Formation of community-based hypertension practice networks: success, obstacles, and lessons learned.

PubMed

Dart, Richard A; Egan, Brent M

2014-06-01

Community-based practice networks for research and improving the quality of care are growing in size and number but have variable success rates. In this paper, the authors review recent efforts to initiate a community-based hypertension network modeled after the successful Outpatient Quality Improvement Network (O'QUIN) project, located at the Medical University of South Carolina. Key lessons learned and new directions to be explored are highlighted. ©2014 Wiley Periodicals, Inc.

Professional Learning Networks Designed for Teacher Learning

ERIC Educational Resources Information Center

Trust, Torrey

2012-01-01

In the information age, students must learn to navigate and evaluate an expanding network of information. Highly effective teachers model this process of information analysis and knowledge acquisition by continually learning through collaboration, professional development, and studying pedagogical techniques and best practices. Many teachers have…

The Role of Networks of Practice and Webs of Influencers on Farmers' Engagement with and Learning about Agricultural Innovations

ERIC Educational Resources Information Center

Oreszczyn, Sue; Lane, Andy; Carr, Susan

2010-01-01

Drawing on the UK research project, "Farmers' understandings of GM crops within local communities", this paper considers the application of the concepts of communities of practice and networks of practice in the agricultural context. A brief review of theories about communities of practice and networks of practice is given and some of…

Improving the English-Speaking Skills of Young Learners through Mobile Social Networking

ERIC Educational Resources Information Center

Sun, Zhong; Lin, Chin-Hsi; You, Jiaxin; Shen, Hai jiao; Qi, Song; Luo, Liming

2017-01-01

Most students of English as a foreign language (EFL) lack sufficient opportunities to practice their English-speaking skills. However, the recent development of social-networking sites (SNSs) and mobile learning, and especially mobile-assisted language learning, represents new opportunities for these learners to practice speaking English in a…

Learning Communities for Curriculum Change: Key Factors in an Educational Change Process in New Zealand

ERIC Educational Resources Information Center

Edwards, Frances

2012-01-01

Increasingly school change processes are being facilitated through the formation and operation of groups of teachers working together for improved student outcomes. These groupings are variously referred to as networks, networked learning communities, communities of practice, professional learning communities, learning circles or clusters. The…

Practices and Strategies of Self-Initiated Language Learning in an Online Social Network Discussion Forum: A Descriptive Case Study

ERIC Educational Resources Information Center

Hsieh, Hsiu-Wei

2012-01-01

The proliferation of information and communication technologies and the prevalence of online social networks have facilitated the opportunities for informal learning of foreign languages. However, little educational research has been conducted on how individuals utilize those social networks to take part in self-initiated language learning without…

Networking in medical education: creating and connecting.

PubMed

Supe, Avinash N

2008-03-01

Social networking is being increasingly used as a tool of choice for communications and collaborations in business and higher education. Learning and practice become inseparable when professionals work in communities of practice that create interpersonal bonds and promote collective learning. Individual learning that arises from the critical reconstruction of practice, in the presence of peers and other health professionals, enhances a physician's capability of clinical judgment and evidence-based practice. As such, it would be wise for medical schools, whose responsibility it is to prepare students to make a transition to adult life with the skills they need to succeed in both arenas, to reckon with it.

Collaborative Academic Projects on Social Network Sites to Socialize EAP Students into Academic Communities of Practice

ERIC Educational Resources Information Center

Dashtestani, Reza

2018-01-01

Learning English for academic purposes (EAP) can help university students promote their academic literacy through socializing them into academic communities of practice. This study examined the impact of the use of collaborative projects on three social network sites on EAP students' attitudes towards EAP and academic content learning. Three…

To Enhance Collaborative Learning and Practice Network Knowledge with a Virtualization Laboratory and Online Synchronous Discussion

ERIC Educational Resources Information Center

Hwang, Wu-Yuin; Kongcharoen, Chaknarin; Ghinea, Gheorghita

2014-01-01

Recently, various computer networking courses have included additional laboratory classes in order to enhance students' learning achievement. However, these classes need to establish a suitable laboratory where each student can connect network devices to configure and test functions within different network topologies. In this case, the Linux…

Network Learning for Educational Change. Professional Learning

ERIC Educational Resources Information Center

Veugelers, Wiel, Ed.; O'Hair, Mary John, Ed.

2005-01-01

School-university networks are becoming an important method to enhance educational renewal and student achievement. Networks go beyond tensions of top-down versus bottom-up, school development and professional development of individuals, theory and practice, and formal and informal organizational structures. The theoretical base of networking…

Unlocking the Potential of Urban Communities: Case Studies of Twelve Learning Cities

ERIC Educational Resources Information Center

Valdés-Cotera, Raúl, Ed.; Longworth, Norman, Ed.; Lunardon, Katharina, Ed.; Wang, Mo, Ed.; Jo, Sunok, Ed.; Crowe, Sinéad, Ed.

2015-01-01

UNESCO established the UNESCO Global Network of Learning Cities (GNLC) to encourage the development of learning cities. By providing technical support, capacity development, and a platform where members can share ideas on policies and best practice, this international exchange network helps urban communities create thriving learning cities. The…

Expanding the Reach of Continuing Educational Offerings Through a Web-Based Virtual Network: The Experience of InspireNet.

PubMed

Frisch, Noreen C; Atherton, Pat; Borycki, Elizabeth M; Mickelson, Grace; Black, Agnes; Novak Lauscher, Helen; Cordeiro, Jennifer

2017-01-01

Virtual platforms using webinars, e-posters, e-newsletters, wikis and blogs connect people who have common interests in new ways. When those individuals are healthcare providers, a professional network that operates on a virtual platform can support their needs for learning, professional development and information currency. The practice of e-learning for continuing professional development is emerging , particularly in nursing where shift work shift inhibits their ability to attend conferences and classes. This article reports the experience of the InspireNet network that provided e-learning models to: 1) provide opportunities for healthcare providers to organize themselves into learning communities through development of electronic communities of practice; 2) support learning on demand; and 3) dramatically increase the reach of educational offerings.

Digital Doings: Curating Work-Learning Practices and Ecologies

ERIC Educational Resources Information Center

Thompson, Terrie Lynn

2016-01-01

Workers are faced with wider networks of knowledge generation amplified by the scale, diffusion, and critical mass of digital artefacts and web technologies globally. In this study of mobilities of work-learning practices, I draw on sociomaterial theorizing to explore how the work and everyday learning practices of self-employed workers or…

The ASE Improving Practical Work in Triple Science Learning Skills Network

ERIC Educational Resources Information Center

Barber, Paul; Chapman, Georgina; Ellis-Sackey, Cecilia; Grainger, Beth; Jones, Steve

2011-01-01

In July 2010, the Association for Science Education won a bid to run a "Sharing innovation network" for the Triple Science Support Programme, which is delivered by the Learning Skills Network on behalf of the Department for Education. The network involves schools from the London boroughs of Tower Hamlets and Greenwich. In this article,…

Networked Environments that Create Hybrid Spaces for Learning Science

ERIC Educational Resources Information Center

Otrel-Cass, Kathrin; Khoo, Elaine; Cowie, Bronwen

2014-01-01

Networked learning environments that embed the essence of the Community of Inquiry (CoI) framework utilise pedagogies that encourage dialogic practices. This can be of significance for classroom teaching across all curriculum areas. In science education, networked environments are thought to support student investigations of scientific problems,…

Paradoxes of Social Networking in a Structured Web 2.0 Language Learning Community

ERIC Educational Resources Information Center

Loiseau, Mathieu; Zourou, Katerina

2012-01-01

This paper critically inquires into social networking as a set of mechanisms and associated practices developed in a structured Web 2.0 language learning community. This type of community can be roughly described as learning spaces featuring (more or less) structured language learning resources displaying at least some notions of language learning…

Creating and Sustaining Inquiry Spaces for Teacher Learning and System Transformation

ERIC Educational Resources Information Center

Kaser, Linda; Halbert, Judy

2014-01-01

Over a 15-year period, one Western Canadian province, British Columbia, has been exploring the potential of inquiry learning networks to deepen teacher professional learning and to influence the system as a whole. During this time, we have learned a great deal about shifting practice through inquiry networks. In this article, we provide a…

Compensatory Motor Network Connectivity is Associated with Motor Sequence Learning after Subcortical Stroke

PubMed Central

Wadden, Katie P.; Woodward, Todd S.; Metzak, Paul D.; Lavigne, Katie M.; Lakhani, Bimal; Auriat, Angela M.; Boyd, Lara A.

2015-01-01

Following stroke, functional networks reorganize and the brain demonstrates widespread alterations in cortical activity. Implicit motor learning is preserved after stroke. However the manner in which brain reorganization occurs, and how it supports behaviour within the damaged brain remains unclear. In this functional magnetic resonance imaging (fMRI) study, we evaluated whole brain patterns of functional connectivity during the performance of an implicit tracking task at baseline and retention, following 5 days of practice. Following motor practice, a significant difference in connectivity within a motor network, consisting of bihemispheric activation of the sensory and motor cortices, parietal lobules, cerebellar and occipital lobules, was observed at retention. Healthy subjects demonstrated greater activity within this motor network during sequence learning compared to random practice. The stroke group did not show the same level of functional network integration, presumably due to the heterogeneity of functional reorganization following stroke. In a secondary analysis, a binary mask of the functional network activated from the aforementioned whole brain analyses was created to assess within-network connectivity, decreasing the spatial distribution and large variability of activation that exists within the lesioned brain. The stroke group demonstrated reduced clusters of connectivity within the masked brain regions as compared to the whole brain approach. Connectivity within this smaller motor network correlated with repeated sequence performance on the retention test. Increased functional integration within the motor network may be an important neurophysiological predictor of motor learning-related change in individuals with stroke. PMID:25757996

Systemwide Implementation of Project-Based Learning: The Philadelphia Approach

ERIC Educational Resources Information Center

Schwalm, Jason; Tylek, Karen Smuck

2012-01-01

Citywide implementation of project-based learning highlights the benefits--and the challenges--of promoting exemplary practices across an entire out-of-school time (OST) network. In summer 2009, the City of Philadelphia and its intermediary, the Public Health Management Corporation (PHMC), introduced project-based learning to a network of more…

Higher Education Scholars' Participation and Practices on Twitter

ERIC Educational Resources Information Center

Veletsianos, G.

2012-01-01

Scholars participate in online social networks for professional purposes. In such networks, learning takes the form of participation and identity formation through engagement in and contribution to networked practices. While current literature describes the possible benefits of online participation, empirical research on scholars' use of online…

Teacher Learning and the Development of Inclusive Practices and Policies: Framing and Context

ERIC Educational Resources Information Center

Howes, Andrew; Booth, Tony; Dyson, Alan; Frankham, Jo

2005-01-01

The process of a school becoming more inclusive involves teacher learning. The Economic and Social Research Council Teaching and Learning Research Programme (ESRC TLRP) research and development Network "Understanding and Developing Inclusive Practices in Schools" presented an opportunity both to understand the nature of this learning and…

Frameworks for Understanding the Nature of Interactions, Networking, and Community in a Social Networking Site for Academic Practice

ERIC Educational Resources Information Center

Conole, Grainne; Galley, Rebecca; Culver, Juliette

2011-01-01

This paper describes a new social networking site, Cloudworks, which has been developed to enable discussion and sharing of learning and teaching ideas/designs and to promote reflective academic practice. The site aims to foster new forms of social and participatory practices (peer critiquing, sharing, user-generated content, aggregation, and…

A Collaborative Learning Network Approach to Improvement: The CUSP Learning Network.

PubMed

Weaver, Sallie J; Lofthus, Jennifer; Sawyer, Melinda; Greer, Lee; Opett, Kristin; Reynolds, Catherine; Wyskiel, Rhonda; Peditto, Stephanie; Pronovost, Peter J

2015-04-01

Collaborative improvement networks draw on the science of collaborative organizational learning and communities of practice to facilitate peer-to-peer learning, coaching, and local adaption. Although significant improvements in patient safety and quality have been achieved through collaborative methods, insight regarding how collaborative networks are used by members is needed. Improvement Strategy: The Comprehensive Unit-based Safety Program (CUSP) Learning Network is a multi-institutional collaborative network that is designed to facilitate peer-to-peer learning and coaching specifically related to CUSP. Member organizations implement all or part of the CUSP methodology to improve organizational safety culture, patient safety, and care quality. Qualitative case studies developed by participating members examine the impact of network participation across three levels of analysis (unit, hospital, health system). In addition, results of a satisfaction survey designed to evaluate member experiences were collected to inform network development. Common themes across case studies suggest that members found value in collaborative learning and sharing strategies across organizational boundaries related to a specific improvement strategy. The CUSP Learning Network is an example of network-based collaborative learning in action. Although this learning network focuses on a particular improvement methodology-CUSP-there is clear potential for member-driven learning networks to grow around other methods or topic areas. Such collaborative learning networks may offer a way to develop an infrastructure for longer-term support of improvement efforts and to more quickly diffuse creative sustainment strategies.

Institutionalizing Community-Based Learning and Research: The Case for External Networks

ERIC Educational Resources Information Center

Shrader, Elizabeth; Saunders, Mary Anne; Marullo, Sam; Benatti, Sylvia; Weigert, Kathleen Maas

2008-01-01

Conversations continue as to whether and how community-based learning and research (CBLR) can be most effectively integrated into the mission and practice of institutions of higher education (IHEs). In 2005, eight District of Columbia- (DC-) area universities affiliated with the Community Research and Learning (CoRAL) Network engaged in a planning…

Neuroimaging studies of practice-related change: fMRI and meta-analytic evidence of a domain-general control network for learning.

PubMed

Chein, Jason M; Schneider, Walter

2005-12-01

Functional magnetic resonance imaging and a meta-analysis of prior neuroimaging studies were used to characterize cortical changes resulting from extensive practice and to evaluate a dual-processing account of the neural mechanisms underlying human learning. Three core predictions of the dual processing theory are evaluated: 1) that practice elicits generalized reductions in regional activity by reducing the load on the cognitive control mechanisms that scaffold early learning; 2) that these control mechanisms are domain-general; and 3) that no separate processing pathway emerges as skill develops. To evaluate these predictions, a meta-analysis of prior neuroimaging studies and a within-subjects fMRI experiment contrasting unpracticed to practiced performance in a paired-associate task were conducted. The principal effect of practice was found to be a reduction in the extent and magnitude of activity in a cortical network spanning bilateral dorsal prefrontal, left ventral prefrontal, medial frontal (anterior cingulate), left insular, bilateral parietal, and occipito-temporal (fusiform) areas. These activity reductions are shown to occur in common regions across prior neuroimaging studies and for both verbal and nonverbal paired-associate learning in the present fMRI experiment. The implicated network of brain regions is interpreted as a domain-general system engaged specifically to support novice, but not practiced, performance.

On the applicability of STDP-based learning mechanisms to spiking neuron network models

NASA Astrophysics Data System (ADS)

Sboev, A.; Vlasov, D.; Serenko, A.; Rybka, R.; Moloshnikov, I.

2016-11-01

The ways to creating practically effective method for spiking neuron networks learning, that would be appropriate for implementing in neuromorphic hardware and at the same time based on the biologically plausible plasticity rules, namely, on STDP, are discussed. The influence of the amount of correlation between input and output spike trains on the learnability by different STDP rules is evaluated. A usability of alternative combined learning schemes, involving artificial and spiking neuron models is demonstrated on the iris benchmark task and on the practical task of gender recognition.

Students' Framing of Language Learning Practices in Social Networking Sites

ERIC Educational Resources Information Center

Lantz-Andersson, Annika; Vigmo, Sylvi; Bowen, Rhonwen

2012-01-01

The amount of time that people, especially young people, spend on communicative activities in social media is rapidly increasing. We are facing new arenas with great potential for learning in general and for language learning in particular, but their impact on learning is not yet acknowledged as such in educational practice (e.g., Conole, 2010;…

Edmodo social learning network for elementary school mathematics learning

NASA Astrophysics Data System (ADS)

Ariani, Y.; Helsa, Y.; Ahmad, S.; Prahmana, RCI

2017-12-01

A developed instructional media can be as printed media, visual media, audio media, and multimedia. The development of instructional media can also take advantage of technological development by utilizing Edmodo social network. This research aims to develop a digital classroom learning model using Edmodo social learning network for elementary school mathematics learning which is practical, valid and effective in order to improve the quality of learning activities. The result of this research showed that the prototype of mathematics learning device for elementary school students using Edmodo was in good category. There were 72% of students passed the assessment as a result of Edmodo learning. Edmodo has become a promising way to engage students in a collaborative learning process.

Interconnecting Networks of Practice for Professional Learning

ERIC Educational Resources Information Center

Mackey, Julie; Evans, Terry

2011-01-01

The article explores the complementary connections between communities of practice and the ways in which individuals orchestrate their engagement with others to further their professional learning. It does so by reporting on part of a research project conducted in New Zealand on teachers' online professional learning in a university graduate…

(Re/Dis)assembling Learning Practices Online with Fluid Objects and Spaces

ERIC Educational Resources Information Center

Thompson, Terrie Lynn

2012-01-01

Actor network theory (ANT) is used to explore how work-learning is enacted in informal online communities and illustrates how researchers might use sociomaterial approaches to uncover complexities, uncertainties, and specificities of work-learning practices. Participants in this study were self-employed workers. The relational and material aspects…

Social learning within a community of practice: Investigating interactions about evaluation among zoo education professionals.

PubMed

Khalil, Kathayoon; Ardoin, Nicole M; Wojcik, Deborah

2017-04-01

The accessibility and ubiquity of zoos and aquariums-which reach over 700 million people worldwide annually-make them critical sites for science and environmental learning. Through educational offerings, these sites can generate excitement and curiosity about nature and motivate stewardship behavior, but only if their programs are high quality and meet the needs of their audiences. Evaluation is, therefore, critical: knowing what works, for whom, and under what conditions must be central to these organizations. Yet, many zoo and aquarium educators find evaluation to be daunting, and they are challenged to implement evaluations and/or use the findings iteratively in program development and improvement. This article examines how zoo education professionals engage with one another in a learning community related to evaluation. We use a communities of practice lens and social network analysis to understand the structure of this networked learning community, considering changes over time. Our findings suggest that individuals' roles in a networked learning community are influenced by factors such as communicative convenience and one's perceptions of others' evaluation expertise, which also contribute to forming and sustaining professional relationships. This study illuminates how project-based professional networks can become communities of practice. Copyright © 2016 Elsevier Ltd. All rights reserved.

Rethinking the learning of belief network probabilities

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

Musick, R.

Belief networks are a powerful tool for knowledge discovery that provide concise, understandable probabilistic models of data. There are methods grounded in probability theory to incrementally update the relationships described by the belief network when new information is seen, to perform complex inferences over any set of variables in the data, to incorporate domain expertise and prior knowledge into the model, and to automatically learn the model from data. This paper concentrates on part of the belief network induction problem, that of learning the quantitative structure (the conditional probabilities), given the qualitative structure. In particular, the current practice of rotemore » learning the probabilities in belief networks can be significantly improved upon. We advance the idea of applying any learning algorithm to the task of conditional probability learning in belief networks, discuss potential benefits, and show results of applying neutral networks and other algorithms to a medium sized car insurance belief network. The results demonstrate from 10 to 100% improvements in model error rates over the current approaches.« less

Disruptive innovation, labor markets, and Big Valley STEM School: network analysis in STEM education

NASA Astrophysics Data System (ADS)

Ellison, Scott; Allen, Ben

2018-03-01

A defining characteristic of contemporary trends in global education policy is the promotion of STEM learning in the primary, secondary, and tertiary sectors of education as a means to generate innovation and prosperity in the economy. Intertwined with common sensical assumptions about future labor markets and the transformative potential of technology in education, STEM has become a hegemonic discourse informing policy formation and educational practice. In Gramscian terms, the struggle over STEM as a discursive practice, between proponents of instrumental learning of marketable economic skills and those of education towards humanistic goals, reveals insights about the ideological characteristics of the push for STEM learning. This article explores the power dynamics behind the push for STEM learning as an ideological discourse propagated by global networks of elite policy actors and enacted by non-elite policy actors at the school level. The findings point toward a disjuncture between the discourse of elite policy actors in the US, the realities of STEM labor markets, and the actualization of this policy discourse into classroom practice. The implications of this study indicate that analyses of vertical power relations in network governance in STEM education should attend to the semiotics, materiality, and mutability of networked spaces.

GP Networks as enablers of quality of care: implementing a practice engagement framework in a General Practice Network.

PubMed

Pearce, Christopher; Shearer, Marianne; Gardner, Karina; Kelly, Jill; Xu, Tony Baixian

2012-01-01

This paper describes how the Melbourne East General Practice Network supports general practice to enable quality of care, it describes the challenges and enablers of change, and the evidence of practice capacity building and improved quality of care. Primary care is well known as a place where quality, relatively inexpensive medical care occurs. General practice is made up of multiple small sites with fragmented systems and a funding system that challenges a whole-of-practice approach to clinical care. General Practice Networks support GPs to synthesise complexity and crystallise solutions that enhance general practice beyond current capacity. Through a culture of change management, GP Networks create the link between the practice and the big picture of the whole health system and reduce the isolation of general practice. They distribute information (evidence-based learning and resources) and provide individualised support, responding to practice need and capacity.

Patients, practices, and relationships: challenges and lessons learned from the Kentucky Ambulatory Network (KAN) CaRESS clinical trial.

PubMed

Love, Margaret M; Pearce, Kevin A; Williamson, M Ann; Barron, Mary A; Shelton, Brent J

2006-01-01

The Cardiovascular Risk Education and Social Support (CaRESS) study is a randomized controlled trial that evaluates a social support intervention toward reducing cardiovascular risk in type 2 diabetic patients. It involves multiple community-based practice sites from the Kentucky Ambulatory Network (KAN), which is a regional primary care practice-based research network (PBRN). CaRESS also implements multiple modes of data collection. The purpose of this methods article is to share lessons learned that might be useful to others developing or implementing complex studies that consent patients in PBRNs. Key points include building long-term relationships with the clinicians, adaptability when integrating into practice sites, adequate funding to support consistent data management and statistical support during all phases of the study, and creativity and perseverance for recruiting patients and practices while maintaining the integrity of the protocol.

Best Practices in Service Learning: Building a National Community College Network, 1994-1997. AACC Project Brief.

ERIC Educational Resources Information Center

Robinson, Gail; Barnett, Lynn

As part of the Learn and Serve America Program of the Corporation for National Service, the American Association of Community Colleges (AACC) has helped develop campus-based programs that have instigated a growing community college service learning network. Ten colleges, selected in a national competition for grants ranging from $2,000 to $12,000…

Implications of Social Network Sites for Teaching and Learning. Where We Are and Where We Want to Go

ERIC Educational Resources Information Center

Manca, Stefania; Ranieri, Maria

2017-01-01

This conceptual paper deals with some of the implications that the use of social network sites, though not originally developed and conceived for learning purposes, have for schools and academic activities when they are used as tools able to modify and innovate teaching/learning practices and academic culture. Beside the differences that…

Does a University Teacher Need to Change e-Learning Beliefs and Practices When Using a Social Networking Site? A Longitudinal Case Study

ERIC Educational Resources Information Center

Scott, Karen M.

2013-01-01

While much of the e-learning development at universities in the past 15 years has been on institutionally supported Learning Management Systems (LMSs), alternative educational technologies are being taken up following the rapid growth in emerging technologies, including social networking sites (SNSs). While teachers may choose educational…

"Development Radar": The Co-Configuration of a Tool in a Learning Network

ERIC Educational Resources Information Center

Toiviainen, Hanna; Kerosuo, Hannele; Syrjala, Tuula

2009-01-01

Purpose: The paper aims to argue that new tools are needed for operating, developing and learning in work-life networks where academic and practice knowledge are intertwined in multiple levels of and in boundary-crossing across activities. At best, tools for learning are designed in a process of co-configuration, as the analysis of one tool,…

Educational Design and Networked Learning: Patterns, Pattern Languages and Design Practice

ERIC Educational Resources Information Center

Goodyear, Peter

2005-01-01

There is a growing demand for advice about effective, time efficient ways of using ICT to support student learning in higher education. This paper uses one such area of activity--networked learning--as a context in which to outline a novel approach to educational design. The paper makes two main contributions. It provides a high level view of the…

Fixed Point Learning Based Intelligent Traffic Control System

NASA Astrophysics Data System (ADS)

Zongyao, Wang; Cong, Sui; Cheng, Shao

2017-10-01

Fixed point learning has become an important tool to analyse large scale distributed system such as urban traffic network. This paper presents a fixed point learning based intelligence traffic network control system. The system applies convergence property of fixed point theorem to optimize the traffic flow density. The intelligence traffic control system achieves maximum road resources usage by averaging traffic flow density among the traffic network. The intelligence traffic network control system is built based on decentralized structure and intelligence cooperation. No central control is needed to manage the system. The proposed system is simple, effective and feasible for practical use. The performance of the system is tested via theoretical proof and simulations. The results demonstrate that the system can effectively solve the traffic congestion problem and increase the vehicles average speed. It also proves that the system is flexible, reliable and feasible for practical use.

SISL and SIRL: Two knowledge dissemination models with leader nodes on cooperative learning networks

NASA Astrophysics Data System (ADS)

Li, Jingjing; Zhang, Yumei; Man, Jiayu; Zhou, Yun; Wu, Xiaojun

2017-02-01

Cooperative learning is one of the most effective teaching methods, which has been widely used. Students' mutual contact forms a cooperative learning network in this process. Our previous research demonstrated that the cooperative learning network has complex characteristics. This study aims to investigating the dynamic spreading process of the knowledge in the cooperative learning network and the inspiration of leaders in this process. To this end, complex network transmission dynamics theory is utilized to construct the knowledge dissemination model of a cooperative learning network. Based on the existing epidemic models, we propose a new susceptible-infected-susceptible-leader (SISL) model that considers both students' forgetting and leaders' inspiration, and a susceptible-infected-removed-leader (SIRL) model that considers students' interest in spreading and leaders' inspiration. The spreading threshold λcand its impact factors are analyzed. Then, numerical simulation and analysis are delivered to reveal the dynamic transmission mechanism of knowledge and leaders' role. This work is of great significance to cooperative learning theory and teaching practice. It also enriches the theory of complex network transmission dynamics.

Understanding the Context of Learning in an Online Social Network for Health Professionals' Informal Learning.

PubMed

Li, Xin; Gray, Kathleen; Verspoor, Karin; Barnett, Stephen

2017-01-01

Online social networks (OSN) enable health professionals to learn informally, for example by sharing medical knowledge, or discussing practice management challenges and clinical issues. Understanding the learning context in OSN is necessary to get a complete picture of the learning process, in order to better support this type of learning. This study proposes critical contextual factors for understanding the learning context in OSN for health professionals, and demonstrates how these contextual factors can be used to analyse the learning context in a designated online learning environment for health professionals.

Social Networking and Democratic Practices as Spheres for Innovative Musical Learning

ERIC Educational Resources Information Center

Thorgersen, Cecilia Ferm; Georgii-Hemming, Eva

2012-01-01

This chapter takes into account and discusses innovative learning in the 21st digital and communicative century based on life-world-phenomenology and Hannah Arendt's view of democracy. From this point of view, the authors address and discuss how democratic practices can offer innovative musical learning in relation to what is taking place in…

Integration and segregation of large-scale brain networks during short-term task automatization

PubMed Central

Mohr, Holger; Wolfensteller, Uta; Betzel, Richard F.; Mišić, Bratislav; Sporns, Olaf; Richiardi, Jonas; Ruge, Hannes

2016-01-01

The human brain is organized into large-scale functional networks that can flexibly reconfigure their connectivity patterns, supporting both rapid adaptive control and long-term learning processes. However, it has remained unclear how short-term network dynamics support the rapid transformation of instructions into fluent behaviour. Comparing fMRI data of a learning sample (N=70) with a control sample (N=67), we find that increasingly efficient task processing during short-term practice is associated with a reorganization of large-scale network interactions. Practice-related efficiency gains are facilitated by enhanced coupling between the cingulo-opercular network and the dorsal attention network. Simultaneously, short-term task automatization is accompanied by decreasing activation of the fronto-parietal network, indicating a release of high-level cognitive control, and a segregation of the default mode network from task-related networks. These findings suggest that short-term task automatization is enabled by the brain's ability to rapidly reconfigure its large-scale network organization involving complementary integration and segregation processes. PMID:27808095

Enhanced Learning through Electronic Communities: A Research Review.

ERIC Educational Resources Information Center

Burgstahler, Sheryl; Swift, Catherine

This report, in support of the project "Enhanced Learning through Electronic Communities," investigated successful practices of electronic communities. A literature review was conducted and a survey was sent to 15 system operators of networks that had a community-based focus with ancillary educational components and networks that focused primarily…

Recognition of Tacit Skills: Sustaining Learning Outcomes in Adult Learning and Work Re-Entry

ERIC Educational Resources Information Center

Evans, Karen; Kersh, Natasha; Kontiainen, Seppo

2004-01-01

This paper is based on the project "Recognition of Tacit Skills and Knowledge in Work Re-entry" carried out as a part of the ESRC-funded Research Network "Improving Incentives to Learning in the Workplace". The network aims to contribute to improved practice among a wide range of practitioners. The study has investigated the part played by tacit…

Grower networks support adoption of innovations in pollination management: The roles of social learning, technical learning, and personal experience.

PubMed

Garbach, Kelly; Morgan, Geoffrey P

2017-12-15

Management decisions underpinning availability of ecosystem services and the organisms that provide them in agroecosystems, such as pollinators and pollination services, have emerged as a foremost consideration for both conservation and crop production goals. There is growing evidence that innovative management practices can support diverse pollinators and increase crop pollination. However, there is also considerable debate regarding factors that support adoption of these innovative practices. This study investigated pollination management practices and related knowledge systems in a major crop producing region of southwest Michigan in the United States, where 367 growers were surveyed to evaluate adoption of three innovative practices that are at various stages of adoption. The goals of this quantitative, social survey were to investigate grower experience with concerns and benefits associated with each practice, as well as the influence of grower networks, which are comprised of contacts that reflect potential pathways for social and technical learning. The results demonstrated that 17% of growers adopted combinations of bees (e.g. honey bees, Apis mellifera, with other species), representing an innovation in use by early adopters; 49% of growers adopted flowering cover crops, an innovation in use by the early majority 55% of growers retained permanent habitat for pollinators, an innovation in use by the late majority. Not all growers adopted innovative practices. We found that growers' personal experience with potential benefits and concerns related to the management practices had significant positive and negative relationships, respectively, with adoption of all three innovations. The influence of these communication links likely has different levels of importance, depending on the stage of the adoption that a practice is experiencing in the agricultural community. Social learning was positively associated with adopting the use of combinations of bees, highlighting the potentially critical roles of peer-to-peer networks and social learning in supporting early stages of adoption of innovations. Engaging with grower networks and understanding grower experience with benefits and concerns associated with innovative practices is needed to inform outreach, extension, and policy efforts designed to stimulate management innovations in agroecosystems. Copyright © 2017 Elsevier Ltd. All rights reserved.

The networked student: A design-based research case study of student constructed personal learning environments in a middle school science course

NASA Astrophysics Data System (ADS)

Drexler, Wendy

This design-based research case study applied a networked learning approach to a seventh grade science class at a public school in the southeastern United States. Students adapted emerging Web applications to construct personal learning environments for in-depth scientific inquiry of poisonous and venomous life forms. The personal learning environments constructed used Application Programming Interface (API) widgets to access, organize, and synthesize content from a number of educational Internet resources and social network connections. This study examined the nature of personal learning environments; the processes students go through during construction, and patterns that emerged. The project was documented from both an instructional and student-design perspective. Findings revealed that students applied the processes of: practicing digital responsibility; practicing digital literacy; organizing content; collaborating and socializing; and synthesizing and creating. These processes informed a model of the networked student that will serve as a framework for future instructional designs. A networked learning approach that incorporates these processes into future designs has implications for student learning, teacher roles, professional development, administrative policies, and delivery. This work is significant in that it shifts the focus from technology innovations based on tools to student empowerment based on the processes required to support learning. It affirms the need for greater attention to digital literacy and responsibility in K12 schools as well as consideration for those skills students will need to achieve success in the 21st century. The design-based research case study provides a set of design principles for teachers to follow when facilitating student construction of personal learning environments.

A Case Study of the Implementation of Social Models of Teaching in E-Learning: "The Social Networks in Education", Online Course of the Inter-Orthodox Centre of the Church of Greece

ERIC Educational Resources Information Center

Komninou, Ioanna

2018-01-01

The development of e-learning has caused a growing interest in learning models that may have the best results. We believe that it is good practice to implement social learning models in the field of online education. In this case, the implementation of complex instruction in online training courses for teachers, on "Social Networks in…

Making practice transparent through e-portfolio.

PubMed

Stewart, Sarah M

2013-12-01

Midwives are required to maintain a professional portfolio as part of their statutory requirements. Some midwives are using open social networking tools and processes to develop an e-portfolio. However, confidentiality of patient and client data and professional reputation have to be taken into consideration when using online public spaces for reflection. There is little evidence about how midwives use social networking tools for ongoing learning. It is uncertain how reflecting in an e-portfolio with an audience impacts on learning outcomes. This paper investigates ways in which reflective midwifery practice be carried out using e-portfolio in open, social networking platforms using collaborative processes. Using an auto-ethnographic approach I explored my e-portfolio and selected posts that had attracted six or more comments. I used thematic analysis to identify themes within the textual conversations in the posts and responses posted by readers. The analysis identified that my collaborative e-portfolio had four themes: to provide commentary and discuss issues; to reflect and process learning; to seek advice, brainstorm and process ideas for practice, projects and research, and provide evidence of professional development. E-portfolio using open social networking tools and processes is a viable option for midwives because it facilitates collaborative reflection and shared learning. However, my experience shows that concerns about what people think, and client confidentiality does impact on the nature of open reflection and learning outcomes. I conclude this paper with a framework for managing midwifery statutory obligations using online public spaces and social networking tools. Copyright © 2013 Australian College of Midwives. Published by Elsevier Ltd. All rights reserved.

Learning Leaders Leading for Learning: A Cross-Case Analysis of How Participation in an Instructional Rounds Network Shapes Superintendents' Instructional Leadership Practices

ERIC Educational Resources Information Center

Schiavino-Narvaez, Beth

2012-01-01

The leadership practice of superintendents spans three domains: instructional, managerial, and political (Johnson, 1996; Cuban, 1998; Nestor-Baker and Hoy, 2001; Lashaway, 2002). Despite the fact that superintendents lead organizations whose main business is teaching and learning, they spend most of their time in the political and managerial…

Postcolonial Practices for a Global Virtual Group: The Case of the International Network for Learning and Teaching Geography in Higher Education (INLT)

ERIC Educational Resources Information Center

Hay, Iain

2008-01-01

This paper offers a critical review of the role of the International Network for Learning and Teaching geography in higher education (INLT) in the production of geographical knowledge. Through an examination of the Network's membership and activities, it explores some of the ways in which INLT--as a global virtual group--may be inadvertently…

Contextual Language Learning: Educational Potential and Use of Social Networking Technology in Higher Education

ERIC Educational Resources Information Center

Huang, Chung-Kai; Lin, Chun-Yu; Villarreal, Daniel Steve

2014-01-01

This study investigates the potential and use of social networking technology, specifically Facebook, to support a community of practice in an undergraduate-level classroom setting. Facebook is used as a tool with which to provide supplementary language learning materials to develop learners' English writing skills. We adopted the technology…

Information Resources Usage in Project Management Digital Learning System

ERIC Educational Resources Information Center

Davidovitch, Nitza; Belichenko, Margarita; Kravchenko, Yurii

2017-01-01

The article combines a theoretical approach to structuring knowledge that is based on the integrated use of fuzzy semantic network theory predicates, Boolean functions, theory of complexity of network structures and some practical aspects to be considered in the distance learning at the university. The paper proposes a methodological approach that…

On Location Learning: Authentic Applied Science with Networked Augmented Realities

ERIC Educational Resources Information Center

Rosenbaum, Eric; Klopfer, Eric; Perry, Judy

2007-01-01

The learning of science can be made more like the practice of science through authentic simulated experiences. We have created a networked handheld Augmented Reality environment that combines the authentic role-playing of Augmented Realities and the underlying models of Participatory Simulations. This game, known as Outbreak @ The Institute, is…

Social Partnerships: Practices, Paradoxes and Prospects of Local Learning Networks

ERIC Educational Resources Information Center

Seddon, Terri; Clemans, Allie; Billett, Stephen

2005-01-01

This paper discusses the formation, character and contradictions of social partnerships. We report on a specific initiative, the Local Learning and Employment Networks (LLEN) established by the Victorian Government in Australia in 2001, documenting the nature of this initiative and how it is playing out. We draw attention to some of the tensions…

A European Languages Virtual Network Proposal

NASA Astrophysics Data System (ADS)

García-Peñalvo, Francisco José; González-González, Juan Carlos; Murray, Maria

ELVIN (European Languages Virtual Network) is a European Union (EU) Lifelong Learning Programme Project aimed at creating an informal social network to support and facilitate language learning. The ELVIN project aims to research and develop the connection between social networks, professional profiles and language learning in an informal educational context. At the core of the ELVIN project, there will be a web 2.0 social networking platform that connects employees/students for language practice based on their own professional/academic needs and abilities, using all relevant technologies. The ELVIN remit involves the examination of both methodological and technological issues inherent in achieving a social-based learning platform that provides the user with their own customized Personal Learning Environment for EU language acquisition. ELVIN started in November 2009 and this paper presents the project aims and objectives as well as the development and implementation of the web platform.

Military Interoperable Digital Hospital Testbed (MIDHT)

DTIC Science & Technology

2010-07-01

solutions to optimize healthcare resources for rural communities and identify lessons learned and best practices that benefit both the global MHS...providers and three CHS facilities on their business practices and process flows. Research initiatives will focus on the impact of an electronic...strategic goals and the Nationwide Health Information Network (NHIN). The MIDHT will continue to identify lessons learned/best practices that benefit

The Role of Networked Learning in Academics' Writing

ERIC Educational Resources Information Center

McCulloch, Sharon; Tusting, Karin; Hamilton, Mary

2017-01-01

This article explores academics' writing practices, focusing on the ways in which they use digital platforms in their processes of collaborative learning. It draws on interview data from a research project that has involved working closely with academics across different disciplines and institutions to explore their writing practices,…

Networked Learning and Network Science: Potential Applications to Health Professionals' Continuing Education and Development.

PubMed

Margolis, Alvaro; Parboosingh, John

2015-01-01

Prior interpersonal relationships and interactivity among members of professional associations may impact the learning process in continuing medical education (CME). On the other hand, CME programs that encourage interactivity between participants may impact structures and behaviors in these professional associations. With the advent of information and communication technologies, new communication spaces have emerged that have the potential to enhance networked learning in national and international professional associations and increase the effectiveness of CME for health professionals. In this article, network science, based on the application of network theory and other theories, is proposed as an approach to better understand the contribution networking and interactivity between health professionals in professional communities make to their learning and adoption of new practices over time. © 2015 The Alliance for Continuing Education in the Health Professions, the Society for Academic Continuing Medical Education, and the Council on Continuing Medical Education, Association for Hospital Medical Education.

Tracking Plasticity: Effects of Long-Term Rehearsal in Expert Dancers Encoding Music to Movement

PubMed Central

Bar, Rachel J.; DeSouza, Joseph F. X.

2016-01-01

Our knowledge of neural plasticity suggests that neural networks show adaptation to environmental and intrinsic change. In particular, studies investigating the neuroplastic changes associated with learning and practicing motor tasks have shown that practicing such tasks results in an increase in neural activation in several specific brain regions. However, studies comparing experts and non-experts suggest that experts employ less neuronal activation than non-experts when performing a familiar motor task. Here, we aimed to determine the long-term changes in neural networks associated with learning a new dance in professional ballet dancers over 34 weeks. Subjects visualized dance movements to music while undergoing fMRI scanning at four time points over 34-weeks. Results demonstrated that initial learning and performance at seven weeks led to increases in activation in cortical regions during visualization compared to the first week. However, at 34 weeks, the cortical networks showed reduced activation compared to week seven. Specifically, motor learning and performance over the 34 weeks showed the typical inverted-U-shaped function of learning. Further, our result demonstrate that learning of a motor sequence of dance movements to music in the real world can be visualized by expert dancers using fMRI and capture highly significant modeled fits of the brain network variance of BOLD signals from early learning to expert level performance. PMID:26824475

Remix as Professional Learning: Educators' Iterative Literacy Practice in CLMOOC

ERIC Educational Resources Information Center

Smith, Anna; West-Puckett, Stephanie; Cantrill, Christina; Zamora, Mia

2016-01-01

The Connected Learning Massive Open Online Collaboration (CLMOOC) is an online professional development experience designed as an openly networked, production-centered, participatory learning collaboration for educators. Addressing the paucity of research that investigates learning processes in MOOC experiences, this paper examines the situated…

Netlearning and Learning through Networks

ERIC Educational Resources Information Center

Wiberg, Mikael

2007-01-01

Traditional non-computerized learning environments are typically founded on an understanding of learning as acquiring silence for an effective "individual learning process". Recently, it has also been reported that the high expectations for the impact of computer-based technology on educational practice have not been realized. This paper…

Enhancing Use of Learning Sciences Research in Planning for and Supporting Educational Change: Leveraging and Building Social Networks

ERIC Educational Resources Information Center

Penuel, William R.; Bell, Philip; Bevan, Bronwyn; Buffington, Pam; Falk, Joni

2016-01-01

This paper explores practical ways to engage two areas of educational scholarship--research on science learning and research on social networks--to inform efforts to plan and support implementation of new standards. The standards, the "Next Generation Science Standards" (NGSS; NGSS Lead States in Next generation science standards: For…

Models of Community Learning Networks in Canada = Modeles de reseaux d'apprentissage communautaires au Canada.

ERIC Educational Resources Information Center

Human Resources Development Canada, Hull (Quebec). Office of Learning Technologies.

Canada-based community learning networks (CLNs) were examined to provide an operational definition of CLNs, design a framework for their review and analysis, and identify best practices in CLNs. Data were collected from three sources: interviews with 16 key stakeholders in CLNs, literature review, and case studies of five Canadian CLNs. The…

Examining Digital Literacy Practices on Social Network Sites

ERIC Educational Resources Information Center

Buck, Amber

2012-01-01

Young adults represent the most avid users of social network sites, and they are also the most concerned with their online identity management, according to the Pew Internet and American Life Project. These practices represent important literate activity today, as individuals who are writing online learn to negotiate interfaces, user agreements,…

Learning to teach in a coteaching community of practice

NASA Astrophysics Data System (ADS)

Gallo-Fox, Jennifer

2009-12-01

As a result of the standards and accountability reforms of the past two decades, heightened attention has been focused upon student learning in the K-12 classrooms, classroom teacher practice, and teacher preparation. This has led to the acknowledgement of limitations of traditional field practicum and that these learning experiences are not well understood (Bullough et al., 2003; Clift & Brady, 2005). Alternative models for student teaching, including those that foster social learning experiences, have been developed. However, research is necessary to understand the implications of these models for preservice teacher learning. Drawing on sociocultural theoretical frameworks and ethnographic perspectives (Gee and Green, 1998), this qualitative research study examined the learning experiences of a cohort of eight undergraduate preservice secondary science teachers who cotaught with eight cooperating teachers for their full practicum semester. In this model, interns planned and taught alongside multiple cooperating teachers and other interns. This study centers on the social and cultural learning that occurred within this networked model and the ways that the interns developed as high school science teachers within a coteaching community of practice (Wenger, 1998). This study utilized the following data sources: Intern and cooperating teachers interviews, field observations, meeting recordings, and program documentation. Analysis focused on community and interpersonal planes of development (Rogoff, 1995) in order understand of the nature of the learning experiences and the learning that was afforded through participant interactions. Several conclusions were made after the data were analyzed. On a daily basis, the interns participated in a wide range of cultural practices and in the activities of the community. The coteaching model challenged the idiosyncratic nature of traditional student teaching models by creating opportunities to learn across various classroom contexts. In different classrooms, there were markedly different constructions of teacher practice and participant roles. The implementation of the coteaching model also resulted in the creation of an interconnected network of colleagues. In the resulting learning community, coteachers supported one another's developing practice and critically examined their shared practice.

Does Gender Matter? Collaborative Learning in a Virtual Corporate Community of Practice

ERIC Educational Resources Information Center

Tomcsik, Rachel E.

2010-01-01

The purpose of this study was to investigate how gender identity construction in virtuality and actuality affect collaborative learning in a corporate community of practice. As part of a virtual ethnographic design, participants were employees from a major American corporation who were interested specifically in social networking applications. The…

Facilitative Components of Collaborative Learning: A Review of Nine Health Research Networks.

PubMed

Leroy, Lisa; Rittner, Jessica Levin; Johnson, Karin E; Gerteis, Jessie; Miller, Therese

2017-02-01

Collaborative research networks are increasingly used as an effective mechanism for accelerating knowledge transfer into policy and practice. This paper explored the characteristics and collaborative learning approaches of nine health research networks. Semi-structured interviews with representatives from eight diverse US health services research networks conducted between November 2012 and January 2013 and program evaluation data from a ninth. The qualitative analysis assessed each network's purpose, duration, funding sources, governance structure, methods used to foster collaboration, and barriers and facilitators to collaborative learning. The authors reviewed detailed notes from the interviews to distill salient themes. Face-to-face meetings, intentional facilitation and communication, shared vision, trust among members and willingness to work together were key facilitators of collaborative learning. Competing priorities for members, limited funding and lack of long-term support and geographic dispersion were the main barriers to coordination and collaboration across research network members. The findings illustrate the importance of collaborative learning in research networks and the challenges to evaluating the success of research network functionality. Conducting readiness assessments and developing process and outcome evaluation metrics will advance the design and show the impact of collaborative research networks. Copyright © 2017 Longwoods Publishing.

