collective learning systems: Topics by Science.gov

Sample records for collective learning systems

Collective Learning: A Way over the Ridge to a New Organizational Attractor

ERIC Educational Resources Information Center

Backstrom, Tomas

2004-01-01

A theoretical model of collective learning has been developed based on complex systems theory. The need for collective learning is illustrated by an empirical study of an "unsuccessful" organizational-renewal project in a Swedish Telecom firm. The conclusion, using chaordic systems thinking as a diagnostic framework, is that its interior…
Race to the Top--Early Learning Challenge (RTT-ELC): Descriptive Study of Tiered Quality Rating and Improvement Systems (TQRIS). Master Data Collection Protocol

ERIC Educational Resources Information Center

Mathematica Policy Research, Inc., 2015

2015-01-01

This master data collection protocol describes the data that Mathematica collected for the Race to the Top-Early Learning Challenge Study of Tiered Quality Rating and Improvement Systems. This study was conducted for the Department of Education's Institute of Education Sciences. The data were collected from reviews of applications, documents, and…
Web-Based Intelligent E-Learning Systems: Technologies and Applications

ERIC Educational Resources Information Center

Ma, Zongmin

2006-01-01

Collecting and presenting the latest research and development results from the leading researchers in the field of e-learning systems, Web-Based Intelligent E-Learning Systems: Technologies and Applications provides a single record of current research and practical applications in Web-based intelligent e-learning systems. This book includes major…
DOE Office of Scientific and Technical Information (OSTI.GOV)

Jamieson, Kevin; Davis, IV, Warren L.

Active learning methods automatically adapt data collection by selecting the most informative samples in order to accelerate machine learning. Because of this, real-world testing and comparing active learning algorithms requires collecting new datasets (adaptively), rather than simply applying algorithms to benchmark datasets, as is the norm in (passive) machine learning research. To facilitate the development, testing and deployment of active learning for real applications, we have built an open-source software system for large-scale active learning research and experimentation. The system, called NEXT, provides a unique platform for realworld, reproducible active learning research. This paper details the challenges of building themore » system and demonstrates its capabilities with several experiments. The results show how experimentation can help expose strengths and weaknesses of active learning algorithms, in sometimes unexpected and enlightening ways.« less
A Learning Framework for Knowledge Building and Collective Wisdom Advancement in Virtual Learning Communities

ERIC Educational Resources Information Center

Gan, Yongcheng; Zhu, Zhiting

2007-01-01

This study represents an effort to construct a learning framework for knowledge building and collective wisdom advancement in a virtual learning community (VLC) from the perspectives of system wholeness, intelligence wholeness and dynamics, learning models, and knowledge management. It also tries to construct the zone of proximal development (ZPD)…
SABER-School Finance: Data Collection Instrument

ERIC Educational Resources Information Center

King, Elizabeth; Patrinos, Harry; Rogers, Halsey

2015-01-01

The aim of the SABER-school finance initiative is to collect, analyze and disseminate comparable data about education finance systems across countries. SABER-school finance assesses education finance systems along six policy goals: (i) ensuring basic conditions for learning; (ii) monitoring learning conditions and outcomes; (iii) overseeing…
Collective learning for the emergence of social norms in networked multiagent systems.

PubMed

Yu, Chao; Zhang, Minjie; Ren, Fenghui

2014-12-01

Social norms such as social rules and conventions play a pivotal role in sustaining system order by regulating and controlling individual behaviors toward a global consensus in large-scale distributed systems. Systematic studies of efficient mechanisms that can facilitate the emergence of social norms enable us to build and design robust distributed systems, such as electronic institutions and norm-governed sensor networks. This paper studies the emergence of social norms via learning from repeated local interactions in networked multiagent systems. A collective learning framework, which imitates the opinion aggregation process in human decision making, is proposed to study the impact of agent local collective behaviors on the emergence of social norms in a number of different situations. In the framework, each agent interacts repeatedly with all of its neighbors. At each step, an agent first takes a best-response action toward each of its neighbors and then combines all of these actions into a final action using ensemble learning methods. Extensive experiments are carried out to evaluate the framework with respect to different network topologies, learning strategies, numbers of actions, influences of nonlearning agents, and so on. Experimental results reveal some significant insights into the manipulation and control of norm emergence in networked multiagent systems achieved through local collective behaviors.
Coupled replicator equations for the dynamics of learning in multiagent systems

NASA Astrophysics Data System (ADS)

Sato, Yuzuru; Crutchfield, James P.

2003-01-01

Starting with a group of reinforcement-learning agents we derive coupled replicator equations that describe the dynamics of collective learning in multiagent systems. We show that, although agents model their environment in a self-interested way without sharing knowledge, a game dynamics emerges naturally through environment-mediated interactions. An application to rock-scissors-paper game interactions shows that the collective learning dynamics exhibits a diversity of competitive and cooperative behaviors. These include quasiperiodicity, stable limit cycles, intermittency, and deterministic chaos—behaviors that should be expected in heterogeneous multiagent systems described by the general replicator equations we derive.
A Lecture Supporting System Based on Real-Time Learning Analytics

ERIC Educational Resources Information Center

Shimada, Atsushi; Konomi, Shin'ichi

2017-01-01

A new lecture supporting system based on real-time learning analytics is proposed. Our target is on-site classrooms where teachers give their lectures, and a lot of students listen to teachers' explanation, conduct exercises etc. We utilize not only an e-Learning system, but also an e-Book system to collect real-time learning activities during the…
Optimal Sensor Management and Signal Processing for New EMI Systems

DTIC Science & Technology

2010-09-01

adaptive techniques that would improve the speed of data collection and increase the mobility of a TEMTADS system. Although an active learning technique...data, SIG has simulated the active selection based on the data already collected at Camp SLO. In this setup, the active learning approach was constrained...to work only on a 5x5 grid (corresponding to twenty five transmitters and co-located receivers). The first technique assumes that active learning will
Privacy Impact Assessment for the Contract Payment System

EPA Pesticide Factsheets

The Contract Payment System collects contact information and other Personally Identifiable Information (PII). Learn how this data is collected in the system, how it will be used, access to the data, the purpose of data collection, and record retention.
Development and Classroom Implementation of an Environmental Data Creation and Sharing Tool

ERIC Educational Resources Information Center

Brogan, Daniel S.; McDonald, Walter M.; Lohani, Vinod K.; Dymond, Randel L.; Bradner, Aaron J.

2016-01-01

Education is essential for solving the complex water-related challenges facing society. The Learning Enhanced Watershed Assessment System (LEWAS) and the Online Watershed Learning System (OWLS) provide data creation and data sharing infrastructures, respectively, that combine to form an environmental learning tool. This system collects, integrates…
Privacy Impact Assessment for the Integrated Contracts Management System

EPA Pesticide Factsheets

The Integrated Contracts Management System collects contact information and other Personally Identifiable Information (PII). Learn how this data will be collected in the system, how it will be used, access to the data, and the purpose of data collection.
Privacy Impact Assessment for the Peer Reviewer Panelist Information System

EPA Pesticide Factsheets

This system collects contact and employment information. Learn how this data will be collected in the system, how it will be used, access to the data, the purpose of data collection, and record retention policies for this data.
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…
Exploring Learning Performance toward Cognitive Approaches of a Virtual Companion System in LINE App for m-Learning

ERIC Educational Resources Information Center

Hsieh, Sheng-Wen; Wu, Min-Ping

2013-01-01

This paper used a Virtual Companion System (VCS) to examine how specific design variables within virtual learning companion affect the learning process of learners as defined by the cognitive continuum of field-dependent, field-independent and field-mixed learners in LINE app for m-learning. The data were collected from 198 participants in a…
Exploring Adaptability through Learning Layers and Learning Loops

ERIC Educational Resources Information Center

Lof, Annette

2010-01-01

Adaptability in social-ecological systems results from individual and collective action, and multi-level interactions. It can be understood in a dual sense as a system's ability to adapt to disturbance and change, and to navigate system transformation. Inherent in this conception, as found in resilience thinking, are the concepts of learning and…
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.
Privacy Impact Assessment for the Case Management System for Suspension and Debarment

EPA Pesticide Factsheets

This system collects contact information and other Personally Identifiable Information (PII). Learn how this data will be collected in the system, how it will be used, access to the data, the purpose of data collection, and the record retention policies.
Privacy Impact Assessment for the Hotline Allegation System for the Office of Inspector General

EPA Pesticide Factsheets

This system collects contact information and other Personally Identifiable Information (PII). Learn how this data will be collected in the system, how it will be used, access to the data, the purpose of data collection, and record retention policies.

Privacy Impact Assessment for the PC Label System

EPA Pesticide Factsheets

The PC Label System collects contact information for individuals with an interest in EPA's Region 1. Learn how this data will be collected in the system, how it will be used, access to the data, the purpose of data collection, and record retention policies
Create Learning Systems, Not Silos

ERIC Educational Resources Information Center

Hirsh, Stephanie; Psencik, Kay; Brown, Frederick

2018-01-01

In a learning system, central office personnel assume collective responsibility for schools and go about their work very differently. They are responsible not only for departments and programs, but also for student learning. They demonstrate that responsibility by engaging in data informed conversations about student achievement. Central office…
Business Center

EPA Pesticide Factsheets

Learn how to do business with EPA's Clean Air Markets, including registering to use the Emissions Collection and Monitoring Plan System (ECMPS), the CAMD Business System (CBS), and learn how to submit monitored emissions data.
Privacy Impact Assessment for EZHire

EPA Pesticide Factsheets

The EZHire System collects contact and other Personally Identifiable Information (PII). Learn how this is collected for the system, how it will be used, access to the data, the purpose of data collection, and record retention policies for this data.
Privacy Impact Assessment for the Office of Administrative Services Information System

EPA Pesticide Factsheets

The Office of Administrative Services Information System collects contact information and other Personally Identifiable Information (PII). Learn how this data is collected, used, access to the data, and the purpose of data collection.
How Does Self-Regulated Learning Relate to Active Procrastination and Other Learning Behaviors?

ERIC Educational Resources Information Center

Yamada, Masanori; Goda, Yoshiko; Matsuda, Takeshi; Saito, Yutaka; Kato, Hiroshi; Miyagawa, Hiroyuki

2016-01-01

This research investigates the relationship between self-regulated learning awareness, procrastination, and learning behaviors in a blended learning environment. Participants included 179 first-grade university students attending a blended learning-style class that used a learning management system. Data were collected using questionnaires on…
The Design of Collectives of Agents to Control Non-Markovian Systems

NASA Technical Reports Server (NTRS)

Lawson, John W.; Wolpert, David H.

2004-01-01

The Collective Intelligence (COIN) framework concerns the design of collectives of reinforcement-learning agents such that their interaction causes a provided "world" utility function concerning the entire collective to be maximized. Previously, we applied that framework to scenarios involving Markovian dynamics where no re-evolution of the system from counter-factual initial conditions (an often expensive calculation) is permitted. This approach sets the individual utility function of each agent to be both aligned with the world utility, and at the same time, easy for the associated agents to optimize. Here we extend that approach to systems involving non-Markovian dynamics. In computer simulations, we compare our techniques with each other and with conventional "team games". We show whereas in team games performance often degrades badly with time, it steadily improves when our techniques are used. We also investigate situations where the system's dimensionality is effectively reduced. We show that this leads to difficulties in the agents ability to learn. The implication is that learning is a property only of high-enough dimensional systems.
The Design of Collectives of Agents to Control Non-Markovian Systems

NASA Technical Reports Server (NTRS)

Lawson, John W.; Wolpert, David H.; Clancy, Daniel (Technical Monitor)

2002-01-01

The 'Collective Intelligence' (COIN) framework concerns the design of collectives of reinforcement-learning agents such that their interaction causes a provided 'world' utility function concerning the entire collective to be maximized. Previously, we applied that framework to scenarios involving Markovian dynamics where no re-evolution of the system from counter-factual initial conditions (an often expensive calculation) is permitted. This approach sets the individual utility function of each agent to be both aligned with the world utility, and at the same time, easy for the associated agents to optimize. Here we extend that approach to systems involving non-Markovian dynamics. In computer simulations, we compare our techniques with each other and with conventional-'team games'. We show whereas in team games performance often degrades badly with time, it steadily improves when our techniques are used. We also investigate situations where the system's dimensionality is effectively reduced. We show that this leads to difficulties in the agents' ability to learn. The implication is that 'learning' is a property only of high-enough dimensional systems.
Privacy Impact Assessment for the Mailing System for the Office of Public Affairs

EPA Pesticide Factsheets

The Mailing System for the Office of Public Affairs collects contact and geographic information. Learn how this data will be collected in the system, how it will be used, access to the data, the purpose of data collection, and record retention policies.
Constructing Learning: Adversarial and Collaborative Working in the British Construction Industry

ERIC Educational Resources Information Center

Bishop, Dan; Felstead, Alan; Fuller, Alison; Jewson, Nick; Unwin, Lorna; Kakavelakis, Konstantinos

2009-01-01

This paper examines two competing systems of work organisation in the British construction industry and their consequences for learning. Under the traditional "adversarial" system, conflict, hostility and litigation between contractors are commonplace. Such a climate actively militates against collective learning and knowledge sharing between…
Clustering and Profiling Students According to Their Interactions with an Intelligent Tutoring System Fostering Self-Regulated Learning

ERIC Educational Resources Information Center

Bouchet, Francois; Harley, Jason M.; Trevors, Gregory J.; Azevedo, Roger

2013-01-01

In this paper, we present the results obtained using a clustering algorithm (Expectation-Maximization) on data collected from 106 college students learning about the circulatory system with MetaTutor, an agent-based Intelligent Tutoring System (ITS) designed to foster self-regulated learning (SRL). The three extracted clusters were validated and…
The Relationship among Self-Regulated Learning, Procrastination, and Learning Behaviors in Blended Learning Environment

ERIC Educational Resources Information Center

Yamada, Masanori; Goda, Yoshiko; Matsuda, Takeshi; Kato, Hiroshi; Miyagawa, Hiroyuki

2015-01-01

This research aims to investigate the relationship among the awareness of self-regulated learning (SRL), procrastination, and learning behaviors in blended learning environment. One hundred seventy nine freshmen participated in this research, conducted in the blended learning style class using learning management system. Data collection was…
Privacy Impact Assessment for the Correspondence Management System

EPA Pesticide Factsheets

The Correspondence Management System collects basic contact information (name, address, e-mail and phone number). Learn how this data is collected, how it will be used, access to the data, the purpose of data collection, and record retention policies.
Privacy Impact Assessment for the Enforcement Action Response System

EPA Pesticide Factsheets

The Enforcement Action Response System collects waste transaction information, and liability determination information. Learn how this data is collected, how it will be used, access to the data, the purpose of data collection, and record retention policies
Privacy Impact Assessment for the Integrated Grants Management System

EPA Pesticide Factsheets

The Integrated Management System collects contact information and other Personally Identifiable Information (PII). Learn how this data is collected, how it will be used, access to the data, the purpose of data collection, and record retention policies.
Privacy Impact Assessment for Withdrawal Petition Management for the National Pollutant Discharge Elimination System

EPA Pesticide Factsheets

This system collects the names and contact information of petition submitting persons. Learn how this data will be collected in the system, how it will be used, access to the data, the purpose of data collection, and record retention policies for this data
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.
A User-Centric Adaptive Learning System for E-Learning 2.0

ERIC Educational Resources Information Center

Huang, Shiu-Li; Shiu, Jung-Hung

2012-01-01

The success of Web 2.0 inspires e-learning to evolve into e-learning 2.0, which exploits collective intelligence to achieve user-centric learning. However, searching for suitable learning paths and content for achieving a learning goal is time consuming and troublesome on e-learning 2.0 platforms. Therefore, introducing formal learning in these…
Privacy Impact Assessment for the External Compliance Program Discrimination Complaint Files

EPA Pesticide Factsheets

The External Compliance Program Discrimination Complaint Files System collects information on administrative complaints. Learn how this data will be collected in the system, how it will be used, access to the data, and the purpose of data collection.
Privacy Impact Assessment for the Registration and Tracking System for SunWise

EPA Pesticide Factsheets

The Registration and Tracking System for SunWise collects contact information and demographics about each educator. Learn how this data is collected, used, access to the data, the purpose of data collection, and record retention policies.

Privacy Impact Assessment for the Emergency Management Portal

EPA Pesticide Factsheets

The Emergency Management Portal System collects cleanup site data, and personnel readiness data. Learn how this data will be collected in the system, how it will be used, access to the data, the purpose of data collection, and record retention policies.
Privacy Impact Assessment for the Inspector General Enterprise Management System

EPA Pesticide Factsheets

This system collects personally identifiable information (PII), including social security numbers, date of birth, etc. Learn how this data is collected, how it will be used, access to the data, the purpose of data collection, and record retention policies.
Privacy Impact Assessment for the Enforcement Superfund Tracking System

EPA Pesticide Factsheets

This Enforcement Superfund Tracking System (ESTS) collects publicly available information from the California Secretary of State on businesses. Learn how this data is collected, how it will be used, access to the data, and the purpose of data collection.
Learning and Collective Knowledge Construction With Social Media: A Process-Oriented Perspective

PubMed Central

Kimmerle, Joachim; Moskaliuk, Johannes; Oeberst, Aileen; Cress, Ulrike

2015-01-01

Social media are increasingly being used for educational purposes. The first part of this article briefly reviews literature that reports on educational applications of social media tools. The second part discusses theories that may provide a basis for analyzing the processes that are relevant for individual learning and collective knowledge construction. We argue that a systems-theoretical constructivist approach is appropriate to examine the processes of educational social media use, namely, self-organization, the internalization of information, the externalization of knowledge, and the interplay of externalization and internalization providing the basis of a co-evolution of cognitive and social systems. In the third part we present research findings that illustrate and support this systems-theoretical framework. Concluding, we discuss the implications for educational design and for future research on learning and collective knowledge construction with social media. PMID:26246643
Privacy Impact Assessment for the EPA Acquisition System

EPA Pesticide Factsheets

The EPA Acquisition System collects data on the business process of acquiring goods in support of the Agency's mission. Learn how this data is collected, how it will be used, access to the data, the purpose of data collection, and record retention policies
Privacy Impact Assessment for the Research Grant, Cooperative Agreement and Fellowship Application Files

EPA Pesticide Factsheets

This system collects research proposal information. Learn how this data will be collected in the system, how it will be used, access to the data, the purpose of data collection, and record retention policies for this data.
Privacy Impact Assessment for the Federal Docket Management System

EPA Pesticide Factsheets

This system collects the name and contact information for users who submit comments, in addition to agency users. Learn how the data is collected, how it will be used, access to the data, the purpose of data collection, and record retention policies.
Privacy Impact Assessment for the Lead-based Paint System of Records

EPA Pesticide Factsheets

The Lead-based Paint System of Records collects personally identifiable information, test scores, and submitted fees. Learn how this data is collected, how it will be used, access to the data, the purpose of data collection, and record retention policies.
Privacy Impact Assessment for the Las Vegas Finance Center Local Area Network

EPA Pesticide Factsheets

This system collects contact information and other Personally Identifiable Information (PII). Learn how this data will be collected in the system, how it will be used, access to the data, the purpose of data collection, and record retention policies.
Practical Measurement and Productive Persistence: Strategies for Using Digital Learning System Data to Drive Improvement

ERIC Educational Resources Information Center

Krumm, Andrew E.; Beattie, Rachel; Takahashi, Sola; D'Angelo, Cynthia; Feng, Mingyu; Cheng, Britte

2016-01-01

This paper outlines the development of practical measures of productive persistence using digital learning system data. Practical measurement refers to data collection and analysis approaches originating from improvement science; productive persistence refers to the combination of academic and social mindsets as well as learning behaviours that…
Collecting, Integrating, and Disseminating Patient-Reported Outcomes for Research in a Learning Healthcare System

PubMed Central

Harle, Christopher A.; Lipori, Gloria; Hurley, Robert W.

2016-01-01

Introduction: Advances in health policy, research, and information technology have converged to increase the electronic collection and use of patient-reported outcomes (PROs). Therefore, it is important to share lessons learned in implementing PROs in research information systems. Case Description: The purpose of this case study is to describe a novel information system for electronic PROs and lessons learned in implementing that system to support research in an academic health center. The system incorporates freely available and commercial software and involves clinical and research workflows that support the collection, transformation, and research use of PRO data. The software and processes that comprise the system serve three main functions, (i) collecting electronic PROs in clinical care, (ii) integrating PRO data with non-patient generated clinical data, and (iii) disseminating data to researchers through the institution’s research informatics infrastructure, including the i2b2 (Informatics for Integrating Biology and the Bedside) system. Strategies: Our successful design and implementation was driven by three overarching strategies. First, we selected and implemented multiple interfaced technologies to support PRO collection, management, and research use. Second, we aimed to use standardized approaches to measuring PROs, sending PROs between systems, and disseminating PROs. Finally, we focused on using technologies and processes that aligned with existing clinical research information management strategies within our organization. Conclusion: These experiences and lessons may help future implementers and researchers enhance the scale and sustainable use of systems for research use of PROs. PMID:27563683
Collecting, Integrating, and Disseminating Patient-Reported Outcomes for Research in a Learning Healthcare System.

PubMed

Harle, Christopher A; Lipori, Gloria; Hurley, Robert W

2016-01-01

Advances in health policy, research, and information technology have converged to increase the electronic collection and use of patient-reported outcomes (PROs). Therefore, it is important to share lessons learned in implementing PROs in research information systems. The purpose of this case study is to describe a novel information system for electronic PROs and lessons learned in implementing that system to support research in an academic health center. The system incorporates freely available and commercial software and involves clinical and research workflows that support the collection, transformation, and research use of PRO data. The software and processes that comprise the system serve three main functions, (i) collecting electronic PROs in clinical care, (ii) integrating PRO data with non-patient generated clinical data, and (iii) disseminating data to researchers through the institution's research informatics infrastructure, including the i2b2 (Informatics for Integrating Biology and the Bedside) system. Our successful design and implementation was driven by three overarching strategies. First, we selected and implemented multiple interfaced technologies to support PRO collection, management, and research use. Second, we aimed to use standardized approaches to measuring PROs, sending PROs between systems, and disseminating PROs. Finally, we focused on using technologies and processes that aligned with existing clinical research information management strategies within our organization. These experiences and lessons may help future implementers and researchers enhance the scale and sustainable use of systems for research use of PROs.
Approach for Using Learner Satisfaction to Evaluate the Learning Adaptation Policy

ERIC Educational Resources Information Center

Jeghal, Adil; Oughdir, Lahcen; Tairi, Hamid; Radouane, Abdelhay

2016-01-01

The learning adaptation is a very important phase in a learning situation in human learning environments. This paper presents the authors' approach used to evaluate the effectiveness of learning adaptive systems. This approach is based on the analysis of learner satisfaction notices collected by a questionnaire on a learning situation; to analyze…
Privacy Impact Assessment for the Libby Asbestos Exposure Assessment Records

EPA Pesticide Factsheets

This system collects health screening results for individuals in Libby, Montana. Learn how this data will be collected in the system, how it will be used, access to the data, the purpose of data collection, and record retention policies for this data.
Privacy Impact Assessment for the Engine and Vehicle Automated Commercial Environment

EPA Pesticide Factsheets

The system collects basic contact information (name, address, e-mail and phone numbers). Learn how this data will be collected in the system, how it will be used, access to the data, the purpose of data collection, and record retention policies.
Privacy Impact Assessment for the Application for Tips and Complaints

EPA Pesticide Factsheets

The Application for Tips and Complaints System collects information on potential violators. Learn how this data is collected in the system, how it will be used, access to the data, the purpose of data collection, and record retention policies for this data
Privacy Impact Assessment for the Freedom of Information Act Online

EPA Pesticide Factsheets

This system collects the name and contact information for Freedom of Information Act requestors. Learn how the data will be collected in the system, how it will be used, access to the data, the purpose of data collection, and record retention policies.
Privacy Impact Assessment for the Federal Docket Management System/eRulemaking

EPA Pesticide Factsheets

This system collects the name and contact information for users who submit comments, in addition to agency users. Learn how the data is collected, how it will be used, access to the data, the purpose of data collection, and record retention policies.
Privacy Impact Assessment for the National Service Center for Environmental Publications and National Environmental Publications Internet Site

EPA Pesticide Factsheets

This system collects contact information. Learn how this data is collected in the system, how it will be used, access to the data, the purpose of data collection, and record retention policies for this data.
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'.

Learning and dynamics in social systems. Comment on "Collective learning modeling based on the kinetic theory of active particles" by D. Burini et al.

NASA Astrophysics Data System (ADS)

Dolfin, Marina

2016-03-01

The interesting novelty of the paper by Burini et al. [1] is that the authors present a survey and a new approach of collective learning based on suitable development of methods of the kinetic theory [2] and theoretical tools of evolutionary game theory [3]. Methods of statistical dynamics and kinetic theory lead naturally to stochastic and collective dynamics. Indeed, the authors propose the use of games where the state of the interacting entities is delivered by probability distributions.
Using Knowledge-Based Systems to Support Learning of Organizational Knowledge: A Case Study

NASA Technical Reports Server (NTRS)

Cooper, Lynne P.; Nash, Rebecca L.; Phan, Tu-Anh T.; Bailey, Teresa R.

2003-01-01

This paper describes the deployment of a knowledge system to support learning of organizational knowledge at the Jet Propulsion Laboratory (JPL), a US national research laboratory whose mission is planetary exploration and to 'do what no one has done before.' Data collected over 19 weeks of operation were used to assess system performance with respect to design considerations, participation, effectiveness of communication mechanisms, and individual-based learning. These results are discussed in the context of organizational learning research and implications for practice.
A Visualization System for Predicting Learning Activities Using State Transition Graphs

ERIC Educational Resources Information Center

Okubo, Fumiya; Shimada, Atsushi; Taniguchi, Yuta

2017-01-01

In this paper, we present a system for visualizing learning logs of a course in progress together with predictions of learning activities of the following week and the final grades of students by state transition graphs. Data are collected from 236 students attending the course in progress and from 209 students attending the past course for…
Program Management Collection. "LINCS" Resource Collection News

ERIC Educational Resources Information Center

Literacy Information and Communication System, 2011

2011-01-01

This edition of "'LINCS' Resource Collection News" features the Program Management Collection, which covers the topics of Assessment, Learning Disabilities, and Program Improvement. Each month Collections News features one of the three "LINCS" (Literacy Information and Communication System) Resource Collections--Basic Skills, Program Management,…
Privacy Impact Assessment for the Confidential Business Information Records Access System for the Toxic Control Substances Act

EPA Pesticide Factsheets

This system collects submission data from the Toxic Substances Control Act (TSCA) and contact information for EPA contractors and employees who are CBI cleared. Learn how this data is collected, how it will be used, and the purpose of data collection.
Loans for Lifelong Learning.

ERIC Educational Resources Information Center

Fletcher, Mick, Ed.

This collection of eight papers looks at how a system of loans for lifelong learning in Great Britain and New Zealand might be positioned. It examines where such loans might work best and where they seem inappropriate. In particular, the collection assembles the available evidence about the role and impact of loans in the world of education and…
Privacy Impact Assessment for the Superfund Cost Recovery Package Imaging and On-Line System

EPA Pesticide Factsheets

This system collects financial data, associated documents, including travel, payroll and voucher data. Learn about how this data is collected, used, accessed, and the record retention policies for this data.
Quality evaluation on an e-learning system in continuing professional education of nurses.

PubMed

Lin, I-Chun; Chien, Yu-Mei; Chang, I-Chiu

2006-01-01

Maintaining high quality in Web-based learning is a powerful means of increasing the overall efficiency and effectiveness of distance learning. Many studies have evaluated Web-based learning but seldom evaluate from the information systems (IS) perspective. This study applied the famous IS Success model in measuring the quality of a Web-based learning system using a Web-based questionnaire for data collection. One hundred and fifty four nurses participated in the survey. Based on confirmatory factor analysis, the variables of the research model fit for measuring the quality of a Web-based learning system. As Web-based education continues to grow worldwide, the results of this study may assist the system adopter (hospital executives), the learner (nurses), and the system designers in making reasonable and informed judgments with regard to the quality of Web-based learning system in continuing professional education.
Privacy Impact Assessment for the Wellness Program Medical Records

EPA Pesticide Factsheets

The Wellness Program Medical Records System collects contact information and other Personally Identifiable Information (PII). Learn how this data is collected, used, accessed, the purpose of data collection, and record retention policies.
Privacy Impact Assessment for MyWorkplace

EPA Pesticide Factsheets

Users of this system can retrieve names, personal contact information, and hierarchy information about individuals. Learn how the data will be collected in the system, how it will be used, access to the data, and the purpose of data collection.
Modeling and Simulation of An Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning

ERIC Educational Resources Information Center

Al-Hmouz, A.; Shen, Jun; Al-Hmouz, R.; Yan, Jun

2012-01-01

With recent advances in mobile learning (m-learning), it is becoming possible for learning activities to occur everywhere. The learner model presented in our earlier work was partitioned into smaller elements in the form of learner profiles, which collectively represent the entire learning process. This paper presents an Adaptive Neuro-Fuzzy…
The Learning Healthcare System and Cardiovascular Care: A Scientific Statement From the American Heart Association.

PubMed

Maddox, Thomas M; Albert, Nancy M; Borden, William B; Curtis, Lesley H; Ferguson, T Bruce; Kao, David P; Marcus, Gregory M; Peterson, Eric D; Redberg, Rita; Rumsfeld, John S; Shah, Nilay D; Tcheng, James E

2017-04-04

The learning healthcare system uses health information technology and the health data infrastructure to apply scientific evidence at the point of clinical care while simultaneously collecting insights from that care to promote innovation in optimal healthcare delivery and to fuel new scientific discovery. To achieve these goals, the learning healthcare system requires systematic redesign of the current healthcare system, focusing on 4 major domains: science and informatics, patient-clinician partnerships, incentives, and development of a continuous learning culture. This scientific statement provides an overview of how these learning healthcare system domains can be realized in cardiovascular disease care. Current cardiovascular disease care innovations in informatics, data uses, patient engagement, continuous learning culture, and incentives are profiled. In addition, recommendations for next steps for the development of a learning healthcare system in cardiovascular care are presented. © 2017 American Heart Association, Inc.
K-4 Keepers Collection: A Service Learning Teacher Professional Development Program

NASA Astrophysics Data System (ADS)

Schwerin, T. G.; Blaney, L.; Myers, R. J.

2011-12-01

This poster focuses on the K-4 Keepers Collection, a service-learning program developed for the Earth System Science Education Alliance (ESSEA). ESSEA is a NOAA-, NASA- and NSF-supported program of teacher professional development that increases teachers' pedagogical content knowledge of climate-related Earth system science. The ESSEA program -- whether used in formal higher education courses or frequented by individual teachers who look for classroom activities in the environmental sciences -- provides a full suite of activities, lessons and units for teachers' use. The ESSEA network consists of 45 universities and education centers addressing climate and environment issues. K-4 Keepers Collection - ESSEA K-4 module collections focus on five specific themes of content development: spheres, Polar Regions, oceans, climate and service learning. The K-4 Keepers collection provides the opportunity for teachers to explore topics and learning projects promoting stewardship of the Earth's land, water, air and living things. Examination of the impacts of usage and pollution on water, air, land and living things through service-learning projects allows students to become informed stewards. All of the modules include short-term sample projects that either educate or initiate action involving caring for the environment. The K-4 Keepers course requires teachers to develop similar short or long-term projects for implementation in their classrooms. Objectives include: 1. Increase elementary teachers' environmental literacy addressing ocean, coastal, Great Lakes, stewardship, weather and climate science standards and using NOAA and NASA resources. 2. Develop elementary teachers' efficacy in employing service learning projects focused on conserving and preserving Earth's land, air, water and living things. 3. Prepare college faculty to incorporate service learning and environmental literacy into their courses through professional development and modules on the ESSEA website.
Short-Run Learning Dynamics under a Test-Based Accountability System: Evidence from Pakistan. Policy Research Working Paper 5465

ERIC Educational Resources Information Center

Barrera-Osorio, Felipe; Raju, Dhushyanth

2010-01-01

Low student learning is a common finding in much of the developing world. This paper uses a relatively unique dataset of five semiannual rounds of standardized test data to characterize and explain the short-term changes in student learning. The data are collected as part of the quality assurance system for a public-private partnership program…
Unraveling Students' Interaction around a Tangible Interface Using Multimodal Learning Analytics

ERIC Educational Resources Information Center

Schneider, Bertrand; Blikstein, Paulo

2015-01-01

In this paper, we describe multimodal learning analytics (MMLA) techniques to analyze data collected around an interactive learning environment. In a previous study (Schneider & Blikstein, submitted), we designed and evaluated a Tangible User Interface (TUI) where dyads of students were asked to learn about the human hearing system by…
Scaling Up and Zooming In: Big Data and Personalization in Language Learning

ERIC Educational Resources Information Center

Godwin-Jones, Robert

2017-01-01

From its earliest days, practitioners of computer-assisted language learning (CALL) have collected data from computer-mediated learning environments. Indeed, that has been a central aspect of the field from the beginning. Usage logs provided valuable insights into how systems were used and how effective they were for language learning. That…
Designing an Adaptive Web-Based Learning System Based on Students' Cognitive Styles Identified Online

ERIC Educational Resources Information Center

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

2012-01-01

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

EPA Pesticide Factsheets

This system collects Logical Evidence Files, which include data from workstations, laptops, SharePoint and document repositories. Learn how the data is collected, used, who has access, the purpose of data collection, and record retention policies.
The Collective Knowledge of Social Tags: Direct and Indirect Influences on Navigation, Learning, and Information Processing

ERIC Educational Resources Information Center

Cress, Ulrike; Held, Christoph; Kimmerle, Joachim

2013-01-01

Tag clouds generated in social tagging systems can capture the collective knowledge of communities. Using as a basis spreading activation theories, information foraging theory, and the co-evolution model of cognitive and social systems, we present here a model for an "extended information scent," which proposes that both collective and individual…
Overcoming Language and Literacy Barriers: Using Student Response System Technology to Collect Quality Program Evaluation Data from Immigrant Participants

ERIC Educational Resources Information Center

Walker, Susan K.; Mao, Dung

2016-01-01

Student response system technology was employed for parenting education program evaluation data collection with Karen adults. The technology, with translation and use of an interpreter, provided an efficient and secure method that respected oral language and collective learning preferences and accommodated literacy needs. The method was popular…

Assessment in the Learning Organization: Shifting the Paradigm.

ERIC Educational Resources Information Center

Costa, Arthur L., Ed.; Kallick, Bena, Ed.

This collection provides a new perspective for understanding what assessment can do to promote continuous improvement in education. The concepts of systems thinking, continued learning, mental models, shared vision, and team building are highlighted in the selections, which include: (1) "A Systems Approach to Assessing School Culture"…
What have we learned about intelligent transportation systems? Chapter 2, What have we learned about freeway incident and emergency management and electronic toll collection?

DOT National Transportation Integrated Search

2000-12-01

The intelligent infrastructure is often the most visible manifestation of intelligent transportation systems (ITS) along with roads, freeways, and incident management is often among the first ITS elements implemented. They can significantly contribut...
Participatory Monitoring and Evaluation--A Prototype Internal Learning System for Livelihood and Micro-Credit Programs.

ERIC Educational Resources Information Center

Noponen, Helzi

1997-01-01

An internal learning system (ILS), created for a development organization operating savings/credit programs with poor women, is used for data collection, monitoring, and evaluation. The ILS is participatory, pictorial, decentralized, and flexible; it documents socioeconomic impact and supports action planning. (SK)
How Positioning Shapes Opportunities for Student Agency in Schools

ERIC Educational Resources Information Center

York, Adam; Kirshner, Ben

2015-01-01

This chapter shows how student positioning by adults shapes opportunities for students to learn collective systemic agency including practices such as organizing others, developing a systemic analysis, and taking action in complex institutions, such as schools. We argue that these learning opportunities are expanded when education professionals…
Authentic scientific data collection in support of an integrative model-based class: A framework for student engagement in the classroom

NASA Astrophysics Data System (ADS)

Sorensen, A. E.; Dauer, J. M.; Corral, L.; Fontaine, J. J.

2017-12-01

A core component of public scientific literacy, and thereby informed decision-making, is the ability of individuals to reason about complex systems. In response to students having difficulty learning about complex systems, educational research suggests that conceptual representations, or mental models, may help orient student thinking. Mental models provide a framework to support students in organizing and developing ideas. The PMC-2E model is a productive tool in teaching ideas of modeling complex systems in the classroom because the conceptual representation framework allows for self-directed learning where students can externalize systems thinking. Beyond mental models, recent work emphasizes the importance of facilitating integration of authentic science into the formal classroom. To align these ideas, a university class was developed around the theme of carnivore ecology, founded on PMC-2E framework and authentic scientific data collection. Students were asked to develop a protocol, collect, and analyze data around a scientific question in partnership with a scientist, and then use data to inform their own learning about the system through the mental model process. We identified two beneficial outcomes (1) scientific data is collected to address real scientific questions at a larger scale and (2) positive outcomes for student learning and views of science. After participating in the class, students report enjoying class structure, increased support for public understanding of science, and shifts in nature of science and interest in pursuing science metrics on post-assessments. Further work is ongoing investigating the linkages between engaging in authentic scientific practices that inform student mental models, and how it might promote students' systems-thinking skills, implications for student views of nature of science, and development of student epistemic practices.
Quantitative Aspects about the Interactions of Professors in the Learning Management System during a Final Undergraduate Project Distance Discipline

ERIC Educational Resources Information Center

Cechinel, Cristian

2014-01-01

This work presents a quantitative study of the use of a Learning Management System (LMS) by the professors of a distance learning course, focused on the guidance given for the students' Final Undergraduate Project. Data taken from the logs of 34 professors in two distinct virtual rooms were collected. After pre-processing the data, a series of…
Privacy Impact Assessment for the Childcare Tuition Assistance Program

EPA Pesticide Factsheets

This system collects contact information and other Personally Identifiable Information (PII). Learn how this data is collected, how it will be used, access to the data, the purpose of data collection, and record retention policies for this data.
Privacy Impact Assessment for the Central Data Exchange

EPA Pesticide Factsheets

The Central Data Exchange collects personally identifiable information, including personal answers to security questions. Learn how this data will be collected in the system, how it will be used, access to the data, and the purpose of data collection.
75 FR 52373 - Submission for OMB Review; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2010-08-25

... Administration. Type of Review: NEW. Title of Collection: Evaluation of the Technology-Based Learning Grants. OMB... technology based learning. The initiative increases worker access to training while stimulating the development of innovative models and uses for technology based learning in the public workforce system. For...
Privacy Impact Assessment for the Integrated Financial Management System

EPA Pesticide Factsheets

This system contact information and Social Security Numbers for individuals who owe, or are owed money by the EPA. Learn how this data is collected, how it will be used, access to the data, the purpose of data collection, and record retention policies.
75 FR 15692 - Proposed Information Collection; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2010-03-30

..., reporting burden (time and financial resources) is minimized, collection instruments are clearly understood... Performance Reporting System, also referred to as the Learn and Serve Systems and Information Exchange (LASSIE... information is necessary for the proper performance of the functions of the Corporation, including whether the...
Building a Rapid Learning Health Care System for Oncology: Why CancerLinQ Collects Identifiable Health Information to Achieve Its Vision.

