforcement learning agents using those goals outperform both 'natural' extensions of single agent algorithms and global reinforcement, learning solutions based on 'team games'.

Managing and learning with multiple models: Objectives and optimization algorithms

USGS Publications Warehouse

Probert, William J. M.; Hauser, C.E.; McDonald-Madden, E.; Runge, M.C.; Baxter, P.W.J.; Possingham, H.P.

2011-01-01

The quality of environmental decisions should be gauged according to managers' objectives. Management objectives generally seek to maximize quantifiable measures of system benefit, for instance population growth rate. Reaching these goals often requires a certain degree of learning about the system. Learning can occur by using management action in combination with a monitoring system. Furthermore, actions can be chosen strategically to obtain specific kinds of information. Formal decision making tools can choose actions to favor such learning in two ways: implicitly via the optimization algorithm that is used when there is a management objective (for instance, when using adaptive management), or explicitly by quantifying knowledge and using it as the fundamental project objective, an approach new to conservation.This paper outlines three conservation project objectives - a pure management objective, a pure learning objective, and an objective that is a weighted mixture of these two. We use eight optimization algorithms to choose actions that meet project objectives and illustrate them in a simulated conservation project. The algorithms provide a taxonomy of decision making tools in conservation management when there is uncertainty surrounding competing models of system function. The algorithms build upon each other such that their differences are highlighted and practitioners may see where their decision making tools can be improved. ?? 2010 Elsevier Ltd.

TEES 2.2: Biomedical Event Extraction for Diverse Corpora

PubMed Central

2015-01-01

Background The Turku Event Extraction System (TEES) is a text mining program developed for the extraction of events, complex biomedical relationships, from scientific literature. Based on a graph-generation approach, the system detects events with the use of a rich feature set built via dependency parsing. The TEES system has achieved record performance in several of the shared tasks of its domain, and continues to be used in a variety of biomedical text mining tasks. Results The TEES system was quickly adapted to the BioNLP'13 Shared Task in order to provide a public baseline for derived systems. An automated approach was developed for learning the underlying annotation rules of event type, allowing immediate adaptation to the various subtasks, and leading to a first place in four out of eight tasks. The system for the automated learning of annotation rules is further enhanced in this paper to the point of requiring no manual adaptation to any of the BioNLP'13 tasks. Further, the scikit-learn machine learning library is integrated into the system, bringing a wide variety of machine learning methods usable with TEES in addition to the default SVM. A scikit-learn ensemble method is also used to analyze the importances of the features in the TEES feature sets. Conclusions The TEES system was introduced for the BioNLP'09 Shared Task and has since then demonstrated good performance in several other shared tasks. By applying the current TEES 2.2 system to multiple corpora from these past shared tasks an overarching analysis of the most promising methods and possible pitfalls in the evolving field of biomedical event extraction are presented. PMID:26551925

TEES 2.2: Biomedical Event Extraction for Diverse Corpora.

PubMed

Björne, Jari; Salakoski, Tapio

2015-01-01

The Turku Event Extraction System (TEES) is a text mining program developed for the extraction of events, complex biomedical relationships, from scientific literature. Based on a graph-generation approach, the system detects events with the use of a rich feature set built via dependency parsing. The TEES system has achieved record performance in several of the shared tasks of its domain, and continues to be used in a variety of biomedical text mining tasks. The TEES system was quickly adapted to the BioNLP'13 Shared Task in order to provide a public baseline for derived systems. An automated approach was developed for learning the underlying annotation rules of event type, allowing immediate adaptation to the various subtasks, and leading to a first place in four out of eight tasks. The system for the automated learning of annotation rules is further enhanced in this paper to the point of requiring no manual adaptation to any of the BioNLP'13 tasks. Further, the scikit-learn machine learning library is integrated into the system, bringing a wide variety of machine learning methods usable with TEES in addition to the default SVM. A scikit-learn ensemble method is also used to analyze the importances of the features in the TEES feature sets. The TEES system was introduced for the BioNLP'09 Shared Task and has since then demonstrated good performance in several other shared tasks. By applying the current TEES 2.2 system to multiple corpora from these past shared tasks an overarching analysis of the most promising methods and possible pitfalls in the evolving field of biomedical event extraction are presented.

Deep Learning in Label-free Cell Classification

PubMed Central

Chen, Claire Lifan; Mahjoubfar, Ata; Tai, Li-Chia; Blaby, Ian K.; Huang, Allen; Niazi, Kayvan Reza; Jalali, Bahram

2016-01-01

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells. PMID:26975219

Deep Learning in Label-free Cell Classification

NASA Astrophysics Data System (ADS)

Chen, Claire Lifan; Mahjoubfar, Ata; Tai, Li-Chia; Blaby, Ian K.; Huang, Allen; Niazi, Kayvan Reza; Jalali, Bahram

2016-03-01

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.

Graded Multiple Choice Questions: Rewarding Understanding and Preventing Plagiarism

NASA Astrophysics Data System (ADS)

Denyer, G. S.; Hancock, D.

2002-08-01

This paper describes an easily implemented method that allows the generation and analysis of graded multiple-choice examinations. The technique, which uses standard functions in user-end software (Microsoft Excel 5+), can also produce several different versions of an examination that can be employed to prevent the reward of plagarism. The manuscript also discusses the advantages of having a graded marking system for the elimination of ambiguities, use in multi-step calculation questions, and questions that require extrapolation or reasoning. The advantages of the scrambling strategy, which maintains the same question order, is discussed with reference to student equity. The system provides a non-confrontational mechanism for dealing with cheating in large-class multiple-choice examinations, as well as providing a reward for problem solving over surface learning.

Meaning-Led Learning for Pupils with Severe and Profound and Multiple Learning Difficulties

ERIC Educational Resources Information Center

Goss, Phil

2006-01-01

This paper proposes that learning and teaching for pupils with severe and profound and multiple learning difficulties could be enhanced by a closer focus on emotional factors and on the careful identification of what is meaningful for them. Phil Goss, senior lecturer in counselling and psychotherapy at the University of Central Lancashire draws on…

Cross-Platform Learning: On the Nature of Children's Learning from Multiple Media Platforms

ERIC Educational Resources Information Center

Fisch, Shalom M.

2013-01-01

It is increasingly common for an educational media project to span several media platforms (e.g., TV, Web, hands-on materials), assuming that the benefits of learning from multiple media extend beyond those gained from one medium alone. Yet research typically has investigated learning from a single medium in isolation. This paper reviews several…

Creating Ripples: An Exploration of Sansei Women's Experiences of Expressive Practices in a Holistic Approach to Learning about Oppression and Privilege

ERIC Educational Resources Information Center

Kaya, Katherine

2013-01-01

Although much has been written recently about holistic orientations to transformative learning, including its theoretical foundations and frameworks for designing learning experiences that engage multiple epistemologies, little is known about learners' experiences and about how engaging multiple epistemologies can foster learning that is…

Disability-Aware Adaptive and Personalised Learning for Students with Multiple Disabilities

ERIC Educational Resources Information Center

Nganji, Julius T.; Brayshaw, Mike

2017-01-01

Purpose: The purpose of this paper is to address how virtual learning environments (VLEs) can be designed to include the needs of learners with multiple disabilities. Specifically, it employs AI to show how specific learning materials from a huge repository of learning materials can be recommended to learners with various disabilities. This is…

Assessing the use of multiple sources in student essays.

PubMed

Hastings, Peter; Hughes, Simon; Magliano, Joseph P; Goldman, Susan R; Lawless, Kimberly

2012-09-01

The present study explored different approaches for automatically scoring student essays that were written on the basis of multiple texts. Specifically, these approaches were developed to classify whether or not important elements of the texts were present in the essays. The first was a simple pattern-matching approach called "multi-word" that allowed for flexible matching of words and phrases in the sentences. The second technique was latent semantic analysis (LSA), which was used to compare student sentences to original source sentences using its high-dimensional vector-based representation. Finally, the third was a machine-learning technique, support vector machines, which learned a classification scheme from the corpus. The results of the study suggested that the LSA-based system was superior for detecting the presence of explicit content from the texts, but the multi-word pattern-matching approach was better for detecting inferences outside or across texts. These results suggest that the best approach for analyzing essays of this nature should draw upon multiple natural language processing approaches.

Cognitive-Developmental Learning for a Humanoid Robot: A Caregiver’s Gift

DTIC Science & Technology

2004-05-01

system . We propose a real- time algorithm to infer depth and build 3-dimensional coarse maps for objects through the analysis of cues provided by an... system is well defined at the boundary of these regions (although the derivatives are not). A time domain analysis is presented for a piece-linear... Analysis of Multivariable Systems ......................... 266 D.3.1 Networks of Multiple Neural Oscillators ................. 266 D.3.2 Networks of

Feedback-related brain activity predicts learning from feedback in multiple-choice testing.

PubMed

Ernst, Benjamin; Steinhauser, Marco

2012-06-01

Different event-related potentials (ERPs) have been shown to correlate with learning from feedback in decision-making tasks and with learning in explicit memory tasks. In the present study, we investigated which ERPs predict learning from corrective feedback in a multiple-choice test, which combines elements from both paradigms. Participants worked through sets of multiple-choice items of a Swahili-German vocabulary task. Whereas the initial presentation of an item required the participants to guess the answer, corrective feedback could be used to learn the correct response. Initial analyses revealed that corrective feedback elicited components related to reinforcement learning (FRN), as well as to explicit memory processing (P300) and attention (early frontal positivity). However, only the P300 and early frontal positivity were positively correlated with successful learning from corrective feedback, whereas the FRN was even larger when learning failed. These results suggest that learning from corrective feedback crucially relies on explicit memory processing and attentional orienting to corrective feedback, rather than on reinforcement learning.

Multiple neural network approaches to clinical expert systems

NASA Astrophysics Data System (ADS)

Stubbs, Derek F.

1990-08-01

We briefly review the concept of computer aided medical diagnosis and more extensively review the the existing literature on neural network applications in the field. Neural networks can function as simple expert systems for diagnosis or prognosis. Using a public database we develop a neural network for the diagnosis of a major presenting symptom while discussing the development process and possible approaches. MEDICAL EXPERTS SYSTEMS COMPUTER AIDED DIAGNOSIS Biomedicine is an incredibly diverse and multidisciplinary field and it is not surprising that neural networks with their many applications are finding more and more applications in the highly non-linear field of biomedicine. I want to concentrate on neural networks as medical expert systems for clinical diagnosis or prognosis. Expert Systems started out as a set of computerized " ifthen" rules. Everything was reduced to boolean logic and the promised land of computer experts was said to be in sight. It never came. Why? First the computer code explodes as the number of " ifs" increases. All the " ifs" have to interact. Second experts are not very good at reducing expertise to language. It turns out that experts recognize patterns and have non-verbal left-brain intuition decision processes. Third learning by example rather than learning by rule is the way natural brains works and making computers work by rule-learning is hideously labor intensive. Neural networks can learn from example. They learn the results

The Television Generation, Television Literacy, and Television Trends.

ERIC Educational Resources Information Center

Cohen, Jodi R.

Unlike the linear, serial process of reading books, learning to "read" television is a parallel process in which multiple pieces of information are simultaneously received. Perceiving images, only one aspect of understanding television, requires the concurrent processing of information that is compounded within a symbol system. The…

ViNEL: A Virtual Networking Lab for Cyber Defense Education

ERIC Educational Resources Information Center

Reinicke, Bryan; Baker, Elizabeth; Toothman, Callie

2018-01-01

Professors teaching cyber security classes often face challenges when developing workshops for their students: How does one quickly and efficiently configure and deploy an operating system for a temporary learning/testing environment? Faculty teaching these classes spend countless hours installing, configuring and deploying multiple system…

Joint Theater Trauma System: Strategic Overview

DTIC Science & Technology

2011-01-01

Ready for CENTCOM SG) – Multiple amputation management • (Ready for SME vetting) JTTS Trauma Conference Lessons Learned • New therapies – Tranexamic ... acid (used by UK @ Bastion) • Consensus not enough good data to support ubiquitous use • Limited use in patients with hyperfibrinolysis? – Renal

Response to Intervention: Is the Sky Falling?

ERIC Educational Resources Information Center

Dougherty Stahl, Katherine A.

2016-01-01

Response to Intervention (RTI) is a multiple tiered system of instructional interventions that may also serve to identify children with Specific Learning Disabilities (particularly in reading and spelling). This article summarizes the findings of Evaluation of Response to Intervention Practices for Elementary School Reading (Balu, Zhu, Doolittle,…

Neural Correlates of Morphology Acquisition through a Statistical Learning Paradigm.

PubMed

Sandoval, Michelle; Patterson, Dianne; Dai, Huanping; Vance, Christopher J; Plante, Elena

2017-01-01

The neural basis of statistical learning as it occurs over time was explored with stimuli drawn from a natural language (Russian nouns). The input reflected the "rules" for marking categories of gendered nouns, without making participants explicitly aware of the nature of what they were to learn. Participants were scanned while listening to a series of gender-marked nouns during four sequential scans, and were tested for their learning immediately after each scan. Although participants were not told the nature of the learning task, they exhibited learning after their initial exposure to the stimuli. Independent component analysis of the brain data revealed five task-related sub-networks. Unlike prior statistical learning studies of word segmentation, this morphological learning task robustly activated the inferior frontal gyrus during the learning period. This region was represented in multiple independent components, suggesting it functions as a network hub for this type of learning. Moreover, the results suggest that subnetworks activated by statistical learning are driven by the nature of the input, rather than reflecting a general statistical learning system.

Neural Correlates of Morphology Acquisition through a Statistical Learning Paradigm

PubMed Central

Sandoval, Michelle; Patterson, Dianne; Dai, Huanping; Vance, Christopher J.; Plante, Elena

2017-01-01

The neural basis of statistical learning as it occurs over time was explored with stimuli drawn from a natural language (Russian nouns). The input reflected the “rules” for marking categories of gendered nouns, without making participants explicitly aware of the nature of what they were to learn. Participants were scanned while listening to a series of gender-marked nouns during four sequential scans, and were tested for their learning immediately after each scan. Although participants were not told the nature of the learning task, they exhibited learning after their initial exposure to the stimuli. Independent component analysis of the brain data revealed five task-related sub-networks. Unlike prior statistical learning studies of word segmentation, this morphological learning task robustly activated the inferior frontal gyrus during the learning period. This region was represented in multiple independent components, suggesting it functions as a network hub for this type of learning. Moreover, the results suggest that subnetworks activated by statistical learning are driven by the nature of the input, rather than reflecting a general statistical learning system. PMID:28798703

The mediating effect of context variation in mixed practice for transfer of basic science.

PubMed

Kulasegaram, Kulamakan; Min, Cynthia; Howey, Elizabeth; Neville, Alan; Woods, Nicole; Dore, Kelly; Norman, Geoffrey

2015-10-01

Applying a previously learned concept to a novel problem is an important but difficult process called transfer. Practicing multiple concepts together (mixed practice mode) has been shown superior to practicing concepts separately (blocked practice mode) for transfer. This study examined the effect of single and multiple practice contexts for both mixed and blocked practice modalities on transfer performance. We looked at performance on near transfer (familiar contexts) cases and far transfer (unfamiliar contexts) cases. First year psychology students (n = 42) learned three physiological concepts in a 2 × 2 factorial study (one or two practice contexts and blocked or mixed practice). Each concept was practiced with two clinical cases; practice context was defined as the number of organ systems used (one system per concept vs. two systems). In blocked practice, two practice cases followed each concept; in mixed practice, students learned all concepts before seeing six practice cases. Transfer testing consisted of correctly classifying and explaining 15 clinical cases involving near and far transfer. The outcome was ratings of quality of explanations on a 0-3 scale. The repeated measures analysis showed a significant near versus far by organ system interaction [F(1,38) = 3.4, p < 0.002] with practice with a single context showing lower far transfer scores than near transfer [0.58 (0.37)-0.83 (0.37)] compared to the two contexts which had similar far and near transfer scores [1.19 (0.50)-1.01 (0.38)]. Practicing with two organ contexts had a significant benefit for far transfer regardless of mixed or blocked practice; the single context mixed practice group had the lowest far transfer performance; this was a large effect size (Cohen's d = 0.81). Using only one practice context during practice significantly lowers performance even with the usually superior mixed practice mode. Novices should be exposed to multiple contexts and mixed practice to facilitate transfer.

Academic Achievement from Using the Learning Medium Via a Tablet Device Based on Multiple Intelligences in Grade 1 Elementary Student.

PubMed

Nuallaong, Winitra; Nuallaong, Thanya; Preechadirek, Nongluck

2015-04-01

To measure academic achievement of the multiple intelligence-based learning medium via a tablet device. This is a quasi-experimental research study (non-randomized control group pretest-posttest design) in 62 grade 1 elementary students (33 males and 29 females). Thirty-one students were included in an experimental group using purposive sampling by choosing a student who had highest multiple intelligence test scores in logical-mathematic. Then, this group learned by the new learning medium via a tablet which the application matched to logical-mathematic multiple intelligence. Another 31 students were included in a control group using simple random sampling and then learning by recitation. Both groups did pre-test and post-test vocabulary. Thirty students in the experimental group and 24 students in the control group increased post-test scores (odds ratio = 8.75). Both groups made significant increasing in post-test scores. The experimental group increased 9.07 marks (95% CI 8.20-9.93) significantly higher than the control group which increased 4.39 marks (95% CI 3.06-5.72) (t = -6.032, df = 51.481, p < 0.001). Although learning from either multiple intelligence-based learning medium via a tablet or recitation can contribute academic achievement, learningfrom the new medium contributed more achievement than recitation. The new learning medium group had higher post-test scores 8.75 times than the recitation group. Therefore, the new learning medium is more effective than the traditional recitation in terms of academic achievement. This study has limitations because samples came from the same school. However, the previous study in Thailand did notfind a logical-mathematical multiple intelligence difference among schools. In the future, long-term research to find how the new learning medium affects knowledge retention will support the advantage for life-long learning.