Learning by Doing Approach in the Internet Environment to Improve the Teaching Efficiency of Information Technology

NASA Astrophysics Data System (ADS)

Zhang, X.-S.; Xie, Hua

This paper presents a learning-by-doing method in the Internet environment to enhance the results of information technology education by experimental work in the classroom of colleges. In this research, an practical approach to apply the "learning by doing" paradigm in Internet-based learning, both for higher educational environments and life-long training systems, taking into account available computer and network resources, such as blogging, podcasting, social networks, wiki etc. We first introduce the different phases in the learning process, which aimed at showing to the readers that the importance of the learning by doing paradigm, which is not implemented in many Internet-based educational environments. Secondly, we give the concept of learning by doing in the different perfective. Then, we identify the most important trends in this field, and give a real practical case for the application of this approach. The results show that the attempt methods are much better than traditional teaching methods.

Disseminating Innovations in Teaching Value-Based Care Through an Online Learning Network.

PubMed

Gupta, Reshma; Shah, Neel T; Moriates, Christopher; Wallingford, September; Arora, Vineet M

2017-08-01

A national imperative to provide value-based care requires new strategies to teach clinicians about high-value care. We developed a virtual online learning network aimed at disseminating emerging strategies in teaching value-based care. The online Teaching Value in Health Care Learning Network includes monthly webinars that feature selected innovators, online discussion forums, and a repository for sharing tools. The learning network comprises clinician-educators and health system leaders across North America. We conducted a cross-sectional online survey of all webinar presenters and the active members of the network, and we assessed program feasibility. Six months after the program launched, there were 277 learning community members in 22 US states. Of the 74 active members, 50 (68%) completed the evaluation. Active members represented independently practicing physicians and trainees in 7 specialties, nurses, educators, and health system leaders. Nearly all speakers reported that the learning network provided them with a unique opportunity to connect with a different audience and achieve greater recognition for their work. Of the members who were active in the learning network, most reported that strategies gleaned from the network were helpful, and some adopted or adapted these innovations at their home institutions. One year after the program launched, the learning network had grown to 364 total members. The learning network helped participants share and implement innovations to promote high-value care. The model can help disseminate innovations in emerging areas of health care transformation, and is sustainable without ongoing support after a period of start-up funding.

A Model of Active Ageing through Elder Learning: The Elder Academy Network in Hong Kong

ERIC Educational Resources Information Center

Tam, Maureen

2013-01-01

This article presents the Elder Academy (EA) Network as the policy and practice in promoting active ageing through elder learning in Hong Kong. First, the article examines how the change in demographics and the prevalent trend of an ageing population have propelled the government in Hong Kong to tackle issues and challenges brought about by an…

A Comparison of Users' Personal Information Sharing Awareness, Habits, and Practices in Social Networking Sites and E-Learning Systems

ERIC Educational Resources Information Center

Ball, Albert L.

2012-01-01

Although reports of identity theft continue to be widely published, users continue to post an increasing amount of personal information online, especially within social networking sites (SNS) and e-learning systems (ELS). Research has suggested that many users lack awareness of the threats that risky online personal information sharing poses to…

How do general practice residents use social networking sites in asynchronous distance learning?

PubMed

Maisonneuve, Hubert; Chambe, Juliette; Lorenzo, Mathieu; Pelaccia, Thierry

2015-09-21

Blended learning environments - involving both face-to-face and remote interactions - make it easier to adapt learning programs to constraints such as residents' location and low teacher-student ratio. Social networking sites (SNS) such as Facebook®, while not originally intended to be used as learning environments, may be adapted for the distance-learning part of training programs. The purpose of our study was to explore the use of SNS for asynchronous distance learning in a blended learning environment as well as its influence on learners' face-to-face interactions. We conducted a qualitative study and carried out semi-structured interviews. We performed purposeful sampling for maximal variation to include eight general practice residents in 2(nd) and 3(rd) year training. A thematic analysis was performed. The social integration of SNS facilitates the engagement of users in their learning tasks. This may also stimulate students' interactions and group cohesion when members meet up in person. Most of the general practice residents who work in the blended learning environment we studied had a positive appraisal on their use of SNS. In particular, we report a positive impact on their engagement in learning and their participation in discussions during face-to-face instruction. Further studies are needed in order to evaluate the effectiveness of SNS in blended learning environments and the appropriation of SNS by teachers.

Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science.

PubMed

Mocanu, Decebal Constantin; Mocanu, Elena; Stone, Peter; Nguyen, Phuong H; Gibescu, Madeleine; Liotta, Antonio

2018-06-19

Through the success of deep learning in various domains, artificial neural networks are currently among the most used artificial intelligence methods. Taking inspiration from the network properties of biological neural networks (e.g. sparsity, scale-freeness), we argue that (contrary to general practice) artificial neural networks, too, should not have fully-connected layers. Here we propose sparse evolutionary training of artificial neural networks, an algorithm which evolves an initial sparse topology (Erdős-Rényi random graph) of two consecutive layers of neurons into a scale-free topology, during learning. Our method replaces artificial neural networks fully-connected layers with sparse ones before training, reducing quadratically the number of parameters, with no decrease in accuracy. We demonstrate our claims on restricted Boltzmann machines, multi-layer perceptrons, and convolutional neural networks for unsupervised and supervised learning on 15 datasets. Our approach has the potential to enable artificial neural networks to scale up beyond what is currently possible.

"Learning a Different Form of Communication": Experiences of Networked Learning and Reflections on Practice

ERIC Educational Resources Information Center

Levy, Philippa

2006-01-01

This paper focuses on learners' experiences of text-based computer-mediated communication (CMC) as a means of self-expression, dialogue and debate. A detailed case study narrative and a reflective commentary are presented, drawn from a personal, practice-based inquiry into the design and facilitation of a professional development course for which…

Construction of Course Ubiquitous Learning Based on Network

ERIC Educational Resources Information Center

Wang, Xue; Zhang, Wei; Yang, Xinhui

2017-01-01

Ubiquitous learning has been more and more recognized, which describes a new generation of learning from a new point of view. Ubiquitous learning will bring the new teaching practice and teaching reform, which will become an essential way of learning in 21st century. Taking translation course as a case study, this research constructed a system of…

GA-based fuzzy reinforcement learning for control of a magnetic bearing system.

PubMed

Lin, C T; Jou, C P

2000-01-01

This paper proposes a TD (temporal difference) and GA (genetic algorithm)-based reinforcement (TDGAR) learning method and applies it to the control of a real magnetic bearing system. The TDGAR learning scheme is a new hybrid GA, which integrates the TD prediction method and the GA to perform the reinforcement learning task. The TDGAR learning system is composed of two integrated feedforward networks. One neural network acts as a critic network to guide the learning of the other network (the action network) which determines the outputs (actions) of the TDGAR learning system. The action network can be a normal neural network or a neural fuzzy network. Using the TD prediction method, the critic network can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the action network. The action network uses the GA to adapt itself according to the internal reinforcement signal. The key concept of the TDGAR learning scheme is to formulate the internal reinforcement signal as the fitness function for the GA such that the GA can evaluate the candidate solutions (chromosomes) regularly, even during periods without external feedback from the environment. This enables the GA to proceed to new generations regularly without waiting for the arrival of the external reinforcement signal. This can usually accelerate the GA learning since a reinforcement signal may only be available at a time long after a sequence of actions has occurred in the reinforcement learning problem. The proposed TDGAR learning system has been used to control an active magnetic bearing (AMB) system in practice. A systematic design procedure is developed to achieve successful integration of all the subsystems including magnetic suspension, mechanical structure, and controller training. The results show that the TDGAR learning scheme can successfully find a neural controller or a neural fuzzy controller for a self-designed magnetic bearing system.

Involvement, Collaboration and Engagement: Social Networks through a Pedagogical Lens

ERIC Educational Resources Information Center

Seifert, Tami

2016-01-01

Social networks facilitate activities that promote involvement, collaboration and engagement. Modelling of best practices using social networks enhances its usage by participants, increases participants confidence as to its implementation and creates a paradigm shift to a more personalized, participatory and collaborative learning and a more…

Closer to Learning: Social Networks, Trust, and Professional Communities

ERIC Educational Resources Information Center

Liou, Yi-Hwa; Daly, Alan J.

2014-01-01

Researchers, educators, and policymakers suggest the use of professional learning communities as one important approach to the improvement of teaching and learning. However, relatively little research examines the interplay of professional interactions (structural social capital) around instructional practices and key elements of professional…

Developmental implications of children's brain networks and learning.

PubMed

Chan, John S Y; Wang, Yifeng; Yan, Jin H; Chen, Huafu

2016-10-01

The human brain works as a synergistic system where information exchanges between functional neuronal networks. Rudimentary networks are observed in the brain during infancy. In recent years, the question of how functional networks develop and mature in children has been a hotly discussed topic. In this review, we examined the developmental characteristics of functional networks and the impacts of skill training on children's brains. We first focused on the general rules of brain network development and on the typical and atypical development of children's brain networks. After that, we highlighted the essentials of neural plasticity and the effects of learning on brain network development. We also discussed two important theoretical and practical concerns in brain network training. Finally, we concluded by presenting the significance of network training in typically and atypically developed brains.

What's in a Name: Dimensions of Social Learning in Teacher Groups

ERIC Educational Resources Information Center

Vrieling, E.; van den Beemt, A.; de Laat, M.

2016-01-01

Induced by a literature review, this paper presents a framework of dimensions and indicators highlighting the underpinning aspects and values of social learning within teacher groups. Notions of social networks, communities of practice and learning teams were taken as the main perspectives to influence this social learning framework. The review…

Teachers, Arts Practice and Pedagogy

ERIC Educational Resources Information Center

Franks, Anton; Thomson, Pat; Hall, Chris; Jones, Ken

2014-01-01

What are possible overlaps between arts practice and school pedagogy? How is teacher subjectivity and pedagogy affected when teachers engage with arts practice, in particular, theatre practices? We draw on research conducted into the Learning Performance Network (LPN), a project that involved school teachers working with the Royal Shakespeare…

Sustainability, Participatory Culture, and the Performance of Democracy: Ascendant Sites of Theory and Practice in Art Education

ERIC Educational Resources Information Center

Blandy, Doug

2011-01-01

Art education is a systemic and extensive network within which children, youth, and adults make and learn about material culture. This lecture considers three sites of theory and practice that I see as ascendant in circulating through this network. These sites are sustainability, participatory culture, and performing democracy. I argue that…

Facilitative Components of Collaborative Learning: A Review of Nine Health Research Networks

PubMed Central

Rittner, Jessica Levin; Johnson, Karin E.; Gerteis, Jessie; Miller, Therese

2017-01-01

Objective: Collaborative research networks are increasingly used as an effective mechanism for accelerating knowledge transfer into policy and practice. This paper explored the characteristics and collaborative learning approaches of nine health research networks. Data sources/study setting: Semi-structured interviews with representatives from eight diverse US health services research networks conducted between November 2012 and January 2013 and program evaluation data from a ninth. Study design: The qualitative analysis assessed each network's purpose, duration, funding sources, governance structure, methods used to foster collaboration, and barriers and facilitators to collaborative learning. Data collection: The authors reviewed detailed notes from the interviews to distill salient themes. Principal findings: Face-to-face meetings, intentional facilitation and communication, shared vision, trust among members and willingness to work together were key facilitators of collaborative learning. Competing priorities for members, limited funding and lack of long-term support and geographic dispersion were the main barriers to coordination and collaboration across research network members. Conclusion: The findings illustrate the importance of collaborative learning in research networks and the challenges to evaluating the success of research network functionality. Conducting readiness assessments and developing process and outcome evaluation metrics will advance the design and show the impact of collaborative research networks. PMID:28277202

Social Media as a Professional Tool

ERIC Educational Resources Information Center

Principal, 2011

2011-01-01

Social networking is more than catching up with family and long-lost friends; it's turned into a professional resource for educators to exchange ideas and expand their professional learning network (PLN). According to the report "School Principals and Social Networking in Education: Practices, Policies, and Realities in 2010," most responding…

InnovateEDU, Inc.: Brooklyn Laboratory Charter Schools (LAB)

ERIC Educational Resources Information Center

EDUCAUSE, 2015

2015-01-01

Entrepreneurial learning is the backbone of this Brooklyn charter school network which opened in Fall 2014 to serve grades 6-12, including English language learners and students with disabilities. LAB's academic model combines empirically effective learning practices with innovative implementation strategies, including a blended learning model…

Guidelines for Network Security in the Learning Environment.

ERIC Educational Resources Information Center

Littman, Marlyn Kemper

1996-01-01

Explores security challenges and practical approaches to safeguarding school networks against invasion. Highlights include security problems; computer viruses; privacy assaults; Internet invasions; building a security policy; authentication; passwords; encryption; firewalls; and acceptable use policies. (Author/LRW)

Workplace Learning in the New Zealand Apple Industry Network: A New Co-Design Method for Government "Practice Making"

ERIC Educational Resources Information Center

Hill, Roberta; Capper, Phillip; Wilson, Ken; Whatman, Richard; Wong, Karen

2007-01-01

Purpose: The purpose of this paper is to describe how, from 2004-2006, a New Zealand research team experimented with the "change laboratory" learning process to create a new method of government policy development and implementation, referred to as "practice-making". The apple industry in Hawke's Bay was chosen because of the…

A Community of Practice Focused on Resiliency in Graduate Nursing Students

ERIC Educational Resources Information Center

Wildes, Megan

2016-01-01

The purpose of this project was to create a Community of Practice (CoP) focused on resiliency in graduate nursing students. CoPs are networks of people who collectively learn and share in learning as a social experience. By engaging a CoP that focused on resiliency in graduate nursing students, the aim was to positively support students' sense of…

Implementing Role-Changing Versus Time-Changing Innovations in Health Care: Differences in Helpfulness of Staff Improvement Teams, Management, and Network for Learning.

PubMed

Nembhard, Ingrid M; Morrow, Christopher T; Bradley, Elizabeth H

2015-12-01

Health care organizations often fail in their effort to implement care-improving innovations. This article differentiates role-changing innovations, altering what workers do, from time-changing innovations, altering when tasks are performed or for how long. We examine our hypothesis that the degree to which access to groups that can alter organizational learning--staff, management, and external network--facilitates implementation depends on innovation type. Our longitudinal study using ordinal logistic regression and survey data on 517 hospitals' implementation of evidence-based practices for treating heart attack confirmed our thesis for factors granting access to each group: improvement team's representativeness (of affected staff), senior management engagement, and network membership. Although team representativeness and network membership were positively associated with implementing role-changing practices, senior management engagement was not. In contrast, senior management engagement was positively associated with implementing time-changing practices, whereas team representativeness was not, and network membership was not unless there was limited management engagement. These findings advance implementation science by explaining mixed results across past studies: Nature of change for workers alters potential facilitators' effects on implementation. © The Author(s) 2015.

"Follow" Me: Networked Professional Learning for Teachers

ERIC Educational Resources Information Center

Holmes, Kathryn; Preston, Greg; Shaw, Kylie; Buchanan, Rachel

2013-01-01

Effective professional learning for teachers is fundamental for any school system aiming to make transformative and sustainable change to teacher practice. This paper investigates the efficacy of Twitter as a medium for teachers to participate in professional learning by analysing the tweets of 30 influential users of the popular medium. We find…

Blending Online Learning with Traditional Approaches: Changing Practices

ERIC Educational Resources Information Center

Condie, Rae; Livingston, Kay

2007-01-01

Considerable claims have been made for the development of e-learning, either as stand-alone programmes or alongside more traditional approaches to teaching and learning, for students across school and tertiary education. National initiatives have improved the position of schools in terms of access to hardware and electronic networking, software…

Teaching Social Work Practice Research to Enhance Research-Minded Expertise

ERIC Educational Resources Information Center

Satka, Mirja; Kääriäinen, Aino; Yliruka, Laura

2016-01-01

The emphasis on student cognitive knowledge and expertise in social work education has been shifting more toward reflective learning that features learning networks and dialogical interaction. In the context of innovative knowledge communities for promoting social work expertise, educators have become facilitators of learning that is expanding…

The Need for Innovative Methods of Teaching and Learning Chemistry in Higher Education--Reflections from a Project of the European Chemistry Thematic Network

ERIC Educational Resources Information Center

Eilks, Ingo; Byers, Bill

2010-01-01

This paper summarizes the work and conclusions of a working group established by the European Chemistry Thematic Network (ECTN). The aim of the working group was to identify potential areas for innovative approaches to the teaching and learning of chemistry in Higher Education, and to survey good practice throughout the EU. The paper starts by…

Smart Girls, Black Girls, Mean Girls, and Bullies: At the Intersection of Identities and the Mediating Role of Young Girls' Social Network in Mathematical Communities of Practice

ERIC Educational Resources Information Center

Gholson, Maisie; Martin, Danny B.

2014-01-01

By taking an intersectional and emic view to studying a group of African American girls in a third-grade class, we attempted to capture the complexity of mathematics learning for these girls. Traditionally, children's social networks in school are framed as external to mathematics content learning. Our preliminary analyses of student interviews…

Neurophysiological correlates of visuo-motor learning through mental and physical practice.

PubMed

Allami, Nadia; Brovelli, Andrea; Hamzaoui, El Mehdi; Regragui, Fakhita; Paulignan, Yves; Boussaoud, Driss

2014-03-01

We have previously shown that mental rehearsal can replace up to 75% of physical practice for learning a visuomotor task (Allami, Paulignan, Brovelli, & Boussaoud, (2008). Experimental Brain Research, 184, 105-113). Presumably, mental rehearsal must induce brain changes that facilitate motor learning. We tested this hypothesis by recording scalp electroencephalographic activity (EEG) in two groups of subjects. In one group, subjects executed a reach to grasp task for 240 trials. In the second group, subjects learned the task through a combination of mental rehearsal for the initial 180 trials followed by the execution of 60 trials. Thus, one group physically executed the task for 240 trials, the other only for 60 trials. Amplitudes and latencies of event-related potentials (ERPs) were compared across groups at different stages during learning. We found that ERP activity increases dramatically with training and reaches the same amplitude over the premotor regions in the two groups, despite large differences in physically executed trials. These findings suggest that during mental rehearsal, neuronal changes occur in the motor networks that make physical practice after mental rehearsal more effective in configuring functional networks for skilful behaviour. Copyright © 2013 Elsevier Ltd. All rights reserved.

Frame prediction using recurrent convolutional encoder with residual learning

NASA Astrophysics Data System (ADS)

Yue, Boxuan; Liang, Jun

2018-05-01

The prediction for the frame of a video is difficult but in urgent need in auto-driving. Conventional methods can only predict some abstract trends of the region of interest. The boom of deep learning makes the prediction for frames possible. In this paper, we propose a novel recurrent convolutional encoder and DE convolutional decoder structure to predict frames. We introduce the residual learning in the convolution encoder structure to solve the gradient issues. The residual learning can transform the gradient back propagation to an identity mapping. It can reserve the whole gradient information and overcome the gradient issues in Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN). Besides, compared with the branches in CNNs and the gated structures in RNNs, the residual learning can save the training time significantly. In the experiments, we use UCF101 dataset to train our networks, the predictions are compared with some state-of-the-art methods. The results show that our networks can predict frames fast and efficiently. Furthermore, our networks are used for the driving video to verify the practicability.

Medical education practice-based research networks: Facilitating collaborative research.

PubMed

Schwartz, Alan; Young, Robin; Hicks, Patricia J

2016-01-01

Research networks formalize and institutionalize multi-site collaborations by establishing an infrastructure that enables network members to participate in research, propose new studies, and exploit study data to move the field forward. Although practice-based clinical research networks are now widespread, medical education research networks are rapidly emerging. In this article, we offer a definition of the medical education practice-based research network, a brief description of networks in existence in July 2014 and their features, and a more detailed case study of the emergence and early growth of one such network, the Association of Pediatric Program Directors Longitudinal Educational Assessment Research Network (APPD LEARN). We searched for extant networks through peer-reviewed literature and the world-wide web. We identified 15 research networks in medical education founded since 2002 with membership ranging from 8 to 120 programs. Most focus on graduate medical education in primary care or emergency medicine specialties. We offer four recommendations for the further development and spread of medical education research networks: increasing faculty development, obtaining central resources, studying networks themselves, and developing networks of networks.

Medical education practice-based research networks: Facilitating collaborative research

PubMed Central

Schwartz, Alan; Young, Robin; Hicks, Patricia J.; APPD LEARN, For

2016-01-01

Abstract Background: Research networks formalize and institutionalize multi-site collaborations by establishing an infrastructure that enables network members to participate in research, propose new studies, and exploit study data to move the field forward. Although practice-based clinical research networks are now widespread, medical education research networks are rapidly emerging. Aims: In this article, we offer a definition of the medical education practice-based research network, a brief description of networks in existence in July 2014 and their features, and a more detailed case study of the emergence and early growth of one such network, the Association of Pediatric Program Directors Longitudinal Educational Assessment Research Network (APPD LEARN). Methods: We searched for extant networks through peer-reviewed literature and the world-wide web. Results: We identified 15 research networks in medical education founded since 2002 with membership ranging from 8 to 120 programs. Most focus on graduate medical education in primary care or emergency medicine specialties. Conclusions: We offer four recommendations for the further development and spread of medical education research networks: increasing faculty development, obtaining central resources, studying networks themselves, and developing networks of networks. PMID:25319404

The CIRTL Network: A Professional Development Network for Future STEM Faculty

NASA Astrophysics Data System (ADS)

Herbert, B. E.

2011-12-01

The Center for the Integration of Research, Teaching, and Learning (CIRTL) is an NSF Center for Learning and Teaching in higher education using the professional development of graduate students and post-doctoral scholars as the leverage point to develop a national STEM faculty committed to implementing and advancing effective teaching practices for diverse student audiences as part of successful professional careers. The goal of CIRTL is to improve the STEM learning of all students at every college and university, and thereby to increase the diversity in STEM fields and the STEM literacy of the nation. The CIRTL network seeks to support change at a number of levels to support its goals: individual, classroom, institutional, and national. To bring about change, which is never easy, the CIRTL network has developed a conceptual model or change model that is thought to support the program objectives. Three central concepts, Teaching-as-Research, Learning Communities, and Learning-through-Diversity, underlie the design of all CIRTL activities. STEM faculty use research methods to systematically and reflectively improve learning outcomes. This work is done within a community of shared learning and discovery, and explicitly recognizes that effective teaching capitalizes on the rich array of experiences, backgrounds, and skills among the students and instructors to enhance the learning of all. This model is being refined and tested through a networked-design experiment, where the model is tested in diverse settings. Established in fall 2006, the CIRTL Network comprises the University of Colorado at Boulder (CU), Howard University, Michigan State University, Texas A&M University, Vanderbilt University, and the University of Wisconsin-Madison. The diversity of these institutions is by design: private/public; large/moderate size; majority-/minority-serving; geographic location. This talk will describe the theoretical constructs and efficacy of Teaching-as Research as a central design element of the CIRTL network model. Teaching-as-Research involves the deliberate, systematic, and reflective use of research methods to develop and implement teaching practices that advance the learning experiences and outcomes of students. CIRTL envision three types of learning outcomes for CIRTL participants: CIRTL Fellow, CIRTL Practitioner, and CIRTL Scholar. These three, tiered learning outcomes recognize the role of the CIRTL pillars in effective teaching (Fellow), scholarly teaching that builds on the CIRTL pillars to demonstrably improve learning and make the results public (Practitioner), and finally scholarship that advances teaching and learning under peer review (Scholar). CIRTL program outcomes conceived in this way permit anyone to enter the CIRTL Network learning community from a wide variety of disciplines, needs, and past experiences, and to achieve success as an instructor in diverse contexts.

Large-Scale High School Reform through School Improvement Networks: Exploring Possibilities for "Developmental Evaluation"

ERIC Educational Resources Information Center

Peurach, Donald J.; Lenhoff, Sarah Winchell; Glazer, Joshua L.

2016-01-01

Recognizing school improvement networks as a leading strategy for large-scale high school reform, this analysis examines developmental evaluation as an approach to examining school improvement networks as "learning systems" able to produce, use, and refine practical knowledge in large numbers of schools. Through a case study of one…

From Networked Learning to Operational Practice: Constructing and Transferring Superintendent Knowledge in a Regional Instructional Rounds Network

ERIC Educational Resources Information Center

Travis, Timothy J.

2015-01-01

Instructional rounds are an emerging network structure with processes and protocols designed to develop superintendents' knowledge and skills in leading large-scale improvement, to enable superintendents to build an infrastructure that supports the work of improvement, to assist superintendents in distributing leadership throughout their district,…

Material Enactments of Identities and Learning in Everyday Community Practices: Implications for Pedagogy

ERIC Educational Resources Information Center

Aberton, Helen

2012-01-01

In recent years there has been an upsurge of interest in applying actor-network theory (ANT) to educational research and analysis. This article presents an account of how an ANT analysis of socio-material practices with a focus on objects can bring informal learning and identity formation to view. It is based on a doctoral study of the everyday…

Geometrical structure of Neural Networks: Geodesics, Jeffrey's Prior and Hyper-ribbons

NASA Astrophysics Data System (ADS)

Hayden, Lorien; Alemi, Alex; Sethna, James

2014-03-01

Neural networks are learning algorithms which are employed in a host of Machine Learning problems including speech recognition, object classification and data mining. In practice, neural networks learn a low dimensional representation of high dimensional data and define a model manifold which is an embedding of this low dimensional structure in the higher dimensional space. In this work, we explore the geometrical structure of a neural network model manifold. A Stacked Denoising Autoencoder and a Deep Belief Network are trained on handwritten digits from the MNIST database. Construction of geodesics along the surface and of slices taken from the high dimensional manifolds reveal a hierarchy of widths corresponding to a hyper-ribbon structure. This property indicates that neural networks fall into the class of sloppy models, in which certain parameter combinations dominate the behavior. Employing this information could prove valuable in designing both neural network architectures and training algorithms. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No . DGE-1144153.

Project ECHO: A Telementoring Network Model for Continuing Professional Development.

PubMed

Arora, Sanjeev; Kalishman, Summers G; Thornton, Karla A; Komaromy, Miriam S; Katzman, Joanna G; Struminger, Bruce B; Rayburn, William F

2017-01-01

A major challenge with current systems of CME is the inability to translate the explosive growth in health care knowledge into daily practice. Project ECHO (Extension for Community Healthcare Outcomes) is a telementoring network designed for continuing professional development (CPD) and improving patient outcomes. The purpose of this article was to describe how the model has complied with recommendations from several authoritative reports about redesigning and enhancing CPD. This model links primary care clinicians through a knowledge network with an interprofessional team of specialists from an academic medical center who provide telementoring and ongoing education enabling community clinicians to treat patients with a variety of complex conditions. Knowledge and skills are shared during weekly condition-specific videoconferences. The model exemplifies learning as described in the seven levels of CPD by Moore (participation, satisfaction, learning, competence, performance, patient, and community health). The model is also aligned with recommendations from four national reports intended to redesign knowledge transfer in improving health care. Efforts in learning sessions focus on information that is relevant to practice, focus on evidence, education methodology, tailoring of recommendations to individual needs and community resources, and interprofessionalism. Project ECHO serves as a telementoring network model of CPD that aligns with current best practice recommendations for CME. This transformative initiative has the potential to serve as a leading model for larger scale CPD, nationally and globally, to enhance access to care, improve quality, and reduce cost.

ICADx: interpretable computer aided diagnosis of breast masses

NASA Astrophysics Data System (ADS)

Kim, Seong Tae; Lee, Hakmin; Kim, Hak Gu; Ro, Yong Man

2018-02-01

In this study, a novel computer aided diagnosis (CADx) framework is devised to investigate interpretability for classifying breast masses. Recently, a deep learning technology has been successfully applied to medical image analysis including CADx. Existing deep learning based CADx approaches, however, have a limitation in explaining the diagnostic decision. In real clinical practice, clinical decisions could be made with reasonable explanation. So current deep learning approaches in CADx are limited in real world deployment. In this paper, we investigate interpretability in CADx with the proposed interpretable CADx (ICADx) framework. The proposed framework is devised with a generative adversarial network, which consists of interpretable diagnosis network and synthetic lesion generative network to learn the relationship between malignancy and a standardized description (BI-RADS). The lesion generative network and the interpretable diagnosis network compete in an adversarial learning so that the two networks are improved. The effectiveness of the proposed method was validated on public mammogram database. Experimental results showed that the proposed ICADx framework could provide the interpretability of mass as well as mass classification. It was mainly attributed to the fact that the proposed method was effectively trained to find the relationship between malignancy and interpretations via the adversarial learning. These results imply that the proposed ICADx framework could be a promising approach to develop the CADx system.

Supporting Evidence Use in Networked Professional Learning: The Role of the Middle Leader

ERIC Educational Resources Information Center

LaPointe-McEwan, Danielle; DeLuca, Christopher; Klinger, Don A.

2017-01-01

Background: In Canada, contemporary collaborative professional learning models for educators utilise multiple forms of evidence to inform practice. Commonly, two forms of evidence are prioritised: (a) research-based evidence and (b) classroom-based evidence of student learning. In Ontario, the integration of these two forms of evidence within…

Mobile-Learning: Thai HE Student Perceptions and Potential Technological Impacts

ERIC Educational Resources Information Center

James, Paul T. J.

2011-01-01

Higher education appears to be changing in the Thailand, as students, especially younger students whose social networking concerns match their obsession with mobile technology, may take issue with past academic teaching patterns and practices and opt for more contemporary approaches such as mobile-learning (m-Learning). This research used a…

Learning Nursing in the Workplace Community: The Generation of Professional Capital

NASA Astrophysics Data System (ADS)

Gobbi, Mary

This chapter explores the connections between learning, working and professional communities in nursing. It draws on experiences and research in nursing practice and education, where not only do isolated professionals learn as a result of their actions for patients and others, but those professionals are part of a community whose associated networks enable learning to occur. Several characteristics of this professional community are shared with those found in Communities of Practice (CoPs) (Lave and Wenger, 1991; Wenger, 1998), but the balance and importance of many elements can differ. For instance, whilst Lave and Wenger (1991) describe many aspects of situated learning in CoPs that apply to nurses, their model is of little help in understanding the ways in which other professions as well as patients/clients and carers influence the development of nursing practice. Therefore, I shall argue that it is not just the Community of Practice that we need to consider

Advancing Professional Development Through a Community of Practice: the New England Network for Faculty Affairs.

PubMed

Power, Christine M; Thorndyke, Luanne E; Milner, Robert J; Lowney, Kathleen; Irvin, Charles G; Fonseca-Kelly, Zoe; Benjamin, Emelia J; Bhasin, Robina M; Connelly, Maureen T

2018-01-01

In an era of competing priorities, funding is increasingly restricted for offices of faculty affairs and development. Opportunities for professional staff to grow and network through attendance at national meetings and to share best practices are limited. We sought to describe a community of practice established to enhance the professional development of faculty affairs professionals and to document its impact. We outlined the process of formation of the New England Network for Faculty Affairs (NENFA), reviewed the pedagogical approaches to professional development, and surveyed members to evaluate the impact of NENFA on their activities, professional network and their institutions. After a successful 2011 initial meeting, NENFA created an organizing committee and conducted a needs assessment among potential members. NENFA's charter, mission, goals, and structure were based on survey results. NENFA's regional community of practice grew to 31 institutions and held 10 meetings over 5 years. Meetings have examined a faculty development topic in depth using multiple learning formats to engage participants from academic medical centers and allied professions. Results from a 2015 member survey confirmed the value of NENFA. Multiple members documented changes in practice as a result of participating. NENFA has been sustained by volunteer leadership, collaboration, and the value that the group has brought to its members. We propose that a "community of practice" offers an effective model for collaborative learning among individuals at different institutions within a competitive health care environment. We recommend that the approach be replicated in other regions.

Enhancing Practice Improvement by Facilitating Practitioner Interactivity: New Roles for Providers of Continuing Medical Education

ERIC Educational Resources Information Center

Parboosingh, I. John; Reed, Virginia A.; Palmer, James Caldwell; Bernstein, Henry H.

2011-01-01

Research into networking and interactivity among practitioners is providing new information that has the potential to enhance the effectiveness of practice improvement initiatives. This commentary reviews the evidence that practitioner interactivity can facilitate emergent learning and behavior change that lead to practice improvements. Insights…

Learning in Social Networks: Rationale and Ideas for Its Implementation in Higher Education

ERIC Educational Resources Information Center

Alvarez, Ibis M.; Olivera-Smith, Marialexa

2013-01-01

The internet has fast become a prevalent medium for collaboration between people and social networks, in particular, have gained vast popularity and relevance over the past few years. Within this framework, our paper will analyse the role played by social networks in current teaching practices. Specifically, we focus on the principles guiding the…

Learning from Instructional Rounds

ERIC Educational Resources Information Center

City, Elizabeth A.

2011-01-01

Instructional rounds are a disciplined way for educators to work together to improve a school's instructional core. The practice combines three common elements of improvement: classroom observation, an improvement strategy, and a network. Instructional rounds differ from supervision and evaluation in that people doing rounds learn something…

Lessons learned from the design of chemical space networks and opportunities for new applications.

PubMed

Vogt, Martin; Stumpfe, Dagmar; Maggiora, Gerald M; Bajorath, Jürgen

2016-03-01

The concept of chemical space is of fundamental relevance in chemical informatics and computer-aided drug discovery. In a series of articles published in the Journal of Computer-Aided Molecular Design, principles of chemical space design were evaluated, molecular networks proposed as an alternative to conventional coordinate-based chemical reference spaces, and different types of chemical space networks (CSNs) constructed and analyzed. Central to the generation of CSNs was the way in which molecular similarity relationships were assessed and a primary focal point was the network-based representation of biologically relevant chemical space. The design and comparison of CSNs based upon alternative similarity measures can be viewed as an evolutionary path with interesting lessons learned along the way. CSN design has matured to the point that such chemical space representations can be used in practice. In this contribution, highlights from the sequence of CSN design efforts are discussed in context, providing a perspective for future practical applications.

Lessons learned from the design of chemical space networks and opportunities for new applications

NASA Astrophysics Data System (ADS)

Vogt, Martin; Stumpfe, Dagmar; Maggiora, Gerald M.; Bajorath, Jürgen

2016-03-01

The concept of chemical space is of fundamental relevance in chemical informatics and computer-aided drug discovery. In a series of articles published in the Journal of Computer- Aided Molecular Design, principles of chemical space design were evaluated, molecular networks proposed as an alternative to conventional coordinate-based chemical reference spaces, and different types of chemical space networks (CSNs) constructed and analyzed. Central to the generation of CSNs was the way in which molecular similarity relationships were assessed and a primary focal point was the network-based representation of biologically relevant chemical space. The design and comparison of CSNs based upon alternative similarity measures can be viewed as an evolutionary path with interesting lessons learned along the way. CSN design has matured to the point that such chemical space representations can be used in practice. In this contribution, highlights from the sequence of CSN design efforts are discussed in context, providing a perspective for future practical applications.

A functional-dependencies-based Bayesian networks learning method and its application in a mobile commerce system.

PubMed

Liao, Stephen Shaoyi; Wang, Huai Qing; Li, Qiu Dan; Liu, Wei Yi

2006-06-01

This paper presents a new method for learning Bayesian networks from functional dependencies (FD) and third normal form (3NF) tables in relational databases. The method sets up a linkage between the theory of relational databases and probabilistic reasoning models, which is interesting and useful especially when data are incomplete and inaccurate. The effectiveness and practicability of the proposed method is demonstrated by its implementation in a mobile commerce system.

Using social media to enhance career development opportunities for health promotion professionals.

PubMed

Roman, Leah A

2014-07-01

For health promotion professionals, social media offers many ways to engage with a broader range of colleagues; participate in professional development events; promote expertise, products, or services; and learn about career-enhancing opportunities such as funding and fellowships. Previous work has recommended "building networking into what you are already doing." This article provides updated and new social media resources, as well as practical examples and strategies to promote effective use of social media. Social media offers health promotion professionals cost-effective opportunities to enhance their career by building communities of practice, participating in professional development events, and enriching classroom learning. Developing the skills necessary to use social media for networking is important in the public health workforce, especially as social media is increasingly used in academic and practice settings. © 2014 Society for Public Health Education.

Three learning phases for radial-basis-function networks.

PubMed

Schwenker, F; Kestler, H A; Palm, G

2001-05-01

In this paper, learning algorithms for radial basis function (RBF) networks are discussed. Whereas multilayer perceptrons (MLP) are typically trained with backpropagation algorithms, starting the training procedure with a random initialization of the MLP's parameters, an RBF network may be trained in many different ways. We categorize these RBF training methods into one-, two-, and three-phase learning schemes. Two-phase RBF learning is a very common learning scheme. The two layers of an RBF network are learnt separately; first the RBF layer is trained, including the adaptation of centers and scaling parameters, and then the weights of the output layer are adapted. RBF centers may be trained by clustering, vector quantization and classification tree algorithms, and the output layer by supervised learning (through gradient descent or pseudo inverse solution). Results from numerical experiments of RBF classifiers trained by two-phase learning are presented in three completely different pattern recognition applications: (a) the classification of 3D visual objects; (b) the recognition hand-written digits (2D objects); and (c) the categorization of high-resolution electrocardiograms given as a time series (ID objects) and as a set of features extracted from these time series. In these applications, it can be observed that the performance of RBF classifiers trained with two-phase learning can be improved through a third backpropagation-like training phase of the RBF network, adapting the whole set of parameters (RBF centers, scaling parameters, and output layer weights) simultaneously. This, we call three-phase learning in RBF networks. A practical advantage of two- and three-phase learning in RBF networks is the possibility to use unlabeled training data for the first training phase. Support vector (SV) learning in RBF networks is a different learning approach. SV learning can be considered, in this context of learning, as a special type of one-phase learning, where only the output layer weights of the RBF network are calculated, and the RBF centers are restricted to be a subset of the training data. Numerical experiments with several classifier schemes including k-nearest-neighbor, learning vector quantization and RBF classifiers trained through two-phase, three-phase and support vector learning are given. The performance of the RBF classifiers trained through SV learning and three-phase learning are superior to the results of two-phase learning, but SV learning often leads to complex network structures, since the number of support vectors is not a small fraction of the total number of data points.

Networking to improve end of life care.

PubMed

McGivern, Gerry

2009-01-01

Network organisations are increasingly common in healthcare. This paper describes an example of clinically led networking, which improved end of life care (EOLC) in care homes, differentiating between a 'network' as a formal entity and the more informal process of 'networking'. The paper begins with a brief discussion of networks and their development in healthcare, then an overview of EOLC policy, the case setting and methods. The paper describes four key features of this networking; (1) how it enabled discussions and implemented processes to help people address difficult taboos about dying; (2) how personal communication and 'distributed leadership' facilitated learning; (3) how EOLC occasionally lapsed during the handover of patient care, where personal relationship and communication were weaker; and (4) how successful learning and sharing of best practice was fragile and could be potentially undermined by wider financial pressures in the NHS.

Know How? Show How: Experienced Teachers Share Best Practices through Ontario Program

ERIC Educational Resources Information Center

Amato, Lindy; Anthony, Paul; Strachan, Jim

2014-01-01

Launched in 2007, the Teacher Learning and Leadership Program, out of Ontario, Canada, operates on the belief that classroom teachers know their learning needs and the needs of their students best. Additionally, the program assumes teachers have the greatest knowledge of how to build and foster multiple learning networks in order to share their…

Layering Networked and Symphonic Selves: A Critical Role for e-Portfolios in Employability through Integrative Learning

ERIC Educational Resources Information Center

Cambridge, Darren

2008-01-01

Purpose: E-portfolios, which document and facilitate learning and performance, have recently attracted interest in the USA, UK, and Europe as means to increase employability and support lifelong learning. This article aims to critically examine these objectives in order to guide the future e-portfolio practice. Design/methodology/approach: Social…

Online English Learning Using Internet for English-as-a-Foreign-Language Students.

ERIC Educational Resources Information Center

Wang, Lih-Ching Chen; Dalton, David W.

Learning to communicate in English is an essential tool to access many resources via worldwide networks in the global society. Like students from many other countries, students in Taiwan study English for years, but lack opportunities to practice. For English-as-a-Second-Language students, the World Wide Web provides a learning environment in…

Knowledge sharing and organizational learning in the context of hospital infection prevention.

PubMed

Rangachari, Pavani

2010-01-01

Recently, hospitals that have been successful in preventing infections have labeled their improvement approaches as either the Toyota Production System (TPS) approach or the Positive Deviance (PD) approach. PD has been distinguished from TPS as being a bottom-up approach to improvement, as against top-down. Facilities that have employed both approaches have suggested that PD may be more effective than TPS for infection prevention. This article integrates organizational learning, institutional, and knowledge network theories to develop a theoretical framework for understanding the structure and evolution of effective knowledge-sharing networks in health care organizations, that is, networks most conducive to learning and improvement. Contrary to arguments put forth by hospital success stories, the framework suggests that networks rich in brokerage and hierarchy (ie, top-down, "TPS-like" structures) may be more effective for learning and improvement in health care organizations, compared with a networks rich in density (ie, bottom-up, "PD-like" structures). The theoretical framework and ensuing analysis help identify several gaps in the literature related to organization learning and improvement in the infection prevention context. This, in turn, helps put forth recommendations for health management research and practice.

A VIRTUAL LEARNING COMMUNITY TO FACILITATE SUSTAINABLE BEHAVIOR

EPA Science Inventory

Research to date on virtual learning communities suggests that electronic interaction can be a useful way to impact new skills and to encourage innovative practices by creating networked systems of mutual support. We expect that by being able to exchange information, trade tip...

Defence Adademies and Colleges 2009 International Conference. Network Centric Learning: Towards Authentic ePractices, 25 - 27 March 2009

DTIC Science & Technology

2009-03-27

to learning and collaborative working • Developing more immersive learning where learning is promoted through experiencing the style of thinking of... Student Talk in Promoting Quality Learning in Science Classroom”, MS. Morrison, P., Barlow, M., Bethel, G. and Clothier, S. (2005), “Proficient Soldier...on student perceptions of learning effectiveness. 1 Computer self-efficacy: “The learner’s perception of their ability to carry out a series of

Vibration control of building structures using self-organizing and self-learning neural networks

NASA Astrophysics Data System (ADS)

Madan, Alok

2005-11-01

Past research in artificial intelligence establishes that artificial neural networks (ANN) are effective and efficient computational processors for performing a variety of tasks including pattern recognition, classification, associative recall, combinatorial problem solving, adaptive control, multi-sensor data fusion, noise filtering and data compression, modelling and forecasting. The paper presents a potentially feasible approach for training ANN in active control of earthquake-induced vibrations in building structures without the aid of teacher signals (i.e. target control forces). A counter-propagation neural network is trained to output the control forces that are required to reduce the structural vibrations in the absence of any feedback on the correctness of the output control forces (i.e. without any information on the errors in output activations of the network). The present study shows that, in principle, the counter-propagation network (CPN) can learn from the control environment to compute the required control forces without the supervision of a teacher (unsupervised learning). Simulated case studies are presented to demonstrate the feasibility of implementing the unsupervised learning approach in ANN for effective vibration control of structures under the influence of earthquake ground motions. The proposed learning methodology obviates the need for developing a mathematical model of structural dynamics or training a separate neural network to emulate the structural response for implementation in practice.

SU-E-T-23: A Developing Australian Network for Datamining and Modelling Routine Radiotherapy Clinical Data and Radiomics Information for Rapid Learning and Clinical Decision Support

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

Thwaites, D; Holloway, L; Bailey, M

2015-06-15

Purpose: Large amounts of routine radiotherapy (RT) data are available, which can potentially add clinical evidence to support better decisions. A developing collaborative Australian network, with a leading European partner, aims to validate, implement and extend European predictive models (PMs) for Australian practice and assess their impact on future patient decisions. Wider objectives include: developing multi-institutional rapid learning, using distributed learning approaches; and assessing and incorporating radiomics information into PMs. Methods: Two initial standalone pilots were conducted; one on NSCLC, the other on larynx, patient datasets in two different centres. Open-source rapid learning systems were installed, for data extraction andmore » mining to collect relevant clinical parameters from the centres’ databases. The European DSSs were learned (“training cohort”) and validated against local data sets (“clinical cohort”). Further NSCLC studies are underway in three more centres to pilot a wider distributed learning network. Initial radiomics work is underway. Results: For the NSCLC pilot, 159/419 patient datasets were identified meeting the PM criteria, and hence eligible for inclusion in the curative clinical cohort (for the larynx pilot, 109/125). Some missing data were imputed using Bayesian methods. For both, the European PMs successfully predicted prognosis groups, but with some differences in practice reflected. For example, the PM-predicted good prognosis NSCLC group was differentiated from a combined medium/poor prognosis group (2YOS 69% vs. 27%, p<0.001). Stage was less discriminatory in identifying prognostic groups. In the good prognosis group two-year overall survival was 65% in curatively and 18% in palliatively treated patients. Conclusion: The technical infrastructure and basic European PMs support prognosis prediction for these Australian patient groups, showing promise for supporting future personalized treatment decisions, improved treatment quality and potential practice changes. The early indications from the distributed learning and radiomics pilots strengthen this. Improved routine patient data quality should strengthen such rapid learning systems.« less

Electronic Networks: Crossing Boundaries/Creating Communities.