PubMed

Shah, Alaap; Stewart, Andrew K; Kolacevski, Andrej; Michels, Dina; Miller, Robert

2016-03-01

The ever-increasing volume of scientific discoveries, clinical knowledge, novel diagnostic tools, and treatment options juxtaposed with rising costs in health care challenge physicians to identify, prioritize, and use new information rapidly to deliver efficient and high-quality care to a growing and aging patient population. CancerLinQ, a rapid learning health care system in oncology, is an initiative of the American Society of Clinical Oncology and its Institute for Quality that addresses these challenges by collecting information from the electronic health records of large numbers of patients with cancer. CancerLinQ is first and foremost a quality measurement and reporting system through which oncologists can harness the depth and power of their patients' clinical records and other data to assess, monitor, and improve the care they deliver. However, in light of privacy and security concerns with regard to collection, use, and disclosure of patient information, this article addresses the need to collect protected health information as defined under the Health Insurance Portability and Accountability Act of 1996 to drive rapid learning through CancerLinQ. © 2016 by American Society of Clinical Oncology.
Game-powered machine learning

PubMed Central

Barrington, Luke; Turnbull, Douglas; Lanckriet, Gert

2012-01-01

Searching for relevant content in a massive amount of multimedia information is facilitated by accurately annotating each image, video, or song with a large number of relevant semantic keywords, or tags. We introduce game-powered machine learning, an integrated approach to annotating multimedia content that combines the effectiveness of human computation, through online games, with the scalability of machine learning. We investigate this framework for labeling music. First, a socially-oriented music annotation game called Herd It collects reliable music annotations based on the “wisdom of the crowds.” Second, these annotated examples are used to train a supervised machine learning system. Third, the machine learning system actively directs the annotation games to collect new data that will most benefit future model iterations. Once trained, the system can automatically annotate a corpus of music much larger than what could be labeled using human computation alone. Automatically annotated songs can be retrieved based on their semantic relevance to text-based queries (e.g., “funky jazz with saxophone,” “spooky electronica,” etc.). Based on the results presented in this paper, we find that actively coupling annotation games with machine learning provides a reliable and scalable approach to making searchable massive amounts of multimedia data. PMID:22460786
Game-powered machine learning.

PubMed

Barrington, Luke; Turnbull, Douglas; Lanckriet, Gert

2012-04-24

Searching for relevant content in a massive amount of multimedia information is facilitated by accurately annotating each image, video, or song with a large number of relevant semantic keywords, or tags. We introduce game-powered machine learning, an integrated approach to annotating multimedia content that combines the effectiveness of human computation, through online games, with the scalability of machine learning. We investigate this framework for labeling music. First, a socially-oriented music annotation game called Herd It collects reliable music annotations based on the "wisdom of the crowds." Second, these annotated examples are used to train a supervised machine learning system. Third, the machine learning system actively directs the annotation games to collect new data that will most benefit future model iterations. Once trained, the system can automatically annotate a corpus of music much larger than what could be labeled using human computation alone. Automatically annotated songs can be retrieved based on their semantic relevance to text-based queries (e.g., "funky jazz with saxophone," "spooky electronica," etc.). Based on the results presented in this paper, we find that actively coupling annotation games with machine learning provides a reliable and scalable approach to making searchable massive amounts of multimedia data.
Agent Reward Shaping for Alleviating Traffic Congestion

NASA Technical Reports Server (NTRS)

Tumer, Kagan; Agogino, Adrian

2006-01-01

Traffic congestion problems provide a unique environment to study how multi-agent systems promote desired system level behavior. What is particularly interesting in this class of problems is that no individual action is intrinsically "bad" for the system but that combinations of actions among agents lead to undesirable outcomes, As a consequence, agents need to learn how to coordinate their actions with those of other agents, rather than learn a particular set of "good" actions. This problem is ubiquitous in various traffic problems, including selecting departure times for commuters, routes for airlines, and paths for data routers. In this paper we present a multi-agent approach to two traffic problems, where far each driver, an agent selects the most suitable action using reinforcement learning. The agent rewards are based on concepts from collectives and aim to provide the agents with rewards that are both easy to learn and that if learned, lead to good system level behavior. In the first problem, we study how agents learn the best departure times of drivers in a daily commuting environment and how following those departure times alleviates congestion. In the second problem, we study how agents learn to select desirable routes to improve traffic flow and minimize delays for. all drivers.. In both sets of experiments,. agents using collective-based rewards produced near optimal performance (93-96% of optimal) whereas agents using system rewards (63-68%) barely outperformed random action selection (62-64%) and agents using local rewards (48-72%) performed worse than random in some instances.
Exploring Online Learning Data Using Fractal Dimensions. Research Report. ETS RR-17-15

ERIC Educational Resources Information Center

Guo, Hongwen

2017-01-01

Data collected from online learning and tutoring systems for individual students showed strong autocorrelation or dependence because of content connection, knowledge-based dependency, or persistence of learning behavior. When the response data show little dependence or negative autocorrelations for individual students, it is suspected that…
Exploring nursing e-learning systems success based on information system success model.

PubMed

Chang, Hui-Chuan; Liu, Chung-Feng; Hwang, Hsin-Ginn

2011-12-01

E-learning is thought of as an innovative approach to enhance nurses' care service knowledge. Extensive research has provided rich information toward system development, courses design, and nurses' satisfaction with an e-learning system. However, a comprehensive view in understanding nursing e-learning system success is an important but less focused-on topic. The purpose of this research was to explore net benefits of nursing e-learning systems based on the updated DeLone and McLean's Information System Success Model. The study used a self-administered questionnaire to collected 208 valid nurses' responses from 21 of Taiwan's medium- and large-scale hospitals that have implemented nursing e-learning systems. The result confirms that the model is sufficient to explore the nurses' use of e-learning systems in terms of intention to use, user satisfaction, and net benefits. However, while the three exogenous quality factors (system quality, information quality, and service quality) were all found to be critical factors affecting user satisfaction, only information quality showed a direct effect on the intention to use. This study provides useful insights for evaluating nursing e-learning system qualities as well as an understanding of nurses' intentions and satisfaction related to performance benefits.
The Relationship between Prospective Teachers' Belief Systems and Writing-to-Learn

ERIC Educational Resources Information Center

Demirbag, Mehmet; Kingir, Sevgi; Çepni, Salih

2015-01-01

The purpose of this study is to investigate the relationship between prospective teachers' belief systems and writing-to-learn. The participants comprised eight freshmen from the Department of Elementary Science Education at a public university in Turkey. The data were collected using semi-structured interviews.The results indicated that…
Factors Predicting Online University Students' Use of a Mobile Learning Management System (m-LMS)

ERIC Educational Resources Information Center

Joo, Young Ju; Kim, Nari; Kim, Nam Hee

2016-01-01

This study analyzed the relationships among factors predicting online university students' actual usage of a mobile learning management system (m-LMS) through a structural model. Data from 222 students in a Korean online university were collected to investigate integrated relationships among their perceived ease of use, perceived usefulness,…
Privacy Act System of Records: Confidential Business Information Tracking System, EPA-20

EPA Pesticide Factsheets

Learn about the Confidential Business Information Tracking System, including who is covered in the system, the purpose of data collection, routine uses for the system's records, and other security procedures.

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.
Privacy Impact Assessment for the Claims Office Master Files

EPA Pesticide Factsheets

The Claims Office Master Files System collects information on companies in debt to the EPA. Learn how this data is collected, how it will be used, access to the data, the purpose of data collection, and record retention policies for this data.
Privacy Impact Assessment for the Medical and Research Study Records of Human Volunteers

EPA Pesticide Factsheets

The Medical & Research Study Records of Human Volunteers System collects demographic and medical information on subjects who participate in research. Learn how this data is collected, used, access to the data, and the purpose of data collection.
77 FR 23264 - Agency Information Collection Activities: Proposed Collection; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2012-04-18

... the existing home health prospective payment system. The study team's analytic plan focuses on... populations that cannot be learned through analyses of administrative data. Form Number: CMS-10429 (OCN: 0938...
A Research Agenda for Professional Learning Communities: Moving Forward

ERIC Educational Resources Information Center

Hairon, Salleh; Goh, Jonathan Wee Pin; Chua, Catherine Siew Kheng; Wang, Li-yi

2017-01-01

Professional learning communities (PLCs) as a means of raising the teaching profession are becoming more attractive in education systems seeking to improve school improvement processes and outcomes. The main intention is to increase the individual and collective capacity of teachers so as to support school-wide capacity for teaching and learning.…
The Knowledge Web: Learning and Collaborating on the Net. Open and Distance Learning Series.

ERIC Educational Resources Information Center

Eisenstadt, Marc, Ed.; Vincent, Tom, Ed.

This book contains a collection of examples of new and effective uses of the World Wide Web in education from the Knowledge Media Institute (KMi) at the Open University (Great Britain). The publication is organized in three main sections--"Learning Media,""Collaboration and Presence," and "Knowledge Systems on the…
The Intentional Use of Learning Management Systems (LMS) to Improve Outcomes in Studio

ERIC Educational Resources Information Center

MacKenzie, Andrew; Muminovic, Milica; Oerlemans, Karin

2017-01-01

At the University of Canberra, Australia, the design and architecture faculty are trialling a range of approaches to incorporating learning technologies in the first year foundation studio to improve student learning outcomes. For this study researchers collected information on students' access to their assignment information and feedback from the…
Individual and Collective Reflection: How to Meet the Needs of Development in Teaching

ERIC Educational Resources Information Center

Nissila, Sade-Pirkko

2005-01-01

The following five core ideas explain how learning organizations function as wholes. The core ideas are central when school is examined as a learning organization. Personal mastery, mental models, team learning, shared visions and system thinking offer different angles to examine the organization. (1) Personal mastery. Without personal commitment,…
Methods and Frequency of Sharing of Learning Resources by Medical Students

ERIC Educational Resources Information Center

Judd, Terry; Elliott, Kristine

2017-01-01

University students have ready access to quality learning resources through learning management systems (LMS), online library collections and generic search tools. However, anecdotal evidence suggests they sometimes turn to peer-based sharing rather than sourcing resources directly. We know little about this practice--how common it is, what sort…
Complexity-Based Learning and Teaching: A Case Study in Higher Education

ERIC Educational Resources Information Center

Fabricatore, Carlo; López, María Ximena

2014-01-01

This paper presents a learning and teaching strategy based on complexity science and explores its impacts on a higher education game design course. The strategy aimed at generating conditions fostering individual and collective learning in educational complex adaptive systems, and led the design of the course through an iterative and adaptive…
Developing Collective Learning Extension for Rapidly Evolving Information System Courses

ERIC Educational Resources Information Center

Agarwal, Nitin; Ahmed, Faysal

2017-01-01

Due to rapidly evolving Information System (IS) technologies, instructors find themselves stuck in the constant game of catching up. On the same hand students find their skills obsolete almost as soon as they graduate. As part of IS curriculum and education, we need to emphasize more on teaching the students "how to learn" while keeping…
A Clustering Methodology of Web Log Data for Learning Management Systems

ERIC Educational Resources Information Center

Valsamidis, Stavros; Kontogiannis, Sotirios; Kazanidis, Ioannis; Theodosiou, Theodosios; Karakos, Alexandros

2012-01-01

Learning Management Systems (LMS) collect large amounts of data. Data mining techniques can be applied to analyse their web data log files. The instructors may use this data for assessing and measuring their courses. In this respect, we have proposed a methodology for analysing LMS courses and students' activity. This methodology uses a Markov…
Willingness to Adopt or Reuse an E-Learning System: The Perspectives of Self-Determination and Perceived Characteristics of Innovation

ERIC Educational Resources Information Center

Chang, Hsin Hsin; Fu, Chen Su; Huang, Ching Ying

2017-01-01

Adopting self-determination theory and the perceived characteristics of innovation as the theoretical background, this study investigates the school teachers' willingness to adopt and reuse an e-learning system. Three hundred and eighty-eight valid questionnaires were collected for analysis using structural equation modelling. The results…
Adding Innovation Diffusion Theory to the Technology Acceptance Model: Supporting Employees' Intentions to Use E-Learning Systems

ERIC Educational Resources Information Center

Lee, Yi-Hsuan; Hsieh, Yi-Chuan; Hsu, Chia-Ning

2011-01-01

This study intends to investigate factors affecting business employees' behavioral intentions to use the e-learning system. Combining the innovation diffusion theory (IDT) with the technology acceptance model (TAM), the present study proposes an extended technology acceptance model. The proposed model was tested with data collected from 552…
Privacy Act System of Records: Libby Asbestos Exposure Assessment Records, EPA-48

EPA Pesticide Factsheets

Learn about the Libby Asbestos Exposure Assessment Records System, including who is covered in the system, the purpose of data collection, routine uses for the system's records, and other security procedure.
Privacy Act System of Records: EPA Telecommunications Detail Records, EPA-32

EPA Pesticide Factsheets

Learn more about the EPA Telecommunications Detail Records System, including who is covered in the system, the purpose of data collection, routine uses for the system's records, and other security procedures.
Privacy Impact Assessment for the Financial Audit Version of the Office of Inspector General AutoAudit

EPA Pesticide Factsheets

This system collects contact information and other Personally Identifiable Information (PII). Learn how this data is collected, how it will be used, access to the data, the purpose of data collection, and record retention policies for this data.
Privacy Impact Assessment for the Request and Appeal Files for the Freedom of Information Act

EPA Pesticide Factsheets

This system collects contact information from Freedom of Information Act (FOIA) requestors. Learn how this data is collected, how it will be used, access to the data, the purpose of data collection, and record retention policies for this data.
Operation and Maintenance of Wastewater Collection Systems: A Field Study Training Program.

ERIC Educational Resources Information Center

California State Univ., Sacramento. Dept. of Civil Engineering.

This manual was prepared by experienced wastewater collection system workers to provide a home study course to develop new qualified workers and expand the abilities of existing workers. The objective of this manual is to provide the knowledge and skills necessary for certification. Participants learn to effectively operate and maintain wastewater…
Reinforced dynamics for enhanced sampling in large atomic and molecular systems

NASA Astrophysics Data System (ADS)

Zhang, Linfeng; Wang, Han; E, Weinan

2018-03-01

A new approach for efficiently exploring the configuration space and computing the free energy of large atomic and molecular systems is proposed, motivated by an analogy with reinforcement learning. There are two major components in this new approach. Like metadynamics, it allows for an efficient exploration of the configuration space by adding an adaptively computed biasing potential to the original dynamics. Like deep reinforcement learning, this biasing potential is trained on the fly using deep neural networks, with data collected judiciously from the exploration and an uncertainty indicator from the neural network model playing the role of the reward function. Parameterization using neural networks makes it feasible to handle cases with a large set of collective variables. This has the potential advantage that selecting precisely the right set of collective variables has now become less critical for capturing the structural transformations of the system. The method is illustrated by studying the full-atom explicit solvent models of alanine dipeptide and tripeptide, as well as the system of a polyalanine-10 molecule with 20 collective variables.

Privacy Act System of Records: Employee Counseling and Assistance Program Records, EPA-27

EPA Pesticide Factsheets

Learn about the Employee Counseling and Assistance Program Records System, including who is covered in the system, the purpose of data collection, routine uses for the system's records, and other security procedures.
Privacy Act System of Records: EPA Personnel Emergency Contact Files, EPA-44

EPA Pesticide Factsheets

Learn about the EPA Personnel Emergency Contact Files System, including including who is covered in the system, the purpose of data collection, routine uses for the system's records, and other security procedure.
Privacy Act System of Records: Invention Reports Submitted to the EPA, EPA-38

EPA Pesticide Factsheets

Learn about the Invention Reports Submitted to the EPA System, including who is covered in the system, the purpose of data collection, routine uses for the system's records, and other security procedures.
A Usability Study of Users' Perceptions toward a Multimedia Computer-Assisted Learning Tool for Neuroanatomy

ERIC Educational Resources Information Center

Gould, Douglas J.; Terrell, Mark A.; Fleming, Jo

2008-01-01

This usability study evaluated users' perceptions of a multimedia prototype for a new e-learning tool: Anatomy of the Central Nervous System: A Multimedia Course. Usability testing is a collection of formative evaluation methods that inform the developmental design of e-learning tools to maximize user acceptance, satisfaction, and adoption.…
Using Noninvasive Wearable Computers to Recognize Human Emotions from Physiological Signals

NASA Astrophysics Data System (ADS)

Lisetti, Christine Lætitia; Nasoz, Fatma

2004-12-01

We discuss the strong relationship between affect and cognition and the importance of emotions in multimodal human computer interaction (HCI) and user modeling. We introduce the overall paradigm for our multimodal system that aims at recognizing its users' emotions and at responding to them accordingly depending upon the current context or application. We then describe the design of the emotion elicitation experiment we conducted by collecting, via wearable computers, physiological signals from the autonomic nervous system (galvanic skin response, heart rate, temperature) and mapping them to certain emotions (sadness, anger, fear, surprise, frustration, and amusement). We show the results of three different supervised learning algorithms that categorize these collected signals in terms of emotions, and generalize their learning to recognize emotions from new collections of signals. We finally discuss possible broader impact and potential applications of emotion recognition for multimodal intelligent systems.
Apprentissage auto-dirige: Quand les chiffres parlent (Self-Directed Learning: Figures Speak for Themselves).

ERIC Educational Resources Information Center

Abe, Daniele; Gremmo, Marie-Jose

The functioning of the semi-autonomous learning system (SAAS) at the Centre de Recherches et d'Applications Pedagogiques en Langues (CRAPEL) was surveyed for the academic year 1980-81. Detailed data were collected about learners' aims, language level, and their assessment of the SAAS, as well as about the way they actually used the system. This…
Classroom Assessments of 6000 Teachers: What Do the Results Show about the Effectiveness of Teaching and Learning?

ERIC Educational Resources Information Center

Hill, Flo H.; And Others

This paper presents the results of a series of summary analyses of descriptive statistics concerning 5,720 Louisiana teachers who were assessed with the System for Teaching and Learning Assessment and Review (STAR)--a comprehensive on-the-job statewide teacher assessment system--during the second pilot year (1989-90). Data were collected by about…
Language Learning Activities of Distance EFL Learners in the Turkish Open Education System as the Indicator of Their Learner Autonomy

ERIC Educational Resources Information Center

Altunay, Dilek

2013-01-01

This study investigates the noncompulsory language learning activities performed by a group of distance EFL learners in the Turkish Open Education System. Performance of these activities has been considered as an indicator of their learner autonomy. The data were collected through an online questionnaire and interviews. The study shows that in…
An Examination of Faculty and Student Online Activity: Predictive Relationships of Student Academic Success in a Learning Management System (LMS)

ERIC Educational Resources Information Center

Stamm, Randy Lee

2013-01-01

The purpose of this mixed method research study was to examine relationships in student and instructor activity logs and student performance benchmarks specific to enabling early intervention by the instructor in a Learning Management System (LMS). Instructor feedback was collected through a survey instrument to demonstrate perceived importance of…
Examination of Factors Impacting Student Satisfaction with a New Learning Management System

ERIC Educational Resources Information Center

Green, Lucy Santos; Inan, Fethi A.; Denton, Bree

2012-01-01

The purpose of this study was to determine factors that influenced student satisfaction with a new learning management system and to identify which of these factors were most important. The data was collected using an an online survey tool that was administered to students enrolled in courses designed and taught by faculty who participated in a…
Web-Based Learning Information System for Web 3.0

NASA Astrophysics Data System (ADS)

Rego, Hugo; Moreira, Tiago; García-Peñalvo, Francisco Jose

With the emergence of Web/eLearning 3.0 we have been developing/adjusting AHKME in order to face this great challenge. One of our goals is to allow the instructional designer and teacher to access standardized resources and evaluate the possibility of integration and reuse in eLearning systems, not only content but also the learning strategy. We have also integrated some collaborative tools for the adaptation of resources, as well as the collection of feedback from users to provide feedback to the system. We also provide tools for the instructional designer to create/customize specifications/ontologies to give structure and meaning to resources, manual and automatic search with recommendation of resources and instructional design based on the context, as well as recommendation of adaptations in learning resources. We also consider the concept of mobility and mobile technology applied to eLearning, allowing access by teachers and students to learning resources, regardless of time and space.
Privacy Impact Assessment for the Criminal Investigate Index and Files for the Office of Criminal Enforcement, Forensics and Training

EPA Pesticide Factsheets

This system collects contact and other information related to the Criminal Investigative Division's cases. Learn how this data is collected, how it will be used, access to the data, the purpose of data collection, and record retention policies.
Modeling and enhanced sampling of molecular systems with smooth and nonlinear data-driven collective variables

NASA Astrophysics Data System (ADS)

Hashemian, Behrooz; Millán, Daniel; Arroyo, Marino

2013-12-01

Collective variables (CVs) are low-dimensional representations of the state of a complex system, which help us rationalize molecular conformations and sample free energy landscapes with molecular dynamics simulations. Given their importance, there is need for systematic methods that effectively identify CVs for complex systems. In recent years, nonlinear manifold learning has shown its ability to automatically characterize molecular collective behavior. Unfortunately, these methods fail to provide a differentiable function mapping high-dimensional configurations to their low-dimensional representation, as required in enhanced sampling methods. We introduce a methodology that, starting from an ensemble representative of molecular flexibility, builds smooth and nonlinear data-driven collective variables (SandCV) from the output of nonlinear manifold learning algorithms. We demonstrate the method with a standard benchmark molecule, alanine dipeptide, and show how it can be non-intrusively combined with off-the-shelf enhanced sampling methods, here the adaptive biasing force method. We illustrate how enhanced sampling simulations with SandCV can explore regions that were poorly sampled in the original molecular ensemble. We further explore the transferability of SandCV from a simpler system, alanine dipeptide in vacuum, to a more complex system, alanine dipeptide in explicit water.
Modeling and enhanced sampling of molecular systems with smooth and nonlinear data-driven collective variables.

PubMed

Hashemian, Behrooz; Millán, Daniel; Arroyo, Marino

2013-12-07

Collective variables (CVs) are low-dimensional representations of the state of a complex system, which help us rationalize molecular conformations and sample free energy landscapes with molecular dynamics simulations. Given their importance, there is need for systematic methods that effectively identify CVs for complex systems. In recent years, nonlinear manifold learning has shown its ability to automatically characterize molecular collective behavior. Unfortunately, these methods fail to provide a differentiable function mapping high-dimensional configurations to their low-dimensional representation, as required in enhanced sampling methods. We introduce a methodology that, starting from an ensemble representative of molecular flexibility, builds smooth and nonlinear data-driven collective variables (SandCV) from the output of nonlinear manifold learning algorithms. We demonstrate the method with a standard benchmark molecule, alanine dipeptide, and show how it can be non-intrusively combined with off-the-shelf enhanced sampling methods, here the adaptive biasing force method. We illustrate how enhanced sampling simulations with SandCV can explore regions that were poorly sampled in the original molecular ensemble. We further explore the transferability of SandCV from a simpler system, alanine dipeptide in vacuum, to a more complex system, alanine dipeptide in explicit water.
Clinical data miner: an electronic case report form system with integrated data preprocessing and machine-learning libraries supporting clinical diagnostic model research.

PubMed

Installé, Arnaud Jf; Van den Bosch, Thierry; De Moor, Bart; Timmerman, Dirk

2014-10-20

Using machine-learning techniques, clinical diagnostic model research extracts diagnostic models from patient data. Traditionally, patient data are often collected using electronic Case Report Form (eCRF) systems, while mathematical software is used for analyzing these data using machine-learning techniques. Due to the lack of integration between eCRF systems and mathematical software, extracting diagnostic models is a complex, error-prone process. Moreover, due to the complexity of this process, it is usually only performed once, after a predetermined number of data points have been collected, without insight into the predictive performance of the resulting models. The objective of the study of Clinical Data Miner (CDM) software framework is to offer an eCRF system with integrated data preprocessing and machine-learning libraries, improving efficiency of the clinical diagnostic model research workflow, and to enable optimization of patient inclusion numbers through study performance monitoring. The CDM software framework was developed using a test-driven development (TDD) approach, to ensure high software quality. Architecturally, CDM's design is split over a number of modules, to ensure future extendability. The TDD approach has enabled us to deliver high software quality. CDM's eCRF Web interface is in active use by the studies of the International Endometrial Tumor Analysis consortium, with over 4000 enrolled patients, and more studies planned. Additionally, a derived user interface has been used in six separate interrater agreement studies. CDM's integrated data preprocessing and machine-learning libraries simplify some otherwise manual and error-prone steps in the clinical diagnostic model research workflow. Furthermore, CDM's libraries provide study coordinators with a method to monitor a study's predictive performance as patient inclusions increase. To our knowledge, CDM is the only eCRF system integrating data preprocessing and machine-learning libraries. This integration improves the efficiency of the clinical diagnostic model research workflow. Moreover, by simplifying the generation of learning curves, CDM enables study coordinators to assess more accurately when data collection can be terminated, resulting in better models or lower patient recruitment costs.
Extraterrestrial Materials: The Role of Synchrotron Radiation Analyses in the Study of Our Solar System

ScienceCinema

Sutton, Stephen R. [University of Chicago, Chicago, Illinois, United States

2017-12-09

Sample-return missions and natural collection processes have provided us with a surprisingly extensive collection of matter from Solar System bodies other than the Earth. These collections include samples from the Moon, Mars, asteroids, interplanetary dust, and, recently, from the Sun (solar wind) and a comet. This presentation will describe some of these materials, how they were collected, and what we have learned from them. Synchrotron radiation analyses of these materials are playing an increasingly valuable role in unraveling the histories and properities of the parent Solar System bodies.
Spawning Ideas--Moving from Ideas to Action: Quality Tools for Collective Problem-Solving and Continuous Learning.

ERIC Educational Resources Information Center

Flor, Richard F.; Troskey, Matthew D.

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

PubMed Central

Tribello, Gareth A.; Ceriotti, Michele; Parrinello, Michele

2010-01-01

A new self-learning algorithm for accelerated dynamics, reconnaissance metadynamics, is proposed that is able to work with a very large number of collective coordinates. Acceleration of the dynamics is achieved by constructing a bias potential in terms of a patchwork of one-dimensional, locally valid collective coordinates. These collective coordinates are obtained from trajectory analyses so that they adapt to any new features encountered during the simulation. We show how this methodology can be used to enhance sampling in real chemical systems citing examples both from the physics of clusters and from the biological sciences. PMID:20876135
Building environment analysis based on temperature and humidity for smart energy systems.

PubMed

Yun, Jaeseok; Won, Kwang-Ho

2012-10-01

In this paper, we propose a new HVAC (heating, ventilation, and air conditioning) control strategy as part of the smart energy system that can balance occupant comfort against building energy consumption using ubiquitous sensing and machine learning technology. We have developed ZigBee-based wireless sensor nodes and collected realistic temperature and humidity data during one month from a laboratory environment. With the collected data, we have established a building environment model using machine learning algorithms, which can be used to assess occupant comfort level. We expect the proposed HVAC control strategy will be able to provide occupants with a consistently comfortable working or home environment.
Privacy Act System of Records: Office of the Inspector General AutoAudit, EPA-50

EPA Pesticide Factsheets

Learn more about the Office of the Inspector General AutoAudit System, including who is covered in the system, the purpose of data collection, routine uses for the system's records, and other security procedures.

Privacy Act System of Records: Medical and Research Study Records of Human Volunteers, EPA-34

EPA Pesticide Factsheets

Learn about the Medical and Research Study Records of Human Volunteers System, including who is covered in the system, the purpose of data collection, routine uses for the system's records, and other security procedures.
Learning as an Apprentice in Sweden: A Comparative Study on Affordances for Vocational Learning in School and Work Life Apprentice Education

ERIC Educational Resources Information Center

Fjellström, Magnus; Kristmansson, Per

2016-01-01

Purpose: The purpose of this paper is to compare, and identify both similarities and differences in, affordances for vocational learning in two contrasting education systems, for construction worker and shop salesperson apprentices, in Swedish contexts. Design/methodology/approach: Data were collected through interviews and observations in two…
Attitudes to the Rights and Rewards for Author Contributions to Repositories for Teaching and Learning

ERIC Educational Resources Information Center

Bates, Melanie; Loddington, Steve; Manuel, Sue; Oppenheim, Charles

2007-01-01

In the United Kingdom over the past few years there has been a dramatic growth of national and regional repositories to collect and disseminate resources related to teaching and learning. Most notable of these are the Joint Information Systems Committee's Online Repository for [Learning and Teaching] Materials as well as the Higher Education…
Learning Resource Center at the Baraboo Campus of the University of Wisconsin Center System.

ERIC Educational Resources Information Center

Umhoefer, Aural

The Learning Resource Center (LRC) at the Baraboo campus of the University of Wisconsin was designed to be an integral part of the teaching program, and to embody the multimedia approach to individual self-paced learning by using the most appropriate medium or combination of media for a given instructional situation. The collection includes books,…
Replacing the Lab Manual with a Learning Management System in Physics Investigations for K-4 Pre-Service Teachers

ERIC Educational Resources Information Center

Sobolewski, Stanley; Numan, Muhammad Z.

2018-01-01

The traditional laboratory investigation uses a procedure written on paper; students then record their responses on a supplied data page or laboratory notebook. In an attempt to make this process more efficient, the use of a Learning Management System (in this case D2L) was used to present the material and collect student feedback. Each student…
An adaptive learning control system for aircraft

NASA Technical Reports Server (NTRS)

Mekel, R.; Nachmias, S.

1978-01-01

A learning control system and its utilization as a flight control system for F-8 Digital Fly-By-Wire (DFBW) research aircraft is studied. The system has the ability to adjust a gain schedule to account for changing plant characteristics and to improve its performance and the plant's performance in the course of its own operation. Three subsystems are detailed: (1) the information acquisition subsystem which identifies the plant's parameters at a given operating condition; (2) the learning algorithm subsystem which relates the identified parameters to predetermined analytical expressions describing the behavior of the parameters over a range of operating conditions; and (3) the memory and control process subsystem which consists of the collection of updated coefficients (memory) and the derived control laws. Simulation experiments indicate that the learning control system is effective in compensating for parameter variations caused by changes in flight conditions.
Privacy Impact Assessment for the Training Registration and Administration Records for the Office of Criminal Enforcement, Forensics and Training and the National Enforcement Training Institute

EPA Pesticide Factsheets

This system collects student data for NETI's online university, including contact information and course data. Learn how this data is collected, used, accessed, the purpose of data collection, and record retention policies for the data.
Privacy Act System of Records: Freedom of Information Act Request and Appeal File, EPA-9

EPA Pesticide Factsheets

Learn more about the Freedom of Information Act Request and Appeal File System, including who is covered in the system, the purpose of data collection, routine uses for the system's records, and other security procedures.
Neurodynamical model of collective brain

NASA Technical Reports Server (NTRS)

Zak, Michail

1992-01-01

A dynamical system which mimics collective purposeful activities of a set of units of intelligence is introduced and discussed. A global control of the unit activities is replaced by the probabilistic correlations between them. These correlations are learned during a long term period of performing collective tasks, and are stored in the synaptic interconnections. The model is represented by a system of ordinary differential equations with terminal attractors and repellers, and does not contain any man-made digital devices.
Construction of teacher knowledge in context: Preparing elementary teachers to teach mathematics and science

NASA Astrophysics Data System (ADS)

Lowery, Maye Norene Vail

1998-12-01

The purposes of this study were to further the understanding of how preservice teacher construct teacher knowledge and pedagogical content knowledge of elementary mathematics and science and to determine the extent of that knowledge in a school-based setting. Preservice teachers, university instructors, inservice teachers, and other school personnel were involved in this context-specific study. Evidence of the preservice teachers' knowledge construction (its acquisition, its dimensions, and the social context) was collected through the use of a qualitative methodology. Collected data included individual and group interviews, course documents, artifacts, and preservice teaching portfolios. Innovative aspects of this integrated mathematics and science elementary methods course included standards-based instruction with immediate access to field experiences. Grade-level teams of preservice and inservice teachers planned and implemented lessons in mathematics and science for elementary students. An on-site, portable classroom building served as a mathematics and science teaching and learning laboratory. A four-stage analysis was performed, revealing significant patterns of learning. An ecosystem of learning within a constructivist learning environment was identified to contain three systems: the university system; the school system; and the cohort of learners system. A mega system for the construction of teacher knowledge was revealed in the final analysis. Learning venues were discovered to be the conduits of learning in a situated learning context. Analysis and synthesis of data revealed an extensive acquisition of teacher knowledge and pedagogical content knowledge through identified learning components. Patience, flexibility, and communication were identified as necessities for successful teaching. Learning components included: collaboration with inservice teachers; implementation of discovery learning and hands-on/minds-on learning; small groupwork; lesson planning; classroom management; and application of standards-based instruction. Prolonged, extensive classroom involvement provided familiarity with the ability levels of elementary students. Gains in positive attitudes and confidence in teaching mathematics and science were identified as direct results of this experience. This may be attributed to the immersion in the school-based setting (hands-on) and the standards-based approach (minds-on) methods course. The results are written in case study form using thick description with an emphasis on preservice teachers.
Pilot of a System for Collecting Daily Classroom Data on Learning by Using Microcomputers.

ERIC Educational Resources Information Center

Mayer, Victor J.; Raudebaugh, William

This report describes a microcomputer system which collects data from students in classrooms on a daily basis and is then used to evaluate concept achievement and attitude changes through a time series analysis. Two pilot studies in two junior high schools in Ohio are detailed, where eighth grade students' progress in an earth science study unit…
A Study of Social Cognitive Theory: The Relationship between Professional Learning Communities and Collective Teacher Efficacy in International School Settings

ERIC Educational Resources Information Center

Hardin, James

2010-01-01

In "Self-Efficacy: The Exercise of Control" (1997), Albert Bandura writes, "Teachers operate collectively within an interactive social system rather than as isolates" (p. 243). Bandura's attention to the existence of the communal systems that exist in schools is an appreciation shared by many educational reformers, especially those who advocate…
A Security Monitoring Framework For Virtualization Based HEP Infrastructures

NASA Astrophysics Data System (ADS)

Gomez Ramirez, A.; Martinez Pedreira, M.; Grigoras, C.; Betev, L.; Lara, C.; Kebschull, U.; ALICE Collaboration

2017-10-01

High Energy Physics (HEP) distributed computing infrastructures require automatic tools to monitor, analyze and react to potential security incidents. These tools should collect and inspect data such as resource consumption, logs and sequence of system calls for detecting anomalies that indicate the presence of a malicious agent. They should also be able to perform automated reactions to attacks without administrator intervention. We describe a novel framework that accomplishes these requirements, with a proof of concept implementation for the ALICE experiment at CERN. We show how we achieve a fully virtualized environment that improves the security by isolating services and Jobs without a significant performance impact. We also describe a collected dataset for Machine Learning based Intrusion Prevention and Detection Systems on Grid computing. This dataset is composed of resource consumption measurements (such as CPU, RAM and network traffic), logfiles from operating system services, and system call data collected from production Jobs running in an ALICE Grid test site and a big set of malware samples. This malware set was collected from security research sites. Based on this dataset, we will proceed to develop Machine Learning algorithms able to detect malicious Jobs.
What can ecosystems learn? Expanding evolutionary ecology with learning theory.

PubMed

Power, Daniel A; Watson, Richard A; Szathmáry, Eörs; Mills, Rob; Powers, Simon T; Doncaster, C Patrick; Czapp, Błażej

2015-12-08

The structure and organisation of ecological interactions within an ecosystem is modified by the evolution and coevolution of the individual species it contains. Understanding how historical conditions have shaped this architecture is vital for understanding system responses to change at scales from the microbial upwards. However, in the absence of a group selection process, the collective behaviours and ecosystem functions exhibited by the whole community cannot be organised or adapted in a Darwinian sense. A long-standing open question thus persists: Are there alternative organising principles that enable us to understand and predict how the coevolution of the component species creates and maintains complex collective behaviours exhibited by the ecosystem as a whole? Here we answer this question by incorporating principles from connectionist learning, a previously unrelated discipline already using well-developed theories on how emergent behaviours arise in simple networks. Specifically, we show conditions where natural selection on ecological interactions is functionally equivalent to a simple type of connectionist learning, 'unsupervised learning', well-known in neural-network models of cognitive systems to produce many non-trivial collective behaviours. Accordingly, we find that a community can self-organise in a well-defined and non-trivial sense without selection at the community level; its organisation can be conditioned by past experience in the same sense as connectionist learning models habituate to stimuli. This conditioning drives the community to form a distributed ecological memory of multiple past states, causing the community to: a) converge to these states from any random initial composition; b) accurately restore historical compositions from small fragments; c) recover a state composition following disturbance; and d) to correctly classify ambiguous initial compositions according to their similarity to learned compositions. We examine how the formation of alternative stable states alters the community's response to changing environmental forcing, and we identify conditions under which the ecosystem exhibits hysteresis with potential for catastrophic regime shifts. This work highlights the potential of connectionist theory to expand our understanding of evo-eco dynamics and collective ecological behaviours. Within this framework we find that, despite not being a Darwinian unit, ecological communities can behave like connectionist learning systems, creating internal conditions that habituate to past environmental conditions and actively recalling those conditions.
Skill acquisition while operating in-vehicle information systems: interface design determines the level of safety-relevant distractions.

PubMed

Jahn, Georg; Krems, Josef F; Gelau, Christhard

2009-04-01

This study tested whether the ease of learning to use human-machine interfaces of in-vehicle information systems (IVIS) can be assessed at standstill. Assessing the attentional demand of IVIS should include an evaluation of ease of learning, because the use of IVIS at low skill levels may create safety-relevant distractions. Skill acquisition in operating IVIS was quantified by fitting the power law of practice to training data sets collected in a driving study and at standstill. Participants practiced manual destination entry with two route guidance systems differing in cognitive demand. In Experiment 1, a sample of middle-aged participants was trained while steering routes of varying driving demands. In Experiment 2, another sample of middle-aged participants was trained at standstill. In Experiment 1, display glance times were less affected by driving demands than by total task times and decreased at slightly higher speed-up rates (0.02 higher on average) than task times collected at standstill in Experiment 2. The system interface that minimized cognitive demand was operated more quickly and was easier to learn. Its system delays increased static task times, which still predicted 58% of variance in display glance times compared with even 76% for the second system. The ease of learning to use an IVIS interface and the decrease in attentional demand with training can be assessed at standstill. Fitting the power law of practice to static task times yields parameters that predict display glance times while driving, which makes it possible to compare interfaces with regard to ease of learning.
ACT-R Electronic Bookshelf: An Adaptive System To Support Learning ACT-R on the Web.