Multiple Intelligences for Differentiated Learning

ERIC Educational Resources Information Center

Williams, R. Bruce

2007-01-01

There is an intricate literacy to Gardner's multiple intelligences theory that unlocks key entry points for differentiated learning. Using a well-articulated framework, rich with graphic representations, Williams provides a comprehensive discussion of multiple intelligences. He moves the teacher and students from curiosity, to confidence, to…

Creating E-Commerce Start-Ups with Information Systems Students: Lessons Learned from New Venture Successes and Failures

ERIC Educational Resources Information Center

Abrahams, Alan

2010-01-01

In this paper, we review a variety of e-commerce startups created by senior information systems students, under the author's guidance, over a number of years at multiple universities. We compare the characteristics of the start-ups and comment on various factors which appear to have contributed to their success or failure. Our recommendations are…

Online Homework Put to the Test: A Report on the Impact of Two Online Learning Systems on Student Performance in General Chemistry

ERIC Educational Resources Information Center

Eichler, Jack F.; Peeples, Junelyn

2013-01-01

Two different online homework systems were administered to students in a first-quarter general chemistry course. This study used a multiple regression model to control for the students' academic and socioeconomic background, and it was found that students who completed the online homework activities performed significantly better on a common…

Historical framework to explain long-term coupled human and natural system feedbacks: application to a multiple-ownership forest landscape in the northern Great Lakes region, USA

Treesearch

Michelle M. Steen-Adams; Nancy Langston; Mark D. O. Adams; David J. Mladenoff

2015-01-01

Current and future human and forest landscape conditions are influenced by the cumulative, unfolding history of socialecological interactions. Examining past system responses, especially unintended consequences, can reveal valuable insights that promote learning and adaptation in forest policy and management. Temporal couplings are complex, however; they can be...

Assuring quality health care outcomes: lessons learned from car dealers?

PubMed Central

Dimsdale, Joel E

2017-01-01

Health care systems want quality but struggle to find the right tools because, typically, they track quality in only one or two ways. Because of the complexity of health care, high quality will emerge only when health care systems employ multiple approaches, including, importantly, patient-reported outcome perspectives. Sustained changes are unlikely to emerge in the absence of such multipronged interventions. PMID:28123314

Assessing the Validity of the Qualistar Early Learning Quality Rating and Improvement System as a Tool for Improving Child-Care Quality

ERIC Educational Resources Information Center

Zellman, Gail L.; Perlman, Michal; Le, Vi-Nhuan; Setodji, Claude Messan

2008-01-01

As a result of the generally low quality of child care in the United States and the increased emphasis on accountability in education policy, quality rating systems (QRSs) are proliferating in the child-care arena. QRSs assess child-care providers on multiple dimensions of quality and integrate these assessments into an easily understood summary…

Creating a Culture of Continuous Assessment to Improve Student Learning through Curriculum Review

ERIC Educational Resources Information Center

Kalu, Frances; Dyjur, Patti

2018-01-01

This chapter describes a curriculum review framework that fosters continuous assessment through collaboration with multiple stakeholders, alignment with program level learning outcomes, evaluation based on multiple sources of evidence, and facilitated development of action plans to improve student learning.

Multiagent cooperation and competition with deep reinforcement learning.

PubMed

Tampuu, Ardi; Matiisen, Tambet; Kodelja, Dorian; Kuzovkin, Ilya; Korjus, Kristjan; Aru, Juhan; Aru, Jaan; Vicente, Raul

2017-01-01

Evolution of cooperation and competition can appear when multiple adaptive agents share a biological, social, or technological niche. In the present work we study how cooperation and competition emerge between autonomous agents that learn by reinforcement while using only their raw visual input as the state representation. In particular, we extend the Deep Q-Learning framework to multiagent environments to investigate the interaction between two learning agents in the well-known video game Pong. By manipulating the classical rewarding scheme of Pong we show how competitive and collaborative behaviors emerge. We also describe the progression from competitive to collaborative behavior when the incentive to cooperate is increased. Finally we show how learning by playing against another adaptive agent, instead of against a hard-wired algorithm, results in more robust strategies. The present work shows that Deep Q-Networks can become a useful tool for studying decentralized learning of multiagent systems coping with high-dimensional environments.

Multiagent cooperation and competition with deep reinforcement learning

PubMed Central

Kodelja, Dorian; Kuzovkin, Ilya; Korjus, Kristjan; Aru, Juhan; Aru, Jaan; Vicente, Raul

2017-01-01

Evolution of cooperation and competition can appear when multiple adaptive agents share a biological, social, or technological niche. In the present work we study how cooperation and competition emerge between autonomous agents that learn by reinforcement while using only their raw visual input as the state representation. In particular, we extend the Deep Q-Learning framework to multiagent environments to investigate the interaction between two learning agents in the well-known video game Pong. By manipulating the classical rewarding scheme of Pong we show how competitive and collaborative behaviors emerge. We also describe the progression from competitive to collaborative behavior when the incentive to cooperate is increased. Finally we show how learning by playing against another adaptive agent, instead of against a hard-wired algorithm, results in more robust strategies. The present work shows that Deep Q-Networks can become a useful tool for studying decentralized learning of multiagent systems coping with high-dimensional environments. PMID:28380078

Learning During Stressful Times

PubMed Central

Shors, Tracey J.

2012-01-01

Stressful life events can have profound effects on our cognitive and motor abilities, from those that could be construed as adaptive to those not so. In this review, I discuss the general notion that acute stressful experience necessarily impairs our abilities to learn and remember. The effects of stress on operant conditioning, that is, learned helplessness, as well as those on classical conditioning procedures are discussed in the context of performance and adaptation. Studies indicating sex differences in learning during stressful times are discussed, as are those attributing different responses to the existence of multiple memory systems and nonlinear relationships. The intent of this review is to highlight the apparent plasticity of the stress response, how it might have evolved to affect both performance and learning processes, and the potential problems with interpreting stress effects on learning as either good or bad. An appreciation for its plasticity may provide new avenues for investigating its underlying neuronal mechanisms. PMID:15054128

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

NASA Astrophysics Data System (ADS)

Babaie, Hassan; Atchison, Chris; Sunderraman, Rajshekhar

2014-05-01

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

Perceptual-motor skill learning in Gilles de la Tourette syndrome. Evidence for multiple procedural learning and memory systems.

PubMed

Marsh, Rachel; Alexander, Gerianne M; Packard, Mark G; Zhu, Hongtu; Peterson, Bradley S

2005-01-01

Procedural learning and memory systems likely comprise several skills that are differentially affected by various illnesses of the central nervous system, suggesting their relative functional independence and reliance on differing neural circuits. Gilles de la Tourette syndrome (GTS) is a movement disorder that involves disturbances in the structure and function of the striatum and related circuitry. Recent studies suggest that patients with GTS are impaired in performance of a probabilistic classification task that putatively involves the acquisition of stimulus-response (S-R)-based habits. Assessing the learning of perceptual-motor skills and probabilistic classification in the same samples of GTS and healthy control subjects may help to determine whether these various forms of procedural (habit) learning rely on the same or differing neuroanatomical substrates and whether those substrates are differentially affected in persons with GTS. Therefore, we assessed perceptual-motor skill learning using the pursuit-rotor and mirror tracing tasks in 50 patients with GTS and 55 control subjects who had previously been compared at learning a task of probabilistic classifications. The GTS subjects did not differ from the control subjects in performance of either the pursuit rotor or mirror-tracing tasks, although they were significantly impaired in the acquisition of a probabilistic classification task. In addition, learning on the perceptual-motor tasks was not correlated with habit learning on the classification task in either the GTS or healthy control subjects. These findings suggest that the differing forms of procedural learning are dissociable both functionally and neuroanatomically. The specific deficits in the probabilistic classification form of habit learning in persons with GTS are likely to be a consequence of disturbances in specific corticostriatal circuits, but not the same circuits that subserve the perceptual-motor form of habit learning.

The Role of Parenting for the Adjustment of Children with and without Learning Disabilities: A Person-Oriented Approach

ERIC Educational Resources Information Center

Barkauskiene, Rasa

2009-01-01

A person-oriented approach was used to examine the role of parenting in the associations between single learning disabilities and multiple learning disabilities and the adjustment difficulties in 8-11-year-olds. The results revealed that multiple, but not single, learning disabilities were associated with greater difficulties in emotional and…

Improving wave forecasting by integrating ensemble modelling and machine learning

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Multiplication Fact Fluency Using Doubles

ERIC Educational Resources Information Center

Flowers, Judith M.; Rubenstein, Rheta N.

2010-01-01

Not knowing multiplication facts creates a gap in a student's mathematics development and undermines confidence and disposition toward further mathematical learning. Learning multiplication facts is a first step in proportional reasoning, "the capstone of elementary arithmetic and the gateway to higher mathematics" (NRC 2001, p. 242). Proportional…

In it together: Organizational learning through participation in environmental assessment

NASA Astrophysics Data System (ADS)

Fitzpatrick, Patricia

2005-07-01

Can organizations learn through participation in environmental assessment (EA)? This was the central research question of a study that explored the linkages among sustainable development, EA, public participation, and learning. To address this question, the research design involved a comparative case study of two concurrent but geographically separate projects, the Wuskwatim generation station and transmission lines projects (Wuskwatim projects), and the Snap Lake Diamonds Project (Snap Lake project). The Wuskwatim projects involve the construction of a low head dam and three 230 kV transmission line segments in Northern Manitoba, Canada. The Snap Lake Project involves the construction and operation of a diamond mine 220 km northwest of Yellowknife, Northwest Territories, Canada, at the headwaters of the Lockhart River drainage system. The EAs of these proposed developments provided multiple opportunities for public (and organizational) involvement in the review, including comments on the scope of the assessment, information requests, and public hearings. Data collection included participant observation, semi-structured interviews with EA participants, and documentation generated through the course of the reviews. Data were organized using QSR Nvivo, a database software system. In this dissertation, three key contributions are made. The theoretical framework that draws together a number of separate but related fields of study---communicative action, discursive democracy, transformative learning, organizational learning---is the first contribution. The second is verification that organizations learn through participation in EA. Third, empirical support is presented far the assertion that transformative learning can address change beyond that experienced by the individual, to account for both policy-oriented and organizational learning. Related to the second contribution, results indicate that participants of EA engage in Teaming on multiple scales. Furthermore, learning outcomes include both instrumental and communicative learning. Instrumental learning included an increased understanding of technical issues and assessment tools, such as information requests. Communicative teaming outcomes included the importance of dialogue as a means of resolving issues and a refinement of strategies For promoting organizational positions. At an organizational scale, teaming by state actors, including government and tribunals, emphasized mechanisms designed to improve performance within existing structures, or "single-loop learning". Public actors, however, identified more outcomes associated with changes to their theory-in-use, designed to change the structure of the EA process, or "double-loop learning". The discussion of learning supports the application of transformative learning as a framework for considering different scales of learning, the third contribution to research. Findings revealed that individuals and organizations use project specific EA as an opportunity to compel the development and implementation of sustainable initiatives. These findings suggest that higher order learning for sustainability may be occurring through project based EA. Results also revealed the importance of creating opportunities for discussion and debate as a means of engaging organizations in and encouraging learning through EA. These findings support Habermas' emphasis on dialogue as a means of negotiating political systems.

Unsupervised learning of contextual constraints in neural networks for simultaneous visual processing of multiple objects

NASA Astrophysics Data System (ADS)

Marshall, Jonathan A.

1992-12-01

A simple self-organizing neural network model, called an EXIN network, that learns to process sensory information in a context-sensitive manner, is described. EXIN networks develop efficient representation structures for higher-level visual tasks such as segmentation, grouping, transparency, depth perception, and size perception. Exposure to a perceptual environment during a developmental period serves to configure the network to perform appropriate organization of sensory data. A new anti-Hebbian inhibitory learning rule permits superposition of multiple simultaneous neural activations (multiple winners), while maintaining contextual consistency constraints, instead of forcing winner-take-all pattern classifications. The activations can represent multiple patterns simultaneously and can represent uncertainty. The network performs parallel parsing, credit attribution, and simultaneous constraint satisfaction. EXIN networks can learn to represent multiple oriented edges even where they intersect and can learn to represent multiple transparently overlaid surfaces defined by stereo or motion cues. In the case of stereo transparency, the inhibitory learning implements both a uniqueness constraint and permits coactivation of cells representing multiple disparities at the same image location. Thus two or more disparities can be active simultaneously without interference. This behavior is analogous to that of Prazdny's stereo vision algorithm, with the bonus that each binocular point is assigned a unique disparity. In a large implementation, such a NN would also be able to represent effectively the disparities of a cloud of points at random depths, like human observers, and unlike Prazdny's method

Useful Pedagogies or Financial Hardships? Interactive Response Technology (Clickers) in the Large College Classroom

ERIC Educational Resources Information Center

Boatright-Horowitz, Su L.

2009-01-01

Interactive response systems "clickers" can provide multiple benefits to the students and faculty who use them, including immediate performance feedback and greater student engagement in learning. My own exploration of this technology has yielded five pedagogically different types of polling questions, specifically measurement of student…

Multiple Sensory-Motor Pathways Lead to Coordinated Visual Attention

ERIC Educational Resources Information Center

Yu, Chen; Smith, Linda B.

2017-01-01

Joint attention has been extensively studied in the developmental literature because of overwhelming evidence that the ability to socially coordinate visual attention to an object is essential to healthy developmental outcomes, including language learning. The goal of this study was to understand the complex system of sensory-motor behaviors that…

Neural Correlates of Olfactory Learning: Critical Role of Centrifugal Neuromodulation

ERIC Educational Resources Information Center

Fletcher, Max L.; Chen, Wei R.

2010-01-01

The mammalian olfactory system is well established for its remarkable capability of undergoing experience-dependent plasticity. Although this process involves changes at multiple stages throughout the central olfactory pathway, even the early stages of processing, such as the olfactory bulb and piriform cortex, can display a high degree of…

Data Sharing to Inform School-Based Asthma Services

ERIC Educational Resources Information Center

Portwood, Sharon G.; Nelson, Elissa B.

2013-01-01

Background: This article examines results and lessons learned from a collaborative project involving a large urban school district, its county health department, multiple community partners, and the local university to establish an effective system for data sharing to inform monitoring and evaluation of the Charlotte Mecklenburg Schools (CMS)…

Building Mental Models by Dissecting Physical Models

ERIC Educational Resources Information Center

Srivastava, Anveshna

2016-01-01

When students build physical models from prefabricated components to learn about model systems, there is an implicit trade-off between the physical degrees of freedom in building the model and the intensity of instructor supervision needed. Models that are too flexible, permitting multiple possible constructions require greater supervision to…

Molecular mechanisms of fear learning and memory.

PubMed

Johansen, Joshua P; Cain, Christopher K; Ostroff, Linnaea E; LeDoux, Joseph E

2011-10-28

Pavlovian fear conditioning is a particularly useful behavioral paradigm for exploring the molecular mechanisms of learning and memory because a well-defined response to a specific environmental stimulus is produced through associative learning processes. Synaptic plasticity in the lateral nucleus of the amygdala (LA) underlies this form of associative learning. Here, we summarize the molecular mechanisms that contribute to this synaptic plasticity in the context of auditory fear conditioning, the form of fear conditioning best understood at the molecular level. We discuss the neurotransmitter systems and signaling cascades that contribute to three phases of auditory fear conditioning: acquisition, consolidation, and reconsolidation. These studies suggest that multiple intracellular signaling pathways, including those triggered by activation of Hebbian processes and neuromodulatory receptors, interact to produce neural plasticity in the LA and behavioral fear conditioning. Collectively, this body of research illustrates the power of fear conditioning as a model system for characterizing the mechanisms of learning and memory in mammals and potentially for understanding fear-related disorders, such as PTSD and phobias. Copyright © 2011 Elsevier Inc. All rights reserved.

Order Matters: Sequencing Scale-Realistic Versus Simplified Models to Improve Science Learning

NASA Astrophysics Data System (ADS)

Chen, Chen; Schneps, Matthew H.; Sonnert, Gerhard

2016-10-01

Teachers choosing between different models to facilitate students' understanding of an abstract system must decide whether to adopt a model that is simplified and striking or one that is realistic and complex. Only recently have instructional technologies enabled teachers and learners to change presentations swiftly and to provide for learning based on multiple models, thus giving rise to questions about the order of presentation. Using disjoint individual growth modeling to examine the learning of astronomical concepts using a simulation of the solar system on tablets for 152 high school students (age 15), the authors detect both a model effect and an order effect in the use of the Orrery, a simplified model that exaggerates the scale relationships, and the True-to-scale, a proportional model that more accurately represents the realistic scale relationships. Specifically, earlier exposure to the simplified model resulted in diminution of the conceptual gain from the subsequent realistic model, but the realistic model did not impede learning from the following simplified model.

Positive Attitude Toward Math Supports Early Academic Success: Behavioral Evidence and Neurocognitive Mechanisms.

PubMed

Chen, Lang; Bae, Se Ri; Battista, Christian; Qin, Shaozheng; Chen, Tianwen; Evans, Tanya M; Menon, Vinod

2018-03-01

Positive attitude is thought to impact academic achievement and learning in children, but little is known about its underlying neurocognitive mechanisms. Using a large behavioral sample of 240 children, we found that positive attitude toward math uniquely predicted math achievement, even after we accounted for multiple other cognitive-affective factors. We then investigated the neural mechanisms underlying the link between positive attitude and academic achievement in two independent cohorts of children (discovery cohort: n = 47; replication cohort: n = 28) and tested competing hypotheses regarding the differential roles of affective-motivational and learning-memory systems. In both cohorts, we found that positive attitude was associated with increased engagement of the hippocampal learning-memory system. Structural equation modeling further revealed that, in both cohorts, increased hippocampal activity and more frequent use of efficient memory-based strategies mediated the relation between positive attitude and higher math achievement. Our study is the first to elucidate the neurocognitive mechanisms by which positive attitude influences learning and academic achievement.

Managing Multiple Goals in Real Learning Contexts

ERIC Educational Resources Information Center

Mansfield, Caroline F.