ERIC Educational Resources Information Center

Howard, Tharon, Ed.; Benson, Chris, Ed.; Gooch, Rocky; Goswami, Dixie

Written by practicing teachers about actual instructional computing projects, this book provides information teachers need to integrate instructional technologies into their classrooms. The book is divided into three parts. Part 1, "New Tools for the Classroom: An Introduction to Networked Learning," includes chapters: (1) "Getting Started in a…

Elements of Network-Based Assessment

ERIC Educational Resources Information Center

Gibson, David

2007-01-01

Elements of network-based assessment systems are envisioned based on recent advances in knowledge and practice in learning theory, assessment design and delivery, and semantic web interoperability. The architecture takes advantage of the meditating role of technology as well as recent models of assessment systems. This overview of the elements…

Online People Tagging: Social (Mobile) Network(ing) Services and Work-Based Learning

ERIC Educational Resources Information Center

Cook, John; Pachler, Norbert

2012-01-01

Social and mobile technologies offer users unprecedented opportunities for communicating, interacting, sharing, meaning-making, content and context generation. And, these affordances are in constant flux driven by a powerful interplay between technological innovation and emerging cultural practices. Significantly, also, they are starting to…

From struggles to resource gains in interprofessional service networks: Key findings from a multiple case study.

PubMed

Toiviainen, Hanna; Kira, Mari

2017-07-01

In interprofessional service networks, employees cross professional boundaries to collaborate with colleagues and clients with expertise and values different from their own. It can be a struggle to adopt shared work practices and deal with "multivoicedness." At the same time, networks allow members to engage in meaningful service provision, gain a broader understanding of the service provided, and obtain social support. Intertwined network struggles and resource gains have received limited attention in the interprofessional care literature to date. The aim of the study was to investigate the learning potential of the co-existing struggles and resource gains. This article reports findings from two interprofessional networks. Interviews were conducted with 19 employees and thematically analysed. Three types of struggles and six types of resource gains of networking were identified. The struggles relate, first, to the assumptions of networking following similar practices to those in a home organisation; second, to the challenges of dealing with the multivoicedness of networking; and, third, to the experienced gap between the networking ideals and the reality of cooperation. At the same time, the network members experience gains in emotional resources (e.g., stronger sense of meaningfulness at work), cognitive resources (e.g., understanding the customer needs from alternative perspectives), and social resources (e.g., being able to rely on other professionals' competence). Learning potential emerged from the dynamics between coexisting struggles and resource gains.

The OCHIN community information network: bringing together community health centers, information technology, and data to support a patient-centered medical village.

PubMed

Devoe, Jennifer E; Sears, Abigail

2013-01-01

Creating integrated, comprehensive care practices requires access to data and informatics expertise. Information technology (IT) resources are not readily available to individual practices. One model of shared IT resources and learning is a "patient-centered medical village." We describe the OCHIN Community Health Information Network as an example of this model; community practices have come together collectively to form an organization that leverages shared IT expertise, resources, and data, providing members with the means to fully capitalize on new technologies that support improved care. This collaborative facilitates the identification of "problem sheds" through surveillance of network-wide data, enables shared learning regarding best practices, and provides a "community laboratory" for practice-based research. As an example of a community of solution, OCHIN uses health IT and data-sharing innovations to enhance partnerships between public health leaders, clinicians in community health centers, informatics experts, and policy makers. OCHIN community partners benefit from the shared IT resource (eg, a linked electronic health record, centralized data warehouse, informatics, and improvement expertise). This patient-centered medical village provides (1) the collective mechanism to build community-tailored IT solutions, (2) "neighbors" to share data and improvement strategies, and (3) infrastructure to support innovations based on electronic health records across communities, using experimental approaches.

Refining process of nursing skill movie manual by peer comments of social network system.

PubMed

Majima, Yukie; Maekawa, Yasuko; Shimada, Satoshi; Izumi, Takako

2014-01-01

The nursing practical knowledge represented by nursing skill is highly tacit and is therefore difficult to verbalize. The purpose of this study is to build a new learning community for nursing education (nursing social e-learning model) that is refined and developed autonomously and continuously. We used the social network system (SNS) that can be participated in a variety of stakeholder of medical personnel in order to hear comments for the content of learning to practice nursing skill. We had the nurses make the nursing skill movie manual. Through this process to get the opinions about the movie contents from others, we inspected what kind of opinions and feelings occurred to the nurses. As a result, the nurses were able to see objectively the own nursing skills, to do self-reflection. They had the awareness to improve the nursing skills.

Collaborative networks for both improvement and research.

PubMed

Clancy, Carolyn M; Margolis, Peter A; Miller, Marlene

2013-06-01

Moving significant therapeutic discoveries beyond early biomedical translation or T1 science and into practice involves: (1) T2 science, identifying "the right treatment for the right patient in the right way at the right time" (eg, patient-centered outcomes research) and tools to implement this knowledge (eg, guidelines, registries); and (2) T3 studies addressing how to achieve health care delivery change. Collaborative improvement networks can serve as large-scale, health system laboratories to engage clinicians, researchers, patients, and parents in testing approaches to translate research into practice. Improvement networks are of particular importance for pediatric T2 and T3 research, as evidence to establish safety and efficacy of therapeutic interventions in children is often lacking. Networks for improvement and research are also consistent with the Institute of Medicine's Learning Healthcare Systems model in which learning networks provide a system for improving care and outcomes and generate new knowledge in near real-time. Creation of total population registries in collaborative network sites provides large, representative study samples with high-quality data that can be used to generate evidence and to inform clinical decision-making. Networks use collaboration, data, and quality-improvement methods to standardize practice. Therefore, variation in outcomes due to unreliable and unnecessary care delivery is reduced, increasing statistical power, and allowing a consistent baseline from which to test new strategies. In addition, collaborative networks for improvement and research offer the opportunity to not only make improvements but also to study improvements to determine which interventions and combination of strategies work best in what settings.

Efficient Ways to Learn Weather Radar Polarimetry

ERIC Educational Resources Information Center

Cao, Qing; Yeary, M. B.; Zhang, Guifu

2012-01-01

The U.S. weather radar network is currently being upgraded with dual-polarization capability. Weather radar polarimetry is an interdisciplinary area of engineering and meteorology. This paper presents efficient ways to learn weather radar polarimetry through several basic and practical topics. These topics include: 1) hydrometeor scattering model…

Using Technology to Enhance Collaborative Learning

ERIC Educational Resources Information Center

Wasonga, Teresa A.

2007-01-01

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

Successful Teachers Practice Perpetual Learning

ERIC Educational Resources Information Center

Main, Marisa

2007-01-01

Successful teaching involves continuous learning, stimulation, motivation, and networking with other art educators. To help art teachers improve themselves, SchoolArts magazine recently organized the Folk Art Traditions and Beyond Seminar at Ghost Ranch in Santa Fe. In this article, the author describes the highlights of the Folk Art Traditions…

Improving Audio Quality in Distance Learning Applications.

ERIC Educational Resources Information Center

Richardson, Craig H.

This paper discusses common causes of problems encountered with audio systems in distance learning networks and offers practical suggestions for correcting the problems. Problems and discussions are divided into nine categories: (1) acoustics, including reverberant classrooms leading to distorted or garbled voices, as well as one-dimensional audio…

Proceedings for the Annual Symposium and Exhibition on Situational Awareness in the Tactical Air Environment, (2nd), Held at Patuxent River, Maryland, on 3-4 June 1997

DTIC Science & Technology

1997-06-01

made based on a learning mechanism. Traditional statistical regression and neural network approaches offer some utility, but suffer from practical...Columbus, OH. Kraiger, K., Ford, J. K., & Salas, E. (1993). Application of cognitive, skill- based , and affective theories of learning outcomes to new...and Feature Effects 151 Enhanced Spatial State Feedback for Night Vision Goggle Displays 159 Statistical Network Applications of Decision Aiding for

bnstruct: an R package for Bayesian Network structure learning in the presence of missing data.

PubMed

Franzin, Alberto; Sambo, Francesco; Di Camillo, Barbara

2017-04-15

A Bayesian Network is a probabilistic graphical model that encodes probabilistic dependencies between a set of random variables. We introduce bnstruct, an open source R package to (i) learn the structure and the parameters of a Bayesian Network from data in the presence of missing values and (ii) perform reasoning and inference on the learned Bayesian Networks. To the best of our knowledge, there is no other open source software that provides methods for all of these tasks, particularly the manipulation of missing data, which is a common situation in practice. The software is implemented in R and C and is available on CRAN under a GPL licence. francesco.sambo@unipd.it. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Measuring Creative Potential: Using Social Network Analysis to Monitor a Learners' Creative Capacity

ERIC Educational Resources Information Center

Dawson, Shane; Tan, Jennifer Pei Ling; McWilliam, Erica

2011-01-01

Despite the burgeoning rhetoric from political, social and educational commentators regarding creativity and learning and teaching, there is a paucity of scalable and measurable examples of creativity-centric pedagogical practice. This paper makes an argument for the application of social network visualisations to inform and support…

Teacher Advice-Seeking: Relating Centrality and Expertise in Middle School Mathematics Social Networks

ERIC Educational Resources Information Center

Berebitsky, Dan; Andrews-Larson, Christine

2017-01-01

Background/Context: Teachers' relationships with principals, instructional coaches, and other teachers have important implications for the improvement of their instructional practice and student learning. In particular, teachers who access content-specific instructional expertise through their social networks are more likely to exhibit and sustain…

Social Media for Informal Minority Language Learning: Exploring Welsh Learners' Practices

ERIC Educational Resources Information Center

Jones, Ann

2015-01-01

Conole and Alevizou's social media typology (Conole and Alevizou, 2010) includes amongst its ten categories: media sharing; conversational arenas and chat; social networking and blogging. These are all media with which language learners are increasingly engaging (Lamy and Zourou, 2013). Social networking tools, in particular, which encourage…

Deep learning in bioinformatics.

PubMed

Min, Seonwoo; Lee, Byunghan; Yoon, Sungroh

2017-09-01

In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-the-art performance in various fields. Accordingly, application of deep learning in bioinformatics to gain insight from data has been emphasized in both academia and industry. Here, we review deep learning in bioinformatics, presenting examples of current research. To provide a useful and comprehensive perspective, we categorize research both by the bioinformatics domain (i.e. omics, biomedical imaging, biomedical signal processing) and deep learning architecture (i.e. deep neural networks, convolutional neural networks, recurrent neural networks, emergent architectures) and present brief descriptions of each study. Additionally, we discuss theoretical and practical issues of deep learning in bioinformatics and suggest future research directions. We believe that this review will provide valuable insights and serve as a starting point for researchers to apply deep learning approaches in their bioinformatics studies. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

The Accelerating Roles of Higher Education in Regions through the European Lifelong Learning Initiative

ERIC Educational Resources Information Center

Nemeth, Balazs

2010-01-01

This article assesses the network development and promotion of the learning region model in HEIs in the framework of the European Higher Education Area (EHEA), focusing on quality, partnership and social equality in the Hungarian context. It argues that the learning city-region model can be used and put into practice in many different ways for a…

The ACCE Position Statement on Media Enriched Learning Communities: "If We're Not Standing on the Edge and Thinking Ahead We're Taking up Too Much Room"

ERIC Educational Resources Information Center

Kember, Deborah; Brandenburg, Tony; Murphy, Angela

2007-01-01

The ACCE position statement for creating media enriched learning communities targets all stakeholders in educational policy and practice who influence the future of learning, schools, and systems. Stakeholders include policy-makers in government, national and international organisations, professional networks and institutions, school leaders,…

Inferring causal molecular networks: empirical assessment through a community-based effort.

PubMed

Hill, Steven M; Heiser, Laura M; Cokelaer, Thomas; Unger, Michael; Nesser, Nicole K; Carlin, Daniel E; Zhang, Yang; Sokolov, Artem; Paull, Evan O; Wong, Chris K; Graim, Kiley; Bivol, Adrian; Wang, Haizhou; Zhu, Fan; Afsari, Bahman; Danilova, Ludmila V; Favorov, Alexander V; Lee, Wai Shing; Taylor, Dane; Hu, Chenyue W; Long, Byron L; Noren, David P; Bisberg, Alexander J; Mills, Gordon B; Gray, Joe W; Kellen, Michael; Norman, Thea; Friend, Stephen; Qutub, Amina A; Fertig, Elana J; Guan, Yuanfang; Song, Mingzhou; Stuart, Joshua M; Spellman, Paul T; Koeppl, Heinz; Stolovitzky, Gustavo; Saez-Rodriguez, Julio; Mukherjee, Sach

2016-04-01

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.

Nourishing networks: an interprofessional learning model and its application to the Australian rural health workforce.

PubMed

Little, F; Brown, L; Grotowski, M; Harris, D

2012-01-01

Access to continuing professional development for rural health clinicians requires strategies to overcome barriers associated with finances, travel and a lack of resources. Approaches to providing professional development need to transcend conventional educational methods and consider interprofessional educational opportunities to meet the diverse needs of the rural health workforce. Rural clinicians often work in professional isolation and frequently work collaboratively with clinicians from a range of other health disciplines. Interprofessional learning and practice is therefore important in a rural areas as clinicians working in these settings are often more reliant on each other and require an understanding of other's roles to provide effective health care. In addition, specialist services are limited in rural areas, with health professionals increasingly required to perform extended roles at an advanced-practice level. A model for interprofessional learning has been developed to attempt to address the barriers related to the delivery of interprofessional education in the rural health setting in Australia. This model demonstrates a flexible approach to interprofessional learning which meets different educational needs across a number of health disciplines, and is tailored to varying levels of expertise. It incorporates three learning approaches: traditional learning, flexible learning and advanced practice. Each of these components of the model are described and the Nourishing Networks program is provided as an example of the application of the model in a rural setting, utilising 'eating disorders' as the educational topic. Interprofessional learning can be delivered effectively in a rural setting by utilising technology to help bridge the isolation experienced in rural practice. Challenges in delivering the interprofessional learning program included: engaging rural general practitioners, utilising technology and maintaining participant engagement. The use of technology is essential to access a broad group of rural clinicians however, there are limitations in its use that must be acknowledged. The pilot of the Stepped Interprofessional Rural Learning Model and its application to eating disorders has scope for use in delivering education for other health topics.

A study of network education application on nursing staff continuing education effectiveness and staff's satisfaction.

PubMed

Lin, Juin-Shu; Yen-Chi, Liao; Lee, Ting-Ting

2006-01-01

The rapid development of computer technology pushes Internet's popularity and makes daily services more timely and convenient. Meanwhile, it also becomes a trend for nursing practice to implement network education model to break the distance barriers and for nurses to obtain more knowledge. The purpose of this study was to investigate the relationship of nursing staff's information competency, satisfaction and outcomes of network education. After completing 4 weeks of network education, a total of 218 nurses answered the on-line questionnaires. The results revealed that nurses who joined the computer training course for less than 3 hours per week, without networking connection devices and with college degree, had the lower nursing informatics competency; while nurses who were older, at N4 position, with on-line course experience and participated for more than 4 hours each week, had higher nursing informatics competency. Those who participated in the network education course less than 4 hours per week were less satisfied. There were significant differences between nursing positions before and after having the network education. Nurses who had higher nursing information competency also had higher satisfaction toward the network education. Network education not only enhances learners' computer competency but also improves their learning satisfaction. By promoting the network education and improving nurses' hardware/software skills and knowledge, nurses can use networks to access learning resources. Healthcare institutions should also enhance computer infrastructure, and to establish the standards for certificate courses to increase the learning motivation and learning outcome.

Social networking and Internet use among pelvic floor patients: a multicenter survey.

PubMed

Mazloomdoost, Donna; Kanter, Gregory; Chan, Robert C; Deveaneau, Nicolette; Wyman, Allison M; Von Bargen, Emily C; Chaudhry, Zaid; Elshatanoufy, Solafa; Miranne, Jeannine M; Chu, Christine M; Pauls, Rachel N; Arya, Lily A; Antosh, Danielle D

2016-11-01

Internet resources are becoming increasingly important for patients seeking medical knowledge. It is imperative to understand patient use and preferences for using the Internet and social networking websites to optimize patient education. The purpose of this study was to evaluate social networking and Internet use among women with pelvic floor complaints to seek information for their conditions as well as describe the likelihood, preferences, and predictors of website usage. This was a cross-sectional, multicenter study of women presenting to clinical practices of 10 female pelvic medicine and reconstructive surgery fellowship programs across the United States, affiliated with the Fellows' Pelvic Research Network. New female patients presenting with pelvic floor complaints, including urinary incontinence, pelvic organ prolapse, and fecal incontinence were eligible. Participants completed a 24 item questionnaire designed by the authors to assess demographic information, general Internet use, preferences regarding social networking websites, referral patterns, and resources utilized to learn about their pelvic floor complaints. Internet use was quantified as high (≥4 times/wk), moderate (2-3 times/wk), or minimal (≤1 time/wk). Means were used for normally distributed data and medians for data not meeting this assumption. Fisher's exact and χ 2 tests were used to evaluate the associations between variables and Internet use. A total of 282 surveys were analyzed. The majority of participants, 83.3%, were white. The mean age was 55.8 years old. Referrals to urogynecology practices were most frequently from obstetrician/gynecologists (39.9%) and primary care providers (27.8%). Subjects were well distributed geographically, with the largest representation from the South (38.0%). Almost one third (29.9%) were most bothered by prolapse complaints, 22.0% by urgency urinary incontinence, 20.9% by stress urinary incontinence, 14.9% by urgency/frequency symptoms, and 4.1% by fecal incontinence. The majority, 75.0%, described high Internet use, whereas 8.5% moderately and 4.8% minimally used the Internet. Women most often used the Internet for personal motivations including medical research (76.4%), and 42.6% reported Google to be their primary search engine. Despite this, only 4.9% primarily used the Internet to learn about their pelvic floor condition, more commonly consulting an obstetrician-gynecologist for this information (39.4%). The majority (74.1%) held a social networking account, and 45.9% visited these daily. Nearly half, 41.7%, expressed the desire to use social networking websites to learn about their condition. Women <65 years old were significantly more likely to have high Internet use (83.4% vs 68.8%, P = .018) and to desire using social networking websites to learn about their pelvic floor complaint (P = .008). The presenting complaint was not associated with Internet use (P = .905) or the desire to use social networking websites to learn about pelvic floor disorders (P = .201). Women presenting to urogynecology practices have high Internet use and a desire to learn about their conditions via social networking websites. Despite this, obstetrician-gynecologists remain a common resource for information. Nonetheless, urogynecology practices and national organizations would likely benefit from increasing their Internet resources for patient education in pelvic floor disorders, though patients should be made aware of available resources. Copyright © 2016 Elsevier Inc. All rights reserved.

Role of Social Knowledge Networking technology in facilitating meaningful use of Electronic Health Record medication reconciliation.

PubMed

Rangachari, Pavani

2016-06-01

Despite the federal policy impetus towards EHR Medication Reconciliation, hospital adherence has lagged for one chief reason; low physician engagement, which in turn emanates from lack of consensus in regard to which physician is responsible for managing a patient's medication list, and the importance of medication reconciliation as a tool for improving patient safety and quality of care. The Technology-in-Practice (TIP) framework stresses the role of human action in enacting structures of technology use or "technologies-in-practice." Applying the TIP framework to the EHR Medication Reconciliation context, helps frame the problem as one of low physician engagement in performing EHR Medication Reconciliation, translating to limited-use-EHR-in-practice. Concurrently, the problem suggests a hierarchical network structure, reflecting limited communication among hospital administrators and clinical providers on the importance of EHR Medication Reconciliation in improving patient safety. Integrating the TIP literature with the more recent knowledge-in-Practice (KIP) literature suggests that EHR-in-practice could be transformed from "limited use" to "meaningful use" through the use of Social Knowledge Networking (SKN) Technology to create new social network structures, and enable engagement, learning, and practice change. Correspondingly, the objectives of this paper are to: 1) Conduct a narrative review of the literature on "technology use," to understand how technologies-in-practice may be transformed from limited use to meaningful use; 2) Conduct a narrative review of the literature on "organizational change implementation," to understand how changes in technology use could be successfully implemented and sustained in a healthcare organizational context; and 3) Apply lessons learned from the narrative literature reviews to identify strategies for the meaningful use and successful implementation of EHR Medication Reconciliation technology.

The Diversity Project: An Ethnography of Social Justice Experiential Education Programming

ERIC Educational Resources Information Center

Vernon, Franklin

2016-01-01

Whilst adventure-based experiential education traditions have long-standing claims of progressive, democratic learning potential, little research has examined practice from within democratic theories of participation and learning. Focusing on a complex network making up a disturbing interaction in an outdoor education programme, I posit forms of…

Expanding Learning Opportunities with Transmedia Practices: "Inanimate Alice" as an Exemplar

ERIC Educational Resources Information Center

Fleming, Laura

2013-01-01

The proliferation of digital and networking technologies enables us to rethink, restructure, and redefine teaching and learning. Transmedia storytelling takes advantage of the rapid convergence of media and allows teachers and learners to participate in rich virtual (and physical) environments that have been shown to foster students' real…

Ties That Bind: The Practice of Social Networks. Number Two in a Series of Reports on Social Networks from The Annie E. Casey Foundation

ERIC Educational Resources Information Center

Bailey, Terri J.

2006-01-01

This report documents the Annie E. Casey Foundation's effort to learn from families, communities and organizations around the country about their experiences with social network strategies and approaches. Using the voices and experiences of the families and organizations visited, the report summarizes findings from these visits and helps lay the…

Using Communities of Practice to Foster Faculty Development in Higher Education

ERIC Educational Resources Information Center

Teeter, Christopher; Fenton, Nancy; Nicholson, Karen; Flynn, Terry; Kim, Joseph; McKay, Muriel; O'Shaughnessy, Bridget; Vajoczki, Sue

2011-01-01

Communities of practice are becoming more widespread within higher education, yet little research has explored how these social learning networks can enhance faculty development. The focus of this paper is to describe the first-year experience of a community of practice initiative at McMaster University that was designed to engage groups of…

Core Practices: Fuel Superintendents' Equity Focus

ERIC Educational Resources Information Center

Thompson, Scott

2016-01-01

For eight years, more than a dozen district superintendents in New Jersey have joined together for a full day each month during the school year to listen to and learn from each other as a community of practice. Known as the New Jersey Network of Superintendents, this community of practice has a tight focus on advancing equity through improvement…

On the sample complexity of learning for networks of spiking neurons with nonlinear synaptic interactions.

PubMed

Schmitt, Michael

2004-09-01

We study networks of spiking neurons that use the timing of pulses to encode information. Nonlinear interactions model the spatial groupings of synapses on the neural dendrites and describe the computations performed at local branches. Within a theoretical framework of learning we analyze the question of how many training examples these networks must receive to be able to generalize well. Bounds for this sample complexity of learning can be obtained in terms of a combinatorial parameter known as the pseudodimension. This dimension characterizes the computational richness of a neural network and is given in terms of the number of network parameters. Two types of feedforward architectures are considered: constant-depth networks and networks of unconstrained depth. We derive asymptotically tight bounds for each of these network types. Constant depth networks are shown to have an almost linear pseudodimension, whereas the pseudodimension of general networks is quadratic. Networks of spiking neurons that use temporal coding are becoming increasingly more important in practical tasks such as computer vision, speech recognition, and motor control. The question of how well these networks generalize from a given set of training examples is a central issue for their successful application as adaptive systems. The results show that, although coding and computation in these networks is quite different and in many cases more powerful, their generalization capabilities are at least as good as those of traditional neural network models.

Activity Theory and the Transformation of Pedagogic Practice

ERIC Educational Resources Information Center

Yamazumi, Katsuhiro

2006-01-01

Today, work and other societal practices are experiencing accelerating paradigm shifts from mass-production-based systems toward new systems based on networking between organizations, collaboration, and partnerships. This shift requires new paradigms in the fields of education, learning, and development. As human activity quickly changes to…

Neural correlates of skill acquisition: decreased cortical activity during a serial interception sequence learning task.

PubMed

Gobel, Eric W; Parrish, Todd B; Reber, Paul J

2011-10-15

Learning of complex motor skills requires learning of component movements as well as the sequential structure of their order and timing. Using a Serial Interception Sequence Learning (SISL) task, participants learned a sequence of precisely timed interception responses through training with a repeating sequence. Following initial implicit learning of the repeating sequence, functional MRI data were collected during performance of that known sequence and compared with activity evoked during novel sequences of actions, novel timing patterns, or both. Reduced activity was observed during the practiced sequence in a distributed bilateral network including extrastriate occipital, parietal, and premotor cortical regions. These reductions in evoked activity likely reflect improved efficiency in visuospatial processing, spatio-motor integration, motor planning, and motor execution for the trained sequence, which is likely supported by nondeclarative skill learning. In addition, the practiced sequence evoked increased activity in the left ventral striatum and medial prefrontal cortex, while the posterior cingulate was more active during periods of better performance. Many prior studies of perceptual-motor skill learning have found increased activity in motor areas of the frontal cortex (e.g., motor and premotor cortex, SMA) and striatal areas (e.g., the putamen). The change in activity observed here (i.e., decreased activity across a cortical network) may reflect skill learning that is predominantly expressed through more accurate performance rather than decreased reaction time. Copyright © 2011 Elsevier Inc. All rights reserved.

Neural Correlates of Skill Acquisition: Decreased Cortical Activity During a Serial Interception Sequence Learning Task

PubMed Central

Gobel, Eric W.; Parrish, Todd B.; Reber, Paul J.

2011-01-01

Learning of complex motor skills requires learning of component movements as well as the sequential structure of their order and timing. Using a Serial Interception Sequence Learning (SISL) task, participants learned a sequence of precisely timed interception responses through training with a repeating sequence. Following initial implicit learning of the repeating sequence, functional MRI data were collected during performance of that known sequence and compared with activity evoked during novel sequences of actions, novel timing patterns, or both. Reduced activity was observed during the practiced sequence in a distributed bilateral network including extrastriate occipital, parietal, and premotor cortical regions. These reductions in evoked activity likely reflect improved efficiency in visuospatial processing, spatio-motor integration, motor planning, and motor execution for the trained sequence, which is likely supported by nondeclarative skill learning. In addition, the practiced sequence evoked increased activity in the left ventral striatum and medial prefrontal cortex, while the posterior cingulate was more active during periods of better performance. Many prior studies of perceptual-motor skill learning have found increased activity in motor areas of frontal cortex (e.g., motor and premotor cortex, SMA) and striatal areas (e.g., the putamen). The change in activity observed here (i.e., decreased activity across a cortical network) may reflect skill learning that is predominantly expressed through more accurate performance rather than decreased reaction time. PMID:21771663

Networking Skills as a Career Development Practice: Lessons from the Earth Science Women's Network (ESWN)

NASA Astrophysics Data System (ADS)

Hastings, M. G.; Kontak, R.; Holloway, T.; Marin-Spiotta, E.; Steiner, A. L.; Wiedinmyer, C.; Adams, A. S.; de Boer, A. M.; Staudt, A. C.; Fiore, A. M.

2010-12-01

Professional networking is often cited as an important component of scientific career development, yet there are few resources for early career scientists to develop and build networks. Personal networks can provide opportunities to learn about organizational culture and procedures, expectations, advancement opportunities, and best practices. They provide access to mentors and job placement opportunities, new scientific collaborations, speaker and conference invitations, increased scientific visibility, reduced isolation, and a stronger feeling of community. There is evidence in the literature that a sense of community positively affects the engagement and retention of underrepresented groups, including women, in science. Thus women scientists may particularly benefit from becoming part of a network. The Earth Science Women’s Network (ESWN) began in 2002 as an informal peer-to-peer mentoring initiative among a few recent Ph.D.s. The network has grown exponentially to include over 1000 women scientists across the globe. Surveys of our membership about ESWN report positive impacts on the careers of women in Earth sciences, particularly those in early career stages. Through ESWN, women share both professional and personal advice, establish research collaborations, communicate strategies on work/life balance, connect with women at various stages of their careers, and provide perspectives from cultures across the globe. We present lessons learned through the formal and informal activities promoted by ESWN in support of the career development of women Earth scientists.

The OpenForest Portal as an Open Learning Ecosystem: Co-Developing in the Study of a Multidisciplinary Phenomenon in a Cultural Context

ERIC Educational Resources Information Center

Liljeström, Anu; Enkenberg, Jorma; Vanninen, Petteri; Vartiainen, Henriikka; Pöllänen, Sinikka

2014-01-01

This paper discusses the OpenForest portal and its related multidisciplinary learning project. The OpenForest portal is an open learning environment and ecosystem, in which students can participate in co-developing and co-creating practices. The aim of the OpenForest ecosystem is to create an extensive interactive network of diverse learning…

Bringing Interpretability and Visualization with Artificial Neural Networks

ERIC Educational Resources Information Center

Gritsenko, Andrey

2017-01-01

Extreme Learning Machine (ELM) is a training algorithm for Single-Layer Feed-forward Neural Network (SLFN). The difference in theory of ELM from other training algorithms is in the existence of explicitly-given solution due to the immutability of initialed weights. In practice, ELMs achieve performance similar to that of other state-of-the-art…

Situated Learning through Social Networking Communities: The Development of Joint Enterprise, Mutual Engagement, and a Shared Repertoire

ERIC Educational Resources Information Center

Mills, Nicole

2011-01-01

Scholars praise social networking tools for their ability to engage and motivate iGeneration students in meaningful communicative practice, content exchange, and collaboration (Greenhow, Robelia, & Hughes, 2009; Ziegler, 2007). To gain further insight about the nature of student participation, knowledge acquisition, and relationship development…

Coral-View: A Network-Based Design Environment for Collaborative Learning

ERIC Educational Resources Information Center

Sun, Chuen-Tsai; Lin, Sunny S. J.

2004-01-01

The vast majority of complex engineering tasks in today's business world are completed using a team-oriented approach. Therefore, teaching collaborative skills to university students can be viewed as a practical means of enhancing their employability. With these goals in mind, the authors developed a network environment that helps Taiwanese…

Social Network Perspectives Reveal Strength of Academic Developers as Weak Ties

ERIC Educational Resources Information Center

Matthews, Kelly E.; Crampton, Andrea; Hill, Matthew; Johnson, Elizabeth D.; Sharma, Manjula D.; Varsavsky, Cristina

2015-01-01

Social network perspectives acknowledge the influence of disciplinary cultures on academics' teaching beliefs and practices with implications for academic developers. The contribution of academic developers in 18 scholarship of teaching and learning (SoTL) projects situated in the sciences are explored by drawing on data from a two-year national…

Shaping Networked Theatre: Experience Architectures, Behaviours and Creative Pedagogies

ERIC Educational Resources Information Center

Sutton, Paul

2012-01-01

Since 2006 the UK based applied theatre company C&T has been using its experience and expertise in mixing drama, learning and digital media to create a new online utility for shaping collaborative educational drama experiences. C&T describes this practice as "Networked Theatre". This article describes both the motivations for…

Teaching adults-best practices that leverage the emerging understanding of the neurobiology of learning.

PubMed

Mahan, John D; Stein, David S

2014-07-01

It is important in teaching adults to recognize the essential characteristics of adult learners and how these characteristics define their learning priorities and activities. The seven key premises and practices for teaching adults provide a good guide for those interested in helping adults learn. The emerging science of the neurobiology of learning provides powerful new insights into how learning occurs in the complex integrated neural network that characterizes the adult. Differentiation of the two types of thinking: System 1 (fast, intuitive, and, often, emotional) and System 2 (slower, deliberate, and logical). System 1 thinking helps explain the basis for quick decisions and reliance of humans on heuristics (or rules of thumb) that leads to the type of convenient thinking associated with errors of thinking and judgment. We now know that the learning experience has an objective location-in the temporal and parietal lobes-as persistent dynamic networks of neurons and neuronal connections. Learning is initially stored in transient working memory (relatively limited capacity and time frame) and then moved under the right conditions to more long-lasting/stable memory (with larger capacity) that is stored for future access and development. It is clear that memories are not static and are not destined, once developed, to forever remain as stable constructs; rather, memories are dynamic, always available for modulation and alteration, and heavily invested with context, emotion, and other operant factors. The framework for such neural networks involves new neuronal connections, enhanced neuronal synaptic transmission, and neuron generation. Ten key teaching and learning concepts derived from recent neurobiology studies on learning and memory are presented. As the neurobiology of learning is better defined, the basis for how adults best learn, and even the preferences they display, can be employed as the physiological foundation for our best methods to effectively teach adults and facilitate their learning. Copyright © 2014 Mosby, Inc. All rights reserved.

ENERGY-NET (Energy, Environment and Society Learning Network): Best Practices to Enhance Informal Geoscience Learning

NASA Astrophysics Data System (ADS)

Rossi, R.; Elliott, E. M.; Bain, D.; Crowley, K. J.; Steiner, M. A.; Divers, M. T.; Hopkins, K. G.; Giarratani, L.; Gilmore, M. E.

2014-12-01

While energy links all living and non-living systems, the integration of energy, the environment, and society is often not clearly represented in 9 - 12 classrooms and informal learning venues. However, objective public learning that integrates these components is essential for improving public environmental literacy. ENERGY-NET (Energy, Environment and Society Learning Network) is a National Science Foundation funded initiative that uses an Earth Systems Science framework to guide experimental learning for high school students and to improve public learning opportunities regarding the energy-environment-society nexus in a Museum setting. One of the primary objectives of the ENERGY-NET project is to develop a rich set of experimental learning activities that are presented as exhibits at the Carnegie Museum of Natural History in Pittsburgh, Pennsylvania (USA). Here we detail the evolution of the ENERGY-NET exhibit building process and the subsequent evolution of exhibit content over the past three years. While preliminary plans included the development of five "exploration stations" (i.e., traveling activity carts) per calendar year, the opportunity arose to create a single, larger topical exhibit per semester, which was assumed to have a greater impact on museum visitors. Evaluative assessments conducted to date reveal important practices to be incorporated into ongoing exhibit development: 1) Undergraduate mentors and teen exhibit developers should receive additional content training to allow richer exhibit materials. 2) The development process should be distributed over as long a time period as possible and emphasize iteration. This project can serve as a model for other collaborations between geoscience departments and museums. In particular, these practices may streamline development of public presentations and increase the effectiveness of experimental learning activities.

A Participatory Evaluation of the Use of Social Networking Tools in a High School Math Class

ERIC Educational Resources Information Center

Wormald, Randy J.

2012-01-01

As we move into the 21st century, the needs of our students are more variable than ever. There has been a proliferation of social networking usage in society yet there has been little use of those emerging tools in schools as a means to enhance student learning. It is a common practice in school districts to block social networking sites and…

Toward Development of Distance Learning Environment in the Grid

ERIC Educational Resources Information Center

Li, Kuan-Ching; Tsai, Yin-Te; Tsai, Chuan-Ko

2008-01-01

In recent years, with the rapid development of communication and network technologies, distance learning has been popularized and it became one of the most well-known teaching methods, due to its practicability. Over the Internet, learners are free to access new knowledge without restrictions on time or location. However, current distance learning…

Global Conceptualization of the Professional Learning Community Process: Transitioning from Country Perspectives to International Commonalities

ERIC Educational Resources Information Center

Huffman, Jane B.; Olivier, Dianne F.; Wang, Ting; Chen, Peiying; Hairon, Salleh; Pang, Nicholas

2016-01-01

The authors seek to find common PLC structures and actions among global educational systems to enhance understanding and practice. Six international researchers formed the Global Professional Learning Community Network (GloPLCNet), conducted literature reviews of each country's involvement with PLC actions, and noted similarities and common…

Measuring Learning Progressions Using Bayesian Modeling in Complex Assessments

ERIC Educational Resources Information Center

Rutstein, Daisy Wise

2012-01-01

This research examines issues regarding model estimation and robustness in the use of Bayesian Inference Networks (BINs) for measuring Learning Progressions (LPs). It provides background information on LPs and how they might be used in practice. Two simulation studies are performed, along with real data examples. The first study examines the case…

Putting ECD into Practice: The Interplay of Theory and Data in Evidence Models within a Digital Learning Environment

ERIC Educational Resources Information Center

Rupp, Andre A.; Levy, Roy; Dicerbo, Kristen E.; Sweet, Shauna J.; Crawford, Aaron V.; Calico, Tiago; Benson, Martin; Fay, Derek; Kunze, Katie L.; Mislevy, Robert J.; Behrens, John T.

2012-01-01

In this paper we describe the development and refinement of "evidence rules" and "measurement models" within the "evidence model" of the "evidence-centered design" (ECD) framework in the context of the "Packet Tracer" digital learning environment of the "Cisco Networking Academy." Using…

Adult Learners and Professional Development: Peer-to-Peer Learning in a Networked Community

ERIC Educational Resources Information Center

Guldberg, Karen

2008-01-01

This paper analyses how adult learners on a professional development course learn and develop through online dialogue. The research uses Wenger's community of practice framework, and assesses whether the concept of "legitimate peripheral participation" is useful in relation to this specific case study in which the students are practitioners and…

Can Public Education Coexist with Participatory Culture?

ERIC Educational Resources Information Center

Losh, Elizabeth; Jenkins, Henry

2012-01-01

Participatory culture has many mechanisms to support peer-to-peer learning as young people enter interest-driven and friendship-driven networks. In this article, the authors argue that school librarians can help bridge the gap between the excitement of having students experiment with new forms of social learning and new digital-media practices,…

Creative Networks of Practice Using Web 2.0 Tools

ERIC Educational Resources Information Center

Orava, Jukka; Worrall, Pete

2011-01-01

This paper examines the professional implications for teachers and managers in new and evolving forms of professional development using Web 2.0 tools in a European context. Research findings are presented from the "Creative Use of Media" learning event developed through a European eTwinning Learning Lab initiative in spring of 2009. The…

A Servant of Two Masters: Designing Research To Advance Knowledge and Practice.

ERIC Educational Resources Information Center

James, Mary; Pedder, David; Swaffield, Sue; Conner, Colin; Frost, David; MacBeath, John

This paper describes aspects of the design and implementation of "Learning How To Learnin Classrooms, Schools, and Networks," a major development and research project within the Teaching and Learning Research Programme in the United Kingdom. It focuses on how a group of Cambridge academics and researchers, working in partnership with other…

Growing a professional network to over 3000 members in less than 4 years: evaluation of InspireNet, British Columbia's virtual nursing health services research network.

PubMed

Frisch, Noreen; Atherton, Pat; Borycki, Elizabeth; Mickelson, Grace; Cordeiro, Jennifer; Novak Lauscher, Helen; Black, Agnes

2014-02-21

Use of Web 2.0 and social media technologies has become a new area of research among health professionals. Much of this work has focused on the use of technologies for health self-management and the ways technologies support communication between care providers and consumers. This paper addresses a new use of technology in providing a platform for health professionals to support professional development, increase knowledge utilization, and promote formal/informal professional communication. Specifically, we report on factors necessary to attract and sustain health professionals' use of a network designed to increase nurses' interest in and use of health services research and to support knowledge utilization activities in British Columbia, Canada. "InspireNet", a virtual professional network for health professionals, is a living laboratory permitting documentation of when and how professionals take up Web 2.0 and social media. Ongoing evaluation documents our experiences in establishing, operating, and evaluating this network. Overall evaluation methods included (1) tracking website use, (2) conducting two member surveys, and (3) soliciting member feedback through focus groups and interviews with those who participated in electronic communities of practice (eCoPs) and other stakeholders. These data have been used to learn about the types of support that seem relevant to network growth. Network growth exceeded all expectations. Members engaged with varying aspects of the network's virtual technologies, such as teams of professionals sharing a common interest, research teams conducting their work, and instructional webinars open to network members. Members used wikis, blogs, and discussion groups to support professional work, as well as a members' database with contact information and areas of interest. The database is accessed approximately 10 times per day. InspireNet public blog posts are accessed roughly 500 times each. At the time of writing, 21 research teams conduct their work virtually using the InspireNet platform; 10 topic-based Action Teams meet to address issues of mutual concern. Nursing and other health professionals, even those who rated themselves as computer literate, required significant mentoring and support in their efforts to adopt their practice to a virtual environment. There was a steep learning curve for professionals to learn to work in a virtual environment and to benefit from the available technologies. Virtual professional networks can be positioned to make a significant contribution to ongoing professional practice and to creating environments supportive of information sharing, mentoring, and learning across geographical boundaries. Nonetheless, creation of a Web 2.0 and social media platform is not sufficient, in and of itself, to attract or sustain a vibrant community of professionals interested in improving their practice. Essential support includes instruction in the use of Web-based activities and time management, a biweekly e-Newsletter, regular communication from leaders, and an annual face-to-face conference.

Developing a Community of Practice for HIV Care: Supporting Knowledge Translation in a Regional Training Initiative.

PubMed

Gallagher, Donna M; Hirschhorn, Lisa R; Lorenz, Laura S; Piya, Priyatam

2017-01-01

Ensuring knowledgeable, skilled HIV providers is challenged by rapid advances in the field, diversity of patients and providers, and the need to retain experienced providers while training new providers. These challenges highlight the need for education strategies, including training and clinical consultation to support translation of new knowledge to practice. New England AIDS Education and Training Center (NEAETC) provides a range of educational modalities including academic peer detailing and distance support to HIV providers in six states. We describe the interprofessional perspectives of HIV providers who participated in this regional program to understand success and areas for strengthening pedagogical modality, content, and impact on clinical practice. This 2013 to 2014 mixed-methods study analyzed quantitative programmatic data to understand changes in training participants and modalities and used semistructured interviews with 30 HIV providers and coded for preidentified and emerging themes. Since 2010, NEAETC evolved modalities to a greater focus on active learning (case discussion, clinical consultation), decreasing didactic training by half (18-9%). This shift was designed to move from knowledge transfer to translation, and qualitative findings supported the value of active learning approaches. Providers valued interactive trainings and presentation of cases supporting knowledge translation. On-site training encouraged peer networking and sharing of lessons learned. Diversity in learning priorities across providers and sites validated NEAETC's approach of tailoring topics to local needs and encouraging regional networking. Tailored approaches resulted in improved provider-reported capacity, peer learning, and support. Future evaluations should explore the impact of this multipronged approach on supporting a community of practice and empowerment of provider teams.

Large-scale coupling dynamics of instructed reversal learning.

PubMed

Mohr, Holger; Wolfensteller, Uta; Ruge, Hannes

2018-02-15

The ability to rapidly learn from others by instruction is an important characteristic of human cognition. A recent study found that the rapid transfer from initial instructions to fluid behavior is supported by changes of functional connectivity between and within several large-scale brain networks, and particularly by the coupling of the dorsal attention network (DAN) with the cingulo-opercular network (CON). In the present study, we extended this approach to investigate how these brain networks interact when stimulus-response mappings are altered by novel instructions. We hypothesized that residual stimulus-response associations from initial practice might negatively impact the ability to implement novel instructions. Using functional imaging and large-scale connectivity analysis, we found that functional coupling between the CON and DAN was generally at a higher level during initial than reversal learning. Examining the learning-related connectivity dynamics between the CON and DAN in more detail by means of multivariate patterns analyses, we identified a specific subset of connections which showed a particularly high increase in connectivity during initial learning compared to reversal learning. This finding suggests that the CON-DAN connections can be separated into two functionally dissociable yet spatially intertwined subsystems supporting different aspects of short-term task automatization. Copyright © 2017 Elsevier Inc. All rights reserved.

TH-A-12A-01: Medical Physicist's Role in Digital Information Security: Threats, Vulnerabilities and Best Practices

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

McDonald, K; Curran, B

I. Information Security Background (Speaker = Kevin McDonald) Evolution of Medical Devices Living and Working in a Hostile Environment Attack Motivations Attack Vectors Simple Safety Strategies Medical Device Security in the News Medical Devices and Vendors Summary II. Keeping Radiation Oncology IT Systems Secure (Speaker = Bruce Curran) Hardware Security Double-lock Requirements “Foreign” computer systems Portable Device Encryption Patient Data Storage System Requirements Network Configuration Isolating Critical Devices Isolating Clinical Networks Remote Access Considerations Software Applications / Configuration Passwords / Screen Savers Restricted Services / access Software Configuration Restriction Use of DNS to restrict accesse. Patches / Upgrades Awareness Intrusionmore » Prevention Intrusion Detection Threat Risk Analysis Conclusion Learning Objectives: Understanding how Hospital IT Requirements affect Radiation Oncology IT Systems. Illustrating sample practices for hardware, network, and software security. Discussing implementation of good IT security practices in radiation oncology. Understand overall risk and threats scenario in a networked environment.« less

Learning Management Systems: Practical Considerations for the Selection and Implementation of an E-learning Platform for the Navy

DTIC Science & Technology

2007-01-28

is interested in B2B and B2C e-commerce, enterprise resource planning, e-procurement, supply-chain management, data mining, and knowledge discovery... social networking tools, collaborative spaces, knowledge management, “connecting-enabling” protocols like RSS, and other tools. The intent of the ILE...delivered to them, what learning pedagogy is appropriate for them, the optimal level of social interaction for learning, and available resources

Associate degree nursing in a community-based health center network: lessons in collaboration.

PubMed

Connolly, Charlene; Wilson, Diane; Missett, Regina; Dooley, Wanda C; Avent, Pamela A; Wright, Ronda

2004-02-01

This exemplar highlights the ability of community experiences to enhance nursing students' understanding of the principles of community-based care: advocating self-care; focusing on prevention, family, culture, and community; providing continuity of care; and collaborating. An innovative teaching-practice model (i.e., a nurse-managed "network" of clinics), incorporating service-learning, was created. The Network's purposes are to provide practice sites in community-based primary care settings for student clinical rotations, increasing the awareness of the civic and social responsibility to provide quality health care for disadvantaged populations; and to reduce health disparities by increasing access to free primary health care, including health promotion and disease prevention, for disadvantaged individuals. Network clients receive free health care, referrals, and guidance to effectively obtain additional health care resources for themselves and their families. The Network is a national pioneer in modeling the delivery of primary care services through a faculty-student practice plan, with leadership emanating from a community college.

Does a Formal Wiki Event Contribute to the Formation of a Network of Practice? A Social Capital Perspective on the Potential for Informal Learning

ERIC Educational Resources Information Center

Rehm, Martin; Littlejohn, Allison; Rienties, Bart

2018-01-01

Informal learning in blended and online settings plays an increasingly important role in the continuous professional development of individuals. Yet, how do individuals engage into these types of activities? We argue that social capital theory can provide valuable insights into how people behave and decide to take part in (in)formal learning.…

A globally convergent MC algorithm with an adaptive learning rate.