ERIC Educational Resources Information Center

Brusilovsky, Peter; Anderson, John

This paper describes the electronic ACT-R Bookshelf, a system which supports learning ACT-R, a well-known theory in the field of cognitive psychology, over the World Wide Web. ACT-R Bookshelf is a collection of electronic books on various aspects of ACT-R. The primary role of ACT-R Bookshelf is to serve as a 24-hour information resource for…
A Symphony of Skills

ERIC Educational Resources Information Center

Sharratt, Lyn; Planche, Beate

2018-01-01

Skilled collaborative leaders are in high demand in schools, school systems, and districts worldwide. The success of schools as learning organizations hinges on how well people can work together as they seek to build collective capacity and problem solve to improve student outcomes. Collaborative learning has now emerged as the vital strategy for…
Expeditionary Learning in Information Systems: Definition, Implementation, and Assessment

ERIC Educational Resources Information Center

Abrahams, Alan S.; Singh, Tirna

2013-01-01

In the natural sciences, collecting, cataloguing, and comparing living specimens have long been a popular, collaborative mode of discovery and learning. New species are discovered, and the relationships between species are theorized. From Aristotle's "History of Animals" to Darwin's "On the Origin of Species", and beyond, this…
Designing Learning Object Repositories as Systems for Managing Educational Communities Knowledge

ERIC Educational Resources Information Center

Sampson, Demetrios G.; Zervas, Panagiotis

2013-01-01

Over the past years, a number of international initiatives that recognize the importance of sharing and reusing digital educational resources among educational communities through the use of Learning Object Repositories (LORs) have emerged. Typically, these initiatives focus on collecting digital educational resources that are offered by their…
Phases and Patterns of Group Development in Virtual Learning Teams

ERIC Educational Resources Information Center

Yoon, Seung Won; Johnson, Scott D.

2008-01-01

With the advancement of Internet communication technologies, distributed work groups have great potential for remote collaboration and use of collective knowledge. Adopting the Complex Adaptive System (CAS) perspective (McGrath, Arrow, & Berdhal, "Personal Soc Psychol Rev" 4 (2000) 95), which views virtual learning teams as an adaptive and…

Learning in Authentic Contexts: Projects Integrating Spatial Technologies and Fieldwork

ERIC Educational Resources Information Center

Huang, Kuo-Hung

2011-01-01

In recent years, professional practice has been an issue of concern in higher education. The purpose of this study is to design students' projects to facilitate collaborative learning in authentic contexts. Ten students majoring in Management Information Systems conducted fieldwork with spatial technologies to collect data and provided information…
Adolescents' Perceptions of Chronic Self-Concept, Peer Relations, and Learning Conditions

ERIC Educational Resources Information Center

Liu, Weiping; Eckert, Thomas

2014-01-01

Based on Lewin's Field Theory, Bronfenbrenner's Bioecological Systems Theory and social network analysis, the authors collected data from 405 Chinese adolescents about their peer relations, chronic self-concept levels and learning condition variables through questionnaire distributing, and from their teachers about their annual average academic…
Lessons Learned while Exploring Cloud-Native Architectures for NASA EOSDIS Applications and Systems

NASA Technical Reports Server (NTRS)

Pilone, Dan

2016-01-01

As new, high data rate missions begin collecting data, the NASAs Earth Observing System Data and Information System (EOSDIS) archive is projected to grow roughly 20x to over 300PBs by 2025. To prepare for the dramatic increase in data and enable broad scientific inquiry into larger time series and datasets, NASA has been exploring the impact of applying cloud technologies throughout EOSDIS. In this talk we will provide an overview of NASAs prototyping and lessons learned in applying cloud architectures.
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
Building Environment Analysis based on Temperature and Humidity for Smart Energy Systems

PubMed Central

Yun, Jaeseok; Won, Kwang-Ho

2012-01-01

In this paper, we propose a new HVAC (heating, ventilation, and air conditioning) control strategy as part of the smart energy system that can balance occupant comfort against building energy consumption using ubiquitous sensing and machine learning technology. We have developed ZigBee-based wireless sensor nodes and collected realistic temperature and humidity data during one month from a laboratory environment. With the collected data, we have established a building environment model using machine learning algorithms, which can be used to assess occupant comfort level. We expect the proposed HVAC control strategy will be able to provide occupants with a consistently comfortable working or home environment. PMID:23202004
Learning management system and e-learning tools: an experience of medical students' usage and expectations.

PubMed

Back, David A; Behringer, Florian; Haberstroh, Nicole; Ehlers, Jan P; Sostmann, Kai; Peters, Harm

2016-08-20

To investigate medical students´ utilization of and problems with a learning management system and its e-learning tools as well as their expectations on future developments. A single-center online survey has been carried out to investigate medical students´ (n = 505) usage and perception concerning the learning management system Blackboard, and provided e-learning tools. Data were collected with a standardized questionnaire consisting of 70 items and analyzed by quantitative and qualitative methods. The participants valued lecture notes (73.7%) and Wikipedia (74%) as their most important online sources for knowledge acquisition. Missing integration of e-learning into teaching was seen as the major pitfall (58.7%). The learning management system was mostly used for study information (68.3%), preparation of exams (63.3%) and lessons (54.5%). Clarity (98.3%), teaching-related contexts (92.5%) and easy use of e-learning offers (92.5%) were rated highest. Interactivity was most important in free-text comments (n = 123). It is desired that contents of a learning management system support an efficient learning. Interactivity of tools and their conceptual integration into face-to-face teaching are important for students. The learning management system was especially important for organizational purposes and the provision of learning materials. Teachers should be aware that free online sources such as Wikipedia enjoy a high approval as source of knowledge acquisition. This study provides an empirical basis for medical schools and teachers to improve their offerings in the field of digital learning for their students.
Learning management system and e-learning tools: an experience of medical students' usage and expectations

PubMed Central

Back, David A.; Behringer, Florian; Haberstroh, Nicole; Ehlers, Jan P.; Sostmann, Kai

2016-01-01

Objectives To investigate medical students´ utilization of and problems with a learning management system and its e-learning tools as well as their expectations on future developments. Methods A single-center online survey has been carried out to investigate medical students´ (n = 505) usage and perception concerning the learning management system Blackboard, and provided e-learning tools. Data were collected with a standardized questionnaire consisting of 70 items and analyzed by quantitative and qualitative methods. Results The participants valued lecture notes (73.7%) and Wikipedia (74%) as their most important online sources for knowledge acquisition. Missing integration of e-learning into teaching was seen as the major pitfall (58.7%). The learning management system was mostly used for study information (68.3%), preparation of exams (63.3%) and lessons (54.5%). Clarity (98.3%), teaching-related contexts (92.5%) and easy use of e-learning offers (92.5%) were rated highest. Interactivity was most important in free-text comments (n = 123). Conclusions It is desired that contents of a learning management system support an efficient learning. Interactivity of tools and their conceptual integration into face-to-face teaching are important for students. The learning management system was especially important for organizational purposes and the provision of learning materials. Teachers should be aware that free online sources such as Wikipedia enjoy a high approval as source of knowledge acquisition. This study provides an empirical basis for medical schools and teachers to improve their offerings in the field of digital learning for their students. PMID:27544782
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.
Learning and coding in biological neural networks

NASA Astrophysics Data System (ADS)

Fiete, Ila Rani

How can large groups of neurons that locally modify their activities learn to collectively perform a desired task? Do studies of learning in small networks tell us anything about learning in the fantastically large collection of neurons that make up a vertebrate brain? What factors do neurons optimize by encoding sensory inputs or motor commands in the way they do? In this thesis I present a collection of four theoretical works: each of the projects was motivated by specific constraints and complexities of biological neural networks, as revealed by experimental studies; together, they aim to partially address some of the central questions of neuroscience posed above. We first study the role of sparse neural activity, as seen in the coding of sequential commands in a premotor area responsible for birdsong. We show that the sparse coding of temporal sequences in the songbird brain can, in a network where the feedforward plastic weights must translate the sparse sequential code into a time-varying muscle code, facilitate learning by minimizing synaptic interference. Next, we propose a biologically plausible synaptic plasticity rule that can perform goal-directed learning in recurrent networks of voltage-based spiking neurons that interact through conductances. Learning is based on the correlation of noisy local activity with a global reward signal; we prove that this rule performs stochastic gradient ascent on the reward. Thus, if the reward signal quantifies network performance on some desired task, the plasticity rule provably drives goal-directed learning in the network. To assess the convergence properties of the learning rule, we compare it with a known example of learning in the brain. Song-learning in finches is a clear example of a learned behavior, with detailed available neurophysiological data. With our learning rule, we train an anatomically accurate model birdsong network that drives a sound source to mimic an actual zebrafinch song. Simulation and theoretical results on the scalability of this rule show that learning with stochastic gradient ascent may be adequately fast to explain learning in the bird. Finally, we address the more general issue of the scalability of stochastic gradient learning on quadratic cost surfaces in linear systems, as a function of system size and task characteristics, by deriving analytical expressions for the learning curves.
The Development of Inquiry Learning Materials to Complete Content Life System Organization in Junior High School Students

NASA Astrophysics Data System (ADS)

Mayasari, F.; Raharjo; Supardi, Z. A. I.

2018-01-01

This research aims to develop the material eligibility to complete the inquiry learning of student in the material organization system of junior high school students. Learning materials developed include syllabi, lesson plans, students’ textbook, worksheets, and learning achievement test. This research is the developmental research which employ Dick and Carey model to develop learning material. The experiment was done in Junior High School 4 Lamongan regency using One Group Pretest-Posttest Design. The data collection used validation, observation, achievement test, questionnaire administration, and documentation. Data analysis techniques used quantitative and qualitative descriptive.The results showed that the developed learning material was valid and can be used. Learning activity accomplished with good category, where student activities were observed. The aspects of attitudes were observed during the learning process are honest, responsible, and confident. Student learning achievement gained an average of 81, 85 in complete category, with N-Gain 0, 75 for a high category. The activities and student response to learning was very well categorized. Based on the results, this researcher concluded that the device classified as feasible of inquiry-based learning (valid, practical, and effective) system used on the material organization of junior high school students.
Utilization and Monetization of Healthcare Data in Developing Countries.

PubMed

Bram, Joshua T; Warwick-Clark, Boyd; Obeysekare, Eric; Mehta, Khanjan

2015-06-01

In developing countries with fledgling healthcare systems, the efficient deployment of scarce resources is paramount. Comprehensive community health data and machine learning techniques can optimize the allocation of resources to areas, epidemics, or populations most in need of medical aid or services. However, reliable data collection in low-resource settings is challenging due to a wide range of contextual, business-related, communication, and technological factors. Community health workers (CHWs) are trusted community members who deliver basic health education and services to their friends and neighbors. While an increasing number of programs leverage CHWs for last mile data collection, a fundamental challenge to such programs is the lack of tangible incentives for the CHWs. This article describes potential applications of health data in developing countries and reviews the challenges to reliable data collection. Four practical CHW-centric business models that provide incentive and accountability structures to facilitate data collection are presented. Creating and strengthening the data collection infrastructure is a prerequisite for big data scientists, machine learning experts, and public health administrators to ultimately elevate and transform healthcare systems in resource-poor settings.
Utilization and Monetization of Healthcare Data in Developing Countries

PubMed Central

Bram, Joshua T.; Warwick-Clark, Boyd; Obeysekare, Eric; Mehta, Khanjan

2015-01-01

Abstract In developing countries with fledgling healthcare systems, the efficient deployment of scarce resources is paramount. Comprehensive community health data and machine learning techniques can optimize the allocation of resources to areas, epidemics, or populations most in need of medical aid or services. However, reliable data collection in low-resource settings is challenging due to a wide range of contextual, business-related, communication, and technological factors. Community health workers (CHWs) are trusted community members who deliver basic health education and services to their friends and neighbors. While an increasing number of programs leverage CHWs for last mile data collection, a fundamental challenge to such programs is the lack of tangible incentives for the CHWs. This article describes potential applications of health data in developing countries and reviews the challenges to reliable data collection. Four practical CHW-centric business models that provide incentive and accountability structures to facilitate data collection are presented. Creating and strengthening the data collection infrastructure is a prerequisite for big data scientists, machine learning experts, and public health administrators to ultimately elevate and transform healthcare systems in resource-poor settings. PMID:26487984
Learning from error: leading a culture of safety.

PubMed

Gibson, Russell; Armstrong, Alexander; Till, Alex; McKimm, Judy

2017-07-02

A recent shift towards more collective leadership in the NHS can help to achieve a culture of safety, particularly through encouraging frontline staff to participate and take responsibility for improving safety through learning from error and near misses. Leaders must ensure that they provide psychological safety, organizational fairness and learning systems for staff to feel confident in raising concerns, that they have the autonomy and skills to lead continual improvement, and that they have responsibility for spreading this learning within and across organizations.
Scalability Issues for Remote Sensing Infrastructure: A Case Study.

PubMed

Liu, Yang; Picard, Sean; Williamson, Carey

2017-04-29

For the past decade, a team of University of Calgary researchers has operated a large "sensor Web" to collect, analyze, and share scientific data from remote measurement instruments across northern Canada. This sensor Web receives real-time data streams from over a thousand Internet-connected sensors, with a particular emphasis on environmental data (e.g., space weather, auroral phenomena, atmospheric imaging). Through research collaborations, we had the opportunity to evaluate the performance and scalability of their remote sensing infrastructure. This article reports the lessons learned from our study, which considered both data collection and data dissemination aspects of their system. On the data collection front, we used benchmarking techniques to identify and fix a performance bottleneck in the system's memory management for TCP data streams, while also improving system efficiency on multi-core architectures. On the data dissemination front, we used passive and active network traffic measurements to identify and reduce excessive network traffic from the Web robots and JavaScript techniques used for data sharing. While our results are from one specific sensor Web system, the lessons learned may apply to other scientific Web sites with remote sensing infrastructure.
Using an improved association rules mining optimization algorithm in web-based mobile-learning system

NASA Astrophysics Data System (ADS)

Huang, Yin; Chen, Jianhua; Xiong, Shaojun

2009-07-01

Mobile-Learning (M-learning) makes many learners get the advantages of both traditional learning and E-learning. Currently, Web-based Mobile-Learning Systems have created many new ways and defined new relationships between educators and learners. Association rule mining is one of the most important fields in data mining and knowledge discovery in databases. Rules explosion is a serious problem which causes great concerns, as conventional mining algorithms often produce too many rules for decision makers to digest. Since Web-based Mobile-Learning System collects vast amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships between attributes of learners, assessments, the solution strategies adopted by learners and so on. Therefore ,this paper focus on a new data-mining algorithm, combined with the advantages of genetic algorithm and simulated annealing algorithm , called ARGSA(Association rules based on an improved Genetic Simulated Annealing Algorithm), to mine the association rules. This paper first takes advantage of the Parallel Genetic Algorithm and Simulated Algorithm designed specifically for discovering association rules. Moreover, the analysis and experiment are also made to show the proposed method is superior to the Apriori algorithm in this Mobile-Learning system.
Tailoring Systems Engineering for Rapid Acquisition

DTIC Science & Technology

2014-03-27

center’s focus would be the collection of lessons learned and the dissemination of the basic knowledge to the members who are conducting rapid acquisition...dictates that they rarely do lessons learned . Adding in the turnover of personnel and they reported that they make the same mistakes over and over...weapon system program of record designated by the CSAF. This is where the interviewee heard the phrase “when skating on thin ice your best asset is
Time-Extended Payoffs for Collectives of Autonomous Agents

NASA Technical Reports Server (NTRS)

Tumer, Kagan; Agogino, Adrian K.

2002-01-01

A collective is a set of self-interested agents which try to maximize their own utilities, along with a a well-defined, time-extended world utility function which rates the performance of the entire system. In this paper, we use theory of collectives to design time-extended payoff utilities for agents that are both aligned with the world utility, and are "learnable", i.e., the agents can readily see how their behavior affects their utility. We show that in systems where each agent aims to optimize such payoff functions, coordination arises as a byproduct of the agents selfishly pursuing their own goals. A game theoretic analysis shows that such payoff functions have the net effect of aligning the Nash equilibrium, Pareto optimal solution and world utility optimum, thus eliminating undesirable behavior such as agents working at cross-purposes. We then apply collective-based payoff functions to the token collection in a gridworld problem where agents need to optimize the aggregate value of tokens collected across an episode of finite duration (i.e., an abstracted version of rovers on Mars collecting scientifically interesting rock samples, subject to power limitations). We show that, regardless of the initial token distribution, reinforcement learning agents using collective-based payoff functions significantly outperform both natural extensions of single agent algorithms and global reinforcement learning solutions based on "team games".
Next-Generation Machine Learning for Biological Networks.

PubMed

Camacho, Diogo M; Collins, Katherine M; Powers, Rani K; Costello, James C; Collins, James J

2018-06-14

Machine learning, a collection of data-analytical techniques aimed at building predictive models from multi-dimensional datasets, is becoming integral to modern biological research. By enabling one to generate models that learn from large datasets and make predictions on likely outcomes, machine learning can be used to study complex cellular systems such as biological networks. Here, we provide a primer on machine learning for life scientists, including an introduction to deep learning. We discuss opportunities and challenges at the intersection of machine learning and network biology, which could impact disease biology, drug discovery, microbiome research, and synthetic biology. Copyright © 2018 Elsevier Inc. All rights reserved.
Effectiveness of e-learning in hospitals.

PubMed

Chuo, Yinghsiang; Liu, Chuangchun; Tsai, Chunghung

2015-01-01

Because medical personnel share different work shifts (i.e., three work shifts) and do not have a fixed work schedule, implementing timely, flexible, and quick e-learning methods for their continued education is imperative. Hospitals are currently focusing on developing e-learning. This study aims to explore the key factors that influence the effectiveness of e-learning in medical personnel. This study recruited medical personnel as the study participants and collected sample data by using the questionnaire survey method. This study is based on the information systems success model (IS success model), a significant model in MIS research. This study found that the factors (i.e., information quality, service quality, convenience, and learning climate) influence the e-learning satisfaction and in turn influence effectiveness in medical personnel. This study provided recommendations to medical institutions according to the derived findings, which can be used as a reference when establishing e-learning systems in the future.
Using Collective Intelligence to Route Internet Traffic

NASA Technical Reports Server (NTRS)

Wolpert, David H.; Tumer, Kagan; Frank, Jeremy

1998-01-01

A Collective Intelligence (COIN) is a community of interacting reinforcement learning (RL) algorithms designed so that their collective behavior maximizes a global utility function. We introduce the theory of COINs, then present experiments using that theory to design COINs to control internet traffic routing. These experiments indicate that COINs outperform previous RL-based systems for such routing that have previously been investigated.

Negotiating Control and Protecting the Private: History Teachers and the Virginia Standards of Learning.

ERIC Educational Resources Information Center

Smith, Ann Marie

This study investigated what Virginia high school social studies teachers thought about the new Standards of Learning (SOL) mandates. The grounded theory ethnographic study collected data through interviews with five teachers, observations of social studies department meetings and classes, and school system and SOL program documents. Findings show…
QSIA--A Web-Based Environment for Learning, Assessing and Knowledge Sharing in Communities

ERIC Educational Resources Information Center

Rafaeli, Sheizaf; Barak, Miri; Dan-Gur, Yuval; Toch, Eran

2004-01-01

This paper describes a Web-based and distributed system named QSIA that serves as an environment for learning, assessing and knowledge sharing. QSIA--Questions Sharing and Interactive Assignments--offers a unified infrastructure for developing, collecting, managing and sharing of knowledge items. QSIA enhances collaboration in authoring via online…
Measuring and Visualizing Students' Behavioral Engagement in Writing Activities

ERIC Educational Resources Information Center

Liu, Ming; Calvo, Rafael A.; Pardo, Abelardo; Martin, Andrew

2015-01-01

Engagement is critical to the success of learning activities such as writing, and can be promoted with appropriate feedback. Current engagement measures rely mostly on data collected by observers or self-reported by the participants. In this paper, we describe a learning analytic system called Tracer, which derives behavioral engagement measures…
The Ecology of Cooperative Learning in Elementary Physical Education Classes

ERIC Educational Resources Information Center

Dyson, Ben P.; Linehan, Nicole Rhodes; Hastie, Peter A.

2010-01-01

The purpose of this study was to describe and interpret the instructional ecology of Cooperative Learning in elementary physical education classes. Data collection included a modified version of the task structure system (Siedentop, 1994), interviews, field notes, and a teacher's journal. T-tests of the quantitative data revealed that instruction…
Are e-Books for Everyone? An Evaluation of Academic e-Book Platforms' Accessibility Features

ERIC Educational Resources Information Center

Mune, Christina; Agee, Ann

2016-01-01

With the increasing prevalence of e-books in academic library collections, faculty and librarians have begun to express concern regarding the accessibility of these digital texts for students with physical or learning disabilities. To begin addressing these concerns, the California State University System's Affordable Learning Solutions…
Patient-Centered Precision Health In A Learning Health Care System: Geisinger's Genomic Medicine Experience.

PubMed

Williams, Marc S; Buchanan, Adam H; Davis, F Daniel; Faucett, W Andrew; Hallquist, Miranda L G; Leader, Joseph B; Martin, Christa L; McCormick, Cara Z; Meyer, Michelle N; Murray, Michael F; Rahm, Alanna K; Schwartz, Marci L B; Sturm, Amy C; Wagner, Jennifer K; Williams, Janet L; Willard, Huntington F; Ledbetter, David H

2018-05-01

Health care delivery is increasingly influenced by the emerging concepts of precision health and the learning health care system. Although not synonymous with precision health, genomics is a key enabler of individualized care. Delivering patient-centered, genomics-informed care based on individual-level data in the current national landscape of health care delivery is a daunting challenge. Problems to overcome include data generation, analysis, storage, and transfer; knowledge management and representation for patients and providers at the point of care; process management; and outcomes definition, collection, and analysis. Development, testing, and implementation of a genomics-informed program requires multidisciplinary collaboration and building the concepts of precision health into a multilevel implementation framework. Using the principles of a learning health care system provides a promising solution. This article describes the implementation of population-based genomic medicine in an integrated learning health care system-a working example of a precision health program.
Avatar Web-Based Self-Report Survey System Technology for Public Health Research: Technical Outcome Results and Lessons Learned.

PubMed

Savel, Craig; Mierzwa, Stan; Gorbach, Pamina M; Souidi, Samir; Lally, Michelle; Zimet, Gregory; Interventions, Aids

2016-01-01

This paper reports on a specific Web-based self-report data collection system that was developed for a public health research study in the United States. Our focus is on technical outcome results and lessons learned that may be useful to other projects requiring such a solution. The system was accessible from any device that had a browser that supported HTML5. Report findings include: which hardware devices, Web browsers, and operating systems were used; the rate of survey completion; and key considerations for employing Web-based surveys in a clinical trial setting.
Distributed Control with Collective Intelligence

NASA Technical Reports Server (NTRS)

Wolpert, David H.; Wheeler, Kevin R.; Tumer, Kagan

1998-01-01

We consider systems of interacting reinforcement learning (RL) algorithms that do not work at cross purposes , in that their collective behavior maximizes a global utility function. We call such systems COllective INtelligences (COINs). We present the theory of designing COINs. Then we present experiments validating that theory in the context of two distributed control problems: We show that COINs perform near-optimally in a difficult variant of Arthur's bar problem [Arthur] (and in particular avoid the tragedy of the commons for that problem), and we also illustrate optimal performance in the master-slave problem.
Statistical Analysis Tools for Learning in Engineering Laboratories.

ERIC Educational Resources Information Center

Maher, Carolyn A.

1990-01-01

Described are engineering programs that have used automated data acquisition systems to implement data collection and analyze experiments. Applications include a biochemical engineering laboratory, heat transfer performance, engineering materials testing, mechanical system reliability, statistical control laboratory, thermo-fluid laboratory, and a…
An integrated system for interactive continuous learning of categorical knowledge

NASA Astrophysics Data System (ADS)

Skočaj, Danijel; Vrečko, Alen; Mahnič, Marko; Janíček, Miroslav; Kruijff, Geert-Jan M.; Hanheide, Marc; Hawes, Nick; Wyatt, Jeremy L.; Keller, Thomas; Zhou, Kai; Zillich, Michael; Kristan, Matej

2016-09-01

This article presents an integrated robot system capable of interactive learning in dialogue with a human. Such a system needs to have several competencies and must be able to process different types of representations. In this article, we describe a collection of mechanisms that enable integration of heterogeneous competencies in a principled way. Central to our design is the creation of beliefs from visual and linguistic information, and the use of these beliefs for planning system behaviour to satisfy internal drives. The system is able to detect gaps in its knowledge and to plan and execute actions that provide information needed to fill these gaps. We propose a hierarchy of mechanisms which are capable of engaging in different kinds of learning interactions, e.g. those initiated by a tutor or by the system itself. We present the theory these mechanisms are build upon and an instantiation of this theory in the form of an integrated robot system. We demonstrate the operation of the system in the case of learning conceptual models of objects and their visual properties.
System Characteristics, Satisfaction and E-Learning Usage: A Structural Equation Model (SEM)

ERIC Educational Resources Information Center

Ramayah, T.; Lee, Jason Wai Chow

2012-01-01

With the advent of the Internet, more and more public universities in Malaysia are putting in effort to introduce e-learning in their respective universities. Using a structured questionnaire derived from the literature, data was collected from 250 undergraduate students from a public university in Penang, Malaysia. Data was analyzed using AMOS…
Open Secrets: Using the Internet to Learn about the Influence of Money in Politics

ERIC Educational Resources Information Center

Scheuerell, Scott K.

2008-01-01

With the 2008 election quickly approaching, candidates continue the scramble to fund their campaigns--collecting money from individuals, corporations, and labor unions. Students can learn a great deal about the political system by examining how politicians are financed. The vast majority of high school students do not understand the influence of…
New Ways of Using Computers in Language Teaching. New Ways in TESOL Series II. Innovative Classroom Techniques.

ERIC Educational Resources Information Center

Boswood, Tim, Ed.

A collection of classroom approaches and activities using computers for language learning is presented. Some require sophisticated installations, but most do not, and most use software readily available on most workplace computer systems. The activities were chosen because they use sound language learning strategies. The book is divided into five…
Listening Strategy Use and Influential Factors in Web-Based Computer Assisted Language Learning

ERIC Educational Resources Information Center

Chen, L.; Zhang, R.; Liu, C.

2014-01-01

This study investigates second and foreign language (L2) learners' listening strategy use and factors that influence their strategy use in a Web-based computer assisted language learning (CALL) system. A strategy inventory, a factor questionnaire and a standardized listening test were used to collect data from a group of 82 Chinese students…
Perceiving Permutations as Distinct Outcomes: The Accommodation of a Complex Knowledge System

ERIC Educational Resources Information Center

Kapon, Shulamit; Ron, Gila; Hershkowitz, Rina; Dreyfus, Tommy

2015-01-01

There is ample evidence that reasoning about stochastic phenomena is often subject to systematic bias even after instruction. Few studies have examined the detailed learning processes involved in learning probability. This paper examines a case study drawn from a large corpus of data collected as part of a research project that dealt with the…
Feasibility of Cloud Computing Implementation for eLearning in Secondary Schools in Tanzania

ERIC Educational Resources Information Center

Mwakisole, Kennedy F.; Kissaka, Mussa M.; Mtebe, Joel S.

2018-01-01

This article assessed the feasibility of implementing eLearning systems in a cloud-based infrastructure for secondary schools in Tanzania. The study adopted questionnaire and document reviews as data collection tools. A total of 820 students successfully returned the questionnaire from seven secondary schools in Tanzania. The study found that 11%…
The Use of a Real Life Simulated Problem Based Learning Activity in a Corporate Environment

ERIC Educational Resources Information Center

Laurent, Mark A.

2013-01-01

This narrative study examines using a real life simulated problem base learning activity during education of clinical staff, which is expected to design and develop clinically correct electronic charting systems. Expertise in healthcare does not readily transcend to the realm of manipulating software to collect patient data that is pertinent to…
Multi-agent Reinforcement Learning Model for Effective Action Selection

NASA Astrophysics Data System (ADS)

Youk, Sang Jo; Lee, Bong Keun

Reinforcement learning is a sub area of machine learning concerned with how an agent ought to take actions in an environment so as to maximize some notion of long-term reward. In the case of multi-agent, especially, which state space and action space gets very enormous in compared to single agent, so it needs to take most effective measure available select the action strategy for effective reinforcement learning. This paper proposes a multi-agent reinforcement learning model based on fuzzy inference system in order to improve learning collect speed and select an effective action in multi-agent. This paper verifies an effective action select strategy through evaluation tests based on Robocop Keep away which is one of useful test-beds for multi-agent. Our proposed model can apply to evaluate efficiency of the various intelligent multi-agents and also can apply to strategy and tactics of robot soccer system.
Hydrophobic/Hydrophilic Cooperative Janus System for Enhancement of Fog Collection.

PubMed

Cao, Moyuan; Xiao, Jiasheng; Yu, Cunming; Li, Kan; Jiang, Lei

2015-09-09

Harvesting micro-droplets from fog is a promising method for solving global freshwater crisis. Different types of fog collectors have been extensively reported during the last decade. The improvement of fog collection can be attributed to the immediate transportation of harvested water, the effective regeneration of the fog gathering surface, etc. Through learning from the nature's strategy for water preservation, the hydrophobic/hydrophilic cooperative Janus system that achieved reinforced fog collection ability is reported here. Directional delivery of the surface water, decreased re-evaporation rate of the harvested water, and thinner boundary layer of the collecting surface contribute to the enhancement of collection efficiency. Further designed cylinder Janus collector can facilely achieve a continuous process of efficient collection, directional transportation, and spontaneous preservation of fog water. This Janus fog harvesting system should improve the understanding of micro-droplet collection system and offer ideas to solve water resource crisis. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Collective states in social systems with interacting learning agents

NASA Astrophysics Data System (ADS)

Semeshenko, Viktoriya; Gordon, Mirta B.; Nadal, Jean-Pierre

2008-08-01

We study the implications of social interactions and individual learning features on consumer demand in a simple market model. We consider a social system of interacting heterogeneous agents with learning abilities. Given a fixed price, agents repeatedly decide whether or not to buy a unit of a good, so as to maximize their expected utilities. This model is close to Random Field Ising Models, where the random field corresponds to the idiosyncratic willingness to pay. We show that the equilibrium reached depends on the nature of the information agents use to estimate their expected utilities. It may be different from the systems’ Nash equilibria.

Real-time individualized training vectors for experiential learning.

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

Willis, Matt; Tucker, Eilish Marie; Raybourn, Elaine Marie

2011-01-01

Military training utilizing serious games or virtual worlds potentially generate data that can be mined to better understand how trainees learn in experiential exercises. Few data mining approaches for deployed military training games exist. Opportunities exist to collect and analyze these data, as well as to construct a full-history learner model. Outcomes discussed in the present document include results from a quasi-experimental research study on military game-based experiential learning, the deployment of an online game for training evidence collection, and results from a proof-of-concept pilot study on the development of individualized training vectors. This Lab Directed Research & Development (LDRD)more » project leveraged products within projects, such as Titan (Network Grand Challenge), Real-Time Feedback and Evaluation System, (America's Army Adaptive Thinking and Leadership, DARWARS Ambush! NK), and Dynamic Bayesian Networks to investigate whether machine learning capabilities could perform real-time, in-game similarity vectors of learner performance, toward adaptation of content delivery, and quantitative measurement of experiential learning.« less
Students academic performance based on behavior

NASA Astrophysics Data System (ADS)

Maulida, Juwita Dien; Kariyam

2017-12-01

Utilization of data in an information system that can be used for decision making that utilizes existing data warehouse to help dig useful information to make decisions correctly and accurately. Experience API (xAPI) is one of the enabling technologies for collecting data, so xAPI can be used as a data warehouse that can be used for various needs. One software application whose data is collected in xAPI is LMS. LMS is a software used in an electronic learning process that can handle all aspects of learning, by using LMS can also be known how the learning process and the aspects that can affect learning achievement. One of the aspects that can affect the learning achievement is the background of each student, which is not necessarily the student with a good background is an outstanding student or vice versa. Therefore, an action is needed to anticipate this problem. Prediction of student academic performance using Naive Bayes algorithm obtained accuracy of 67.7983% and error 32.2917%.
Equipment Issues regarding the Collection of Video Data for Research

ERIC Educational Resources Information Center

Kung, Rebecca Lippmann; Kung, Peter; Linder, Cedric

2005-01-01

Physics education research increasingly makes use of video data for analysis of student learning and teaching practice. Collection of these data is conceptually simple but execution is often fraught with costly and time-consuming complications. This pragmatic paper discusses the development of systems to record and permanently archive audio and…
Teaching Inquiry using NASA Earth-System Science: Lessons Learned for Blended, Scaffolded Professional Development

NASA Astrophysics Data System (ADS)

Ellis, T. D.; TeBockhorst, D.

2013-12-01

Teaching Inquiry using NASA Earth-System Science (TINES) is a NASA EPOESS funded program exploring blended professional development for pre- and in-service educators to learn how to conduct meaningful inquiry lessons and projects in the K-12 classroom. This project combines trainings in GLOBE observational protocols and training in the use of NASA Earth Science mission data in a backward-faded scaffolding approach to teaching and learning about scientific inquiry. It also features a unique partnership with the National Science Teachers Association Learning Center to promote cohort building and blended professional development with access to NSTA's collection of resources. In this presentation, we will discuss lessons learned in year one and two of this program and how we plan to further develop this program over the next two years.
Bringing a Time-Depth Perspective to Collective Animal Behaviour.

PubMed

Biro, Dora; Sasaki, Takao; Portugal, Steven J

2016-07-01

The field of collective animal behaviour examines how relatively simple, local interactions between individuals in groups combine to produce global-level outcomes. Existing mathematical models and empirical work have identified candidate mechanisms for numerous collective phenomena but have typically focused on one-off or short-term performance. We argue that feedback between collective performance and learning - giving the former the capacity to become an adaptive, and potentially cumulative, process - is a currently poorly explored but crucial mechanism in understanding collective systems. We synthesise material ranging from swarm intelligence in social insects through collective movements in vertebrates to collective decision making in animal and human groups, to propose avenues for future research to identify the potential for changes in these systems to accumulate over time. Copyright © 2016 Elsevier Ltd. All rights reserved.
Shuttle Case Study Collection Website Development

NASA Technical Reports Server (NTRS)

Ransom, Khadijah S.; Johnson, Grace K.

2012-01-01

As a continuation from summer 2012, the Shuttle Case Study Collection has been developed using lessons learned documented by NASA engineers, analysts, and contractors. Decades of information related to processing and launching the Space Shuttle is gathered into a single database to provide educators with an alternative means to teach real-world engineering processes. The goal is to provide additional engineering materials that enhance critical thinking, decision making, and problem solving skills. During this second phase of the project, the Shuttle Case Study Collection website was developed. Extensive HTML coding to link downloadable documents, videos, and images was required, as was training to learn NASA's Content Management System (CMS) for website design. As the final stage of the collection development, the website is designed to allow for distribution of information to the public as well as for case study report submissions from other educators online.
The RADAR Test Methodology: Evaluating a Multi-Task Machine Learning System with Humans in the Loop

DTIC Science & Technology

2006-10-01

burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing...data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information . Send comments regarding this...burden estimate or any other aspect of this collection of information , including suggestions for reducing this burden, to Washington Headquarters Services
Mobile Devices and Apps as Scaffolds to Science Learning in the Primary Classroom

ERIC Educational Resources Information Center

Falloon, Garry

2017-01-01

Considerable work over many years has explored the contribution technology can make to science learning, at all levels of education. In the school sector, historically this has focused on the use of fixed, desktop-based or semi-mobile laptop systems for purposes such as experiment data collection or analysis, or as a means of engaging or…
The Adoption of Blended E-Learning Technology in Vietnam Using a Revision of the Technology Acceptance Model

ERIC Educational Resources Information Center

Tran, Khanh Ngo Nhu

2016-01-01

This study examines factors that determine the attitudes of learners toward a blended e-learning system (BELS) using data collected by questionnaire from a sample of 396 students involved in a BELS environment in Vietnam. A theoretical model is derived from previous studies and is analyzed and developed using structural equation modeling…
Pedagogical Praxis: The Professions as Models for Learning in the Age of the Smart Machine. WCER Working Paper No. 2003-6

ERIC Educational Resources Information Center

Shaffer, David W.

2003-01-01

Successful curricula are not collections of isolated elements; rather, effective learning environments function as coherent systems (Brown & Campione, 1996; see also Papert, 1980; Shaffer, 1998). The theory of pedagogical praxis begins with the premise that computers and other information technologies make it easier for students to become active…
Evaluation of Online Log Variables That Estimate Learners' Time Management in a Korean Online Learning Context

ERIC Educational Resources Information Center

Jo, Il-Hyun; Park, Yeonjeong; Yoon, Meehyun; Sung, Hanall

2016-01-01

The purpose of this study was to identify the relationship between the psychological variables and online behavioral patterns of students, collected through a learning management system (LMS). As the psychological variable, time and study environment management (TSEM), one of the sub-constructs of MSLQ, was chosen to verify a set of time-related…
A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring Systems

ERIC Educational Resources Information Center

Liu, Ran; Stamper, John; Davenport, Jodi

2018-01-01

Temporal analyses are critical to understanding learning processes, yet understudied in education research. Data from different sources are often collected at different grain sizes, which are difficult to integrate. Making sense of data at many levels of analysis, including the most detailed levels, is highly time-consuming. In this paper, we…
Linking Complexity with Cultural Historical Activity Theory

ERIC Educational Resources Information Center

McMurtry, Angus

2006-01-01

This paper explores the similarities and differences between complexity science's and cultural-historical activity theory's understandings of human learning. Notable similarities include their emphasis on the importance of social systems or collectives in understanding human knowledge and practices, as well as their characterization of systems'…
The antecedents of e-learning outcome: an examination of system quality, technology readiness, and learning behavior.

PubMed

Ho, Li-An

2009-01-01

The rapid advancement of Internet and computer technology has not only influenced the way we live, but also the way we learn. Due to the implementation of e-learning in urban junior high schools in Taiwan, it has become essential to find out how external and internal factors affect junior high school students' online learning behavior, which consequently affects their learning outcome. The present study aims to propose a conceptual structural equation model to investigate the relationships among e-Learning system quality (eLSQ), technology readiness (TR), learning behavior (LB), and learning outcome (LO), and to demonstrate the direct and indirect effect of eLSQ and TR on LO from the perspectives of LB. Data collected from 10 urban junior high schools in Taiwan (N = 376) were analyzed using structural equation modeling. Results reveal that both eLSQ and TR have a direct and significant impact on LB. However, eLSQ and TR influence LO indirectly through LB. In addition, LB has a direct and positive significant influence on LO. Managerial implications are proposed and research limitations are discussed.
Assessment of Web-Based Authentication Methods in the U.S.: Comparing E-Learning Systems to Internet Healthcare Information Systems

ERIC Educational Resources Information Center

Mattord, Herbert J.