2009-01-01

Understanding students' multiple goals in real learning contexts is an emerging area of importance for educators and researchers investigating student motivation in classrooms. This qualitative study conducted over an academic year investigates the multiple goals articulated by seven 11-year-old students and explores relationships between goals…

Knowledge Organization through Multiple Representations in a Computer-Supported Collaborative Learning Environment

ERIC Educational Resources Information Center

Namdar, Bahadir; Shen, Ji

2018-01-01

Computer-supported collaborative learning (CSCL) environments provide learners with multiple representational tools for storing, sharing, and constructing knowledge. However, little is known about how learners organize knowledge through multiple representations about complex socioscientific issues. Therefore, the purpose of this study was to…

On the fusion of tuning parameters of fuzzy rules and neural network

NASA Astrophysics Data System (ADS)

Mamuda, Mamman; Sathasivam, Saratha

2017-08-01

Learning fuzzy rule-based system with neural network can lead to a precise valuable empathy of several problems. Fuzzy logic offers a simple way to reach at a definite conclusion based upon its vague, ambiguous, imprecise, noisy or missing input information. Conventional learning algorithm for tuning parameters of fuzzy rules using training input-output data usually end in a weak firing state, this certainly powers the fuzzy rule and makes it insecure for a multiple-input fuzzy system. In this paper, we introduce a new learning algorithm for tuning the parameters of the fuzzy rules alongside with radial basis function neural network (RBFNN) in training input-output data based on the gradient descent method. By the new learning algorithm, the problem of weak firing using the conventional method was addressed. We illustrated the efficiency of our new learning algorithm by means of numerical examples. MATLAB R2014(a) software was used in simulating our result The result shows that the new learning method has the best advantage of training the fuzzy rules without tempering with the fuzzy rule table which allowed a membership function of the rule to be used more than one time in the fuzzy rule base.

Nicotinic Receptors in the Dorsal and Ventral Hippocampus Differentially Modulate Contextual Fear Conditioning

PubMed Central

Kenney, Justin W.; Raybuck, Jonathan D.; Gould, Thomas J.

2012-01-01

Nicotine administration alters various forms of hippocampus-dependent learning and memory. Increasing work has found that the dorsal and ventral hippocampus differentially contribute to multiple behaviors. Thus, the present study examined whether the effects of nicotine in the dorsal and ventral hippocampus have distinct influences on contextual fear learning in male C57BL/6J mice. Direct infusion of nicotine into the dorsal hippocampus resulted in an enhancement of contextual fear learning, whereas nicotine infused into the ventral hippocampus resulted in deficits. Nicotine infusions into the ventral hippocampus did not alter hippocampus-independent cued fear conditioning or time spent in the open arm of the elevated plus maze, a measure of anxiety, suggesting the effects are due to alterations in contextual learning and not other general processes. Finally, results from using direct infusions of MLA, a low-affinity α7 nicotinic acetylcholine receptor (nAChR) antagonist, in conjunction with systemic nicotine, provide evidence that α7-nAChRs in the ventral hippocampus mediate the detrimental effect of ventral hippocampal nicotine on contextual fear learning. These results suggest that with systemic nicotine administration, competition exists between the dorsal and ventral hippocampus for behavioral control over contextual learning. PMID:22271264

I came, I saw, I reflected: a qualitative study into learning outcomes of international electives for Japanese and British medical students.

PubMed

Nishigori, Hiroshi; Otani, Takashi; Plint, Simon; Uchino, Minako; Ban, Nobutaro

2009-05-01

Although medical students have increasingly more opportunities to participate in international electives, their experiences are usually unstructured and the literature referring to their learning outcomes, educational environment, and assessment is scanty. This study was undertaken to clarify qualitatively what students learn from their international electives. We carried out semi-structured individual interviews with 15 Japanese students studying clinical medicine in British medical schools and six British students studying in Japanese medical schools. The thematic synthesis method was used in analysing the transcribed data and triangulation by multiple researchers was used to achieve higher reliability. The main learning outcomes identified were skills in history taking and physical examination with clinical reasoning and in management of diseases rarely seen in the students' own countries; awareness of clinical ethics and merits and demerits of different systems of healthcare and medical education; sensitivity to issues in doctor-patient relationships and work ethics; enhancement of cultural competence; and personal development. Most learning outcomes of international electives are culture- or system-dependent. Students achieved outcomes related closely to medical professionalism, mainly through reflection. International electives may give students opportunities to learn both professionalism and cultural competence.

Dynamic adaptive learning for decision-making supporting systems

NASA Astrophysics Data System (ADS)

He, Haibo; Cao, Yuan; Chen, Sheng; Desai, Sachi; Hohil, Myron E.

2008-03-01

This paper proposes a novel adaptive learning method for data mining in support of decision-making systems. Due to the inherent characteristics of information ambiguity/uncertainty, high dimensionality and noisy in many homeland security and defense applications, such as surveillances, monitoring, net-centric battlefield, and others, it is critical to develop autonomous learning methods to efficiently learn useful information from raw data to help the decision making process. The proposed method is based on a dynamic learning principle in the feature spaces. Generally speaking, conventional approaches of learning from high dimensional data sets include various feature extraction (principal component analysis, wavelet transform, and others) and feature selection (embedded approach, wrapper approach, filter approach, and others) methods. However, very limited understandings of adaptive learning from different feature spaces have been achieved. We propose an integrative approach that takes advantages of feature selection and hypothesis ensemble techniques to achieve our goal. Based on the training data distributions, a feature score function is used to provide a measurement of the importance of different features for learning purpose. Then multiple hypotheses are iteratively developed in different feature spaces according to their learning capabilities. Unlike the pre-set iteration steps in many of the existing ensemble learning approaches, such as adaptive boosting (AdaBoost) method, the iterative learning process will automatically stop when the intelligent system can not provide a better understanding than a random guess in that particular subset of feature spaces. Finally, a voting algorithm is used to combine all the decisions from different hypotheses to provide the final prediction results. Simulation analyses of the proposed method on classification of different US military aircraft databases show the effectiveness of this method.

Students' Construction of External Representations in Design-Based Learning Situations

ERIC Educational Resources Information Center

de Vries, Erica

2006-01-01

This article develops a theoretical framework for the study of students' construction of mixed multiple external representations in design-based learning situations involving an adaptation of professional tasks and tools to a classroom setting. The framework draws on research on professional design processes and on learning with multiple external…

Multiple Intelligences, Motivations and Learning Experience Regarding Video-Assisted Subjects in a Rural University

ERIC Educational Resources Information Center

Hajhashemi, Karim; Caltabiano, Nerina; Anderson, Neil; Tabibzadeh, Seyed Asadollah

2018-01-01

This study investigates multiple intelligences in relation to online video experiences, age, gender, and mode of learning from a rural Australian university. The inter-relationships between learners' different intelligences and their motivations and learning experience with the supplementary online videos utilised in their subjects are…

The Effect Of The Materials Based On Multiple Intelligence Theory Upon The Intelligence Groups' Learning Process

NASA Astrophysics Data System (ADS)

Oral, I.; Dogan, O.

2007-04-01

The aim of this study is to find out the effect of the course materials based on Multiple Intelligence Theory upon the intelligence groups' learning process. In conclusion, the results proved that the materials prepared according to Multiple Intelligence Theory have a considerable effect on the students' learning process. This effect was particularly seen on the student groups of the musical-rhythmic, verbal-linguistic, interpersonal-social and naturalist intelligence.

The Role of Multiple Neuromodulators in Reinforcement Learning That Is Based on Competition between Eligibility Traces.

PubMed

Huertas, Marco A; Schwettmann, Sarah E; Shouval, Harel Z

2016-01-01

The ability to maximize reward and avoid punishment is essential for animal survival. Reinforcement learning (RL) refers to the algorithms used by biological or artificial systems to learn how to maximize reward or avoid negative outcomes based on past experiences. While RL is also important in machine learning, the types of mechanistic constraints encountered by biological machinery might be different than those for artificial systems. Two major problems encountered by RL are how to relate a stimulus with a reinforcing signal that is delayed in time (temporal credit assignment), and how to stop learning once the target behaviors are attained (stopping rule). To address the first problem synaptic eligibility traces were introduced, bridging the temporal gap between a stimulus and its reward. Although, these were mere theoretical constructs, recent experiments have provided evidence of their existence. These experiments also reveal that the presence of specific neuromodulators converts the traces into changes in synaptic efficacy. A mechanistic implementation of the stopping rule usually assumes the inhibition of the reward nucleus; however, recent experimental results have shown that learning terminates at the appropriate network state even in setups where the reward nucleus cannot be inhibited. In an effort to describe a learning rule that solves the temporal credit assignment problem and implements a biologically plausible stopping rule, we proposed a model based on two separate synaptic eligibility traces, one for long-term potentiation (LTP) and one for long-term depression (LTD), each obeying different dynamics and having different effective magnitudes. The model has been shown to successfully generate stable learning in recurrent networks. Although, the model assumes the presence of a single neuromodulator, evidence indicates that there are different neuromodulators for expressing the different traces. What could be the role of different neuromodulators for expressing the LTP and LTD traces? Here we expand on our previous model to include several neuromodulators, and illustrate through various examples how different these contribute to learning reward-timing within a wide set of training paradigms and propose further roles that multiple neuromodulators can play in encoding additional information of the rewarding signal.

Integrating Multiple Intelligences and Learning Styles on Solving Problems, Achievement in, and Attitudes towards Math in Six Graders with Learning Disabilities in Cooperative Groups

ERIC Educational Resources Information Center

Eissa, Mourad Ali; Mostafa, Amaal Ahmed

2013-01-01

This study investigated the effect of using differentiated instruction by integrating multiple intelligences and learning styles on solving problems, achievement in, and attitudes towards math in six graders with learning disabilities in cooperative groups. A total of 60 students identified with LD were invited to participate. The sample was…

A Critical Reflection on the Multiple Roles Required to Facilitate Mutual Learning during Service-Learning in Creative Arts Education

ERIC Educational Resources Information Center

Meyer, Merna; Wood, Lesley

2017-01-01

In this article, I critically reflect on my own learning during a community-based, service-learning pilot project, highlighting the multiple roles that were required of me as facilitator. I provided opportunity for student teachers in a Creative Arts module to engage with youth from a local township community. The purpose of the participatory…

Teaching and Learning Activity Sequencing System using Distributed Genetic Algorithms

NASA Astrophysics Data System (ADS)

Matsui, Tatsunori; Ishikawa, Tomotake; Okamoto, Toshio

The purpose of this study is development of a supporting system for teacher's design of lesson plan. Especially design of lesson plan which relates to the new subject "Information Study" is supported. In this study, we developed a system which generates teaching and learning activity sequences by interlinking lesson's activities corresponding to the various conditions according to the user's input. Because user's input is multiple information, there will be caused contradiction which the system should solve. This multiobjective optimization problem is resolved by Distributed Genetic Algorithms, in which some fitness functions are defined with reference models on lesson, thinking and teaching style. From results of various experiments, effectivity and validity of the proposed methods and reference models were verified; on the other hand, some future works on reference models and evaluation functions were also pointed out.

The portfolio approach to competency-based assessment at the Cleveland Clinic Lerner College of Medicine.

PubMed

Dannefer, Elaine F; Henson, Lindsey C

2007-05-01

Despite the rapid expansion of interest in competency-based assessment, few descriptions of assessment systems specifically designed for a competency-based curriculum have been reported. The purpose of this article is to describe the design of a portfolio approach to a comprehensive, competency-based assessment system that is fully integrated with the curriculum to foster an educational environment focused on learning. The educational design goal of the Cleveland Clinic Lerner College of Medicine of Case Western Reserve University was to create an integrated educational program-curriculum and instructional methods, student assessment processes, and learning environment-to prepare medical students for success in careers as physician investigators. The first class in the five-year program matriculated in 2004. To graduate, a student must demonstrate mastery of nine competencies: research, medical knowledge, communication, professionalism, clinical skills, clinical reasoning, health care systems, personal development, and reflective practice. The portfolio provides a tool for collecting and managing multiple types of assessment evidence from multiple contexts and sources within the curriculum to document competence and promote reflective practice skills. This article describes how the portfolio was developed to provide both formative and summative assessment of student achievement in relation to the program's nine competencies.

Physical Realization of a Supervised Learning System Built with Organic Memristive Synapses

NASA Astrophysics Data System (ADS)

Lin, Yu-Pu; Bennett, Christopher H.; Cabaret, Théo; Vodenicarevic, Damir; Chabi, Djaafar; Querlioz, Damien; Jousselme, Bruno; Derycke, Vincent; Klein, Jacques-Olivier

2016-09-01

Multiple modern applications of electronics call for inexpensive chips that can perform complex operations on natural data with limited energy. A vision for accomplishing this is implementing hardware neural networks, which fuse computation and memory, with low cost organic electronics. A challenge, however, is the implementation of synapses (analog memories) composed of such materials. In this work, we introduce robust, fastly programmable, nonvolatile organic memristive nanodevices based on electrografted redox complexes that implement synapses thanks to a wide range of accessible intermediate conductivity states. We demonstrate experimentally an elementary neural network, capable of learning functions, which combines four pairs of organic memristors as synapses and conventional electronics as neurons. Our architecture is highly resilient to issues caused by imperfect devices. It tolerates inter-device variability and an adaptable learning rule offers immunity against asymmetries in device switching. Highly compliant with conventional fabrication processes, the system can be extended to larger computing systems capable of complex cognitive tasks, as demonstrated in complementary simulations.

Physical Realization of a Supervised Learning System Built with Organic Memristive Synapses.

PubMed

Lin, Yu-Pu; Bennett, Christopher H; Cabaret, Théo; Vodenicarevic, Damir; Chabi, Djaafar; Querlioz, Damien; Jousselme, Bruno; Derycke, Vincent; Klein, Jacques-Olivier

2016-09-07

Multiple modern applications of electronics call for inexpensive chips that can perform complex operations on natural data with limited energy. A vision for accomplishing this is implementing hardware neural networks, which fuse computation and memory, with low cost organic electronics. A challenge, however, is the implementation of synapses (analog memories) composed of such materials. In this work, we introduce robust, fastly programmable, nonvolatile organic memristive nanodevices based on electrografted redox complexes that implement synapses thanks to a wide range of accessible intermediate conductivity states. We demonstrate experimentally an elementary neural network, capable of learning functions, which combines four pairs of organic memristors as synapses and conventional electronics as neurons. Our architecture is highly resilient to issues caused by imperfect devices. It tolerates inter-device variability and an adaptable learning rule offers immunity against asymmetries in device switching. Highly compliant with conventional fabrication processes, the system can be extended to larger computing systems capable of complex cognitive tasks, as demonstrated in complementary simulations.

Student Mastery of the Sun-Earth-Moon System in a Flipped Classroom of Pre-service Elementary Education Students

NASA Astrophysics Data System (ADS)

Larsen, Kristine

2014-01-01

One of the current trends in pedagogy at all levels(K-college) is the so-called ‘flipped classroom’, in which students prepare for a class meeting through self-study of the material. It is based on a rejection of the classic model of the faculty member as the ‘sage on the stage’ instead, responsibility for learning shifts to the individual student. The faculty member takes on the role of learning facilitator or mentor, and focuses the students’ learning by crafting and administering timely formative assessments (in multiple formats and applied multiple times) that aid both students and the faculty member in tracking the students’ mastery of the learning outcomes. In a flipped, freshman-only, section of SCI 111 Elementary Earth-Physical Sciences (a required introductory science course for pre-service elementary school teachers) the students learned through a combination of individual and group hands-on in-class activities, technology (including PowerPoint presentations and short videos viewed prior to attending class), in-class worksheets, and in-class discussions. Students self-differentiated in how they interacted with the available teaching materials, deciding which activities to spend the most time on based on their individual needs (based on an online quiz taken the night before the class period, and their personal self-confidence with the material). Available in-class activities and worksheets were developed by the faculty member based on student scores on the online quiz as well as personal messages submitted through the course management system the night before the class meeting. While this placed a significant burden on the faculty member in terms of course preparation, it allowed for just-in-time teaching to take place. This poster describes the results of student mastery of content centered on the sun-earth-moon system (specifically seasons, moon phases, and eclipses) as compared to traditional classroom sections.

Multiple Intelligences and Language Learning Strategies: Investigating Possible Relations

ERIC Educational Resources Information Center

Akbari, Ramin; Hosseini, Kobra

2008-01-01

The present study was conducted to investigate the existence of any possible relationship between the use of language learning strategies and multiple intelligences' scores of foreign language learners of English. Ninety subjects participated in the study. To measure the participants' multiple intelligence scores, MIDAS, a commercially designed…

Enhancing Undergraduate Chemistry Learning by Helping Students Make Connections among Multiple Graphical Representations

ERIC Educational Resources Information Center

Rau, Martina A.

2015-01-01

Multiple representations are ubiquitous in chemistry education. To benefit from multiple representations, students have to make connections between them. However, connection making is a difficult task for students. Prior research shows that supporting connection making enhances students' learning in math and science domains. Most prior research…

Students' Difficulties With Multiple Representations in Introductory Mechanics

ERIC Educational Resources Information Center

Nguyen, Dong-Hai; Rebello, N. Sanjay

2011-01-01

Research in physics education indicates that the use of multiple representations in teaching and learning helps students become better problem-solvers. We report on a study to investigate students' difficulties in solving mechanics problems presented in multiple representations. We conducted teaching/learning interviews with 20 students in a…

Cost-Benefit Arbitration Between Multiple Reinforcement-Learning Systems.

PubMed

Kool, Wouter; Gershman, Samuel J; Cushman, Fiery A

2017-09-01

Human behavior is sometimes determined by habit and other times by goal-directed planning. Modern reinforcement-learning theories formalize this distinction as a competition between a computationally cheap but inaccurate model-free system that gives rise to habits and a computationally expensive but accurate model-based system that implements planning. It is unclear, however, how people choose to allocate control between these systems. Here, we propose that arbitration occurs by comparing each system's task-specific costs and benefits. To investigate this proposal, we conducted two experiments showing that people increase model-based control when it achieves greater accuracy than model-free control, and especially when the rewards of accurate performance are amplified. In contrast, they are insensitive to reward amplification when model-based and model-free control yield equivalent accuracy. This suggests that humans adaptively balance habitual and planned action through on-line cost-benefit analysis.