PubMed

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

2012-02-01

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

Who Exactly Is the Moderator? A Consideration of Online Knowledge Management Network Moderation in Educational Organisations

ERIC Educational Resources Information Center

Gairin-Sallan, Joaquin; Rodriguez-Gomez, David; Armengol-Asparo, Carme

2010-01-01

In the knowledge society, the appearance and development of new networked working and learning environments is increasingly common. In the "Accelera" project, which is the basis for this paper, we have developed an online community of practice which enables experiences and knowledge to be shared between various educational agents, and…

Connected to Learn: Teachers' Experiences with Networked Technologies in the Classroom

ERIC Educational Resources Information Center

Johnson, Matthew; Riel, Richard; Germain-Froese, Bernie

2016-01-01

To get a better understanding of how networked technologies are impacting teachers and their teaching practices, in 2015 MediaSmarts partnered with the Canadian Teachers' Federation to survey 4,043 K-12 teachers and school administrators who were teaching in classroom settings across the country. The survey explored the extent to which networked…

Place-Making in Higher Education: Co-Creating Engagement and Knowledge Practices in the Networked Age

ERIC Educational Resources Information Center

Swist, Teresa; Kuswara, Andreas

2016-01-01

The pedagogical locations, functions and possibilities of higher education continuously unfold as mobile technologies, digital content and social practices intersect at a rapid pace. There is an urgent need to understand better how student learning is situated within this complex system and interrelates with broader sociotechnical knowledge…

Beginning with the Particular: Reimagining Professional Development as a Feminist Practice

ERIC Educational Resources Information Center

Schultz, Katherine

2011-01-01

This article analyzes the work of a long-term network of teachers, the Philadelphia Teachers Learning Cooperative, with a focus on their descriptive practices. Drawing on three years of ethnographic documentation of weekly meetings and a historical archive of meetings over 30 years, I characterize the teachers' knowledge about teaching and…

Reality Is Our Laboratory: Communities of Practice in Applied Computer Science

ERIC Educational Resources Information Center

Rohde, M.; Klamma, R.; Jarke, M.; Wulf, V.

2007-01-01

The present paper presents a longitudinal study of the course "High-tech Entrepreneurship and New Media." The course design is based on socio-cultural theories of learning and considers the role of social capital in entrepreneurial networks. By integrating student teams into the communities of practice of local start-ups, we offer…

Evidence-Based, Student-Centered Instructional Practices. CAELA Network Brief

ERIC Educational Resources Information Center

Peyton, Joy Kreeft; Moore, Sarah Catherine K.; Young, Sarah

2010-01-01

The field of adult education has a longstanding tradition of student-centered approaches to learning. More recently, there has been an increased emphasis in K-12 and adult education on using evidence-based instructional practices. While there has been some tendency to dichotomize these two approaches, instruction of any kind is more effective when…

Peer learning partnerships: exploring the experience of pre-registration nursing students.

PubMed

Christiansen, Angela; Bell, Amelia

2010-03-01

This paper explores the impact of a peer learning initiative developed to facilitate, purposefully, mutually supportive learning relationships between student nurses in the practice setting. Finding effective strategies to support learning in the practice setting has been the focus of professional concern for a considerable time. In the UK clinical mentorship is seen as pivotal to ensuring fitness to practice; however, recent debate on the nature of learning has revealed the clinical workplace as a rich learning environment where learning occurs not only through hierarchical relationships, but also from a network of peer relationships. Formalising peer relationships through peer assisted learning is increasingly suggested as a strategy to support workplace learning and support novice students' transition to the clinical setting. Despite the developing literature in this field there is limited understanding about how students experience facilitated peer relationships. An interpretive qualitative design. Focus group interviews were used to collect interactive and situated discourse from nursing students who had recently participated in peer learning partnerships (n = 54). Narrative data were analysed thematically. Findings suggest that active support from a fellow student reduced the feelings of social isolation experienced by novice students in initial clinical placements, helping them to deal more effectively with the challenges faced and reducing the factors that have an impact on attrition. In addition, the reciprocity of the peer learning partnerships facilitated understanding of mentorship and created a heightened sense of readiness for registration and professional practice. Peer learning partnerships facilitated by mentors in clinical practice can support the transition to nursing for first year students and can help more experienced students gain a confidence and a heightened readiness for mentorship and registered practice. Facilitated peer learning partnerships can enhance the student experience in the practice setting and can help maximise opportunities for learning and support. This suggests that peer assisted learning is a legitimate area for innovation and further research.

Building research infrastructure in community health centers: a Community Health Applied Research Network (CHARN) report.

PubMed

Likumahuwa, Sonja; Song, Hui; Singal, Robbie; Weir, Rosy Chang; Crane, Heidi; Muench, John; Sim, Shao-Chee; DeVoe, Jennifer E

2013-01-01

This article introduces the Community Health Applied Research Network (CHARN), a practice-based research network of community health centers (CHCs). Established by the Health Resources and Services Administration in 2010, CHARN is a network of 4 community research nodes, each with multiple affiliated CHCs and an academic center. The four nodes (18 individual CHCs and 4 academic partners in 9 states) are supported by a data coordinating center. Here we provide case studies detailing how CHARN is building research infrastructure and capacity in CHCs, with a particular focus on how community practice-academic partnerships were facilitated by the CHARN structure. The examples provided by the CHARN nodes include many of the building blocks of research capacity: communication capacity and "matchmaking" between providers and researchers; technology transfer; research methods tailored to community practice settings; and community institutional review board infrastructure to enable community oversight. We draw lessons learned from these case studies that we hope will serve as examples for other networks, with special relevance for community-based networks seeking to build research infrastructure in primary care settings.

Building Research Infrastructure in Community Health Centers: A Community Health Applied Research Network (CHARN) Report

PubMed Central

Likumahuwa, Sonja; Song, Hui; Singal, Robbie; Weir, Rosy Chang; Crane, Heidi; Muench, John; Sim, Shao-Chee; DeVoe, Jennifer E.

2015-01-01

This article introduces the Community Health Applied Research Network (CHARN), a practice-based research network of community health centers (CHCs). Established by the Health Resources and Services Administration in 2010, CHARN is a network of 4 community research nodes, each with multiple affiliated CHCs and an academic center. The four nodes (18 individual CHCs and 4 academic partners in 9 states) are supported by a data coordinating center. Here we provide case studies detailing how CHARN is building research infrastructure and capacity in CHCs, with a particular focus on how community practice-academic partnerships were facilitated by the CHARN structure. The examples provided by the CHARN nodes include many of the building blocks of research capacity: communication capacity and “matchmaking” between providers and researchers; technology transfer; research methods tailored to community practice settings; and community institutional review board infrastructure to enable community oversight. We draw lessons learned from these case studies that we hope will serve as examples for other networks, with special relevance for community-based networks seeking to build research infrastructure in primary care settings. PMID:24004710

Building Professional Learning Communities in Special Education through Social Networking: Directions for Future Research

ERIC Educational Resources Information Center

Hardman, Elizabeth L.

2011-01-01

This paper examines the challenges inherent in building professional learning communities (PLCs) in special education and describes how two Web 2.0 tools were used to build a community that engages general and special education teachers, school administrators, and teacher educators in implementing research based inclusive practices that are known…

The Implementation of a Collaborative Action Research Programme for Developing Inclusive Practices: Social Learning in Small Internal Networks

ERIC Educational Resources Information Center

Angelides, Panayiotis; Georgiou, Renos; Kyriakou, Kyriaki

2008-01-01

The idea of inclusive education has featured very highly in the educational priorities of many educational systems. However, the same educational systems are very often criticised because of the difficulties of their teachers to respond to inclusive environments of learning, where all children, despite their abilities, receive equal opportunities…

An Automated Method to Generate e-Learning Quizzes from Online Language Learner Writing

ERIC Educational Resources Information Center

Flanagan, Brendan; Yin, Chengjiu; Hirokawa, Sachio; Hashimoto, Kiyota; Tabata, Yoshiyuki

2013-01-01

In this paper, the entries of Lang-8, which is a Social Networking Site (SNS) site for learning and practicing foreign languages, were analyzed and found to contain similar rates of errors for most error categories reported in previous research. These similarly rated errors were then processed using an algorithm to determine corrections suggested…

Disciplining the Practice of Creative Inquiry: The Suppression of Difference in Teacher Education

ERIC Educational Resources Information Center

Edwards, Gail; Blake, Anthony

2007-01-01

In this paper we suggest that the pursuit of ahistorical, universal truths in education is antithetical to creativity, learning and motivation in preservice teachers. We argue that learning to teach is a dynamic process embedded in networks of power in which educational truths are politically "accomplished" rather than innocently discovered. We…

Community-Sourcing a New Marketing Course: Collaboration in Social Media

ERIC Educational Resources Information Center

Schirr, Gary R.

2013-01-01

This paper shows the value of an online personal learning network or community in educational innovation. It shows how theories and best practices from service and product innovation, as well the theories of learning communities, were applied using social media to facilitate the grant proposal and course development processes for a new course in…

Blog-Based Professional Development of English Teachers in Mumbai: The Potential of Innovative Practice under Scrutiny

ERIC Educational Resources Information Center

Khan, Atiya

2017-01-01

The professional development of teachers in India is still, by and large, based on formal and outdated professional learning traditions, often characterised by crash courses and one-off workshops. In education, blogs have proven to be an effective means of establishing and maintaining collaborative learning networks and helping members reflect on…

Crossing Boundaries in Facebook: Students' Framing of Language Learning Activities as Extended Spaces

ERIC Educational Resources Information Center

Lantz-Andersson, Annika; Vigmo, Sylvi; Bowen, Rhonwen

2013-01-01

Young people's interaction online is rapidly increasing, which enables new spaces for communication; the impact on learning, however, is not yet acknowledged in education. The aim of this exploratory case study is to scrutinize how students frame their interaction in social networking sites (SNS) in school practices and what that implies for…

Merging Social Networking Environments and Formal Learning Environments to Support and Facilitate Interprofessional Instruction

PubMed Central

King, Sharla; Greidanus, Elaine; Carbonaro, Michael; Drummond, Jane; Patterson, Steven

2009-01-01

This study describes the redesign of an interprofessional team development course for health science students. A theoretical model is hypothesized as a framework for the redesign process, consisting of two themes: 1) the increasing trend among post-secondary students to participate in social networking (e.g., Facebook, Second Life) and 2) the need for healthcare educators to provide interprofessional training that results in effective communities of practice and better patient care. The redesign focused on increasing the relevance of the course through the integration of custom-designed technology to facilitate social networking during their interprofessional education. Results suggest that students in an educationally structured social networking environment can be guided to join learning communities quickly and access course materials. More research and implementation work is required to effectively develop interprofessional health sciences communities in a combined face-to-face and on-line social networking context. PMID:20165519

Networking to improve end of life care

PubMed Central

2009-01-01

Network organisations are increasingly common in healthcare. This paper describes an example of clinically led networking, which improved end of life care (EOLC) in care homes, differentiating between a ‘network’ as a formal entity and the more informal process of ‘networking’. The paper begins with a brief discussion of networks and their development in healthcare, then an overview of EOLC policy, the case setting and methods. The paper describes four key features of this networking; (1) how it enabled discussions and implemented processes to help people address difficult taboos about dying; (2) how personal communication and ‘distributed leadership’ facilitated learning; (3) how EOLC occasionally lapsed during the handover of patient care, where personal relationship and communication were weaker; and (4) how successful learning and sharing of best practice was fragile and could be potentially undermined by wider financial pressures in the NHS. PMID:25949588

Merging social networking environments and formal learning environments to support and facilitate interprofessional instruction.

PubMed

King, Sharla; Greidanus, Elaine; Carbonaro, Michael; Drummond, Jane; Patterson, Steven

2009-04-28

This study describes the redesign of an interprofessional team development course for health science students. A theoretical model is hypothesized as a framework for the redesign process, consisting of two themes: 1) the increasing trend among post-secondary students to participate in social networking (e.g., Facebook, Second Life) and 2) the need for healthcare educators to provide interprofessional training that results in effective communities of practice and better patient care. The redesign focused on increasing the relevance of the course through the integration of custom-designed technology to facilitate social networking during their interprofessional education. Results suggest that students in an educationally structured social networking environment can be guided to join learning communities quickly and access course materials. More research and implementation work is required to effectively develop interprofessional health sciences communities in a combined face-to-face and on-line social networking context.

Hardware-Efficient On-line Learning through Pipelined Truncated-Error Backpropagation in Binary-State Networks

PubMed Central

Mostafa, Hesham; Pedroni, Bruno; Sheik, Sadique; Cauwenberghs, Gert

2017-01-01

Artificial neural networks (ANNs) trained using backpropagation are powerful learning architectures that have achieved state-of-the-art performance in various benchmarks. Significant effort has been devoted to developing custom silicon devices to accelerate inference in ANNs. Accelerating the training phase, however, has attracted relatively little attention. In this paper, we describe a hardware-efficient on-line learning technique for feedforward multi-layer ANNs that is based on pipelined backpropagation. Learning is performed in parallel with inference in the forward pass, removing the need for an explicit backward pass and requiring no extra weight lookup. By using binary state variables in the feedforward network and ternary errors in truncated-error backpropagation, the need for any multiplications in the forward and backward passes is removed, and memory requirements for the pipelining are drastically reduced. Further reduction in addition operations owing to the sparsity in the forward neural and backpropagating error signal paths contributes to highly efficient hardware implementation. For proof-of-concept validation, we demonstrate on-line learning of MNIST handwritten digit classification on a Spartan 6 FPGA interfacing with an external 1Gb DDR2 DRAM, that shows small degradation in test error performance compared to an equivalently sized binary ANN trained off-line using standard back-propagation and exact errors. Our results highlight an attractive synergy between pipelined backpropagation and binary-state networks in substantially reducing computation and memory requirements, making pipelined on-line learning practical in deep networks. PMID:28932180

Hardware-Efficient On-line Learning through Pipelined Truncated-Error Backpropagation in Binary-State Networks.

PubMed

Mostafa, Hesham; Pedroni, Bruno; Sheik, Sadique; Cauwenberghs, Gert

2017-01-01

Artificial neural networks (ANNs) trained using backpropagation are powerful learning architectures that have achieved state-of-the-art performance in various benchmarks. Significant effort has been devoted to developing custom silicon devices to accelerate inference in ANNs. Accelerating the training phase, however, has attracted relatively little attention. In this paper, we describe a hardware-efficient on-line learning technique for feedforward multi-layer ANNs that is based on pipelined backpropagation. Learning is performed in parallel with inference in the forward pass, removing the need for an explicit backward pass and requiring no extra weight lookup. By using binary state variables in the feedforward network and ternary errors in truncated-error backpropagation, the need for any multiplications in the forward and backward passes is removed, and memory requirements for the pipelining are drastically reduced. Further reduction in addition operations owing to the sparsity in the forward neural and backpropagating error signal paths contributes to highly efficient hardware implementation. For proof-of-concept validation, we demonstrate on-line learning of MNIST handwritten digit classification on a Spartan 6 FPGA interfacing with an external 1Gb DDR2 DRAM, that shows small degradation in test error performance compared to an equivalently sized binary ANN trained off-line using standard back-propagation and exact errors. Our results highlight an attractive synergy between pipelined backpropagation and binary-state networks in substantially reducing computation and memory requirements, making pipelined on-line learning practical in deep networks.

Improving Uptake of Key Perinatal Interventions Using Statewide Quality Collaboratives.

PubMed

Pai, Vidya V; Lee, Henry C; Profit, Jochen

2018-06-01

Regional and statewide quality improvement collaboratives have been instrumental in implementing evidence-based practices and facilitating quality improvement initiatives within neonatology. Statewide collaboratives emerged from larger collaborative organizations, like the Vermont Oxford Network, and play an increasing role in collecting and interpreting data, setting priorities for improvement, disseminating evidence-based clinical practice guidelines, and creating regional networks for synergistic learning. In this review, we highlight examples of successful statewide collaborative initiatives, as well as challenges that exist in initiating and sustaining collaborative efforts. Copyright © 2018 Elsevier Inc. All rights reserved.

Development of a university-based emergency department network: lessons learned.

PubMed

Pimentel, Laura; Hirshon, Jon Mark; Barrueto, Fermin; Browne, Brian J

2012-10-01

As part of the growth of emergency medical care in our state, our university-based emergency medicine practice developed a network of affiliated emergency department (ED) practices. The original practices were academic and based on a faculty practice model; more recent network development incorporated a community practice model less focused on academics. This article discusses the growth of that network, with a focus on the recent addition of a county-wide two-hospital emergency medicine practice. During the transition of the two EDs from a contract management group to the university network, six critical areas in need of restructuring were identified: 1) departmental leadership, 2) recruitment and retention of clinical staff members, 3) staffing strategies, 4) relationships with key constituents, 5) clinical operations, supplies, and equipment, and 6) compensation structure. The impact of changes was measured by comparison of core measures, efficiency metrics, patient volumes, admissions, and transfers to the academic medical center before and after the implementation of our practice model. Our review and modification of these components significantly improved the quality and efficiency of care at the community hospital system. The consistent presence of board certified emergency physicians optimized utilization of clinical resources in the community hospital and the academic health system. This dynamic led to a mutually beneficial merger of these major state healthcare systems. Copyright © 2012 Elsevier Inc. All rights reserved.

Is Multitask Deep Learning Practical for Pharma?

PubMed

Ramsundar, Bharath; Liu, Bowen; Wu, Zhenqin; Verras, Andreas; Tudor, Matthew; Sheridan, Robert P; Pande, Vijay

2017-08-28

Multitask deep learning has emerged as a powerful tool for computational drug discovery. However, despite a number of preliminary studies, multitask deep networks have yet to be widely deployed in the pharmaceutical and biotech industries. This lack of acceptance stems from both software difficulties and lack of understanding of the robustness of multitask deep networks. Our work aims to resolve both of these barriers to adoption. We introduce a high-quality open-source implementation of multitask deep networks as part of the DeepChem open-source platform. Our implementation enables simple python scripts to construct, fit, and evaluate sophisticated deep models. We use our implementation to analyze the performance of multitask deep networks and related deep models on four collections of pharmaceutical data (three of which have not previously been analyzed in the literature). We split these data sets into train/valid/test using time and neighbor splits to test multitask deep learning performance under challenging conditions. Our results demonstrate that multitask deep networks are surprisingly robust and can offer strong improvement over random forests. Our analysis and open-source implementation in DeepChem provide an argument that multitask deep networks are ready for widespread use in commercial drug discovery.

Utilizing a Collaborative Learning Model to Promote Early Extubation Following Infant Heart Surgery.

PubMed

Mahle, William T; Nicolson, Susan C; Hollenbeck-Pringle, Danielle; Gaies, Michael G; Witte, Madolin K; Lee, Eva K; Goldsworthy, Michelle; Stark, Paul C; Burns, Kristin M; Scheurer, Mark A; Cooper, David S; Thiagarajan, Ravi; Sivarajan, V Ben; Colan, Steven D; Schamberger, Marcus S; Shekerdemian, Lara S

2016-10-01

To determine whether a collaborative learning strategy-derived clinical practice guideline can reduce the duration of endotracheal intubation following infant heart surgery. Prospective and retrospective data collected from the Pediatric Heart Network in the 12 months pre- and post-clinical practice guideline implementation at the four sites participating in the collaborative (active sites) compared with data from five Pediatric Heart Network centers not participating in collaborative learning (control sites). Ten children's hospitals. Data were collected for infants following two-index operations: 1) repair of isolated coarctation of the aorta (birth to 365 d) and 2) repair of tetralogy of Fallot (29-365 d). There were 240 subjects eligible for the clinical practice guideline at active sites and 259 subjects at control sites. Development and application of early extubation clinical practice guideline. After clinical practice guideline implementation, the rate of early extubation at active sites increased significantly from 11.7% to 66.9% (p < 0.001) with no increase in reintubation rate. The median duration of postoperative intubation among active sites decreased from 21.2 to 4.5 hours (p < 0.001). No statistically significant change in early extubation rates was found in the control sites 11.7% to 13.7% (p = 0.63). At active sites, clinical practice guideline implementation had no statistically significant impact on median ICU length of stay (71.9 hr pre- vs 69.2 hr postimplementation; p = 0.29) for the entire cohort. There was a trend toward shorter ICU length of stay in the tetralogy of Fallot subgroup (71.6 hr pre- vs 54.2 hr postimplementation, p = 0.068). A collaborative learning strategy designed clinical practice guideline significantly increased the rate of early extubation with no change in the rate of reintubation. The early extubation clinical practice guideline did not significantly change postoperative ICU length of stay.

Perception as a Route for Motor Skill Learning: Perspectives from Neuroscience.

PubMed

Ossmy, Ori; Mukamel, Roy

2018-04-22

Learning a motor skill requires physical practice that engages neural networks involved in movement. These networks have also been found to be engaged during perception of sensory signals associated with actions. Nonetheless, despite extensive evidence for the existence of such sensory-evoked neural activity in motor pathways, much less is known about their contribution to learning and actual changes in behavior. Primate studies usually involve an overlearned task while studies in humans have largely focused on characterizing activity of the action observation network (AON) in the context of action understanding, theory of mind, and social interactions. Relatively few studies examined neural plasticity induced by perception and its role in transfer of motor knowledge. Here, we review this body of literature and point to future directions for the development of alternative, physiologically grounded ways in which sensory signals could be harnessed to improve motor skills. Copyright © 2018. Published by Elsevier Ltd.

An application of programmatic assessment for learning (PAL) system for general practice training.

PubMed

Schuwirth, Lambert; Valentine, Nyoli; Dilena, Paul

2017-01-01

Aim: Programmatic assessment for learning (PAL) is becoming more and more popular as a concept but its implementation is not without problems. In this paper we describe the design principles behind a PAL program in a general practice training context. Design principles: The PAL program was designed to optimise the meaningfulness of assessment information for the registrar and to make him/her use that information to self regulate their learning. The main principles in the program were cognitivist and transformative. The main cognitive principles we used were fostering the understanding of deep structures and stimulating transfer by making registrars constantly connect practice experiences with background knowledge. Ericsson's deliberate practice approach was built in with regard to the provision of feedback combined with Pintrich's model of self regulation. Mezirow's transformative learning and insights from social network theory on collaborative learning were used to support the registrars in their development to become GP professionals. Finally the principal of test enhanced learning was optimised. Epilogue: We have provided this example explain the design decisions behind our program, but not want to present our program as the solution to any given situation.

GPON FTTH trial: lessons learned

NASA Astrophysics Data System (ADS)

Weis, Erik; Hölzl, Rainer; Breuer, Dirk; Lange, Christoph

2009-11-01

This paper reports on a FTTH field trial with GPON (Gigabit-capable passive optical network) technology in the network of Deutsche Telekom in the region of the cities of Berlin and Potsdam. Focus of this trial was to gain practical experience regarding GPON technology, fibre installation in existing ducts with micro duct technology, fibre cabling in customer buildings and impact on operational processes. Furthermore it is reported on an initial Deutsche Telekom FTTB deployment based on GPON technology in the city of Dresden with the main targets to obtain practical deployment and operation experiences with fibre-based access networks and to provide broadband access to a part of the city formerly not servable by DSL (digital subscriber line) technology.

A safety mechanism for observational learning.

PubMed

Badets, Arnaud; Boutin, Arnaud; Michelet, Thomas

2018-04-01

This empirical article presents the first evidence of a "safety mechanism" based on an observational-learning paradigm. It is accepted that during observational learning, a person can use different strategies to learn a motor skill, but it is unknown whether the learner is able to circumvent the encoding of an uncompleted observed skill. In this study, participants were tested in a dyadic protocol in which an observer watched a participant practicing two different motor sequences during a learning phase. During this phase, one of the two motor sequences was interrupted by a stop signal that precluded motor learning. The results of the subsequent retention test revealed that both groups learned the two motor sequences, but only the physical practice group showed worse performance for the interrupted sequence. The observers were consequently able to use a safety strategy to learn both sequences equally. Our findings are discussed in light of the implications of the action observation network for sequence learning and the cognitive mechanisms of error-based observation.

Adopting Social Networking Sites (SNSs) as Interactive Communities among English Foreign Language (EFL) Learners in Writing: Opportunities and Challenges

ERIC Educational Resources Information Center

Razak, Norizan Abdul; Saeed, Murad; Ahmad, Zulkifli

2013-01-01

As most traditional classroom environments in English as Foreign Language (EFL) still restrict learners' collaboration and interaction in college writing classes, today, the majority of EFL learners are accessing Social Networking Sites (SNSs) as online communities of practice (CoPs) for adopting informal collaborative learning as a way of…

Research Capacity-Building with New Technologies within New Communities of Practice: Reflections on the First Year of the Teacher Education Research Network

ERIC Educational Resources Information Center

Fowler, Zoe; Stanley, Grant; Murray, Jean; Jones, Marion; McNamara, Olwen

2013-01-01

This article focuses on a virtual research environment (VRE) and how it facilitated the networking of teacher educators participating in an Economic and Social Research Council-funded research capacity-building project. Using the theoretical lenses of situated learning and socio-cultural approaches to literacy, participants' ways of engaging with…

Disrupting the Implementation Gap with Digital Technology in Healthcare Distance Education: Critical Insights from an e-Mentoring Intensional Network Practitioner Research Project

ERIC Educational Resources Information Center

Singh, Gurmit

2013-01-01

Effective professional distance education is urgently needed to develop a well-trained workforce and improve impact on healthcare. However, distance education initiatives have had mixed results in improving practice. Often, successful implementation fails to leverage insights on the social and emergent nature of learning in networks. This paper…

Unpacking (In)formal Learning in an Academic Development Programme: A Mixed-Method Social Network Perspective

ERIC Educational Resources Information Center

Rienties, Bart; Hosein, Anesa

2015-01-01

How and with whom academics develop and maintain formal and informal networks for reflecting on their teaching practice has received limited attention even though academic development (AD) programmes have become an almost ubiquitous feature of higher education. The primary goal of this mixed-method study is to unpack how 114 academics in an AD…

White Matter Microstructural Correlates of Superior Long-term Skill Gained Implicitly under Randomized Practice

PubMed Central

Song, Sunbin; Sharma, Nikhil; Buch, Ethan R.

2012-01-01

We value skills we have learned intentionally, but equally important are skills acquired incidentally without ability to describe how or what is learned, referred to as implicit. Randomized practice schedules are superior to grouped schedules for long-term skill gained intentionally, but its relevance for implicit learning is not known. In a parallel design, we studied healthy subjects who learned a motor sequence implicitly under randomized or grouped practice schedule and obtained diffusion-weighted images to identify white matter microstructural correlates of long-term skill. Randomized practice led to superior long-term skill compared with grouped practice. Whole-brain analyses relating interindividual variability in fractional anisotropy (FA) to long-term skill demonstrated that 1) skill in randomized learners correlated with FA within the corticostriatal tract connecting left sensorimotor cortex to posterior putamen, while 2) skill in grouped learners correlated with FA within the right forceps minor connecting homologous regions of the prefrontal cortex (PFC) and the corticostriatal tract connecting lateral PFC to anterior putamen. These results demonstrate first that randomized practice schedules improve long-term implicit skill more than grouped practice schedules and, second, that the superior skill acquired through randomized practice can be related to white matter microstructure in the sensorimotor corticostriatal network. PMID:21914632

Design of a universal two-layered neural network derived from the PLI theory

NASA Astrophysics Data System (ADS)

Hu, Chia-Lun J.

2004-05-01

The if-and-only-if (IFF) condition that a set of M analog-to-digital vector-mapping relations can be learned by a one-layered-feed-forward neural network (OLNN) is that all the input analog vectors dichotomized by the i-th output bit must be positively, linearly independent, or PLI. If they are not PLI, then the OLNN just cannot learn no matter what learning rules is employed because the solution of the connection matrix does not exist mathematically. However, in this case, one can still design a parallel-cascaded, two-layered, perceptron (PCTLP) to acheive this general mapping goal. The design principle of this "universal" neural network is derived from the major mathematical properties of the PLI theory - changing the output bits of the dependent relations existing among the dichotomized input vectors to make the PLD relations PLI. Then with a vector concatenation technique, the required mapping can still be learned by this PCTLP system with very high efficiency. This paper will report in detail the mathematical derivation of the general design principle and the design procedures of the PCTLP neural network system. It then will be verified in general by a practical numerical example.

Neural networks for function approximation in nonlinear control

NASA Technical Reports Server (NTRS)

Linse, Dennis J.; Stengel, Robert F.

1990-01-01

Two neural network architectures are compared with a classical spline interpolation technique for the approximation of functions useful in a nonlinear control system. A standard back-propagation feedforward neural network and a cerebellar model articulation controller (CMAC) neural network are presented, and their results are compared with a B-spline interpolation procedure that is updated using recursive least-squares parameter identification. Each method is able to accurately represent a one-dimensional test function. Tradeoffs between size requirements, speed of operation, and speed of learning indicate that neural networks may be practical for identification and adaptation in a nonlinear control environment.

What do we do? Practices and learning strategies of medical education leaders.

PubMed

Lieff, Susan; Albert, Mathieu

2012-01-01

Continuous changes in undergraduate and postgraduate medical education require faculty to assume a variety of new leadership roles. While numerous faculty development programmes have been developed, there is little evidence about the specific practices of medical education leaders or their learning strategies to help inform their design. This study aimed to explore what medical education leaders' actually do, their learning strategies and recommendations for faculty development. A total of 16 medical education leaders from a variety of contexts within the faculty of medicine of a large North American medical school participated in semi-structured interviews to explore the nature of their work and the learning strategies they employ. Using thematic analysis, interview transcripts were coded inductively and then clustered into emergent themes. Findings clustered into four key themes of practice: (1) intrapersonal (e.g., self-awareness), (2) interpersonal (e.g., fostering informal networks), (3) organizational (e.g., creating a shared vision) and (4) systemic (e.g. strategic navigation). Learning strategies employed included learning from experience and example, reflective practice, strategic mentoring or advanced training. Our findings illuminate a four-domain framework for understanding medical education leader practices and their learning preferences. While some of these findings are not unknown in the general leadership literature, our understanding of their application in medical education is unique. These practices and preferences have a potential utility for conceptualizing a coherent and relevant approach to the design of faculty development strategies for medical education leadership.

The child and adolescent psychiatry trials network (CAPTN): infrastructure development and lessons learned

PubMed Central

Shapiro, Mark; Silva, Susan G; Compton, Scott; Chrisman, Allan; DeVeaugh-Geiss, Joseph; Breland-Noble, Alfiee; Kondo, Douglas; Kirchner, Jerry; March, John S

2009-01-01

Background In 2003, the National Institute of Mental Health funded the Child and Adolescent Psychiatry Trials Network (CAPTN) under the Advanced Center for Services and Intervention Research (ACSIR) mechanism. At the time, CAPTN was believed to be both a highly innovative undertaking and a highly speculative one. One reviewer even suggested that CAPTN was "unlikely to succeed, but would be a valuable learning experience for the field." Objective To describe valuable lessons learned in building a clinical research network in pediatric psychiatry, including innovations intended to decrease barriers to research participation. Methods The CAPTN Team has completed construction of the CAPTN network infrastructure, conducted a large, multi-center psychometric study of a novel adverse event reporting tool, and initiated a large antidepressant safety registry and linked pharmacogenomic study focused on severe adverse events. Specific challenges overcome included establishing structures for network organization and governance; recruiting over 150 active CAPTN participants and 15 child psychiatry training programs; developing and implementing procedures for site contracts, regulatory compliance, indemnification and malpractice coverage, human subjects protection training and IRB approval; and constructing an innovative electronic casa report form (eCRF) running on a web-based electronic data capture system; and, finally, establishing procedures for audit trail oversight requirements put forward by, among others, the Food and Drug Administration (FDA). Conclusion Given stable funding for network construction and maintenance, our experience demonstrates that judicious use of web-based technologies for profiling investigators, investigator training, and capturing clinical trials data, when coupled to innovative approaches to network governance, data management and site management, can reduce the costs and burden and improve the feasibility of incorporating clinical research into routine clinical practice. Having successfully achieved its initial aim of constructing a network infrastructure, CAPTN is now a capable platform for large safety registries, pharmacogenetic studies, and randomized practical clinical trials in pediatric psychiatry. PMID:19320979

Inferring causal molecular networks: empirical assessment through a community-based effort

PubMed Central

Hill, Steven M.; Heiser, Laura M.; Cokelaer, Thomas; Unger, Michael; Nesser, Nicole K.; Carlin, Daniel E.; Zhang, Yang; Sokolov, Artem; Paull, Evan O.; Wong, Chris K.; Graim, Kiley; Bivol, Adrian; Wang, Haizhou; Zhu, Fan; Afsari, Bahman; Danilova, Ludmila V.; Favorov, Alexander V.; Lee, Wai Shing; Taylor, Dane; Hu, Chenyue W.; Long, Byron L.; Noren, David P.; Bisberg, Alexander J.; Mills, Gordon B.; Gray, Joe W.; Kellen, Michael; Norman, Thea; Friend, Stephen; Qutub, Amina A.; Fertig, Elana J.; Guan, Yuanfang; Song, Mingzhou; Stuart, Joshua M.; Spellman, Paul T.; Koeppl, Heinz; Stolovitzky, Gustavo; Saez-Rodriguez, Julio; Mukherjee, Sach

2016-01-01

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks. PMID:26901648

Engaging the University in Building Communities of Practice for Aging in Place

ERIC Educational Resources Information Center

McDonald, Jessyna M.

2011-01-01

Based upon the principles of the Engaged University (Kellogg Commission 2001), The Institute of Gerontology (IOG) at the University of the District of Columbia developed a model for the scholarship of engagement by building communities of practice within the aging network which may support and enhance student learning outcomes and experiences. The…

Navigating the Challenges of Becoming a Culturally Responsive Teacher: Supportive Networking May Be the Key

ERIC Educational Resources Information Center

Nilsson, Nina L.; Kong, Ailing; Hubert, Shantel

2016-01-01

Research shows graduates of teacher education programs do not always transfer, or apply, the best practices they learn to instructional practice due to factors related to course features, the student, and workplace environment (e.g., Brown & Bentley, 2004; de Jong et al., 2010). This study examined the challenges a secondary-level English…

The importance of social and collaborative learning for online continuing medical education (OCME): directions for future development and research.

PubMed

Sandars, John; Kokotailo, Patricia; Singh, Gurmit

2012-01-01

There is an increasing use of online continuing medical education (OCME), but the potential use of social and collaborative learning to change professional performance and improve patient care has yet to be fully realised. The integration of the main themes from the presentations and comments from participants at a symposium at AMEE 2011. Sociological perspectives on change in professional performance highlight the need for social and collaborative learning in OCME so that learners can share information (explicit knowledge) and opinion (tacit knowledge). The educational topic should be relevant to the complexity of professional practice and use iterative cycles of implementation and critical reflection in social networks so that proposed solutions can be tested in actual practice. The challenge of developing effective online discussions for collaborative learning is recognised. The provision of OCME requires a shift in both policy and practice to emphasise the importance of social and collaborative learning. Further research is recommended, especially to evaluate the implementation and impact of social and collaborative learning for OCME on patient care and the use of newer Web 2.0 approaches.

Motivation, Empowerment, and Innovation: Teachers' Beliefs about How Participating in the Edmodo Math Subject Community Shapes Teaching and Learning

ERIC Educational Resources Information Center

Trust, Torrey

2017-01-01

Educators around the world participate in virtual communities, social media sites, and online networks in order to gain support and ideas for improving their practice. Many researchers have explored how and why teachers participate in these online spaces; however, there is limited research on how participation might impact teaching and learning.…

Student Agency: An Analysis of Students' Networked Relations across the Informal and Formal Learning Domains

ERIC Educational Resources Information Center

Rappa, Natasha Anne; Tang, Kok-Sing

2017-01-01

Agency is a construct facilitating our examination of when and how young people extend their own learning across contexts. However, little is known about the role played by adolescent learners' sense of agency. This paper reports two cases of students' agentively employing and developing science literacy practices--one in Singapore and the other…

Mobile Learning Approaches for U.S. Army Training

DTIC Science & Technology

2010-08-01

2.0 tools on smartphones may promote student-centered learning pedagogies (e.g., Cochrane & Bateman, 2010) and provide learners with more fruitful...and effective relationships with their instructors and peers.1 That is, Web 2.0 tools facilitate learners‟ creative practices, participation...1 Web 1.0 tools focused on presenting information to users whereas Web 2.0 tools focused on providing social networking

A neural network construction method for surrogate modeling of physics-based analysis

NASA Astrophysics Data System (ADS)

Sung, Woong Je

In this thesis existing methodologies related to the developmental methods of neural networks have been surveyed and their approaches to network sizing and structuring are carefully observed. This literature review covers the constructive methods, the pruning methods, and the evolutionary methods and questions about the basic assumption intrinsic to the conventional neural network learning paradigm, which is primarily devoted to optimization of connection weights (or synaptic strengths) for the pre-determined connection structure of the network. The main research hypothesis governing this thesis is that, without breaking a prevailing dichotomy between weights and connectivity of the network during learning phase, the efficient design of a task-specific neural network is hard to achieve because, as long as connectivity and weights are searched by separate means, a structural optimization of the neural network requires either repetitive re-training procedures or computationally expensive topological meta-search cycles. The main contribution of this thesis is designing and testing a novel learning mechanism which efficiently learns not only weight parameters but also connection structure from a given training data set, and positioning this learning mechanism within the surrogate modeling practice. In this work, a simple and straightforward extension to the conventional error Back-Propagation (BP) algorithm has been formulated to enable a simultaneous learning for both connectivity and weights of the Generalized Multilayer Perceptron (GMLP) in supervised learning tasks. A particular objective is to achieve a task-specific network having reasonable generalization performance with a minimal training time. The dichotomy between architectural design and weight optimization is reconciled by a mechanism establishing a new connection for a neuron pair which has potentially higher error-gradient than one of the existing connections. Interpreting an instance of the absence of connection as a zero-weight connection, the potential contribution to training error reduction of any present or absent connection can readily be evaluated using the BP algorithm. Instead of being broken, the connections that contribute less remain frozen with constant weight values optimized to that point but they are excluded from further weight optimization until reselected. In this way, a selective weight optimization is executed only for the dynamically maintained pool of high gradient connections. By searching the rapidly changing weights and concentrating optimization resources on them, the learning process is accelerated without either a significant increase in computational cost or a need for re-training. This results in a more task-adapted network connection structure. Combined with another important criterion for the division of a neuron which adds a new computational unit to a network, a highly fitted network can be grown out of the minimal random structure. This particular learning strategy can belong to a more broad class of the variable connectivity learning scheme and the devised algorithm has been named Optimal Brain Growth (OBG). The OBG algorithm has been tested on two canonical problems; a regression analysis using the Complicated Interaction Regression Function and a classification of the Two-Spiral Problem. A comparative study with conventional Multilayer Perceptrons (MLPs) consisting of single- and double-hidden layers shows that OBG is less sensitive to random initial conditions and generalizes better with only a minimal increase in computational time. This partially proves that a variable connectivity learning scheme has great potential to enhance computational efficiency and reduce efforts to select proper network architecture. To investigate the applicability of the OBG to more practical surrogate modeling tasks, the geometry-to-pressure mapping of a particular class of airfoils in the transonic flow regime has been sought using both the conventional MLP networks with pre-defined architecture and the OBG-developed networks started from the same initial MLP networks. Considering wide variety in airfoil geometry and diversity of flow conditions distributed over a range of flow Mach numbers and angles of attack, the new method shows a great potential to capture fundamentally nonlinear flow phenomena especially related to the occurrence of shock waves on airfoil surfaces in transonic flow regime. (Abstract shortened by UMI.).

Neural dynamic programming and its application to control systems

NASA Astrophysics Data System (ADS)

Seong, Chang-Yun

There are few general practical feedback control methods for nonlinear MIMO (multi-input-multi-output) systems, although such methods exist for their linear counterparts. Neural Dynamic Programming (NDP) is proposed as a practical design method of optimal feedback controllers for nonlinear MIMO systems. NDP is an offspring of both neural networks and optimal control theory. In optimal control theory, the optimal solution to any nonlinear MIMO control problem may be obtained from the Hamilton-Jacobi-Bellman equation (HJB) or the Euler-Lagrange equations (EL). The two sets of equations provide the same solution in different forms: EL leads to a sequence of optimal control vectors, called Feedforward Optimal Control (FOC); HJB yields a nonlinear optimal feedback controller, called Dynamic Programming (DP). DP produces an optimal solution that can reject disturbances and uncertainties as a result of feedback. Unfortunately, computation and storage requirements associated with DP solutions can be problematic, especially for high-order nonlinear systems. This dissertation presents an approximate technique for solving the DP problem based on neural network techniques that provides many of the performance benefits (e.g., optimality and feedback) of DP and benefits from the numerical properties of neural networks. We formulate neural networks to approximate optimal feedback solutions whose existence DP justifies. We show the conditions under which NDP closely approximates the optimal solution. Finally, we introduce the learning operator characterizing the learning process of the neural network in searching the optimal solution. The analysis of the learning operator provides not only a fundamental understanding of the learning process in neural networks but also useful guidelines for selecting the number of weights of the neural network. As a result, NDP finds---with a reasonable amount of computation and storage---the optimal feedback solutions to nonlinear MIMO control problems that would be very difficult to solve with DP. NDP was demonstrated on several applications such as the lateral autopilot logic for a Boeing 747, the minimum fuel control of a double-integrator plant with bounded control, the backward steering of a two-trailer truck, and the set-point control of a two-link robot arm.

Visuomotor coordination and cortical connectivity of modular motor learning.

PubMed

Burgos, Pablo I; Mariman, Juan J; Makeig, Scott; Rivera-Lillo, Gonzalo; Maldonado, Pedro E

2018-05-15

The ability to transfer sensorimotor skill components to new actions and the capacity to use skill components from whole actions are characteristic of the adaptability of the human sensorimotor system. However, behavioral evidence suggests complex limitations for transfer after combined or modular learning of motor adaptations. Also, to date, only behavioral analysis of the consequences of the modular learning has been reported, with little understanding of the sensorimotor mechanisms of control and the interaction between cortical areas. We programmed a video game with distorted kinematic and dynamic features to test the ability to combine sensorimotor skill components learned modularly (composition) and the capacity to use separate sensorimotor skill components learned in combination (decomposition). We examined motor performance, eye-hand coordination, and EEG connectivity. When tested for integrated learning, we found that combined practice initially performed better than separated practice, but differences disappeared after integrated practice. Separate learning promotes fewer anticipatory control mechanisms (depending more on feedback control), evidenced in a lower gaze leading behavior and in higher connectivity between visual and premotor domains, in comparison with the combined practice. The sensorimotor system can acquire motor modules in a separated or integrated manner. However, the system appears to require integrated practice to coordinate the adaptations with the skill learning and the networks involved in the integrated behavior. This integration seems to be related to the acquisition of anticipatory mechanism of control and with the decrement of feedback control. © 2018 Wiley Periodicals, Inc.

Network-based machine learning and graph theory algorithms for precision oncology.

PubMed

Zhang, Wei; Chien, Jeremy; Yong, Jeongsik; Kuang, Rui

2017-01-01

Network-based analytics plays an increasingly important role in precision oncology. Growing evidence in recent studies suggests that cancer can be better understood through mutated or dysregulated pathways or networks rather than individual mutations and that the efficacy of repositioned drugs can be inferred from disease modules in molecular networks. This article reviews network-based machine learning and graph theory algorithms for integrative analysis of personal genomic data and biomedical knowledge bases to identify tumor-specific molecular mechanisms, candidate targets and repositioned drugs for personalized treatment. The review focuses on the algorithmic design and mathematical formulation of these methods to facilitate applications and implementations of network-based analysis in the practice of precision oncology. We review the methods applied in three scenarios to integrate genomic data and network models in different analysis pipelines, and we examine three categories of network-based approaches for repositioning drugs in drug-disease-gene networks. In addition, we perform a comprehensive subnetwork/pathway analysis of mutations in 31 cancer genome projects in the Cancer Genome Atlas and present a detailed case study on ovarian cancer. Finally, we discuss interesting observations, potential pitfalls and future directions in network-based precision oncology.

Modular neural networks: a survey.

PubMed

Auda, G; Kamel, M

1999-04-01

Modular Neural Networks (MNNs) is a rapidly growing field in artificial Neural Networks (NNs) research. This paper surveys the different motivations for creating MNNs: biological, psychological, hardware, and computational. Then, the general stages of MNN design are outlined and surveyed as well, viz., task decomposition techniques, learning schemes and multi-module decision-making strategies. Advantages and disadvantages of the surveyed methods are pointed out, and an assessment with respect to practical potential is provided. Finally, some general recommendations for future designs are presented.

A National Strategy to Develop Pragmatic Clinical Trials Infrastructure

PubMed Central

Guise, Jeanne‐Marie; Dolor, Rowena J.; Meissner, Paul; Tunis, Sean; Krishnan, Jerry A.; Pace, Wilson D.; Saltz, Joel; Hersh, William R.; Michener, Lloyd; Carey, Timothy S.

2014-01-01

Abstract An important challenge in comparative effectiveness research is the lack of infrastructure to support pragmatic clinical trials, which compare interventions in usual practice settings and subjects. These trials present challenges that differ from those of classical efficacy trials, which are conducted under ideal circumstances, in patients selected for their suitability, and with highly controlled protocols. In 2012, we launched a 1‐year learning network to identify high‐priority pragmatic clinical trials and to deploy research infrastructure through the NIH Clinical and Translational Science Awards Consortium that could be used to launch and sustain them. The network and infrastructure were initiated as a learning ground and shared resource for investigators and communities interested in developing pragmatic clinical trials. We followed a three‐stage process of developing the network, prioritizing proposed trials, and implementing learning exercises that culminated in a 1‐day network meeting at the end of the year. The year‐long project resulted in five recommendations related to developing the network, enhancing community engagement, addressing regulatory challenges, advancing information technology, and developing research methods. The recommendations can be implemented within 24 months and are designed to lead toward a sustained national infrastructure for pragmatic trials. PMID:24472114

An exploration of peer-assisted learning in undergraduate nursing students in paediatric clinical settings: An ethnographic study.