2012-01-01

Organizations continue to rely on password-based authentication methods to control access to many Web-based systems. This research study developed a benchmarking instrument intended to assess authentication methods used in Web-based information systems (IS). It developed an Authentication Method System Index (AMSI) to analyze collected data from…
Coaching as Professional Learning: Guidance for Implementing Effective Coaching Systems

ERIC Educational Resources Information Center

Vermont Agency of Education, 2016

2016-01-01

To build collective capacity within organizations, schools and districts across the world have implemented coaching as an effective method for systemic reform. Vermont in particular has a wide variety of coaches, including instructional coaches and systems coaches, as well as a variety of interpretations of the coaching practice. Many schools…
Using Mobile Learning to Improve the Reflection: A Case Study of Traffic Violation

ERIC Educational Resources Information Center

Lan, Yu-Feng; Huang, Shin-Ming

2012-01-01

The purpose of this study was to integrate mobile communication technologies and a global positioning system (GPS) to construct an instant, convenient report of the mobile network service system named the Mobile Traffic Violation Reporting System (MTVRS), to improve learners' traffic violation reflection level. Data were collected using a…
Avatar Web-Based Self-Report Survey System Technology for Public Health Research: Technical Outcome Results and Lessons Learned

PubMed Central

Savel, Craig; Mierzwa, Stan; Gorbach, Pamina M.; Souidi, Samir; Lally, Michelle; Zimet, Gregory; Interventions, AIDS

2016-01-01

This paper reports on a specific Web-based self-report data collection system that was developed for a public health research study in the United States. Our focus is on technical outcome results and lessons learned that may be useful to other projects requiring such a solution. The system was accessible from any device that had a browser that supported HTML5. Report findings include: which hardware devices, Web browsers, and operating systems were used; the rate of survey completion; and key considerations for employing Web-based surveys in a clinical trial setting. PMID:28149445
Collective learning modeling based on the kinetic theory of active particles

NASA Astrophysics Data System (ADS)

Burini, D.; De Lillo, S.; Gibelli, L.

2016-03-01

This paper proposes a systems approach to the theory of perception and learning in populations composed of many living entities. Starting from a phenomenological description of these processes, a mathematical structure is derived which is deemed to incorporate their complexity features. The modeling is based on a generalization of kinetic theory methods where interactions are described by theoretical tools of game theory. As an application, the proposed approach is used to model the learning processes that take place in a classroom.
75 FR 13305 - Comment Request for Information Collection for Evaluation of the Technology-Based Learning Grants...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-03-19

... Collection for Evaluation of the Technology-Based Learning Grants: New Collection AGENCY: Employment and... Technology- Based Learning Grants Evaluation. A copy of the proposed information collection request (ICR) can... INFORMATION: I. Background The Evaluation of the Technology-Based Learning (TBL) Grants is a two-year...

Collective Learning and Path Plasticity as Means to Regional Economic Resilience: The Case of Stuttgart

ERIC Educational Resources Information Center

Wink, Rüdiger; Kirchner, Laura; Koch, Florian; Speda, Daniel

2015-01-01

This paper links two strands of literature (collective learning and resilience) by looking at experiences with collective learning as precondition of regional economic resilience. Based on a qualitative empirical study, the emergence of collective learning structures in the Stuttgart region after a macroeconomic and structural crisis at the…
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.
Beyond Effectiveness: A Pragmatic Evaluation Framework for Learning and Continuous Quality Improvement of e-Learning Interventions in Healthcare.

PubMed

Dafalla, Tarig Dafalla Mohamed; Kushniruk, Andre W; Borycki, Elizabeth M

2015-01-01

A pragmatic evaluation framework for evaluating the usability and usefulness of an e-learning intervention for a patient clinical information scheduling system is presented in this paper. The framework was conceptualized based on two different but related concepts (usability and usefulness) and selection of appropriate and valid methods of data collection and analysis that included: (1) Low-Cost Rapid Usability Engineering (LCRUE), (2) Cognitive Task Analysis (CTA), (3) Heuristic Evaluation (HE) criteria for web-based learning, and (4) Software Usability Measurement Inventory (SUMI). The results of the analysis showed some areas where usability that were related to General Interface Usability (GIU), instructional design and content was problematic; some of which might account for the poorly rated aspects of usability when subjectively measured. This paper shows that using a pragmatic framework can be a useful way, not only for measuring the usability and usefulness, but also for providing a practical objective evidences for learning and continuous quality improvement of e-learning systems. The findings should be of interest to educators, developers, designers, researchers, and usability practitioners involved in the development of e-learning systems in healthcare. This framework could be an appropriate method for assessing the usability, usefulness and safety of health information systems both in the laboratory and in the clinical context.
Conducting a Large Public Health Data Collection Project in Uganda: Methods, Tools, and Lessons Learned

ERIC Educational Resources Information Center

Stover, Bert; Lubega, Flavia; Namubiru, Aidah; Bakengesa, Evelyn; Luboga, Samuel Abimerech; Makumbi, Frederick; Kiwanuka, Noah; Ndizihiwe, Assay; Mukooyo, Eddie; Hurley, Erin; Lim, Travis; Borse, Nagesh N.; Bernhardt, James; Wood, Angela; Sheppard, Lianne; Barnhart, Scott; Hagopian, Amy

2018-01-01

We report on the implementation experience of carrying out data collection and other activities for a public health evaluation study on whether U.S. President's Emergency Plan for AIDS Relief (PEPFAR) investment improved utilization of health services and health system strengthening in Uganda. The retrospective study period focused on the PEPFAR…
Supporting Collaborative Learning and Problem-Solving in a Constraint-Based CSCL Environment for UML Class Diagrams

ERIC Educational Resources Information Center

Baghaei, Nilufar; Mitrovic, Antonija; Irwin, Warwick

2007-01-01

We present COLLECT-UML, a constraint-based intelligent tutoring system (ITS) that teaches object-oriented analysis and design using Unified Modelling Language (UML). UML is easily the most popular object-oriented modelling technology in current practice. While teaching how to design UML class diagrams, COLLECT-UML also provides feedback on…
Using an Ecomap as a Tool for Qualitative Data Collection in Organizations

ERIC Educational Resources Information Center

Bennett, Jo; Grant, Natalie S.

2016-01-01

An ecomap is a social work data collection tool that is used to gather data about a participant's environment. Derived from Bronfenbrenner's ecological system theory, the ecomap can be used in adult education and human resource development to record information of in-and-out-of-work and learning experiences and show how these interactions support…
ClearTK 2.0: Design Patterns for Machine Learning in UIMA

PubMed Central

Bethard, Steven; Ogren, Philip; Becker, Lee

2014-01-01

ClearTK adds machine learning functionality to the UIMA framework, providing wrappers to popular machine learning libraries, a rich feature extraction library that works across different classifiers, and utilities for applying and evaluating machine learning models. Since its inception in 2008, ClearTK has evolved in response to feedback from developers and the community. This evolution has followed a number of important design principles including: conceptually simple annotator interfaces, readable pipeline descriptions, minimal collection readers, type system agnostic code, modules organized for ease of import, and assisting user comprehension of the complex UIMA framework. PMID:29104966
ClearTK 2.0: Design Patterns for Machine Learning in UIMA.

PubMed

Bethard, Steven; Ogren, Philip; Becker, Lee

2014-05-01

ClearTK adds machine learning functionality to the UIMA framework, providing wrappers to popular machine learning libraries, a rich feature extraction library that works across different classifiers, and utilities for applying and evaluating machine learning models. Since its inception in 2008, ClearTK has evolved in response to feedback from developers and the community. This evolution has followed a number of important design principles including: conceptually simple annotator interfaces, readable pipeline descriptions, minimal collection readers, type system agnostic code, modules organized for ease of import, and assisting user comprehension of the complex UIMA framework.
An exploratory investigation of teaching innovations and learning factors in a lean manufacturing systems engineering course

NASA Astrophysics Data System (ADS)

Choomlucksana, Juthamas; Doolen, Toni L.

2017-11-01

The use of collaborative activities and simulation sessions in engineering education has been explored previously. However, few studies have investigated the relationship of these types of teaching innovations with other learner characteristics, such as self-efficacy and background knowledge. This study explored the effects of collaborative activities and simulation sessions on learning and the relationships between self-efficacy beliefs, background knowledge, and learning. Data were collected from two different terms in an upper division engineering course entitled Lean Manufacturing Systems Engineering. Findings indicated that the impact of collaborative activities and simulation sessions appears to be different, depending on the concepts being taught. Simulation sessions were found to have a significant effect on self-efficacy beliefs, and background knowledge had a mixed effect on learning. Overall the results of this study highlight the complex set of relationships between classroom innovations, learner characteristics, and learning.
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.
Full-Scale Incineration System Demonstration at the Naval Battalion Construction Center, Gulfport, Mississippi. Volume 8. Delisting

DTIC Science & Technology

1991-07-01

concerning disposition of soil that is considered hazardous after treatment. The report also documents the data collected in support of soil disposition...regulatory and technical lessons learned concerning disposition of soil after treatment. The report also documents the data collected in support of soil...were undertaken to support delisting of the soil, including the Wii / verification test burn, a RCRA trial burn, and data collected during routine
Investigating Users' Requirements

PubMed Central

Walker, Deborah S.; Lee, Wen-Yu; Skov, Neil M.; Berger, Carl F.; Athley, Brian D.

2002-01-01

Objective: User data and information about anatomy education were used to guide development of a learning environment that is efficient and effective. The research question focused on how to design instructional software suitable for the educational goals of different groups of users of the Visible Human data set. The ultimate goal of the study was to provide options for students and teachers to use different anatomy learning modules corresponding to key topics, for course work and professional training. Design: The research used both qualitative and quantitative methods. It was driven by the belief that good instructional design must address learning context information and pedagogic content information. The data collection emphasized measurement of users' perspectives, experience, and demands in anatomy learning. Measurement: Users' requirements elicited from 12 focus groups were combined and rated by 11 researchers. Collective data were sorted and analyzed by use of multidimensional scaling and cluster analysis. Results: A set of functions and features in high demand across all groups of users was suggested by the results. However, several subgroups of users shared distinct demands. The design of the learning modules will encompass both unified core components and user-specific applications. The design templates will allow sufficient flexibility for dynamic insertion of different learning applications for different users. Conclusion: This study describes how users' requirements, associated with users' learning experiences, were systematically collected and analyzed and then transformed into guidelines informing the iterative design of multiple learning modules. Information about learning challenges and processes was gathered to define essential anatomy teaching strategies. A prototype instrument to design and polish the Visible Human user interface system is currently being developed using ideas and feedback from users. PMID:12087112
Communication and Control in Organizations: Applying the Work of James Thompson and Gregory Bateson to Interpretive Research.

DTIC Science & Technology

1985-07-01

learning ’, ’adaptations’, ’process’, and ’ abstraction ...hierarchy. Rather, he argues through a combina- tion of cybernetic epistemology, learning theory, and cognitive models, that patterned collective...systemic ability to perceive and respond to pattern and variance in the ’environment’. Each level of abstraction can be considered a ’meta’
Building the Army of the Republic of Vietnam’s Logistical System: Lessons Learned

DTIC Science & Technology

2016-05-26

to any’ penalty for failing to comply with a collection ot information if it does not display a currently valid OMB control number. PLEASE DO NOT...reliant on US Army capability to a regional depot system that assumed control of all logistical operations as the US Army withdrew. The ARVN logistical...described body of work is determining the reasoning behind US military’s approach, and its overall effectiveness for the South Vietnam. From the collective
Digital Preservation and Deep Infrastructure; Dublin Core Metadata Initiative Progress Report and Workplan for 2002; Video Gaming, Education and Digital Learning Technologies: Relevance and Opportunities; Digital Collections of Real World Objects; The MusArt Music-Retrieval System: An Overview; eML: Taking Mississippi Libraries into the 21st Century.

ERIC Educational Resources Information Center

Granger, Stewart; Dekkers, Makx; Weibel, Stuart L.; Kirriemuir, John; Lensch, Hendrik P. A.; Goesele, Michael; Seidel, Hans-Peter; Birmingham, William; Pardo, Bryan; Meek, Colin; Shifrin, Jonah; Goodvin, Renee; Lippy, Brooke

2002-01-01

One opinion piece and five articles in this issue discuss: digital preservation infrastructure; accomplishments and changes in the Dublin Core Metadata Initiative in 2001 and plans for 2002; video gaming and how it relates to digital libraries and learning technologies; overview of a music retrieval system; and the online version of the…
Hospitals as learning organizations: fostering innovation through interactive learning.

PubMed

Dias, Casimiro; Escoval, Ana

2015-01-01

The article aims to provide an analytical understanding of hospitals as "learning organizations." It further analyzes the development of learning organizations as a way to enhance innovation and performance in the hospital sector. The article pulls together primary data on organizational flexibility, innovation, and performance from 95 administrators from hospital boards in Portugal, collected through a survey, interviews with hospital's boards, and a nominal group technique with a panel of experts on health systems. Results show that a combination of several organizational traits of the learning organization enhances its capacity for innovation development. The logistic model presented reveals that hospitals classified as "advanced learning organizations" have 5 times more chance of developing innovation than "basic learning organizations." Empirical findings further pointed out incentives, standards, and measurement requirements as key elements for integration of service delivery systems and expansion of the current capacity for structured and real-time learning in the hospital sector. The major implication arising from this study is that policy needs to combine instruments that promote innovation opportunities and incentives, with instruments stimulating the further development of the core components of learning organizations. Such a combination of policy instruments has the potential to ensure a wide external cooperation through a learning infrastructure.
CCML--Exchanging Marked-Up Documents in a Networked Writing Classroom.

ERIC Educational Resources Information Center

Ogata, Hiroaki; Yano, Yoneo; Wakita, Riko

1998-01-01

Describes an on-line mark-up-based composition learning environment system called CoCoA (Communicative Collection Assisting System). This system allows students and teachers to engage in marked-up documents via the Internet, and its environment is very similar to a real-world one in which people use pen and paper. CCML also facilitates teachers to…
Web-based newborn screening system for metabolic diseases: machine learning versus clinicians.

PubMed

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

2013-05-23

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

PubMed Central

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

2013-01-01

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

ERIC Educational Resources Information Center

Erickson, Jacqueline M.

This critical qualitative study explored the obstacles older women encounter as they pursue doctorates. Introductory material identifies steps in a critical qualitative approach including monological data collection, preliminary reconstructive analysis, dialogical data generation, and description and explanation of system relationships. Four women…

Scalability Issues for Remote Sensing Infrastructure: A Case Study

PubMed Central

Liu, Yang; Picard, Sean; Williamson, Carey

2017-01-01

For the past decade, a team of University of Calgary researchers has operated a large “sensor Web” to collect, analyze, and share scientific data from remote measurement instruments across northern Canada. This sensor Web receives real-time data streams from over a thousand Internet-connected sensors, with a particular emphasis on environmental data (e.g., space weather, auroral phenomena, atmospheric imaging). Through research collaborations, we had the opportunity to evaluate the performance and scalability of their remote sensing infrastructure. This article reports the lessons learned from our study, which considered both data collection and data dissemination aspects of their system. On the data collection front, we used benchmarking techniques to identify and fix a performance bottleneck in the system’s memory management for TCP data streams, while also improving system efficiency on multi-core architectures. On the data dissemination front, we used passive and active network traffic measurements to identify and reduce excessive network traffic from the Web robots and JavaScript techniques used for data sharing. While our results are from one specific sensor Web system, the lessons learned may apply to other scientific Web sites with remote sensing infrastructure. PMID:28468262
Comprehension: an overlooked component in augmented language development.

PubMed

Sevcik, Rose A

2006-02-15

Despite the importance of children's receptive skills as a foundation for later productive word use, the role of receptive language traditionally has received very limited attention since the focus in linguistic development has centered on language production. For children with significant developmental disabilities and communication impairments, augmented language systems have been devised as a tool both for language input and output. The role of both speech and symbol comprehension skills is emphasized in this paper. Data collected from two longitudinal studies of children and youth with severe disabilities and limited speech serve as illustrations in this paper. The acquisition and use of the System for Augmenting Language (SAL) was studied in home and school settings. Communication behaviors of the children and youth and their communication partners were observed and language assessment measures were collected. Two patterns of symbol learning and achievement--beginning and advanced--were observed. Extant speech comprehension skills brought to the augmented language learning task impacted the participants' patterns of symbol learning and use. Though often overlooked, the importance of speech and symbol comprehension skills were underscored in the studies described. Future areas for research are identified.
Chase: Control of Heterogeneous Autonomous Sensors for Situational Awareness

DTIC Science & Technology

2016-08-03

remained the discovery and analysis of new foundational methodology for information collection and fusion that exercises rigorous feedback control over...simultaneously achieve quantified information and physical objectives. New foundational methodology for information collection and fusion that exercises...11.2.1. In the general area of novel stochastic systems analysis it seems appropriate to mention the pioneering work on non -Bayesian distributed learning
Sample Manipulation System for Sample Analysis at Mars

NASA Technical Reports Server (NTRS)

Mumm, Erik; Kennedy, Tom; Carlson, Lee; Roberts, Dustyn

2008-01-01

The Sample Analysis at Mars (SAM) instrument will analyze Martian samples collected by the Mars Science Laboratory Rover with a suite of spectrometers. This paper discusses the driving requirements, design, and lessons learned in the development of the Sample Manipulation System (SMS) within SAM. The SMS stores and manipulates 74 sample cups to be used for solid sample pyrolysis experiments. Focus is given to the unique mechanism architecture developed to deliver a high packing density of sample cups in a reliable, fault tolerant manner while minimizing system mass and control complexity. Lessons learned are presented on contamination control, launch restraint mechanisms for fragile sample cups, and mechanism test data.
Nicephor[e]: a web-based solution for teaching forensic and scientific photography.

PubMed

Voisard, R; Champod, C; Furrer, J; Curchod, J; Vautier, A; Massonnet, G; Buzzini, P

2007-04-11

Nicephor[e] is a project funded by "Swiss Virtual Campus" and aims at creating a distant or mixed web-based learning system in forensic and scientific photography and microscopy. The practical goal is to organize series of on-line modular courses corresponding to the educational requirements of undergraduate academic programs. Additionally, this program could be used in the context of continuing educational programs. The architecture of the project is designed to guarantee a high level of knowledge in forensic and scientific photographic techniques, and to have an easy content production and the ability to create a number of different courses sharing the same content. The e-learning system Nicephor[e] consists of three different parts. The first one is a repository of learning objects that gathers all theoretical subject matter of the project such as texts, animations, images, and films. This repository is a web content management system (Typo3) that permits creating, publishing, and administrating dynamic content via a web browser as well as storing it into a database. The flexibility of the system's architecture allows for an easy updating of the content to follow the development of photographic technology. The instructor of a course can decide which modular contents need to be included in the course, and in which order they will be accessed by students. All the modular courses are developed in a learning management system (WebCT or Moodle) that can deal with complex learning scenarios, content distribution, students, tests, and interaction with instructor. Each course has its own learning scenario based on the goals of the course and the student's profile. The content of each course is taken from the content management system. It is then structured in the learning management system according to the pedagogical goals defined by the instructor. The modular courses are created in a highly interactive setting and offer autoevaluating tests to the students. The last part of the system is a digital assets management system (Extensis Portfolio). The practical portion of each course is to produce images of different marks or objects. The collection of all this material produced, indexed by the students and corrected by the instructor is essential to the development of a knowledge base of photographic techniques applied to a specific forensic subject. It represents also an extensible collection of different marks from known sources obtained under various conditions. It allows to reuse these images for creating image-based case files.
Systems-Oriented Workplace Learning Experiences for Early Learners: Three Models.

PubMed

O'Brien, Bridget C; Bachhuber, Melissa R; Teherani, Arianne; Iker, Theresa M; Batt, Joanne; O'Sullivan, Patricia S

2017-05-01

Early workplace learning experiences may be effective for learning systems-based practice. This study explores systems-oriented workplace learning experiences (SOWLEs) for early learners to suggest a framework for their development. The authors used a two-phase qualitative case study design. In Phase 1 (spring 2014), they prepared case write-ups based on transcribed interviews from 10 SOWLE leaders at the authors' institution and, through comparative analysis of cases, identified three SOWLE models. In Phase 2 (summer 2014), studying seven 8-week SOWLE pilots, the authors used interview and observational data collected from the seven participating medical students, two pharmacy students, and site leaders to construct case write-ups of each pilot and to verify and elaborate the models. In Model 1, students performed specific patient care activities that addressed a system gap. Some site leaders helped students connect the activities to larger systems problems and potential improvements. In Model 2, students participated in predetermined systems improvement (SI) projects, gaining experience in the improvement process. Site leaders had experience in SI and often had significant roles in the projects. In Model 3, students worked with key stakeholders to develop a project and conduct a small test of change. They experienced most elements of an improvement cycle. Site leaders often had experience with SI and knew how to guide and support students' learning. Each model could offer systems-oriented learning opportunities provided that key elements are in place including site leaders facile in SI concepts and able to guide students in SOWLE activities.
A new standardized data collection system for interdisciplinary thyroid cancer management: Thyroid COBRA.

PubMed

Tagliaferri, Luca; Gobitti, Carlo; Colloca, Giuseppe Ferdinando; Boldrini, Luca; Farina, Eleonora; Furlan, Carlo; Paiar, Fabiola; Vianello, Federica; Basso, Michela; Cerizza, Lorenzo; Monari, Fabio; Simontacchi, Gabriele; Gambacorta, Maria Antonietta; Lenkowicz, Jacopo; Dinapoli, Nicola; Lanzotti, Vito; Mazzarotto, Renzo; Russi, Elvio; Mangoni, Monica

2018-07-01

The big data approach offers a powerful alternative to Evidence-based medicine. This approach could guide cancer management thanks to machine learning application to large-scale data. Aim of the Thyroid CoBRA (Consortium for Brachytherapy Data Analysis) project is to develop a standardized web data collection system, focused on thyroid cancer. The Metabolic Radiotherapy Working Group of Italian Association of Radiation Oncology (AIRO) endorsed the implementation of a consortium directed to thyroid cancer management and data collection. The agreement conditions, the ontology of the collected data and the related software services were defined by a multicentre ad hoc working-group (WG). Six Italian cancer centres were firstly started the project, defined and signed the Thyroid COBRA consortium agreement. Three data set tiers were identified: Registry, Procedures and Research. The COBRA-Storage System (C-SS) appeared to be not time-consuming and to be privacy respecting, as data can be extracted directly from the single centre's storage platforms through a secured connection that ensures reliable encryption of sensible data. Automatic data archiving could be directly performed from Image Hospital Storage System or the Radiotherapy Treatment Planning Systems. The C-SS architecture will allow "Cloud storage way" or "distributed learning" approaches for predictive model definition and further clinical decision support tools development. The development of the Thyroid COBRA data Storage System C-SS through a multicentre consortium approach appeared to be a feasible tool in the setup of complex and privacy saving data sharing system oriented to the management of thyroid cancer and in the near future every cancer type. Copyright © 2018 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.
Collective learning modeling based on the kinetic theory of active particles.

PubMed

Burini, D; De Lillo, S; Gibelli, L

2016-03-01

This paper proposes a systems approach to the theory of perception and learning in populations composed of many living entities. Starting from a phenomenological description of these processes, a mathematical structure is derived which is deemed to incorporate their complexity features. The modeling is based on a generalization of kinetic theory methods where interactions are described by theoretical tools of game theory. As an application, the proposed approach is used to model the learning processes that take place in a classroom. Copyright © 2015 Elsevier B.V. All rights reserved.
Inductive System Health Monitoring

NASA Technical Reports Server (NTRS)

Iverson, David L.

2004-01-01

The Inductive Monitoring System (IMS) software was developed to provide a technique to automatically produce health monitoring knowledge bases for systems that are either difficult to model (simulate) with a computer or which require computer models that are too complex to use for real time monitoring. IMS uses nominal data sets collected either directly from the system or from simulations to build a knowledge base that can be used to detect anomalous behavior in the system. Machine learning and data mining techniques are used to characterize typical system behavior by extracting general classes of nominal data from archived data sets. IMS is able to monitor the system by comparing real time operational data with these classes. We present a description of learning and monitoring method used by IMS and summarize some recent IMS results.
Natuculture Systems: Addressing Students' STEM and Agriculture Knowledge

NASA Astrophysics Data System (ADS)

Joyce, Alexander Augusto

The purpose of this study was to assess the inclusion of a Natuculture systems learning experience into selected high school STEM courses to determine high school students' interests in majoring in STEM and for pursuing careers in agricultural sciences. Natuculture is defined as "any human-made system that mimics nature in human-disturbed landscapes". The research occurred at an urban area high school located in the Piedmont region of North Carolina. Fifty-three students in grades 9-12 participated during an academic semester learning experience which included planting, maintenance, & harvesting for an oasissofa. Data was collected using a questionnaire and reflective journals to gather students' attitudes towards agriculture and science and knowledge towards agriculture. Results showed that while the experiences did not improve students' interest in pursuing careers in agricultural sciences, overall, they did increase their knowledge of concepts related to agriculture. It was concluded that students benefit from experiential learning experiences. Based on the study, it is recommended that future research follow up with students to learn of their educational and career choices in agriculture and future learning experiences include curricula that integrates agricultural topics with STEM courses.
Problem Solving Model for Science Learning

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

NASA Astrophysics Data System (ADS)

Dattolo, Antonina; Luccio, Flaminia L.

Web-based education enables learners and teachers to access a wide quantity of continuously updated educational sources. In order to support the learning process, a system has to provide some fundamental features, such as simple mechanisms for the identification of the collection of “interesting” documents, adequate structures for storing, organizing and visualizing these documents, and appropriate mechanisms for creating personalized adaptive paths and views for learners.
Outcomes-Based Authentic Learning, Portfolio Assessment, and a Systems Approach to "Complex Problem-Solving": Related Pillars for Enhancing the Innovative Role of PBL in Future Higher Education

ERIC Educational Resources Information Center

Richards, Cameron

2015-01-01

The challenge of better reconciling individual and collective aspects of innovative problem-solving can be productively addressed to enhance the role of PBL as a key focus of the creative process in future higher education. This should involve "active learning" approaches supported by related processes of teaching, assessment and…
How do primary health care teams learn to integrate intimate partner violence (IPV) management? A realist evaluation protocol.

PubMed

Goicolea, Isabel; Vives-Cases, Carmen; San Sebastian, Miguel; Marchal, Bruno; Kegels, Guy; Hurtig, Anna-Karin

2013-03-23

Despite the existence of ample literature dealing, on the one hand, with the integration of innovations within health systems and team learning, and, on the other hand, with different aspects of the detection and management of intimate partner violence (IPV) within healthcare facilities, research that explores how health innovations that go beyond biomedical issues-such as IPV management-get integrated into health systems, and that focuses on healthcare teams' learning processes is, to the best of our knowledge, very scarce if not absent. This realist evaluation protocol aims to ascertain: why, how, and under what circumstances primary healthcare teams engage (if at all) in a learning process to integrate IPV management in their practices; and why, how, and under what circumstances team learning processes lead to the development of organizational culture and values regarding IPV management, and the delivery of IPV management services. This study will be conducted in Spain using a multiple-case study design. Data will be collected from selected cases (primary healthcare teams) through different methods: individual and group interviews, routinely collected statistical data, documentary review, and observation. Cases will be purposively selected in order to enable testing the initial middle-range theory (MRT). After in-depth exploration of a limited number of cases, additional cases will be chosen for their ability to contribute to refining the emerging MRT to explain how primary healthcare learn to integrate intimate partner violence management. Evaluations of health sector responses to IPV are scarce, and even fewer focus on why, how, and when the healthcare services integrate IPV management. There is a consensus that healthcare professionals and healthcare teams play a key role in this integration, and that training is important in order to realize changes. However, little is known about team learning of IPV management, both in terms of how to trigger such learning and how team learning is connected with changes in organizational culture and values, and in service delivery. This realist evaluation protocol aims to contribute to this knowledge by conducting this project in a country, Spain, where great endeavours have been made towards the integration of IPV management within the health system.
Authentic science experiences as a vehicle for assessing orientation towards science and science careers relative to identity and agency: a response to ``learning from the path followed by Brad''

NASA Astrophysics Data System (ADS)

Chinn, Pauline W. U.

2009-09-01

This response draws from the literature on adaptive learning, traditional ecological knowledge, and social-ecological systems to show that Brad's choice is not a simple decision between traditional ecological knowledge and authentic science. This perspective recognizes knowledge systems as dynamic, cultural and historical activities characterized by diverse worldviews and ways of constructing and legitimizing knowledge. Brad's decision is seen as an example of adaptive learning, identity development and personal/collective agency oriented to increasing tribal influence in resource management decisions and policies. I will conclude that science literacy for all is not served by a transcendent, universal, Western modern view of science.
[A radiological case collection with interactive character as a new element in the education of medical students].

PubMed

Heye, T; Kurz, P; Eiers, M; Kauffmann, G W; Schipp, A

2008-04-01

Evaluation of an interactive, multimedia case-based learning platform for the radiological education of medical students. An interactive electronic learning platform for the education of medical students was built in HTML format independent of the operating system in the context of the Heidelberg Curriculum Medicinale (HeiCuMed). A case collection of 30 common and authentic clinical cases is used as the central theme and clinical background. The user has to work on each case by making decisions regarding a selection of diagnostic modalities and by analyzing the chosen studies. After a reasonable selection and sequence of diagnostic radiological modalities and their interpretation, a diagnosis has to be made. An extensive collection of normal findings for any modality is available for the user as a reference in correlation with the pathology at anytime within each case. The case collection consists of 2053 files with 1109 Internet pages (HTML) and 869 image files (.jpeg) with approximately 10 000 crosslinks (links). The case collection was evaluated by a questionnaire (scale 1 - 5) at the end of the radiological student course. The development of the results of the radiological course exam was analyzed to investigate any effect on the learning performance after the case collection was introduced. 97.6 % of the course participants would use the case collection beyond the radiological student course to learn radiology in their medical studies. The handling of the case collection was rated excellent in 36.9 %, good in 54.6 %, satisfactory in 8 % and unsatisfactory in 0.4 %. 41 % felt that the case collection was overall excellent, 49.2 % good, 7.8 % satisfactory, 1.6 % unsatisfactory and 0.4 % poor. A positive trend in the development of the results in the radiological course exam with less variance after the introduction of the case collection was found but failed statistical significance. A platform-independent, interactive, multimedia learning platform with authentic clinical cases and multiple choice elements for the user is the ideal method for supporting and expanding medical education in radiology. The usefulness and the reasonable exertion of diagnostic modalities are conveyed in a practical context as teaching goals. The high acceptance among students is based on the interactivity and use of multimedia.
Student Team Projects in Information Systems Development: Measuring Collective Creative Efficacy

ERIC Educational Resources Information Center

Cheng, Hsiu-Hua; Yang, Heng-Li

2011-01-01

For information systems development project student teams, learning how to improve software development processes is an important training. Software process improvement is an outcome of a number of creative behaviours. Social cognitive theory states that the efficacy of judgment influences behaviours. This study explores the impact of three types…
Implementation of Web-Based Argumentation in Facilitating Elementary School Students to Learn Environmental Issues

ERIC Educational Resources Information Center

Wang, T. H.

2014-01-01

This research develops a Web-based argumentation system named the Web-based Interactive Argumentation System (WIAS). WIAS can provide teachers with the scaffolding for argumentation instruction. Students can propose their statements, collect supporting evidence and share and discuss with peers online. This research adopts a quasi-experimental…
From Intuition to Evidence: A Data-Driven Approach to Transforming CS Education

ERIC Educational Resources Information Center

Allevato, Anthony J.

2012-01-01

Educators in many disciplines are too often forced to rely on intuition about how students learn and the effectiveness of teaching to guide changes and improvements to their curricula. In computer science, systems that perform automated collection and assessment of programming assignments are seeing increased adoption, and these systems generate a…
Lecture Rule No. 1: Cell Phones ON, Please! A Low-Cost Personal Response System for Learning and Teaching

ERIC Educational Resources Information Center

Lee, Albert W. M.; Ng, Joseph K. Y.; Wong, Eva Y. W.; Tan, Alfred; Lau, April K. Y.; Lai, Stephen F. Y.

2013-01-01

phone, that can be used to replace the "clicker" as a personal response device. Our mobile phone-based response system (iQlickers) collects and analyzes the answers or opinions sent in by the students as SMS (short message service) messages. The statistic of the…

Knowledge exchange systems for youth health and chronic disease prevention: a tri-provincial case study.

PubMed

Murnaghan, D; Morrison, W; Griffith, E J; Bell, B L; Duffley, L A; McGarry, K; Manske, S

2013-09-01

The research teams undertook a case study design using a common analytical framework to investigate three provincial (Prince Edward Island, New Brunswick and Manitoba) knowledge exchange systems. These three knowledge exchange systems seek to generate and enhance the use of evidence in policy development, program planning and evaluation to improve youth health and chronic disease prevention. We applied a case study design to explore the lessons learned, that is, key conditions or processes contributing to the development of knowledge exchange capacity, using a multi-data collection method to gain an in-depth understanding. Data management, synthesis and analysis activities were concurrent, iterative and ongoing. The lessons learned were organized into seven "clusters." Key findings demonstrated that knowledge exchange is a complex process requiring champions, collaborative partnerships, regional readiness and the adaptation of knowledge exchange to diverse stakeholders. Overall, knowledge exchange systems can increase the capacity to exchange and use evidence by moving beyond collecting and reporting data. Areas of influence included development of new partnerships, expanded knowledge-sharing activities, and refinement of policy and practice approaches related to youth health and chronic disease prevention.
Ultrasound in telemedicine: its impact in high-risk obstetric health care delivery.

PubMed

Long, Megan Chang; Angtuaco, Teresita; Lowery, Curtis

2014-09-01

The aim of this study was to determine the impact of Antenatal and Neonatal Guidelines, Education, and Learning System (ANGELS), a statewide telemedicine project, on health care delivery to patients with high-risk pregnancies in Arkansas. With institutional review board approval, a Health Insurance Portability and Accountability Act-compliant retrospective review, in which the requirement for informed patient consent was waived, was performed. The population studied is the Arkansas maternal Medicaid population. Data for evaluation were collected from maternal Medicaid claims, ANGELS administrative records, and birth records from the Arkansas Vital Statistics record system. Data collected from before the inception of ANGELS (2001-2003) were compared with data collected after the inception of ANGELS (2004-2007).Antenatal and Neonatal Guidelines, Education, and Learning System is a multidisciplinary, multifaceted telemedicine program designed in Arkansas to enhance high-risk obstetric health care delivery across the state. An essential component of the program is real-time interactive targeted level II ultrasound examination of patients. Since the inception of the ANGELS program in 2003, a growing number of telemedicine consultations and real-time ultrasound examinations are being performed every year. The number and percentage of high-risk pregnancies identified each year show a slight decrease since inception of the ANGELS program, and findings suggest that identification of high-risk pregnancies is shifting from the second trimester to the first trimester, but trends vary over time. Antenatal and Neonatal Guidelines, Education, and Learning System has created a telemedicine network across the state that has made possible, among many other things, access to real-time level II ultrasound examinations and consultations. This program has ultimately led to improved prenatal access across the state.
Charting molecular free-energy landscapes with an atlas of collective variables

NASA Astrophysics Data System (ADS)

Hashemian, Behrooz; Millán, Daniel; Arroyo, Marino

2016-11-01

Collective variables (CVs) are a fundamental tool to understand molecular flexibility, to compute free energy landscapes, and to enhance sampling in molecular dynamics simulations. However, identifying suitable CVs is challenging, and is increasingly addressed with systematic data-driven manifold learning techniques. Here, we provide a flexible framework to model molecular systems in terms of a collection of locally valid and partially overlapping CVs: an atlas of CVs. The specific motivation for such a framework is to enhance the applicability and robustness of CVs based on manifold learning methods, which fail in the presence of periodicities in the underlying conformational manifold. More generally, using an atlas of CVs rather than a single chart may help us better describe different regions of conformational space. We develop the statistical mechanics foundation for our multi-chart description and propose an algorithmic implementation. The resulting atlas of data-based CVs are then used to enhance sampling and compute free energy surfaces in two model systems, alanine dipeptide and β-D-glucopyranose, whose conformational manifolds have toroidal and spherical topologies.
Apollo Missions to the Lunar Surface

NASA Technical Reports Server (NTRS)

Graff, Paige V.

2018-01-01

Six Apollo missions to the Moon, from 1969-1972, enabled astronauts to collect and bring lunar rocks and materials from the lunar surface to Earth. Apollo lunar samples are curated by NASA Astromaterials at the NASA Johnson Space Center in Houston, TX. Samples continue to be studied and provide clues about our early Solar System. Learn more and view collected samples at: https://curator.jsc.nasa.gov/lunar.
Using Computers Intelligently in Tertiary Education. A Collection of Papers Presented to the Australian Society for Computers in Learning (Sydney, New South Wales, Australia, November 29-December 3, 1987).

ERIC Educational Resources Information Center

Barrett, John, Ed.; Hedberg, John, Ed.

The 63 papers in this collection include two keynote addresses: "Patient Simulation Using Interactive Video: An Application" (Joseph V. Henderson), and "Intelligent Tutoring Systems: Practice Opportunities and Explanatory Models" (Alan Lesgold). The remaining papers are grouped under five topics: (1) Artificial Intelligence,…
Can a virtual reality surgical simulation training provide a self-driven and mentor-free skills learning? Investigation of the practical influence of the performance metrics from the virtual reality robotic surgery simulator on the skill learning and associated cognitive workloads.