Cognitive Training for Impaired Neural Systems in Neuropsychiatric Illness

PubMed Central

Vinogradov, Sophia; Fisher, Melissa; de Villers-Sidani, Etienne

2012-01-01

Neuropsychiatric illnesses are associated with dysfunction in distributed prefrontal neural systems that underlie perception, cognition, social interactions, emotion regulation, and motivation. The high degree of learning-dependent plasticity in these networks—combined with the availability of advanced computerized technology—suggests that we should be able to engineer very specific training programs that drive meaningful and enduring improvements in impaired neural systems relevant to neuropsychiatric illness. However, cognitive training approaches for mental and addictive disorders must take into account possible inherent limitations in the underlying brain ‘learning machinery' due to pathophysiology, must grapple with the presence of complex overlearned maladaptive patterns of neural functioning, and must find a way to ally with developmental and psychosocial factors that influence response to illness and to treatment. In this review, we briefly examine the current state of knowledge from studies of cognitive remediation in psychiatry and we highlight open questions. We then present a systems neuroscience rationale for successful cognitive training for neuropsychiatric illnesses, one that emphasizes the distributed nature of neural assemblies that support cognitive and affective processing, as well as their plasticity. It is based on the notion that, during successful learning, the brain represents the relevant perceptual and cognitive/affective inputs and action outputs with disproportionately larger and more coordinated populations of neurons that are distributed (and that are interacting) across multiple levels of processing and throughout multiple brain regions. This approach allows us to address limitations found in earlier research and to introduce important principles for the design and evaluation of the next generation of cognitive training for impaired neural systems. We summarize work to date using such neuroscience-informed methods and indicate some of the exciting future directions of this field. PMID:22048465

Models of vocal learning in the songbird: Historical frameworks and the stabilizing critic.

PubMed

Nick, Teresa A

2015-10-01

Birdsong is a form of sensorimotor learning that involves a mirror-like system that activates with both song hearing and production. Early models of song learning, based on behavioral measures, identified key features of vocal plasticity, such as the requirements for memorization of a tutor song and auditory feedback during song practice. The concept of a comparator, which compares the memory of the tutor song to auditory feedback, featured prominently. Later models focused on linking anatomically-defined neural modules to behavioral concepts, such as the comparator. Exploiting the anatomical modularity of the songbird brain, localized lesions illuminated mechanisms of the neural song system. More recent models have integrated neuronal mechanisms identified in other systems with observations in songbirds. While these models explain multiple aspects of song learning, they must incorporate computational elements based on unknown biological mechanisms to bridge the motor-to-sensory delay and/or transform motor signals into the sensory domain. Here, I introduce the stabilizing critic hypothesis, which enables sensorimotor learning by (1) placing a purely sensory comparator afferent of the song system and (2) endowing song system disinhibitory interneuron networks with the capacity both to bridge the motor-sensory delay through prolonged bursting and to stabilize song segments selectively based on the comparator signal. These proposed networks stabilize an otherwise variable signal generated by both putative mirror neurons and a cortical-basal ganglia-thalamic loop. This stabilized signal then temporally converges with a matched premotor signal in the efferent song motor cortex, promoting spike-timing-dependent plasticity in the premotor circuitry and behavioral song learning. © 2014 Wiley Periodicals, Inc.

MIT/Draper Technology Development Partnership Project: Systems Analysis and On-Station Propulsion Subsystem Design.

DTIC Science & Technology

1998-08-04

manufacturing Military and commercial applications Large market developing for multiple- satellite constellations Will have a high demand if...identified, and market assessments for five different possible projects are discussed. Lessons learned during the first semester of project work are...24 1.2.6 Market Assessments of Five Concepts 26 1.2.7 Project Selection 28 Chapter 2 Requirements Analysis and Top-Level System Architecture 30

Incomplete Multisource Transfer Learning.

PubMed

Ding, Zhengming; Shao, Ming; Fu, Yun

2018-02-01

Transfer learning is generally exploited to adapt well-established source knowledge for learning tasks in weakly labeled or unlabeled target domain. Nowadays, it is common to see multiple sources available for knowledge transfer, each of which, however, may not include complete classes information of the target domain. Naively merging multiple sources together would lead to inferior results due to the large divergence among multiple sources. In this paper, we attempt to utilize incomplete multiple sources for effective knowledge transfer to facilitate the learning task in target domain. To this end, we propose an incomplete multisource transfer learning through two directional knowledge transfer, i.e., cross-domain transfer from each source to target, and cross-source transfer. In particular, in cross-domain direction, we deploy latent low-rank transfer learning guided by iterative structure learning to transfer knowledge from each single source to target domain. This practice reinforces to compensate for any missing data in each source by the complete target data. While in cross-source direction, unsupervised manifold regularizer and effective multisource alignment are explored to jointly compensate for missing data from one portion of source to another. In this way, both marginal and conditional distribution discrepancy in two directions would be mitigated. Experimental results on standard cross-domain benchmarks and synthetic data sets demonstrate the effectiveness of our proposed model in knowledge transfer from incomplete multiple sources.

Fuzzy adaptive iterative learning coordination control of second-order multi-agent systems with imprecise communication topology structure

NASA Astrophysics Data System (ADS)

Chen, Jiaxi; Li, Junmin

2018-02-01

In this paper, we investigate the perfect consensus problem for second-order linearly parameterised multi-agent systems (MAS) with imprecise communication topology structure. Takagi-Sugeno (T-S) fuzzy models are presented to describe the imprecise communication topology structure of leader-following MAS, and a distributed adaptive iterative learning control protocol is proposed with the dynamic of leader unknown to any of the agent. The proposed protocol guarantees that the follower agents can track the leader perfectly on [0,T] for the consensus problem. Under alignment condition, a sufficient condition of the consensus for closed-loop MAS is given based on Lyapunov stability theory. Finally, a numerical example and a multiple pendulum system are given to illustrate the effectiveness of the proposed algorithm.

Looking through the lenses of science literacy and cultural diversity: learning from Helena's mistake

NASA Astrophysics Data System (ADS)

Chinn, Pauline W. U.

2012-06-01

Maria Andrée focuses on an immigrant student whose error in a laboratory activity leads to a novel, colorful outcome that she excitedly shares with peers. After engaging in class activities for a few weeks she returns to her earlier dislike of science, saying: "I hate science, particularly Chemistry." The classroom activity system focused on reproduction of school knowledge did not expand to accommodate Helena's "new activity system with an object of learning science." This essay suggests teachers be prepared to teach diverse students in ways supporting multiple ways to engage in science. This becomes possible when teachers view their classrooms as dynamic, participatory activity systems that support content mastery as contributing to but not being identical to science identity and science literacy.

The Effects of Study Tasks in a Computer-Based Chemistry Learning Environment

NASA Astrophysics Data System (ADS)

Urhahne, Detlef; Nick, Sabine; Poepping, Anna Christin; Schulz, Sarah Jayne

2013-12-01

The present study examines the effects of different study tasks on the acquisition of knowledge about acids and bases in a computer-based learning environment. Three different task formats were selected to create three treatment conditions: learning with gap-fill and matching tasks, learning with multiple-choice tasks, and learning only from text and figures without any additional tasks. Participants were 196 ninth-grade students who learned with a self-developed multimedia program in a pretest-posttest control group design. Research results reveal that gap-fill and matching tasks were most effective in promoting knowledge acquisition, followed by multiple-choice tasks, and no tasks at all. The findings are in line with previous research on this topic. The effects can possibly be explained by the generation-recognition model, which predicts that gap-fill and matching tasks trigger more encompassing learning processes than multiple-choice tasks. It is concluded that instructional designers should incorporate more challenging study tasks for enhancing the effectiveness of computer-based learning environments.

Implementation of a General Real-Time Visual Anomaly Detection System Via Soft Computing

NASA Technical Reports Server (NTRS)

Dominguez, Jesus A.; Klinko, Steve; Ferrell, Bob; Steinrock, Todd (Technical Monitor)

2001-01-01

The intelligent visual system detects anomalies or defects in real time under normal lighting operating conditions. The application is basically a learning machine that integrates fuzzy logic (FL), artificial neural network (ANN), and generic algorithm (GA) schemes to process the image, run the learning process, and finally detect the anomalies or defects. The system acquires the image, performs segmentation to separate the object being tested from the background, preprocesses the image using fuzzy reasoning, performs the final segmentation using fuzzy reasoning techniques to retrieve regions with potential anomalies or defects, and finally retrieves them using a learning model built via ANN and GA techniques. FL provides a powerful framework for knowledge representation and overcomes uncertainty and vagueness typically found in image analysis. ANN provides learning capabilities, and GA leads to robust learning results. An application prototype currently runs on a regular PC under Windows NT, and preliminary work has been performed to build an embedded version with multiple image processors. The application prototype is being tested at the Kennedy Space Center (KSC), Florida, to visually detect anomalies along slide basket cables utilized by the astronauts to evacuate the NASA Shuttle launch pad in an emergency. The potential applications of this anomaly detection system in an open environment are quite wide. Another current, potentially viable application at NASA is in detecting anomalies of the NASA Space Shuttle Orbiter's radiator panels.

Impact of network aided platforms as educational tools on academic performance and attitude of pharmacology students.

PubMed

Khan, Aftab Ahmed; Siddiqui, Adel Zia; Mohsin, Syed Fareed; Momani, Mohammed Mahmoud Al; Mirza, Eraj Humayun

2017-01-01

This cross-sectional study aimed to examine the impact of learning management system and WhatsApp application as educational tools on students' academic achievement and attitude. The sample population was the students of six medical colleges of Riyadh, Saudi Arabia attending Medical Pharmacology's semester course in Bachelor of Medicine, Bachelor of Surgery (MBBS) program from September 2016 to January 2017. An exploratory approach was adopted based on a comparison between students exposed to only in-class lectures (Group-N), in-class lectures together with WhatsApp platform to disseminate the lecture slides (Group-W) and students group with in-class lectures facility blended with Learning Management System (LMS) and WhatsApp platform (Group-WL). The students' grades were assessed using unified multiple choice questions at the end of the semester. Data were analyzed using descriptive statistics and Pearson correlation (p<0.01). Using learning management system (LMS) and/or WhatsApp messenger tool showed a significant positive correlation in improving students' grades. Additionally, use of WhatsApp enhances students' in-class attendance though statistically insignificant. The results are pivotal for a paradigm shift of in-class lectures and discussion to mobile learning (M-learning). M-learning through WhatsApp may be as an alternative, innovative, and collaborative tool in achieving the required goals in medical education.

Impact of network aided platforms as educational tools on academic performance and attitude of pharmacology students

PubMed Central

Khan, Aftab Ahmed; Siddiqui, Adel Zia; Mohsin, Syed Fareed; Momani, Mohammed Mahmoud Al; Mirza, Eraj Humayun

2017-01-01

Objective: This cross-sectional study aimed to examine the impact of learning management system and WhatsApp application as educational tools on students’ academic achievement and attitude. Methods: The sample population was the students of six medical colleges of Riyadh, Saudi Arabia attending Medical Pharmacology’s semester course in Bachelor of Medicine, Bachelor of Surgery (MBBS) program from September 2016 to January 2017. An exploratory approach was adopted based on a comparison between students exposed to only in-class lectures (Group-N), in-class lectures together with WhatsApp platform to disseminate the lecture slides (Group-W) and students group with in-class lectures facility blended with Learning Management System (LMS) and WhatsApp platform (Group-WL). The students’ grades were assessed using unified multiple choice questions at the end of the semester. Data were analyzed using descriptive statistics and Pearson correlation (p<0.01). Results: Using learning management system (LMS) and/or WhatsApp messenger tool showed a significant positive correlation in improving students’ grades. Additionally, use of WhatsApp enhances students’ in-class attendance though statistically insignificant. Conclusion: The results are pivotal for a paradigm shift of in-class lectures and discussion to mobile learning (M-learning). M-learning through WhatsApp may be as an alternative, innovative, and collaborative tool in achieving the required goals in medical education. PMID:29492081

Active Deep Learning-Based Annotation of Electroencephalography Reports for Cohort Identification

PubMed Central

Maldonado, Ramon; Goodwin, Travis R; Harabagiu, Sanda M

2017-01-01

The annotation of a large corpus of Electroencephalography (EEG) reports is a crucial step in the development of an EEG-specific patient cohort retrieval system. The annotation of multiple types of EEG-specific medical concepts, along with their polarity and modality, is challenging, especially when automatically performed on Big Data. To address this challenge, we present a novel framework which combines the advantages of active and deep learning while producing annotations that capture a variety of attributes of medical concepts. Results obtained through our novel framework show great promise. PMID:28815135

Deep Learning in Label-free Cell Classification

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

Chen, Claire Lifan; Mahjoubfar, Ata; Tai, Li-Chia

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individualmore » cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. In conclusion, this system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.« less

A system for learning statistical motion patterns.

PubMed

Hu, Weiming; Xiao, Xuejuan; Fu, Zhouyu; Xie, Dan; Tan, Tieniu; Maybank, Steve

2006-09-01

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy K-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction.

Deep Learning in Label-free Cell Classification

DOE PAGES

Chen, Claire Lifan; Mahjoubfar, Ata; Tai, Li-Chia; ...

2016-03-15

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individualmore » cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. In conclusion, this system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.« less

Implementation of the Multiple Intelligences Theory in the 21st Century Teaching and Learning Environments: A New Tool for Effective Teaching and Learning in All Levels.

ERIC Educational Resources Information Center

Mbuva, James

This paper focuses on the implementation of the multiple intelligences (MI) theory in 21st century teaching and learning environment, suggesting that it offers a new tool for effective teaching and learning at all levels. The eight current MI include: verbal/linguistic, logical/mathematical, visual/spatial, bodily/kinesthetic, musical/rhythmic,…

The role of intrinsic motivations in attention allocation and shifting

PubMed Central

Di Nocera, Dario; Finzi, Alberto; Rossi, Silvia; Staffa, Mariacarla

2014-01-01

The concepts of attention and intrinsic motivations are of great interest within adaptive robotic systems, and can be exploited in order to guide, activate, and coordinate multiple concurrent behaviors. Attention allocation strategies represent key capabilities of human beings, which are strictly connected with action selection and execution mechanisms, while intrinsic motivations directly affect the allocation of attentional resources. In this paper we propose a model of Reinforcement Learning (RL), where both these capabilities are involved. RL is deployed to learn how to allocate attentional resources in a behavior-based robotic system, while action selection is obtained as a side effect of the resulting motivated attentional behaviors. Moreover, the influence of intrinsic motivations in attention orientation is obtained by introducing rewards associated with curiosity drives. In this way, the learning process is affected not only by goal-specific rewards, but also by intrinsic motivations. PMID:24744746

Using an adapted form of the picture exchange communication system to increase independent requesting in deafblind adults with learning disabilities.

PubMed

Bracken, Maeve; Rohrer, Nicole

2014-02-01

The current study assessed the effectiveness of an adapted form of the Picture Exchange Communication System (PECS) in increasing independent requesting in deafblind adults with learning disabilities. PECS cards were created to accommodate individual needs, including adaptations such as enlarging photographs and using swelled images which consisted of images created on raised line drawing paper. Training included up to Phase III of PECS and procedures ensuring generalizations across individuals and contexts were included. The effects of the intervention were evaluated using a multiple baseline design across participants. Results demonstrated an increase in independent requesting with each of the participants reaching mastery criterion. These results suggest that PECS, in combination with some minor adaptations, may be an effective communicative alternative for individuals who are deafblind and have learning impairments. Copyright © 2013 Elsevier Ltd. All rights reserved.

Do infants retain the statistics of a statistical learning experience? Insights from a developmental cognitive neuroscience perspective

PubMed Central

2017-01-01

Statistical structure abounds in language. Human infants show a striking capacity for using statistical learning (SL) to extract regularities in their linguistic environments, a process thought to bootstrap their knowledge of language. Critically, studies of SL test infants in the minutes immediately following familiarization, but long-term retention unfolds over hours and days, with almost no work investigating retention of SL. This creates a critical gap in the literature given that we know little about how single or multiple SL experiences translate into permanent knowledge. Furthermore, different memory systems with vastly different encoding and retention profiles emerge at different points in development, with the underlying memory system dictating the fidelity of the memory trace hours later. I describe the scant literature on retention of SL, the learning and retention properties of memory systems as they apply to SL, and the development of these memory systems. I propose that different memory systems support retention of SL in infant and adult learners, suggesting an explanation for the slow pace of natural language acquisition in infancy. I discuss the implications of developing memory systems for SL and suggest that we exercise caution in extrapolating from adult to infant properties of SL. This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences’. PMID:27872372

Do infants retain the statistics of a statistical learning experience? Insights from a developmental cognitive neuroscience perspective.

PubMed

Gómez, Rebecca L

2017-01-05

Statistical structure abounds in language. Human infants show a striking capacity for using statistical learning (SL) to extract regularities in their linguistic environments, a process thought to bootstrap their knowledge of language. Critically, studies of SL test infants in the minutes immediately following familiarization, but long-term retention unfolds over hours and days, with almost no work investigating retention of SL. This creates a critical gap in the literature given that we know little about how single or multiple SL experiences translate into permanent knowledge. Furthermore, different memory systems with vastly different encoding and retention profiles emerge at different points in development, with the underlying memory system dictating the fidelity of the memory trace hours later. I describe the scant literature on retention of SL, the learning and retention properties of memory systems as they apply to SL, and the development of these memory systems. I propose that different memory systems support retention of SL in infant and adult learners, suggesting an explanation for the slow pace of natural language acquisition in infancy. I discuss the implications of developing memory systems for SL and suggest that we exercise caution in extrapolating from adult to infant properties of SL.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'. © 2016 The Author(s).

Protein (multi-)location prediction: using location inter-dependencies in a probabilistic framework

PubMed Central

2014-01-01

Motivation Knowing the location of a protein within the cell is important for understanding its function, role in biological processes, and potential use as a drug target. Much progress has been made in developing computational methods that predict single locations for proteins. Most such methods are based on the over-simplifying assumption that proteins localize to a single location. However, it has been shown that proteins localize to multiple locations. While a few recent systems attempt to predict multiple locations of proteins, their performance leaves much room for improvement. Moreover, they typically treat locations as independent and do not attempt to utilize possible inter-dependencies among locations. Our hypothesis is that directly incorporating inter-dependencies among locations into both the classifier-learning and the prediction process can improve location prediction performance. Results We present a new method and a preliminary system we have developed that directly incorporates inter-dependencies among locations into the location-prediction process of multiply-localized proteins. Our method is based on a collection of Bayesian network classifiers, where each classifier is used to predict a single location. Learning the structure of each Bayesian network classifier takes into account inter-dependencies among locations, and the prediction process uses estimates involving multiple locations. We evaluate our system on a dataset of single- and multi-localized proteins (the most comprehensive protein multi-localization dataset currently available, derived from the DBMLoc dataset). Our results, obtained by incorporating inter-dependencies, are significantly higher than those obtained by classifiers that do not use inter-dependencies. The performance of our system on multi-localized proteins is comparable to a top performing system (YLoc+), without being restricted only to location-combinations present in the training set. PMID:24646119

Clinical time series prediction: Toward a hierarchical dynamical system framework.