PubMed

Carey, Matthew C; Chick, Anna; Kent, Bridie; Latour, Jos M

2018-06-01

Peer-assisted leaning relates to the acquisition of knowledge and skills through shared learning of matched equals. The concept has been explored within the field of nurse education across a range of learning environments, but its impact in practice is still relatively unknown. This paper reports on findings when observing paediatric undergraduate nursing students who engage in PAL within the clinical practice setting. The aim of this paper is to report the findings of a study undertaken to explore peer-assisted learning in undergraduate nursing students, studying children's health, in the clinical practice setting. A qualitative ethnographic study using non-participant observations. A range of inpatient paediatric clinical settings across two teaching hospitals. First, second and third year paediatric student nurses enrolled on a Bachelor of Nursing Programme. Non-participant observations were used to observe a range of interactions between the participants when engaging in peer-assisted learning within the same clinical area. A total of 67 h of raw data collected across all observations was analysed using framework analysis to draw together key themes. Of the 20 identified students across two hospitals, 17 agreed to take part in the study. Findings were aggregated into three key themes; 1. Peers as facilitators to develop learning when engaging in peer-assisted learning, 2. Working together to develop clinical practice and deliver care, 3. Positive support and interaction from peers to enhance networking and develop working structure. Peer-assisted learning in undergraduate children's nursing students stimulates students in becoming engaged in their learning experiences in clinical practice and enhance collaborative support within the working environment. The benefits of peer-assisted learning in current clinical practice settings can be challenging. Therefore, education and practice need to be aware of the benefits and their contribution towards future strategies and models of learning. Copyright © 2018 Elsevier Ltd. All rights reserved.

Scotland's knowledge network: a progress report on Knowledge into Action.

PubMed

Wales, Ann; Boyle, Derek

2015-11-01

Launched in 2012, Knowledge into Action is the national knowledge management strategy for the health and social care workforce in Scotland. It is transforming the role of the national digital knowledge service--NHS Education for Scotlands' Knowledge Network--and the NHSS librarian role to offer more active, tailored support for translating knowledge into frontline clinical practice. This includes the development of a national evidence search and summary service, help with converting knowledge into practical and usable formats for easy use at point of care and with using digital tools to share clinicians' learning, experience and expertise. Through this practical support, Knowledge into Action is contributing to quality and safety outcomes across NHS Scotland, building clinicians' capacity and capability in applying knowledge in frontline practice and service improvement. © The Author(s) 2015.

Creating a Community of Practice: Lessons Learned from the Center for Astronomy Education (Invited)

NASA Astrophysics Data System (ADS)

Brissenden, G.

2009-12-01

The Center for Astronomy Education (CAE) is devoted to improving teaching and learning in Astro 101. To accomplish this, a vital part of CAE is our broader community of practice which includes over 1000 instructors, graduate and undergraduate students, and postdocs. It is this greater community of practice that supports each other, helps, and learns from each other beyond what would be possible without it. As our community of practice has grown, we at CAE have learned many lessons about how different facets of CAE can best be used to promote and support our community both as a whole and for individual members. We will discuss the various facets of CAE, such as our online discussion group Astrolrner@CAE (http://astronomy101.jpl.nasa.gov/discussion) and its Guest Moderator program, our CAE Regional Teaching Exchange Coordinator program, our CAE Workshop Presenter Apprenticeship Training program, our online This Month’s Teaching Strategy, monthly newsletters, and various types of socializing and networking sessions we hold at national meetings. But more importantly, we will discuss the lessons we’ve learned about what does and does not work in building community within each of these facets.

Understanding Classrooms through Social Network Analysis: A Primer for Social Network Analysis in Education Research

PubMed Central

Wiggins, Benjamin L.; Goodreau, Steven M.

2014-01-01

Social interactions between students are a major and underexplored part of undergraduate education. Understanding how learning relationships form in undergraduate classrooms, as well as the impacts these relationships have on learning outcomes, can inform educators in unique ways and improve educational reform. Social network analysis (SNA) provides the necessary tool kit for investigating questions involving relational data. We introduce basic concepts in SNA, along with methods for data collection, data processing, and data analysis, using a previously collected example study on an undergraduate biology classroom as a tutorial. We conduct descriptive analyses of the structure of the network of costudying relationships. We explore generative processes that create observed study networks between students and also test for an association between network position and success on exams. We also cover practical issues, such as the unique aspects of human subjects review for network studies. Our aims are to convince readers that using SNA in classroom environments allows rich and informative analyses to take place and to provide some initial tools for doing so, in the process inspiring future educational studies incorporating relational data. PMID:26086650

Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network.

PubMed

Liu, Ziyi; Gao, Junfeng; Yang, Guoguo; Zhang, Huan; He, Yong

2016-02-11

We present a pipeline for the visual localization and classification of agricultural pest insects by computing a saliency map and applying deep convolutional neural network (DCNN) learning. First, we used a global contrast region-based approach to compute a saliency map for localizing pest insect objects. Bounding squares containing targets were then extracted, resized to a fixed size, and used to construct a large standard database called Pest ID. This database was then utilized for self-learning of local image features which were, in turn, used for classification by DCNN. DCNN learning optimized the critical parameters, including size, number and convolutional stride of local receptive fields, dropout ratio and the final loss function. To demonstrate the practical utility of using DCNN, we explored different architectures by shrinking depth and width, and found effective sizes that can act as alternatives for practical applications. On the test set of paddy field images, our architectures achieved a mean Accuracy Precision (mAP) of 0.951, a significant improvement over previous methods.

Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network

PubMed Central

Liu, Ziyi; Gao, Junfeng; Yang, Guoguo; Zhang, Huan; He, Yong

2016-01-01

We present a pipeline for the visual localization and classification of agricultural pest insects by computing a saliency map and applying deep convolutional neural network (DCNN) learning. First, we used a global contrast region-based approach to compute a saliency map for localizing pest insect objects. Bounding squares containing targets were then extracted, resized to a fixed size, and used to construct a large standard database called Pest ID. This database was then utilized for self-learning of local image features which were, in turn, used for classification by DCNN. DCNN learning optimized the critical parameters, including size, number and convolutional stride of local receptive fields, dropout ratio and the final loss function. To demonstrate the practical utility of using DCNN, we explored different architectures by shrinking depth and width, and found effective sizes that can act as alternatives for practical applications. On the test set of paddy field images, our architectures achieved a mean Accuracy Precision (mAP) of 0.951, a significant improvement over previous methods. PMID:26864172

The graph neural network model.

PubMed

Scarselli, Franco; Gori, Marco; Tsoi, Ah Chung; Hagenbuchner, Markus; Monfardini, Gabriele

2009-01-01

Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper, we propose a new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing the data represented in graph domains. This GNN model, which can directly process most of the practically useful types of graphs, e.g., acyclic, cyclic, directed, and undirected, implements a function tau(G,n) is an element of IR(m) that maps a graph G and one of its nodes n into an m-dimensional Euclidean space. A supervised learning algorithm is derived to estimate the parameters of the proposed GNN model. The computational cost of the proposed algorithm is also considered. Some experimental results are shown to validate the proposed learning algorithm, and to demonstrate its generalization capabilities.

DeepX: Deep Learning Accelerator for Restricted Boltzmann Machine Artificial Neural Networks.

PubMed

Kim, Lok-Won

2018-05-01

Although there have been many decades of research and commercial presence on high performance general purpose processors, there are still many applications that require fully customized hardware architectures for further computational acceleration. Recently, deep learning has been successfully used to learn in a wide variety of applications, but their heavy computation demand has considerably limited their practical applications. This paper proposes a fully pipelined acceleration architecture to alleviate high computational demand of an artificial neural network (ANN) which is restricted Boltzmann machine (RBM) ANNs. The implemented RBM ANN accelerator (integrating network size, using 128 input cases per batch, and running at a 303-MHz clock frequency) integrated in a state-of-the art field-programmable gate array (FPGA) (Xilinx Virtex 7 XC7V-2000T) provides a computational performance of 301-billion connection-updates-per-second and about 193 times higher performance than a software solution running on general purpose processors. Most importantly, the architecture enables over 4 times (12 times in batch learning) higher performance compared with a previous work when both are implemented in an FPGA device (XC2VP70).

The Integration of Research, Teaching, and Learning: Preparation of the Future STEM Faculty

NASA Astrophysics Data System (ADS)

Jariwala, Manher

Graduate students at research universities shape the future of STEM undergraduate education in the United States. These future faculty flow into the STEM faculties of several thousand research universities, comprehensive universities, liberal arts colleges, and community and tribal colleges. The Center for the Integration of Research, Teaching, and Learning (CIRTL) uses graduate education as the leverage point to develop STEM faculty with the capability and commitment to implement and improve effective teaching and learning practices. CIRTL has developed, implemented, and evaluated successful strategies based on three core ideas: teaching-as-research, learning communities, and learning-through-diversity. A decade of research demonstrates that STEM future faculty participating in CIRTL learning communities understand, use, and advance high-impact teaching practices. Today the CIRTL Network includes 43 research universities. Ultimately, CIRTL seeks a national STEM faculty who enable all students to learn effectively and achieve STEM literacy, whose teaching enhances recruitment into STEM careers, and whose leadership ensures continued advancement of STEM education.

A position paper of the EFLM Committee on Education and Training and Working Group on Distance Education Programmes/E-Learning: developing an e-learning platform for the education of stakeholders in laboratory medicine.

PubMed

Gruson, Damien; Faure, Gilbert; Gouget, Bernard; Haliassos, Alexandre; Kisikuchin, Darya; Reguengo, Henrique; Topic, Elizabeta; Blaton, Victor

2013-04-01

The progress of information and communication technologies has strongly influenced changes in healthcare and laboratory medicine. E-learning, the learning or teaching through electronic means, contributes to the effective knowledge translation in medicine and healthcare, which is an essential element of a modern healthcare system and for the improvement of patient care. E-learning also represents a great vector for the transfer knowledge into laboratory practice, stimulate multidisciplinary interactions, enhance continuing professional development and promote laboratory medicine. The European Federation of Laboratory Medicine (EFLM) has initiated a distance learning program and the development of a collaborative network for e-learning. The EFLM dedicated working group encourages the organization of distance education programs and e-learning courses as well as critically evaluate information from courses, lectures and documents including electronic learning tools. The objectives of the present paper are to provide some specifications for distance learning and be compatible with laboratory medicine practices.

Knowledge Diffusion in a Grade 4-5 Classroom during a Unit on Civil Engineering: An Analysis of a Classroom Community in Terms of Its Changing Resources and Practices.

ERIC Educational Resources Information Center

Roth, Wolff-Michael

1996-01-01

Employed the actor network theory to examine the transformation of a grade-four classroom community as new resources and practices became available. Found that the diffusion and enculturation metaphors are insufficient to model important aspects of learning in a student-centered classroom. (MOK)

Massive Multiplayer Online Gaming: A Research Framework for Military Training and Education

DTIC Science & Technology

2005-03-01

those required by a military transforming itself to operating under the concept of network centric warfare. The technologies and practice...learning. Simulations are popular in other business situations and management processes. Data files, video clips, and flowcharts might help learners...on nature of these environments is another key motivator. According to Randy Hinrich, Microsoft Research Group Research Manager for Learning

Evolving Best Practice in Learning About Air Quality and Climate Change Science in ACCENT

NASA Astrophysics Data System (ADS)

Schuepbach, E.

2008-12-01

Learning about air quality and climate change science has developed into a transdisciplinary impact generator, moulded by academic-stakeholder partnerships, where complementary skills and competences lead to a culture of dialogue, mutual learning and decision-making. These sweeping changes are mirrored in the evolving best practice within the European Network of Excellence on Atmospheric Composition Change (ACCENT). The Training and Education Programme in ACCENT pursues an integrated approach and innovative avenues to sharing knowledge and communicating air quality and climate change science to various end-user groups, including teachers, policy makers, stakeholders, and the general public. Early career scientists are involved in the process, and are trained to acquire new knowledge in a variety of learning communities and environments. Here, examples of both the open system of teaching within ACCENT training workshops for early career scientists, and the engagement of non-academic audiences in the joint learning process are presented.

Second Language Word Learning through Repetition and Imitation: Functional Networks as a Function of Learning Phase and Language Distance.

PubMed

Ghazi-Saidi, Ladan; Ansaldo, Ana Ines

2017-01-01

Introduction and Aim : Repetition and imitation are among the oldest second language (L2) teaching approaches and are frequently used in the context of L2 learning and language therapy, despite some heavy criticism. Current neuroimaging techniques allow the neural mechanisms underlying repetition and imitation to be examined. This fMRI study examines the influence of verbal repetition and imitation on network configuration. Integration changes within and between the cognitive control and language networks were studied, in a pair of linguistically close languages (Spanish and French), and compared to our previous work on a distant language pair (Ghazi-Saidi et al., 2013). Methods : Twelve healthy native Spanish-speaking (L1) adults, and 12 healthy native Persian-speaking adults learned 130 new French (L2) words, through a computerized audiovisual repetition and imitation program. The program presented colored photos of objects. Participants were instructed to look at each photo and pronounce its name as closely as possible to the native template (imitate). Repetition was encouraged as many times as necessary to learn the object's name; phonological cues were provided if necessary. Participants practiced for 15 min, over 30 days, and were tested while naming the same items during fMRI scanning, at week 1 (shallow learning phase) and week 4 (consolidation phase) of training. To compare this set of data with our previous work on Persian speakers, a similar data analysis plan including accuracy rates (AR), response times (RT), and functional integration values for the language and cognitive control network at each measure point was included, with further L1-L2 direct comparisons across the two populations. Results and Discussion : The evidence shows that learning L2 words through repetition induces neuroplasticity at the network level. Specifically, L2 word learners showed increased network integration after 3 weeks of training, with both close and distant language pairs. Moreover, higher network integration was observed in the learners with the close language pair, suggesting that repetition effects on network configuration vary as a function of task complexity.

Blended learning networks supported by information and communication technology: an intervention for knowledge transformation within family care of older people.

PubMed

Hanson, Elizabeth; Magnusson, Lennart; Sennemark, Eva

2011-08-01

This article describes an innovative practice called Blended Learning Networks (BLNs) whose aim is to enable older people, their families, and care providers to exchange knowledge, learn together, and support each other in local development work so that care is improved for older people. BLNs were established in 31 municipalities, headed up by a local facilitator. They were supported by a national themed network consisting of virtual meetings between local facilitators and national facilitators at the Swedish National Family Care Competence Centre. An evaluation was conducted to explore the utility of the BLNs so that any improvements to the model could be instigated. Focus group interviews were conducted with members of 9 BLNs, and self-evaluation questions were discussed in 16 BLNs. Limitations are that not all BLN members participated in the evaluation, and local facilitators conducting self-evaluations were not trained in focus group dynamics. Virtual focus groups were carried out with 26 of the 31 local facilitators and with the national facilitators. Participants reported an increased understanding of caregiver issues and of each group's roles. Of particular value were the stories shared by caregivers and the potential for change locally due to the involvement of decision makers. The practice demanded considerable skills of the local facilitators. An initial education for new local facilitators was deemed necessary. BLNs is a unique practice of community communications and knowledge transfer as it creates partnerships among all key stakeholder groups that act as a catalyst for improving care for older people.

Reaping benefits from intellectual capital.

PubMed

Weston, Marla J; Estrada, Nicolette A; Carrington, Jane

2007-01-01

The wealth and value of organizations are increasingly based on intellectual capital. Although acquiring talented individuals and investing in employee learning adds value to the organization, reaping the benefits of intellectual capital involves translating the wisdom of employees into reusable and sustained actions. This requires a culture that creates employee commitment, encourages learning, fosters sharing, and involves employees in decision making. An infrastructure to recognize and embed promising and best practices through social networks, evidence-based practice, customization of innovations, and use of information technology results in increased productivity, stronger financial performance, better patient outcomes, and greater employee and customer satisfaction.

Increasing access to learning for the adult basic education learner with learning disabilities: evidence-based accommodation research.

PubMed

Gregg, Noel

2012-01-01

Accommodating adult basic education (ABE) learners with learning disabilities (LD) is common practice across many instructional, testing, and work settings. However, the results from this literature search indicate that very few empirically based studies are available to support or reject the effectiveness of a great deal of accommodation implementation. In addition, in light of the profound changes to literacy taking place in today's digital, networked, and multimodal world, technology is redefining traditional concepts of accessibility and accommodation.

Learning Search Control Knowledge for Deep Space Network Scheduling

NASA Technical Reports Server (NTRS)

Gratch, Jonathan; Chien, Steve; DeJong, Gerald

1993-01-01

While the general class of most scheduling problems is NP-hard in worst-case complexity, in practice, for specific distributions of problems and constraints, domain-specific solutions have been shown to perform in much better than exponential time.

Social media for lifelong learning.

PubMed

Kind, Terry; Evans, Yolanda

2015-04-01

Learning is ongoing, and can be considered a social activity. In this paper we aim to provide a review of the use of social media for lifelong learning. We start by defining lifelong learning, drawing upon principles of continuous professional development and adult learning theory. We searched Embase and MEDLINE from 2004-2014 for search terms relevant to social media and learning. We describe examples of lifelong learners using social media in medical education and healthcare that have been reported in the peer-reviewed literature. Medical or other health professions students may have qualities consistent with being a lifelong learner, yet once individuals move beyond structured learning environments they will need to recognize their own gaps in knowledge and skills over time and be motivated to fill them, thereby incorporating lifelong learning principles into their day-to-day practice. Engagement with social media can parallel engagement in the learning process over time, to the extent that online social networking fosters feedback and collaboration. The use of social media and online networking platforms are a key way to continuously learn in today's information sharing society. Additional research is needed, particularly rigorous studies that extend beyond learner satisfaction to knowledge, behaviour change, and outcomes.

The Significance of Kinship for Medical Education: Reflections on the Use of a Bespoke Social Network to Support Learners' Professional Identities.

PubMed

Hatzipanagos, Stylianos; John, Bernadette; Chiu, Yuan-Li Tiffany

2016-03-03

Social media can support and sustain communities much better than previous generations of learning technologies, where institutional barriers undermined any initiatives for embedding formal and informal learning. Some of the many types of social media have already had an impact on student learning, based on empirical evidence. One of these, social networking, has the potential to support communication in formal and informal spaces. In this paper we report on the evaluation of an institutional social network-King's Social Harmonisation Project (KINSHIP)-established to foster an improved sense of community, enhance communication, and serve as a space to model digital professionalism for students at King's College London, United Kingdom. Our evaluation focused on a study that examined students' needs and perceptions with regard to the provision of a cross-university platform. Data were collected from students, including those in the field of health and social care, in order to recommend a practical way forward to address current needs in this area. The findings indicate that the majority of the respondents were positive about using a social networking platform to develop their professional voice and profiles. Results suggest that timely promotion of the platform, emphasis on interface and learning design, and a clear identity are required in order to gain acceptance as the institutional social networking site. Empirical findings in this study project an advantage of an institutional social network such a KINSHIP over other social networks (eg, Facebook) because access is limited to staff and students and the site is mainly being used for academic purposes.

A community of practice for knowledge translation trainees: an innovative approach for learning and collaboration.

PubMed

Urquhart, Robin; Cornelissen, Evelyn; Lal, Shalini; Colquhoun, Heather; Klein, Gail; Richmond, Sarah; Witteman, Holly O

2013-01-01

A growing number of researchers and trainees identify knowledge translation (KT) as their field of study or practice. Yet, KT educational and professional development opportunities and established KT networks remain relatively uncommon, making it challenging for trainees to develop the necessary skills, networks, and collaborations to optimally work in this area. The Knowledge Translation Trainee Collaborative is a trainee-initiated and trainee-led community of practice established by junior knowledge translation researchers and practitioners to: examine the diversity of knowledge translation research and practice, build networks with other knowledge translation trainees, and advance the field through knowledge generation activities. In this article, we describe how the collaborative serves as an innovative community of practice for continuing education and professional development in knowledge translation and present a logic model that provides a framework for designing an evaluation of its impact as a community of practice. The expectation is that formal and informal networking will lead to knowledge sharing and knowledge generation opportunities that improve individual members' competencies (eg, combination of skills, abilities, and knowledge) in knowledge translation research and practice and contribute to the development and advancement of the knowledge translation field. Copyright © 2013 The Alliance for Continuing Education in the Health Professions, the Society for Academic Continuing Medical Education, and the Council on CME, Association for Hospital Medical Education.

Enhancing practice improvement by facilitating practitioner interactivity: new roles for providers of continuing medical education.

PubMed

Parboosingh, I John; Reed, Virginia A; Caldwell Palmer, James; Bernstein, Henry H

2011-01-01

Research into networking and interactivity among practitioners is providing new information that has the potential to enhance the effectiveness of practice improvement initiatives. This commentary reviews the evidence that practitioner interactivity can facilitate emergent learning and behavior change that lead to practice improvements. Insights from learning theories provide a framework for understanding emergent learning as the product of interactions between individuals in trusted relationships, such as occurs in communities of practice. This framework helps explain why some groups respond more favorably to improvement initiatives than others. Failure to take advantage of practitioner interactivity may explain in part the disappointingly low mean rates of practice improvement reported in studies of the effectiveness of practice improvement projects. Examples of improvement models in primary care settings that explicitly use relationship building and facilitation techniques to enhance practitioner interactivity are provided. Ingredients of a curriculum to teach relationship building in communities of practice and facilitation skills to enhance learning in small group education sessions are explored. Sufficient evidence exists to support the roles of relationships and interactivity in practice improvement initiatives such that we recommend the development of training programs to teach these skills to CME providers. Copyright © 2011 The Alliance for Continuing Medical Education, the Society for Academic Continuing Medical Education, and the Council on CME, Association for Hospital Medical Education.

Cisco Networking Academy Program for high school students: Formative & summative evaluation

NASA Astrophysics Data System (ADS)

Cranford-Wesley, Deanne

This study examined the effectiveness of the Cisco Network Technology Program in enhancing students' technology skills as measured by classroom strategies, student motivation, student attitude, and student learning. Qualitative and quantitative methods were utilized to determine the effectiveness of this program. The study focused on two 11th grade classrooms at Hamtramck High School. Hamtramck, an inner-city community located in Detroit, is racially and ethnically diverse. The majority of students speak English as a second language; more than 20 languages are represented in the school district. More than 70% of the students are considered to be economically at risk. Few students have computers at home, and their access to the few computers at school is limited. Purposive sampling was conducted for this study. The sample consisted of 40 students, all of whom were trained in Cisco Networking Technologies. The researcher examined viable learning strategies in teaching a Cisco Networking class that focused on a web-based approach. Findings revealed that the Cisco Networking Academy Program was an excellent vehicle for teaching networking skills and, therefore, helping to enhance computer skills for the participating students. However, only a limited number of students were able to participate in the program, due to limited computer labs and lack of qualified teaching personnel. In addition, the cumbersome technical language posed an obstacle to students' success in networking. Laboratory assignments were preferred by 90% of the students over lecture and PowerPoint presentations. Practical applications, lab projects, interactive assignments, PowerPoint presentations, lectures, discussions, readings, research, and assessment all helped to increase student learning and proficiency and to enrich the classroom experience. Classroom strategies are crucial to student success in the networking program. Equipment must be updated and utilized to ensure that students are applying practical skills to networking concepts. The results also suggested a high level of motivation and retention in student participants. Students in both classes scored 80% proficiency on the Achievement Motivation Profile Assessment. The identified standard proficiency score was 70%, and both classes exceeded the standard.

Ridge Polynomial Neural Network with Error Feedback for Time Series Forecasting

PubMed Central

Ghazali, Rozaida; Herawan, Tutut

2016-01-01

Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting. It maintains fast learning and the ability to learn the dynamics of the time series over time. Network output feedback is the most common recurrent feedback for many recurrent neural network models. However, not much attention has been paid to the use of network error feedback instead of network output feedback. In this study, we propose a novel model, called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF) that incorporates higher order terms, recurrence and error feedback. To evaluate the performance of RPNN-EF, we used four univariate time series with different forecasting horizons, namely star brightness, monthly smoothed sunspot numbers, daily Euro/Dollar exchange rate, and Mackey-Glass time-delay differential equation. We compared the forecasting performance of RPNN-EF with the ordinary Ridge Polynomial Neural Network (RPNN) and the Dynamic Ridge Polynomial Neural Network (DRPNN). Simulation results showed an average 23.34% improvement in Root Mean Square Error (RMSE) with respect to RPNN and an average 10.74% improvement with respect to DRPNN. That means that using network errors during training helps enhance the overall forecasting performance for the network. PMID:27959927

Ridge Polynomial Neural Network with Error Feedback for Time Series Forecasting.

PubMed

Waheeb, Waddah; Ghazali, Rozaida; Herawan, Tutut

2016-01-01

Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting. It maintains fast learning and the ability to learn the dynamics of the time series over time. Network output feedback is the most common recurrent feedback for many recurrent neural network models. However, not much attention has been paid to the use of network error feedback instead of network output feedback. In this study, we propose a novel model, called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF) that incorporates higher order terms, recurrence and error feedback. To evaluate the performance of RPNN-EF, we used four univariate time series with different forecasting horizons, namely star brightness, monthly smoothed sunspot numbers, daily Euro/Dollar exchange rate, and Mackey-Glass time-delay differential equation. We compared the forecasting performance of RPNN-EF with the ordinary Ridge Polynomial Neural Network (RPNN) and the Dynamic Ridge Polynomial Neural Network (DRPNN). Simulation results showed an average 23.34% improvement in Root Mean Square Error (RMSE) with respect to RPNN and an average 10.74% improvement with respect to DRPNN. That means that using network errors during training helps enhance the overall forecasting performance for the network.

Manifold learning of brain MRIs by deep learning.

PubMed

Brosch, Tom; Tam, Roger

2013-01-01

Manifold learning of medical images plays a potentially important role for modeling anatomical variability within a population with pplications that include segmentation, registration, and prediction of clinical parameters. This paper describes a novel method for learning the manifold of 3D brain images that, unlike most existing manifold learning methods, does not require the manifold space to be locally linear, and does not require a predefined similarity measure or a prebuilt proximity graph. Our manifold learning method is based on deep learning, a machine learning approach that uses layered networks (called deep belief networks, or DBNs) and has received much attention recently in the computer vision field due to their success in object recognition tasks. DBNs have traditionally been too computationally expensive for application to 3D images due to the large number of trainable parameters. Our primary contributions are (1) a much more computationally efficient training method for DBNs that makes training on 3D medical images with a resolution of up to 128 x 128 x 128 practical, and (2) the demonstration that DBNs can learn a low-dimensional manifold of brain volumes that detects modes of variations that correlate to demographic and disease parameters.

Primary motor and premotor cortex in implicit sequence learning--evidence for competition between implicit and explicit human motor memory systems.

PubMed

Kantak, Shailesh S; Mummidisetty, Chaithanya K; Stinear, James W

2012-09-01

Implicit and explicit memory systems for motor skills compete with each other during and after motor practice. Primary motor cortex (M1) is known to be engaged during implicit motor learning, while dorsal premotor cortex (PMd) is critical for explicit learning. To elucidate the neural substrates underlying the interaction between implicit and explicit memory systems, adults underwent a randomized crossover experiment of anodal transcranial direct current stimulation (AtDCS) applied over M1, PMd or sham stimulation during implicit motor sequence (serial reaction time task, SRTT) practice. We hypothesized that M1-AtDCS during practice will enhance online performance and offline learning of the implicit motor sequence. In contrast, we also hypothesized that PMd-AtDCS will attenuate performance and retention of the implicit motor sequence. Implicit sequence performance was assessed at baseline, at the end of acquisition (EoA), and 24 h after practice (retention test, RET). M1-AtDCS during practice significantly improved practice performance and supported offline stabilization compared with Sham tDCS. Performance change from EoA to RET revealed that PMd-AtDCS during practice attenuated offline stabilization compared with M1-AtDCS and sham stimulation. The results support the role of M1 in implementing online performance gains and offline stabilization for implicit motor sequence learning. In contrast, enhancing the activity within explicit motor memory network nodes such as the PMd during practice may be detrimental to offline stabilization of the learned implicit motor sequence. These results support the notion of competition between implicit and explicit motor memory systems and identify underlying neural substrates that are engaged in this competition. © 2012 The Authors. European Journal of Neuroscience © 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

Community, Collective or Movement? Evaluating Theoretical Perspectives on Network Building

NASA Astrophysics Data System (ADS)

Spitzer, W.

2015-12-01

Since 2007, the New England Aquarium has led a national effort to increase the capacity of informal science venues to effectively communicate about climate change. We are now leading the NSF-funded National Network for Ocean and Climate Change Interpretation (NNOCCI), partnering with the Association of Zoos and Aquariums, FrameWorks Institute, Woods Hole Oceanographic Institution, Monterey Bay Aquarium, and National Aquarium, with evaluation conducted by the New Knowledge Organization, Pennsylvania State University, and Ohio State University. NNOCCI enables teams of informal science interpreters across the country to serve as "communication strategists" - beyond merely conveying information they can influence public perceptions, given their high level of commitment, knowledge, public trust, social networks, and visitor contact. We provide in-depth training as well as an alumni network for ongoing learning, implementation support, leadership development, and coalition building. Our goals are to achieve a systemic national impact, embed our work within multiple ongoing regional and national climate change education networks, and leave an enduring legacy. What is the most useful theoretical model for conceptualizing the work of the NNOCCI community? This presentation will examine the pros and cons of three perspectives -- community of practice, collective impact, and social movements. The community of practice approach emphasizes use of common tools, support for practice, social learning, and organic development of leadership. A collective impact model focuses on defining common outcomes, aligning activities toward a common goal, structured collaboration. A social movement emphasizes building group identity and creating a sense of group efficacy. This presentation will address how these models compare in terms of their utility in program planning and evaluation, their fit with the unique characteristics of the NNOCCI community, and their relevance to our program goals.

Innovations in workplace accessibility and accommodation for persons with hearing loss: using social networking and community of practice theory to promote knowledge exchange and change.

PubMed

Shaw, Lynn; Jennings, Mary Beth; Poost-Foroosh, Laya; Hodgins, Heather; Kuchar, Ashley

2013-01-01

Despite widespread availability of assistive technology and the role of occupational therapists and audiologists in workplace health, little is known about how these groups influence the health of workers with hearing loss. Based on a previously conducted study, this paper explores the need for networking and community of practice theory to promote knowledge sharing and use between occupational therapists, audiologists, educators, regulators, workers, and employers. Five occupational therapists and five audiologists participated in in-depth interviews. Grounded theory was used to investigate the processes that hinder or support these professionals in addressing the accommodation needs of and workplace accessibility for workers with hearing loss. Constraints to addressing the needs of workers with hearing loss included: lack of knowledge about professional practice processes, lack of networking, lack of knowledge on current research, and lack of knowledge on the realm of expertise of audiologists by occupational therapists and of occupational therapists by audiologists. Innovations in workplace practice in hearing loss require engagement of occupational therapists, audiologists, and employers in knowledge transfer, networking, and learning. This column introduces two theories that may guide the use and development of evidence, knowledge, and expertise toward innovations in hearing work practice.

The interactive use of networking multimedia--innovative education resource for professionals and patients.

PubMed

Matthies, H K; Walter, G F; Brandis, A; Stan, A C; Ammann, A; von Jan, U; Porth, A J

1999-01-01

The combination of new and rapidly developing interactive multimedia computers and applications with electronic networks will require a restructuring of our traditional approach to strategic planning and organizational structure. Worldwide telecommunication networks (using satellites, cable) are now facilitating the global pooling of healthcare information and medical knowledge independent of location. The development of multimedia information and communication systems demands cooperative working teams of authors, who are able to master several areas of medical knowledge as well as the presentation of these in different multimedia forms. The assemblage of telematics and services offers a base for multimedia applications, for example teleteaching, telelearning, telepublishing, teleconsulting, teleconferencing, telemedicine etc. The expansion of the internet will also lead to the formation of interdisciplinary "Global Education Networks". The theory and practice of education are undergoing dramatic changes. Lifelong learning and adaptation of medical practice to new knowledge and new techniques will be even more important in the future.

Jugando en el Pidi: Active Learning, Early Child Development and Interactive Radio Instruction. Supporting Caregivers, Parents, and Young Children. LearnTech Case Study Series, No. 4.

ERIC Educational Resources Information Center

Bosch, Andrea; Crespo, Cecilia

In 1993, Bolivia was selected as a site to pilot an interactive radio instruction (IRI) project that would provide practical support to adult caregivers and children around early childhood development. Through linkages with health and education networks, PIDI (Programa Integral de Desarrollo Infantil) provided young children under the age of six…

Literacy Networks: Following the Circulation of Texts, Bodies, and Objects in the Schooling and Online Gaming of One Youth

ERIC Educational Resources Information Center

Leander, Kevin M.; Lovvorn, Jason F.

2006-01-01

In this article, we offer an approach to conceiving of the relation between literacy practices and space-time. Literacy, embedded in other forms of activity, has a unique role in producing and organizing space-time relations, and such relations provide for different forms of cognition and learning. Closely examining how literacy practices produce…

Critical Pedagogy--The Practice with Veteran Teachers: The Work of the Eastern Pennsylvania Lead Teacher Consortium. [and] Abandon Ship, Change Course, or Ride It Out: A Reaction to Walker.

ERIC Educational Resources Information Center

Walker, Thomas J.; Johnson, Scott D.

1993-01-01

The Eastern Pennsylvania Lead Teacher Consortium, a regional network for professional development of vocational teachers, demonstrates that lead teachers' work must be tied to student learning outcomes, ideas and practices must be communicated to building-level staff, and regional consortia need a dedicated funding source. (SK)

The philosophical and pedagogical underpinnings of Active Learning in Engineering Education

NASA Astrophysics Data System (ADS)

Christie, Michael; de Graaff, Erik

2017-01-01

In this paper the authors draw on three sequential keynote addresses that they gave at Active Learning in Engineering Education (ALE) workshops in Copenhagen (2012), Caxias do Sol (2014) and San Sebastian (2015). Active Learning in Engineering Education is an informal international network of engineering educators dedicated to improving engineering education through active learning (http://www.ale-net.org/). The paper reiterates themes from those keynotes, namely, the philosophical and pedagogical underpinnings of Active Learning in Engineering Education, the scholarly questions that inspire engineering educators to go on improving their practice and exemplary models designed to activate the learning of engineering students. This paper aims to uncover the bedrock of established educational philosophies and theories that define and support active learning. The paper does not claim to present any new or innovative educational theory. There is already a surfeit of them. Rather, the aim is to assist Engineering Educators who wish to research how they can best activate the learning of their students by providing a readable, reasonable and solid underpinning for best practice in this field.

Determinants of field edge habitat restoration on farms in California's Sacramento Valley.

PubMed

Garbach, Kelly; Long, Rachael Freeman

2017-03-15

Degradation and loss of biodiversity and ecosystem services pose major challenges in simplified agricultural landscapes. Consequently, best management practices to create or restore habitat areas on field edges and other marginal areas have received a great deal of recent attention and policy support. Despite this, remarkably little is known about how landholders (farmers and landowners) learn about field edge management practices and which factors facilitate, or hinder, adoption of field edge plantings. We surveyed 109 landholders in California's Sacramento Valley to determine drivers of adoption of field edge plantings. The results show the important influence of landholders' communication networks, which included two key roles: agencies that provide technical support and fellow landholders. The networks of landholders that adopted field edge plantings included both fellow landholders and agencies, whereas networks of non-adopters included either landholders or agencies. This pattern documents that social learning through peer-to-peer information exchange can serve as a complementary and reinforcing pathway with technical learning that is stimulated by traditional outreach and extension programs. Landholder experience with benefits and concerns associated with field edge plantings were also significant predictors of adoption. Our results suggest that technical learning, stimulated by outreach and extension, may provide critical and necessary support for broad-scale adoption of field-edge plantings, but that this alone may not be sufficient. Instead, outreach and extension efforts may need to be strategically expanded to incorporate peer-to-peer communication, which can provide critical information on benefits and concerns. Copyright © 2016 Elsevier Ltd. All rights reserved.

Summit of the Research Coordination Networks for Undergraduate Biology Education

PubMed Central

Eaton, Carrie Diaz; Allen, Deborah; Anderson, Laurel J.; Bowser, Gillian; Pauley, Mark A.; Williams, Kathy S.; Uno, Gordon E.

2016-01-01

The first summit of projects funded by the National Science Foundation’s Research Coordination Networks for Undergraduate Biology Education (RCN-UBE) program was held January 14–16, 2016, in Washington, DC. Sixty-five scientists and science educators from 38 of the 41 Incubator and Full RCN-UBE awards discussed the value and contributions of RCNs to the national biology education reform effort. The summit illustrated the progress of this innovative UBE track, first awarded in 2009. Participants shared experiences regarding network development and growth, identified best practices and challenges faced in network management, and discussed work accomplished. We report here on key aspects of network evaluation, characteristics of successful networks, and how to sustain and broaden participation in networks. Evidence from successful networks indicates that 5 years (the length of a Full RCN-UBE) may be insufficient time to produce a cohesive and effective network. While online communication promotes the activities of a network and disseminates effective practices, face-to-face meetings are critical for establishing ties between network participants. Creation of these National Science Foundation–funded networks may be particularly useful for consortia of faculty working to address problems or exchange novel solutions discovered while introducing active-learning methods and/or course-based research into their curricula.

Stochastic Prediction and Feedback Control of Router Queue Size in a Virtual Network Environment

DTIC Science & Technology

2014-09-18

predictor equations, while the update equations for measurement can be thought of as corrector equations. 11 2.3.1.1 Predict Equations In the... Adaptive Filters and Self -Learning Systems. Springer London, 2005. [11] Zarchan, P., and Musoff, H. Fundamentals of Kalman filtering: A Practical...iv AFIT-ENG-T-14-S-10 Abstract Modern congestion and routing management algorithms work well for networks with static topologies and moderate

Action Research Monographs. Complete Set. Pennsylvania Action Research Network, 1998-99. A Section 353 Project of the Pennsylvania Department of Education, Bureau of Adult Basic and Literacy Education. A Learning from Practice Project.

ERIC Educational Resources Information Center

Pennsylvania State Univ., McKeesport.

This publication consists of the complete set of 23 monographs developed by the Pennsylvania Action Research Network to supplement the 67 monographs produced over the past 3 years. The specific audience are literacy, General Educational Development (GED), and English-as-a Second Language (ESL) practitioners. The titles are: "Use of…

[Exploration and practice of genetics teaching assisted by network technology platform].

PubMed

Li, Ya-Xuan; Zhang, Fei-Xiong; Zhao, Xin; Cai, Min-Hua; Yan, Yue-Ming; Hu, Ying-Kao

2010-04-01

More teaching techniques have been brought out gradually along with the development of new technologies. On the basis of those traditional teaching methods, a new platform has been set up by the network technology for teaching process. In genetics teaching, it is possible to use the network platform to guide student studying, promote student's learning interest and study independently by themselves. It has been proved, after exploring and applying for many years, that network teaching is one of the most useful methods and has inimitable advantage comparing to the traditional ones in genetics teaching. The establishment of network teaching platform, the advantage and deficiency and relevant strategies were intro-duced in this paper.

A Deep Neural Network Model for Rainfall Estimation UsingPolarimetric WSR-88DP Radar Observations

NASA Astrophysics Data System (ADS)

Tan, H.; Chandra, C. V.; Chen, H.

2016-12-01

Rainfall estimation based on radar measurements has been an important topic for a few decades. Generally, radar rainfall estimation is conducted through parametric algorisms such as reflectivity-rainfall relation (i.e., Z-R relation). On the other hand, neural networks are developed for ground rainfall estimation based on radar measurements. This nonparametric method, which takes into account of both radar observations and rainfall measurements from ground rain gauges, has been demonstrated successfully for rainfall rate estimation. However, the neural network-based rainfall estimation is limited in practice due to the model complexity and structure, data quality, as well as different rainfall microphysics. Recently, the deep learning approach has been introduced in pattern recognition and machine learning areas. Compared to traditional neural networks, the deep learning based methodologies have larger number of hidden layers and more complex structure for data representation. Through a hierarchical learning process, the high level structured information and knowledge can be extracted automatically from low level features of the data. In this paper, we introduce a novel deep neural network model for rainfall estimation based on ground polarimetric radar measurements .The model is designed to capture the complex abstractions of radar measurements at different levels using multiple layers feature identification and extraction. The abstractions at different levels can be used independently or fused with other data resource such as satellite-based rainfall products and/or topographic data to represent the rain characteristics at certain location. In particular, the WSR-88DP radar and rain gauge data collected in Dallas - Fort Worth Metroplex and Florida are used extensively to train the model, and for demonstration purposes. Quantitative evaluation of the deep neural network based rainfall products will also be presented, which is based on an independent rain gauge network.

If we only knew what we know: principles for knowledge sharing across people, practices, and platforms.

PubMed

Dearing, James W; Greene, Sarah M; Stewart, Walter F; Williams, Andrew E

2011-03-01

The improvement of health outcomes for both individual patients and entire populations requires improvement in the array of structures that support decisions and activities by healthcare practitioners. Yet, many gaps remain in how even sophisticated healthcare organizations manage knowledge. Here we describe the value of a trans-institutional network for identifying and capturing how-to knowledge that contributes to improved outcomes. Organizing and sharing on-the-job experience would concentrate and organize the activities of individual practitioners and subject their rapid cycle improvement testing and refinement to a form of collective intelligence for subsequent diffusion back through the network. We use the existing Cancer Research Network as an example of how a loosely structured consortium of healthcare delivery organizations could create and grow an implementation registry to foster innovation and implementation success by communicating what works, how, and which practitioners are using each innovation. We focus on the principles and parameters that could be used as a basis for infrastructure design. As experiential knowledge from across institutions builds within such a system, the system could ultimately motivate rapid learning and adoption of best practices. Implications for research about healthcare IT, invention, and organizational learning are discussed.

Supervised learning from human performance at the computationally hard problem of optimal traffic signal control on a network of junctions

PubMed Central

Box, Simon

2014-01-01

Optimal switching of traffic lights on a network of junctions is a computationally intractable problem. In this research, road traffic networks containing signallized junctions are simulated. A computer game interface is used to enable a human ‘player’ to control the traffic light settings on the junctions within the simulation. A supervised learning approach, based on simple neural network classifiers can be used to capture human player's strategies in the game and thus develop a human-trained machine control (HuTMaC) system that approaches human levels of performance. Experiments conducted within the simulation compare the performance of HuTMaC to two well-established traffic-responsive control systems that are widely deployed in the developed world and also to a temporal difference learning-based control method. In all experiments, HuTMaC outperforms the other control methods in terms of average delay and variance over delay. The conclusion is that these results add weight to the suggestion that HuTMaC may be a viable alternative, or supplemental method, to approximate optimization for some practical engineering control problems where the optimal strategy is computationally intractable. PMID:26064570

Supervised learning from human performance at the computationally hard problem of optimal traffic signal control on a network of junctions.

PubMed

Box, Simon

2014-12-01

Optimal switching of traffic lights on a network of junctions is a computationally intractable problem. In this research, road traffic networks containing signallized junctions are simulated. A computer game interface is used to enable a human 'player' to control the traffic light settings on the junctions within the simulation. A supervised learning approach, based on simple neural network classifiers can be used to capture human player's strategies in the game and thus develop a human-trained machine control (HuTMaC) system that approaches human levels of performance. Experiments conducted within the simulation compare the performance of HuTMaC to two well-established traffic-responsive control systems that are widely deployed in the developed world and also to a temporal difference learning-based control method. In all experiments, HuTMaC outperforms the other control methods in terms of average delay and variance over delay. The conclusion is that these results add weight to the suggestion that HuTMaC may be a viable alternative, or supplemental method, to approximate optimization for some practical engineering control problems where the optimal strategy is computationally intractable.

Nonvolatile Memory Materials for Neuromorphic Intelligent Machines.

PubMed

Jeong, Doo Seok; Hwang, Cheol Seong

2018-04-18

Recent progress in deep learning extends the capability of artificial intelligence to various practical tasks, making the deep neural network (DNN) an extremely versatile hypothesis. While such DNN is virtually built on contemporary data centers of the von Neumann architecture, physical (in part) DNN of non-von Neumann architecture, also known as neuromorphic computing, can remarkably improve learning and inference efficiency. Particularly, resistance-based nonvolatile random access memory (NVRAM) highlights its handy and efficient application to the multiply-accumulate (MAC) operation in an analog manner. Here, an overview is given of the available types of resistance-based NVRAMs and their technological maturity from the material- and device-points of view. Examples within the strategy are subsequently addressed in comparison with their benchmarks (virtual DNN in deep learning). A spiking neural network (SNN) is another type of neural network that is more biologically plausible than the DNN. The successful incorporation of resistance-based NVRAM in SNN-based neuromorphic computing offers an efficient solution to the MAC operation and spike timing-based learning in nature. This strategy is exemplified from a material perspective. Intelligent machines are categorized according to their architecture and learning type. Also, the functionality and usefulness of NVRAM-based neuromorphic computing are addressed. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Evaluating the impact on practice of a west of Berkshire protected learning time initiative in primary care.