PubMed

Lee, Gyusung I; Lee, Mija R

2018-01-01

While it is often claimed that virtual reality (VR) training system can offer self-directed and mentor-free skill learning using the system's performance metrics (PM), no studies have yet provided evidence-based confirmation. This experimental study investigated what extent to which trainees achieved their self-learning with a current VR simulator and whether additional mentoring improved skill learning, skill transfer and cognitive workloads in robotic surgery simulation training. Thirty-two surgical trainees were randomly assigned to either the Control-Group (CG) or Experiment-Group (EG). While the CG participants reviewed the PM at their discretion, the EG participants had explanations about PM and instructions on how to improve scores. Each subject completed a 5-week training using four simulation tasks. Pre- and post-training data were collected using both a simulator and robot. Peri-training data were collected after each session. Skill learning, time spent on PM (TPM), and cognitive workloads were compared between groups. After the simulation training, CG showed substantially lower simulation task scores (82.9 ± 6.0) compared with EG (93.2 ± 4.8). Both groups demonstrated improved physical model tasks performance with the actual robot, but the EG had a greater improvement in two tasks. The EG exhibited lower global mental workload/distress, higher engagement, and a better understanding regarding using PM to improve performance. The EG's TPM was initially long but substantially shortened as the group became familiar with PM. Our study demonstrated that the current VR simulator offered limited self-skill learning and additional mentoring still played an important role in improving the robotic surgery simulation training.
Primary Care and Public Health Activities in Select US Health Centers: Documenting Successes, Barriers, and Lessons Learned

PubMed Central

Shi, Leiyu; Chowdhury, Joya; Sripipatana, Alek; Zhu, Jinsheng; Sharma, Ravi; Hayashi, A. Seiji; Daly, Charles A.; Tomoyasu, Naomi; Nair, Suma; Ngo-Metzger, Quyen

2012-01-01

Objectives. We examined primary care and public health activities among federally funded health centers, to better understand their successes, the barriers encountered, and the lessons learned. Methods. We used qualitative and quantitative methods to collect data from 9 health centers, stratified by administrative division, urban–rural location, and race/ethnicity of patients served. Descriptive data on patient and institutional characteristics came from the Uniform Data System, which collects data from all health centers annually. We administered questionnaires and conducted phone interviews with key informants. Results. Health centers performed well on primary care coordination and community orientation scales and reported conducting many essential public health activities. We identified specific needs for integrating primary care and public health: (1) more funding for collaborations and for addressing the social determinants of health, (2) strong leadership to champion collaborations, (3) trust building among partners, with shared missions and clear expectations of responsibilities, and (4) alignment and standardization of data collection, analysis, and exchange. Conclusions. Lessons learned from health centers should inform strategies to better integrate public health with primary care. PMID:22690975
Habituation based synaptic plasticity and organismic learning in a quantum perovskite.

PubMed

Zuo, Fan; Panda, Priyadarshini; Kotiuga, Michele; Li, Jiarui; Kang, Mingu; Mazzoli, Claudio; Zhou, Hua; Barbour, Andi; Wilkins, Stuart; Narayanan, Badri; Cherukara, Mathew; Zhang, Zhen; Sankaranarayanan, Subramanian K R S; Comin, Riccardo; Rabe, Karin M; Roy, Kaushik; Ramanathan, Shriram

2017-08-14

A central characteristic of living beings is the ability to learn from and respond to their environment leading to habit formation and decision making. This behavior, known as habituation, is universal among all forms of life with a central nervous system, and is also observed in single-cell organisms that do not possess a brain. Here, we report the discovery of habituation-based plasticity utilizing a perovskite quantum system by dynamical modulation of electron localization. Microscopic mechanisms and pathways that enable this organismic collective charge-lattice interaction are elucidated by first-principles theory, synchrotron investigations, ab initio molecular dynamics simulations, and in situ environmental breathing studies. We implement a learning algorithm inspired by the conductance relaxation behavior of perovskites that naturally incorporates habituation, and demonstrate learning to forget: a key feature of animal and human brains. Incorporating this elementary skill in learning boosts the capability of neural computing in a sequential, dynamic environment.Habituation is a learning mechanism that enables control over forgetting and learning. Zuo, Panda et al., demonstrate adaptive synaptic plasticity in SmNiO 3 perovskites to address catastrophic forgetting in a dynamic learning environment via hydrogen-induced electron localization.
Knowledge Transfer in Health Care Through Digitally Collecting Learning Experiences - Results of Witra Care.

PubMed

Behrends, Marianne; Kupka, Thomas; Schmeer, Regina; Meyenburg-Altwarg, Iris; Marschollek, Michael

2016-01-01

The goal of the project Witra Care was to investigate how far the use of mobile technology is suitable to collect experience-based knowledge of nurses. Nine new employees and seven experienced nurses received for six weeks a mobile phone or a tablet pc with a mobile application that allowed them to collect learning object as pictures, videos, audio files or notes. In Witra Care the nurses created 303 learning objects. They have found the collecting of learning experiences was helpful for their learning processes. The learning objects demonstrate various aspects of daily routines in nursing. The results of Witra Care show that the documentation of learning experiences with mobile devices helps to gather information about the practical knowledge in the daily work of nurses, identifies individual learning needs of the employees and supports them in their personal learning processes.
Predicting Virtual World User Population Fluctuations with Deep Learning

PubMed Central

Park, Nuri; Zhang, Qimeng; Kim, Jun Gi; Kang, Shin Jin; Kim, Chang Hun

2016-01-01

This paper proposes a system for predicting increases in virtual world user actions. The virtual world user population is a very important aspect of these worlds; however, methods for predicting fluctuations in these populations have not been well documented. Therefore, we attempt to predict changes in virtual world user populations with deep learning, using easily accessible online data, including formal datasets from Google Trends, Wikipedia, and online communities, as well as informal datasets collected from online forums. We use the proposed system to analyze the user population of EVE Online, one of the largest virtual worlds. PMID:27936009
Predicting Virtual World User Population Fluctuations with Deep Learning.

PubMed

Kim, Young Bin; Park, Nuri; Zhang, Qimeng; Kim, Jun Gi; Kang, Shin Jin; Kim, Chang Hun

2016-01-01

This paper proposes a system for predicting increases in virtual world user actions. The virtual world user population is a very important aspect of these worlds; however, methods for predicting fluctuations in these populations have not been well documented. Therefore, we attempt to predict changes in virtual world user populations with deep learning, using easily accessible online data, including formal datasets from Google Trends, Wikipedia, and online communities, as well as informal datasets collected from online forums. We use the proposed system to analyze the user population of EVE Online, one of the largest virtual worlds.
Habituation based synaptic plasticity and organismic learning in a quantum perovskite

DOE PAGES

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

2017-08-14

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

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

Zuo, Fan; Panda, Priyadarshini; Kotiuga, Michele

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

DTIC Science & Technology

1989-12-01

indicates that the CL research lacks sufficient experimental controls, as well as, fails to use current theories associated with cognitiv-e approaches...comparing/contrasting some of the differences between CL and GPS. Finally, examples of successful CL are given to show the transition from theory to...Schmuck, 1985). For example, Stodolsky (1984) differentiates peer-work groups from teacher- led groups. She suggests that there are five types of
Implementation of learning outcome attainment measurement system in aviation engineering higher education

NASA Astrophysics Data System (ADS)

Salleh, I. Mohd; Mat Rani, M.

2017-12-01

This paper aims to discuss the effectiveness of the Learning Outcome Attainment Measurement System in assisting Outcome Based Education (OBE) for Aviation Engineering Higher Education in Malaysia. Direct assessments are discussed to show the implementation processes that become a key role in the successful outcome measurement system. A case study presented in this paper involves investigation on the implementation of the system in Aircraft Structure course for Bachelor in Aircraft Engineering Technology program in UniKL-MIAT. The data has been collected for five semesters, starting from July 2014 until July 2016. The study instruments used include the report generated in Learning Outcomes Measurements System (LOAMS) that contains information on the course learning outcomes (CLO) individual and course average performance reports. The report derived from LOAMS is analyzed and the data analysis has revealed that there is a positive significant correlation between the individual performance and the average performance reports. The results for analysis of variance has further revealed that there is a significant difference in OBE grade score among the report. Independent samples F-test results, on the other hand, indicate that the variances of the two populations are unequal.
Student-Centered Modules to Support Active Learning in Hydrology: Development Experiences and Users' Perspectives

NASA Astrophysics Data System (ADS)

Tarboton, D. G.; Habib, E. H.; Deshotel, M.; Merck, M. F.; Lall, U.; Farnham, D. J.

2016-12-01

Traditional approaches to undergraduate hydrology and water resource education are textbook based, adopt unit processes and rely on idealized examples of specific applications, rather than examining the contextual relations in the processes and the dynamics connecting climate and ecosystems. The overarching goal of this project is to address the needed paradigm shift in undergraduate education of engineering hydrology and water resources education to reflect parallel advances in hydrologic research and technology, mainly in the areas of new observational settings, data and modeling resources and web-based technologies. This study presents efforts to develop a set of learning modules that are case-based, data and simulation driven and delivered via a web user interface. The modules are based on real-world case studies from three regional hydrologic settings: Coastal Louisiana, Utah Rocky Mountains and Florida Everglades. These three systems provide unique learning opportunities on topics such as: regional-scale budget analysis, hydrologic effects of human and natural changes, flashflood protection, climate-hydrology teleconnections and water resource management scenarios. The technical design and contents of the modules aim to support students' ability for transforming their learning outcomes and skills to hydrologic systems other than those used by the specific activity. To promote active learning, the modules take students through a set of highly engaging learning activities that are based on analysis of hydrologic data and model simulations. The modules include user support in the form of feedback and self-assessment mechanisms that are integrated within the online modules. Module effectiveness is assessed through an improvement-focused evaluation model using a mixed-method research approach guiding collection and analysis of evaluation data. Both qualitative and quantitative data are collected through student learning data, product analysis, and staff interviews. The presentation shares with the audience lessons learned from the development and implementation of the modules, students' feedback, guidelines on design and content attributes that support active learning in hydrology, and challenges encountered during the class implementation and evaluation of the modules.
Skeletons Not Just for Halloween.

ERIC Educational Resources Information Center

Markle, Sandra; And Others

1983-01-01

A teaching unit that helps fourth-, fifth-, and sixth-grade students learn about human and animal skeletal systems is described. The unit focuses on bone characteristics and develops basic science skills, such as observation, data collection, and making and testing hypotheses. (PP)
New Evaluation Vector through the Stanford Mobile Inquiry-Based Learning Environment (SMILE) for Participatory Action Research

PubMed Central

An, Ji-Young

2016-01-01

Objectives This article reviews an evaluation vector model driven from a participatory action research leveraging a collective inquiry system named SMILE (Stanford Mobile Inquiry-based Learning Environment). Methods SMILE has been implemented in a diverse set of collective inquiry generation and analysis scenarios including community health care-specific professional development sessions and community-based participatory action research projects. In each scenario, participants are given opportunities to construct inquiries around physical and emotional health-related phenomena in their own community. Results Participants formulated inquiries as well as potential clinical treatments and hypothetical scenarios to address health concerns or clarify misunderstandings or misdiagnoses often found in their community practices. From medical universities to rural village health promotion organizations, all participatory inquiries and potential solutions can be collected and analyzed. The inquiry and solution sets represent an evaluation vector which helps educators better understand community health issues at a much deeper level. Conclusions SMILE helps collect problems that are most important and central to their community health concerns. The evaluation vector, consisting participatory and collective inquiries and potential solutions, helps the researchers assess the participants' level of understanding on issues around health concerns and practices while helping the community adequately formulate follow-up action plans. The method used in SMILE requires much further enhancement with machine learning and advanced data visualization. PMID:27525157
[The construction of collective portfolios in traditional curriculums: an innovative approach in teaching-learning].

PubMed

Cotta, Rosângela Minardi Mitre; Silva, Luciana Saraiva da; Lopes, Lílian Lelis; Gomes, Karine de Oliveira; Cotta, Fernanda Mitre; Lugarinho, Regina; Mitre, Sandra Minardi

2012-03-01

Education to promote health has traditionally been based on knowledge transmission methodologies. However, the current scenario calls for the training of professionals with a critical-reflective profile, who are able to work in teams. We present the report of an innovative experience using the construction of collective portfolios as instruments of learning, changing attitudes and training of undergraduates, in a traditional subject-based curriculum structure context. It is a descriptive exploratory study, with a qualitative-quantitative approach, based on analysis of collective portfolios (n=9), built by Health Policy students, together with an open questionnaire to students who attended the course (n=58) and also the staging of focus groups (n=3). The use of collective portfolios mobilized students in critical and reflective thinking on Brazilian health policy - the Unified Health System - broadening the concept on the health-disease process and practices related to health services, prioritizing teamwork and the active search for knowledge building, stressing the exercise of otherness, resilience and empowerment.
Learning from Experience: A Collection of Service-Learning Projects Linking Academic Standards to Curriculum.

ERIC Educational Resources Information Center

Babcock, Barbara, Ed.

Service-learning projects combine community service with student learning in a practical way that enhances academic knowledge and improves community environments and fellowship. This compilation is designed to show the service-learning process in action. The collection presents outstanding examples of successful service-learning projects as…

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
An fMRI investigation of the fronto-striatal learning system in women who exhibit eating disorder behaviors

PubMed Central

Celone, Kim A.; Thompson-Brenner, Heather; Ross, Robert S.; Pratt, Elizabeth M.; Stern, Chantal E.

2013-01-01

In the present study, we sought to examine whether the fronto-striatal learning system, which has been implicated in bulimia nervosa, would demonstrate altered BOLD activity during probabilistic category learning in women who met subthreshold criteria for bulimia nervosa (Sub-BN). Sub-BN, which falls within the clinical category of Eating Disorder Not Otherwise Specified (EDNOS), is comprised of individuals who demonstrate recurrent binge eating, efforts to minimize their caloric intake and caloric retention, and elevated levels of concern about shape, weight, and/or eating, but just fail to meet the diagnostic threshold for bulimia nervosa (BN). fMRI data were collected from eighteen women with subthreshold-BN (Sub-BN) and nineteen healthy control women group-matched for age, education and body mass index (MC) during the weather prediction task. Sub-BN participants demonstrated increased caudate nucleus and dorsolateral prefrontal cortex (DLPFC) activation during the learning of probabilistic categories. Though the two subject groups did not differ in behavioral performance, over the course of learning, Sub-BN participants showed a dynamic pattern of brain activity differences when compared to matched control participants. Regions implicated in episodic memory, including the medial temporal lobe (MTL), retrosplenial cortex, middle frontal gyrus, and anterior and posterior cingulate cortex showed decreased activity in the Sub-BN participants compared to MCs during early learning which was followed by increased involvement of the DLPFC during later learning. These findings demonstrate that women with Sub-BN demonstrate differences in fronto-striatal learning system activity, as well as a distinct functional pattern between fronto-striatal and MTL learning systems during the course of implicit probabilistic category learning. PMID:21419229
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.
Web-based, mobile-device friendly, self-report survey system incorporating avatars and gaming console techniques.

PubMed

Savel, Craig; Mierzwa, Stan; Gorbach, Pamina; Lally, Michelle; Zimet, Gregory; Meyer, Kristin; Souidi, Samir; Interventions, Aids

2014-01-01

We describe building an avatar-based self-report data collection tool to be used for a specific HIV prevention research project that is evaluating the feasibility and acceptability of this novel approach to collect self-reported data among youth. We discuss the gathering of requirements, the process of building a prototype of the envisioned system, and the lessons learned during the development of the solution. Specific knowledge is shared regarding technical experience with software development technologies and possible avenues for changes that could be considered if such a self-report survey system is used again. Examples of other gaming and avatar technology systems are included to provide further background.
Web-based, mobile-device friendly, self-report survey system incorporating avatars and gaming console techniques

PubMed Central

Savel, Craig; Mierzwa, Stan; Gorbach, Pamina; Lally, Michelle; Zimet, Gregory; Meyer, Kristin; Souidi, Samir; Interventions, AIDS

2014-01-01

We describe building an avatar-based self-report data collection tool to be used for a specific HIV prevention research project that is evaluating the feasibility and acceptability of this novel approach to collect self-reported data among youth. We discuss the gathering of requirements, the process of building a prototype of the envisioned system, and the lessons learned during the development of the solution. Specific knowledge is shared regarding technical experience with software development technologies and possible avenues for changes that could be considered if such a self-report survey system is used again. Examples of other gaming and avatar technology systems are included to provide further background. PMID:25422726
Determining the Significance of Item Order in Randomized Problem Sets

ERIC Educational Resources Information Center

Pardos, Zachary A.; Heffernan, Neil T.

2009-01-01

Researchers who make tutoring systems would like to know which sequences of educational content lead to the most effective learning by their students. The majority of data collected in many ITS systems consist of answers to a group of questions of a given skill often presented in a random sequence. Following work that identifies which items…
Toward an Integrated Online Learning Environment

NASA Astrophysics Data System (ADS)

Teodorescu, Raluca E.; Pawl, Andrew; Rayyan, Saif; Barrantes, Analia; Pritchard, David E.

2010-10-01

We are building in LON-CAPA an integrated learning environment that will enable the development, dissemination and evaluation of PER-based material. This environment features a collection of multi-level research-based homework sets organized by topic and cognitive complexity. These sets are associated with learning modules that contain very short exposition of the content supplemented by integrated open-access videos, worked examples, simulations, and tutorials (some from ANDES). To assess students' performance accurately with respect to a system-wide standard, we plan to implement Item Response Theory. Together with other PER assessments and purposeful solicitation of student feedback, this will allow us to measure and improve the efficacy of various research-based materials, while getting insights into teaching and learning.
Collective learning and optimal consensus decisions in social animal groups.

PubMed

Kao, Albert B; Miller, Noam; Torney, Colin; Hartnett, Andrew; Couzin, Iain D

2014-08-01

Learning has been studied extensively in the context of isolated individuals. However, many organisms are social and consequently make decisions both individually and as part of a collective. Reaching consensus necessarily means that a single option is chosen by the group, even when there are dissenting opinions. This decision-making process decouples the otherwise direct relationship between animals' preferences and their experiences (the outcomes of decisions). Instead, because an individual's learned preferences influence what others experience, and therefore learn about, collective decisions couple the learning processes between social organisms. This introduces a new, and previously unexplored, dynamical relationship between preference, action, experience and learning. Here we model collective learning within animal groups that make consensus decisions. We reveal how learning as part of a collective results in behavior that is fundamentally different from that learned in isolation, allowing grouping organisms to spontaneously (and indirectly) detect correlations between group members' observations of environmental cues, adjust strategy as a function of changing group size (even if that group size is not known to the individual), and achieve a decision accuracy that is very close to that which is provably optimal, regardless of environmental contingencies. Because these properties make minimal cognitive demands on individuals, collective learning, and the capabilities it affords, may be widespread among group-living organisms. Our work emphasizes the importance and need for theoretical and experimental work that considers the mechanism and consequences of learning in a social context.
Collective Learning and Optimal Consensus Decisions in Social Animal Groups

PubMed Central

Kao, Albert B.; Miller, Noam; Torney, Colin; Hartnett, Andrew; Couzin, Iain D.

2014-01-01

Learning has been studied extensively in the context of isolated individuals. However, many organisms are social and consequently make decisions both individually and as part of a collective. Reaching consensus necessarily means that a single option is chosen by the group, even when there are dissenting opinions. This decision-making process decouples the otherwise direct relationship between animals' preferences and their experiences (the outcomes of decisions). Instead, because an individual's learned preferences influence what others experience, and therefore learn about, collective decisions couple the learning processes between social organisms. This introduces a new, and previously unexplored, dynamical relationship between preference, action, experience and learning. Here we model collective learning within animal groups that make consensus decisions. We reveal how learning as part of a collective results in behavior that is fundamentally different from that learned in isolation, allowing grouping organisms to spontaneously (and indirectly) detect correlations between group members' observations of environmental cues, adjust strategy as a function of changing group size (even if that group size is not known to the individual), and achieve a decision accuracy that is very close to that which is provably optimal, regardless of environmental contingencies. Because these properties make minimal cognitive demands on individuals, collective learning, and the capabilities it affords, may be widespread among group-living organisms. Our work emphasizes the importance and need for theoretical and experimental work that considers the mechanism and consequences of learning in a social context. PMID:25101642
Learning in the model space for cognitive fault diagnosis.

PubMed

Chen, Huanhuan; Tino, Peter; Rodan, Ali; Yao, Xin

2014-01-01

The emergence of large sensor networks has facilitated the collection of large amounts of real-time data to monitor and control complex engineering systems. However, in many cases the collected data may be incomplete or inconsistent, while the underlying environment may be time-varying or unformulated. In this paper, we develop an innovative cognitive fault diagnosis framework that tackles the above challenges. This framework investigates fault diagnosis in the model space instead of the signal space. Learning in the model space is implemented by fitting a series of models using a series of signal segments selected with a sliding window. By investigating the learning techniques in the fitted model space, faulty models can be discriminated from healthy models using a one-class learning algorithm. The framework enables us to construct a fault library when unknown faults occur, which can be regarded as cognitive fault isolation. This paper also theoretically investigates how to measure the pairwise distance between two models in the model space and incorporates the model distance into the learning algorithm in the model space. The results on three benchmark applications and one simulated model for the Barcelona water distribution network confirm the effectiveness of the proposed framework.
75 FR 70931 - Proposed Information Collection Activity; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2010-11-19

... Information Collection Activity; Comment Request Title: Evaluation of Head Start Early Learning Mentor Coach... implementation evaluation of the Head Start Early Learning Mentor-Coach Initiative. The study will collect... awarded funds under the American Recovery and Reinvestment Act of 2009--Early Learning Mentor Coach...
Neuronal avalanches and learning

NASA Astrophysics Data System (ADS)

de Arcangelis, Lucilla

2011-05-01

Networks of living neurons represent one of the most fascinating systems of biology. If the physical and chemical mechanisms at the basis of the functioning of a single neuron are quite well understood, the collective behaviour of a system of many neurons is an extremely intriguing subject. Crucial ingredient of this complex behaviour is the plasticity property of the network, namely the capacity to adapt and evolve depending on the level of activity. This plastic ability is believed, nowadays, to be at the basis of learning and memory in real brains. Spontaneous neuronal activity has recently shown features in common to other complex systems. Experimental data have, in fact, shown that electrical information propagates in a cortex slice via an avalanche mode. These avalanches are characterized by a power law distribution for the size and duration, features found in other problems in the context of the physics of complex systems and successful models have been developed to describe their behaviour. In this contribution we discuss a statistical mechanical model for the complex activity in a neuronal network. The model implements the main physiological properties of living neurons and is able to reproduce recent experimental results. Then, we discuss the learning abilities of this neuronal network. Learning occurs via plastic adaptation of synaptic strengths by a non-uniform negative feedback mechanism. The system is able to learn all the tested rules, in particular the exclusive OR (XOR) and a random rule with three inputs. The learning dynamics exhibits universal features as function of the strength of plastic adaptation. Any rule could be learned provided that the plastic adaptation is sufficiently slow.
Designing Communication and Learning Environments.

ERIC Educational Resources Information Center

Gayeski, Diane M., Ed.

Designing and remodeling educational facilities are becoming more complex with options that include computer-based collaboration, classrooms with multimedia podiums, conference centers, and workplaces with desktop communication systems. This book provides a collection of articles that address educational facility design categorized in the…
Learning User Preferences for Sets of Objects

NASA Technical Reports Server (NTRS)

desJardins, Marie; Eaton, Eric; Wagstaff, Kiri L.

2006-01-01

Most work on preference learning has focused on pairwise preferences or rankings over individual items. In this paper, we present a method for learning preferences over sets of items. Our learning method takes as input a collection of positive examples--that is, one or more sets that have been identified by a user as desirable. Kernel density estimation is used to estimate the value function for individual items, and the desired set diversity is estimated from the average set diversity observed in the collection. Since this is a new learning problem, we introduce a new evaluation methodology and evaluate the learning method on two data collections: synthetic blocks-world data and a new real-world music data collection that we have gathered.
Using "get with the guidelines" to improve cardiovascular secondary prevention.

PubMed

LaBresh, Kenneth A; Gliklich, Richard; Liljestrand, James; Peto, Randolph; Ellrodt, A Gray

2003-10-01

"Get With The Guidelines (GWTG)" was developed and piloted by the American Heart Association (AHA), New England Affiliate; MassPRO, Inc.; and other organizations to reduce the gap in the application of secondary prevention guidelines in hospitalized cardiovascular disease patients. Collaborative learning programs and technology solutions were created for the project. The interactive Web-based patient management tool (PMT) was developed using quality measures derived from the AHA/American College of Cardiology secondary prevention guidelines. It provided data entry, embedded reminders and guideline summaries, and online reports of quality measure performance, including comparisons with the aggregate performance of all hospitals. Multidisciplinary teams from 24 hospitals participated in the 2000-2001 pilot. Four collaborative learning sessions and monthly conference calls supported team interaction. Best-practices sharing and the use of an Internet tool enabled hospitals to change systems and collect data on 1,738 patients. The GWTG program, a template of learning sessions with didactic presentations, best-practices sharing, and collaborative multidisciplinary team meetings supported by the Internet-based data collection and reporting system, can be extended to multiple regions without requiring additional development. Following the completion of the pilot, the AHA adopted GWTG as a national program.
The Organizational Learning Cycle. How We Can Learn Collectively.

ERIC Educational Resources Information Center

Dixon, Nancy

This book, which is designed for individuals interested in changing and developing their organizations, examines the organizational learning cycle and ways of learning collectively. Among the topics discussed in the book's nine chapters are the following: (1) changing nature of work and organizational learning; (2) theoretical framework of…
Hand Rehabilitation Learning System With an Exoskeleton Robotic Glove.

PubMed

Ma, Zhou; Ben-Tzvi, Pinhas; Danoff, Jerome

2016-12-01

This paper presents a hand rehabilitation learning system, the SAFE Glove, a device that can be utilized to enhance the rehabilitation of subjects with disabilities. This system is able to learn fingertip motion and force for grasping different objects and then record and analyze the common movements of hand function including grip and release patterns. The glove is then able to reproduce these movement patterns in playback fashion to assist a weakened hand to accomplish these movements, or to modulate the assistive level based on the user's or therapist's intent for the purpose of hand rehabilitation therapy. Preliminary data have been collected from healthy hands. To demonstrate the glove's ability to manipulate the hand, the glove has been fitted on a wooden hand and the grasping of various objects was performed. To further prove that hands can be safely driven by this haptic mechanism, force sensor readings placed between each finger and the mechanism are plotted. These experimental results demonstrate the potential of the proposed system in rehabilitation therapy.
Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning.

PubMed

Li, Chuan; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego

2016-06-17

Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults.
Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning

PubMed Central

Li, Chuan; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego

2016-01-01

Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults. PMID:27322273
Design a Learning-Oriented Fall Event Reporting System Based on Kirkpatrick Model.

PubMed

Zhou, Sicheng; Kang, Hong; Gong, Yang

2017-01-01

Patient fall has been a severe problem in healthcare facilities around the world due to its prevalence and cost. Routine fall prevention training programs are not as effective as expected. Using event reporting systems is the trend for reducing patient safety events such as falls, although some limitations of the systems exist at current stage. We summarized these limitations through literature review, and developed an improved web-based fall event reporting system. The Kirkpatrick model, widely used in the business area for training program evaluation, has been integrated during the design of our system. Different from traditional event reporting systems that only collect and store the reports, our system automatically annotates and analyzes the reported events, and provides users with timely knowledge support specific to the reported event. The paper illustrates the design of our system and how its features are intended to reduce patient falls by learning from previous errors.

The Spirit of '76: Revolutionizing College Learning Skills. Proceedings of the Annual Conference of the Western College Reading Association (9th, Tucson, Arizona, April 8-10, 1976). Volume IX.

ERIC Educational Resources Information Center

Sugimoto, Roy, Ed.

Drawn from the proceedings of a conference focusing on ways to improve the teaching of college level learning skills, the articles in this collection deal with a variety of topics. Among the topics discussed in the 37 articles are the following: (1) a systems approach to planning, implementing, and evaluating peer counseling programs; (2)…
`Unlearning' has a stabilizing effect in collective memories

NASA Astrophysics Data System (ADS)

Hopfield, J. J.; Feinstein, D. I.; Palmer, R. G.

1983-07-01

Crick and Mitchison1 have presented a hypothesis for the functional role of dream sleep involving an `unlearning' process. We have independently carried out mathematical and computer modelling of learning and `unlearning' in a collective neural network of 30-1,000 neurones. The model network has a content-addressable memory or `associative memory' which allows it to learn and store many memories. A particular memory can be evoked in its entirety when the network is stimulated by any adequate-sized subpart of the information of that memory2. But different memories of the same size are not equally easy to recall. Also, when memories are learned, spurious memories are also created and can also be evoked. Applying an `unlearning' process, similar to the learning processes but with a reversed sign and starting from a noise input, enhances the performance of the network in accessing real memories and in minimizing spurious ones. Although our model was not motivated by higher nervous function, our system displays behaviours which are strikingly parallel to those needed for the hypothesized role of `unlearning' in rapid eye movement (REM) sleep.
[Reflective portfolio: a proposal for teaching and learning geared on competencies].

PubMed

Cotta, Rosângela Minardi Mitre; da Costa, Glauce Dias; Mendonça, Erica Toledo

2013-06-01

This article seeks to analyze the experience of collective construction of portfolios as a teaching-learning method in the discipline of Health Policy, identifying the competencies developed by students. Qualitative research, whose collection and data processing were conducted by means of documental and thematic analysis of 34 portfolios. The "Learning to be" and "Learning to live and work together" competencies were considered according to the proposals of the UNESCO report for Education. The training of critical-reflexive individuals, provided by the portfolio, was particularly observed when students reported the transformation of the negative views that they had about the health care system - an inefficient and precarious policy - to a positive vision - policy which deals with the principles of equity, integrity and universality. This process of critical transformation is the result of the practice and use of communication skills, information management (search, selection, analysis and evaluation of information), leadership, cooperation and human relationships (teamwork, ethics and recognition of diversity), and personal competencies (time management, responsibility and planning), namely important skills in the training of professionals committed to the national health policy.
Evaluation of the clinical implementation of a large-scale online e-learning program on venous blood specimen collection guideline practices.

PubMed

Willman, Britta; Grankvist, Kjell; Bölenius, Karin

2018-05-11

When performed erroneously, the venous blood specimen collection (VBSC) practice steps patient identification, test request management and test tube labeling are at high risk to jeopardize patient safety. VBSC educational programs with the intention to minimize risk of harm to patients are therefore needed. In this study, we evaluate the efficiency of a large-scale online e-learning program on personnel's adherence to VBSC practices and their experience of the e-learning program. An interprofessional team transformed an implemented traditional VBSC education program to an online e-learning program developed to stimulate reflection with focus on the high-risk practice steps. We used questionnaires to evaluate the effect of the e-learning program on personnel's self-reported adherence to VBSC practices compared to questionnaire surveys before and after introduction of the traditional education program. We used content analysis to evaluate the participants free text experience of the VBSC e-learning program. Adherence to the VBSC guideline high-risk practice steps generally increased following the implementation of a traditional educational program followed by an e-learning program. We however found a negative trend over years regarding participation rates and the practice to always send/sign the request form following the introduction of an electronic request system. The participants were in general content with the VBSC e-learning program. Properly designed e-learning programs on VBSC practices supersedes traditional educational programs in usefulness and functionality. Inclusion of questionnaires in the e-learning program is necessary for follow-up of VBSC participant's practices and educational program efficiency.
Collective Learning: Interaction and a Shared Action Arena

ERIC Educational Resources Information Center

Doos, Marianne; Wilhelmson, Lena

2011-01-01

Purpose: The paper seeks to argue for a theoretical contribution that deals with the detection of collective learning. The aim is to examine and clarify the genesis processes of collective learning. The empirical basis is a telecoms context with task-driven networking across both internal and external organisational borders.…
Experience API: Flexible, Decentralized and Activity-Centric Data Collection

ERIC Educational Resources Information Center

Kevan, Jonathan M.; Ryan, Paul R.

2016-01-01

This emerging technology report describes the Experience API (xAPI), a new e-learning specification designed to support the learning community in standardizing and collecting both formal and informal distributed learning activities. Informed by Activity Theory, a framework aligned with constructivism, data is collected in the form of activity…
Management Strategies for Promoting Teacher Collective Learning

ERIC Educational Resources Information Center

Cheng, Eric C. K.

2011-01-01

This paper aims to validate a theoretical model for developing teacher collective learning by using a quasi-experimental design, and explores the management strategies that would provide a school administrator practical steps to effectively promote collective learning in the school organization. Twenty aided secondary schools in Hong Kong were…
Collective Learning in Schools: Exploring the Perceptions of Leadership Trainees

ERIC Educational Resources Information Center

Schechter, Chen

2013-01-01

Purpose: The purpose of this paper is to explore leadership trainees' perceptions of determinants of collective learning in school settings and of the principal's role in collective learning. Design/methodology/approach: In total, 24 interviews were conducted with all leadership trainees in a university-based principal preparatory program. Data…
Experimental program for the operational study of data collection platforms in Bolivia

NASA Technical Reports Server (NTRS)

1977-01-01

The system developed for the SMS/GOES satellite while learning the limitations possessed by this system with regard to the LANDSAT 1 and 2 satellites with respect to the transmission distance and horizon angle is investigated. The advantages possessed by this system in comparison with conventional methods are evaluated so as to permit studying the feasibility of introducing it into this country in the near future.
Handbook of Research on Hybrid Learning Models: Advanced Tools, Technologies, and Applications

ERIC Educational Resources Information Center

Wang, Fu Lee, Ed.; Fong, Joseph, Ed.; Kwan, Reggie, Ed.

2010-01-01

Hybrid learning is now the single-greatest trend in education today due to the numerous educational advantages when both traditional classroom learning and e-learning are implemented collectively. This handbook collects emerging research and pedagogies related to the convergence of teaching and learning methods. This significant "Handbook of…
Participatory monitoring to connect local and global priorities for forest restoration.

PubMed

Evans, Kristen; Guariguata, Manuel R; Brancalion, Pedro H S

2018-06-01

New global initiatives to restore forest landscapes present an unparalleled opportunity to reverse deforestation and forest degradation. Participatory monitoring could play a crucial role in providing accountability, generating local buy in, and catalyzing learning in monitoring systems that need scalability and adaptability to a range of local sites. We synthesized current knowledge from literature searches and interviews to provide lessons for the development of a scalable, multisite participatory monitoring system. Studies show that local people can collect accurate data on forest change, drivers of change, threats to reforestation, and biophysical and socioeconomic impacts that remote sensing cannot. They can do this at one-third the cost of professionals. Successful participatory monitoring systems collect information on a few simple indicators, respond to local priorities, provide appropriate incentives for participation, and catalyze learning and decision making based on frequent analyses and multilevel interactions with other stakeholders. Participatory monitoring could provide a framework for linking global, national, and local needs, aspirations, and capacities for forest restoration. © 2018 The Authors. Conservation Biology published by Wiley Periodicals, Inc. on behalf of Society for Conservation Biology.
Promotion of critical thinking in e-learning: a qualitative study on the experiences of instructors and students

PubMed Central

Gharib, Mitra; Zolfaghari, Mitra; Mojtahedzadeh, Rita; Mohammadi, Aeen; Gharib, Atoosa

2016-01-01

Background With the increasing popularity of e-learning programs, educational stakeholders are attempting to promote critical thinking in the virtual education system. This study aimed to explore the experiences of both the instructors and the students about critical thinking promotion within the virtual education system. Methods This qualitative study recruited the instructors and students from four academic disciplines provided by the Virtual School of Tehran University of Medical Sciences (Tehran, Iran). All programs were master’s degree programs and utilized a blended (combination of e-learning and face to face) training. Semistructured interviews with the participants were used to collect data. Results The participants had a variety of experiences about how to promote critical thinking. These experiences were conceptualized in four main themes, namely, instructional design, educational leadership and management, local evidence, and belief systems. Conclusion The present study clarified the factors affecting critical thinking promotion in e-learning. Not only the instructors but also the educational designers and leaders can benefit from our findings to improve the quality of virtual education programs and promote critical thinking. PMID:27217807
Promotion of critical thinking in e-learning: a qualitative study on the experiences of instructors and students.

PubMed

Gharib, Mitra; Zolfaghari, Mitra; Mojtahedzadeh, Rita; Mohammadi, Aeen; Gharib, Atoosa

2016-01-01

With the increasing popularity of e-learning programs, educational stakeholders are attempting to promote critical thinking in the virtual education system. This study aimed to explore the experiences of both the instructors and the students about critical thinking promotion within the virtual education system. This qualitative study recruited the instructors and students from four academic disciplines provided by the Virtual School of Tehran University of Medical Sciences (Tehran, Iran). All programs were master's degree programs and utilized a blended (combination of e-learning and face to face) training. Semistructured interviews with the participants were used to collect data. The participants had a variety of experiences about how to promote critical thinking. These experiences were conceptualized in four main themes, namely, instructional design, educational leadership and management, local evidence, and belief systems. The present study clarified the factors affecting critical thinking promotion in e-learning. Not only the instructors but also the educational designers and leaders can benefit from our findings to improve the quality of virtual education programs and promote critical thinking.
Toward the Characterization of Non-Formal Pedagogy.

ERIC Educational Resources Information Center

Silberman-Keller, Diana

This study examined characteristic attributes of non-formal education and the non-formal pedagogy directing its teaching and learning processes. Data were collected on organizational and pedagogical characteristics in several out-of-school organizations (youth movements, youth organizations, community centers, bypass educational systems, local…
Iterative learning control with applications in energy generation, lasers and health care.

PubMed

Rogers, E; Tutty, O R

2016-09-01

Many physical systems make repeated executions of the same finite time duration task. One example is a robot in a factory or warehouse whose task is to collect an object in sequence from a location, transfer it over a finite duration, place it at a specified location or on a moving conveyor and then return for the next one and so on. Iterative learning control was especially developed for systems with this mode of operation and this paper gives an overview of this control design method using relatively recent relevant applications in wind turbines, free-electron lasers and health care, as exemplars to demonstrate its applicability.
Deepening Understanding of "Pedagogical Outcomes" through Video Data Collection: A Catalyst for Guided Reflective Learning Conversations

ERIC Educational Resources Information Center

Dann, Chris; Richardson, Tony

2015-01-01

This article examines the case of Catch Me Excel (CeMeE), an electronic feedback system developed to facilitate video, image and written feedback in the workplace to educators about pedagogical-related outcomes. It comprises a sophisticated, technological feedback system of which the resultant data can be used to enhance classroom, schools and…
The Linking Study: An Experiment to Strengthen Teachers' Engagement with Data on Teaching and Learning

ERIC Educational Resources Information Center

Supovitz, Jonathan

2013-01-01

The allure of using data to improve performance is a source of tremendous activity in the education field today. "Data use" has spurred a wide variety of reforms at all different levels of the education system, ranging from infrastructure augmentation to state databases, to district dashboard systems that collect and display an array of…
Facilitating and Learning at the Edge of Chaos: Expanding the Context of Experiential Education.

ERIC Educational Resources Information Center

Oekerman, Carl

Significant recent discoveries within a number of scientific disciplines, collectively referred to as the science of complexity, are creating a major shift in how human beings understand the complex, adaptive systems that make up the world. A complex adaptive system consists of networks of large numbers of agents that interact with each other and…
The Role of Leadership in Facilitating Organisational Learning and Collective Capacity Building

ERIC Educational Resources Information Center

Piranfar, Hosein

2007-01-01

The paper examines the role of leadership in facilitating collective learning and capacity building by utilising ideas from the fields of evolutionary learning, operations strategy, quality, project and risk management. Two contrasting cases are chosen to show how success and failure can depend upon collective capacity building through…
Equipment issues regarding the collection of video data for research

NASA Astrophysics Data System (ADS)

Kung, Rebecca Lippmann; Kung, Peter; Linder, Cedric

2005-12-01

Physics education research increasingly makes use of video data for analysis of student learning and teaching practice. Collection of these data is conceptually simple but execution is often fraught with costly and time-consuming complications. This pragmatic paper discusses the development of systems to record and permanently archive audio and video data in real-time. We focus on a system based upon consumer video DVD recorders, but also give an overview of other technologies and detail issues common to all systems. We detail common yet unexpected complications, particularly with regard to sound quality and compatibility with transcription software. Information specific to fixed and transportable systems, other technology options, and generic and specific equipment recommendations are given in supplemental appendices

Teaching astronomy with dry erase whiteboards

NASA Astrophysics Data System (ADS)

Slater, Timothy F.