PubMed

Liu, Zitao; Hauskrecht, Milos

2015-09-01

Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. We tested our framework by first learning the time series model from data for the patients in the training set, and then using it to predict future time series values for the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive performance. Copyright © 2014 Elsevier B.V. All rights reserved.

Protein (multi-)location prediction: using location inter-dependencies in a probabilistic framework.

PubMed

Simha, Ramanuja; Shatkay, Hagit

2014-03-19

Knowing the location of a protein within the cell is important for understanding its function, role in biological processes, and potential use as a drug target. Much progress has been made in developing computational methods that predict single locations for proteins. Most such methods are based on the over-simplifying assumption that proteins localize to a single location. However, it has been shown that proteins localize to multiple locations. While a few recent systems attempt to predict multiple locations of proteins, their performance leaves much room for improvement. Moreover, they typically treat locations as independent and do not attempt to utilize possible inter-dependencies among locations. Our hypothesis is that directly incorporating inter-dependencies among locations into both the classifier-learning and the prediction process can improve location prediction performance. We present a new method and a preliminary system we have developed that directly incorporates inter-dependencies among locations into the location-prediction process of multiply-localized proteins. Our method is based on a collection of Bayesian network classifiers, where each classifier is used to predict a single location. Learning the structure of each Bayesian network classifier takes into account inter-dependencies among locations, and the prediction process uses estimates involving multiple locations. We evaluate our system on a dataset of single- and multi-localized proteins (the most comprehensive protein multi-localization dataset currently available, derived from the DBMLoc dataset). Our results, obtained by incorporating inter-dependencies, are significantly higher than those obtained by classifiers that do not use inter-dependencies. The performance of our system on multi-localized proteins is comparable to a top performing system (YLoc+), without being restricted only to location-combinations present in the training set.

Emotional arousal and multiple memory systems in the mammalian brain

PubMed Central

Packard, Mark G.; Goodman, Jarid

2012-01-01

Emotional arousal induced by stress and/or anxiety can exert complex effects on learning and memory processes in mammals. Recent studies have begun to link study of the influence of emotional arousal on memory with earlier research indicating that memory is organized in multiple systems in the brain that differ in terms of the “type” of memory they mediate. Specifically, these studies have examined whether emotional arousal may have a differential effect on the “cognitive” and stimulus-response “habit” memory processes sub-served by the hippocampus and dorsal striatum, respectively. Evidence indicates that stress or the peripheral injection of anxiogenic drugs can bias animals and humans toward the use of striatal-dependent habit memory in dual-solution tasks in which both hippocampal and striatal-based strategies can provide an adequate solution. A bias toward the use of habit memory can also be produced by intra-basolateral amygdala (BLA) administration of anxiogenic drugs, consistent with the well documented role of efferent projections of this brain region in mediating the modulatory influence of emotional arousal on memory. In some learning situations, the bias toward the use of habit memory produced by emotional arousal appears to result from an impairing effect on hippocampus-dependent cognitive memory. Further research examining the neural mechanisms linking emotion and the relative use of multiple memory systems should prove useful in view of the potential role for maladaptive habitual behaviors in various human psychopathologies. PMID:22470324

"'Knowledge Growth": A Multiple Case Study of English Literature Graduates' Learning Experiences for Teaching Composition

ERIC Educational Resources Information Center

Richards, Kathleen A.

2013-01-01

This multiple case study investigates the learning processes of postsecondary English literature graduates who teach composition to diverse student groups. Since the context of study in English literature graduate programs concentrates on literature and literary theory, the interest of this study examines how teachers learn to teach composition…

The Impact of Taiwan's University Multiple-Channel Entrance Policy on Student Learning Outcomes

ERIC Educational Resources Information Center

Hsiao-Fang, Lin

2012-01-01

This research explores the impact of Taiwan's university multiple-channel entrance policy on student learning outcomes, using quantitative research to look for differences in the learning experiences of third-year students who were admitted via different methods (examination and placement, application for admission, recommendation and selection,…

High School Students' Motivation to Learn Mathematics: The Role of Multiple Goals

ERIC Educational Resources Information Center

Ng, Chi-hung Clarence

2018-01-01

Using a sample of 310 Year 10 Chinese students from Hong Kong, this survey study examined the effects of multiple goals in learning mathematics. Independent variables were mastery, performance-approach, performance-avoidance, and pro-social goals. Dependent variables included perceived classroom goal structures, teacher's support, learning motives…

Distinct Roles of Dopamine and Subthalamic Nucleus in Learning and Probabilistic Decision Making

ERIC Educational Resources Information Center

Coulthard, Elizabeth J.; Bogacz, Rafal; Javed, Shazia; Mooney, Lucy K.; Murphy, Gillian; Keeley, Sophie; Whone, Alan L.

2012-01-01

Even simple behaviour requires us to make decisions based on combining multiple pieces of learned and new information. Making such decisions requires both learning the optimal response to each given stimulus as well as combining probabilistic information from multiple stimuli before selecting a response. Computational theories of decision making…

Improving semi-automated segmentation by integrating learning with active sampling

NASA Astrophysics Data System (ADS)

Huo, Jing; Okada, Kazunori; Brown, Matthew

2012-02-01

Interactive segmentation algorithms such as GrowCut usually require quite a few user interactions to perform well, and have poor repeatability. In this study, we developed a novel technique to boost the performance of the interactive segmentation method GrowCut involving: 1) a novel "focused sampling" approach for supervised learning, as opposed to conventional random sampling; 2) boosting GrowCut using the machine learned results. We applied the proposed technique to the glioblastoma multiforme (GBM) brain tumor segmentation, and evaluated on a dataset of ten cases from a multiple center pharmaceutical drug trial. The results showed that the proposed system has the potential to reduce user interaction while maintaining similar segmentation accuracy.

Cortical and Hippocampal Correlates of Deliberation during Model-Based Decisions for Rewards in Humans

PubMed Central

Bornstein, Aaron M.; Daw, Nathaniel D.

2013-01-01

How do we use our memories of the past to guide decisions we've never had to make before? Although extensive work describes how the brain learns to repeat rewarded actions, decisions can also be influenced by associations between stimuli or events not directly involving reward — such as when planning routes using a cognitive map or chess moves using predicted countermoves — and these sorts of associations are critical when deciding among novel options. This process is known as model-based decision making. While the learning of environmental relations that might support model-based decisions is well studied, and separately this sort of information has been inferred to impact decisions, there is little evidence concerning the full cycle by which such associations are acquired and drive choices. Of particular interest is whether decisions are directly supported by the same mnemonic systems characterized for relational learning more generally, or instead rely on other, specialized representations. Here, building on our previous work, which isolated dual representations underlying sequential predictive learning, we directly demonstrate that one such representation, encoded by the hippocampal memory system and adjacent cortical structures, supports goal-directed decisions. Using interleaved learning and decision tasks, we monitor predictive learning directly and also trace its influence on decisions for reward. We quantitatively compare the learning processes underlying multiple behavioral and fMRI observables using computational model fits. Across both tasks, a quantitatively consistent learning process explains reaction times, choices, and both expectation- and surprise-related neural activity. The same hippocampal and ventral stream regions engaged in anticipating stimuli during learning are also engaged in proportion to the difficulty of decisions. These results support a role for predictive associations learned by the hippocampal memory system to be recalled during choice formation. PMID:24339770

Interactions Among Working Memory, Reinforcement Learning, and Effort in Value-Based Choice: A New Paradigm and Selective Deficits in Schizophrenia.

PubMed

Collins, Anne G E; Albrecht, Matthew A; Waltz, James A; Gold, James M; Frank, Michael J

2017-09-15

When studying learning, researchers directly observe only the participants' choices, which are often assumed to arise from a unitary learning process. However, a number of separable systems, such as working memory (WM) and reinforcement learning (RL), contribute simultaneously to human learning. Identifying each system's contributions is essential for mapping the neural substrates contributing in parallel to behavior; computational modeling can help to design tasks that allow such a separable identification of processes and infer their contributions in individuals. We present a new experimental protocol that separately identifies the contributions of RL and WM to learning, is sensitive to parametric variations in both, and allows us to investigate whether the processes interact. In experiments 1 and 2, we tested this protocol with healthy young adults (n = 29 and n = 52, respectively). In experiment 3, we used it to investigate learning deficits in medicated individuals with schizophrenia (n = 49 patients, n = 32 control subjects). Experiments 1 and 2 established WM and RL contributions to learning, as evidenced by parametric modulations of choice by load and delay and reward history, respectively. They also showed interactions between WM and RL, where RL was enhanced under high WM load. Moreover, we observed a cost of mental effort when controlling for reinforcement history: participants preferred stimuli they encountered under low WM load. Experiment 3 revealed selective deficits in WM contributions and preserved RL value learning in individuals with schizophrenia compared with control subjects. Computational approaches allow us to disentangle contributions of multiple systems to learning and, consequently, to further our understanding of psychiatric diseases. Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Revealing Adaptive Management of Environmental Flows

NASA Astrophysics Data System (ADS)

Allan, Catherine; Watts, Robyn J.

2018-03-01

Managers of land, water, and biodiversity are working with increasingly complex social ecological systems with high uncertainty. Adaptive management (learning from doing) is an ideal approach for working with this complexity. The competing social and environmental demands for water have prompted interest in freshwater adaptive management, but its success and uptake appear to be slow. Some of the perceived "failure" of adaptive management may reflect the way success is conceived and measured; learning, rarely used as an indicator of success, is narrowly defined when it is. In this paper, we document the process of adaptive flow management in the Edward-Wakool system in the southern Murray-Darling Basin, Australia. Data are from interviews with environmental water managers, document review, and the authors' structured reflection on their experiences of adaptive management and environmental flows. Substantial learning occurred in relation to the management of environmental flows in the Edward-Wakool system, with evidence found in planning documents, water-use reports, technical reports, stakeholder committee minutes, and refereed papers, while other evidence was anecdotal. Based on this case, we suggest it may be difficult for external observers to perceive the success of large adaptive management projects because evidence of learning is dispersed across multiple documents, and learning is not necessarily considered a measure of success. We suggest that documentation and sharing of new insights, and of the processes of learning, should be resourced to facilitate social learning within the water management sector, and to help demonstrate the successes of adaptive management.

Revealing Adaptive Management of Environmental Flows.

PubMed

Allan, Catherine; Watts, Robyn J

2018-03-01

Managers of land, water, and biodiversity are working with increasingly complex social ecological systems with high uncertainty. Adaptive management (learning from doing) is an ideal approach for working with this complexity. The competing social and environmental demands for water have prompted interest in freshwater adaptive management, but its success and uptake appear to be slow. Some of the perceived "failure" of adaptive management may reflect the way success is conceived and measured; learning, rarely used as an indicator of success, is narrowly defined when it is. In this paper, we document the process of adaptive flow management in the Edward-Wakool system in the southern Murray-Darling Basin, Australia. Data are from interviews with environmental water managers, document review, and the authors' structured reflection on their experiences of adaptive management and environmental flows. Substantial learning occurred in relation to the management of environmental flows in the Edward-Wakool system, with evidence found in planning documents, water-use reports, technical reports, stakeholder committee minutes, and refereed papers, while other evidence was anecdotal. Based on this case, we suggest it may be difficult for external observers to perceive the success of large adaptive management projects because evidence of learning is dispersed across multiple documents, and learning is not necessarily considered a measure of success. We suggest that documentation and sharing of new insights, and of the processes of learning, should be resourced to facilitate social learning within the water management sector, and to help demonstrate the successes of adaptive management.

The Complexity of Teaching Density in Middle School

ERIC Educational Resources Information Center

Hashweh, Maher Z.

2016-01-01

Background: Density is difficult to learn and teach in middle schools. This study, hypothesizing that the density concept develops as part of a conceptual system, used a conceptual change approach to teaching density. The approach emphasized the use of multiple strategies to teach the density concept and the associated concepts in the conceptual…

Alcoa North American Extrusions Implements Energy Use Assessments at Multiple Facilities: Office of Industrial Technologies (OIT) BestPractices Aluminum Assessment Case Study

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

U.S. Department of Energy

2001-08-05

This case study is the latest in a series on industrial firms who are implementing energy efficient technologies and system improvements into their manufacturing processes. The case studies document the activities, savings, and lessons learned on these projects.

Web-Based System for Adaptable Rubrics: Case Study on CAD Assessment

ERIC Educational Resources Information Center

Company, Pedro; Contero, Manuel; Otey, Jeffrey; Camba, Jorge D.; Agost, María-Jesús; Pérez-López, David

2017-01-01

This paper describes the implementation and testing of our concept of adaptable rubrics, defined as analytical rubrics that arrange assessment criteria at multiple levels that can be expanded on demand. Because of its adaptable nature, these rubrics cannot be implemented in paper formats, neither are they supported by current Learning Management…

Function modeling improves the efficiency of spatial modeling using big data from remote sensing

Treesearch

John Hogland; Nathaniel Anderson

2017-01-01

Spatial modeling is an integral component of most geographic information systems (GISs). However, conventional GIS modeling techniques can require substantial processing time and storage space and have limited statistical and machine learning functionality. To address these limitations, many have parallelized spatial models using multiple coding libraries and have...

Interactive Learning in the Classroom: Is Student Response Method Related to Performance?

ERIC Educational Resources Information Center

Elicker, Joelle D.; McConnell, Nicole L.

2011-01-01

This study examined three methods of responding to in-class multiple-choice concept questions in an Introduction to Psychology course. Specifically, this study compared exam performance and student reactions using three methods of responding to concept questions: (a) a technology-based network system, (b) hand-held flashcards, and (c) hand…

Plants and Human Affairs: Educational Enhancement Via a Computer.

ERIC Educational Resources Information Center

Crovello, Theodore J.; Smith, W. Nelson

To enhance both teaching and learning in an advanced undergraduate elective course on the interrelationships of plants and human affairs, the computer was used for information retrieval, multiple choice course review, and the running of three simulation models--plant related systems (e.g., the rise in world coffee prices after the 1975 freeze in…

Examining Multiple Stages of Protective Behavior of Information System End-Users

ERIC Educational Resources Information Center

Burns, Mary B.

2012-01-01

The adage, "old habits die hard", is especially relevant when humans learn new protective behaviors (i.e., dental flossing, IS security behaviors). The foundation that underlies many social-cognitive theories used in IS research is that intention to change predicts actual behavior change. Despite intentions to change, humans do not…

Neural Priming in Human Frontal Cortex: Multiple Forms of Learning Reduce Demands on the Prefrontal Executive System

ERIC Educational Resources Information Center

Race, Elizabeth A.; Shanker, Shanti; Wagner, Anthony D.

2009-01-01

Past experience is hypothesized to reduce computational demands in PFC by providing bottom-up predictive information that informs subsequent stimulus-action mapping. The present fMRI study measured cortical activity reductions ("neural priming"/"repetition suppression") during repeated stimulus classification to investigate the mechanisms through…

Chapter 7: Lessons, Conclusions, and Implications of the Saber-Tooth Project.

ERIC Educational Resources Information Center

Ward, Phillip; Doutis, Panayiotis; Evans, Sharon A.

1999-01-01

Summarizes findings from the Saber-Tooth Project related to systemic change and student learning, concluding that vision is everything; workplace conditions must be addressed at multiple levels; strong relationships exist among planning, teaching, and assessment; and improvement in reform may occur due to the cessation of business as usual. This…

"Digit Anatomy": A New Technique for Learning Anatomy Using Motor Memory

ERIC Educational Resources Information Center

Oh, Chang-Seok; Won, Hyung-Sun; Kim, Kyong-Jee; Jang, Dong-Su

2011-01-01

Gestural motions of the hands and fingers are powerful tools for expressing meanings and concepts, and the nervous system has the capacity to retain multiple long-term motor memories, especially including movements of the hands. We developed many sets of successive movements of both hands, referred to as "digit anatomy," and made…

Confidence-Based Assessments within an Adult Learning Environment

ERIC Educational Resources Information Center

Novacek, Paul

2013-01-01

Traditional knowledge assessments rely on multiple-choice type questions that only report a right or wrong answer. The reliance within the education system on this technique infers that a student who provides a correct answer purely through guesswork possesses knowledge equivalent to a student who actually knows the correct answer. A more complete…

Laying a Foundation for Artmaking in the 21st Century: A Description and Some Dilemmas

ERIC Educational Resources Information Center

Salazar, Stacey McKenna

2013-01-01

This article describes a study of teaching and learning in the first--or "foundation"--year of art college. As a multiple embedded case study informed by systems theory, the following cases are described: art colleges, foundation programs, professors, and students. The data were collected through surveys, interviews, classroom…

Who (Else) Is the Teacher? Cautionary Notes on Teacher Accountability Systems

ERIC Educational Resources Information Center

Valli, Linda; Croninger, Robert G.; Walters, Kirk

2007-01-01

This article examines a premise underlying teacher accountability policies, namely, that annual student learning gains can be attributed to individual teachers. After analyzing data collected in fourth- and fifth-grade reading and mathematics classes in 18 schools, the authors identify forms of instructional design that rely on multiple teachers.…

Using Trialogues to Measure English Language Skills

ERIC Educational Resources Information Center

So, Youngsoon; Zapata-Rivera, Diego; Cho, Yeonsuk; Luce, Christine; Battistini, Laura

2015-01-01

We explored the use of technology-assisted, trialogue-based tasks to measure the English language proficiency of students learning English as a second or foreign language. A presumed benefit of the system for language assessment is its suitability for use in scenario-based tasks that integrate multiple language skills. This integration allows test…

Learning Effects of Interactive Whiteboard Pedagogy for Students in Taiwan from the Perspective of Multiple Intelligences

ERIC Educational Resources Information Center

Chen, Hong-Ren; Chiang, Chih-Hao; Lin, Wen-Shan

2013-01-01

With the rapid progress in information technology, interactive whiteboards have become IT-integrated in teaching activities. The theory of multiple intelligences argues that every person possesses multiple intelligences, emphasizing learners' cognitive richness and the possible role of these differences in enhanced learning. This study is the…

Learning from Comparing Multiple Examples: On the Dilemma of "Similar" or "Different"

ERIC Educational Resources Information Center

Guo, Jian-Peng; Pang, Ming Fai; Yang, Ling-Yan; Ding, Yi

2012-01-01

Although researchers have demonstrated that studying multiple examples is more effective than studying one example to facilitate learning, the principles found in the literature for designing multiple examples remain ambiguous. This paper reviews variation theory research on example design which sheds light on unclear issues regarding the effects…

Assessment of Matrix Multiplication Learning with a Rule-Based Analytical Model--"A Bayesian Network Representation"

ERIC Educational Resources Information Center

Zhang, Zhidong

2016-01-01

This study explored an alternative assessment procedure to examine learning trajectories of matrix multiplication. It took rule-based analytical and cognitive task analysis methods specifically to break down operation rules for a given matrix multiplication. Based on the analysis results, a hierarchical Bayesian network, an assessment model,…

Development of Multiple Thinking and Creativity in Organizational Learning

ERIC Educational Resources Information Center

Cheng, Yin Cheong

2005-01-01

Purpose: Based on a typology of contextualized multiple thinking, this paper aims to elaborate how the levels of thinking (data, information, knowledge, and intelligence), and the types of thinking as a whole, can be used to profile the characteristics of multiple thinking in organizational learning, re-conceptualize the nature of creativity in…

A Mobile Service Oriented Multiple Object Tracking Augmented Reality Architecture for Education and Learning Experiences

ERIC Educational Resources Information Center

Rattanarungrot, Sasithorn; White, Martin; Newbury, Paul

2014-01-01

This paper describes the design of our service-oriented architecture to support mobile multiple object tracking augmented reality applications applied to education and learning scenarios. The architecture is composed of a mobile multiple object tracking augmented reality client, a web service framework, and dynamic content providers. Tracking of…

Knowledge production and learning for sustainable landscapes: seven steps using social-ecological systems as laboratories.