PubMed

Stenner, Karen; Iacovou, Nicci

2006-01-01

WHAT IS ALREADY KNOWN IN THIS AREA • Research indicates that Protected Learning Time (PLT) events in primary care enable professionals to network and share ideas. • A variety of educational techniques have been shown to improve performance of: individual practitioners in other settings. • Beyond one-off examples, there is little published evidence that PLT helps to improve practice. WHAT THIS WORK ADDS • It describes a range of ways in which PLT has impacted on practice at the level of the individual, the team and the wider organisation. • It highlights the main benefits of large event PLT according to participants at a Berkshire initiative. The benefits include increased awareness of services, increased understanding of illnesses and improved treatment. SUGGESTIONS FOR FUTURE RESEARCH • Do large PLT events have different outcomes from practice-based PLT? • How does PLT impact on the development of a learning culture? • How can large; learning events best meet the needs of different groups of professionals? • What impact, if any, does the closure of surgeries for PLT have on use of out-of-hours services or subsequent workload?

Cooperation enhanced by indirect reciprocity in spatial prisoner's dilemma games for social P2P systems

NASA Astrophysics Data System (ADS)

Tian, Lin-Lin; Li, Ming-Chu; Wang, Zhen

2016-11-01

With the growing interest in social Peer-to-Peer (P2P) applications, relationships of individuals are further exploited to improve the performances of reputation systems. It is an on-going challenge to investigate how spatial reciprocity aids indirect reciprocity in sustaining cooperation in practical P2P environments. This paper describes the construction of an extended prisoner's dilemma game on square lattice networks with three strategies, i.e., defection, unconditional cooperation, and reciprocal cooperation. Reciprocators discriminate partners according to their reputations based on image scoring, where mistakes in judgment of reputations may occur. The independent structures of interaction and learning neighborhood are discussed, with respect to the situation in which learning environments differ from interaction networks. The simulation results have indicated that the incentive mechanism enhances cooperation better in structured peers than among a well-mixed population. Given the realistic condition of inaccurate reputation scores, defection is still successfully held down when the players interact and learn within the unified neighborhoods. Extensive simulations have further confirmed the positive impact of spatial structure on cooperation with different sizes of lattice neighborhoods. And similar conclusions can also be drawn on regular random networks and scale-free networks. Moreover, for the separated structures of the neighborhoods, the interaction network has a critical effect on the evolution dynamics of cooperation and learning environments only have weaker impacts on the process. Our findings further provide some insights concerning the evolution of collective behaviors in social systems.

The new and improved learning community at Johns Hopkins University School of Medicine resembles that at Hogwarts School of Witchcraft and Wizardry.

PubMed

Stewart, Rosalyn W; Barker, Allison R; Shochet, Robert B; Wright, Scott M

2007-05-01

In July 2005, a learning community was created at Johns Hopkins University School of Medicine (JHUSOM) to foster camaraderie, networking, advising, mentoring, professionalism, clinical skills, and scholarship--The Colleges. The cultural and structural changes that emerged with the creation of this program have resulted in JHUSOM bearing a resemblance to J. K. Rowling's fictional Hogwarts School of Witchcraft and Wizardry. This manuscript will describe the similarities between these two revered schools, and highlight the innovations and improvements made to JHUSOM's learning environment. The intense, stressful, and lengthy professional training required to achieve competency in the practice of medicine and in the practice of witchcraft (albeit fictional) have meaningful parallels. The supportive learning environment at these two schools should afford the next generation of graduates to have an even more enriching experience than those who have come before them.

Cognitive Plasticity and Cortical Modules

PubMed Central

Mercado, Eduardo

2009-01-01

Some organisms learn to calculate, accumulate knowledge, and communicate in ways that others do not. What factors determine which intellectual abilities a particular species or individual can easily acquire? I propose that cognitive-skill learning capacity reflects (a) the availability of specialized cortical circuits, (b) the flexibility with which cortical activity is coordinated, and (c) the customizability of cortical networks. This framework can potentially account for differences in learning capacity across species, individuals, and developmental stages. Understanding the mechanisms that constrain cognitive plasticity is fundamental to developing new technologies and educational practices that maximize intellectual advancements. PMID:19750239

Cognitive Plasticity and Cortical Modules.

PubMed

Mercado, Eduardo

2009-06-01

Some organisms learn to calculate, accumulate knowledge, and communicate in ways that others do not. What factors determine which intellectual abilities a particular species or individual can easily acquire? I propose that cognitive-skill learning capacity reflects (a) the availability of specialized cortical circuits, (b) the flexibility with which cortical activity is coordinated, and (c) the customizability of cortical networks. This framework can potentially account for differences in learning capacity across species, individuals, and developmental stages. Understanding the mechanisms that constrain cognitive plasticity is fundamental to developing new technologies and educational practices that maximize intellectual advancements.

Personalized E- learning System Based on Intelligent Agent

NASA Astrophysics Data System (ADS)

Duo, Sun; Ying, Zhou Cai

Lack of personalized learning is the key shortcoming of traditional e-Learning system. This paper analyzes the personal characters in e-Learning activity. In order to meet the personalized e-learning, a personalized e-learning system based on intelligent agent was proposed and realized in the paper. The structure of system, work process, the design of intelligent agent and the realization of intelligent agent were introduced in the paper. After the test use of the system by certain network school, we found that the system could improve the learner's initiative participation, which can provide learners with personalized knowledge service. Thus, we thought it might be a practical solution to realize self- learning and self-promotion in the lifelong education age.

Learning algorithm in restricted Boltzmann machines using Kullback-Leibler importance estimation procedure

NASA Astrophysics Data System (ADS)

Yasuda, Muneki; Sakurai, Tetsuharu; Tanaka, Kazuyuki

Restricted Boltzmann machines (RBMs) are bipartite structured statistical neural networks and consist of two layers. One of them is a layer of visible units and the other one is a layer of hidden units. In each layer, any units do not connect to each other. RBMs have high flexibility and rich structure and have been expected to applied to various applications, for example, image and pattern recognitions, face detections and so on. However, most of computational models in RBMs are intractable and often belong to the class of NP-hard problem. In this paper, in order to construct a practical learning algorithm for them, we employ the Kullback-Leibler Importance Estimation Procedure (KLIEP) to RBMs, and give a new scheme of practical approximate learning algorithm for RBMs based on the KLIEP.

Joint Prior Learning for Visual Sensor Network Noisy Image Super-Resolution

PubMed Central

Yue, Bo; Wang, Shuang; Liang, Xuefeng; Jiao, Licheng; Xu, Caijin

2016-01-01

The visual sensor network (VSN), a new type of wireless sensor network composed of low-cost wireless camera nodes, is being applied for numerous complex visual analyses in wild environments, such as visual surveillance, object recognition, etc. However, the captured images/videos are often low resolution with noise. Such visual data cannot be directly delivered to the advanced visual analysis. In this paper, we propose a joint-prior image super-resolution (JPISR) method using expectation maximization (EM) algorithm to improve VSN image quality. Unlike conventional methods that only focus on upscaling images, JPISR alternatively solves upscaling mapping and denoising in the E-step and M-step. To meet the requirement of the M-step, we introduce a novel non-local group-sparsity image filtering method to learn the explicit prior and induce the geometric duality between images to learn the implicit prior. The EM algorithm inherently combines the explicit prior and implicit prior by joint learning. Moreover, JPISR does not rely on large external datasets for training, which is much more practical in a VSN. Extensive experiments show that JPISR outperforms five state-of-the-art methods in terms of both PSNR, SSIM and visual perception. PMID:26927114

Learning about learning: Mining human brain sub-network biomarkers from fMRI data

PubMed Central

Dereli, Nazli; Dang, Xuan-Hong; Bassett, Danielle S.; Wymbs, Nicholas F.; Grafton, Scott T.; Singh, Ambuj K.

2017-01-01

Modeling the brain as a functional network can reveal the relationship between distributed neurophysiological processes and functional interactions between brain structures. Existing literature on functional brain networks focuses mainly on a battery of network properties in “resting state” employing, for example, modularity, clustering, or path length among regions. In contrast, we seek to uncover functionally connected subnetworks that predict or correlate with cohort differences and are conserved within the subjects within a cohort. We focus on differences in both the rate of learning as well as overall performance in a sensorimotor task across subjects and develop a principled approach for the discovery of discriminative subgraphs of functional connectivity based on imaging acquired during practice. We discover two statistically significant subgraph regions: one involving multiple regions in the visual cortex and another involving the parietal operculum and planum temporale. High functional coherence in the former characterizes sessions in which subjects take longer to perform the task, while high coherence in the latter is associated with high learning rate (performance improvement across trials). Our proposed methodology is general, in that it can be applied to other cognitive tasks, to study learning or to differentiate between healthy patients and patients with neurological disorders, by revealing the salient interactions among brain regions associated with the observed global state. The discovery of such significant discriminative subgraphs promises a better data-driven understanding of the dynamic brain processes associated with high-level cognitive functions. PMID:29016686

Learning about learning: Mining human brain sub-network biomarkers from fMRI data.

PubMed

Bogdanov, Petko; Dereli, Nazli; Dang, Xuan-Hong; Bassett, Danielle S; Wymbs, Nicholas F; Grafton, Scott T; Singh, Ambuj K

2017-01-01

Modeling the brain as a functional network can reveal the relationship between distributed neurophysiological processes and functional interactions between brain structures. Existing literature on functional brain networks focuses mainly on a battery of network properties in "resting state" employing, for example, modularity, clustering, or path length among regions. In contrast, we seek to uncover functionally connected subnetworks that predict or correlate with cohort differences and are conserved within the subjects within a cohort. We focus on differences in both the rate of learning as well as overall performance in a sensorimotor task across subjects and develop a principled approach for the discovery of discriminative subgraphs of functional connectivity based on imaging acquired during practice. We discover two statistically significant subgraph regions: one involving multiple regions in the visual cortex and another involving the parietal operculum and planum temporale. High functional coherence in the former characterizes sessions in which subjects take longer to perform the task, while high coherence in the latter is associated with high learning rate (performance improvement across trials). Our proposed methodology is general, in that it can be applied to other cognitive tasks, to study learning or to differentiate between healthy patients and patients with neurological disorders, by revealing the salient interactions among brain regions associated with the observed global state. The discovery of such significant discriminative subgraphs promises a better data-driven understanding of the dynamic brain processes associated with high-level cognitive functions.

Socio-material perspectives on interprofessional team and collaborative learning.

PubMed

McMurtry, Angus; Rohse, Shanta; Kilgour, Kelly N

2016-02-01

Interprofessional teamwork and collaboration have become important parts of health care practice and education. Most of the literature on interprofessional learning, however, assumes that learning is something acquired by individuals and readily transferred to other contexts. This assumption severely limits the ways in which interprofessional educators and researchers can conceptualise and support learning related to collaborative interprofessional health care. Socio-material theories provide an alternative to individualistic, acquisition-oriented notions by reconceiving learning in terms of collective dynamics, participation in social communities and active engagement with material contexts. Socio-material literature and theories were reviewed to identify concepts relevant to interprofessional learning. After briefly summarising the origins and key principles of socio-material approaches, the authors draw upon specific socio-material theories--including complexity theory, cultural-historical activity theory and actor-network theory--in order to reconceive how learning happens in interprofessional contexts. This reframing of interprofessional learning focuses less on individuals and more on collective dynamics and the actual social and material relations involved in practice. The paper proposes five ways in which learning may be enacted in interprofessional teamwork and collaboration from a socio-material perspective: (i) diverse contributions; (ii) social interactions and relationships; (iii) synthesis of professional ideas; (iv) integration of material elements, and (v) connections to large-scale organisations. For each of these categories, the paper provides practical illustrations to assist educators and researchers who wish to identify and assess this learning. Although more exploratory than comprehensive, this paper articulates many key aspects of socio-material learning theories and offers practical guidance for those who wish to employ and assess them in interprofessional contexts. © 2016 John Wiley & Sons Ltd.

Integrating Online and Offline Three-Dimensional Deep Learning for Automated Polyp Detection in Colonoscopy Videos.

PubMed

Lequan Yu; Hao Chen; Qi Dou; Jing Qin; Pheng Ann Heng

2017-01-01

Automated polyp detection in colonoscopy videos has been demonstrated to be a promising way for colorectal cancer prevention and diagnosis. Traditional manual screening is time consuming, operator dependent, and error prone; hence, automated detection approach is highly demanded in clinical practice. However, automated polyp detection is very challenging due to high intraclass variations in polyp size, color, shape, and texture, and low interclass variations between polyps and hard mimics. In this paper, we propose a novel offline and online three-dimensional (3-D) deep learning integration framework by leveraging the 3-D fully convolutional network (3D-FCN) to tackle this challenging problem. Compared with the previous methods employing hand-crafted features or 2-D convolutional neural network, the 3D-FCN is capable of learning more representative spatio-temporal features from colonoscopy videos, and hence has more powerful discrimination capability. More importantly, we propose a novel online learning scheme to deal with the problem of limited training data by harnessing the specific information of an input video in the learning process. We integrate offline and online learning to effectively reduce the number of false positives generated by the offline network and further improve the detection performance. Extensive experiments on the dataset of MICCAI 2015 Challenge on Polyp Detection demonstrated the better performance of our method when compared with other competitors.

Supporting nurse mentor development: An exploration of developmental constellations in nursing mentorship practice.

PubMed

MacLaren, Julie-Ann

2018-01-01

Supervised practice as a mentor is currently an integral component of nurse mentor education. However, workplace education literature tends to focus on dyadic mentor-student relationships rather than developmental relationships between colleagues. This paper explores the supportive relationships of nurses undertaking a mentorship qualification, using the novel technique of constellation development to determine the nature of workplace support for this group. Semi-structured interviews were conducted with three recently qualified nurse mentors. All participants developed a mentorship constellation identifying colleagues significant to their own learning in practice. These significant others were also interviewed alongside practice education, and nurse education leads. Constellations were analysed in relation to network size, breadth, strength of relationships, and attributes of individuals. Findings suggest that dyadic forms of supervisory mentorship may not offer the range of skills and attributes that developing mentors require. Redundancy of mentorship attributes within the constellation (overlapping attributes between members) may counteract problems caused when one mentor attempts to fulfil all mentorship roles. Wider nursing teams are well placed to provide the support and supervision required by mentors in training. Where wider and stronger networks were not available to mentorship students, mentorship learning was at risk. Crown Copyright © 2017. Published by Elsevier Ltd. All rights reserved.

The developing brain in a multitasking world

PubMed Central

Rothbart, Mary K.; Posner, Michael I.

2015-01-01

To understand the problem of multitasking, it is necessary to examine the brain’s attention networks that underlie the ability to switch attention between stimuli and tasks and to maintain a single focus among distractors. In this paper we discuss the development of brain networks related to the functions of achieving the alert state, orienting to sensory events, and developing self-control. These brain networks are common to everyone, but their efficiency varies among individuals and reflects both genes and experience. Training can alter brain networks. We consider two forms of training: (1) practice in tasks that involve particular networks, and (2) changes in brain state through such practices as meditation that may influence many networks. Playing action video games and multitasking are themselves methods of training the brain that can lead to improved performance but also to overdependence on media activity. We consider both of these outcomes and ideas about how to resist overdependence on media. Overall, our paper seeks to inform the reader about what has been learned about attention that can influence multitasking over the course of development. PMID:25821335

The developing brain in a multitasking world.

PubMed

Rothbart, Mary K; Posner, Michael I

2015-03-01

To understand the problem of multitasking, it is necessary to examine the brain's attention networks that underlie the ability to switch attention between stimuli and tasks and to maintain a single focus among distractors. In this paper we discuss the development of brain networks related to the functions of achieving the alert state, orienting to sensory events, and developing self-control. These brain networks are common to everyone, but their efficiency varies among individuals and reflects both genes and experience. Training can alter brain networks. We consider two forms of training: (1) practice in tasks that involve particular networks, and (2) changes in brain state through such practices as meditation that may influence many networks. Playing action video games and multitasking are themselves methods of training the brain that can lead to improved performance but also to overdependence on media activity. We consider both of these outcomes and ideas about how to resist overdependence on media. Overall, our paper seeks to inform the reader about what has been learned about attention that can influence multitasking over the course of development.

Prism Adaptation in Schizophrenia

ERIC Educational Resources Information Center

Bigelow, Nirav O.; Turner, Beth M.; Andreasen, Nancy C.; Paulsen, Jane S.; O'Leary, Daniel S.; Ho, Beng-Choon

2006-01-01

The prism adaptation test examines procedural learning (PL) in which performance facilitation occurs with practice on tasks without the need for conscious awareness. Dynamic interactions between frontostriatal cortices, basal ganglia, and the cerebellum have been shown to play key roles in PL. Disruptions within these neural networks have also…

Bringing Together Community Health Centers, Information Technology and Data to Support a Patient-Centered Medical Village from the OCHIN community of solutions

PubMed Central

DeVoe, Jennifer E.; Sears, Abigail

2013-01-01

Creating integrated, comprehensive care practices requires access to data and informatics expertise. Information technology (IT) resources are not readily available to individual practices. One model of shared IT resources and learning is a “patient-centered medical village.” We describe the OCHIN Community Health Information Network as an example of this model where community practices have come together collectively to form an organization which leverages shared IT expertise, resources, and data, providing members with the means to fully capitalize on new technologies that support improved care. This collaborative facilitates the identification of “problem-sheds” through surveillance of network-wide data, enables shared learning regarding best practices, and provides a “community laboratory” for practice-based research. As an example of a Community of Solution, OCHIN utilizes health IT and data-sharing innovations to enhance partnerships between public health leaders, community health center clinicians, informatics experts, and policy makers. OCHIN community partners benefit from the shared IT resource (e.g. a linked electronic health record (EHR), centralized data warehouse, informatics and improvement expertise). This patient-centered medical village provides (1) the collective mechanism to build community tailored IT solutions, (2) “neighbors” to share data and improvement strategies, and (3) infrastructure to support EHR-based innovations across communities, using experimental approaches. PMID:23657695

Assessing Interprofessional education in a student-faculty collaborative practice network.

PubMed

Young, Grace J; Cohen, Marya J; Blanchfield, Bonnie B; Jones, Meissa M; Reidy, Patricia A; Weinstein, Amy R

2017-07-01

Although interprofessional relationships are ubiquitous in clinical practice, undergraduate medical students have limited opportunities to develop these relationships in the clinical setting. A few student-faculty collaborative practice networks (SFCPNs) have been working to address this issue, but limited data exist examining the nature and extent of these practices. A systematic survey at a Harvard-affiliated SFCPN is utilised to evaluate the quantity and quality of interprofessional interactions, isolate improvements, and identify challenges in undergraduate interprofessional education (IPE). Our data corroborate previous findings in which interprofessional clinical learning was shown to have positive effects on student development and align with all four domains of Interprofessional Education Collaborative core competencies, including interprofessional ethics and values, roles and responsibilities, interprofessional communication, and teams and teamwork. These results highlight the unique opportunity and growing necessity of integrating IPE in SFCPNs to endorse the development of collaborative and professional competencies in clinical modalities of patient care.

Journal of the American Board of Family Medicine Sixth Annual Practice-based Research Network theme issue--They just keep getting better and better.

PubMed

Bowman, Marjorie A; Neale, Anne Victoria

2011-01-01

We have quite a rich issue this month related to practice-based research networks (PBRNs)--reflections on where they have been, where they should go, how they should happen; lessons learned about recruiting physicians and patients and new research methods; and several clinical studies from existing PBRNs. We had an amazing number of manuscripts submitted this year for the PBRN issue; as a result, this is a powerful issue. Some are under revision for future issues of the Journal of the American Board of Family Medicine, just as we have some articles from PBRNs appearing in most issues. PBRNs have deepened the family medicine research tradition. The importance of primary care research to build the evidence base of our clinical practice, plus the useful work building the methods of primary care research, distinguishes the pioneers in PBRNs. PBRNs are Health Improvement Networks and national treasures to be nurtured.

The development of a network for community-based obesity prevention: the CO-OPS Collaboration

PubMed Central

2011-01-01

Background Community-based interventions are a promising approach and an important component of a comprehensive response to obesity. In this paper we describe the Collaboration of COmmunity-based Obesity Prevention Sites (CO-OPS Collaboration) in Australia as an example of a collaborative network to enhance the quality and quantity of obesity prevention action at the community level. The core aims of the CO-OPS Collaboration are to: identify and analyse the lessons learned from a range of community-based initiatives aimed at tackling obesity, and; to identify the elements that make community-based obesity prevention initiatives successful and share the knowledge gained with other communities. Methods Key activities of the collaboration to date have included the development of a set of Best Practice Principles and knowledge translation and exchange activities to promote the application (or use) of evidence, evaluation and analysis in practice. Results The establishment of the CO-OPS Collaboration is a significant step toward strengthening action in this area, by bringing together research, practice and policy expertise to promote best practice, high quality evaluation and knowledge translation and exchange. Future development of the network should include facilitation of further evidence generation and translation drawing from process, impact and outcome evaluation of existing community-based interventions. Conclusions The lessons presented in this paper may help other networks like CO-OPS as they emerge around the globe. It is important that networks integrate with each other and share the experience of creating these networks. PMID:21349185

Effectiveness of lectures attended via interactive video conferencing versus in-person in preparing third-year internal medicine clerkship students for Clinical Practice Examinations (CPX).

PubMed

Bertsch, Tania F; Callas, Peter W; Rubin, Alan; Caputo, Michael P; Ricci, Michael A

2007-01-01

The current practice in medical education is to place students at off-site locations. The effectiveness of these students attending remote lectures using interactive videoconferencing needs to be evaluated. To determine whether lecture content covering clinical objectives is learned by medical students located at remote sites. During the University of Vermont medicine clerkship, 52 medical students attended lectures both in person and via 2-way videoconferencing over a telemedicine network. The study used a crossover design, such that all students attended half of the lectures in person and half using videoconferencing. At the end of the clerkship, students were assessed via a Clinical Practice Examination (CPX), with each student completing 1 exam for material learned in person and 1 for material learned over telemedicine. Exam scores did not differ for the 2 lecture modes, with a mean score of 76% for lectures attended in person and a mean score of 78% for lectures attended via telemedicine (p = 0.66). Students learn content focused on clinical learning objectives as well using videoconferencing as they do in the traditional classroom setting.

Lifelong Learning for Clinical Practice: How to Leverage Technology for Telebehavioral Health Care and Digital Continuing Medical Education.

PubMed

Hilty, Donald M; Turvey, Carolyn; Hwang, Tiffany

2018-03-12

Psychiatric practice continues to evolve and play an important role in patients' lives, the field of medicine, and health care delivery. Clinicians must learn a variety of clinical care systems and lifelong learning (LLL) is crucial to apply knowledge, develop skills, and adjust attitudes. Technology is rapidly becoming a key player-in delivery, lifelong learning, and education/training. The evidence base for telepsychiatry/telemental health via videoconferencing has been growing for three decades, but a greater array of technologies have emerged in the last decade (e.g., social media/networking, text, apps). Clinicians are combining telepsychiatry and these technologies frequently and they need to reflect on, learn more about, and develop skills for these technologies. The digital age has solidified the role of technology in continuing medical education and day-to-day practice. Other fields of medicine are also adapting to the digital age, as are graduate and undergraduate medical education and many allied mental health organizations. In the future, there will be more online training, simulation, and/or interactive electronic examinations, perhaps on a monthly cycle rather than a quasi-annual or 10-year cycle of recertification.

Building Ocean Learning Communities: A COSEE Science and Education Partnership

NASA Astrophysics Data System (ADS)

Robigou, V.; Bullerdick, S.; Anderson, A.

2007-12-01

The core mission of the Centers for Ocean Sciences Education Excellence (COSEE) is to promote partnerships between research scientists and educators through a national network of regional and thematic centers. In addition, the COSEEs also disseminate best practices in ocean sciences education, and promote ocean sciences as a charismatic interdisciplinary vehicle for creating a more scientifically literate workforce and citizenry. Although each center is mainly funded through a peer-reviewed grant process by the National Science Foundation (NSF), the centers form a national network that fosters collaborative efforts among the centers to design and implement initiatives for the benefit of the entire network and beyond. Among these initiatives the COSEE network has contributed to the definition, promotion, and dissemination of Ocean Literacy in formal and informal learning settings. Relevant to all research scientists, an Education and Public Outreach guide for scientists is now available at www.tos.org. This guide highlights strategies for engaging scientists in Ocean Sciences Education that are often applicable in other sciences. To address the challenging issue of ocean sciences education informed by scientific research, the COSEE approach supports centers that are partnerships between research institutions, formal and informal education venues, advocacy groups, industry, and others. The COSEE Ocean Learning Communities, is a partnership between the University of Washington College of Ocean and Fishery Sciences and College of Education, the Seattle Aquarium, and a not-for-profit educational organization. The main focus of the center is to foster and create Learning Communities that cultivate contributing, and ocean sciences-literate citizens aware of the ocean's impact on daily life. The center is currently working with volunteer groups around the Northwest region that are actively involved in projects in the marine environment and to empower these diverse groups including research scientists, formal and informal educators, business representatives, and non-profit groups to identify ocean-related problems, and develop solutions to share with their own communities. COSEE OLC practices and studies the skills of developing these collaborations.

Current Research in European Vocational Education and Human Resource Development. Proceedings of the Programme Presented By the Research Network on Vocational Education and Training (VETNET) at the European Conference of Educational Research (ECER) (3rd, Edinburgh, Scotland, September 20-23, 2000).

ERIC Educational Resources Information Center

Manning, Sabine, Ed.; Raffe, David, Ed.

These 24 papers represent the proceedings of a program presented by the research network on vocational education and training (VET). They include "School-Arranged or Market-Governed Workplace Training?" (Ulla Arnell-Gustafsson); "Prospects for Mutual Learning and Transnational Transfer of Innovative Practice in European VET"…

Lessons Learned During the Conduct of Clinical Studies in The Dental PBRN

PubMed Central

Gilbert, Gregg H.; Richman, Joshua S.; Gordan, Valeria V.; Rindal, D. Brad; Fellows, Jeffrey L.; Benjamin, Paul L.; Wallace-Dawson, Martha; Williams, O. Dale

2012-01-01

Effectively addressing challenges of conducting research in nonacademic settings is crucial to its success. A dental practice-based research network called The Dental Practice-Based Research Network (DPBRN) is comprised of practitioner-investigators in two health maintenance organizations, several universities, many U.S. states, and three Scandinavian countries. Our objective in this article is to describe lessons learned from conducting studies in this research context; the studies are conducted by clinicians in community settings who may be doing their first research study. To date, twenty-one studies have been completed or are in implementation. These include a broad range of topic areas, enrollment sizes, and study designs. A total of 1,126 practitioner-investigators have participated in at least one study. After excluding one study because it involved electronic records queries only, these studies included more than 70,000 patient/participant units. Because the DPBRN is committed to being both practitioner- and patient-driven, all studies must be approved by its Executive Committee and a formal study section of academic clinical scientists. As a result of interacting with a diverse range of institutional and regulatory entities, funding agencies, practitioners, clinic staff, patients, academic scientists, and geographic areas, twenty-three key lessons have been learned. Patients’ acceptance of these studies has been very high, judging from high participation rates and their completion of data forms. Early studies substantially informed later studies with regard to study design, practicality, forms design, informed consent process, and training and monitoring methods. Although time-intensive and complex, these solutions improved acceptability of practice-based research to patients, practitioners, and university researchers. PMID:21460266

Accelerating Research Impact in a Learning Health Care System

PubMed Central

Elwy, A. Rani; Sales, Anne E.; Atkins, David

2017-01-01

Background: Since 1998, the Veterans Health Administration (VHA) Quality Enhancement Research Initiative (QUERI) has supported more rapid implementation of research into clinical practice. Objectives: With the passage of the Veterans Access, Choice and Accountability Act of 2014 (Choice Act), QUERI further evolved to support VHA’s transformation into a Learning Health Care System by aligning science with clinical priority goals based on a strategic planning process and alignment of funding priorities with updated VHA priority goals in response to the Choice Act. Design: QUERI updated its strategic goals in response to independent assessments mandated by the Choice Act that recommended VHA reduce variation in care by providing a clear path to implement best practices. Specifically, QUERI updated its application process to ensure its centers (Programs) focus on cross-cutting VHA priorities and specify roadmaps for implementation of research-informed practices across different settings. QUERI also increased funding for scientific evaluations of the Choice Act and other policies in response to Commission on Care recommendations. Results: QUERI’s national network of Programs deploys effective practices using implementation strategies across different settings. QUERI Choice Act evaluations informed the law’s further implementation, setting the stage for additional rigorous national evaluations of other VHA programs and policies including community provider networks. Conclusions: Grounded in implementation science and evidence-based policy, QUERI serves as an example of how to operationalize core components of a Learning Health Care System, notably through rigorous evaluation and scientific testing of implementation strategies to ultimately reduce variation in quality and improve overall population health. PMID:27997456

Structural limitations of learning in a crowd: communication vulnerability and information diffusion in MOOCs.

PubMed

Gillani, Nabeel; Yasseri, Taha; Eynon, Rebecca; Hjorth, Isis

2014-09-23

Massive Open Online Courses (MOOCs) bring together a global crowd of thousands of learners for several weeks or months. In theory, the openness and scale of MOOCs can promote iterative dialogue that facilitates group cognition and knowledge construction. Using data from two successive instances of a popular business strategy MOOC, we filter observed communication patterns to arrive at the "significant" interaction networks between learners and use complex network analysis to explore the vulnerability and information diffusion potential of the discussion forums. We find that different discussion topics and pedagogical practices promote varying levels of 1) "significant" peer-to-peer engagement, 2) participant inclusiveness in dialogue, and ultimately, 3) modularity, which impacts information diffusion to prevent a truly "global" exchange of knowledge and learning. These results indicate the structural limitations of large-scale crowd-based learning and highlight the different ways that learners in MOOCs leverage, and learn within, social contexts. We conclude by exploring how these insights may inspire new developments in online education.

Structural limitations of learning in a crowd: communication vulnerability and information diffusion in MOOCs

PubMed Central

Gillani, Nabeel; Yasseri, Taha; Eynon, Rebecca; Hjorth, Isis

2014-01-01

Massive Open Online Courses (MOOCs) bring together a global crowd of thousands of learners for several weeks or months. In theory, the openness and scale of MOOCs can promote iterative dialogue that facilitates group cognition and knowledge construction. Using data from two successive instances of a popular business strategy MOOC, we filter observed communication patterns to arrive at the “significant” interaction networks between learners and use complex network analysis to explore the vulnerability and information diffusion potential of the discussion forums. We find that different discussion topics and pedagogical practices promote varying levels of 1) “significant” peer-to-peer engagement, 2) participant inclusiveness in dialogue, and ultimately, 3) modularity, which impacts information diffusion to prevent a truly “global” exchange of knowledge and learning. These results indicate the structural limitations of large-scale crowd-based learning and highlight the different ways that learners in MOOCs leverage, and learn within, social contexts. We conclude by exploring how these insights may inspire new developments in online education. PMID:25244925

Structural limitations of learning in a crowd: communication vulnerability and information diffusion in MOOCs

NASA Astrophysics Data System (ADS)

Gillani, Nabeel; Yasseri, Taha; Eynon, Rebecca; Hjorth, Isis

2014-09-01

Massive Open Online Courses (MOOCs) bring together a global crowd of thousands of learners for several weeks or months. In theory, the openness and scale of MOOCs can promote iterative dialogue that facilitates group cognition and knowledge construction. Using data from two successive instances of a popular business strategy MOOC, we filter observed communication patterns to arrive at the ``significant'' interaction networks between learners and use complex network analysis to explore the vulnerability and information diffusion potential of the discussion forums. We find that different discussion topics and pedagogical practices promote varying levels of 1) ``significant'' peer-to-peer engagement, 2) participant inclusiveness in dialogue, and ultimately, 3) modularity, which impacts information diffusion to prevent a truly ``global'' exchange of knowledge and learning. These results indicate the structural limitations of large-scale crowd-based learning and highlight the different ways that learners in MOOCs leverage, and learn within, social contexts. We conclude by exploring how these insights may inspire new developments in online education.

Integrating the transportation system with a university transportation master plan : best practices and lessons learned.

DOT National Transportation Integrated Search

2010-05-01

The University of Texas at El Paso (UTEP) is planning several projects that will have a substantial impact in : the transportation network in El Paso. This research project conducted a study of the integration of the El : Paso metropolitan transporta...

Reframing Teachers' Work for Educational Innovation

ERIC Educational Resources Information Center

Kunnari, Irma; Ilomäki, Liisa

2016-01-01

The universities of applied sciences in Finland aim to support students in achieving work life competences by integrating authentic research, development and innovation (RDI) practices into learning. However, pursuing an educational change from a traditional higher education culture to a networked model of working is challenging for teachers. This…

Navigating the Social Media Learning Curve

ERIC Educational Resources Information Center

Pikalek, Amy J.

2010-01-01

In recent years, terms such as "social media" and "social networking" have become staples in the university continuing education marketer's vocabulary. This article provides both a working knowledge of the social media landscape and practical applications of the concepts using a case study approach from a Midwestern university.…

Photoacoustic image reconstruction via deep learning

NASA Astrophysics Data System (ADS)

Antholzer, Stephan; Haltmeier, Markus; Nuster, Robert; Schwab, Johannes

2018-02-01

Applying standard algorithms to sparse data problems in photoacoustic tomography (PAT) yields low-quality images containing severe under-sampling artifacts. To some extent, these artifacts can be reduced by iterative image reconstruction algorithms which allow to include prior knowledge such as smoothness, total variation (TV) or sparsity constraints. These algorithms tend to be time consuming as the forward and adjoint problems have to be solved repeatedly. Further, iterative algorithms have additional drawbacks. For example, the reconstruction quality strongly depends on a-priori model assumptions about the objects to be recovered, which are often not strictly satisfied in practical applications. To overcome these issues, in this paper, we develop direct and efficient reconstruction algorithms based on deep learning. As opposed to iterative algorithms, we apply a convolutional neural network, whose parameters are trained before the reconstruction process based on a set of training data. For actual image reconstruction, a single evaluation of the trained network yields the desired result. Our presented numerical results (using two different network architectures) demonstrate that the proposed deep learning approach reconstructs images with a quality comparable to state of the art iterative reconstruction methods.

The race to learn: spike timing and STDP can coordinate learning and recall in CA3.

PubMed

Nolan, Christopher R; Wyeth, Gordon; Milford, Michael; Wiles, Janet

2011-06-01

The CA3 region of the hippocampus has long been proposed as an autoassociative network performing pattern completion on known inputs. The dentate gyrus (DG) region is often proposed as a network performing the complementary function of pattern separation. Neural models of pattern completion and separation generally designate explicit learning phases to encode new information and assume an ideal fixed threshold at which to stop learning new patterns and begin recalling known patterns. Memory systems are significantly more complex in practice, with the degree of memory recall depending on context-specific goals. Here, we present our spike-timing separation and completion (STSC) model of the entorhinal cortex (EC), DG, and CA3 network, ascribing to each region a role similar to that in existing models but adding a temporal dimension by using a spiking neural network. Simulation results demonstrate that (a) spike-timing dependent plasticity in the EC-CA3 synapses provides a pattern completion ability without recurrent CA3 connections, (b) the race between activation of CA3 cells via EC-CA3 synapses and activation of the same cells via DG-CA3 synapses distinguishes novel from known inputs, and (c) modulation of the EC-CA3 synapses adjusts the learned versus test input similarity required to evoke a direct CA3 response prior to any DG activity, thereby adjusting the pattern completion threshold. These mechanisms suggest that spike timing can arbitrate between learning and recall based on the novelty of each individual input, ensuring control of the learn-recall decision resides in the same subsystem as the learned memories themselves. The proposed modulatory signal does not override this decision but biases the system toward either learning or recall. The model provides an explanation for empirical observations that a reduction in novelty produces a corresponding reduction in the latency of responses in CA3 and CA1. Copyright © 2010 Wiley-Liss, Inc.

The Recovery-Oriented Care Collaborative: A Practice-Based Research Network to Improve Care for People With Serious Mental Illnesses.

PubMed

Kelly, Erin L; Kiger, Holly; Gaba, Rebecca; Pancake, Laura; Pilon, David; Murch, Lezlie; Knox, Lyndee; Meyer, Mathew; Brekke, John S

2015-11-01

Practice-based research networks (PBRNs) create continuous collaborations among academic researchers and practitioners. Most PBRNs have operated in primary care, and less than 5% of federally registered PBRNs include mental health practitioners. In 2012 the first PBRN in the nation focused on individuals with serious mental illnesses-the Recovery-Oriented Care Collaborative-was established in Los Angeles. This column describes the development of this innovative PBRN through four phases: building an infrastructure, developing a research study, executing the study, and consolidating the PBRN. Key lessons learned are also described, such as the importance of actively engaging direct service providers and clients.

The National Public Health Leadership Institute: evaluation of a team-based approach to developing collaborative public health leaders.

PubMed

Umble, Karl; Steffen, David; Porter, Janet; Miller, Delesha; Hummer-McLaughlin, Kelley; Lowman, Amy; Zelt, Susan

2005-04-01

Recent public health literature contains calls for collaborative public health interventions and for leaders capable of guiding them. The National Public Health Leadership Institute aims to develop collaborative leaders and to strengthen networks of leaders who share knowledge and jointly address public health problems. Evaluation results show that completing the institute training increases collaborative leadership and builds knowledge-sharing and problem-solving networks. These practices and networks strengthen interorganizational relationships, coalitions, services, programs, and policies. Intensive team-and project-based learning are key to the program's impact.

The National Public Health Leadership Institute: Evaluation of a Team-Based Approach to Developing Collaborative Public Health Leaders

PubMed Central

Umble, Karl; Steffen, David; Porter, Janet; Miller, Delesha; Hummer-McLaughlin, Kelley; Lowman, Amy; Zelt, Susan

2005-01-01

Recent public health literature contains calls for collaborative public health interventions and for leaders capable of guiding them. The National Public Health Leadership Institute aims to develop collaborative leaders and to strengthen networks of leaders who share knowledge and jointly address public health problems. Evaluation results show that completing the institute training increases collaborative leadership and builds knowledge-sharing and problem-solving networks. These practices and networks strengthen interorganizational relationships, coalitions, services, programs, and policies. Intensive team-and project-based learning are key to the program’s impact. PMID:15798124

[Applying a social network for the practice and learning of psychiatry].

PubMed

Mondin, Estefanía; Matusevich, Daniel

2014-01-01

Social networking is a virtual space in which people relate and build their identity, share information, publish content and intervene on the content posted by others. We will describe an experiment carried out in the psychiatry service of Italian Hospital in Buenos Aires, in which we use Whatsapp Social Network applied to the development of clinical work and teaching task. From these new ways of relating between professional, emerge a new way to work, participate in groups or try to evaluate various options for dealing with a patient. We analyze the usefulness of this virtual platform as a working tool.

Abductive networks applied to electronic combat

NASA Astrophysics Data System (ADS)

Montgomery, Gerard J.; Hess, Paul; Hwang, Jong S.

1990-08-01

A practical approach to dealing with combinatorial decision problems and uncertainties associated with electronic combat through the use of networks of high-level functional elements called abductive networks is presented. It describes the application of the Abductory Induction Mechanism (AIMTM) a supervised inductive learning tool for synthesizing polynomial abductive networks to the electronic combat problem domain. From databases of historical expert-generated or simulated combat engagements AIM can often induce compact and robust network models for making effective real-time electronic combat decisions despite significant uncertainties or a combinatorial explosion of possible situations. The feasibility of applying abductive networks to realize advanced combat decision aiding capabilities was demonstrated by applying AIM to a set of electronic combat simulations. The networks synthesized by AIM generated accurate assessments of the intent lethality and overall risk associated with a variety of simulated threats and produced reasonable estimates of the expected effectiveness of a group of electronic countermeasures for a large number of simulated combat scenarios. This paper presents the application of abductive networks to electronic combat summarizes the results of experiments performed using AIM discusses the benefits and limitations of applying abductive networks to electronic combat and indicates why abductive networks can often result in capabilities not attainable using alternative approaches. 1. ELECTRONIC COMBAT. UNCERTAINTY. AND MACHINE LEARNING Electronic combat has become an essential part of the ability to make war and has become increasingly complex since

Machiavellian Ways to Academic Cheating: A Mediational and Interactional Model

PubMed Central

Barbaranelli, Claudio; Farnese, Maria L.; Tramontano, Carlo; Fida, Roberta; Ghezzi, Valerio; Paciello, Marinella; Long, Philip

2018-01-01

Academic cheating has become a pervasive practice from primary schools to university. This study aims at investigating this phenomenon through a nomological network which integrates different theoretical frameworks and models, such as trait and social-cognitive theories and models regarding the approaches to learning and contextual/normative environment. Results on a sample of more than 200 Italian university students show that the Amoral Manipulation facet of Machiavellianism, Academic Moral Disengagement, Deep Approach to Learning, and Normative Academic Cheating are significantly associated with Individual Academic Cheating. Moreover, results show a significant latent interaction effect between Normative Academic Cheating and Amoral Manipulation Machiavellianism: “amoral Machiavellians” students are more prone to resort to Academic Cheating in contexts where Academic Cheating is adopted as a practice by their peers, while this effect is not significant in contexts where Academic Cheating is not normative. Results also show that Academic Moral Disengagement and Deep Approach to learning partially mediate the relationship between Amoral Manipulation and Academic Cheating. Practical implications of these results are discussed. PMID:29867663

Communication is the key to success in pragmatic clinical trials in Practice-based Research Networks (PBRNs).

PubMed

Bertram, Susan; Graham, Deborah; Kurland, Marge; Pace, Wilson; Madison, Suzanne; Yawn, Barbara P

2013-01-01

Effective communication is the foundation of feasibility and fidelity in practice-based pragmatic research studies. Doing a study with practices spread over several states requires long-distance communication strategies, including E-mails, faxes, telephone calls, conference calls, and texting. Compared with face-to-face communication, distance communication strategies are less familiar to most study coordinators and research teams. Developing and ensuring comfort with distance communications requires additional time and use of different talents and expertise than those required for face-to-face communication. It is necessary to make sure that messages are appropriate for the medium, clearly crafted, and presented in a manner that facilitates practices receiving and understanding the information. This discussion is based on extensive experience of 2 groups who have worked collaboratively on several large, federally funded, pragmatic trials in a practice-based research network. The goal of this article is to summarize lessons learned to facilitate the work of other research teams.

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

Long, John P.; Hamill, Michael J.; Mitchell, M. G.

A major portion of the Wireless Networking Project at Sandia National Laboratories over the last few years has been to examine IEEE 802.11 wireless networking for possible use at Sandia and if practical, introduce this technology. This project team deployed 802.11a, b, and g Wireless Local Area Networking at Sandia. This report examines the basics of wireless networking and captures key results from project tests and experiments. It also records project members thoughts and designs on wireless LAN architecture and security issues. It documents some of the actions and milestones of this project, including pilot and production deployment of wirelessmore » networking equipment, and captures the team's rationale behind some of the decisions made. Finally, the report examines lessons learned, future directions, and conclusions.« less

Social networking for nurse education: Possibilities, perils and pitfalls.

PubMed

Green, Janet; Wyllie, Aileen; Jackson, Debra

2014-01-01

Abstract In this paper, we consider the potential and implications of using social networking sites such as Facebook® in nurse education. The concept of social networking and the use of Facebook will be explored, as will the theoretical constructs specific to the use of online technology and Web 2.0 tools. Theories around Communities of Inquiry (Garrison, Anderson, & Archer, 2000), Communities of Practice (Wenger, 1998), Activity Theory (Daniels, Cole, & Wertsch, 2007) and Actor-Network theory (Latour, 1997) will be briefly explored, as will the work of Vygotsky (1978), as applies to the social aspects of learning. Boundary issues, such as if and how faculty and students should or could be connected via social networking sites will also be explored.

Building National Capacity for Climate Change Interpretation: The Role of Leaders, Partnerships, and Networks

NASA Astrophysics Data System (ADS)

Spitzer, W.

2015-12-01

Since 2007, the New England Aquarium has led a national effort to increase the capacity of informal science venues to effectively communicate about climate change. We are now leading the NSF-funded National Network for Ocean and Climate Change Interpretation (NNOCCI), partnering with the Association of Zoos and Aquariums, FrameWorks Institute, Woods Hole Oceanographic Institution, Monterey Bay Aquarium, and National Aquarium, with evaluation conducted by the New Knowledge Organization, Pennsylvania State University, and Ohio State University. NNOCCI enables teams of informal science interpreters across the country to serve as "communication strategists" - beyond merely conveying information they can influence public perceptions, given their high level of commitment, knowledge, public trust, social networks, and visitor contact. We provide in-depth training as well as an alumni network for ongoing learning, implementation support, leadership development, and coalition building. Our goals are to achieve a systemic national impact, embed our work within multiple ongoing regional and national climate change education networks, and leave an enduring legacy. Our project represents a cross-disciplinary partnership among climate scientists, social and cognitive scientists, and informal education practitioners. We have built a growing national network of more than 250 alumni, including approximately 15-20 peer leaders who co-lead both in-depth training programs and introductory workshops. We have found that this alumni network has been assuming increasing importance in providing for ongoing learning, support for implementation, leadership development, and coalition building. As we look toward the future, we are exploring potential partnerships with other existing networks, both to sustain our impact and to expand our reach. This presentation will address what we have learned in terms of network impacts, best practices, factors for success, and future directions.

Minimal perceptrons for memorizing complex patterns

NASA Astrophysics Data System (ADS)

Pastor, Marissa; Song, Juyong; Hoang, Danh-Tai; Jo, Junghyo

2016-11-01

Feedforward neural networks have been investigated to understand learning and memory, as well as applied to numerous practical problems in pattern classification. It is a rule of thumb that more complex tasks require larger networks. However, the design of optimal network architectures for specific tasks is still an unsolved fundamental problem. In this study, we consider three-layered neural networks for memorizing binary patterns. We developed a new complexity measure of binary patterns, and estimated the minimal network size for memorizing them as a function of their complexity. We formulated the minimal network size for regular, random, and complex patterns. In particular, the minimal size for complex patterns, which are neither ordered nor disordered, was predicted by measuring their Hamming distances from known ordered patterns. Our predictions agree with simulations based on the back-propagation algorithm.