2016-09-01

In the quest to become a great astronomy teacher, one carefully considers what might be the best textbook, what might be the best homework collection and grading system, which classroom policies promote an active learning environment, and which teaching inclinations and strategies might work best with this year's students. But what about teaching equipment? As you are thinking about next year's teaching hardware needs, a surprisingly effective tool to consider adding to your cabinet that consistently encourages more active learning is a stack of small dry erase whiteboards.
Aboriginal health learning in the forest and cultivated gardens: building a nutritious and sustainable food system.

PubMed

Stroink, Mirella L; Nelson, Connie H

2009-01-01

Sustainable food systems are those in which diverse foods are produced in close proximity to a market. A dynamic, adaptive knowledge base that is grounded in local culture and geography and connected to outside knowledge resources is essential for such food systems to thrive. Sustainable food systems are particularly important to remote and Aboriginal communities, where extensive transportation makes food expensive and of poorer nutritional value. The Learning Garden program was developed and run with two First Nation communities in northwestern Ontario. With this program, the team adopted a holistic and experiential model of learning to begin rebuilding a knowledge base that would support a sustainable local food system. The program involved a series of workshops held in each community and facilitated by a community-based coordinator. Topics included cultivated gardening and forest foods. Results of survey data collected from 20 Aboriginal workshop participants are presented, revealing a moderate to low level of baseline knowledge of the traditional food system, and a reliance on the mainstream food system that is supported by food values that place convenience, ease, and price above the localness or cultural connectedness of the food. Preliminary findings from qualitative data are also presented on the process of learning that occurred in the program and some of the insights we have gained that are relevant to future adaptations of this program.
Machine-learned and codified synthesis parameters of oxide materials

NASA Astrophysics Data System (ADS)

Kim, Edward; Huang, Kevin; Tomala, Alex; Matthews, Sara; Strubell, Emma; Saunders, Adam; McCallum, Andrew; Olivetti, Elsa

2017-09-01

Predictive materials design has rapidly accelerated in recent years with the advent of large-scale resources, such as materials structure and property databases generated by ab initio computations. In the absence of analogous ab initio frameworks for materials synthesis, high-throughput and machine learning techniques have recently been harnessed to generate synthesis strategies for select materials of interest. Still, a community-accessible, autonomously-compiled synthesis planning resource which spans across materials systems has not yet been developed. In this work, we present a collection of aggregated synthesis parameters computed using the text contained within over 640,000 journal articles using state-of-the-art natural language processing and machine learning techniques. We provide a dataset of synthesis parameters, compiled autonomously across 30 different oxide systems, in a format optimized for planning novel syntheses of materials.
Model-Free control performance improvement using virtual reference feedback tuning and reinforcement Q-learning

NASA Astrophysics Data System (ADS)

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

2017-04-01

This paper proposes the combination of two model-free controller tuning techniques, namely linear virtual reference feedback tuning (VRFT) and nonlinear state-feedback Q-learning, referred to as a new mixed VRFT-Q learning approach. VRFT is first used to find stabilising feedback controller using input-output experimental data from the process in a model reference tracking setting. Reinforcement Q-learning is next applied in the same setting using input-state experimental data collected under perturbed VRFT to ensure good exploration. The Q-learning controller learned with a batch fitted Q iteration algorithm uses two neural networks, one for the Q-function estimator and one for the controller, respectively. The VRFT-Q learning approach is validated on position control of a two-degrees-of-motion open-loop stable multi input-multi output (MIMO) aerodynamic system (AS). Extensive simulations for the two independent control channels of the MIMO AS show that the Q-learning controllers clearly improve performance over the VRFT controllers.
From particle systems to learning processes. Comment on "Collective learning modeling based on the kinetic theory of active particles" by Diletta Burini, Silvana De Lillo, and Livio Gibelli

NASA Astrophysics Data System (ADS)

Lachowicz, Mirosław

2016-03-01

The very stimulating paper [6] discusses an approach to perception and learning in a large population of living agents. The approach is based on a generalization of kinetic theory methods in which the interactions between agents are described in terms of game theory. Such an approach was already discussed in Ref. [2-4] (see also references therein) in various contexts. The processes of perception and learning are based on the interactions between agents and therefore the general kinetic theory is a suitable tool for modeling them. However the main question that rises is how the perception and learning processes may be treated in the mathematical modeling. How may we precisely deliver suitable mathematical structures that are able to capture various aspects of perception and learning?
Influence of nucleation seeding on the compressive strength of ordinary Portland cement and alkali activated blast-furnace slag

DOT National Transportation Integrated Search

2011-05-10

The Intelligent Transportation Systems (ITS) Joint Program Office (JPO) of the U.S. Department of Transportation (USDOT) has been collecting the benefits, costs, lessons learned, and deployment status information of ITS. Such information, intended to...
Teacher Learning of Technology Enhanced Formative Assessment

ERIC Educational Resources Information Center

Feldman, Allan; Capobianco, Brenda M.

2008-01-01

This study examined the integration of technology enhanced formative assessment (FA) into teachers' practice. Participants were high school physics teachers interested in improving their use of a classroom response system (CRS) to promote FA. Data were collected using interviews, direct classroom observations, and collaborative discussions. The…
Key findings from the intelligent transportation systems (ITS) program, what have we learned?

DOT National Transportation Integrated Search

2000-06-01

A study has been conducted to evaluate the quality and variability of the International Roughness Index (IRI) data in the Long Term Pavement Performance (LTPP) database. All LTPP profiles collected between June 1989 and October 1997 were visually rev...
National Geological and Geophysical Data Preservation Program: Successes and Lessons Learned

NASA Astrophysics Data System (ADS)

Adrian, B. M.

2014-12-01

The United States Geological Survey (USGS) is widely recognized in the earth science community as possessing extensive collections of geologic and geophysical materials gathered by its research personnel. Since the USGS was established in 1879, hundreds of thousands of samples have been gathered in collections that range from localized, geographically-based assemblages to ones that are national or international in scope. These materials include, but are not limited to, rock and mineral specimens; fossils; drill cores and cuttings; geochemical standards; and soil, sediment, and geochemical samples. The USGS National Geological and Geophysical Data Preservation Program (NGGDPP) was established with the passage of the Energy Policy Act of 2005. Since its implementation, the USGS NGGDPP has taken an active role in providing opportunities to inventory, archive and preserve geologic and geophysical samples, and to make these samples and ancillary data discoverable on the Internet. Preserving endangered geoscience collections is more cost effective than recollecting this information. Preserving these collections, however, is only one part of the process - there also needs to be a means to facilitate open discovery and access to the physical objects and the ancillary digital records. The NGGDPP has celebrated successes such as the development of the USGS Geologic Collections Management System (GCMS), a master catalog and collections management plan, and the implementation and advancement of the National Digital Catalog, a digital inventory and catalog of geological and geophysical data and collections held by the USGS and State geological surveys. Over this period of time there has been many lessons learned. With the successes and lessons learned, NGGDPP is poised to take on challenges the future may bring.
Measures of Success for Earth System Science Education: The DLESE Evaluation Services and the Evaluation Toolkit Collection

NASA Astrophysics Data System (ADS)

McCaffrey, M. S.; Buhr, S. M.; Lynds, S.

2005-12-01

Increased agency emphasis upon the integration of research and education coupled with the ability to provide students with access to digital background materials, learning activities and primary data sources has begun to revolutionize Earth science education in formal and informal settings. The DLESE Evaluation Services team and the related Evaluation Toolkit collection (http://www.dlese.org/cms/evalservices/ ) provides services and tools for education project leads and educators. Through the Evaluation Toolkit, educators may access high-quality digital materials to assess students' cognitive gains, examples of alternative assessments, and case studies and exemplars of authentic research. The DLESE Evaluation Services team provides support for those who are developing evaluation plans on an as-requested basis. In addition, the Toolkit provides authoritative peer reviewed articlesabout evaluation research techniques and strategies of particular importance to geoscience education. This paper will provide an overview of the DLESE Evaluation Toolkit and discuss challenges and best practices for assessing student learning and evaluating Earth system sciences education in a digital world.
Simulation-driven machine learning: Bearing fault classification

NASA Astrophysics Data System (ADS)

Sobie, Cameron; Freitas, Carina; Nicolai, Mike

2018-01-01

Increasing the accuracy of mechanical fault detection has the potential to improve system safety and economic performance by minimizing scheduled maintenance and the probability of unexpected system failure. Advances in computational performance have enabled the application of machine learning algorithms across numerous applications including condition monitoring and failure detection. Past applications of machine learning to physical failure have relied explicitly on historical data, which limits the feasibility of this approach to in-service components with extended service histories. Furthermore, recorded failure data is often only valid for the specific circumstances and components for which it was collected. This work directly addresses these challenges for roller bearings with race faults by generating training data using information gained from high resolution simulations of roller bearing dynamics, which is used to train machine learning algorithms that are then validated against four experimental datasets. Several different machine learning methodologies are compared starting from well-established statistical feature-based methods to convolutional neural networks, and a novel application of dynamic time warping (DTW) to bearing fault classification is proposed as a robust, parameter free method for race fault detection.
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.
SU-E-T-524: Web-Based Radiation Oncology Incident Reporting and Learning System (ROIRLS)

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

Kapoor, R; Palta, J; Hagan, M

Purpose: Describe a Web-based Radiation Oncology Incident Reporting and Learning system that has the potential to improve quality of care for radiation therapy patients. This system is an important facet of continuing effort by our community to maintain and improve safety of radiotherapy.Material and Methods: The VA National Radiation Oncology Program office has embarked on a program to electronically collect adverse events and near miss data of radiation treatment of over 25,000 veterans treated with radiotherapy annually. Software used for this program is deployed on the VAs intranet as a Website. All data entry forms (adverse event or near missmore » reports, work product reports) utilize standard causal, RT process step taxonomies and data dictionaries defined in AAPM and ASTRO reports on error reporting (AAPM Work Group Report on Prevention of Errors and ASTROs safety is no accident report). All reported incidents are investigated by the radiation oncology domain experts. This system encompasses the entire feedback loop of reporting an incident, analyzing it for salient details, and developing interventions to prevent it from happening again. The operational workflow is similar to that of the Aviation Safety Reporting System. This system is also synergistic with ROSIS and SAFRON. Results: The ROIRLS facilitates the collection of data that help in tracking adverse events and near misses and develop new interventions to prevent such incidents. The ROIRLS electronic infrastructure is fully integrated with each registered facility profile data thus minimizing key strokes and multiple entries by the event reporters. Conclusions: OIRLS is expected to improve the quality and safety of a broad spectrum of radiation therapy patients treated in the VA and fulfills our goal of Effecting Quality While Treating Safely The Radiation Oncology Incident Reporting and Learning System software used for this program has been developed, conceptualized and maintained by TSG Innovations Inc. and is deployed on the VA intranet as a Website. The Radiation Oncology Incident Reporting and Learning System software used for this program has been developed, conceptualized and maintained by TSG Innovations Inc. and is deployed on the VA intranet as a Website.« less
A memory learning framework for effective image retrieval.

PubMed

Han, Junwei; Ngan, King N; Li, Mingjing; Zhang, Hong-Jiang

2005-04-01

Most current content-based image retrieval systems are still incapable of providing users with their desired results. The major difficulty lies in the gap between low-level image features and high-level image semantics. To address the problem, this study reports a framework for effective image retrieval by employing a novel idea of memory learning. It forms a knowledge memory model to store the semantic information by simply accumulating user-provided interactions. A learning strategy is then applied to predict the semantic relationships among images according to the memorized knowledge. Image queries are finally performed based on a seamless combination of low-level features and learned semantics. One important advantage of our framework is its ability to efficiently annotate images and also propagate the keyword annotation from the labeled images to unlabeled images. The presented algorithm has been integrated into a practical image retrieval system. Experiments on a collection of 10,000 general-purpose images demonstrate the effectiveness of the proposed framework.
Interactive Online Modules and Videos for Learning Geological Concepts at the University of Toronto Department of Earth Sciences

NASA Astrophysics Data System (ADS)

Veglio, E.; Graves, L. W.; Bank, C. G.

2014-12-01

We designed various computer-based applications and videos as educational resources for undergraduate courses at the University of Toronto in the Earth Science Department. These resources were developed in effort to enhance students' self-learning of key concepts as identified by educators at the department. The interactive learning modules and videos were created using the programs MATLAB and Adobe Creative Suite 5 (Photoshop and Premiere) and range from optical mineralogy (extinction and Becke line), petrology (equilibrium melting in 2-phase systems), crystallography (crystal systems), geophysics (gravity anomaly), and geologic history (evolution of Canada). These resources will be made available for students on internal course websites as well as through the University of Toronto Earth Science's website (www.es.utoronto.ca) where appropriate; the video platform YouTube.com may be used to reach a wide audience and promote the material. Usage of the material will be monitored and feedback will be collected over the next academic year in order to gage the use of these interactive learning tools and to assess if these computer-based applications and videos foster student engagement and active learning, and thus offer an enriched learning experience.
"Making a difference" - Medical students' opportunities for transformational change in health care and learning through quality improvement projects.

PubMed

Bergh, Anne-Marie; Bac, Martin; Hugo, Jannie; Sandars, John

2016-07-11

Quality improvement is increasingly becoming an essential aspect of the medical curriculum, with the intention of improving the health care system to provide better health care. The aim of this study was to explore undergraduate medical students' experiences of their involvement in quality improvement projects during a district health rotation. Student group reports from rotations in learning centres of the University of Pretoria in Mpumalanga Province, South Africa were analysed for the period 2012 to 2015. Interviews were conducted with health care providers at four learning centres in 2013. Three main themes were identified: (1) 'Situated learning', describing students' exposure to the discrepancies between ideal and reality in a real-life situation and how they learned to deal with complex situations, individually and as student group; (2) 'Facing dilemmas', describing how students were challenged about the non-ideal reality; (3) 'Making a difference', describing the impact of the students' projects, with greater understanding of themselves and others through working in teams but also making a change in the health care system. Quality improvement projects can provide an opportunity for both the transformation of health care and for transformative learning, with individual and 'collective' self-authorship.
The Relationship between Elements of Professional Learning Communities and Collective Efficacy

ERIC Educational Resources Information Center

Dockery, Kim P.

2011-01-01

The purpose of this study was to determine the nature of the relationship between levels of implementation of professional learning communities and Collective Efficacy. More specifically, the study sought to determine the relationship between the levels of implementation of dimensions of professional learning communities (Learning, Collaboration…
76 FR 54283 - 30-Day Notice of Proposed Information Collections: Language Learning Survey Questions

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-31

...: Language Learning Survey Questions ACTION: Notice of request for public comment and submission to OMB of... the Paperwork Reduction Act of 1995. Title of Information Collection: Language Learning Programs: Pre... critical language learning instruction. Estimated Number of Respondents: 1,400 annually Estimated Number of...
78 FR 5793 - Agency Information Collection Activities; Comment Request; Evaluation of State Expanded Learning...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-01-28

...; Comment Request; Evaluation of State Expanded Learning Time AGENCY: Department of Education (ED), IES... State Expanded Learning Time. OMB Control Number: 1850-New. Type of Review: New information collection... conduct semi-structured interviews with 21st Century Community Learning Centers (21st CCLC) state...
Continued use of an interactive computer game-based visual perception learning system in children with developmental delay.

PubMed

Lin, Hsien-Cheng; Chiu, Yu-Hsien; Chen, Yenming J; Wuang, Yee-Pay; Chen, Chiu-Ping; Wang, Chih-Chung; Huang, Chien-Ling; Wu, Tang-Meng; Ho, Wen-Hsien

2017-11-01

This study developed an interactive computer game-based visual perception learning system for special education children with developmental delay. To investigate whether perceived interactivity affects continued use of the system, this study developed a theoretical model of the process in which learners decide whether to continue using an interactive computer game-based visual perception learning system. The technology acceptance model, which considers perceived ease of use, perceived usefulness, and perceived playfulness, was extended by integrating perceived interaction (i.e., learner-instructor interaction and learner-system interaction) and then analyzing the effects of these perceptions on satisfaction and continued use. Data were collected from 150 participants (rehabilitation therapists, medical paraprofessionals, and parents of children with developmental delay) recruited from a single medical center in Taiwan. Structural equation modeling and partial-least-squares techniques were used to evaluate relationships within the model. The modeling results indicated that both perceived ease of use and perceived usefulness were positively associated with both learner-instructor interaction and learner-system interaction. However, perceived playfulness only had a positive association with learner-system interaction and not with learner-instructor interaction. Moreover, satisfaction was positively affected by perceived ease of use, perceived usefulness, and perceived playfulness. Thus, satisfaction positively affects continued use of the system. The data obtained by this study can be applied by researchers, designers of computer game-based learning systems, special education workers, and medical professionals. Copyright © 2017 Elsevier B.V. All rights reserved.

Research Messages 2010

ERIC Educational Resources Information Center

National Centre for Vocational Education Research (NCVER), 2011

2011-01-01

Research messages 2010 is a collection of summaries of research projects published by the National Centre for Vocational Education Research (NCVER). The summaries are clustered under five broad themes used by NCVER to organise its research and analysis: Industry and employers; Students and individuals; Teaching and learning: VET system; and VET in…
Hands On Earth Science.

ERIC Educational Resources Information Center

Weisgarber, Sherry L.; Van Doren, Lisa; Hackathorn, Merrianne; Hannibal, Joseph T.; Hansgen, Richard

This publication is a collection of 13 hands-on activities that focus on earth science-related activities and involve students in learning about growing crystals, tectonics, fossils, rock and minerals, modeling Ohio geology, geologic time, determining true north, and constructing scale-models of the Earth-moon system. Each activity contains…
Academic Challenges: Student Outcomes Assessment.

ERIC Educational Resources Information Center

California State Univ., Long Beach. Office of the Chancellor.

A "meta-assessment" was done of 13 pilot projects on student outcomes assessment in a variety of disciplines at 11 campuses in the California State University (CSU) system. These projects had developed both quantitative and qualitative strategies for collecting data on student learning outcomes. The meta-assessment was designed to…
Ecuadorian Promotores Learn to "Facilitate" Rather than to "Direct."

ERIC Educational Resources Information Center

Aubel, Judi; And Others

1991-01-01

In Ecuador, field workers are trained in community organizing and hygiene education methods to promote maintenance of water and sanitation systems. Workshops based on a community development approach teach workers how to gain entry into communities, collect and analyze information, and develop community education programs. (SK)
Overcoming barriers to implementing patient-reported outcomes in an electronic health record: a case report.

PubMed

Harle, Christopher A; Listhaus, Alyson; Covarrubias, Constanza M; Schmidt, Siegfried Of; Mackey, Sean; Carek, Peter J; Fillingim, Roger B; Hurley, Robert W

2016-01-01

In this case report, the authors describe the implementation of a system for collecting patient-reported outcomes and integrating results in an electronic health record. The objective was to identify lessons learned in overcoming barriers to collecting and integrating patient-reported outcomes in an electronic health record. The authors analyzed qualitative data in 42 documents collected from system development meetings, written feedback from users, and clinical observations with practice staff, providers, and patients. Guided by the Unified Theory on the Adoption and Use of Information Technology, 5 emergent themes were identified. Two barriers emerged: (i) uncertain clinical benefit and (ii) time, work flow, and effort constraints. Three facilitators emerged: (iii) process automation, (iv) usable system interfaces, and (v) collecting patient-reported outcomes for the right patient at the right time. For electronic health record-integrated patient-reported outcomes to succeed as useful clinical tools, system designers must ensure the clinical relevance of the information being collected while minimizing provider, staff, and patient burden. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Exploring Collective Mathematical Creativity in Elementary School

ERIC Educational Resources Information Center

Levenson, Esther

2011-01-01

This study combines theories related to collective learning and theories related to mathematical creativity to investigate the notion of collective mathematical creativity in elementary school classrooms. Collective learning takes place when mathematical ideas and actions, initially stemming from an individual, are built upon and reworked,…
Rapid-Learning System for Cancer Care

PubMed Central

Abernethy, Amy P.; Etheredge, Lynn M.; Ganz, Patricia A.; Wallace, Paul; German, Robert R.; Neti, Chalapathy; Bach, Peter B.; Murphy, Sharon B.

2010-01-01

Compelling public interest is propelling national efforts to advance the evidence base for cancer treatment and control measures and to transform the way in which evidence is aggregated and applied. Substantial investments in health information technology, comparative effectiveness research, health care quality and value, and personalized medicine support these efforts and have resulted in considerable progress to date. An emerging initiative, and one that integrates these converging approaches to improving health care, is “rapid-learning health care.” In this framework, routinely collected real-time clinical data drive the process of scientific discovery, which becomes a natural outgrowth of patient care. To better understand the state of the rapid-learning health care model and its potential implications for oncology, the National Cancer Policy Forum of the Institute of Medicine held a workshop entitled “A Foundation for Evidence-Driven Practice: A Rapid-Learning System for Cancer Care” in October 2009. Participants examined the elements of a rapid-learning system for cancer, including registries and databases, emerging information technology, patient-centered and -driven clinical decision support, patient engagement, culture change, clinical practice guidelines, point-of-care needs in clinical oncology, and federal policy issues and implications. This Special Article reviews the activities of the workshop and sets the stage to move from vision to action. PMID:20585094
Occasional Papers in Open and Distance Learning, Number 13.

ERIC Educational Resources Information Center

Donnan, Peter, Ed.

Each of the four papers in this collection is concerned with open learning in one form or another. "Open Learning: Some Current Perspectives" (Ian Barnard) addresses the topic of open learning in general, commenting on contemporary views and developments, and defining the term as a collective for approaches and practices that focus on…
Mastery Learning: Thousands of Students, Thousands of Excellent Learners.

ERIC Educational Resources Information Center

Whiting, Bryan; Van Burgh, Jill Wright; Render, Gary F.

This study investigated the cognitive and affective student learning outcomes of 36 semesters using the mastery learning approach in distributive education classes. Data were collected by a high school teacher, a junior high school teacher, and a university professor, all of whom used mastery learning. Data collected over the years indicated that…
The Individual|Collective Dialectic in the Learning Organization

ERIC Educational Resources Information Center

Lee, Yew-Jin; Roth, Wolff-Michael

2007-01-01

Purpose: The purpose of this paper is to answer two interrelated questions: "Who learns and how in the learning organization?". By implication, many theories of the learning organization are addressed that are based on a static and erroneous separation of individual and collective. Design/methodology/approach: Four episodes from a larger case…
Smart Interpretation - Application of Machine Learning in Geological Interpretation of AEM Data

NASA Astrophysics Data System (ADS)

Bach, T.; Gulbrandsen, M. L.; Jacobsen, R.; Pallesen, T. M.; Jørgensen, F.; Høyer, A. S.; Hansen, T. M.

2015-12-01

When using airborne geophysical measurements in e.g. groundwater mapping, an overwhelming amount of data is collected. Increasingly larger survey areas, denser data collection and limited resources, combines to an increasing problem of building geological models that use all the available data in a manner that is consistent with the geologists knowledge about the geology of the survey area. In the ERGO project, funded by The Danish National Advanced Technology Foundation, we address this problem, by developing new, usable tools, enabling the geologist utilize her geological knowledge directly in the interpretation of the AEM data, and thereby handle the large amount of data, In the project we have developed the mathematical basis for capturing geological expertise in a statistical model. Based on this, we have implemented new algorithms that have been operationalized and embedded in user friendly software. In this software, the machine learning algorithm, Smart Interpretation, enables the geologist to use the system as an assistant in the geological modelling process. As the software 'learns' the geology from the geologist, the system suggest new modelling features in the data. In this presentation we demonstrate the application of the results from the ERGO project, including the proposed modelling workflow utilized on a variety of data examples.
Communication and the Learning Organization.

ERIC Educational Resources Information Center

Sandine, Brian

Organizational learning is fundamentally a communication phenomenon and, as such, communication research is particularly well suited to contribute to the understanding of this occurrence. Three communicative processes are constitutive of learning organizations: (1) collective thinking processes, whose three components are collectivity, idea…
Iterative learning control with applications in energy generation, lasers and health care

PubMed Central

Tutty, O. R.

2016-01-01

Many physical systems make repeated executions of the same finite time duration task. One example is a robot in a factory or warehouse whose task is to collect an object in sequence from a location, transfer it over a finite duration, place it at a specified location or on a moving conveyor and then return for the next one and so on. Iterative learning control was especially developed for systems with this mode of operation and this paper gives an overview of this control design method using relatively recent relevant applications in wind turbines, free-electron lasers and health care, as exemplars to demonstrate its applicability. PMID:27713654
[Organization development of the public health system].

PubMed

Pfaff, Holger; Klein, Jürgen

2002-05-15

Changes in the German health care system require changes in health care institutions. Organizational development (OD) techniques can help them to cope successfully with their changing environment. OD is defined as a collective process of learning aiming to induce intended organizational change. OD is based on social science methods and conducted by process-oriented consultants. In contrast to techniques of organizational design, OD is characterized by employee participation. One of the most important elements of OD is the so-called "survey-feedback-technique". Five examples illustrate how the survey-feedback-technique can be used to facilitate organisational learning. OD technique supports necessary change in health care organizations. It should be used more frequently.
Nurses' perceptions of e-portfolio use for on-the-job training in Taiwan.

PubMed

Tsai, Pei-Rong; Lee, Ting-Ting; Lin, Hung-Ru; Lee-Hsieh, Jane; Mills, Mary Etta

2015-01-01

Electronic portfolios can be used to record user performance and achievements. Currently, clinical learning systems and in-service education systems lack integration of nurses' clinical performance records with their education or training outcomes. For nurses with less than 2 years' work experience (nursing postgraduate year), use of an electronic portfolio is essential. This study aimed to assess the requirements of using electronic portfolios in continuing nursing education for clinical practices. Fifteen nurses were recruited using a qualitative purposive sampling approach between April 2013 and May 2013. After obtaining participants' consent, data were collected in a conference room of the study hospital by one-on-one semistructured in-depth interviews. Through data analyses, the following five main themes related to electronic learning portfolios were identified: instant access to in-service education information, computerized nursing postgraduate year training manual, diversity of system functions and interface designs, need for sufficient computers, and protection of personal documents. Because electronic portfolios are beginning to be used in clinical settings, a well-designed education information system not only can meet the needs of nurses but also can facilitate their learning progress.
Data-driven model reference control of MIMO vertical tank systems with model-free VRFT and Q-Learning.

PubMed

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

2018-02-01

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

NASA Astrophysics Data System (ADS)

Crompton, Helen; Burke, Diane; Gregory, Kristen H.; Gräbe, Catharina

2016-04-01

The use of mobile learning in education is growing at an exponential rate. To best understand how mobile learning is being used, it is crucial to gain a collective understanding of the research that has taken place. This systematic review reveals the trends in mobile learning in science with a comprehensive analysis and synthesis of studies from the year 2000 onward. Major findings include that most of the studies focused on designing systems for mobile learning, followed by a combination of evaluating the effects of mobile learning and investigating the affective domain during mobile learning. The majority of the studies were conducted in the area of life sciences in informal, elementary (5-11 years) settings. Mobile devices were used in this strand of science easily within informal environments with real-world connections. A variety of research methods were employed, providing a rich research perspective. As the use of mobile learning continues to grow, further research regarding the use of mobile technologies in all areas and levels of science learning will help science educators to expand their ability to embrace these technologies.
Combining resources, combining forces: regionalizing hospital library services in a large statewide health system.

PubMed

Martin, Heather J; Delawska-Elliott, Basia

2015-01-01

After a reduction in full-time equivalents, 2 libraries in large teaching hospitals and 2 libraries in small community hospitals in a western US statewide health system saw opportunity for expansion through a regional reorganization. Despite a loss of 2/3 of the professional staff and a budgetary decrease of 27% over the previous 3 years, the libraries were able to grow business, usage, awareness, and collections through organizational innovation and improved efficiency. This paper describes the experience--including process, challenges, and lessons learned--of an organizational shift to regionalized services, collections, and staffing. Insights from this process may help similar organizations going through restructuring.
Integrating clicker technology at nursing conferences: an innovative approach to research data collection.

PubMed

Solecki, Susan; Cornelius, Frances; Draper, Judy; Fisher, Kathleen

2010-06-01

A pilot demonstration of integrating an audience response system, that is, 'clickers' at a nursing education conference as an engaging tool for using the research process for learning through immediate research results is presented. A convenience sample of nursing conference attendees were surveyed using clicker technology before a panel presentation on the 'Impaired Health Professional'. The 208 subjects who used the clickers were mostly women (93%) and were nurse educators (81%) with at least 20 years of nursing experience (75%). The ease of data collection, real-time analysis, the active engagement of both participant and presenter were all findings of this study. The utility of this tool as a stimulus for discussion and learning was also reported. Pilot testing the clicker at an education conference for data collection and educational purposes was an important goal and positive outcome of this study. Researchers and educators are advised on the planning steps required to make this a successful experience.
Collective Health Nursing: the construction of critical thinking about the reality of health.

PubMed

Chaves, Maria Marta Nolasco; Larocca, Liliana Müller; Peres, Aida Maris

2011-12-01

This article presents an analysis of the Collective Health Nursing teaching-learning processes and research in view of the consolidation of the Brazilian National Health System (Sistema Único de Saúde - SUS), performed with the objective to acknowledge the potentiality of the health reality of the population as a strategy to approximate the field of nursing practice and training as a way to revert undesired health situations. Thus, the authors reflect about the work of Collective Health Nursing, as they understand it is a mediator to promoting teaching, learning and knowledge development in this field. The authors believe that those processes, founded on critical thinking, permit to reflect about the contradictions between the current public policy and the actions promoted by the sector, and, this way, contribute to overcome the current health care mode, which has historically been founded on curative actions towards individuals, to assuming a model that acknowledges the health needs and intervenes in the social determination of the health-disease process.

A/R systems reduce delayed and denied reimbursements.

PubMed

Escobar, Carlos

2007-01-01

The day-to-day benefits of a comprehensive billing and collections system are pro-active and preventive--administrators are increasingly learning that accelerating billings hastens collections and, ultimately, facility profitability. Effective billing and collections management services work closely with the facility, matching nightly "dumps" of patient files with transcripts. This marriage of otherwise disparate data creates a billing unit that reduces errors and ensures no billable procedures are lost. Ultimately, the goal of any medical practice that engages an ASP application or outsource solution is not to sit idly by while allowing a billing company to take control of a practice's revenue stream. The ability to track the billing process from transcript submission to payment provides a facility with all information necessary to manage cash flow, revenues, and even personal and practice financial planning.
Individualization for Education at Scale: MIIC Design and Preliminary Evaluation

ERIC Educational Resources Information Center

Brinton, Christopher G.; Rill, Ruediger; Ha, Sangtae; Chiang, Mung; Smith, Robert; Ju, William

2015-01-01

We present the design, implementation, and preliminary evaluation of our Adaptive Educational System (AES): the Mobile Integrated and Individualized Course (MIIC). MIIC is a platform for personalized course delivery which integrates lecture videos, text, assessments, and social learning into a mobile native app, and collects clickstream-level…
Digital Learning Compass: Distance Education State Almanac 2017. Delaware

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Delaware. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Kansas

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Kansas. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Minnesota

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Minnesota. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Utah

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Utah. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Connecticut

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Connecticut. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Wyoming

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Wyoming. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Montana

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Montana. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Iowa

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Iowa. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Alabama

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Alabama. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Nevada

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Nevada. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Mississippi

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Mississippi. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Kentucky

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Kentucky. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Ohio

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Ohio. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Oklahoma

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Oklahoma. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Texas

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Texas. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Vermont

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Vermont. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Colorado

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Colorado. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Arizona

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Arizona . The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Missouri

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Missouri. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Idaho

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Idaho. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Massachusetts

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Massachusetts. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Tennessee

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Tennessee. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Virginia

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Virginia. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Indiana

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Indiana. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Alaska

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Alaska. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Louisiana

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Louisiana. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Nebraska

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Nebraska. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Maine

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Maine. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Wisconsin

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Wisconsin. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Michigan

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Michigan. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Arkansas

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Arkansas . The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Illinois

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Illinois. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Florida

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Florida. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Maryland

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Maryland. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Oregon

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Oregon. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Washington

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Washington. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Hawaii

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Hawaii. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. California

ERIC Educational Resources Information Center

Seaman, Julia A.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of California. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Georgia

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Georgia. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Pennsylvania

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Pennsylvania. The sample for this analysis is comprised of all active, degree-granting…
Evaluating Games-Based Learning

ERIC Educational Resources Information Center

Hainey, Thomas; Connolly, Thomas

2010-01-01

A highly important part of software engineering education is requirements collection and analysis, one of the initial stages of the Software Development Lifecycle. No other conceptual work is as difficult to rectify at a later stage or as damaging to the overall system if performed incorrectly. As software engineering is a field with a reputation…
Druthers! A Collection of Viable Ideas from Rural Schools.

ERIC Educational Resources Information Center

Elliott, Richard D., Comp.

An individualized junior high school, a youth resources program that interweaves high school with supervised work experiences, multi-aged elementary family groupings that mainstream EMR (educable mentally retarded) children, and a single library room transformed into seven optional learning stations using a multi-channel audio system are real…
Creating a Cycle of Continuous Improvement through Instructional Rounds

ERIC Educational Resources Information Center

Meyer-Looze, Catherine L.

2015-01-01

Instructional Rounds is a continuous improvement strategy that focuses on the technical core of educational systems as well as educators collaborating side-by-side. Concentrating on collective learning, this process only makes sense within an overall strategy of improvement. This case study examined the Instructional Rounds process in a northern…
Assessing Student Learning: A Collection of Evaluation Tools

ERIC Educational Resources Information Center

Gottfried, Gail M.; Johnson, Kathy E.; Vosmik, Jordan R.

2009-01-01

Whereas grading systems based on tacit knowledge may be the norm in practice, the recent trend toward educational accountability--from granting organizations, accreditation boards, journals on the teaching of psychology, and even tenure/promotion committees--suggests a real need for reliable, validated assessment measures that can be used to…
77 FR 26522 - Proposed Priority; Technical Assistance on State Data Collection, Analysis, and Reporting...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-04

..., parents of individuals with disabilities, data system experts, representatives of other early learning and... to improve their capacity to meet the Individuals with Disabilities Education Act (IDEA) data... data for infants, toddlers, and young children with disabilities (birth through age 5) served through...
Effect of Geographic Distance on Distance Education: An Empirical Study

ERIC Educational Resources Information Center

Luo, Heng; Robinson, Anthony C.; Detwiler, Jim

2014-01-01

This study investigates the effect of geographic distance on students' distance learning experience with the aim to provide tentative answers to a fundamental question--does geographic distance matter in distance education? Using educational outcome data collected from an online master's program in Geographic Information Systems, this study…
The Influence of E-Learning on Individual and Collective Empowerment in the Public Sector: An Empirical Study of Korean Government Employees

ERIC Educational Resources Information Center

Hur, Mann Hyung; Im, Yeonwook

2013-01-01

Our study explores the influence of e-learning on individual and collective empowerment by using data collected from e-learning class participants of Korea's Cyber-Education Center. For the survey, a questionnaire was sent to each of the 41 central ministries' education and training officers (ETO) via email. The ETOs distributed the questionnaire…
Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography.

PubMed

Samala, Ravi K; Chan, Heang-Ping; Hadjiiski, Lubomir; Helvie, Mark A; Wei, Jun; Cha, Kenny

2016-12-01

Develop a computer-aided detection (CAD) system for masses in digital breast tomosynthesis (DBT) volume using a deep convolutional neural network (DCNN) with transfer learning from mammograms. A data set containing 2282 digitized film and digital mammograms and 324 DBT volumes were collected with IRB approval. The mass of interest on the images was marked by an experienced breast radiologist as reference standard. The data set was partitioned into a training set (2282 mammograms with 2461 masses and 230 DBT views with 228 masses) and an independent test set (94 DBT views with 89 masses). For DCNN training, the region of interest (ROI) containing the mass (true positive) was extracted from each image. False positive (FP) ROIs were identified at prescreening by their previously developed CAD systems. After data augmentation, a total of 45 072 mammographic ROIs and 37 450 DBT ROIs were obtained. Data normalization and reduction of non-uniformity in the ROIs across heterogeneous data was achieved using a background correction method applied to each ROI. A DCNN with four convolutional layers and three fully connected (FC) layers was first trained on the mammography data. Jittering and dropout techniques were used to reduce overfitting. After training with the mammographic ROIs, all weights in the first three convolutional layers were frozen, and only the last convolution layer and the FC layers were randomly initialized again and trained using the DBT training ROIs. The authors compared the performances of two CAD systems for mass detection in DBT: one used the DCNN-based approach and the other used their previously developed feature-based approach for FP reduction. The prescreening stage was identical in both systems, passing the same set of mass candidates to the FP reduction stage. For the feature-based CAD system, 3D clustering and active contour method was used for segmentation; morphological, gray level, and texture features were extracted and merged with a linear discriminant classifier to score the detected masses. For the DCNN-based CAD system, ROIs from five consecutive slices centered at each candidate were passed through the trained DCNN and a mass likelihood score was generated. The performances of the CAD systems were evaluated using free-response ROC curves and the performance difference was analyzed using a non-parametric method. Before transfer learning, the DCNN trained only on mammograms with an AUC of 0.99 classified DBT masses with an AUC of 0.81 in the DBT training set. After transfer learning with DBT, the AUC improved to 0.90. For breast-based CAD detection in the test set, the sensitivity for the feature-based and the DCNN-based CAD systems was 83% and 91%, respectively, at 1 FP/DBT volume. The difference between the performances for the two systems was statistically significant (p-value < 0.05). The image patterns learned from the mammograms were transferred to the mass detection on DBT slices through the DCNN. This study demonstrated that large data sets collected from mammography are useful for developing new CAD systems for DBT, alleviating the problem and effort of collecting entirely new large data sets for the new modality.
Determining the influence of past development experience on the cost of strategic ballistic missile development

NASA Astrophysics Data System (ADS)

Whitney, Dwight E.