PubMed

Angelstam, Per; Elbakidze, Marine; Axelsson, Robert; Dixelius, Malcolm; Törnblom, Johan

2013-03-01

There are multiple challenges regarding use and governance of landscapes' goods, functions and intangible values for ecosystem health and human well-being. One group of challenges is to measure and assess principal sustainability dimensions through performance targets, so stakeholders have transparent information about states and trends. Another group is to develop adaptive governance at multiple levels, and management of larger geographical areas across scales. Addressing these challenges, we present a framework for transdisciplinary research using multiple landscapes as place-based case studies that integrates multiple research disciplines and non-academic actors: (1) identify a suite of landscapes, and for each (2) review landscape history, (3) map stakeholders, use and non-use values, products and land use, (4) analyze institutions, policies and the system of governance, (5) measure ecological, economic, social and cultural sustainability, (6) assess sustainability dimensions and governance, and finally (7) make comparisons and synthesize. Collaboration, communication and dissemination are additional core features. We discuss barriers bridges and bridges for applying this approach.

Learning from peer feedback on student-generated multiple choice questions: Views of introductory physics students

NASA Astrophysics Data System (ADS)

Kay, Alison E.; Hardy, Judy; Galloway, Ross K.

2018-06-01

PeerWise is an online application where students are encouraged to generate a bank of multiple choice questions for their classmates to answer. After answering a question, students can provide feedback to the question author about the quality of the question and the question author can respond to this. Student use of, and attitudes to, this online community within PeerWise was investigated in two large first year undergraduate physics courses, across three academic years, to explore how students interact with the system and the extent to which they believe PeerWise to be useful to their learning. Most students recognized that there is value in engaging with PeerWise, and many students engaged deeply with the system, thinking critically about the quality of their submissions and reflecting on feedback provided to them. Students also valued the breadth of topics and level of difficulty offered by the questions, recognized the revision benefits afforded by the resource, and were often willing to contribute to the community by providing additional explanations and engaging in discussion.

The effect of multiple external representations (MERs) worksheets toward complex system reasoning achievement

NASA Astrophysics Data System (ADS)

Sumarno; Ibrahim, M.; Supardi, Z. A. I.

2018-03-01

The application of a systems approach to assessing biological systems provides hope for a coherent understanding of cell dynamics patterns and their relationship to plant life. This action required the reasoning about complex systems. In other sides, there were a lot of researchers who provided the proof about the instructional successions. They involved the multiple external representations which improved the biological learning. The researcher conducted an investigation using one shoot case study design which involved 30 students in proving that the MERs worksheets could affect the student's achievement of reasoning about complex system. The data had been collected based on test of reasoning about complex system and student's identification result who worked through MERs. The result showed that only partially students could achieve reasoning about system complex, but their MERs skill could support their reasoning ability of complex system. This study could bring a new hope to develop the MERs worksheet as a tool to facilitate the reasoning about complex system.

A Policy Representation Using Weighted Multiple Normal Distribution

NASA Astrophysics Data System (ADS)

Kimura, Hajime; Aramaki, Takeshi; Kobayashi, Shigenobu

In this paper, we challenge to solve a reinforcement learning problem for a 5-linked ring robot within a real-time so that the real-robot can stand up to the trial and error. On this robot, incomplete perception problems are caused from noisy sensors and cheap position-control motor systems. This incomplete perception also causes varying optimum actions with the progress of the learning. To cope with this problem, we adopt an actor-critic method, and we propose a new hierarchical policy representation scheme, that consists of discrete action selection on the top level and continuous action selection on the low level of the hierarchy. The proposed hierarchical scheme accelerates learning on continuous action space, and it can pursue the optimum actions varying with the progress of learning on our robotics problem. This paper compares and discusses several learning algorithms through simulations, and demonstrates the proposed method showing application for the real robot.

Multiple Kernel Learning for Heterogeneous Anomaly Detection: Algorithm and Aviation Safety Case Study

NASA Technical Reports Server (NTRS)

Das, Santanu; Srivastava, Ashok N.; Matthews, Bryan L.; Oza, Nikunj C.

2010-01-01

The world-wide aviation system is one of the most complex dynamical systems ever developed and is generating data at an extremely rapid rate. Most modern commercial aircraft record several hundred flight parameters including information from the guidance, navigation, and control systems, the avionics and propulsion systems, and the pilot inputs into the aircraft. These parameters may be continuous measurements or binary or categorical measurements recorded in one second intervals for the duration of the flight. Currently, most approaches to aviation safety are reactive, meaning that they are designed to react to an aviation safety incident or accident. In this paper, we discuss a novel approach based on the theory of multiple kernel learning to detect potential safety anomalies in very large data bases of discrete and continuous data from world-wide operations of commercial fleets. We pose a general anomaly detection problem which includes both discrete and continuous data streams, where we assume that the discrete streams have a causal influence on the continuous streams. We also assume that atypical sequence of events in the discrete streams can lead to off-nominal system performance. We discuss the application domain, novel algorithms, and also discuss results on real-world data sets. Our algorithm uncovers operationally significant events in high dimensional data streams in the aviation industry which are not detectable using state of the art methods

Mass detection in digital breast tomosynthesis data using convolutional neural networks and multiple instance learning.

PubMed

Yousefi, Mina; Krzyżak, Adam; Suen, Ching Y

2018-05-01

Digital breast tomosynthesis (DBT) was developed in the field of breast cancer screening as a new tomographic technique to minimize the limitations of conventional digital mammography breast screening methods. A computer-aided detection (CAD) framework for mass detection in DBT has been developed and is described in this paper. The proposed framework operates on a set of two-dimensional (2D) slices. With plane-to-plane analysis on corresponding 2D slices from each DBT, it automatically learns complex patterns of 2D slices through a deep convolutional neural network (DCNN). It then applies multiple instance learning (MIL) with a randomized trees approach to classify DBT images based on extracted information from 2D slices. This CAD framework was developed and evaluated using 5040 2D image slices derived from 87 DBT volumes. The empirical results demonstrate that this proposed CAD framework achieves much better performance than CAD systems that use hand-crafted features and deep cardinality-restricted Bolzmann machines to detect masses in DBTs. Copyright © 2018 Elsevier Ltd. All rights reserved.

Emerging perspectives on transforming the healthcare system: redesign strategies and a call for needed research.

PubMed

Doebbeling, Bradley N; Flanagan, Mindy E

2011-12-01

U.S. healthcare requires major redesign of its delivery systems, finances, and incentives. Healthcare operations, leadership, and payors are increasingly recognizing the need for community-business-research partnerships to transform healthcare. New models of continuous learning, research, and development should help focus and sustain redesign efforts. This study summarizes suggested strategies for transformational change in healthcare and identifies needed areas for research to inform, spread, and sustain transformational change. We developed these recommendations based on a series of review papers, invited expert discussion, and a subsequent review in the context of a health system transformation research conference (The Regenstrief Biennial Research Conference). The multidisciplinary audience included health systems researchers, clinicians, informaticians, social and engineering scientists, and operational and business leaders. Conference participants and literature reviews identified key strategies for system redesign with the following themes: using the framework of complex adaptive systems; fostering organizational redesign; developing appropriate performance measures and incentives; creating continuous learning organizations; and integrating health information, technology, and communication into practice. Sustained investment in research and development in these areas is crucial. Multiple issues influence the likelihood that healthcare leaders will make transformational changes in their healthcare systems. Healthcare leaders, clinicians, researchers, journals, and academic institutions, in partnership with payors, government and multiple other stakeholders, should apply the recommendations relevant to their own setting to redesign healthcare delivery, improve cognitive support, and sustain transformation. Fostering further research investments in these areas will increase the impact of transformation on the health and healthcare of the public.

Ensemble positive unlabeled learning for disease gene identification.

PubMed

Yang, Peng; Li, Xiaoli; Chua, Hon-Nian; Kwoh, Chee-Keong; Ng, See-Kiong

2014-01-01

An increasing number of genes have been experimentally confirmed in recent years as causative genes to various human diseases. The newly available knowledge can be exploited by machine learning methods to discover additional unknown genes that are likely to be associated with diseases. In particular, positive unlabeled learning (PU learning) methods, which require only a positive training set P (confirmed disease genes) and an unlabeled set U (the unknown candidate genes) instead of a negative training set N, have been shown to be effective in uncovering new disease genes in the current scenario. Using only a single source of data for prediction can be susceptible to bias due to incompleteness and noise in the genomic data and a single machine learning predictor prone to bias caused by inherent limitations of individual methods. In this paper, we propose an effective PU learning framework that integrates multiple biological data sources and an ensemble of powerful machine learning classifiers for disease gene identification. Our proposed method integrates data from multiple biological sources for training PU learning classifiers. A novel ensemble-based PU learning method EPU is then used to integrate multiple PU learning classifiers to achieve accurate and robust disease gene predictions. Our evaluation experiments across six disease groups showed that EPU achieved significantly better results compared with various state-of-the-art prediction methods as well as ensemble learning classifiers. Through integrating multiple biological data sources for training and the outputs of an ensemble of PU learning classifiers for prediction, we are able to minimize the potential bias and errors in individual data sources and machine learning algorithms to achieve more accurate and robust disease gene predictions. In the future, our EPU method provides an effective framework to integrate the additional biological and computational resources for better disease gene predictions.

Estrogens and Cognition: Friends or Foes?

PubMed Central

Korol, Donna L.; Pisani, Samantha L.

2015-01-01

Estrogens are becoming well known for their robust enhancement on cognition particularly for learning and memory that relies upon functioning of the hippocampus and related neural systems. What is also emerging is that estrogen modulation of cognition is not uniform, at times enhancing yet at other times impairing learning. This review explores the bidirectional effects of estrogens on learning from a multiple memory systems view, focusing on the hippocampus and striatum, whereby modulation by estrogens sorts according to task attributes and neural systems engaged during cognition. We highlight our findings that show the ability to solve hippocampus-sensitive tasks typically improves under relatively high estrogen status while the ability to solve striatum-sensitive tasks degrades with estrogen exposures. Though constrained by dose and timing of exposure, these opposing enhancements and impairments of cognition can be observed following treatments with different estrogenic compounds including the hormone estradiol, the isoflavone genistein found in soybeans, and agonists that are selective for specific estrogen receptors, suggesting that activation of a single receptor type is sufficient to produce the observed shifts in learning strategies. Using this multi-dimensional framework will allow us to extend our thinking of the relationship between estrogens and cognition to other brain regions and cognitive functions. PMID:26149525

Functional imaging of the semantic system: retrieval of sensory-experienced and verbally learned knowledge.

PubMed

Noppeney, Uta; Price, Cathy J

2003-01-01

This paper considers how functional neuro-imaging can be used to investigate the organization of the semantic system and the limitations associated with this technique. The majority of the functional imaging studies of the semantic system have looked for divisions by varying stimulus category. These studies have led to divergent results and no clear anatomical hypotheses have emerged to account for the dissociations seen in behavioral studies. Only a few functional imaging studies have used task as a variable to differentiate the neural correlates of semantic features more directly. We extend these findings by presenting a new study that contrasts tasks that differentially weight sensory (color and taste) and verbally learned (origin) semantic features. Irrespective of the type of semantic feature retrieved, a common semantic system was activated as demonstrated in many previous studies. In addition, the retrieval of verbally learned, but not sensory-experienced, features enhanced activation in medial and lateral posterior parietal areas. We attribute these "verbally learned" effects to differences in retrieval strategy and conclude that evidence for segregation of semantic features at an anatomical level remains weak. We believe that functional imaging has the potential to increase our understanding of the neuronal infrastructure that sustains semantic processing but progress may require multiple experiments until a consistent explanatory framework emerges.

Combined Diffusion Tensor Imaging and Apparent Transverse Relaxation Rate Differentiate Parkinson Disease and Atypical Parkinsonism.

PubMed

Du, G; Lewis, M M; Kanekar, S; Sterling, N W; He, L; Kong, L; Li, R; Huang, X

2017-05-01

Both diffusion tensor imaging and the apparent transverse relaxation rate have shown promise in differentiating Parkinson disease from atypical parkinsonism (particularly multiple system atrophy and progressive supranuclear palsy). The objective of the study was to assess the ability of DTI, the apparent transverse relaxation rate, and their combination for differentiating Parkinson disease, multiple system atrophy, progressive supranuclear palsy, and controls. A total of 106 subjects (36 controls, 35 patients with Parkinson disease, 16 with multiple system atrophy, and 19 with progressive supranuclear palsy) were included. DTI and the apparent transverse relaxation rate measures from the striatal, midbrain, limbic, and cerebellar regions were obtained and compared among groups. The discrimination performance of DTI and the apparent transverse relaxation rate among groups was assessed by using Elastic-Net machine learning and receiver operating characteristic curve analysis. Compared with controls, patients with Parkinson disease showed significant apparent transverse relaxation rate differences in the red nucleus. Compared to those with Parkinson disease, patients with both multiple system atrophy and progressive supranuclear palsy showed more widespread changes, extending from the midbrain to striatal and cerebellar structures. The pattern of changes, however, was different between the 2 groups. For instance, patients with multiple system atrophy showed decreased fractional anisotropy and an increased apparent transverse relaxation rate in the subthalamic nucleus, whereas patients with progressive supranuclear palsy showed an increased mean diffusivity in the hippocampus. Combined, DTI and the apparent transverse relaxation rate were significantly better than DTI or the apparent transverse relaxation rate alone in separating controls from those with Parkinson disease/multiple system atrophy/progressive supranuclear palsy; controls from those with Parkinson disease; those with Parkinson disease from those with multiple system atrophy/progressive supranuclear palsy; and those with Parkinson disease from those with multiple system atrophy; but not those with Parkinson disease from those with progressive supranuclear palsy, or those with multiple system atrophy from those with progressive supranuclear palsy. DTI and the apparent transverse relaxation rate provide different but complementary information for different parkinsonisms. Combined DTI and apparent transverse relaxation rate may be a superior marker for the differential diagnosis of parkinsonisms. © 2017 by American Journal of Neuroradiology.

Tracking Multiple Statistics: Simultaneous Learning of Object Names and Categories in English and Mandarin Speakers

ERIC Educational Resources Information Center

Chen, Chi-hsin; Gershkoff-Stowe, Lisa; Wu, Chih-Yi; Cheung, Hintat; Yu, Chen

2017-01-01

Two experiments were conducted to examine adult learners' ability to extract multiple statistics in simultaneously presented visual and auditory input. Experiment 1 used a cross-situational learning paradigm to test whether English speakers were able to use co-occurrences to learn word-to-object mappings and concurrently form object categories…

Allowing Learners to Choose: Self-Controlled Practice Schedules for Learning Multiple Movement Patterns

ERIC Educational Resources Information Center

Wu, Will F. W.; Magill, Richard A.

2011-01-01

For this study, we investigated the effects of self-controlled practice on learning multiple motor skills. Thirty participants were randomly assigned to self-control or yoked conditions. Participants learned a three-keystroke pattern with three different relative time structures. Those in the self-control group chose one of three relative time…

Constructing and Modeling Algebraic Statements in the Multiplicative Domain: Investigating Fourth-Grade Student and Teacher Learning

ERIC Educational Resources Information Center

Grandau, Laura

2013-01-01

This study of fourth-grade students and teachers explores mathematics teaching and learning that focuses on discovering and modeling algebraic relationships. The study has two parts: an investigation of how students learn to construct algebraic statements and models for comparisons and measurement situations in the multiplicative domain, and an…

Learning with Multiple Representations: Infographics as Cognitive Tools for Authentic Learning in Science Literacy

ERIC Educational Resources Information Center

Gebre, Engida

2018-01-01

This paper presents a descriptive case study where infographics--visual representation of data and ideas--have been used as cognitive tools to facilitate learning with multiple representations in the context of secondary school students' science news reporting. Despite the complementary nature of the two research foci, studies on cognitive tools…

A Participatory Learning Approach to Biochemistry Using Student Authored and Evaluated Multiple-Choice Questions

ERIC Educational Resources Information Center

Bottomley, Steven; Denny, Paul

2011-01-01

A participatory learning approach, combined with both a traditional and a competitive assessment, was used to motivate students and promote a deep approach to learning biochemistry. Students were challenged to research, author, and explain their own multiple-choice questions (MCQs). They were also required to answer, evaluate, and discuss MCQs…

Reading Multiple Texts about Climate Change: The Relationship between Memory for Sources and Text Comprehension

ERIC Educational Resources Information Center

Stromso, Helge I.; Braten, Ivar; Britt, M. Anne

2010-01-01

In many situations, readers are asked to learn from multiple documents. Many studies have found that evaluating the trustworthiness and usefulness of document sources is an important skill in such learning situations. There has been, however, no direct evidence that attending to source information helps readers learn from and interpret a…

"Trying, Failing, Succeeding, and Trying Again and Again": Perspectives of Teachers of Pupils with Severe Profound Multiple Learning Difficulties

ERIC Educational Resources Information Center

Jones, Phyllis; Riley, Michael W.