The Significance of Kinship for Medical Education: Reflections on the Use of a Bespoke Social Network to Support Learners’ Professional Identities

PubMed Central

2016-01-01

Background Social media can support and sustain communities much better than previous generations of learning technologies, where institutional barriers undermined any initiatives for embedding formal and informal learning. Some of the many types of social media have already had an impact on student learning, based on empirical evidence. One of these, social networking, has the potential to support communication in formal and informal spaces. Objective In this paper we report on the evaluation of an institutional social network—King's Social Harmonisation Project (KINSHIP)—established to foster an improved sense of community, enhance communication, and serve as a space to model digital professionalism for students at King’s College London, United Kingdom. Methods Our evaluation focused on a study that examined students’ needs and perceptions with regard to the provision of a cross-university platform. Data were collected from students, including those in the field of health and social care, in order to recommend a practical way forward to address current needs in this area. Results The findings indicate that the majority of the respondents were positive about using a social networking platform to develop their professional voice and profiles. Results suggest that timely promotion of the platform, emphasis on interface and learning design, and a clear identity are required in order to gain acceptance as the institutional social networking site. Conclusions Empirical findings in this study project an advantage of an institutional social network such a KINSHIP over other social networks (eg, Facebook) because access is limited to staff and students and the site is mainly being used for academic purposes. PMID:27731848

Knowledge brokers in a knowledge network: the case of Seniors Health Research Transfer Network knowledge brokers.

PubMed

Conklin, James; Lusk, Elizabeth; Harris, Megan; Stolee, Paul

2013-01-09

The purpose of this paper is to describe and reflect on the role of knowledge brokers (KBs) in the Seniors Health Research Transfer Network (SHRTN). The paper reviews the relevant literature on knowledge brokering, and then describes the evolving role of knowledge brokering in this knowledge network. The description of knowledge brokering provided here is based on a developmental evaluation program and on the experiences of the authors. Data were gathered through qualitative and quantitative methods, analyzed by the evaluators, and interpreted by network members who participated in sensemaking forums. The results were fed back to the network each year in the form of formal written reports that were widely distributed to network members, as well as through presentations to the network's members. The SHRTN evaluation and our experiences as evaluators and KBs suggest that a SHRTN KB facilitates processes of learning whereby people are connected with tacit or explicit knowledge sources that will help them to resolve work-related challenges. To make this happen, KBs engage in a set of relational, technical, and analytical activities that help communities of practice (CoPs) to develop and operate, facilitate exchanges among people with similar concerns and interests, and help groups and individuals to create, explore, and apply knowledge in their practice. We also suggest that the role is difficult to define, emergent, abstract, episodic, and not fully understood. The KB role within this knowledge network has developed and matured over time. The KB adapts to the social and technical affordances of each situation, and fashions a unique and relevant process to create relationships and promote learning and change. The ability to work with teams and to develop relevant models and feasible approaches are critical KB skills. The KB is a leader who wields influence rather than power, and who is prepared to adopt whatever roles and approaches are needed to bring about a valuable result.

Second language social networks and communication-related acculturative stress: the role of interconnectedness

PubMed Central

Doucerain, Marina M.; Varnaamkhaasti, Raheleh S.; Segalowitz, Norman; Ryder, Andrew G.

2015-01-01

Although a substantial amount of cross-cultural psychology research has investigated acculturative stress in general, little attention has been devoted specifically to communication-related acculturative stress (CRAS). In line with the view that cross-cultural adaptation and second language (L2) learning are social and interpersonal phenomena, the present study examines the hypothesis that migrants’ L2 social network size and interconnectedness predict CRAS. The main idea underlying this hypothesis is that L2 social networks play an important role in fostering social and cultural aspects of communicative competence. Specifically, higher interconnectedness may reflect greater access to unmodified natural cultural representations and L2 communication practices, thus fostering communicative competence through observational learning. As such, structural aspects of migrants’ L2 social networks may be protective against acculturative stress arising from chronic communication difficulties. Results from a study of first generation migrant students (N = 100) support this idea by showing that both inclusiveness and density of the participants’ L2 network account for unique variance in CRAS but not in general acculturative stress. These results support the idea that research on cross-cultural adaptation would benefit from disentangling the various facets of acculturative stress and that the structure of migrants’ L2 network matters for language related outcomes. Finally, this study contributes to an emerging body of work that attempts to integrate cultural/cross-cultural research on acculturation and research on intercultural communication and second language learning. PMID:26300809

Second language social networks and communication-related acculturative stress: the role of interconnectedness.

PubMed

Doucerain, Marina M; Varnaamkhaasti, Raheleh S; Segalowitz, Norman; Ryder, Andrew G

2015-01-01

Although a substantial amount of cross-cultural psychology research has investigated acculturative stress in general, little attention has been devoted specifically to communication-related acculturative stress (CRAS). In line with the view that cross-cultural adaptation and second language (L2) learning are social and interpersonal phenomena, the present study examines the hypothesis that migrants' L2 social network size and interconnectedness predict CRAS. The main idea underlying this hypothesis is that L2 social networks play an important role in fostering social and cultural aspects of communicative competence. Specifically, higher interconnectedness may reflect greater access to unmodified natural cultural representations and L2 communication practices, thus fostering communicative competence through observational learning. As such, structural aspects of migrants' L2 social networks may be protective against acculturative stress arising from chronic communication difficulties. Results from a study of first generation migrant students (N = 100) support this idea by showing that both inclusiveness and density of the participants' L2 network account for unique variance in CRAS but not in general acculturative stress. These results support the idea that research on cross-cultural adaptation would benefit from disentangling the various facets of acculturative stress and that the structure of migrants' L2 network matters for language related outcomes. Finally, this study contributes to an emerging body of work that attempts to integrate cultural/cross-cultural research on acculturation and research on intercultural communication and second language learning.

Electronic Communication across the Curriculum.

ERIC Educational Resources Information Center

Reiss, Donna, Ed.; Selfe, Dickie, Ed; Young, Art, Ed.

This collection of 24 essays explores what happens when proponents of writing across the curriculum (WAC) use the latest computer-mediated tools and techniques--including e-mail, asynchronous learning networks, MOOs, and the World Wide Web--to expand and enrich their teaching practices, especially the teaching of writing. Essays and their authors…

CVISN electronic credentialing for commercial vehicles in Washington State, a case study : easier licensing and credentials processing for the motor carrier industry

DOT National Transportation Integrated Search

2004-09-01

The following case study provides an in-depth view of the deployment of Commercial Vehicle Information Systems and Networks (CVISN) Electronic Credentialing in Washington State. It describes successful practices and lessons learned in operations and ...

Examining Mendeley: Designing Learning Opportunities for Digital Scholarship

ERIC Educational Resources Information Center

Hicks, Alison; Sinkinson, Caroline

2015-01-01

Researchers have widely adopted computer programs for reference management, such as Mendeley, due to their ability to support a variety of research practices, including organization and storage of pdfs. These programs also afford participation and networking within new scholarly information landscapes. This paper uses a survey and semi-structured…

The New Literacies: Multiple Perspectives on Research and Practice

ERIC Educational Resources Information Center

Baker, Elizabeth A., Ed.

2010-01-01

With contributions from leading scholars, this compelling volume offers fresh insights into literacy teaching and learning--and the changing nature of literacy itself--in today's K-12 classrooms. The focus is on varied technologies and literacies such as social networking sites, text messaging, and online communities. Cutting-edge approaches to…

Scaling Online Education: Increasing Access to Higher Education

ERIC Educational Resources Information Center

Moloney, Jacqueline F.; Oakley, Burks, II

2010-01-01

Over the past decade, online courses and entire online degree programs have been made available, serving millions of students in higher education. These online courses largely have been designed and taught using the theoretical concepts and practical strategies of Asynchronous Learning Networks (ALN). During 2003-04, approximately two million…

Guide to Entrepreneurship Education: Programmes and Practice

ERIC Educational Resources Information Center

Learning and Skills Network (NJ3), 2009

2009-01-01

One of the most important factors in successful entrepreneurship is for education to nurture the right mindset within students. To develop this mindset, the inclusion of entrepreneurship in a student's education is essential and therefore must be included on the curriculum. This short Learning and Skills Network (LSN) guide identifies areas of…

Compressive Network Analysis

PubMed Central

Jiang, Xiaoye; Yao, Yuan; Liu, Han; Guibas, Leonidas

2014-01-01

Modern data acquisition routinely produces massive amounts of network data. Though many methods and models have been proposed to analyze such data, the research of network data is largely disconnected with the classical theory of statistical learning and signal processing. In this paper, we present a new framework for modeling network data, which connects two seemingly different areas: network data analysis and compressed sensing. From a nonparametric perspective, we model an observed network using a large dictionary. In particular, we consider the network clique detection problem and show connections between our formulation with a new algebraic tool, namely Randon basis pursuit in homogeneous spaces. Such a connection allows us to identify rigorous recovery conditions for clique detection problems. Though this paper is mainly conceptual, we also develop practical approximation algorithms for solving empirical problems and demonstrate their usefulness on real-world datasets. PMID:25620806

Artificial Neural Networks as an Architectural Design Tool-Generating New Detail Forms Based On the Roman Corinthian Order Capital

NASA Astrophysics Data System (ADS)

Radziszewski, Kacper

2017-10-01

The following paper presents the results of the research in the field of the machine learning, investigating the scope of application of the artificial neural networks algorithms as a tool in architectural design. The computational experiment was held using the backward propagation of errors method of training the artificial neural network, which was trained based on the geometry of the details of the Roman Corinthian order capital. During the experiment, as an input training data set, five local geometry parameters combined has given the best results: Theta, Pi, Rho in spherical coordinate system based on the capital volume centroid, followed by Z value of the Cartesian coordinate system and a distance from vertical planes created based on the capital symmetry. Additionally during the experiment, artificial neural network hidden layers optimal count and structure was found, giving results of the error below 0.2% for the mentioned before input parameters. Once successfully trained artificial network, was able to mimic the details composition on any other geometry type given. Despite of calculating the transformed geometry locally and separately for each of the thousands of surface points, system could create visually attractive and diverse, complex patterns. Designed tool, based on the supervised learning method of machine learning, gives possibility of generating new architectural forms- free of the designer’s imagination bounds. Implementing the infinitely broad computational methods of machine learning, or Artificial Intelligence in general, not only could accelerate and simplify the design process, but give an opportunity to explore never seen before, unpredictable forms or everyday architectural practice solutions.

Factors Affecting the Development and Sustainability of Communities of Practice Among Primary Care Physicians in Hong Kong.

PubMed

Poon, Man Kay; Lam, Tai Pong

2017-01-01

Primary care physicians (PCPs) maintain high standards of medical care by partaking in continuous learning. The learning model of communities of practice (COPs) is increasingly being used in the field of health care. This study explores the establishment and maintenance of COPs among PCPs in Hong Kong. Sequential, semi-structured individual interview and focus group interview were conducted to explore the purposes for partaking in continuous learning, as well as barriers and facilitators for attendance among private nonspecialist PCPs in Hong Kong. Data were drawn from the discourses related to COPs. Thematic analysis with constant comparison was performed until data saturation was reached. PCPs voluntarily established COPs to solve clinical problems from the existing networks. Clinical interest, practice orientation, and recruitment of new members through endorsement by the existing members fostered group coherence. Conversation and interaction among members generated the "best" practice with knowledge that was applicable in specific clinical scenarios in primary care setting. COPs rejected commercial sponsorship to minimize corporate influences on learning. Updating medical knowledge, solving clinical problems, maintaining openness, engendering a sense of trust and ownership among members, and fulfilling psychosocial needs were integral to sustainability. Seeking secretariat support to aid in the logistics of meetings, enhancing external learning resources, and facilitation skills training of facilitators from professional bodies may further incentivize members to maintain COPs. Autonomy of group learning activities, recruiting specialists and allied health professionals, training facilitators, and undertaking discussion in multimedia may achieve the sustainability of COPs.

Learning in networks: individual teacher learning versus organizational learning in a regional health-promoting schools network.

PubMed

Flaschberger, Edith; Gugglberger, Lisa; Dietscher, Christina

2013-12-01

To change a school into a health-promoting organization, organizational learning is required. The evaluation of an Austrian regional health-promoting schools network provides qualitative data on the views of the different stakeholders on learning in this network (steering group, network coordinator and representatives of the network schools; n = 26). Through thematic analysis and deep-structure analyses, the following three forms of learning in the network were identified: (A) individual learning through input offered by the network coordination, (B) individual learning between the network schools, i.e. through exchange between the representatives of different schools and (C) learning within the participating schools, i.e. organizational learning. Learning between (B) or within the participating schools (C) seems to be rare in the network; concepts of individual teacher learning are prevalent. Difficulties detected relating to the transfer of information from the network to the member schools included barriers to organizational learning such as the lack of collaboration, coordination and communication in the network schools, which might be effects of the school system in which the observed network is located. To ensure connectivity of the information offered by the network, more emphasis should be put on linking health promotion to school development and the core processes of schools.

Feasibility of a virtual learning collaborative to implement an obesity QI project in 29 pediatric practices.

PubMed

John, Tamara; Morton, Michaela; Weissman, Mark; O'Brien, Ellen; Hamburger, Ellen; Hancock, Yolandra; Moon, Rachel Y

2014-04-01

Quality improvement (QI) activities are required to maintain board certification in pediatrics. However, because of lack of training and resources, pediatricians may feel overwhelmed by the need to implement QI activities. Pediatricians also face challenges when caring for overweight and obese children. To create a virtual (online) QI learning collaborative through which pediatric practices could easily develop and implement a continuous QI process. Prospective cohort. Pediatric practices that were part of the Children's National Health Network were invited to participate, with the option to receive continuing medical education and maintenance of certification credits. s) Practices conducted baseline and monthly chart audits, participated in educational webinars and selected monthly practice changes, using Plan-Do-Study-Act cycles. Practices reported activities monthly and periodic feedback was provided to practices about their performance. s) Improvement in (i) body mass index (BMI) percentile documentation, (ii) appropriate nutritional and activity counseling and (iii) follow-up management for high-risk patients. Twenty-nine practices (120 providers) participated, and 24 practices completed all program activities. Monthly chart audits demonstrated continuous improvement in documentation of BMI, abnormal weight diagnosis, nutrition and activity screening and counseling, weight-related health messages and follow-up management of overweight and obese patients. Impact of QI activities on visit duration and practice efficiency was minimal. A virtual learning collaborative was successful in providing a framework for pediatricians to implement a continuous QI process and achieve practice improvements. This format can be utilized to address multiple health issues.

General Motors' R&D: Managing Innovation Globally

NASA Astrophysics Data System (ADS)

Taub, Alan

2006-03-01

The rapid pace of technology development and the globalization of the automobile industry are major forces driving General Motors to devise new ways to innovate faster and more efficiently. In response, GM has developed a global R&D network that has transformed GM's research and development organization from a U.S.-based enterprise to one that is over 30 percent leveraged with collaboration in 16 countries. This talk will focus on the challenges faced as well as the lessons learned and best practices developed in building this network.

A ubiquitous reflective e-portfolio architecture.

PubMed

Forte, Marcos; de Souza, Wanderley L; da Silva, Roseli F; do Prado, Antonio F; Rodrigues, Jose F

2013-11-01

In nurse and in medicine courses, the use of reflective portfolios as a pedagogical tool is becoming a common practice; in the last years, this practice has gradually migrated from paper-based to electronic-based portfolios. Current approaches for reflective e-portfolios, however, do not widely operate at outdoor sites, where data networks are limited or nonexistent. Considering that many of the activities related to nurse and medicine courses relate to professional practices conducted in such conditions, these network shortcomings restrict the adoption of e-portfolios. The present study describes the requirements specification, design, implementation, and evaluation of the Ubiquitous Reflective E-Portfolio Architecture, a solution proposed to support the development of systems based on mobile and wired access for both online and offline operation. We have implemented a prototype named Professional Practice Module to evaluate the Ubiquitous Reflective E-Portfolio Architecture; the module was based on requirements observed during the professional practice, the paper-based portfolio in use, and related learning meetings in the Medicine Course of a Brazilian University. The evaluation of the system was carried out with a learning group of 2nd year students of the medicine course, who answered to extensive evaluation questionnaires. The prototype proved to be operational in the activities of the professional practice of the Medicine Course object of the study, including homework tasks, patient care, data sharing, and learning meetings. It also demonstrated to be versatile with respect to the availability of the computer network that, many times, was not accessible. Moreover, the students considered the module useful and easy to use, but pointed out difficulties about the keyboard and the display sizes of the netbook devices, and about their operational system. Lastly, most of the students declared preference for the electronic Professional Practice Module in internal and in group activities, and for the paper-based version while in patient attendance. There is evidence that the environment where the professional practice takes place influences the usage of the e-portfolio. Mobile devices were able to support students in their professional practice; however, these devices present characteristics that must be judiciously selected, otherwise, they may limit the execution of important tasks. The main shortcoming identified during the evaluation tests was about the use of the module, and of the access device, during patient attendance. For this reason, we have envisioned a new version of the Professional Practice Module that shall follow a twofold requisite: by one side, it will include all the features of the module, to be used at the university or in the students' homes; from the other side, it will include only the features that are essential for the practice of patient attendance. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Training directionally selective motion pathways can significantly improve reading efficiency

NASA Astrophysics Data System (ADS)

Lawton, Teri

2004-06-01

This study examined whether perceptual learning at early levels of visual processing would facilitate learning at higher levels of processing. This was examined by determining whether training the motion pathways by practicing leftright movement discrimination, as found previously, would improve the reading skills of inefficient readers significantly more than another computer game, a word discrimination game, or the reading program offered by the school. This controlled validation study found that practicing left-right movement discrimination 5-10 minutes twice a week (rapidly) for 15 weeks doubled reading fluency, and significantly improved all reading skills by more than one grade level, whereas inefficient readers in the control groups barely improved on these reading skills. In contrast to previous studies of perceptual learning, these experiments show that perceptual learning of direction discrimination significantly improved reading skills determined at higher levels of cognitive processing, thereby being generalized to a new task. The deficits in reading performance and attentional focus experienced by the person who struggles when reading are suggested to result from an information overload, resulting from timing deficits in the direction-selectivity network proposed by Russell De Valois et al. (2000), that following practice on direction discrimination goes away. This study found that practicing direction discrimination rapidly transitions the inefficient 7-year-old reader to an efficient reader.

Visual Perceptual Learning and Models.

PubMed

Dosher, Barbara; Lu, Zhong-Lin

2017-09-15

Visual perceptual learning through practice or training can significantly improve performance on visual tasks. Originally seen as a manifestation of plasticity in the primary visual cortex, perceptual learning is more readily understood as improvements in the function of brain networks that integrate processes, including sensory representations, decision, attention, and reward, and balance plasticity with system stability. This review considers the primary phenomena of perceptual learning, theories of perceptual learning, and perceptual learning's effect on signal and noise in visual processing and decision. Models, especially computational models, play a key role in behavioral and physiological investigations of the mechanisms of perceptual learning and for understanding, predicting, and optimizing human perceptual processes, learning, and performance. Performance improvements resulting from reweighting or readout of sensory inputs to decision provide a strong theoretical framework for interpreting perceptual learning and transfer that may prove useful in optimizing learning in real-world applications.

Study on Practical Application of Turboprop Engine Condition Monitoring and Fault Diagnostic System Using Fuzzy-Neuro Algorithms

NASA Astrophysics Data System (ADS)

Kong, Changduk; Lim, Semyeong; Kim, Keunwoo

2013-03-01

The Neural Networks is mostly used to engine fault diagnostic system due to its good learning performance, but it has a drawback due to low accuracy and long learning time to build learning data base. This work builds inversely a base performance model of a turboprop engine to be used for a high altitude operation UAV using measuring performance data, and proposes a fault diagnostic system using the base performance model and artificial intelligent methods such as Fuzzy and Neural Networks. Each real engine performance model, which is named as the base performance model that can simulate a new engine performance, is inversely made using its performance test data. Therefore the condition monitoring of each engine can be more precisely carried out through comparison with measuring performance data. The proposed diagnostic system identifies firstly the faulted components using Fuzzy Logic, and then quantifies faults of the identified components using Neural Networks leaned by fault learning data base obtained from the developed base performance model. In leaning the measuring performance data of the faulted components, the FFBP (Feed Forward Back Propagation) is used. In order to user's friendly purpose, the proposed diagnostic program is coded by the GUI type using MATLAB.

A deep learning framework for causal shape transformation.

PubMed

Lore, Kin Gwn; Stoecklein, Daniel; Davies, Michael; Ganapathysubramanian, Baskar; Sarkar, Soumik

2018-02-01

Recurrent neural network (RNN) and Long Short-term Memory (LSTM) networks are the common go-to architecture for exploiting sequential information where the output is dependent on a sequence of inputs. However, in most considered problems, the dependencies typically lie in the latent domain which may not be suitable for applications involving the prediction of a step-wise transformation sequence that is dependent on the previous states only in the visible domain with a known terminal state. We propose a hybrid architecture of convolution neural networks (CNN) and stacked autoencoders (SAE) to learn a sequence of causal actions that nonlinearly transform an input visual pattern or distribution into a target visual pattern or distribution with the same support and demonstrated its practicality in a real-world engineering problem involving the physics of fluids. We solved a high-dimensional one-to-many inverse mapping problem concerning microfluidic flow sculpting, where the use of deep learning methods as an inverse map is very seldom explored. This work serves as a fruitful use-case to applied scientists and engineers in how deep learning can be beneficial as a solution for high-dimensional physical problems, and potentially opening doors to impactful advance in fields such as material sciences and medical biology where multistep topological transformations is a key element. Copyright © 2017 Elsevier Ltd. All rights reserved.

Knowledge brokers in a knowledge network: the case of Seniors Health Research Transfer Network knowledge brokers

PubMed Central

2013-01-01

Background The purpose of this paper is to describe and reflect on the role of knowledge brokers (KBs) in the Seniors Health Research Transfer Network (SHRTN). The paper reviews the relevant literature on knowledge brokering, and then describes the evolving role of knowledge brokering in this knowledge network. Methods The description of knowledge brokering provided here is based on a developmental evaluation program and on the experiences of the authors. Data were gathered through qualitative and quantitative methods, analyzed by the evaluators, and interpreted by network members who participated in sensemaking forums. The results were fed back to the network each year in the form of formal written reports that were widely distributed to network members, as well as through presentations to the network’s members. Results The SHRTN evaluation and our experiences as evaluators and KBs suggest that a SHRTN KB facilitates processes of learning whereby people are connected with tacit or explicit knowledge sources that will help them to resolve work-related challenges. To make this happen, KBs engage in a set of relational, technical, and analytical activities that help communities of practice (CoPs) to develop and operate, facilitate exchanges among people with similar concerns and interests, and help groups and individuals to create, explore, and apply knowledge in their practice. We also suggest that the role is difficult to define, emergent, abstract, episodic, and not fully understood. Conclusions The KB role within this knowledge network has developed and matured over time. The KB adapts to the social and technical affordances of each situation, and fashions a unique and relevant process to create relationships and promote learning and change. The ability to work with teams and to develop relevant models and feasible approaches are critical KB skills. The KB is a leader who wields influence rather than power, and who is prepared to adopt whatever roles and approaches are needed to bring about a valuable result. PMID:23302517

Praxis and reflexivity for interprofessional education: towards an inclusive theoretical framework for learning.

PubMed

Hutchings, Maggie; Scammell, Janet; Quinney, Anne

2013-09-01

While there is growing evidence of theoretical perspectives adopted in interprofessional education, learning theories tend to foreground the individual, focusing on psycho-social aspects of individual differences and professional identity to the detriment of considering social-structural factors at work in social practices. Conversely socially situated practice is criticised for being context-specific, making it difficult to draw generalisable conclusions for improving interprofessional education. This article builds on a theoretical framework derived from earlier research, drawing on the dynamics of Dewey's experiential learning theory and Archer's critical realist social theory, to make a case for a meta-theoretical framework enabling social-constructivist and situated learning theories to be interlinked and integrated through praxis and reflexivity. Our current analysis is grounded in an interprofessional curriculum initiative mediated by a virtual community peopled by health and social care users. Student perceptions, captured through quantitative and qualitative data, suggest three major disruptive themes, creating opportunities for congruence and disjuncture and generating a model of zones of interlinked praxis associated with professional differences and identity, pedagogic strategies and technology-mediated approaches. This model contributes to a framework for understanding the complexity of interprofessional learning and offers bridges between individual and structural factors for engaging with the enablements and constraints at work in communities of practice and networks for interprofessional education.

A bottom-up strategy for establishment of EER in three Nordic countries - the role of networks

NASA Astrophysics Data System (ADS)

Edström, Kristina; Kolmos, Anette; Malmi, Lauri; Bernhard, Jonte; Andersson, Pernille

2018-03-01

This paper investigates the emergence of an engineering education research (EER) community in three Nordic countries: Denmark, Finland and Sweden. First, an overview of the current state of Nordic EER authorship is produced through statistics on international publication. Then, the history of EER and its precursor activities is described in three national narratives. These national storylines are tied together in a description of recent networking activities, aiming to strengthen the EER communities on the Nordic level. Taking these three perspectives together, and drawing on concepts from community of practice theory, network theory and learning network theory, we discuss factors behind the differences in the countries, and draw some conclusions about implications for networking activities in a heterogeneous community. Further, we discuss the role of networks for affording a joint identity.

Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive Computing: What Do These Terms Mean and How Will They Impact Health Care?

PubMed

Bini, Stefano A

2018-02-27

This article was presented at the 2017 annual meeting of the American Association of Hip and Knee Surgeons to introduce the members gathered as the audience to the concepts behind artificial intelligence (AI) and the applications that AI can have in the world of health care today. We discuss the origin of AI, progress to machine learning, and then discuss how the limits of machine learning lead data scientists to develop artificial neural networks and deep learning algorithms through biomimicry. We will place all these technologies in the context of practical clinical examples and show how AI can act as a tool to support and amplify human cognitive functions for physicians delivering care to increasingly complex patients. The aim of this article is to provide the reader with a basic understanding of the fundamentals of AI. Its purpose is to demystify this technology for practicing surgeons so they can better understand how and where to apply it. Copyright © 2018 Elsevier Inc. All rights reserved.

Videodisc Feasibility Study. An Evaluation of the Use of Videodisc as a Distribution Medium.

ERIC Educational Resources Information Center

France, Ralph

This study evaluated the practicality of using videodiscs to distribute the television programs that are part of the courses of the International University Consortium (IUC) for Telecommunications in Learning, a network of colleges and universities in partnership with public broadcasting stations and cable systems. Fifteen videodisc players, along…

Learning with and from Facebook: Uncovering Power Asymmetries in Educational Interactions

ERIC Educational Resources Information Center

Rambe, Patient; Ng'ambi, Dick

2014-01-01

Although social networking sites (SNS) are increasingly popular among students, their academic application is unfolding on trial basis and best practices for integration into mainstream teaching are yet to be fully realised. More importantly, is the need to understand how these sites shape academic relations and participation of heterogeneous…

Using Peer Learning Support Networks and Reflective Practice: The Arkansas Leadership Academy Master Principal Program

ERIC Educational Resources Information Center

Bengtson, Ed, Airola, Denise, Peer, Diana, Davis.

2012-01-01

The Arkansas Leadership Academy (ALA) was established in 1991 and is a nationally recognized statewide partnership that includes 15 universities, 9 professional associations, the Arkansas Departments of Education, Higher Education, Career Education, and several other government and business agencies. In 2011, there were 49 partners involved…

Modelling for Prediction vs. Modelling for Understanding: Commentary on Musso et al. (2013)

ERIC Educational Resources Information Center

Edelsbrunner, Peter; Schneider, Michael

2013-01-01

Musso et al. (2013) predict students' academic achievement with high accuracy one year in advance from cognitive and demographic variables, using artificial neural networks (ANNs). They conclude that ANNs have high potential for theoretical and practical improvements in learning sciences. ANNs are powerful statistical modelling tools but they can…

Text-Based MOOing in Educational Practice: Experiences of Disinhibition

ERIC Educational Resources Information Center

Chester, Andrea

2006-01-01

Purpose: The purpose of this paper is to describe educational MOOs--MUD, object-oriented (text-based, network-accessible virtual environments) and explore how teaching and learning in such a context impacts on students' inhibitions. Design/methodology/approach: Students enrolled in a course on the psychology of cyberspace interacted for 12 weeks…

Promoting Instructional Change: Using Social Network Analysis to Understand the Informal Structure of Academic Departments

ERIC Educational Resources Information Center

Quardokus, Kathleen; Henderson, Charles

2015-01-01

Calls for improvement of undergraduate science education have resulted in numerous initiatives that seek to improve student learning outcomes by promoting changes in faculty teaching practices. Although many of these initiatives focus on individual faculty, researchers consider the academic department to be a highly productive focus for creating…

Linking Resarch and Practice for Site-Based School Renewal.

ERIC Educational Resources Information Center

Castle, Shari; And Others

An analysis of the IBM/NEA Mastery in Learning (MIL) school renewal system, an electronic network that involves school faculties in collegial interaction with researchers and other educators in school reform, is the purpose of this paper. Developed by IBM (International Business Machines) and NEA (National Education Association), the MIL is a…

Feasibility of Using Distributed Wireless Mesh Networks for Medical Emergency Response

PubMed Central

Braunstein, Brian; Trimble, Troy; Mishra, Rajesh; Manoj, B. S.; Rao, Ramesh; Lenert, Leslie

2006-01-01

Achieving reliable, efficient data communications networks at a disaster site is a difficult task. Network paradigms, such as Wireless Mesh Network (WMN) architectures, form one exemplar for providing high-bandwidth, scalable data communication for medical emergency response activity. WMNs are created by self-organized wireless nodes that use multi-hop wireless relaying for data transfer. In this paper, we describe our experience using a mesh network architecture we developed for homeland security and medical emergency applications. We briefly discuss the architecture and present the traffic behavioral observations made by a client-server medical emergency application tested during a large-scale homeland security drill. We present our traffic measurements, describe lessons learned, and offer functional requirements (based on field testing) for practical 802.11 mesh medical emergency response networks. With certain caveats, the results suggest that 802.11 mesh networks are feasible and scalable systems for field communications in disaster settings. PMID:17238308

Quantum neural networks: Current status and prospects for development

NASA Astrophysics Data System (ADS)

Altaisky, M. V.; Kaputkina, N. E.; Krylov, V. A.

2014-11-01

The idea of quantum artificial neural networks, first formulated in [34], unites the artificial neural network concept with the quantum computation paradigm. Quantum artificial neural networks were first systematically considered in the PhD thesis by T. Menneer (1998). Based on the works of Menneer and Narayanan [42, 43], Kouda, Matsui, and Nishimura [35, 36], Altaisky [2, 68], Zhou [67], and others, quantum-inspired learning algorithms for neural networks were developed, and are now used in various training programs and computer games [29, 30]. The first practically realizable scaled hardware-implemented model of the quantum artificial neural network is obtained by D-Wave Systems, Inc. [33]. It is a quantum Hopfield network implemented on the basis of superconducting quantum interference devices (SQUIDs). In this work we analyze possibilities and underlying principles of an alternative way to implement quantum neural networks on the basis of quantum dots. A possibility of using quantum neural network algorithms in automated control systems, associative memory devices, and in modeling biological and social networks is examined.

Research on the transfer learning of the vehicle logo recognition

NASA Astrophysics Data System (ADS)

Zhao, Wei

2017-08-01

The Convolutional Neural Network of Deep Learning has been a huge success in the field of image intelligent transportation system can effectively solve the traffic safety, congestion, vehicle management and other problems of traffic in the city. Vehicle identification is a vital part of intelligent transportation, and the effective information in vehicles is of great significance to vehicle identification. With the traffic system on the vehicle identification technology requirements are getting higher and higher, the vehicle as an important type of vehicle information, because it should not be removed, difficult to change and other features for vehicle identification provides an important method. The current vehicle identification recognition (VLR) is mostly used to extract the characteristics of the method of classification, which for complex classification of its generalization ability to be some constraints, if the use of depth learning technology, you need a lot of training samples. In this paper, the method of convolution neural network based on transfer learning can solve this problem effectively, and it has important practical application value in the task of vehicle mark recognition.

Deep learning application: rubbish classification with aid of an android device

NASA Astrophysics Data System (ADS)

Liu, Sijiang; Jiang, Bo; Zhan, Jie

2017-06-01

Deep learning is a very hot topic currently in pattern recognition and artificial intelligence researches. Aiming at the practical problem that people usually don't know correct classifications some rubbish should belong to, based on the powerful image classification ability of the deep learning method, we have designed a prototype system to help users to classify kinds of rubbish. Firstly the CaffeNet Model was adopted for our classification network training on the ImageNet dataset, and the trained network was deployed on a web server. Secondly an android app was developed for users to capture images of unclassified rubbish, upload images to the web server for analyzing backstage and retrieve the feedback, so that users can obtain the classification guide by an android device conveniently. Tests on our prototype system of rubbish classification show that: an image of one single type of rubbish with origin shape can be better used to judge its classification, while an image containing kinds of rubbish or rubbish with changed shape may fail to help users to decide rubbish's classification. However, the system still shows promising auxiliary function for rubbish classification if the network training strategy can be optimized further.

Shallow Transits—Deep Learning. I. Feasibility Study of Deep Learning to Detect Periodic Transits of Exoplanets

NASA Astrophysics Data System (ADS)

Zucker, Shay; Giryes, Raja

2018-04-01

Transits of habitable planets around solar-like stars are expected to be shallow, and to have long periods, which means low information content. The current bottleneck in the detection of such transits is caused in large part by the presence of red (correlated) noise in the light curves obtained from the dedicated space telescopes. Based on the groundbreaking results deep learning achieves in many signal and image processing applications, we propose to use deep neural networks to solve this problem. We present a feasibility study, in which we applied a convolutional neural network on a simulated training set. The training set comprised light curves received from a hypothetical high-cadence space-based telescope. We simulated the red noise by using Gaussian Processes with a wide variety of hyper-parameters. We then tested the network on a completely different test set simulated in the same way. Our study proves that very difficult cases can indeed be detected. Furthermore, we show how detection trends can be studied and detection biases quantified. We have also checked the robustness of the neural-network performance against practical artifacts such as outliers and discontinuities, which are known to affect space-based high-cadence light curves. Future work will allow us to use the neural networks to characterize the transit model and identify individual transits. This new approach will certainly be an indispensable tool for the detection of habitable planets in the future planet-detection space missions such as PLATO.

Event-driven contrastive divergence for spiking neuromorphic systems.

PubMed

Neftci, Emre; Das, Srinjoy; Pedroni, Bruno; Kreutz-Delgado, Kenneth; Cauwenberghs, Gert

2013-01-01

Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However, the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD) are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F) neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The recurrent activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP) carries out the weight updates in an online, asynchronous fashion. We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality.

Event-driven contrastive divergence for spiking neuromorphic systems

PubMed Central

Neftci, Emre; Das, Srinjoy; Pedroni, Bruno; Kreutz-Delgado, Kenneth; Cauwenberghs, Gert

2014-01-01

Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However, the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD) are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F) neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The recurrent activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP) carries out the weight updates in an online, asynchronous fashion. We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality. PMID:24574952

Striatal and Hippocampal Involvement in Motor Sequence Chunking Depends on the Learning Strategy

PubMed Central

Lungu, Ovidiu; Monchi, Oury; Albouy, Geneviève; Jubault, Thomas; Ballarin, Emanuelle; Burnod, Yves; Doyon, Julien

2014-01-01

Motor sequences can be learned using an incremental approach by starting with a few elements and then adding more as training evolves (e.g., learning a piano piece); conversely, one can use a global approach and practice the whole sequence in every training session (e.g., shifting gears in an automobile). Yet, the neural correlates associated with such learning strategies in motor sequence learning remain largely unexplored to date. Here we used functional magnetic resonance imaging to measure the cerebral activity of individuals executing the same 8-element sequence after they completed a 4-days training regimen (2 sessions each day) following either a global or incremental strategy. A network comprised of striatal and fronto-parietal regions was engaged significantly regardless of the learning strategy, whereas the global training regimen led to additional cerebellar and temporal lobe recruitment. Analysis of chunking/grouping of sequence elements revealed a common prefrontal network in both conditions during the chunk initiation phase, whereas execution of chunk cores led to higher mediotemporal activity (involving the hippocampus) after global than incremental training. The novelty of our results relate to the recruitment of mediotemporal regions conditional of the learning strategy. Thus, the present findings may have clinical implications suggesting that the ability of patients with lesions to the medial temporal lobe to learn and consolidate new motor sequences may benefit from using an incremental strategy. PMID:25148078

Striatal and hippocampal involvement in motor sequence chunking depends on the learning strategy.

PubMed

Lungu, Ovidiu; Monchi, Oury; Albouy, Geneviève; Jubault, Thomas; Ballarin, Emanuelle; Burnod, Yves; Doyon, Julien

2014-01-01

Motor sequences can be learned using an incremental approach by starting with a few elements and then adding more as training evolves (e.g., learning a piano piece); conversely, one can use a global approach and practice the whole sequence in every training session (e.g., shifting gears in an automobile). Yet, the neural correlates associated with such learning strategies in motor sequence learning remain largely unexplored to date. Here we used functional magnetic resonance imaging to measure the cerebral activity of individuals executing the same 8-element sequence after they completed a 4-days training regimen (2 sessions each day) following either a global or incremental strategy. A network comprised of striatal and fronto-parietal regions was engaged significantly regardless of the learning strategy, whereas the global training regimen led to additional cerebellar and temporal lobe recruitment. Analysis of chunking/grouping of sequence elements revealed a common prefrontal network in both conditions during the chunk initiation phase, whereas execution of chunk cores led to higher mediotemporal activity (involving the hippocampus) after global than incremental training. The novelty of our results relate to the recruitment of mediotemporal regions conditional of the learning strategy. Thus, the present findings may have clinical implications suggesting that the ability of patients with lesions to the medial temporal lobe to learn and consolidate new motor sequences may benefit from using an incremental strategy.

Team Science Approach to Developing Consensus on Research Good Practices for Practice-Based Research Networks: A Case Study.

PubMed

Campbell-Voytal, Kimberly; Daly, Jeanette M; Nagykaldi, Zsolt J; Aspy, Cheryl B; Dolor, Rowena J; Fagnan, Lyle J; Levy, Barcey T; Palac, Hannah L; Michaels, LeAnn; Patterson, V Beth; Kano, Miria; Smith, Paul D; Sussman, Andrew L; Williams, Robert; Sterling, Pamela; O'Beirne, Maeve; Neale, Anne Victoria

2015-12-01

Using peer learning strategies, seven experienced PBRNs working in collaborative teams articulated procedures for PBRN Research Good Practices (PRGPs). The PRGPs is a PBRN-specific resource to facilitate PBRN management and staff training, to promote adherence to study protocols, and to increase validity and generalizability of study findings. This paper describes the team science processes which culminated in the PRGPs. Skilled facilitators used team science strategies and methods from the Technology of Participation (ToP®), and the Consensus Workshop Method to support teams to codify diverse research expertise in practice-based research. The participatory nature of "sense-making" moved through identifiable stages. Lessons learned include (1) team input into the scope of the final outcome proved vital to project relevance; (2) PBRNs with diverse domains of research expertise contributed broad knowledge on each topic; and (3) ToP® structured facilitation techniques were critical for establishing trust and clarifying the "sense-making" process. © 2015 Wiley Periodicals, Inc.

Evolving optimised decision rules for intrusion detection using particle swarm paradigm

NASA Astrophysics Data System (ADS)

Sivatha Sindhu, Siva S.; Geetha, S.; Kannan, A.

2012-12-01

The aim of this article is to construct a practical intrusion detection system (IDS) that properly analyses the statistics of network traffic pattern and classify them as normal or anomalous class. The objective of this article is to prove that the choice of effective network traffic features and a proficient machine-learning paradigm enhances the detection accuracy of IDS. In this article, a rule-based approach with a family of six decision tree classifiers, namely Decision Stump, C4.5, Naive Baye's Tree, Random Forest, Random Tree and Representative Tree model to perform the detection of anomalous network pattern is introduced. In particular, the proposed swarm optimisation-based approach selects instances that compose training set and optimised decision tree operate over this trained set producing classification rules with improved coverage, classification capability and generalisation ability. Experiment with the Knowledge Discovery and Data mining (KDD) data set which have information on traffic pattern, during normal and intrusive behaviour shows that the proposed algorithm produces optimised decision rules and outperforms other machine-learning algorithm.

Two web-based laboratories of the FisL@bs network: Hooke's and Snell's laws

NASA Astrophysics Data System (ADS)

de la Torre, L.; Sánchez, J.; Dormido, S.; Sánchez, J. P.; Yuste, M.; Carreras, C.

2011-03-01

FisL@bs is a network of remote and virtual laboratories for physics university education via the Internet that offers students the possibility of performing hands-on experiments in different fields of physics in two ways: simulation and real remote operation. This paper gives a detailed account of a novel way in physics in which distance learning students can gain practical experience autonomously. FisL@bs uses the same structure as AutomatL@bs, a network of virtual and remote laboratories for learning/teaching of control engineering, which has been in operation for four years. Students can experiment with the laboratories offered using an Internet connection and a Java-compatible web browser. This paper, specially intended for university educators but easily comprehensible even for undergraduate students, explains how the portal works and the hardware and software tools used to create it. In addition, it also describes two physics experiments already available: spring elasticity and the laws of reflection and refraction.

Twelve tips for using social media as a medical educator.

PubMed

Kind, Terry; Patel, Pradip D; Lie, Désirée; Chretien, Katherine C

2014-04-01

We now live, learn, teach and practice medicine in the digital era. Social networking sites are used by at least half of all adults. Engagement with social media can be personal, professional, or both, for health-related and educational purposes. Use is often public. Lapses in professionalism can have devastating consequences, but when used well social media can enhance the lives of and learning by health professionals and trainees, ultimately for public good. Both risks and opportunities abound for individuals who participate, and health professionals need tips to enhance use and avoid pitfalls in their use of social media and to uphold their professional values. This article draws upon current evidence, policies, and the authors' experiences to present best practice tips for health professions educators, trainees, and students to build a framework for navigating the digital world in a way that maintains and promotes professionalism. These practical tips help the newcomer to social media get started by identifying goals, establishing comfort, and connecting. Furthermore, users can ultimately successfully contribute, engage, learn, and teach, and model professional behaviors while navigating social media.

Teaching undergraduate students in rural general practice: an evaluation of a new rural campus in England.

PubMed

Bartlett, Maggie; Pritchard, Katie; Lewis, Leo; Hays, Richard B; Mckinley, Robert K

2016-01-01

One approach to facilitating student interactions with patient pathways at Keele University School of Medicine, England, is the placement of medical students for 25% of their clinical placement time in general practices. The largest component is a 15-week 'student attachment' in primary care during the final year, which required the development of a new network of teaching practices in a rural district of England about 90 km (60 mi) from the main campus in North Staffordshire. The new accommodation and education hub was established in 2011-2012 to enable students to become immersed in those communities and learn about medical practice within a rural and remote context. Objectives were to evaluate the rural teaching from the perspectives of four groups: patients, general practice tutors, community hospital staff and students. Learning outcomes (as measured by objective structured clinical examinations) of students learning in rural practices in the final year were compared with those in other practices. Data were gathered from a variety of sources. Students' scores in cohort-wide clinical assessment were compared with those in other locations. Semi-structured interviews were conducted with general practice tutors and community hospital staff. Serial focus groups explored the perceptions of the students, and questionnaires were used to gather the views of patients. Patients reported positive experiences of students in their consultations, with 97% expressing willingness to see students. The majority of patients considered that teaching in general practice was a good thing. They also expressed altruistic ideas about facilitating learning. The tutors were enthusiastic and perceived that teaching had positive impacts on their practices despite negative effects on their workload. The community hospital staff welcomed students and expressed altruistic ideas about helping them learn. There was no significant difference between the rurally placed students' objective structured clinical examination performance and that of their peers in other locations. Some students had difficulty with the isolation from peers and academic activities, and travel was a problem despite their accommodation close to the practices. Students valued the learning opportunities offered by the rural practice placements. The general practice tutors, patients and community hospital staff found teaching to be a positive experience overall and perceived a value to the health system and broader community in students learning locally for substantial periods of time. The evaluation has identified some student concerns about transport times and costs, social isolation, and access to resources and administrative tasks, and these are being addressed.

Public Participation, Education, and Engagement in Drought Planning

NASA Astrophysics Data System (ADS)

Bathke, D. J.; Wall, N.; Haigh, T.; Smith, K. H.; Bernadt, T.

2014-12-01

Drought is a complex problem that typically goes beyond the capacity, resources, and jurisdiction of any single person, program, organization, political boundary, or sector. Thus, by nature, monitoring, planning for, and reducing drought risk must be a collaborative process. The National Drought Mitigation Center, in partnership with the National Integrated Drought Information System (NIDIS) Program Office and others, provides active engagement and education drought professionals, stakeholders, and the general public about managing drought-related risks through resilience planning, monitoring, and education. Using case studies, we discuss recruitment processes, network building, participation techniques, and educational methods as they pertain to a variety of unique audiences with distinct objectives. Examples include collaborative decision-making at a World Meteorological Organization conference; planning, and peer-learning among drought professionals in a community of practice; drought condition monitoring through citizen science networks; research and education dissemination with stakeholder groups; and informal learning activities for all ages. Finally, we conclude with evaluation methods, indicators of success, and lessons learned for increasing the effectiveness of our programs in increasing drought resilience.

Optimizing performance through intrinsic motivation and attention for learning: The OPTIMAL theory of motor learning.