The influence of learning in the form of past relevant experience was examined in data collected for strategic ballistic missiles developed by the United States. A total of twenty-four new missiles were developed and entered service between 1954 and 1990. Missile development costs were collected and analyzed by regression analysis using the learning curve model with factors for past experience and other relevant cost estimating relationships. The purpose of the study was to determine whether prior development experience was a factor in the development cost of these like systems. Of the twenty-four missiles in the population, development costs for twelve of the missiles were collected from the literature. Since the costs were found to be segmented by military service, a discrete input variable for military service was used as one of the cost estimating relationships. Because there were only two US Navy samples, too few to analyze for segmentation and learning rate, they were excluded from the final analysis. The final analysis was on a sample of ten out of eighteen US Army and US Air Force missiles within the population. The result of the analysis found past experience to be a statistically significant factor in describing the development cost of the US Army and US Air Force missiles. The influence equated to a 0.86 progress ratio, indicating prior development experience had a positive (cost-reducing) influence on their development cost. Based on the result, it was concluded that prior development experience was a factor in the development cost of these systems.
Uniform data collection in routine clinical practice in cardiovascular patients for optimal care, quality control and research: The Utrecht Cardiovascular Cohort.

PubMed

Asselbergs, Folkert W; Visseren, Frank Lj; Bots, Michiel L; de Borst, Gert J; Buijsrogge, Marc P; Dieleman, Jan M; van Dinther, Baukje Gf; Doevendans, Pieter A; Hoefer, Imo E; Hollander, Monika; de Jong, Pim A; Koenen, Steven V; Pasterkamp, Gerard; Ruigrok, Ynte M; van der Schouw, Yvonne T; Verhaar, Marianne C; Grobbee, Diederick E

2017-05-01

Background Cardiovascular disease remains the major contributor to morbidity and mortality. In routine care for patients with an elevated cardiovascular risk or with symptomatic cardiovascular disease information is mostly collected in an unstructured manner, making the data of limited use for structural feedback, quality control, learning and scientific research. Objective The Utrecht Cardiovascular Cohort (UCC) initiative aims to create an infrastructure for uniform registration of cardiovascular information in routine clinical practice for patients referred for cardiovascular care at the University Medical Center Utrecht, the Netherlands. This infrastructure will promote optimal care according to guidelines, continuous quality control in a learning healthcare system and creation of a research database. Methods The UCC comprises three parts. UCC-1 comprises enrolment of all eligible cardiovascular patients in whom the same information will be collected, based on the Dutch cardiovascular management guideline. A sample of UCC-1 will be invited for UCC-2. UCC-2 involves an enrichment through extensive clinical measurements with emphasis on heart failure, cerebral ischaemia, arterial aneurysms, diabetes mellitus and elevated blood pressure. UCC-3 comprises on-top studies, with in-depth measurements in smaller groups of participants typically based on dedicated project grants. All participants are followed up for morbidity and mortality through linkage with national registries. Conclusion In a multidisciplinary effort with physicians, patients and researchers the UCC sets a benchmark for a learning cardiovascular healthcare system. UCC offers an invaluable resource for future high quality care as well as for first-class research for investigators.
Charting Collective Knowledge: Supporting Self-Regulated Learning in the Workplace

ERIC Educational Resources Information Center

Littlejohn, Allison; Milligan, Colin; Margaryan, Anoush

2012-01-01

Purpose: This study aims to outline an approach to improving the effectiveness of work-based learning through knowledge creation and enhancing self-regulated learning. The paper presents a case example of a novel approach to learning through knowledge creation in the workplace. This case example is based on empirical data collected through a study…
Electronic patient-reported data capture as a foundation of rapid learning cancer care.

PubMed

Abernethy, Amy P; Ahmad, Asif; Zafar, S Yousuf; Wheeler, Jane L; Reese, Jennifer Barsky; Lyerly, H Kim

2010-06-01

"Rapid learning healthcare" presents a new infrastructure to support comparative effectiveness research. By leveraging heterogeneous datasets (eg, clinical, administrative, genomic, registry, and research), health information technology, and sophisticated iterative analyses, rapid learning healthcare provides a real-time framework in which clinical studies can evaluate the relative impact of therapeutic approaches on a diverse array of measures. This article describes an effort, at 1 academic medical center, to demonstrate what rapid learning healthcare might look like in operation. The article describes the process of developing and testing the components of this new model of integrated clinical/research function, with the pilot site being an academic oncology clinic and with electronic patient-reported outcomes (ePROs) being the foundational dataset. Steps included: feasibility study of the ePRO system; validation study of ePRO collection across 3 cancers; linking ePRO and other datasets; implementation; stakeholder alignment and buy in, and; demonstration through use cases. Two use cases are presented; participants were metastatic breast cancer (n = 65) and gastrointestinal cancer (n = 113) patients at 2 academic medical centers. (1) Patient-reported symptom data were collected with tablet computers; patients with breast and gastrointestinal cancer indicated high levels of sexual distress, which prompted multidisciplinary response, design of an intervention, and successful application for funding to study the intervention's impact. (2) The system evaluated the longitudinal impact of a psychosocial care program provided to patients with breast cancer. Participants used tablet computers to complete PRO surveys; data indicated significant impact on psychosocial outcomes, notably distress and despair, despite advanced disease. Results return to the clinic, allowing iterative update and evaluation. An ePRO-based rapid learning cancer clinic is feasible, providing real-time research-quality data to support comparative effectiveness research.
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.
E-Learning Development Process for Operating System Course in Vocational School

NASA Astrophysics Data System (ADS)

Tuna, J. R.; Manoppo, C. T. M.; Kaparang, D. R.; Mewengkang, A.

2018-02-01

This development research aims to produce learning media in the form of E- Learning media using Edmodo which is interesting, efficient and effective on the subjects of operating system for students of class X TKJ in SMKN 3 Manado. The development model used was developed by S. Thiagarajan et al., Often known as the Four-D model, but this research only uses (define, design, and develop). Trial of the product is done twice (limited and wide). The experimental design used was the before-after experimental design. Data collection techniques used are interview techniques, questionnaires, and tests. The analytical technique used in this development research is descriptive qualitative. These include analysis of attractiveness test, efficiency and effectiveness of E-Learning media using Edmodo. The media attractiveness test was measured using a student response questionnaire. Media efficiency test was obtained through interviews of researchers with operating system subjects teachers and students of class X TKJ 1 at SMKN 3 Manado. While the media effectiveness test obtained from student learning outcomes before and after applying E-Learning media using Edomodo. Then tested by paired sample t test formula. After the media was piloted on the subject of trials (limited and broad), and the results show that E-Learning media using Edmodo is interesting, efficient and effective. It is shown on average student response score of 88.15% with very interesting interpretation. While the average value of student learning outcomes increased from 76.33 to 82.93. The results of differential test (paired sample t-test) the value of t = 11 217 ≥ ttable = 2,045 with significant value = 0.000 <α = 0.050 showing the media E -Learning using Edmodo is effective.
Convolutional neural network-based classification system design with compressed wireless sensor network images.

PubMed

Ahn, Jungmo; Park, JaeYeon; Park, Donghwan; Paek, Jeongyeup; Ko, JeongGil

2018-01-01

With the introduction of various advanced deep learning algorithms, initiatives for image classification systems have transitioned over from traditional machine learning algorithms (e.g., SVM) to Convolutional Neural Networks (CNNs) using deep learning software tools. A prerequisite in applying CNN to real world applications is a system that collects meaningful and useful data. For such purposes, Wireless Image Sensor Networks (WISNs), that are capable of monitoring natural environment phenomena using tiny and low-power cameras on resource-limited embedded devices, can be considered as an effective means of data collection. However, with limited battery resources, sending high-resolution raw images to the backend server is a burdensome task that has direct impact on network lifetime. To address this problem, we propose an energy-efficient pre- and post- processing mechanism using image resizing and color quantization that can significantly reduce the amount of data transferred while maintaining the classification accuracy in the CNN at the backend server. We show that, if well designed, an image in its highly compressed form can be well-classified with a CNN model trained in advance using adequately compressed data. Our evaluation using a real image dataset shows that an embedded device can reduce the amount of transmitted data by ∼71% while maintaining a classification accuracy of ∼98%. Under the same conditions, this process naturally reduces energy consumption by ∼71% compared to a WISN that sends the original uncompressed images.
Facilitating mathematics learning for students with upper extremity disabilities using touch-input system.

PubMed

Choi, Kup-Sze; Chan, Tak-Yin

2015-03-01

To investigate the feasibility of using tablet device as user interface for students with upper extremity disabilities to input mathematics efficiently into computer. A touch-input system using tablet device as user interface was proposed to assist these students to write mathematics. User-switchable and context-specific keyboard layouts were designed to streamline the input process. The system could be integrated with conventional computer systems only with minor software setup. A two-week pre-post test study involving five participants was conducted to evaluate the performance of the system and collect user feedback. The mathematics input efficiency of the participants was found to improve during the experiment sessions. In particular, their performance in entering trigonometric expressions by using the touch-input system was significantly better than that by using conventional mathematics editing software with keyboard and mouse. The participants rated the touch-input system positively and were confident that they could operate at ease with more practice. The proposed touch-input system provides a convenient way for the students with hand impairment to write mathematics and has the potential to facilitate their mathematics learning. Implications for Rehabilitation Students with upper extremity disabilities often face barriers to learning mathematics which is largely based on handwriting. Conventional computer user interfaces are inefficient for them to input mathematics into computer. A touch-input system with context-specific and user-switchable keyboard layouts was designed to improve the efficiency of mathematics input. Experimental results and user feedback suggested that the system has the potential to facilitate mathematics learning for the students.
Connecticut Biodiesel Power Generation Project

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

Grannis, Lee; York, Carla R.

Sabre will continue support of the emissions equipment and VARS issues to ensure all are resolved and the system is functioning as expected. The remote data collection to become more automated. Final project reports for data collection and system performance to be generated. Sabre continued to support the emissions equipment and VARS issues to ensure all are resolved and the system is functioning as expected. The remote data collection became more automated. Final project reports for data collection and system performance were generated and are part of this final report. Some Systems Sensors were replaced due to a lightning strike.more » Sample data charts are shown at the end of the report. During the project, Sabre Engineering provided support to the project team with regarding to troubleshooting technical issues and system integration with the local power utility company. The resulting lessons learned through Sabre’s participation in the project have been valuable to the integrity of the data collected as well as in providing BioPur Light & Power valuable insights into future operations and planning for possible expansion. The system monitoring and data collection system has been operating as designed and continues to provide relevant information to the system operators. The information routinely gathered automatically by the system also contributes to the REN and REC validations which are required to secure credit for these items. During the quarter, the remaining work on the operations and safety manual were completed and released for publication after screen shots were verified. The goal of this effort to provide an accurate set of precautions and procedures for the technology system that can be replicated to other similar system.« less
A study of the relationship between the study process, motivation resources, and motivation problems of nursing students in different educational systems.

PubMed

Yardimci, Figen; Bektaş, Murat; Özkütük, Nilay; Muslu, Gonca Karayağız; Gerçeker, Gülçin Özalp; Başbakkal, Zümrüt

2017-01-01

The study process is related to students' learning approaches and styles. Motivation resources and problems determine students' internal, external, and negative motivation. Analyzing the study process and motivation of students yields important indications about the nature of educational systems in higher education. This study aims to analyze the relationship between the study process, and motivation resources and problems with regard to nursing students in different educational systems in Turkey and to reveal their effects according to a set of variables. This is a descriptive, cross-sectional and correlational study. Traditional, integrated and problem-based learning (PBL) educational programs for nurses involving students from three nursing schools in Turkey. Nursing students (n=330). The data were collected using the Study Process Questionnaire (R-SPQ-2F) and the Motivation Resources and Problems (MRP) Scale. A statistically significant difference was found between the scores on the study process scale, and motivation resources and problems scale among the educational systems. This study determined that the mean scores of students in the PBL system on learning approaches, intrinsic motivation and negative motivation were higher. A positive significant correlation was found between the scales. The study process, and motivation resources and problems were found to be affected by the educational system. This study determined that the PBL educational system more effectively increases students' intrinsic motivation and helps them to acquire learning skills. Copyright Â© 2016 Elsevier Ltd. All rights reserved.

Applying an Activity Theory Lens to Designing Instruction for Learning about the Structure, Behavior, and Function of a Honeybee System

ERIC Educational Resources Information Center

Danish, Joshua A.

2014-01-01

This article reports on a study in which activity theory was used to design, implement, and analyze a 10-week curriculum unit about how honeybees collect nectar with a particular focus on complex systems concepts. Students (n = 42) in a multi-year kindergarten and 1st-grade classroom participated in this study as part of their 10 regular classroom…
Systems Development in a Complex Stakeholder Environment: NTCP Chronicle 2.0

PubMed Central

Callahan, Christopher P.; Petersen, Lisa

2003-01-01

The CDC’s National Tobacco Control Program (NTCP) is developing its Chronicle 2.0 online grant application and progress reporting system. 51 CDC-funded state tobacco control programs currently use Chronicle in its 1.0 version to facilitate the collection of state data supporting progress on key performance measures. This poster highlights the application development process for Chronicle 2.0 and presents lessons learned. PMID:14728306
How to build an information gathering and processing system: lessons from naturally and artificially intelligent systems.

PubMed

Chappell, Jackie; Demery, Zoe P; Arriola-Rios, Veronica; Sloman, Aaron

2012-02-01

Imagine a situation in which you had to design a physical agent that could collect information from its environment, then store and process that information to help it respond appropriately to novel situations. What kinds of information should it attend to? How should the information be represented so as to allow efficient use and re-use? What kinds of constraints and trade-offs would there be? There are no unique answers. In this paper, we discuss some of the ways in which the need to be able to address problems of varying kinds and complexity can be met by different information processing systems. We also discuss different ways in which relevant information can be obtained, and how different kinds of information can be processed and used, by both biological organisms and artificial agents. We analyse several constraints and design features, and show how they relate both to biological organisms, and to lessons that can be learned from building artificial systems. Our standpoint overlaps with Karmiloff-Smith (1992) in that we assume that a collection of mechanisms geared to learning and developing in biological environments are available in forms that constrain, but do not determine, what can or will be learnt by individuals. Copyright © 2011 Elsevier B.V. All rights reserved.
How to Set Up Simulations for Designing Light-Weight Personalised Recommender Systems

NASA Astrophysics Data System (ADS)

Nadolski, Rob; van den Berg, Bert; Berlanga, Adriana; Hummel, Hans; Drachsler, Hendrik; Koper, Rob; Sloep, Peter

For effective competence acquisition, professionals should have a clear overview of what learning actions (LAs) are relevant to them. LAs can use any type of learning resource or events (like a course, assignment, discussion, lesson, website, blog) that intends to help learners to acquire a certain competence when participating in a LN. Such learners need advice in choosing from a large and dynamic collection of LAs those that best fit their current needs and accomplishments. In short, they need support to find their way in a LN.
Monitoring REDD+: From Social Safeguards to Social Learning

NASA Astrophysics Data System (ADS)

Ravikumar, A.; Andersson, K.

2010-12-01

Krister Andersson 1 and Ashwin Ravikumar 1 The UNFCCC requires countries that participate in the REDD+ (Reducing Emissions from Deforestation and Forest Degradation in Developing Countries) program to monitor both forest carbon inventories as well as the governance of REDD+ activities and their social consequences. Exactly how this should be done, however, remains an open question. This paper addresses this question by drawing on existing research on social-ecological systems and new institutional economics. We make the case for a monitoring system that goes beyond a narrow focus of qualitative indicators of REDD+ governance that seek to provide social safeguards for international investors to create a more comprehensive monitoring system that is useful for social learning about how policies affect a variety of forest outcomes. We describe the defining characteristics of five existing approaches to monitoring REDD+ governance. Applying evaluative criteria of affordability, comprehensiveness, transparency, uncertainty specification, and explanatory potential, we analyze the extent to which each of the programs contribute to broader social learning processes in participating countries. Our analysis finds that it makes sense to move from the current narrow focus of monitoring for control to monitoring for social learning. Particularly valuable to participating REDD+ actors would be the creation of learning systems that can help policy makers to identify opportunities for policy improvements, with the ultimate goal of making REDD+ more effective, efficient, and equitable. Such learning is not possible, however, without timely and systematic collection of data on the relationships between forests and forest users. 1University of Colorado at Boulder, Environmental Studies Program, Boulder, CO 80309-0397
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.
Earth System Science Education for the 21st Century: Progress and Plans

NASA Astrophysics Data System (ADS)

Ruzek, M.; Johnson, D. R.; Wake, C.; Aron, J.

2005-12-01

Earth System Science Education for the 21st Century (ESSE 21) is a collaborative undergraduate/graduate Earth system science education program sponsored by NASA offering small grants to colleges and universities with special emphasis on including minority institutions to engage faculty and scientists in the development of Earth system science courses, curricula, degree programs and shared learning resources. The annual ESSE 21 meeting in Fairbanks in August, 2005 provided an opportunity for 70 undergraduate educators and scientists to share their best classroom learning resources through a series of short presentations, posters and skills workshops. This poster will highlight meeting results, advances in the development of ESS learning modules, and describe a community-led proposal to develop in the coming year a Design Guide for Undergraduate Earth system Science Education to be based upon the experience of the 63 NASA-supported ESSE teams over the past 15 years. As a living document on the Web, the Design Guide would utilize and share ESSE experiences that: - Advance understanding of the Earth as a system - Apply ESS to the Vision for Space Exploration - Create environments appropriate for teaching and learning ESS - Improve STEM literacy and broaden career paths - Transform institutional priorities and approaches to ESS - Embrace ESS within Minority Serving Institutions - Build collaborative interdisciplinary partnerships - Develop ESS learning resources and modules The Design Guide aims to be a synthesis of just how ESS has been and is being implemented in the college and university environment, listing items essential for undergraduate Earth system education that reflect the collective wisdom of the ESS education community. The Design Guide will focus the vision for ESS in the coming decades, define the challenges, and explore collaborative processes that utilize the next generation of information and communication technology.
Global adaptation in networks of selfish components: emergent associative memory at the system scale.

PubMed

Watson, Richard A; Mills, Rob; Buckley, C L

2011-01-01

In some circumstances complex adaptive systems composed of numerous self-interested agents can self-organize into structures that enhance global adaptation, efficiency, or function. However, the general conditions for such an outcome are poorly understood and present a fundamental open question for domains as varied as ecology, sociology, economics, organismic biology, and technological infrastructure design. In contrast, sufficient conditions for artificial neural networks to form structures that perform collective computational processes such as associative memory/recall, classification, generalization, and optimization are well understood. Such global functions within a single agent or organism are not wholly surprising, since the mechanisms (e.g., Hebbian learning) that create these neural organizations may be selected for this purpose; but agents in a multi-agent system have no obvious reason to adhere to such a structuring protocol or produce such global behaviors when acting from individual self-interest. However, Hebbian learning is actually a very simple and fully distributed habituation or positive feedback principle. Here we show that when self-interested agents can modify how they are affected by other agents (e.g., when they can influence which other agents they interact with), then, in adapting these inter-agent relationships to maximize their own utility, they will necessarily alter them in a manner homologous with Hebbian learning. Multi-agent systems with adaptable relationships will thereby exhibit the same system-level behaviors as neural networks under Hebbian learning. For example, improved global efficiency in multi-agent systems can be explained by the inherent ability of associative memory to generalize by idealizing stored patterns and/or creating new combinations of subpatterns. Thus distributed multi-agent systems can spontaneously exhibit adaptive global behaviors in the same sense, and by the same mechanism, as with the organizational principles familiar in connectionist models of organismic learning.
Tunnel Ventilation Control Using Reinforcement Learning Methodology

NASA Astrophysics Data System (ADS)

Chu, Baeksuk; Kim, Dongnam; Hong, Daehie; Park, Jooyoung; Chung, Jin Taek; Kim, Tae-Hyung

The main purpose of tunnel ventilation system is to maintain CO pollutant concentration and VI (visibility index) under an adequate level to provide drivers with comfortable and safe driving environment. Moreover, it is necessary to minimize power consumption used to operate ventilation system. To achieve the objectives, the control algorithm used in this research is reinforcement learning (RL) method. RL is a goal-directed learning of a mapping from situations to actions without relying on exemplary supervision or complete models of the environment. The goal of RL is to maximize a reward which is an evaluative feedback from the environment. In the process of constructing the reward of the tunnel ventilation system, two objectives listed above are included, that is, maintaining an adequate level of pollutants and minimizing power consumption. RL algorithm based on actor-critic architecture and gradient-following algorithm is adopted to the tunnel ventilation system. The simulations results performed with real data collected from existing tunnel ventilation system and real experimental verification are provided in this paper. It is confirmed that with the suggested controller, the pollutant level inside the tunnel was well maintained under allowable limit and the performance of energy consumption was improved compared to conventional control scheme.
75 FR 69152 - Agency Information Collection Activities: Requests for Comments; Clearance of a New Information...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-11-10

... 40863. The FAA/AST will collect lessons learned from members of the commercial space [[Page 69153... collection. Background: The FAA/AST collects lessons learned from members of the commercial space industry in... amateur rocket community, experimental permit holders, licensed launch and reentry operators, and licensed...
75 FR 63822 - Proposed Information Collection; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2010-10-18

... design to assess the impact of Learn and Serve America-funded service-learning activities on student...-funded service-learning activities on ninth and tenth grade students' academic achievement, academic... will be collected from students on their academic and civic engagement; teachers on the implementation...
Spontaneous neuronal activity as a self-organized critical phenomenon

NASA Astrophysics Data System (ADS)

de Arcangelis, L.; Herrmann, H. J.

2013-01-01

Neuronal avalanches are a novel mode of activity in neuronal networks, experimentally found in vitro and in vivo, and exhibit a robust critical behaviour. Avalanche activity can be modelled within the self-organized criticality framework, including threshold firing, refractory period and activity-dependent synaptic plasticity. The size and duration distributions confirm that the system acts in a critical state, whose scaling behaviour is very robust. Next, we discuss the temporal organization of neuronal avalanches. This is given by the alternation between states of high and low activity, named up and down states, leading to a balance between excitation and inhibition controlled by a single parameter. During these periods both the single neuron state and the network excitability level, keeping memory of past activity, are tuned by homeostatic mechanisms. Finally, we verify if a system with no characteristic response can ever learn in a controlled and reproducible way. Learning in the model occurs via plastic adaptation of synaptic strengths by a non-uniform negative feedback mechanism. Learning is a truly collective process and the learning dynamics exhibits universal features. Even complex rules can be learned provided that the plastic adaptation is sufficiently slow.
Force Sensor Based Tool Condition Monitoring Using a Heterogeneous Ensemble Learning Model

PubMed Central

Wang, Guofeng; Yang, Yinwei; Li, Zhimeng

2014-01-01

Tool condition monitoring (TCM) plays an important role in improving machining efficiency and guaranteeing workpiece quality. In order to realize reliable recognition of the tool condition, a robust classifier needs to be constructed to depict the relationship between tool wear states and sensory information. However, because of the complexity of the machining process and the uncertainty of the tool wear evolution, it is hard for a single classifier to fit all the collected samples without sacrificing generalization ability. In this paper, heterogeneous ensemble learning is proposed to realize tool condition monitoring in which the support vector machine (SVM), hidden Markov model (HMM) and radius basis function (RBF) are selected as base classifiers and a stacking ensemble strategy is further used to reflect the relationship between the outputs of these base classifiers and tool wear states. Based on the heterogeneous ensemble learning classifier, an online monitoring system is constructed in which the harmonic features are extracted from force signals and a minimal redundancy and maximal relevance (mRMR) algorithm is utilized to select the most prominent features. To verify the effectiveness of the proposed method, a titanium alloy milling experiment was carried out and samples with different tool wear states were collected to build the proposed heterogeneous ensemble learning classifier. Moreover, the homogeneous ensemble learning model and majority voting strategy are also adopted to make a comparison. The analysis and comparison results show that the proposed heterogeneous ensemble learning classifier performs better in both classification accuracy and stability. PMID:25405514
Force sensor based tool condition monitoring using a heterogeneous ensemble learning model.

PubMed

Wang, Guofeng; Yang, Yinwei; Li, Zhimeng

2014-11-14

Tool condition monitoring (TCM) plays an important role in improving machining efficiency and guaranteeing workpiece quality. In order to realize reliable recognition of the tool condition, a robust classifier needs to be constructed to depict the relationship between tool wear states and sensory information. However, because of the complexity of the machining process and the uncertainty of the tool wear evolution, it is hard for a single classifier to fit all the collected samples without sacrificing generalization ability. In this paper, heterogeneous ensemble learning is proposed to realize tool condition monitoring in which the support vector machine (SVM), hidden Markov model (HMM) and radius basis function (RBF) are selected as base classifiers and a stacking ensemble strategy is further used to reflect the relationship between the outputs of these base classifiers and tool wear states. Based on the heterogeneous ensemble learning classifier, an online monitoring system is constructed in which the harmonic features are extracted from force signals and a minimal redundancy and maximal relevance (mRMR) algorithm is utilized to select the most prominent features. To verify the effectiveness of the proposed method, a titanium alloy milling experiment was carried out and samples with different tool wear states were collected to build the proposed heterogeneous ensemble learning classifier. Moreover, the homogeneous ensemble learning model and majority voting strategy are also adopted to make a comparison. The analysis and comparison results show that the proposed heterogeneous ensemble learning classifier performs better in both classification accuracy and stability.
STEM based learning to facilitate middle school students’ conceptual change, creativity and collaboration in organization of living system topic

NASA Astrophysics Data System (ADS)

Rustaman, N. Y.; Afianti, E.; Maryati, S.

2018-05-01

A study using one group pre-post-test experimental design on Life organization system topic was carried out to investigate student’s tendency in learning abstract concept, their creativity and collaboration in designing and producing cell models through STEM-based learning. A number of seventh grade students in Cianjur district were involved as research subjects (n=34). Data were collected using two tier test for tracing changes in student conception before and after the application of STEM-based learning, and rubrics in creativity design (adopted from Torrance) and product on cell models (individually, in group), and rubric for self-assessment and observed skills on collaboration adapted from Marzano’s for life-long learning. Later the data obtained were analyzed qualitatively by interpreting the tendency of data presented in matrix sorted by gender. Research findings showed that the percentage of student’s scientific concept mastery is moderate in general. Their creativity in making a cell model design varied in category (expressing, emergent, excellent, not yet evident). Student’s collaboration varied from excellent, fair, good, less once, to less category in designing cell model. It was found that STEM based learning can facilitate students conceptual change, creativity and collaboration.
Computerized tomography platform using beta rays

NASA Astrophysics Data System (ADS)

Paetkau, Owen; Parsons, Zachary; Paetkau, Mark

2017-12-01

A computerized tomography (CT) system using a 0.1 μCi Sr-90 beta source, Geiger counter, and low density foam samples was developed. A simple algorithm was used to construct images from the data collected with the beta CT scanner. The beta CT system is analogous to X-ray CT as both types of radiation are sensitive to density variations. This system offers a platform for learning opportunities in an undergraduate laboratory, covering topics such as image reconstruction algorithms, radiation exposure, and the energy dependence of absorption.
Temporal Cyber Attack Detection.

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

Ingram, Joey Burton; Draelos, Timothy J.; Galiardi, Meghan

Rigorous characterization of the performance and generalization ability of cyber defense systems is extremely difficult, making it hard to gauge uncertainty, and thus, confidence. This difficulty largely stems from a lack of labeled attack data that fully explores the potential adversarial space. Currently, performance of cyber defense systems is typically evaluated in a qualitative manner by manually inspecting the results of the system on live data and adjusting as needed. Additionally, machine learning has shown promise in deriving models that automatically learn indicators of compromise that are more robust than analyst-derived detectors. However, to generate these models, most algorithms requiremore » large amounts of labeled data (i.e., examples of attacks). Algorithms that do not require annotated data to derive models are similarly at a disadvantage, because labeled data is still necessary when evaluating performance. In this work, we explore the use of temporal generative models to learn cyber attack graph representations and automatically generate data for experimentation and evaluation. Training and evaluating cyber systems and machine learning models requires significant, annotated data, which is typically collected and labeled by hand for one-off experiments. Automatically generating such data helps derive/evaluate detection models and ensures reproducibility of results. Experimentally, we demonstrate the efficacy of generative sequence analysis techniques on learning the structure of attack graphs, based on a realistic example. These derived models can then be used to generate more data. Additionally, we provide a roadmap for future research efforts in this area.« less
An analysis of mathematical connection ability based on student learning style on visualization auditory kinesthetic (VAK) learning model with self-assessment

NASA Astrophysics Data System (ADS)

Apipah, S.; Kartono; Isnarto

2018-03-01

This research aims to analyze the quality of VAK learning with self-assessment toward the ability of mathematical connection performed by students and to analyze students’ mathematical connection ability based on learning styles in VAK learning model with self-assessment. This research applies mixed method type with concurrent embedded design. The subject of this research consists of VIII grade students from State Junior High School 9 Semarang who apply visual learning style, auditory learning style, and kinesthetic learning style. The data of learning style is collected by using questionnaires, the data of mathematical connection ability is collected by performing tests, and the data of self-assessment is collected by using assessment sheets. The quality of learning is qualitatively valued from planning stage, realization stage, and valuation stage. The result of mathematical connection ability test is analyzed quantitatively by mean test, conducting completeness test, mean differentiation test, and mean proportional differentiation test. The result of the research shows that VAK learning model results in well-qualified learning regarded from qualitative and quantitative sides. Students with visual learning style perform the highest mathematical connection ability, students with kinesthetic learning style perform average mathematical connection ability, and students with auditory learning style perform the lowest mathematical connection ability.
Broiler chickens can benefit from machine learning: support vector machine analysis of observational epidemiological data

PubMed Central

Hepworth, Philip J.; Nefedov, Alexey V.; Muchnik, Ilya B.; Morgan, Kenton L.

2012-01-01

Machine-learning algorithms pervade our daily lives. In epidemiology, supervised machine learning has the potential for classification, diagnosis and risk factor identification. Here, we report the use of support vector machine learning to identify the features associated with hock burn on commercial broiler farms, using routinely collected farm management data. These data lend themselves to analysis using machine-learning techniques. Hock burn, dermatitis of the skin over the hock, is an important indicator of broiler health and welfare. Remarkably, this classifier can predict the occurrence of high hock burn prevalence with accuracy of 0.78 on unseen data, as measured by the area under the receiver operating characteristic curve. We also compare the results with those obtained by standard multi-variable logistic regression and suggest that this technique provides new insights into the data. This novel application of a machine-learning algorithm, embedded in poultry management systems could offer significant improvements in broiler health and welfare worldwide. PMID:22319115
Broiler chickens can benefit from machine learning: support vector machine analysis of observational epidemiological data.

PubMed

Hepworth, Philip J; Nefedov, Alexey V; Muchnik, Ilya B; Morgan, Kenton L

2012-08-07

Machine-learning algorithms pervade our daily lives. In epidemiology, supervised machine learning has the potential for classification, diagnosis and risk factor identification. Here, we report the use of support vector machine learning to identify the features associated with hock burn on commercial broiler farms, using routinely collected farm management data. These data lend themselves to analysis using machine-learning techniques. Hock burn, dermatitis of the skin over the hock, is an important indicator of broiler health and welfare. Remarkably, this classifier can predict the occurrence of high hock burn prevalence with accuracy of 0.78 on unseen data, as measured by the area under the receiver operating characteristic curve. We also compare the results with those obtained by standard multi-variable logistic regression and suggest that this technique provides new insights into the data. This novel application of a machine-learning algorithm, embedded in poultry management systems could offer significant improvements in broiler health and welfare worldwide.

Teaching People and Machines to Enhance Images

NASA Astrophysics Data System (ADS)

Berthouzoz, Floraine Sara Martianne

Procedural tasks such as following a recipe or editing an image are very common. They require a person to execute a sequence of operations (e.g. chop onions, or sharpen the image) in order to achieve the goal of the task. People commonly use step-by-step tutorials to learn these tasks. We focus on software tutorials, more specifically photo manipulation tutorials, and present a set of tools and techniques to help people learn, compare and automate photo manipulation procedures. We describe three different systems that are each designed to help with a different stage in acquiring procedural knowledge. Today, people primarily rely on hand-crafted tutorials in books and on websites to learn photo manipulation procedures. However, putting together a high quality step-by-step tutorial is a time-consuming process. As a consequence, many online tutorials are poorly designed which can lead to confusion and slow down the learning process. We present a demonstration-based system for automatically generating succinct step-by-step visual tutorials of photo manipulations. An author first demonstrates the manipulation using an instrumented version of GIMP (GNU Image Manipulation Program) that records all changes in interface and application state. From the example recording, our system automatically generates tutorials that illustrate the manipulation using images, text, and annotations. It leverages automated image labeling (recognition of facial features and outdoor scene structures in our implementation) to generate more precise text descriptions of many of the steps in the tutorials. A user study finds that our tutorials are effective for learning the steps of a procedure; users are 20-44% faster and make 60-95% fewer errors when using our tutorials than when using screencapture video tutorials or hand-designed tutorials. We also demonstrate a new interface that allows learners to navigate, explore and compare large collections (i.e. thousands) of photo manipulation tutorials based on their command-level structure. Sites such as tutorialized.com or good-tutorials.com collect tens of thousands of photo manipulation tutorials. These collections typically contain many different tutorials for the same task. For example, there are many different tutorials that describe how to recolor the hair of a person in an image. Learners often want to compare these tutorials to understand the different ways a task can be done. They may also want to identify common strategies that are used across tutorials for a variety of tasks. However, the large number of tutorials in these collections and their inconsistent formats can make it difficult for users to systematically explore and compare them. Current tutorial collections do not exploit the underlying command-level structure of tutorials, and to explore the collection users have to either page through long lists of tutorial titles or perform keyword searches on the natural language tutorial text. We present a new browsing interface to help learners navigate, explore and compare collections of photo manipulation tutorials based on their command-level structure. Our browser indexes tutorials by their commands, identifies common strategies within the tutorial collection, and highlights the similarities and differences between sets of tutorials that execute the same task. User feedback suggests that our interface is easy to understand and use, and that users find command-level browsing to be useful for exploring large tutorial collections. They strongly preferred to explore tutorial collections with our browser over keyword search. Finally, we present a framework for generating content-adaptive macros (programs) that can transfer complex photo manipulation procedures to new target images. After learners master a photo manipulation procedure, they often repeatedly apply it to multiple images. For example, they might routinely apply the same vignetting effect to all their photographs. This process can be very tedious especially for procedures that involve many steps. While image manipulation programs provide basic macro authoring tools that allow users to record and then replay a sequence of operations, these macros are very brittle and cannot adapt to new images. We present a more comprehensive approach for generating content-adaptive macros that can automatically transfer operations to new target images. To create these macro, we make use of multiple training demonstrations. Specifically, we use automated image labeling and machine learning techniques to to adapt the parameters of each operation to the new image content. We show that our framework is able to learn a large class of the most commonly-used manipulations using as few as 20 training demonstrations. Our content-adaptive macros allow users to transfer photo manipulation procedures with a single button click and thereby significantly simplify repetitive procedures.
76 FR 72414 - Agency Information Collection Activities: Proposed Collection; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-23

... participating in a CME activity to determine the degree to which the learning activities with integrated EHC... awareness of and willingness to learn about results from comparative effectiveness research studies. The... learning programs for delivery through the Eisenberg Center for Clinical Decisions and Communications...
Kees: a Practical Ict Solution for Rural Areas

NASA Astrophysics Data System (ADS)

Dai, Xiaoye; Tabirca, Sabin; Lenihan, Eamon

This paper introduces a practical e-learning system, identified as Knowledge Exchange E-learning System (abbr. KEES), for knowledge distribution in rural areas. Particularly, this paper is about providing a virtual teaching and learning environment for small holders in agriculture in those rural areas. E-learning is increasingly influencing the agricultural education (information and knowledge learning) in all forms and the current e-learning in agricultural education appears in informal and formal methods in many developed countries and some developing areas such as Asian Pacific regions. KEES is a solution to provide education services including other services of information distribution and knowledge sharing to local farmers, local institutes or local collection of farmers. The design of KEES is made to meet the needs of knowledge capacity building, experience sharing, skill upgrading, and information exchanging in agriculture for different conditions in rural areas. The system allows the online lecture/training materials to be distributed simultaneously with all multimedia resources through different file formats across different platforms. The teaching/training content can be contextless and broad, allowing for greater participation by more small holders, commercial farmers, extension workers, agriculturists, educators, and other agriculture-related experts. The relative inconsistency in content gives farmers more localised and useful knowledge. The framework of KEES has been designed to be a three-tier architecture logic workflow, which can configure the progressive approach for KEES to pass on and respond to different requests/communications between the client side and the server.
Learning to make collective decisions: the impact of confidence escalation.

PubMed

Mahmoodi, Ali; Bang, Dan; Ahmadabadi, Majid Nili; Bahrami, Bahador

2013-01-01

Little is known about how people learn to take into account others' opinions in joint decisions. To address this question, we combined computational and empirical approaches. Human dyads made individual and joint visual perceptual decision and rated their confidence in those decisions (data previously published). We trained a reinforcement (temporal difference) learning agent to get the participants' confidence level and learn to arrive at a dyadic decision by finding the policy that either maximized the accuracy of the model decisions or maximally conformed to the empirical dyadic decisions. When confidences were shared visually without verbal interaction, RL agents successfully captured social learning. When participants exchanged confidences visually and interacted verbally, no collective benefit was achieved and the model failed to predict the dyadic behaviour. Behaviourally, dyad members' confidence increased progressively and verbal interaction accelerated this escalation. The success of the model in drawing collective benefit from dyad members was inversely related to confidence escalation rate. The findings show an automated learning agent can, in principle, combine individual opinions and achieve collective benefit but the same agent cannot discount the escalation suggesting that one cognitive component of collective decision making in human may involve discounting of overconfidence arising from interactions.
The CREST Simulation Development Process: Training the Next Generation.