2017-01-01

This article explores the perspectives of seven teachers in England who teach pupils with severe profound and multiple learning difficulties about their learning to teach this group of students. Teachers' views were captured through a combination of synchronous and asynchronous online communications. Four themes emerged from teachers' perspectives…

Learning Compositional Shape Models of Multiple Distance Metrics by Information Projection.

PubMed

Luo, Ping; Lin, Liang; Liu, Xiaobai

2016-07-01

This paper presents a novel compositional contour-based shape model by incorporating multiple distance metrics to account for varying shape distortions or deformations. Our approach contains two key steps: 1) contour feature generation and 2) generative model pursuit. For each category, we first densely sample an ensemble of local prototype contour segments from a few positive shape examples and describe each segment using three different types of distance metrics. These metrics are diverse and complementary with each other to capture various shape deformations. We regard the parameterized contour segment plus an additive residual ϵ as a basic subspace, namely, ϵ -ball, in the sense that it represents local shape variance under the certain distance metric. Using these ϵ -balls as features, we then propose a generative learning algorithm to pursue the compositional shape model, which greedily selects the most representative features under the information projection principle. In experiments, we evaluate our model on several public challenging data sets, and demonstrate that the integration of multiple shape distance metrics is capable of dealing various shape deformations, articulations, and background clutter, hence boosting system performance.

Core principles of assessment in competency-based medical education.

PubMed

Lockyer, Jocelyn; Carraccio, Carol; Chan, Ming-Ka; Hart, Danielle; Smee, Sydney; Touchie, Claire; Holmboe, Eric S; Frank, Jason R

2017-06-01

The meaningful assessment of competence is critical for the implementation of effective competency-based medical education (CBME). Timely ongoing assessments are needed along with comprehensive periodic reviews to ensure that trainees continue to progress. New approaches are needed to optimize the use of multiple assessors and assessments; to synthesize the data collected from multiple assessors and multiple types of assessments; to develop faculty competence in assessment; and to ensure that relationships between the givers and receivers of feedback are appropriate. This paper describes the core principles of assessment for learning and assessment of learning. It addresses several ways to ensure the effectiveness of assessment programs, including using the right combination of assessment methods and conducting careful assessor selection and training. It provides a reconceptualization of the role of psychometrics and articulates the importance of a group process in determining trainees' progress. In addition, it notes that, to reach its potential as a driver in trainee development, quality care, and patient safety, CBME requires effective information management and documentation as well as ongoing consideration of ways to improve the assessment system.

The chemotherapeutic agent paclitaxel selectively impairs reversal learning while sparing prior learning, new learning and episodic memory.

PubMed

Panoz-Brown, Danielle; Carey, Lawrence M; Smith, Alexandra E; Gentry, Meredith; Sluka, Christina M; Corbin, Hannah E; Wu, Jie-En; Hohmann, Andrea G; Crystal, Jonathon D

2017-10-01

Chemotherapy is widely used to treat patients with systemic cancer. The efficacy of cancer therapies is frequently undermined by adverse side effects that have a negative impact on the quality of life of cancer survivors. Cancer patients who receive chemotherapy often experience chemotherapy-induced cognitive impairment across a variety of domains including memory, learning, and attention. In the current study, the impact of paclitaxel, a taxane derived chemotherapeutic agent, on episodic memory, prior learning, new learning, and reversal learning were evaluated in rats. Neurogenesis was quantified post-treatment in the dentate gyrus of the same rats using immunostaining for 5-Bromo-2'-deoxyuridine (BrdU) and Ki67. Paclitaxel treatment selectively impaired reversal learning while sparing episodic memory, prior learning, and new learning. Furthermore, paclitaxel-treated rats showed decreases in markers of hippocampal cell proliferation, as measured by markers of cell proliferation assessed using immunostaining for Ki67 and BrdU. This work highlights the importance of using multiple measures of learning and memory to identify the pattern of impaired and spared aspects of chemotherapy-induced cognitive impairment. Copyright © 2017 Elsevier Inc. All rights reserved.

On the development of an expert system for wheelchair selection

NASA Technical Reports Server (NTRS)

Madey, Gregory R.; Bhansin, Charlotte A.; Alaraini, Sulaiman A.; Nour, Mohamed A.

1994-01-01

The presentation of wheelchairs for the Multiple Sclerosis (MS) patients involves the examination of a number of complicated factors including ambulation status, length of diagnosis, and funding sources, to name a few. Consequently, only a few experts exist in this area. To aid medical therapists with the wheelchair selection decision, a prototype medical expert system (ES) was developed. This paper describes and discusses the steps of designing and developing the system, the experiences of the authors, and the lessons learned from working on this project. Wheelchair Advisor, programmed in CLIPS, serves as diagnosis, classification, prescription, and training tool in the MS field. Interviews, insurance letters, forms, and prototyping were used to gain knowledge regarding the wheelchair selection problem. Among the lessons learned are that evolutionary prototyping is superior to the conventional system development life-cycle (SDLC), the wheelchair selection is a good candidate for ES applications, and that ES can be applied to other similar medical subdomains.

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

Beaver, Justin M; Borges, Raymond Charles; Buckner, Mark A

Critical infrastructure Supervisory Control and Data Acquisition (SCADA) systems were designed to operate on closed, proprietary networks where a malicious insider posed the greatest threat potential. The centralization of control and the movement towards open systems and standards has improved the efficiency of industrial control, but has also exposed legacy SCADA systems to security threats that they were not designed to mitigate. This work explores the viability of machine learning methods in detecting the new threat scenarios of command and data injection. Similar to network intrusion detection systems in the cyber security domain, the command and control communications in amore » critical infrastructure setting are monitored, and vetted against examples of benign and malicious command traffic, in order to identify potential attack events. Multiple learning methods are evaluated using a dataset of Remote Terminal Unit communications, which included both normal operations and instances of command and data injection attack scenarios.« less

Web-based curriculum improves residents' knowledge of health care business.

PubMed

Hauge, Linnea S; Frischknecht, Adam C; Gauger, Paul G; Hirshfield, Laura E; Harkins, Deborah; Butz, David A; Taheri, Paul A

2010-12-01

Curricular options for teaching and evaluating surgery residents' outcomes in systems-based practice are limited. A Web-based curriculum, MDContent, developed collaboratively by experts in business and surgery, provides learning experiences in the business of health care. The purpose of this study is to describe surgery residents' experience and learning outcomes associated with the curriculum. Twenty-eight PGY3 to 6 general and plastic surgery residents were enrolled in the Web-based curriculum. Twenty-two residents (79%) completed the pretest, 11 modules, the post-test, and the course evaluation by the end of 1 year. The pretest and the post-test were 30-item multiple-choice exams based on a blueprint of the curricular objectives. Descriptive statistics were calculated on course evaluation and module completion data. Paired t-tests were used to compare pre- and post-test performance. Content analysis was performed on course evaluation written responses. Residents' performance on the multiple choice exam improved significantly (p = 0.0001) from the pre-test (mean 59%, SD 12.1) to the post-test (mean 78%, SD 9.4), with an average gain of 19 percentage points. Participants rated their Web-based learning experience as very positive, with a majority of residents agreeing that the content was well organized, relevant, and an excellent learning experience around content not taught elsewhere in medical school or residency. Participation in a Web-based curriculum on health care business improves surgery residents' knowledge about health care business concepts and principles. Residents with varying levels of interest in health care business provide positive ratings about their learning experience and indications that lessons learned would be applied in their clinical practice. MDContent is a feasible and effective method for teaching and assessing systems-based practice concepts. Copyright © 2010 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

Learning the organization: a model for health system analysis for new nurse administrators.

PubMed

Clark, Mary Jo

2004-01-01

Health systems are large and complex organizations in which multiple components and processes influence system outcomes. In order to effectively position themselves in such organizations, nurse administrators new to a system must gain a rapid understanding of overall system operation. Such understanding is facilitated by use of a model for system analysis. The model presented here examines the dynamic interrelationships between and among internal and external elements as they affect system performance. External elements to be analyzed include environmental factors and characteristics of system clientele. Internal elements flow from the mission and goals of the system and include system culture, services, resources, and outcomes.

RSTensorFlow: GPU Enabled TensorFlow for Deep Learning on Commodity Android Devices

PubMed Central

Alzantot, Moustafa; Wang, Yingnan; Ren, Zhengshuang; Srivastava, Mani B.

2018-01-01

Mobile devices have become an essential part of our daily lives. By virtue of both their increasing computing power and the recent progress made in AI, mobile devices evolved to act as intelligent assistants in many tasks rather than a mere way of making phone calls. However, popular and commonly used tools and frameworks for machine intelligence are still lacking the ability to make proper use of the available heterogeneous computing resources on mobile devices. In this paper, we study the benefits of utilizing the heterogeneous (CPU and GPU) computing resources available on commodity android devices while running deep learning models. We leveraged the heterogeneous computing framework RenderScript to accelerate the execution of deep learning models on commodity Android devices. Our system is implemented as an extension to the popular open-source framework TensorFlow. By integrating our acceleration framework tightly into TensorFlow, machine learning engineers can now easily make benefit of the heterogeneous computing resources on mobile devices without the need of any extra tools. We evaluate our system on different android phones models to study the trade-offs of running different neural network operations on the GPU. We also compare the performance of running different models architectures such as convolutional and recurrent neural networks on CPU only vs using heterogeneous computing resources. Our result shows that although GPUs on the phones are capable of offering substantial performance gain in matrix multiplication on mobile devices. Therefore, models that involve multiplication of large matrices can run much faster (approx. 3 times faster in our experiments) due to GPU support. PMID:29629431

RSTensorFlow: GPU Enabled TensorFlow for Deep Learning on Commodity Android Devices.

PubMed

Alzantot, Moustafa; Wang, Yingnan; Ren, Zhengshuang; Srivastava, Mani B

2017-06-01

Mobile devices have become an essential part of our daily lives. By virtue of both their increasing computing power and the recent progress made in AI, mobile devices evolved to act as intelligent assistants in many tasks rather than a mere way of making phone calls. However, popular and commonly used tools and frameworks for machine intelligence are still lacking the ability to make proper use of the available heterogeneous computing resources on mobile devices. In this paper, we study the benefits of utilizing the heterogeneous (CPU and GPU) computing resources available on commodity android devices while running deep learning models. We leveraged the heterogeneous computing framework RenderScript to accelerate the execution of deep learning models on commodity Android devices. Our system is implemented as an extension to the popular open-source framework TensorFlow. By integrating our acceleration framework tightly into TensorFlow, machine learning engineers can now easily make benefit of the heterogeneous computing resources on mobile devices without the need of any extra tools. We evaluate our system on different android phones models to study the trade-offs of running different neural network operations on the GPU. We also compare the performance of running different models architectures such as convolutional and recurrent neural networks on CPU only vs using heterogeneous computing resources. Our result shows that although GPUs on the phones are capable of offering substantial performance gain in matrix multiplication on mobile devices. Therefore, models that involve multiplication of large matrices can run much faster (approx. 3 times faster in our experiments) due to GPU support.

Energizing the nursing lecture: Application of the Theory of Multiple Intelligence Learning.

PubMed

Amerson, Roxanne

2006-01-01

Nurse educators struggle to find ways to create learning opportunities that are interactive and appeal to the needs of various students. The key to energizing the nursing lecture is to create an environment that encourages students to be active participants. It is essential to use creativity to design cognitive strategies that appeal to students' learning preferences. This article discusses the methods one educator has used to implement the Theory of Multiple Intelligence Learning in the classroom. Specific cognitive strategies that address the learning preferences of each intelligence are discussed.

An Examination of Multiple Intelligence Domains and Learning Styles of Pre-Service Mathematics Teachers: Their Reflections on Mathematics Education

ERIC Educational Resources Information Center

Ozgen, Kemal; Tataroglu, Berna; Alkan, Huseyin

2011-01-01

The present study aims to identify pre-service mathematics teachers' multiple intelligence domains and learning style profiles, and to establish relationships between them. Employing the survey model, the study was conducted with the participation of 243 pre-service mathematics teachers. The study used the "multiple intelligence domains…

DL-sQUAL: A Multiple-Item Scale for Measuring Service Quality of Online Distance Learning Programs

ERIC Educational Resources Information Center

Shaik, Naj; Lowe, Sue; Pinegar, Kem

2006-01-01

Education is a service with multiplicity of student interactions over time and across multiple touch points. Quality teaching needs to be supplemented by consistent quality supporting services for programs to succeed under the competitive distance learning landscape. ServQual and e-SQ scales have been proposed for measuring quality of traditional…

Effects of Multiple Simulation Presentation among Students of Different Anxiety Levels in the Learning of Probability

ERIC Educational Resources Information Center

Fong, Soon Fook; Por, Fei Ping; Tang, Ai Ling

2012-01-01

The purpose of this study was to investigate the effects of multiple simulation presentation in interactive multimedia are on the achievement of students with different levels of anxiety in the learning of Probability. The interactive multimedia courseware was developed in two different modes, which were Multiple Simulation Presentation (MSP) and…

An Empirical Examination of the Association between Multiple Intelligences and Language Learning Self-Efficacy among TEFL University Students

ERIC Educational Resources Information Center

Moafian, Fatemeh; Ebrahimi, Mohammad Reza

2015-01-01

The current study investigated the association between multiple intelligences and language learning efficacy expectations among TEFL (Teaching English as a Foreign Language) university students. To fulfill the aim of the study, 108 junior and senior TEFL students were asked to complete the "Multiple Intelligence Developmental Assessment…

Comparative study on legislation of utilization of construction wastes as resources in china and abroad

NASA Astrophysics Data System (ADS)

Wenfeng, Liu; Zhaomeng, Wang; Hongmei, Hou

2018-05-01

The dilemma of the “Building wastes Besieged City” has gradually become a national problem. Historical experience in the world shows that establishing a systematic and complete legal system is an effective way and powerful weapon to ensure the comprehensive utilization of building wastes resources. Based on the domestic conditions, the state focuses on the problems and learns from the legislation experience of Chinese and foreign construction wastes recycling laws and regulations, to design the legal system form multiple fields, multiple angles, and multiple levels as much as possible to achieve maximum environmental, social, and economic benefits. This article mainly summarizes the characteristics and outstanding experience of the legislation of the comprehensive utilization of construction wastes as resources in foreign countries, as well as the existing problems of Chinese relevant legal regulations, and provides reference for future research and implementation of relevant legislation.

Neurocognitive accounts of developmental dyscalculia and its remediation.

PubMed

Iuculano, T

2016-01-01

Numbers are one of the most pervasive stimulus categories in our environment and an integral foundation of modern society. Yet, up to 20% of individuals fail to understand, represent, and manipulate numbers and form the basis of arithmetic, a condition termed developmental dyscalculia (DD). Multiple cognitive and neural systems including those that serve numerical, mnemonic, visuospatial, and cognitive control functions have independently been implicated in the etiology of DD, yet most studies have not taken a comprehensive or dynamic view of the disorder. This chapter supports the view of DD as a multifaceted neurodevelopmental disorder that is the result of multiple aberrancies at one or multiple levels of the information processing hierarchy, which supports successful arithmetic learning, and suggests that interventions should target all these systems to achieve successful outcomes, at the behavioral and neural levels. © 2016 Elsevier B.V. All rights reserved.

Effective learning strategies for real-time image-guided adaptive control of multiple-source hyperthermia applicators.

PubMed

Cheng, Kung-Shan; Dewhirst, Mark W; Stauffer, Paul R; Das, Shiva

2010-03-01

This paper investigates overall theoretical requirements for reducing the times required for the iterative learning of a real-time image-guided adaptive control routine for multiple-source heat applicators, as used in hyperthermia and thermal ablative therapy for cancer. Methods for partial reconstruction of the physical system with and without model reduction to find solutions within a clinically practical timeframe were analyzed. A mathematical analysis based on the Fredholm alternative theorem (FAT) was used to compactly analyze the existence and uniqueness of the optimal heating vector under two fundamental situations: (1) noiseless partial reconstruction and (2) noisy partial reconstruction. These results were coupled with a method for further acceleration of the solution using virtual source (VS) model reduction. The matrix approximation theorem (MAT) was used to choose the optimal vectors spanning the reduced-order subspace to reduce the time for system reconstruction and to determine the associated approximation error. Numerical simulations of the adaptive control of hyperthermia using VS were also performed to test the predictions derived from the theoretical analysis. A thigh sarcoma patient model surrounded by a ten-antenna phased-array applicator was retained for this purpose. The impacts of the convective cooling from blood flow and the presence of sudden increase of perfusion in muscle and tumor were also simulated. By FAT, partial system reconstruction directly conducted in the full space of the physical variables such as phases and magnitudes of the heat sources cannot guarantee reconstructing the optimal system to determine the global optimal setting of the heat sources. A remedy for this limitation is to conduct the partial reconstruction within a reduced-order subspace spanned by the first few maximum eigenvectors of the true system matrix. By MAT, this VS subspace is the optimal one when the goal is to maximize the average tumor temperature. When more than 6 sources present, the steps required for a nonlinear learning scheme is theoretically fewer than that of a linear one, however, finite number of iterative corrections is necessary for a single learning step of a nonlinear algorithm. Thus, the actual computational workload for a nonlinear algorithm is not necessarily less than that required by a linear algorithm. Based on the analysis presented herein, obtaining a unique global optimal heating vector for a multiple-source applicator within the constraints of real-time clinical hyperthermia treatments and thermal ablative therapies appears attainable using partial reconstruction with minimum norm least-squares method with supplemental equations. One way to supplement equations is the inclusion of a method of model reduction.

Ego depletion interferes with rule-defined category learning but not non-rule-defined category learning.

PubMed

Minda, John P; Rabi, Rahel

2015-01-01

Considerable research on category learning has suggested that many cognitive and environmental factors can have a differential effect on the learning of rule-defined (RD) categories as opposed to the learning of non-rule-defined (NRD) categories. Prior research has also suggested that ego depletion can temporarily reduce the capacity for executive functioning and cognitive flexibility. The present study examined whether temporarily reducing participants' executive functioning via a resource depletion manipulation would differentially impact RD and NRD category learning. Participants were either asked to write a story with no restrictions (the control condition), or without using two common letters (the ego depletion condition). Participants were then asked to learn either a set of RD categories or a set of NRD categories. Resource depleted participants performed more poorly than controls on the RD task, but did not differ from controls on the NRD task, suggesting that self regulatory resources are required for successful RD category learning. These results lend support to multiple systems theories and clarify the role of self-regulatory resources within this theory.

Ego depletion interferes with rule-defined category learning but not non-rule-defined category learning

PubMed Central

Minda, John P.; Rabi, Rahel

2015-01-01

Considerable research on category learning has suggested that many cognitive and environmental factors can have a differential effect on the learning of rule-defined (RD) categories as opposed to the learning of non-rule-defined (NRD) categories. Prior research has also suggested that ego depletion can temporarily reduce the capacity for executive functioning and cognitive flexibility. The present study examined whether temporarily reducing participants’ executive functioning via a resource depletion manipulation would differentially impact RD and NRD category learning. Participants were either asked to write a story with no restrictions (the control condition), or without using two common letters (the ego depletion condition). Participants were then asked to learn either a set of RD categories or a set of NRD categories. Resource depleted participants performed more poorly than controls on the RD task, but did not differ from controls on the NRD task, suggesting that self regulatory resources are required for successful RD category learning. These results lend support to multiple systems theories and clarify the role of self-regulatory resources within this theory. PMID:25688220

Intrinsic motivation, curiosity, and learning: Theory and applications in educational technologies.

PubMed

Oudeyer, P-Y; Gottlieb, J; Lopes, M

2016-01-01

This chapter studies the bidirectional causal interactions between curiosity and learning and discusses how understanding these interactions can be leveraged in educational technology applications. First, we review recent results showing how state curiosity, and more generally the experience of novelty and surprise, can enhance learning and memory retention. Then, we discuss how psychology and neuroscience have conceptualized curiosity and intrinsic motivation, studying how the brain can be intrinsically rewarded by novelty, complexity, or other measures of information. We explain how the framework of computational reinforcement learning can be used to model such mechanisms of curiosity. Then, we discuss the learning progress (LP) hypothesis, which posits a positive feedback loop between curiosity and learning. We outline experiments with robots that show how LP-driven attention and exploration can self-organize a developmental learning curriculum scaffolding efficient acquisition of multiple skills/tasks. Finally, we discuss recent work exploiting these conceptual and computational models in educational technologies, showing in particular how intelligent tutoring systems can be designed to foster curiosity and learning. © 2016 Elsevier B.V. All rights reserved.

Lessons Learned for Improving Spacecraft Ground Operations

NASA Technical Reports Server (NTRS)

Bell, Michael; Henderson, Gena; Stambolian, Damon

2013-01-01

NASA policy requires each Program or Project to develop a plan for how they will address Lessons Learned. Projects have the flexibility to determine how best to promote and implement lessons learned. A large project might budget for a lessons learned position to coordinate elicitation, documentation and archival of the project lessons. The lessons learned process crosses all NASA Centers and includes the contactor community. o The Office of The Chief Engineer at NASA Headquarters in Washington D.C., is the overall process owner, and field locations manage the local implementation. One tool used to transfer knowledge between program and projects is the Lessons Learned Information System (LLIS). Most lessons come from NASA in partnership with support contractors. A search for lessons that might impact a new design is often performed by a contractor team member. Knowledge is not found with only one person, one project team, or one organization. Sometimes, another project team, or person, knows something that can help your project or your task. Knowledge sharing is an everyday activity at the Kennedy Space Center through storytelling, Kennedy Engineering Academy presentations and through searching the Lessons Learned Information system. o Project teams search the lessons repository to ensure the best possible results are delivered. o The ideas from the past are not always directly applicable but usually spark new ideas and innovations. Teams have a great responsibility to collect and disseminate these lessons so that they are shared with future generations of space systems designers. o Leaders should set a goal for themselves to host a set numbers of lesson learned events each year and do more to promote multiple methods of lessons learned activities. o High performing employees are expected to share their lessons, however formal knowledge sharing presentation are not the norm for many employees.

Toward a science of learning systems: a research agenda for the high-functioning Learning Health System.

PubMed

Friedman, Charles; Rubin, Joshua; Brown, Jeffrey; Buntin, Melinda; Corn, Milton; Etheredge, Lynn; Gunter, Carl; Musen, Mark; Platt, Richard; Stead, William; Sullivan, Kevin; Van Houweling, Douglas

2015-01-01

The capability to share data, and harness its potential to generate knowledge rapidly and inform decisions, can have transformative effects that improve health. The infrastructure to achieve this goal at scale--marrying technology, process, and policy--is commonly referred to as the Learning Health System (LHS). Achieving an LHS raises numerous scientific challenges. The National Science Foundation convened an invitational workshop to identify the fundamental scientific and engineering research challenges to achieving a national-scale LHS. The workshop was planned by a 12-member committee and ultimately engaged 45 prominent researchers spanning multiple disciplines over 2 days in Washington, DC on 11-12 April 2013. The workshop participants collectively identified 106 research questions organized around four system-level requirements that a high-functioning LHS must satisfy. The workshop participants also identified a new cross-disciplinary integrative science of cyber-social ecosystems that will be required to address these challenges. The intellectual merit and potential broad impacts of the innovations that will be driven by investments in an LHS are of great potential significance. The specific research questions that emerged from the workshop, alongside the potential for diverse communities to assemble to address them through a 'new science of learning systems', create an important agenda for informatics and related disciplines. © The Author 2014. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Enhancing Spatial Resolution of Remotely Sensed Imagery Using Deep Learning

NASA Astrophysics Data System (ADS)

Beck, J. M.; Bridges, S.; Collins, C.; Rushing, J.; Graves, S. J.

2017-12-01

Researchers at the Information Technology and Systems Center at the University of Alabama in Huntsville are using Deep Learning with Convolutional Neural Networks (CNNs) to develop a method for enhancing the spatial resolutions of moderate resolution (10-60m) multispectral satellite imagery. This enhancement will effectively match the resolutions of imagery from multiple sensors to provide increased global temporal-spatial coverage for a variety of Earth science products. Our research is centered on using Deep Learning for automatically generating transformations for increasing the spatial resolution of remotely sensed images with different spatial, spectral, and temporal resolutions. One of the most important steps in using images from multiple sensors is to transform the different image layers into the same spatial resolution, preferably the highest spatial resolution, without compromising the spectral information. Recent advances in Deep Learning have shown that CNNs can be used to effectively and efficiently upscale or enhance the spatial resolution of multispectral images with the use of an auxiliary data source such as a high spatial resolution panchromatic image. In contrast, we are using both the spatial and spectral details inherent in low spatial resolution multispectral images for image enhancement without the use of a panchromatic image. This presentation will discuss how this technology will benefit many Earth Science applications that use remotely sensed images with moderate spatial resolutions.

Evolution and Reconstruction of Learning Cities for Sustainable Actions

ERIC Educational Resources Information Center

Watson, Connie; Wu, Aimee Tiu

2015-01-01

This chapter describes how the concept of learning cities evolved from the "learning society" and the lifelong education and learning movements, and advances multiple forms of communities of learning.

Machine learning research 1989-90

NASA Technical Reports Server (NTRS)

Porter, Bruce W.; Souther, Arthur

1990-01-01

Multifunctional knowledge bases offer a significant advance in artificial intelligence because they can support numerous expert tasks within a domain. As a result they amortize the costs of building a knowledge base over multiple expert systems and they reduce the brittleness of each system. Due to the inevitable size and complexity of multifunctional knowledge bases, their construction and maintenance require knowledge engineering and acquisition tools that can automatically identify interactions between new and existing knowledge. Furthermore, their use requires software for accessing those portions of the knowledge base that coherently answer questions. Considerable progress was made in developing software for building and accessing multifunctional knowledge bases. A language was developed for representing knowledge, along with software tools for editing and displaying knowledge, a machine learning program for integrating new information into existing knowledge, and a question answering system for accessing the knowledge base.

Change Management in Dental Education: A Professional Learning Community.

PubMed

Palatta, Anthony M

2018-06-01

Professional learning communities (PLCs) are defined as "a group of people sharing and critically interrogating their practice in an ongoing, reflective, collaborative, inclusive, learning-oriented, growth-promoting way." PLCs have been found to be an effective change management strategy in business and education when confronted by rapid change. The American Dental Education Association's Commission on Change and Innovation in Dental Education new national program-ADEA CCI 2.0-includes the development of a PLC. By employing an "engage and learn" model PLC centered on continuous quality improvement and systems thinking, dental faculty can identify internal and external barriers to change that could lead to innovative solutions to complex issues. This article argues that a PLC is a viable change management strategy to counteract the effect of multiple external forces impacting dental education and thus to develop future-ready faculty.

Designing and evaluating a STEM teacher learning opportunity in the research university.

PubMed

Hardré, Patricia L; Ling, Chen; Shehab, Randa L; Herron, Jason; Nanny, Mark A; Nollert, Matthias U; Refai, Hazem; Ramseyer, Christopher; Wollega, Ebisa D

2014-04-01

This study examines the design and evaluation strategies for a year-long teacher learning and development experience, including their effectiveness, efficiency and recommendations for strategic redesign. Design characteristics include programmatic features and outcomes: cognitive, affective and motivational processes; interpersonal and social development; and performance activities. Program participants were secondary math and science teachers, partnered with engineering faculty mentors, in a research university-based education and support program. Data from multiple sources demonstrated strengths and weaknesses in design of the program's learning environment, including: face-to-face and via digital tools; on-site and distance community interactions; and strategic evaluation tools and systems. Implications are considered for the strategic design and evaluation of similar grant-funded research experiences intended to support teacher learning, development and transfer. Copyright © 2013 Elsevier Ltd. All rights reserved.

Design Research on Mathematics Education: Investigating the Progress of Indonesian Fifth Grade Students' Learning on Multiplication of Fractions with Natural Numbers

ERIC Educational Resources Information Center

Shanty, Nenden Octavarulia; Hartono, Yusuf; Putri, Ratu Ilma Indra; de Haan, Dede

2011-01-01

This study aimed at investigating the progress of students' learning on multiplication fractions with natural numbers through the five activity levels based on Realistic Mathematics Education (RME) approach proposed by Streefland. Design research was chosen to achieve this research goal. In design research, the Hypothetical Learning Trajectory…

Self-Regulated Learning in a TELE at the Universite de Technologie de Compiegne: An Analysis from Multiple Perspectives

ERIC Educational Resources Information Center

Trigano, Philippe

2006-01-01

Self-regulation has become a very important topic in the field of learning and instruction. At the same time, the introduction of new technologies in the field of Information and Communication Technologies (ICT) has made it possible to create rich Technology-Enhanced Learning Environments (TELEs) with multiple affordances for supporting…

Multiple Competencies and Self-Regulated Learning: Implications for Multicultural Education. Research in Multicultural Education and International Perspectives.

ERIC Educational Resources Information Center

Chiu, Chi-yue, Ed.; Salili, Farideh, Ed.; Hong, Ying-yi, Ed.

This book presents 13 papers from a 1998 conference in Hong Kong that examined how to apply psychology to enhance learning and teaching quality and focused on multicultural education: (1) "The Role of Multiple Competencies and Self-Regulated Learning in Multicultural Education" (Chi-yue Chiu, Farideh Salili, and Ying-yi Hong); (2)…

Multiple-Choice Testing Using Immediate Feedback--Assessment Technique (IF AT®) Forms: Second-Chance Guessing vs. Second-Chance Learning?

ERIC Educational Resources Information Center

Merrel, Jeremy D.; Cirillo, Pier F.; Schwartz, Pauline M.; Webb, Jeffrey A.

2015-01-01

Multiple choice testing is a common but often ineffective method for evaluating learning. A newer approach, however, using Immediate Feedback Assessment Technique (IF AT®, Epstein Educational Enterprise, Inc.) forms, offers several advantages. In particular, a student learns immediately if his or her answer is correct and, in the case of an…

The Effects of Self-Explanation and Metacognitive Instruction on Undergraduate Students' Learning of Statistics Materials Containing Multiple External Representations in a Web-Based Environment

ERIC Educational Resources Information Center

Hsu, Yu-Chang

2009-01-01

Students in the Science, Technology, Engineering, and Mathematics (STEM) fields are confronted with multiple external representations (MERs) in their learning materials. The ability to learn from and communicate with these MERs requires not only that students comprehend each representation individually but also that students recognize how the…

Searching for Variables and Models to Investigate Mediators of Learning from Multiple Representations

ERIC Educational Resources Information Center

Rau, Martina A.; Scheines, Richard

2012-01-01

Although learning from multiple representations has been shown to be effective in a variety of domains, little is known about the mechanisms by which it occurs. We analyzed log data on error-rate, hint-use, and time-spent obtained from two experiments with a Cognitive Tutor for fractions. The goal of the experiments was to compare learning from…

Dorsolateral striatal lesions impair navigation based on landmark-goal vectors but facilitate spatial learning based on a “cognitive map”

PubMed Central

Poulter, Steven L.; Austen, Joe M.

2015-01-01

In three experiments, the nature of the interaction between multiple memory systems in rats solving a variation of a spatial task in the water maze was investigated. Throughout training rats were able to find a submerged platform at a fixed distance and direction from an intramaze landmark by learning a landmark-goal vector. Extramaze cues were also available for standard place learning, or “cognitive mapping,” but these cues were valid only within each session, as the position of the platform moved around the pool between sessions together with the intramaze landmark. Animals could therefore learn the position of the platform by taking the consistent vector from the landmark across sessions or by rapidly encoding the new platform position on each session with reference to the extramaze cues. Excitotoxic lesions of the dorsolateral striatum impaired vector-based learning but facilitated cognitive map-based rapid place learning when the extramaze cues were relatively poor (Experiment 1) but not when they were more salient (Experiments 2 and 3). The way the lesion effects interacted with cue availability is consistent with the idea that the memory systems involved in the current navigation task are functionally cooperative yet associatively competitive in nature. PMID:25691518

The role of the basal ganglia in learning and memory: Insight from Parkinson's disease

PubMed Central

2013-01-01

It has long been known that memory is not a single process. Rather, there are different kinds of memory that are supported by distinct neural systems. This idea stemmed from early findings of dissociable patterns of memory impairments in patients with selective damage to different brain regions. These studies highlighted the role of the basal ganglia in non-declarative memory, such as procedural or habit learning, contrasting it with the known role of the medial temporal lobes in declarative memory. In recent years, major advances across multiple areas of neuroscience have revealed an important role for the basal ganglia in motivation and decision making. These findings have led to new discoveries about the role of the basal ganglia in learning and highlighted the essential role of dopamine in specific forms of learning. Here we review these recent advances with an emphasis on novel discoveries from studies of learning in patients with Parkinson's disease. We discuss how these findings promote the development of current theories away from accounts that emphasize the verbalizability of the contents of memory and towards a focus on the specific computations carried out by distinct brain regions. Finally, we discuss new challenges that arise in the face of accumulating evidence for dynamic and interconnected memory systems that jointly contribute to learning. PMID:21945835

Linking Learning, Teaching, and Development.

ERIC Educational Resources Information Center

Fiddler, Morris; Marienau, Catherine

1995-01-01

Learning-centered teaching links learning and development by creating a climate of exchange; using assessment to increase awareness of learning needs; promoting learning to learn; holding learners accountable; using multiple strategies for different learning styles; and involving learners in realistic and challenging goals. (SK)

The cost of selective attention in category learning: Developmental differences between adults and infants

PubMed Central

Best, Catherine A.; Yim, Hyungwook; Sloutsky, Vladimir M.

2013-01-01

Selective attention plays an important role in category learning. However, immaturities of top-down attentional control during infancy coupled with successful category learning suggest that early category learning is achieved without attending selectively. Research presented here examines this possibility by focusing on category learning in infants (6–8 months old) and adults. Participants were trained on a novel visual category. Halfway through the experiment, unbeknownst to participants, the to-be-learned category switched to another category, where previously relevant features became irrelevant and previously irrelevant features became relevant. If participants attend selectively to the relevant features of the first category, they should incur a cost of selective attention immediately after the unknown category switch. Results revealed that adults demonstrated a cost, as evidenced by a decrease in accuracy and response time on test trials as well as a decrease in visual attention to newly relevant features. In contrast, infants did not demonstrate a similar cost of selective attention as adults despite evidence of learning both to-be-learned categories. Findings are discussed as supporting multiple systems of category learning and as suggesting that learning mechanisms engaged by adults may be different from those engaged by infants. PMID:23773914

Data Mining Student Answers with Moodle to Investigate Learning Pathways in an Introductory Geohazards Course

NASA Astrophysics Data System (ADS)

Sit, S. M.; Brudzinski, M. R.; Colella, H. V.

2012-12-01

The recent growth of online learning in higher education is primarily motivated by a desire to (a) increase the availability of learning experiences for learners who cannot, or choose not, to attend traditional face-to-face offerings, (b) assemble and disseminate instructional content more cost-efficiently, or (c) enable instructors to handle more students while maintaining a learning outcome quality that is equivalent to that of comparable face-to-face instruction. However, a less recognized incentive is that online learning also provides an opportunity for data mining, or efficient discovery of non-obvious valuable patterns from a large collection of data, that can be used to investigate learning pathways as opposed to focusing solely on assessing student outcomes. Course management systems that enable online courses provide a means to collect a vast amount of information to analyze students' behavior and the learning process in general. One of the most commonly used is Moodle (modular object-oriented developmental learning environment), a free learning management system that enables creation of powerful, flexible, and engaging online courses and experiences. In order to examine student learning pathways, the online learning modules we are constructing take advantage of Moodle capabilities to provide immediate formative feedback, verifying answers as correct or incorrect and elaborating on knowledge components to guide students towards the correct answer. By permitting multiple attempts in which credit is diminished for each incorrect answer, we provide opportunities to use data mining strategies to assess thousands of students' actions for evidence of problem solving strategies and mastery of concepts. We will show preliminary results from application of this approach to a ~90 student introductory geohazard course that is migrating toward online instruction. We hope more continuous assessment of students' performances will help generate cognitive models that can inform instructional redesign, improve overall efficiency of student learning, and, potentially, be used to create an intelligent tutoring system.