PubMed

Sweet, Robert M

2017-04-01

The challenges of training and assessing endourologic skill have driven the development of new training systems. The Center for Research in Education and Simulation Technologies (CREST) has developed a team and a methodology to facilitate this development process. Backwards design principles were applied. A panel of experts first defined desired clinical and educational outcomes. Outcomes were subsequently linked to learning objectives. Gross task deconstruction was performed, and the primary domain was classified as primarily involving decision-making, psychomotor skill, or communication. A more detailed cognitive task analysis was performed to elicit and prioritize relevant anatomy/tissues, metrics, and errors. Reference anatomy was created using a digital anatomist and clinician working off of a clinical data set. Three dimensional printing can facilitate this process. When possible, synthetic or virtual tissue behavior and textures were recreated using data derived from human tissue. Embedded sensors/markers and/or computer-based systems were used to facilitate the collection of objective metrics. A learning Verification and validation occurred throughout the engineering development process. Nine endourology-relevant training systems were created by CREST with this approach. Systems include basic laparoscopic skills (BLUS), vesicourethral anastomosis, pyeloplasty, cystoscopic procedures, stent placement, rigid and flexible ureteroscopy, GreenLight PVP (GL Sim), Percutaneous access with C-arm (CAT), Nephrolithotomy (NLM), and a vascular injury model. Mixed modalities have been used, including "smart" physical models, virtual reality, augmented reality, and video. Substantial validity evidence for training and assessment has been collected on systems. An open source manikin-based modular platform is under development by CREST with the Department of Defense that will unify these and other commercial task trainers through the common physiology engine, learning management system, standard data connectors, and standards. Using the CREST process has and will ensure that the systems we create meet the needs of training and assessing endourologic skills.
Online Learning Flight Control for Intelligent Flight Control Systems (IFCS)

NASA Technical Reports Server (NTRS)

Niewoehner, Kevin R.; Carter, John (Technical Monitor)

2001-01-01

The research accomplishments for the cooperative agreement 'Online Learning Flight Control for Intelligent Flight Control Systems (IFCS)' include the following: (1) previous IFC program data collection and analysis; (2) IFC program support site (configured IFC systems support network, configured Tornado/VxWorks OS development system, made Configuration and Documentation Management Systems Internet accessible); (3) Airborne Research Test Systems (ARTS) II Hardware (developed hardware requirements specification, developing environmental testing requirements, hardware design, and hardware design development); (4) ARTS II software development laboratory unit (procurement of lab style hardware, configured lab style hardware, and designed interface module equivalent to ARTS II faceplate); (5) program support documentation (developed software development plan, configuration management plan, and software verification and validation plan); (6) LWR algorithm analysis (performed timing and profiling on algorithm); (7) pre-trained neural network analysis; (8) Dynamic Cell Structures (DCS) Neural Network Analysis (performing timing and profiling on algorithm); and (9) conducted technical interchange and quarterly meetings to define IFC research goals.
Teachers Rally around Writing: Shared Leadership Transforms Low-Performing Colorado Elementary

ERIC Educational Resources Information Center

Frazee, Dana; Frunzi, Kay; Hein, Heather

2013-01-01

To improve the quality of instruction and the achievement of all students, effective school leaders develop capacity, advocate, and create support systems for professional learning. Creating a team of teachers to share leadership, responsibility, and accountability for achieving collective goals is essential. This was the task of a first-time…
Temoa: An Open Educational Resources Portal to Seek, Investigate and Inquire

ERIC Educational Resources Information Center

Gómez-Zermeño, Marcela Georgina; de la Garza, Lorena Yadira Alemán

2015-01-01

Temoa is a distributor of knowledge that provides a multilingual public catalog of collections of Open Educational Resources (OER). Temoa seeks to support the educational community to find the resources and materials that meet their needs for teaching and learning, through a specialized search system and collaborative social tools. Temoa was…
Helping To Establish a Culture of Learning and Teaching in South Africa. Education Africa Forum. Second Edition.

ERIC Educational Resources Information Center

Fieldgate, Karin, Ed.

This annual collection of papers examines changes in the South African educational system as the country has developed a democratic government. The papers are: "An Interview with the Deputy Minister of Education, Father Smangaliso Mkhatshwa" (Lizeka Mda); "An Interview with Adrienne Bird" (Justice Malala); "An Interview…
Preparing Future Teacher Leaders: Lessons from Exemplary School Systems

ERIC Educational Resources Information Center

Schrum, Lynne; Levin, Barbara B.

2013-01-01

In this paper, we argue that teachers have an opportunity to take on leadership roles in technology-rich schools and districts. Based on data collected during a year-long project to investigate award-winning schools and districts, we used observations, interviews and focus groups, and document analysis to glean lessons learned from leaders and…
English Collocation Learning through Corpus Data: On-Line Concordance and Statistical Information

ERIC Educational Resources Information Center

Ohtake, Hiroshi; Fujita, Nobuyuki; Kawamoto, Takeshi; Morren, Brian; Ugawa, Yoshihiro; Kaneko, Shuji

2012-01-01

We developed an English Collocations On Demand system offering on-line corpus and concordance information to help Japanese researchers acquire a better command of English collocation patterns. The Life Science Dictionary Corpus consists of approximately 90,000,000 words collected from life science related research papers published in academic…
Digital Learning Compass: Distance Education State Almanac 2017. North Dakota

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of North Dakota. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. West Virginia

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of West Virginia. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. South Dakota

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of South Dakota. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. North Carolina

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of North Carolina. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. Rhode Island

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Rhode Island. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. New Hampshire

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of New Hampshire. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. New Jersey

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of New Jersey. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. New Mexico

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of New Mexico. The sample for this analysis is comprised of all active, degree-granting…
Digital Learning Compass: Distance Education State Almanac 2017. New York

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of New York. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. South Carolina

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of South Carolina. The sample for this analysis is comprised of all active, degree-granting…
Increasing Student Intrinsic Motivation and Self-Efficacy through Gamification Pedagogy

ERIC Educational Resources Information Center

Banfield, James; Wilkerson, Brad

2014-01-01

The aim of this study was to assess gamification as a method of experiential learning theory (ELT) on student motivation and self-efficacy to perform System Engineering/Information Assurance (IA) tasks. The study was a basic qualitative method, whereby data was collected via semi-structured interview and then analyzed for recurring themes and…
Acquiring Software Project Specifications in a Virtual World

ERIC Educational Resources Information Center

Ng, Vincent; Tang, Zoe

2012-01-01

In teaching software engineering, it is often interesting to introduce real life scenarios for students to experience and to learn how to collect information from respective clients. The ideal arrangement is to have some real clients willing to spend time to provide their ideas of a target system through interviews. However, this arrangement…
Content Consumption and Hierarchical Structures of Web-Supported Courses

ERIC Educational Resources Information Center

Hershkovitz, Arnon; Hardof-Jaffe, Sharon; Nachmias, Rafi

2014-01-01

This study presents an empirical investigation of the relationship between the hierarchical structure of content delivered to students within a Learning Management System (LMS) and its actual consumption. To this end, campus-wide data relating to 1,203 courses were collected from the LMS' servers and were subsequently analyzed using data mining…
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.…
Collective Problem-Solving: The Role of Self-Efficacy, Skill, and Prior Knowledge

ERIC Educational Resources Information Center

Geifman, Dorit; Raban, Daphne R.

2015-01-01

Self-efficacy is essential to learning but what happens when learning is done as a result of a collective process? What is the role of individual self-efficacy in collective problem solving? This research examines the manifestation of self-efficacy in prediction markets that are configured as collective problem-solving platforms and whether…
EDSN Development Lessons Learned

NASA Technical Reports Server (NTRS)

Chartres, James; Sanchez, Hugo S.; Hanson, John

2014-01-01

The Edison Demonstration of Smallsat Networks (EDSN) is a technology demonstration mission that provides a proof of concept for a constellation or swarm of satellites performing coordinated activities. Networked swarms of small spacecraft will open new horizons in astronomy, Earth observations and solar physics. Their range of applications include the formation of synthetic aperture radars for Earth sensing systems, large aperture observatories for next generation telescopes and the collection of spatially distributed measurements of time varying systems, probing the Earths magnetosphere, Earth-Sun interactions and the Earths geopotential. EDSN is a swarm of eight 1.5U Cubesats with crosslink, downlink and science collection capabilities developed by the NASA Ames Research Center under the Small Spacecraft Technology Program (SSTP) within the NASA Space Technology Mission Directorate (STMD). This paper describes the concept of operations of the mission and planned scientific measurements. The development of the 8 satellites for EDSN necessitated the fabrication of prototypes, Flatsats and a total of 16 satellites to support the concurrent engineering and rapid development. This paper has a specific focus on the development, integration and testing of a large number of units including the lessons learned throughout the project development.
Making sense of the shadows: priorities for creating a learning healthcare system based on routinely collected data

PubMed Central

Deeny, Sarah R; Steventon, Adam

2015-01-01

Socrates described a group of people chained up inside a cave, who mistook shadows of objects on a wall for reality. This allegory comes to mind when considering ‘routinely collected data’—the massive data sets, generated as part of the routine operation of the modern healthcare service. There is keen interest in routine data and the seemingly comprehensive view of healthcare they offer, and we outline a number of examples in which they were used successfully, including the Birmingham OwnHealth study, in which routine data were used with matched control groups to assess the effect of telephone health coaching on hospital utilisation. Routine data differ from data collected primarily for the purposes of research, and this means that analysts cannot assume that they provide the full or accurate clinical picture, let alone a full description of the health of the population. We show that major methodological challenges in using routine data arise from the difficulty of understanding the gap between patient and their ‘data shadow’. Strategies to overcome this challenge include more extensive data linkage, developing analytical methods and collecting more data on a routine basis, including from the patient while away from the clinic. In addition, creating a learning health system will require greater alignment between the analysis and the decisions that will be taken; between analysts and people interested in quality improvement; and between the analysis undertaken and public attitudes regarding appropriate use of data. PMID:26065466
The importance of knowledge-based technology.

PubMed

Cipriano, Pamela F

2012-01-01

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

PubMed

Scalise, Kathleen; Cheng, Nancy Yen-Wen; Oskui, Nargas

2009-01-01

Since architecture students studying design drawing are usually assessed qualitatively on the basis of their final products, the challenges and stages of their learning have remained masked. To clarify the challenges in design drawing, we have been using the BEAR Assessment System and Rasch family models to measure levels of understanding for individuals and groups, in order to correct pedagogical assumptions and tune teaching materials. This chapter discusses the analysis of 81 drawings created by architectural students to solve a space layout problem, collected and analyzed with digital pen-and-paper technology. The approach allows us to map developmental performance criteria and perceive achievement overlaps in learning domains assumed separate, and then re-conceptualize a three-part framework to represent learning in architectural drawing. Results and measurement evidence from the assessment and Rasch modeling are discussed.
Teacher’s Perception about the Use of E-Learning/Edmodo in Educational Activities

NASA Astrophysics Data System (ADS)

Yanti, H.; Setiawan, A.; Nurhabibah; Yannuar

2018-02-01

This study examined the perception of the teachers about the use of e- learning/Edmodo in their educational activities. The teachers consist of diverse subject. Their perceptions were investigated in terms of three aspects: effects of the use of this technology on their perceived motivation, the perceived usefulness and the perceived ease of use of this technology. Edmodo was set up a Learning Management System (LMS) in an online discussion group of subject. The study was conducted in descriptive method. The data were collected by using a questionnaire, interview, and documentation technique. The findings of the study indicated that the teachers perceived that e-learning/Edmodo is a useful and also easy to use technology. It was found out that the teachers are satisfied with advantages of the use of this new technology in their LMS.
76 FR 76393 - Notice of Proposed Information Collection Requests

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-07

..., including through the use of information technology. Dated: December 2, 2011. Darrin King, Director... Collection: 21st Century Community Learning Centers: Lessons Learned Guides. OMB Control Number: 1875--NEW... Community Learning Centers (21st CCLC) program that will assist the U.S. Department of Education staff in...
Collective (Team) Learning Process Models: A Conceptual Review

ERIC Educational Resources Information Center

Knapp, Randall

2010-01-01

Teams have become a key resource for learning and accomplishing work in organizations. The development of collective learning in specific contexts is not well understood, yet has become critical to organizational success. The purpose of this conceptual review is to inform human resource development (HRD) practice about specific team behaviors and…
Using Data Collection Apps and Single-Case Designs to Research Transformative Learning in Adults

ERIC Educational Resources Information Center

Roessger, Kevin M.; Greenleaf, Arie; Hoggan, Chad

2017-01-01

To overcome situational hurdles when researching transformative learning in adults, we outline a research approach using single-case research designs and smartphone data collection apps. This approach allows researchers to better understand learners' current lived experiences and determine the effects of transformative learning interventions on…
78 FR 22254 - Agency Information Collection Activities; Submission to the Office of Management and Budget for...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-04-15

... State Expanded Learning Time AGENCY: Institute of Education Sciences/National Center for Education... State Expanded Learning Time. OMB Control Number: 1850-NEW. Type of Review: a new collection... conduct semi-structured interviews with 21st Century Community Learning Centers (21st CCLC) state...
[Developing team reflexivity as a learning and working tool for medical teams].

PubMed

Riskin, Arieh; Bamberger, Peter

2014-01-01

Team reflexivity is a collective activity in which team members review their previous work, and develop ideas on how to modify their work behavior in order to achieve better future results. It is an important learning tool and a key factor in explaining the varying effectiveness of teams. Team reflexivity encompasses both self-awareness and agency, and includes three main activities: reflection, planning, and adaptation. The model of briefing-debriefing cycles promotes team reflexivity. Its key elements include: Pre-action briefing--setting objectives, roles, and strategies the mission, as well as proposing adaptations based on what was previously learnt from similar procedures; Post-action debriefing--reflecting on the procedure performed and reviewing the extent to which objectives were met, and what can be learnt for future tasks. Given the widespread attention to team-based work systems and organizational learning, efforts should be made toward ntroducing team reflexivity in health administration systems. Implementation could be difficult because most teams in hospitals are short-lived action teams formed for a particular event, with limited time and opportunity to consciously reflect upon their actions. But it is precisely in these contexts that reflexive processes have the most to offer instead of the natural impulsive collective logics. Team reflexivity suggests a potential solution to the major problems of iatorgenesis--avoidable medical errors, as it forces all team members to participate in a reflexive process together. Briefing-debriefing technology was studied mainly in surgical teams and was shown to enhance team-based learning and to improve quality-related outcomes and safety.
Collection Development Policies for the RWC Learning Resources Center.

ERIC Educational Resources Information Center

Wilson, Lucy, Comp.

This manual begins by providing background on the program, collection, and acquisition processes of the Raymond Walters College (RWC) Learning Resources Center. The next section describes collection development policies for: (1) the academic departments (Animal Health; Behavioral Sciences; Biology; Business and Economics; Chemistry; Dental…
Enhancing acronym/abbreviation knowledge bases with semantic information.

PubMed

Torii, Manabu; Liu, Hongfang

2007-10-11

In the biomedical domain, a terminology knowledge base that associates acronyms/abbreviations (denoted as SFs) with the definitions (denoted as LFs) is highly needed. For the construction such terminology knowledge base, we investigate the feasibility to build a system automatically assigning semantic categories to LFs extracted from text. Given a collection of pairs (SF,LF) derived from text, we i) assess the coverage of LFs and pairs (SF,LF) in the UMLS and justify the need of a semantic category assignment system; and ii) automatically derive name phrases annotated with semantic category and construct a system using machine learning. Utilizing ADAM, an existing collection of (SF,LF) pairs extracted from MEDLINE, our system achieved an f-measure of 87% when assigning eight UMLS-based semantic groups to LFs. The system has been incorporated into a web interface which integrates SF knowledge from multiple SF knowledge bases. Web site: http://gauss.dbb.georgetown.edu/liblab/SFThesurus.
ENT COBRA (Consortium for Brachytherapy Data Analysis): interdisciplinary standardized data collection system for head and neck patients treated with interventional radiotherapy (brachytherapy).

PubMed

Tagliaferri, Luca; Kovács, György; Autorino, Rosa; Budrukkar, Ashwini; Guinot, Jose Luis; Hildebrand, Guido; Johansson, Bengt; Monge, Rafael Martìnez; Meyer, Jens E; Niehoff, Peter; Rovirosa, Angeles; Takàcsi-Nagy, Zoltàn; Dinapoli, Nicola; Lanzotti, Vito; Damiani, Andrea; Soror, Tamer; Valentini, Vincenzo

2016-08-01

Aim of the COBRA (Consortium for Brachytherapy Data Analysis) project is to create a multicenter group (consortium) and a web-based system for standardized data collection. GEC-ESTRO (Groupe Européen de Curiethérapie - European Society for Radiotherapy & Oncology) Head and Neck (H&N) Working Group participated in the project and in the implementation of the consortium agreement, the ontology (data-set) and the necessary COBRA software services as well as the peer reviewing of the general anatomic site-specific COBRA protocol. The ontology was defined by a multicenter task-group. Eleven centers from 6 countries signed an agreement and the consortium approved the ontology. We identified 3 tiers for the data set: Registry (epidemiology analysis), Procedures (prediction models and DSS), and Research (radiomics). The COBRA-Storage System (C-SS) is not time-consuming as, thanks to the use of "brokers", data can be extracted directly from the single center's storage systems through a connection with "structured query language database" (SQL-DB), Microsoft Access(®), FileMaker Pro(®), or Microsoft Excel(®). The system is also structured to perform automatic archiving directly from the treatment planning system or afterloading machine. The architecture is based on the concept of "on-purpose data projection". The C-SS architecture is privacy protecting because it will never make visible data that could identify an individual patient. This C-SS can also benefit from the so called "distributed learning" approaches, in which data never leave the collecting institution, while learning algorithms and proposed predictive models are commonly shared. Setting up a consortium is a feasible and practicable tool in the creation of an international and multi-system data sharing system. COBRA C-SS seems to be well accepted by all involved parties, primarily because it does not influence the center's own data storing technologies, procedures, and habits. Furthermore, the method preserves the privacy of all patients.

NASA Electronic Library System (NELS): The system impact of security

NASA Technical Reports Server (NTRS)

Mcgregor, Terry L.

1993-01-01

This paper discusses security issues as they relate to the NASA Electronic Library System which is currently in use as the repository system for AdaNET System Version 3 (ASV3) being operated by MountainNET, Inc. NELS was originally designed to provide for public, development, and secure collections and objects. The secure feature for collections and objects was deferred in the initial system for implementation at a later date. The NELS system is now 9 months old and many lessons have been learned about the use and maintenance of library systems. MountainNET has 9 months of experience in operating the system and gathering feedback from the ASV3 user community. The user community has expressed an interest in seeing security features implemented in the current system. The time has come to take another look at the whole issue of security for the NELS system. Two requirements involving security have been put forth by MountainNET for the ASV3 system. The first is to incorporate at the collection level a security scheme to allow restricted access to collections. This should be invisible to end users and be controlled by librarians. The second is to allow inclusion of applications which can be executed only by a controlled group of users; for example, an application which can be executed by librarians only. The requirements provide a broad framework in which to work. These requirements raise more questions than answers. To explore the impact of these requirements a top down approach will be used.
A Digital Architecture for a Network-Based Learning Health System: Integrating Chronic Care Management, Quality Improvement, and Research.

PubMed

Marsolo, Keith; Margolis, Peter A; Forrest, Christopher B; Colletti, Richard B; Hutton, John J

2015-01-01

We collaborated with the ImproveCareNow Network to create a proof-of-concept architecture for a network-based Learning Health System. This collaboration involved transitioning an existing registry to one that is linked to the electronic health record (EHR), enabling a "data in once" strategy. We sought to automate a series of reports that support care improvement while also demonstrating the use of observational registry data for comparative effectiveness research. We worked with three leading EHR vendors to create EHR-based data collection forms. We automated many of ImproveCareNow's analytic reports and developed an application for storing protected health information and tracking patient consent. Finally, we deployed a cohort identification tool to support feasibility studies and hypothesis generation. There is ongoing uptake of the system. To date, 31 centers have adopted the EHR-based forms and 21 centers are uploading data to the registry. Usage of the automated reports remains high and investigators have used the cohort identification tools to respond to several clinical trial requests. The current process for creating EHR-based data collection forms requires groups to work individually with each vendor. A vendor-agnostic model would allow for more rapid uptake. We believe that interfacing network-based registries with the EHR would allow them to serve as a source of decision support. Additional standards are needed in order for this vision to be achieved, however. We have successfully implemented a proof-of-concept Learning Health System while providing a foundation on which others can build. We have also highlighted opportunities where sponsors could help accelerate progress.
78 FR 27192 - Information Collection; Submission for OMB Review, Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-09

... (ICR) entitled Learn and Serve Progress Report Information Collection for review and approval in... techniques or other forms of information technology, e.g., permitting electronic submissions of responses.... Description: CNCS is seeking approval of Learn and Serve Progress Report Information Collection which is used...
For Parents Particularly. Collecting Keeps Your Mind Busy!

ERIC Educational Resources Information Center

Newman, Rita

1995-01-01

Notes that the hobby of collecting is a way families can reinforce and expand a child's learning when not in school by encouraging development of classification, categorizing, and decision-making skills. Describes resources on and what can be learned by collecting rocks, seashells, cards, and stamps. (DR)
Explicating mathematical thinking in differential equations using a computer algebra system

NASA Astrophysics Data System (ADS)

Zeynivandnezhad, Fereshteh; Bates, Rachel

2018-07-01

The importance of developing students' mathematical thinking is frequently highlighted in literature regarding the teaching and learning of mathematics. Despite this importance, most curricula and instructional activities for undergraduate mathematics fail to bring the learner beyond the mathematics. The purpose of this study was to enhance students' mathematical thinking by implementing a computer algebra system and active learning pedagogical approaches. students' mathematical thinking processes were analyzed while completing specific differential equations tasks based on posed prompts and questions and Instrumental Genesis. Data were collected from 37 engineering students in a public Malaysian university. This study used the descriptive and interpretive qualitative research design to investigate the students' perspectives of emerging mathematical understanding and approaches to learning mathematics in an undergraduate differential equations course. Results of this study concluded that students used a variety of mathematical thinking processes in a non-sequential manner. Additionally, the outcomes provide justification for continued use of technologies such as computer algebra systems in undergraduate mathematics courses and the need for further studies to uncover the various processes students utilize to complete specific mathematical tasks.
Practice-based learning and improvement: a dream that can become a reality.

PubMed

Manning, Phil R

2003-01-01

Systematically enhancing learning from experience (practice-based learning) dominates the teachings of Sir William Osler and adult learning theorists such as Eduard Lindeman, Malcolm Knowles, and Cyril Houle. Because of time constraints, most physicians have not implemented methods that systematically facilitate learning from day-to-day work, but improvements in information technology offer the promise of making systematic practice-based learning practical. At least four ingredients need to be incorporated to significantly enhance learning from experience: a database that makes it possible to study individual practices; methods for supplying short, quick answers to questions while seeing patients; a reminder system to avoid errors of omission; and the opportunity to discuss practice data with colleagues. Great progress has been made, but significant barriers still must be overcome before a majority of physicians will participate. In particular, methods of data collection must be simplified, the delivery of point-of-care information and reminders must become more automatic, and physicians must develop skills to make the discussion of practice data acceptable, stimulating, and not unduly punitive.
A double closed loop to enhance the quality of life of Parkinson's Disease patients: REMPARK system.

PubMed

Samà, Albert; Pérez-López, Carlos; Rodríguez-Martín, Daniel; Moreno-Aróstegui, J Manuel; Rovira, Jordi; Ahlrichs, Claas; Castro, Rui; Cevada, João; Graça, Ricardo; Guimarães, Vânia; Pina, Bernardo; Counihan, Timothy; Lewy, Hadas; Annicchiarico, Roberta; Bayés, Angels; Rodríguez-Molinero, Alejandro; Cabestany, Joan

2014-01-01

This paper presents REMPARK system, a novel approach to deal with Parkinson's Disease (PD). REMPARK system comprises two closed loops of actuation onto PD. The first loop consists in a wearable system that, based on a belt-worn movement sensor, detects movement alterations that activate an auditory cueing system controlled by a smartphone in order to improve patient's gait. The belt-worn sensor analyzes patient's movement through real-time learning algorithms that were developed on the basis of a database previously collected from 93 PD patients. The second loop consists in disease management based on the data collected during long periods and that enables neurologists to tailor medication of their PD patients and follow the disease evolution. REMPARK system is going to be tested in 40 PD patients in Spain, Ireland, Italy and Israel. This paper describes the approach followed to obtain this system, its components, functionalities and trials in which the system will be validated.
Bat detective-Deep learning tools for bat acoustic signal detection.

PubMed

Mac Aodha, Oisin; Gibb, Rory; Barlow, Kate E; Browning, Ella; Firman, Michael; Freeman, Robin; Harder, Briana; Kinsey, Libby; Mead, Gary R; Newson, Stuart E; Pandourski, Ivan; Parsons, Stuart; Russ, Jon; Szodoray-Paradi, Abigel; Szodoray-Paradi, Farkas; Tilova, Elena; Girolami, Mark; Brostow, Gabriel; Jones, Kate E

2018-03-01

Passive acoustic sensing has emerged as a powerful tool for quantifying anthropogenic impacts on biodiversity, especially for echolocating bat species. To better assess bat population trends there is a critical need for accurate, reliable, and open source tools that allow the detection and classification of bat calls in large collections of audio recordings. The majority of existing tools are commercial or have focused on the species classification task, neglecting the important problem of first localizing echolocation calls in audio which is particularly problematic in noisy recordings. We developed a convolutional neural network based open-source pipeline for detecting ultrasonic, full-spectrum, search-phase calls produced by echolocating bats. Our deep learning algorithms were trained on full-spectrum ultrasonic audio collected along road-transects across Europe and labelled by citizen scientists from www.batdetective.org. When compared to other existing algorithms and commercial systems, we show significantly higher detection performance of search-phase echolocation calls with our test sets. As an example application, we ran our detection pipeline on bat monitoring data collected over five years from Jersey (UK), and compared results to a widely-used commercial system. Our detection pipeline can be used for the automatic detection and monitoring of bat populations, and further facilitates their use as indicator species on a large scale. Our proposed pipeline makes only a small number of bat specific design decisions, and with appropriate training data it could be applied to detecting other species in audio. A crucial novelty of our work is showing that with careful, non-trivial, design and implementation considerations, state-of-the-art deep learning methods can be used for accurate and efficient monitoring in audio.
Bat detective—Deep learning tools for bat acoustic signal detection

PubMed Central

Barlow, Kate E.; Firman, Michael; Freeman, Robin; Harder, Briana; Kinsey, Libby; Mead, Gary R.; Newson, Stuart E.; Pandourski, Ivan; Russ, Jon; Szodoray-Paradi, Abigel; Tilova, Elena; Girolami, Mark; Jones, Kate E.

2018-01-01

Passive acoustic sensing has emerged as a powerful tool for quantifying anthropogenic impacts on biodiversity, especially for echolocating bat species. To better assess bat population trends there is a critical need for accurate, reliable, and open source tools that allow the detection and classification of bat calls in large collections of audio recordings. The majority of existing tools are commercial or have focused on the species classification task, neglecting the important problem of first localizing echolocation calls in audio which is particularly problematic in noisy recordings. We developed a convolutional neural network based open-source pipeline for detecting ultrasonic, full-spectrum, search-phase calls produced by echolocating bats. Our deep learning algorithms were trained on full-spectrum ultrasonic audio collected along road-transects across Europe and labelled by citizen scientists from www.batdetective.org. When compared to other existing algorithms and commercial systems, we show significantly higher detection performance of search-phase echolocation calls with our test sets. As an example application, we ran our detection pipeline on bat monitoring data collected over five years from Jersey (UK), and compared results to a widely-used commercial system. Our detection pipeline can be used for the automatic detection and monitoring of bat populations, and further facilitates their use as indicator species on a large scale. Our proposed pipeline makes only a small number of bat specific design decisions, and with appropriate training data it could be applied to detecting other species in audio. A crucial novelty of our work is showing that with careful, non-trivial, design and implementation considerations, state-of-the-art deep learning methods can be used for accurate and efficient monitoring in audio. PMID:29518076
Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning.

PubMed

Norouzzadeh, Mohammad Sadegh; Nguyen, Anh; Kosmala, Margaret; Swanson, Alexandra; Palmer, Meredith S; Packer, Craig; Clune, Jeff

2018-06-19

Having accurate, detailed, and up-to-date information about the location and behavior of animals in the wild would improve our ability to study and conserve ecosystems. We investigate the ability to automatically, accurately, and inexpensively collect such data, which could help catalyze the transformation of many fields of ecology, wildlife biology, zoology, conservation biology, and animal behavior into "big data" sciences. Motion-sensor "camera traps" enable collecting wildlife pictures inexpensively, unobtrusively, and frequently. However, extracting information from these pictures remains an expensive, time-consuming, manual task. We demonstrate that such information can be automatically extracted by deep learning, a cutting-edge type of artificial intelligence. We train deep convolutional neural networks to identify, count, and describe the behaviors of 48 species in the 3.2 million-image Snapshot Serengeti dataset. Our deep neural networks automatically identify animals with >93.8% accuracy, and we expect that number to improve rapidly in years to come. More importantly, if our system classifies only images it is confident about, our system can automate animal identification for 99.3% of the data while still performing at the same 96.6% accuracy as that of crowdsourced teams of human volunteers, saving >8.4 y (i.e., >17,000 h at 40 h/wk) of human labeling effort on this 3.2 million-image dataset. Those efficiency gains highlight the importance of using deep neural networks to automate data extraction from camera-trap images, reducing a roadblock for this widely used technology. Our results suggest that deep learning could enable the inexpensive, unobtrusive, high-volume, and even real-time collection of a wealth of information about vast numbers of animals in the wild. Copyright © 2018 the Author(s). Published by PNAS.
A model of formative assessment practice in secondary science classrooms using an audience response system

NASA Astrophysics Data System (ADS)

Shirley, Melissa L.

Formative assessment involves the probing of students' ideas to determine their level of understanding during the instructional sequence. Often conceptualized as a cycle, formative assessment consists of the teacher posing an instructional task to students, collecting data about student understanding, and engaging in follow-up strategies such as clarifying student understanding and adjusting instruction to meet learning needs. Despite having been shown to increase student achievement in a variety of classroom settings, formative assessment remains a relative weak area of teacher practice. Methods that enhance formative assessment strategies may therefore have a positive effect on student achievement. Audience response systems comprise a broad category of technologies that support richer classroom interaction and have the potential to facilitate formative assessment. Results from a large national research study, Classroom Connectivity in Promoting Mathematics and Science Achievement (CCMS), show that students in algebra classrooms where the teacher has implemented a type of audience response system experience significantly higher achievement gains compared to a control group. This suggests a role for audience response systems in promoting rich formative assessment. The importance of incorporating formative assessment strategies into regular classroom practice is widely recognized. However, it remains challenging to identify whether rich formative assessment is occurring during a particular class session. This dissertation uses teacher interviews and classroom observations to develop a fine-grained model of formative assessment in secondary science classrooms employing a type of audience response system. This model can be used by researchers and practitioners to characterize components of formative assessment practice in classrooms. A major component of formative assessment practice is the collection and aggregation of evidence of student learning. This dissertation proposes the use of the assessment episode to characterize extended cycles of teacher-student interactions. Further, the model presented here provides a new methodology to describe the teacher's use of questioning and subsequent classroom discourse to uncover student learning. Additional components of this model of formative assessment focus on the recognition of student learning by the teacher and the resultant changes in instructional practice to enhance student understanding.
Computerized Hammer Sounding Interpretation for Concrete Assessment with Online Machine Learning.

PubMed

Ye, Jiaxing; Kobayashi, Takumi; Iwata, Masaya; Tsuda, Hiroshi; Murakawa, Masahiro

2018-03-09

Developing efficient Artificial Intelligence (AI)-enabled systems to substitute the human role in non-destructive testing is an emerging topic of considerable interest. In this study, we propose a novel hammering response analysis system using online machine learning, which aims at achieving near-human performance in assessment of concrete structures. Current computerized hammer sounding systems commonly employ lab-scale data to validate the models. In practice, however, the response signal patterns can be far more complicated due to varying geometric shapes and materials of structures. To deal with a large variety of unseen data, we propose a sequential treatment for response characterization. More specifically, the proposed system can adaptively update itself to approach human performance in hammering sounding data interpretation. To this end, a two-stage framework has been introduced, including feature extraction and the model updating scheme. Various state-of-the-art online learning algorithms have been reviewed and evaluated for the task. To conduct experimental validation, we collected 10,940 response instances from multiple inspection sites; each sample was annotated by human experts with healthy/defective condition labels. The results demonstrated that the proposed scheme achieved favorable assessment accuracy with high efficiency and low computation load.
Toward a science of learning systems: a research agenda for the high-functioning Learning Health System

PubMed Central

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

Objective 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. Materials and methods 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. Results 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. Conclusions 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. PMID:25342177
Participating in Authentic Science with the Aid of Learning Progressions through Mission Earth Workshops

NASA Astrophysics Data System (ADS)

Lewis, P. M., Jr.; Taylor, J.; Harte, T.; Czajkowski, K. P.

2016-12-01

"MISSION EARTH: Fusing GLOBE with NASA Assets to Build Systemic Innovation In STEM Education" is one of the new education cooperative agreements funded by the NASA Science Mission Directorate. Students will learn how to conduct "real science" through hands-on data collection using Global Learning and Observations to Benefit the Environment (GLOBE) protocols combined with other NASA science educational materials. This project aims to work with educators spanning the full K-12 range, requiring three grade bands of learning progressions and vertical alignment among materials and resources to best meet classroom needs. From K to 12 students have vastly different abilities to conduct and learn from scientific investigations. Hand-picked NASA assets will provide appropriate exposure across the curriculum and grade bands, and we are developing unique learning progressions that bring together GLOBE protocols for data collection and learning activities, NASA data sets through MY NASA DATA for data comparison, and more. The individual materials are not limited to science, but also include all elements of STEM with literacy components added in where appropriate. This will give the students an opportunity to work on better understanding the world around them in a well-rounded way, and offer cross-subject/classroom exposure to improve student understanding. To ensure that these learning progressions can continue to be used in the classroom in the future, alignment to the Next Generation Science Standards will help frame all of the materials and products. The learning progressions will be living documents that will change based on context. After several iterations, it is our goal to produce learning progressions for grades K-12 that will allow any STEM teacher to pick up and infuse NASA and GLOBE in their classroom at any location and at any time in their school year. This presentation will share results from the first year of development for this project.
Teaching & Learning in College: A Resource for Educators. Fourth Edition.

ERIC Educational Resources Information Center

Wheeler, Gary S., Ed.

This collection offers insights into the state of teaching and learning for graduate students and relatively new higher education faculty. The chapters in this resource collection are: (1) "The Role of Community in Learning: Making Connections for Your Classroom and Campus, Your Students and Colleagues" (Milton D. Cox); (2) "Diversity and New…
Self-directed Learning Favors Local, Rather than Global, Uncertainty

ERIC Educational Resources Information Center

Markant, Douglas B.; Settles, Burr; Gureckis, Todd M.

2016-01-01

Collecting (or "sampling") information that one expects to be useful is a powerful way to facilitate learning. However, relatively little is known about how people decide which information is worth sampling over the course of learning. We describe several alternative models of how people might decide to collect a piece of information…
Learning through Observations: The Potential of Collective Worship in Primary Schools

ERIC Educational Resources Information Center

Mogra, Imran

2017-01-01

This article reports the learning achieved by a group of trainee teachers about acts of collective worship (CW) organised in English primary schools. Using data gathered from non-participant observation questionnaires, it describes, from the viewpoint of observers, three main findings related to children and their learning, the position of CW in…
Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography

PubMed Central

Chan, Heang-Ping; Hadjiiski, Lubomir; Helvie, Mark A.; Wei, Jun; Cha, Kenny

2016-01-01

Purpose: Develop a computer-aided detection (CAD) system for masses in digital breast tomosynthesis (DBT) volume using a deep convolutional neural network (DCNN) with transfer learning from mammograms. Methods: A data set containing 2282 digitized film and digital mammograms and 324 DBT volumes were collected with IRB approval. The mass of interest on the images was marked by an experienced breast radiologist as reference standard. The data set was partitioned into a training set (2282 mammograms with 2461 masses and 230 DBT views with 228 masses) and an independent test set (94 DBT views with 89 masses). For DCNN training, the region of interest (ROI) containing the mass (true positive) was extracted from each image. False positive (FP) ROIs were identified at prescreening by their previously developed CAD systems. After data augmentation, a total of 45 072 mammographic ROIs and 37 450 DBT ROIs were obtained. Data normalization and reduction of non-uniformity in the ROIs across heterogeneous data was achieved using a background correction method applied to each ROI. A DCNN with four convolutional layers and three fully connected (FC) layers was first trained on the mammography data. Jittering and dropout techniques were used to reduce overfitting. After training with the mammographic ROIs, all weights in the first three convolutional layers were frozen, and only the last convolution layer and the FC layers were randomly initialized again and trained using the DBT training ROIs. The authors compared the performances of two CAD systems for mass detection in DBT: one used the DCNN-based approach and the other used their previously developed feature-based approach for FP reduction. The prescreening stage was identical in both systems, passing the same set of mass candidates to the FP reduction stage. For the feature-based CAD system, 3D clustering and active contour method was used for segmentation; morphological, gray level, and texture features were extracted and merged with a linear discriminant classifier to score the detected masses. For the DCNN-based CAD system, ROIs from five consecutive slices centered at each candidate were passed through the trained DCNN and a mass likelihood score was generated. The performances of the CAD systems were evaluated using free-response ROC curves and the performance difference was analyzed using a non-parametric method. Results: Before transfer learning, the DCNN trained only on mammograms with an AUC of 0.99 classified DBT masses with an AUC of 0.81 in the DBT training set. After transfer learning with DBT, the AUC improved to 0.90. For breast-based CAD detection in the test set, the sensitivity for the feature-based and the DCNN-based CAD systems was 83% and 91%, respectively, at 1 FP/DBT volume. The difference between the performances for the two systems was statistically significant (p-value < 0.05). Conclusions: The image patterns learned from the mammograms were transferred to the mass detection on DBT slices through the DCNN. This study demonstrated that large data sets collected from mammography are useful for developing new CAD systems for DBT, alleviating the problem and effort of collecting entirely new large data sets for the new modality. PMID:27908154
An Insight into E-Collections

ERIC Educational Resources Information Center

Albert, Angeline Sheba; Navaraj, A. Johnson

2006-01-01

The present paper gives a brief introduction about E-collections. It discusses the e-books, e-journals, utility, features, advantages and issues for the development of e-collections. E-books will offer a rich learning experience, reinforced with audio, video, 3D animation and collaborative learning tools. E-journals on the other hand are…
Interactive Multimedia-Based Animation: A Study of Effectiveness on Fashion Design Technology Learning

NASA Astrophysics Data System (ADS)

Wiana, W.

2018-01-01

The learning process is believed will reach optimal results if facilitated by diversity of learning’s device from aspects of the approach, method, media or it’s evaluation system, in individually, groups, or as well as classical. One of the learning’s Device can be developed in an attempt to improve the results of the study is Computer Based Learning (CBL). CBL was developed aim to help students to understand the concepts of the learning material which presented interactively by the system and able to provide information and learning process better. This research is closely related to efforts to improve the quality of Fashion design in digital format learning, with specific targets to generate interactive multimedia-based animation as effective media and learning resources for fashion design learning. Applications that are generated may be an option for delivering learning material as well as to engender interest in learning as well as understanding with students against the subject matter so that it can improve the learning achievements of students. The instruments used to collect data is a test sheet of mastering the concept which developed on the basis of indicators understanding the concept of fashion design, the material elements and principles of fashion design as well as application on making fashion design. As for the skills test is done through test performance to making fashion design in digital format. The results of testing against the mastery of concepts and skills of fashion designing in digital formatted shows that experimental group obtained significantly higher qualifications compared to the control group. That means that the use of interactive multimedia-based animation, effective to increased mastery of concepts and skills on making fashion design in digital format.

Some links on this page may take you to non-federal websites. Their policies may differ from this site.