PubMed

Wulf, Gabriele; Lewthwaite, Rebecca

2016-10-01

Effective motor performance is important for surviving and thriving, and skilled movement is critical in many activities. Much theorizing over the past few decades has focused on how certain practice conditions affect the processing of task-related information to affect learning. Yet, existing theoretical perspectives do not accommodate significant recent lines of evidence demonstrating motivational and attentional effects on performance and learning. These include research on (a) conditions that enhance expectancies for future performance, (b) variables that influence learners' autonomy, and (c) an external focus of attention on the intended movement effect. We propose the OPTIMAL (Optimizing Performance through Intrinsic Motivation and Attention for Learning) theory of motor learning. We suggest that motivational and attentional factors contribute to performance and learning by strengthening the coupling of goals to actions. We provide explanations for the performance and learning advantages of these variables on psychological and neuroscientific grounds. We describe a plausible mechanism for expectancy effects rooted in responses of dopamine to the anticipation of positive experience and temporally associated with skill practice. Learner autonomy acts perhaps largely through an enhanced expectancy pathway. Furthermore, we consider the influence of an external focus for the establishment of efficient functional connections across brain networks that subserve skilled movement. We speculate that enhanced expectancies and an external focus propel performers' cognitive and motor systems in productive "forward" directions and prevent "backsliding" into self- and non-task focused states. Expected success presumably breeds further success and helps consolidate memories. We discuss practical implications and future research directions.

A Community-Building Framework for Collaborative Research Coordination across the Education and Biology Research Disciplines.

PubMed

Pelaez, Nancy; Anderson, Trevor R; Gardner, Stephanie M; Yin, Yue; Abraham, Joel K; Bartlett, Edward L; Gormally, Cara; Hurney, Carol A; Long, Tammy M; Newman, Dina L; Sirum, Karen; Stevens, Michael T

2018-06-01

Since 2009, the U.S. National Science Foundation Directorate for Biological Sciences has funded Research Coordination Networks (RCN) aimed at collaborative efforts to improve participation, learning, and assessment in undergraduate biology education (UBE). RCN-UBE projects focus on coordination and communication among scientists and educators who are fostering improved and innovative approaches to biology education. When faculty members collaborate with the overarching goal of advancing undergraduate biology education, there is a need to optimize collaboration between participants in order to deeply integrate the knowledge across disciplinary boundaries. In this essay we propose a novel guiding framework for bringing colleagues together to advance knowledge and its integration across disciplines, the "Five 'C's' of Collaboration: Commitment, Collegiality, Communication, Consensus, and Continuity." This guiding framework for professional network practice is informed by both relevant literature and empirical evidence from community-building experience within the RCN-UBE Advancing Competencies in Experimentation-Biology (ACE-Bio) Network. The framework is presented with practical examples to illustrate how it might be used to enhance collaboration between new and existing participants in the ACE-Bio Network as well as within other interdisciplinary networks.

Learning Analytics for Networked Learning Models

ERIC Educational Resources Information Center

Joksimovic, Srecko; Hatala, Marek; Gaševic, Dragan

2014-01-01

Teaching and learning in networked settings has attracted significant attention recently. The central topic of networked learning research is human-human and human-information interactions occurring within a networked learning environment. The nature of these interactions is highly complex and usually requires a multi-dimensional approach to…

Curriculum Analytics: Application of Social Network Analysis for Improving Strategic Curriculum Decision-Making in a Research-Intensive University

ERIC Educational Resources Information Center

Dawson, Shane; Hubball, Harry

2014-01-01

This paper provides insight into the use of curriculum analytics to enhance learning-centred curricula in diverse higher education contexts. Engagement in evidence-based practice to evaluate and monitor curricula is vital to the success and sustainability of efforts to reform undergraduate and graduate programs. Emerging technology-enabled inquiry…

The Formation of Communities of Practice in a Network of Schools Serving Culturally and Linguistically Diverse Students

ERIC Educational Resources Information Center

Scanlan, Martin; Zisselsberger, Margarita

2015-01-01

Culturally and linguistically diverse (CLD) students comprise the most rapidly expanding, and among the most educationally marginalized, group in the United States. CLD students' opportunities to learn are often diminished through service delivery models that are deficit-oriented, viewing linguistic diversity as a challenge to overcome, not a…

Educational Technology for the Global Village: Worldwide Innovation and Best Practices

ERIC Educational Resources Information Center

Lloyd, Les, Ed.; Barreneche, Gabriel I., Ed.

2014-01-01

With this timely book, editors Les Lloyd and Gabriel Barreneche present an eye-opening look at projects that are innovating with technology to improve education and, indeed, the very quality of people's lives around the world. From collaborative learning communities and social networks to Web 2.0 tools, MOOCs, and mobiles, experts discuss an array…

Using a Networked Improvement Community Approach to Design and Scale up Social Psychological Interventions in Schools. Conference Paper

ERIC Educational Resources Information Center

Barron, Kenneth E.; Hulleman, Chris S.; Inouye, R. Bryce; Hartka, Thomas A.

2015-01-01

In our session, we showcase work from a researcher-practitioner partnership between James Madison University, the University of Virginia, and Harrisonburg City Public Schools that is focused on developing a continuous improvement process to translate social-psychological interventions into teaching practices that enhance motivation and learning.…

Building Interdisciplinary Qualitative Research Networks: Reflections on Qualitative Research Group (QRG) at the University of Manitoba

ERIC Educational Resources Information Center

Roger, Kerstin Stieber; Halas, Gayle

2012-01-01

As qualitative research methodologies continue to evolve and develop, both students and experienced researchers are showing greater interest in learning about and developing new approaches. To meet this need, faculty at the University of Manitoba created the Qualitative Research Group (QRG), a community of practice that utilizes experiential…

Organizing English Learner Instruction in New Immigrant Destinations: District Infrastructure and Subject-Specific School Practice

ERIC Educational Resources Information Center

Hopkins, Megan; Lowenhaupt, Rebecca; Sweet, Tracy M.

2015-01-01

In the context of shifting demographics and standards-based reform, school districts in new immigrant destinations are charged with designing infrastructures that support teaching and learning for English learners (ELs) in core academic subjects. This article uses qualitative data and social network analysis to examine how one district in the…

"It Sort of Feels Uncomfortable": Problematising the Assessment of Reflective Practice

ERIC Educational Resources Information Center

Tummons, Jonathan

2011-01-01

This article forms part of an exploration of assessment on one part-time higher education course: a professional qualification for teachers and trainers in the learning and skills sector, which is delivered on a franchise basis across a network of colleges in the north of England. This article proposes that the validity of the assessment of…

Wrestling with Data: Learning Network Grapples with How to Gather and Analyze Valuable Information

ERIC Educational Resources Information Center

Rasmussen, Harriette Thurber

2012-01-01

As facilitator, the author noted some trepidation in the room as the eight secondary principals from Eugene (Oregon.) School District 4J quietly discussed questions that surfaced through their hopes and fears exercise. Could the practice of visiting classrooms together help them to better lead instruction in their buildings? Would this process…

WeFiLab: A Web-Based WiFi Laboratory Platform for Wireless Networking Education

ERIC Educational Resources Information Center

Cui, Lin; Tso, Fung Po; Yao, Di; Jia, Weijia

2012-01-01

Remote access to physical laboratories for education has received significant attention from both researchers and educators as it provides access at reduced cost in sharing manner of real devices and gives students practical training. With the rapid growing of wireless technologies, it has become an essential of learning to have the hand-on…

Teaching and Learning English Functional Writing: Investigating Egyptian EFL Student Teachers' Currently-Needed Functional Writing Skills

ERIC Educational Resources Information Center

Abdallah, Mahmoud M. S.

2014-01-01

At an age marked by the emergence of new literacies, vast technological developments, and social networking practices, language is currently approached from a pragmatic perspective that recognises its functional use to meet realistic communicative goals. Taking this into account, the present study sought to identify the functional writing skills…

Connected Language Learning: A Tutor's Perspective

ERIC Educational Resources Information Center

Guilbaud, Benoît

2015-01-01

In this article, the author reflects upon the impact that networked technologies has had on his professional life and teaching practice. Using these tools has undoubtedly helped shape the way he looks at his own professional development and they have certainly contributed to the fact that he views himself as a life-long learner. The author's…

Sharing is Winning: Cooperative Learning about Atmospheric Composition Change

NASA Astrophysics Data System (ADS)

Schuepbach, E.

2010-09-01

This contribution presents evolving good practice in disseminating the body of know-how, skills and competencies within the networked community of atmospheric scientists as established in ACCENT. The promotion of early-career scientists, and encouraging the next generation to move into the field were among the key issues addressed by the "Training and Education" programme in the European Network of Excellence in Atmospheric Composition Change (ACCENT). Dissemination avenues include a virtual knowledge train carrying the wealth of high-quality scientific learning material developed with experts involved in the ACCENT network. Learning opportunities on current research in atmospheric composition change in Europe were also created during face-to-face training workshops. Real-life examples of pressing air quality issues were addressed in meetings with stakeholder groups that offered opportunities for mutual learning in inspiring partnerships. In order to increase the expertise in atmospheric composition change across Europe, activities were organized with the general public (e.g., Café Scientifique), where the participating early-career scientists were confronted with questions from lay people. For interested teachers, didactic translations of compact overviews on air quality science topics developed in ACCENT offer links with the typical European science curriculum and go beyond school book content. Some of the educational events, methods and tools are described in a booklet published in 2009 ("We Care for Clean Air!", ISBN 978-88-95665-01-6). The electronic version and all training material can be downloaded from www.accent-network.org/portal/education - a valuable resource for teachers and learners around the globe.

On-the-Job Evidence-Based Medicine Training for Clinician-Scientists of the Next Generation

PubMed Central

Leung, Elaine YL; Malick, Sadia M; Khan, Khalid S

2013-01-01

Clinical scientists are at the unique interface between laboratory science and frontline clinical practice for supporting clinical partnerships for evidence-based practice. In an era of molecular diagnostics and personalised medicine, evidence-based laboratory practice (EBLP) is also crucial in aiding clinical scientists to keep up-to-date with this expanding knowledge base. However, there are recognised barriers to the implementation of EBLP and its training. The aim of this review is to provide a practical summary of potential strategies for training clinician-scientists of the next generation. Current evidence suggests that clinically integrated evidence-based medicine (EBM) training is effective. Tailored e-learning EBM packages and evidence-based journal clubs have been shown to improve knowledge and skills of EBM. Moreover, e-learning is no longer restricted to computer-assisted learning packages. For example, social media platforms such as Twitter have been used to complement existing journal clubs and provide additional post-publication appraisal information for journals. In addition, the delivery of an EBLP curriculum has influence on its success. Although e-learning of EBM skills is effective, having EBM trained teachers available locally promotes the implementation of EBM training. Training courses, such as Training the Trainers, are now available to help trainers identify and make use of EBM training opportunities in clinical practice. On the other hand, peer-assisted learning and trainee-led support networks can strengthen self-directed learning of EBM and research participation among clinical scientists in training. Finally, we emphasise the need to evaluate any EBLP training programme using validated assessment tools to help identify the most crucial ingredients of effective EBLP training. In summary, we recommend on-the-job training of EBM with additional focus on overcoming barriers to its implementation. In addition, future studies evaluating the effectiveness of EBM training should use validated outcome tools, endeavour to achieve adequate power and consider the effects of EBM training on learning environment and patient outcomes. PMID:24151345

On-the-Job Evidence-Based Medicine Training for Clinician-Scientists of the Next Generation.

PubMed

Leung, Elaine Yl; Malick, Sadia M; Khan, Khalid S

2013-08-01

Clinical scientists are at the unique interface between laboratory science and frontline clinical practice for supporting clinical partnerships for evidence-based practice. In an era of molecular diagnostics and personalised medicine, evidence-based laboratory practice (EBLP) is also crucial in aiding clinical scientists to keep up-to-date with this expanding knowledge base. However, there are recognised barriers to the implementation of EBLP and its training. The aim of this review is to provide a practical summary of potential strategies for training clinician-scientists of the next generation. Current evidence suggests that clinically integrated evidence-based medicine (EBM) training is effective. Tailored e-learning EBM packages and evidence-based journal clubs have been shown to improve knowledge and skills of EBM. Moreover, e-learning is no longer restricted to computer-assisted learning packages. For example, social media platforms such as Twitter have been used to complement existing journal clubs and provide additional post-publication appraisal information for journals. In addition, the delivery of an EBLP curriculum has influence on its success. Although e-learning of EBM skills is effective, having EBM trained teachers available locally promotes the implementation of EBM training. Training courses, such as Training the Trainers, are now available to help trainers identify and make use of EBM training opportunities in clinical practice. On the other hand, peer-assisted learning and trainee-led support networks can strengthen self-directed learning of EBM and research participation among clinical scientists in training. Finally, we emphasise the need to evaluate any EBLP training programme using validated assessment tools to help identify the most crucial ingredients of effective EBLP training. In summary, we recommend on-the-job training of EBM with additional focus on overcoming barriers to its implementation. In addition, future studies evaluating the effectiveness of EBM training should use validated outcome tools, endeavour to achieve adequate power and consider the effects of EBM training on learning environment and patient outcomes.

Recurrent Neural Networks With Auxiliary Memory Units.

PubMed

Wang, Jianyong; Zhang, Lei; Guo, Quan; Yi, Zhang

2018-05-01

Memory is one of the most important mechanisms in recurrent neural networks (RNNs) learning. It plays a crucial role in practical applications, such as sequence learning. With a good memory mechanism, long term history can be fused with current information, and can thus improve RNNs learning. Developing a suitable memory mechanism is always desirable in the field of RNNs. This paper proposes a novel memory mechanism for RNNs. The main contributions of this paper are: 1) an auxiliary memory unit (AMU) is proposed, which results in a new special RNN model (AMU-RNN), separating the memory and output explicitly and 2) an efficient learning algorithm is developed by employing the technique of error flow truncation. The proposed AMU-RNN model, together with the developed learning algorithm, can learn and maintain stable memory over a long time range. This method overcomes both the learning conflict problem and gradient vanishing problem. Unlike the traditional method, which mixes the memory and output with a single neuron in a recurrent unit, the AMU provides an auxiliary memory neuron to maintain memory in particular. By separating the memory and output in a recurrent unit, the problem of learning conflicts can be eliminated easily. Moreover, by using the technique of error flow truncation, each auxiliary memory neuron ensures constant error flow during the learning process. The experiments demonstrate good performance of the proposed AMU-RNNs and the developed learning algorithm. The method exhibits quite efficient learning performance with stable convergence in the AMU-RNN learning and outperforms the state-of-the-art RNN models in sequence generation and sequence classification tasks.

Multiple brain networks underpinning word learning from fluent speech revealed by independent component analysis.

PubMed

López-Barroso, Diana; Ripollés, Pablo; Marco-Pallarés, Josep; Mohammadi, Bahram; Münte, Thomas F; Bachoud-Lévi, Anne-Catherine; Rodriguez-Fornells, Antoni; de Diego-Balaguer, Ruth

2015-04-15

Although neuroimaging studies using standard subtraction-based analysis from functional magnetic resonance imaging (fMRI) have suggested that frontal and temporal regions are involved in word learning from fluent speech, the possible contribution of different brain networks during this type of learning is still largely unknown. Indeed, univariate fMRI analyses cannot identify the full extent of distributed networks that are engaged by a complex task such as word learning. Here we used Independent Component Analysis (ICA) to characterize the different brain networks subserving word learning from an artificial language speech stream. Results were replicated in a second cohort of participants with a different linguistic background. Four spatially independent networks were associated with the task in both cohorts: (i) a dorsal Auditory-Premotor network; (ii) a dorsal Sensory-Motor network; (iii) a dorsal Fronto-Parietal network; and (iv) a ventral Fronto-Temporal network. The level of engagement of these networks varied through the learning period with only the dorsal Auditory-Premotor network being engaged across all blocks. In addition, the connectivity strength of this network in the second block of the learning phase correlated with the individual variability in word learning performance. These findings suggest that: (i) word learning relies on segregated connectivity patterns involving dorsal and ventral networks; and (ii) specifically, the dorsal auditory-premotor network connectivity strength is directly correlated with word learning performance. Copyright © 2015 Elsevier Inc. All rights reserved.

The management of advanced practitioner preparation: a work-based challenge.

PubMed

Livesley, Joan; Waters, Karen; Tarbuck, Paul

2009-07-01

This paper explores the collaborative development of a Master's level advanced practice programme in the context of the radical reform and remodelling of the UK's National Health Service. Some of the educational, managerial and practice challenges are discussed. Changes to education and training in response to key strategic reviews undertaken by the Greater Manchester Strategic Health Authority (North West of England) established a need to develop nurses and allied health care practitioners to advanced practitioner level. This paper considers how employers, commissioners and educationalists worked together to produce a Master's level programme to prepare nurses and other health care practitioners for sustainable advanced practice roles. Developing innovative and effective curricula to meet the needs of post graduate students from varied backgrounds preparing to practice in different contexts with different client groups is challenging. However, the development of individual learning pathways and work-based learning ensures that the student's work and intended advanced practice role remains at the centre of their learning. Analysis of each student's knowledge and skill deficits alongside an analysis of the organization's readiness to support them as qualified advanced practitioners (APs) is instrumental in ensuring that organizations are ready to support practitioners in new roles. Work-based learning and collaboration between students, employers and higher education institutions can be used to enable managers and students to unravel the network of factors which affect advanced practice in health and social care. Additionally, collaborative working can help to create opportunities to develop strategies that will facilitate change. Implications for nursing management Sustainable change concerned with the introduction of advanced practitioner roles present a real challenge for managers at a strategic and operational level. Commissioning flexible, collaborative and service-led educational programmes can assist in ensuring that change is sustainable and produce practitioners who are fit for practice, purpose and award.

Factors which motivate the use of social networks by students.

PubMed

González Sanmamed, Mercedes; Muñoz Carril, Pablo C; Dans Álvarez de Sotomayor, Isabel

2017-05-01

The aim of this research was to identify those factors which motivate the use of social networks by 4th year students in Secondary Education between the ages of 15 and 18. 1,144 students from 29 public and private schools took part. The data were analysed using Partial Least Squares Structural Equation Modelling technique. Versatility was confirmed to be the variable which most influences the motivation of students in their use of social networks. The positive relationship between versatility in the use of social networks and educational uses was also significant. The characteristics of social networks are analysed according to their versatility and how this aspect makes them attractive to students. The positive effects of social networks are discussed in terms of educational uses and their contribution to school learning. There is also a warning about the risks associated with misuse of social networks, and finally, the characteristics and conditions for the development of good educational practice through social networks are identified.

Characterization of essential proteins based on network topology in proteins interaction networks

NASA Astrophysics Data System (ADS)

Bakar, Sakhinah Abu; Taheri, Javid; Zomaya, Albert Y.

2014-06-01

The identification of essential proteins is theoretically and practically important as (1) it is essential to understand the minimal surviving requirements for cellular lives, and (2) it provides fundamental for development of drug. As conducting experimental studies to identify essential proteins are both time and resource consuming, here we present a computational approach in predicting them based on network topology properties from protein-protein interaction networks of Saccharomyces cerevisiae. The proposed method, namely EP3NN (Essential Proteins Prediction using Probabilistic Neural Network) employed a machine learning algorithm called Probabilistic Neural Network as a classifier to identify essential proteins of the organism of interest; it uses degree centrality, closeness centrality, local assortativity and local clustering coefficient of each protein in the network for such predictions. Results show that EP3NN managed to successfully predict essential proteins with an accuracy of 95% for our studied organism. Results also show that most of the essential proteins are close to other proteins, have assortativity behavior and form clusters/sub-graph in the network.

Evaluation of a discussion forum for knowledge sharing among emergency practitioners: a social network approach.

PubMed

Curran, Janet; Abidi, Syed Sibte Raza

2006-01-01

Peer to peer knowledge sharing is recognized as a key contributor to the development of expert practice for health care professionals. Emergency departments with access to extensive expertise, such as in urban hospital settings, present greater potential for rich collaborative learning opportunities as compared with rural settings where expertise is at times scarce. Collaborative technologies such as electronic discussion boards may assist in leveling the "knowledge" playing field and increase opportunities for the growth of a strong social network for emergency clinicians. A social network perspective is used to explore the effectiveness of a discussion forum to support knowledge sharing among emergency practitioners in rural and urban emergency departments in Nova Scotia.

Children's Specialized Hospital and GetWellNetwork Collaborate to Improve Patient Education and Outcomes Using an Innovative Approach.

PubMed

Kompany, Laura; Luis, Kiersten; Manganaro, Julie; Motacki, Kathleen; Mustacchio, Elaine; Provenzano, Donna

2016-01-01

Patient education in a pediatric setting is unique. There are different patient ages, degrees of learning, and diagnoses to take into account when educating children and their families. A new and innovative trend in practice called Interactive Patient Care (IPC) integrates technology into care processes to advance pediatric nursing education and patient and family satisfaction. Children's Specialized Hospital is the first pediatric rehabilitation hospital to develop and implement this type of program using an IPC platform from the GetWellNetwork. With the implementation of the GetWellNetwork, Children's Specialized Hospital has achieved positive results in patient satisfaction, health care utilization, quality, and safety measures.

Surgical education and training in an outer metropolitan hospital: a qualitative study of surgical trainers and trainees.

PubMed

Nestel, Debra; Harlim, Jennifer; Bryant, Melanie; Rampersad, Rajay; Hunter-Smith, David; Spychal, Bob

2017-08-01

The landscape of surgical training is changing. The anticipated increase in the numbers of surgical trainees and the shift to competency-based surgical training places pressures on an already stretched health service. With these pressures in mind, we explored trainers' and trainees' experiences of surgical training in a less traditional rotation, an outer metropolitan hospital. We considered practice-based learning theories to make meaning of surgical training in this setting, in particular Actor-network theory. We adopted a qualitative approach and purposively sampled surgical trainers and trainees to participate in individual interviews and focus groups respectively. Transcripts were made and thematically analysed. Institutional human research ethics approval was obtained. Four surgical trainers and fourteen trainees participated. Almost without exception, participants' report training needs to be well met. Emergent inter-related themes were: learning as social activity; learning and programmatic factors; learning and physical infrastructure; and, learning and organizational structure. This outer metropolitan hospital is suited to the provision of surgical training with the current rotational system for trainees. The setting offers experiences that enable consolidation of learning providing a rich and varied overall surgical training program. Although relational elements of learning were paramount they occurred within a complex environment. Actor-network theory was used to give meaning to emergent themes acknowledging that actors (both people and objects) and their interactions combine to influence training quality, shifting the focus of responsibility for learning away from individuals to the complex interactions in which they work and learn.

The educational impact of the Specialty Care Access Network-Extension of Community Healthcare Outcomes program.

PubMed

Salgia, Reena J; Mullan, Patricia B; McCurdy, Heather; Sales, Anne; Moseley, Richard H; Su, Grace L

2014-11-01

With the aging hepatitis C cohort and increasing prevalence of fatty liver disease, the burden on primary care providers (PCPs) to care for patients with liver disease is growing. In response, the Veterans Administration implemented initiatives for primary care-specialty referral to increase PCP competency in complex disease management. The Specialty Care Access Network-Extension of Community Healthcare Outcomes (SCAN-ECHO) program initiative was designed to transfer subspecialty knowledge to PCPs through case-based distance learning combined with real-time consultation. There is limited information regarding the initiative's ability to engage PCPs to learn and influence their practice. We surveyed PCPs to determine the factors that led to their participation in this program and the educational impact of participation. Of 51 potential participants, 24 responded to an anonymous survey. More than 75% of respondents participated more than one time in a SCAN-ECHO clinic. Providers were motivated to participate by a desire to learn more about liver disease, to apply the knowledge gained to future patients, and to save their patients time traveling to another center for specialty consultation. Seventy-one percent responded that the didactic component and case-based discussion were equally important. It is important that participation changed clinical practice: 75% of providers indicated they had personally discussed the information they learned from the case presentations with their colleague(s), and 42% indicated they helped a colleague care for their patient with the knowledge learned during discussions of other participants' cases. This study shows that the SCAN-ECHO videoconferencing program between PCPs and specialists can educate providers in the delivery of specialty care from a distance and potentially improve healthcare delivery.

A Knowledge Transfer Study of the Utility of the Nova Scotia Seniors’ Mental Health Network in Implementing Seniors’ Mental Health National Guidelines

PubMed Central

Bosma, Mark; Cassidy, Keri-Leigh; Le Clair, J Kenneth; Helsdingen, Sherri; Devichand, Pratima

2011-01-01

Background The Canadian Coalition for Seniors’ Mental Health (CCSMH) developed national best-practice guidelines in seniors’ mental health. Promoting adoption of new guidelines is challenging, as paper dissemination alone has limited impact on practice change. Purpose We hypothesized that the existing knowledge transfer (KT) mechanisms of the Nova Scotia Seniors’ Mental Health Network would prove useful in transferring the CCSMH best-practice guidelines. Methods In this observational KT study, CCSMH best-practice guidelines were delivered through two interactive, case-based teaching modules on Depression & Suicide, and Delirium via a provincial tele-education program and local face-to-face sessions. Usefulness of KT was measured using self-report evaluations of material quality and learning. Evaluation results from the two session topics and from tele-education versus face-to-face sessions were compared. Results Sessions were well attended (N = 347), with a high evaluation return rate (287, 83%). Most participants reported enhanced knowledge in seniors’ mental health and intended to apply knowledge to practice. Ratings did not differ significantly between KT session topics or modes of delivery. Conclusions The KT mechanisms of a provincial seniors’ mental health network facilitated knowledge acquisition and the intention of using national guidelines on seniors’ mental health among Nova Scotian clinicians. Key elements of accelerating KT used in this initiative are discussed. PMID:23251305

Preparedness and Emergency Response Learning Centers: supporting the workforce for national health security.

PubMed

Richmond, Alyson L; Sobelson, Robyn K; Cioffi, Joan P

2014-01-01

The importance of a competent and prepared national public health workforce, ready to respond to threats to the public's health, has been acknowledged in numerous publications since the 1980s. The Preparedness and Emergency Response Learning Centers (PERLCs) were funded by the Centers for Disease Control and Prevention in 2010 to continue to build upon a decade of focused activities in public health workforce preparedness development initiated under the Centers for Public Health Preparedness program (http://www.cdc.gov/phpr/cphp/). All 14 PERLCs were located within Council on Education for Public Health (CEPH) accredited schools of public health. These centers aimed to improve workforce readiness and competence through the development, delivery, and evaluation of targeted learning programs designed to meet specific requirements of state, local, and tribal partners. The PERLCs supported organizational and community readiness locally, regionally, or nationally through the provision of technical consultation and dissemination of specific, practical tools aligned with national preparedness competency frameworks and public health preparedness capabilities. Public health agencies strive to address growing public needs and a continuous stream of current and emerging public health threats. The PERLC network represented a flexible, scalable, and experienced national learning system linking academia with practice. This system improved national health security by enhancing individual, organizational, and community performance through the application of public health science and learning technologies to frontline practice.

Neural sensitivity to statistical regularities as a fundamental biological process that underlies auditory learning: the role of musical practice.

PubMed

François, Clément; Schön, Daniele

2014-02-01

There is increasing evidence that humans and other nonhuman mammals are sensitive to the statistical structure of auditory input. Indeed, neural sensitivity to statistical regularities seems to be a fundamental biological property underlying auditory learning. In the case of speech, statistical regularities play a crucial role in the acquisition of several linguistic features, from phonotactic to more complex rules such as morphosyntactic rules. Interestingly, a similar sensitivity has been shown with non-speech streams: sequences of sounds changing in frequency or timbre can be segmented on the sole basis of conditional probabilities between adjacent sounds. We recently ran a set of cross-sectional and longitudinal experiments showing that merging music and speech information in song facilitates stream segmentation and, further, that musical practice enhances sensitivity to statistical regularities in speech at both neural and behavioral levels. Based on recent findings showing the involvement of a fronto-temporal network in speech segmentation, we defend the idea that enhanced auditory learning observed in musicians originates via at least three distinct pathways: enhanced low-level auditory processing, enhanced phono-articulatory mapping via the left Inferior Frontal Gyrus and Pre-Motor cortex and increased functional connectivity within the audio-motor network. Finally, we discuss how these data predict a beneficial use of music for optimizing speech acquisition in both normal and impaired populations. Copyright © 2013 Elsevier B.V. All rights reserved.

Robust Learning of High-dimensional Biological Networks with Bayesian Networks

NASA Astrophysics Data System (ADS)

Nägele, Andreas; Dejori, Mathäus; Stetter, Martin

Structure learning of Bayesian networks applied to gene expression data has become a potentially useful method to estimate interactions between genes. However, the NP-hardness of Bayesian network structure learning renders the reconstruction of the full genetic network with thousands of genes unfeasible. Consequently, the maximal network size is usually restricted dramatically to a small set of genes (corresponding with variables in the Bayesian network). Although this feature reduction step makes structure learning computationally tractable, on the downside, the learned structure might be adversely affected due to the introduction of missing genes. Additionally, gene expression data are usually very sparse with respect to the number of samples, i.e., the number of genes is much greater than the number of different observations. Given these problems, learning robust network features from microarray data is a challenging task. This chapter presents several approaches tackling the robustness issue in order to obtain a more reliable estimation of learned network features.

How social network analysis can be used to monitor online collaborative learning and guide an informed intervention

PubMed Central

Fors, Uno; Tedre, Matti; Nouri, Jalal

2018-01-01

To ensure online collaborative learning meets the intended pedagogical goals (is actually collaborative and stimulates learning), mechanisms are needed for monitoring the efficiency of online collaboration. Various studies have indicated that social network analysis can be particularly effective in studying students’ interactions in online collaboration. However, research in education has only focused on the theoretical potential of using SNA, not on the actual benefits they achieved. This study investigated how social network analysis can be used to monitor online collaborative learning, find aspects in need of improvement, guide an informed intervention, and assess the efficacy of intervention using an experimental, observational repeated-measurement design in three courses over a full-term duration. Using a combination of SNA-based visual and quantitative analysis, we monitored three SNA constructs for each participant: the level of interactivity, the role, and position in information exchange, and the role played by each participant in the collaboration. On the group level, we monitored interactivity and group cohesion indicators. Our monitoring uncovered a non-collaborative teacher-centered pattern of interactions in the three studied courses as well as very few interactions among students, limited information exchange or negotiation, and very limited student networks dominated by the teacher. An intervention based on SNA-generated insights was designed. The intervention was structured into five actions: increasing awareness, promoting collaboration, improving the content, preparing teachers, and finally practicing with feedback. Evaluation of the intervention revealed that it has significantly enhanced student-student interactions and teacher-student interactions, as well as produced a collaborative pattern of interactions among most students and teachers. Since efficient and communicative activities are essential prerequisites for successful content discussion and for realizing the goals of collaboration, we suggest that our SNA-based approach will positively affect teaching and learning in many educational domains. Our study offers a proof-of-concept of what SNA can add to the current tools for monitoring and supporting teaching and learning in higher education. PMID:29566058

How social network analysis can be used to monitor online collaborative learning and guide an informed intervention.

PubMed

Saqr, Mohammed; Fors, Uno; Tedre, Matti; Nouri, Jalal

2018-01-01

To ensure online collaborative learning meets the intended pedagogical goals (is actually collaborative and stimulates learning), mechanisms are needed for monitoring the efficiency of online collaboration. Various studies have indicated that social network analysis can be particularly effective in studying students' interactions in online collaboration. However, research in education has only focused on the theoretical potential of using SNA, not on the actual benefits they achieved. This study investigated how social network analysis can be used to monitor online collaborative learning, find aspects in need of improvement, guide an informed intervention, and assess the efficacy of intervention using an experimental, observational repeated-measurement design in three courses over a full-term duration. Using a combination of SNA-based visual and quantitative analysis, we monitored three SNA constructs for each participant: the level of interactivity, the role, and position in information exchange, and the role played by each participant in the collaboration. On the group level, we monitored interactivity and group cohesion indicators. Our monitoring uncovered a non-collaborative teacher-centered pattern of interactions in the three studied courses as well as very few interactions among students, limited information exchange or negotiation, and very limited student networks dominated by the teacher. An intervention based on SNA-generated insights was designed. The intervention was structured into five actions: increasing awareness, promoting collaboration, improving the content, preparing teachers, and finally practicing with feedback. Evaluation of the intervention revealed that it has significantly enhanced student-student interactions and teacher-student interactions, as well as produced a collaborative pattern of interactions among most students and teachers. Since efficient and communicative activities are essential prerequisites for successful content discussion and for realizing the goals of collaboration, we suggest that our SNA-based approach will positively affect teaching and learning in many educational domains. Our study offers a proof-of-concept of what SNA can add to the current tools for monitoring and supporting teaching and learning in higher education.

Fuzzy logic and neural network technologies

NASA Technical Reports Server (NTRS)

Villarreal, James A.; Lea, Robert N.; Savely, Robert T.

1992-01-01

Applications of fuzzy logic technologies in NASA projects are reviewed to examine their advantages in the development of neural networks for aerospace and commercial expert systems and control. Examples of fuzzy-logic applications include a 6-DOF spacecraft controller, collision-avoidance systems, and reinforcement-learning techniques. The commercial applications examined include a fuzzy autofocusing system, an air conditioning system, and an automobile transmission application. The practical use of fuzzy logic is set in the theoretical context of artificial neural systems (ANSs) to give the background for an overview of ANS research programs at NASA. The research and application programs include the Network Execution and Training Simulator and faster training algorithms such as the Difference Optimized Training Scheme. The networks are well suited for pattern-recognition applications such as predicting sunspots, controlling posture maintenance, and conducting adaptive diagnoses.

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

ERIC Educational Resources Information Center

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

2015-01-01

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

Teachers' Motives for Learning in Networks: Costs, Rewards and Community Interest

ERIC Educational Resources Information Center

van den Beemt, Antoine; Ketelaar, Evelien; Diepstraten, Isabelle; de Laat, Maarten

2018-01-01

Background: This paper discusses teachers' perspectives on learning networks and their motives for participating in these networks. Although it is widely held that teachers' learning may be developed through learning networks, not all teachers participate in such networks. Purpose: The theme of reciprocity, central to studies in the area of…

Up the ANTe: Understanding Entrepreneurial Leadership Learning through Actor-Network Theory

ERIC Educational Resources Information Center

Smith, Sue; Kempster, Steve; Barnes, Stewart

2017-01-01

This article explores the role of educators in supporting the development of entrepreneurial leadership learning by creating peer learning networks of owner-managers of small businesses. Using actor-network theory, the authors think through the process of constructing and maintaining a peer learning network (conceived of as an actor-network) and…

Learning by stimulation avoidance: A principle to control spiking neural networks dynamics

PubMed Central

Sinapayen, Lana; Ikegami, Takashi

2017-01-01

Learning based on networks of real neurons, and learning based on biologically inspired models of neural networks, have yet to find general learning rules leading to widespread applications. In this paper, we argue for the existence of a principle allowing to steer the dynamics of a biologically inspired neural network. Using carefully timed external stimulation, the network can be driven towards a desired dynamical state. We term this principle “Learning by Stimulation Avoidance” (LSA). We demonstrate through simulation that the minimal sufficient conditions leading to LSA in artificial networks are also sufficient to reproduce learning results similar to those obtained in biological neurons by Shahaf and Marom, and in addition explains synaptic pruning. We examined the underlying mechanism by simulating a small network of 3 neurons, then scaled it up to a hundred neurons. We show that LSA has a higher explanatory power than existing hypotheses about the response of biological neural networks to external simulation, and can be used as a learning rule for an embodied application: learning of wall avoidance by a simulated robot. In other works, reinforcement learning with spiking networks can be obtained through global reward signals akin simulating the dopamine system; we believe that this is the first project demonstrating sensory-motor learning with random spiking networks through Hebbian learning relying on environmental conditions without a separate reward system. PMID:28158309

Learning by stimulation avoidance: A principle to control spiking neural networks dynamics.

PubMed

Sinapayen, Lana; Masumori, Atsushi; Ikegami, Takashi

2017-01-01

Learning based on networks of real neurons, and learning based on biologically inspired models of neural networks, have yet to find general learning rules leading to widespread applications. In this paper, we argue for the existence of a principle allowing to steer the dynamics of a biologically inspired neural network. Using carefully timed external stimulation, the network can be driven towards a desired dynamical state. We term this principle "Learning by Stimulation Avoidance" (LSA). We demonstrate through simulation that the minimal sufficient conditions leading to LSA in artificial networks are also sufficient to reproduce learning results similar to those obtained in biological neurons by Shahaf and Marom, and in addition explains synaptic pruning. We examined the underlying mechanism by simulating a small network of 3 neurons, then scaled it up to a hundred neurons. We show that LSA has a higher explanatory power than existing hypotheses about the response of biological neural networks to external simulation, and can be used as a learning rule for an embodied application: learning of wall avoidance by a simulated robot. In other works, reinforcement learning with spiking networks can be obtained through global reward signals akin simulating the dopamine system; we believe that this is the first project demonstrating sensory-motor learning with random spiking networks through Hebbian learning relying on environmental conditions without a separate reward system.

Learning-based computing techniques in geoid modeling for precise height transformation

NASA Astrophysics Data System (ADS)

Erol, B.; Erol, S.

2013-03-01

Precise determination of local geoid is of particular importance for establishing height control in geodetic GNSS applications, since the classical leveling technique is too laborious. A geoid model can be accurately obtained employing properly distributed benchmarks having GNSS and leveling observations using an appropriate computing algorithm. Besides the classical multivariable polynomial regression equations (MPRE), this study attempts an evaluation of learning based computing algorithms: artificial neural networks (ANNs), adaptive network-based fuzzy inference system (ANFIS) and especially the wavelet neural networks (WNNs) approach in geoid surface approximation. These algorithms were developed parallel to advances in computer technologies and recently have been used for solving complex nonlinear problems of many applications. However, they are rather new in dealing with precise modeling problem of the Earth gravity field. In the scope of the study, these methods were applied to Istanbul GPS Triangulation Network data. The performances of the methods were assessed considering the validation results of the geoid models at the observation points. In conclusion the ANFIS and WNN revealed higher prediction accuracies compared to ANN and MPRE methods. Beside the prediction capabilities, these methods were also compared and discussed from the practical point of view in conclusions.

A Spatiotemporal Prediction Framework for Air Pollution Based on Deep RNN

NASA Astrophysics Data System (ADS)

Fan, J.; Li, Q.; Hou, J.; Feng, X.; Karimian, H.; Lin, S.

2017-10-01

Time series data in practical applications always contain missing values due to sensor malfunction, network failure, outliers etc. In order to handle missing values in time series, as well as the lack of considering temporal properties in machine learning models, we propose a spatiotemporal prediction framework based on missing value processing algorithms and deep recurrent neural network (DRNN). By using missing tag and missing interval to represent time series patterns, we implement three different missing value fixing algorithms, which are further incorporated into deep neural network that consists of LSTM (Long Short-term Memory) layers and fully connected layers. Real-world air quality and meteorological datasets (Jingjinji area, China) are used for model training and testing. Deep feed forward neural networks (DFNN) and gradient boosting decision trees (GBDT) are trained as baseline models against the proposed DRNN. Performances of three missing value fixing algorithms, as well as different machine learning models are evaluated and analysed. Experiments show that the proposed DRNN framework outperforms both DFNN and GBDT, therefore validating the capacity of the proposed framework. Our results also provides useful insights for better understanding of different strategies that handle missing values.

RELM: developing a serious game to teach evidence-based medicine in an academic health sciences setting.

PubMed

Gleason, Ann Whitney

2015-01-01

Gaming as a means of delivering online education continues to gain in popularity. Online games provide an engaging and enjoyable way of learning. Gaming is especially appropriate for case-based teaching, and provides a conducive environment for adult independent learning. With funding from the National Network of Libraries of Medicine, Pacific Northwest Region (NN/LM PNR), the University of Washington (UW) Health Sciences Library, and the UW School of Medicine are collaborating to create an interactive, self-paced online game that teaches players to employ the steps in practicing evidence-based medicine. The game encourages life-long learning and literacy skills and could be used for providing continuing medical education.

Teaching the bioinformatics of signaling networks: an integrated approach to facilitate multi-disciplinary learning.

PubMed

Korcsmaros, Tamas; Dunai, Zsuzsanna A; Vellai, Tibor; Csermely, Peter

2013-09-01

The number of bioinformatics tools and resources that support molecular and cell biology approaches is continuously expanding. Moreover, systems and network biology analyses are accompanied more and more by integrated bioinformatics methods. Traditional information-centered university teaching methods often fail, as (1) it is impossible to cover all existing approaches in the frame of a single course, and (2) a large segment of the current bioinformation can become obsolete in a few years. Signaling network offers an excellent example for teaching bioinformatics resources and tools, as it is both focused and complex at the same time. Here, we present an outline of a university bioinformatics course with four sample practices to demonstrate how signaling network studies can integrate biochemistry, genetics, cell biology and network sciences. We show that several bioinformatics resources and tools, as well as important concepts and current trends, can also be integrated to signaling network studies. The research-type hands-on experiences we show enable the students to improve key competences such as teamworking, creative and critical thinking and problem solving. Our classroom course curriculum can be re-formulated as an e-learning material or applied as a part of a specific training course. The multi-disciplinary approach and the mosaic setup of the course have the additional benefit to support the advanced teaching of talented students.

Modular, Hierarchical Learning By Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Baldi, Pierre F.; Toomarian, Nikzad

1996-01-01

Modular and hierarchical approach to supervised learning by artificial neural networks leads to neural networks more structured than neural networks in which all neurons fully interconnected. These networks utilize general feedforward flow of information and sparse recurrent connections to achieve dynamical effects. The modular organization, sparsity of modular units and connections, and fact that learning is much more circumscribed are all attractive features for designing neural-network hardware. Learning streamlined by imitating some aspects of biological neural networks.

The PBRN Initiative

PubMed Central

Curro, F.A.; Vena, D.; Naftolin, F.; Terracio, L.; Thompson, V.P.

2012-01-01

The NIDCR-supported Practice-based Research Network initiative presents dentistry with an unprecedented opportunity by providing a pathway for modifying and advancing the profession. It encourages practitioner participation in the transfer of science into practice for the improvement of patient care. PBRNs vary in infrastructure and design, and sustaining themselves in the long term may involve clinical trial validation by regulatory agencies. This paper discusses the PBRN concept in general and uses the New York University College of Dentistry’s Practitioners Engaged in Applied Research and Learning (PEARL) Network as a model to improve patient outcomes. The PEARL Network is structured to ensure generalizability of results, data integrity, and to provide an infrastructure in which scientists can address clinical practitioner research interests. PEARL evaluates new technologies, conducts comparative effectiveness research, participates in multidisciplinary clinical studies, helps evaluate alternative models of healthcare, educates and trains future clinical faculty for academic positions, expands continuing education to include “benchmarking” as a form of continuous feedback to practitioners, adds value to dental schools’ educational programs, and collaborates with the oral health care and pharmaceutical industries and medical PBRNs to advance the dental profession and further the integration of dental research and practice into contemporary healthcare (NCT00867997, NCT01268605). PMID:22699662

Case Presentations Demonstrating Periodontal Treatment Variation: PEARL Network.

PubMed

Curro, Frederick A; Grill, Ashley C; Matthews, Abigail G; Martin, John; Kalenderian, Elisabeth; Craig, Ronald G; Naftolin, Frederick; Thompson, Van P

2015-06-01

Variation in periodontal terminology can affect the diagnosis and treatment plan as assessed by practicing general dentists in the Practitioners Engaged in Applied Research and Learning (PEARL) Network. General dentists participating in the PEARL Network are highly screened, credentialed, and qualified and may not be representative of the general population of dentists. Ten randomized case presentations ranging from periodontal health to gingivitis, to mild, moderate, and severe periodontitis were randomly presented to respondents. Descriptive comparisons were made between these diagnosis groups in terms of the treatment recommendations following diagnosis. PEARL practitioners assessing periodontal clinical scenarios were found to either over- or under-diagnose the case presentations, which affected treatment planning, while the remaining responses concurred with respect to the diagnosis. The predominant diagnosis was compared with that assigned by two practicing periodontists. There was variation in treatment based on the diagnosis for gingivitis and the lesser forms of periodontitis. Data suggests that a lack of clarity of periodontal terminology affects both diagnosis and treatment planning, and terminology may be improved by having diagnosis codes, which could be used to assess treatment outcomes. This article provides data to support best practice for the use of diagnosis coding and integration of dentistry with medicine using ICD-10 terminology.

mEducator: A Best Practice Network for Repurposing and Sharing Medical Educational Multi-type Content

NASA Astrophysics Data System (ADS)

Bamidis, Panagiotis D.; Kaldoudi, Eleni; Pattichis, Costas

Although there is an abundance of medical educational content available in individual EU academic institutions, this is not widely available or easy to discover and retrieve, due to lack of standardized content sharing mechanisms. The mEducator EU project will face this lack by implementing and experimenting between two different sharing mechanisms, namely, one based one mashup technologies, and one based on semantic web services. In addition, the mEducator best practice network will critically evaluate existing standards and reference models in the field of e-learning in order to enable specialized state-of-the-art medical educational content to be discovered, retrieved, shared, repurposed and re-used across European higher academic institutions. Educational content included in mEducator covers and represents the whole range of medical educational content, from traditional instructional teaching to active learning and experiential teaching/studying approaches. It spans the whole range of types, from text to exam sheets, algorithms, teaching files, computer programs (simulators or games) and interactive objects (like virtual patients and electronically traced anatomies), while it covers a variety of topics. In this paper, apart from introducing the relevant project concepts and strategies, emphasis is also placed on the notion of (dynamic) user-generated content, its advantages and peculiarities, as well as, gaps in current research and technology practice upon its embedding into existing standards.

Command and Control Concepts and Solutions for Major Events Safety and Security: Lessons Learned from the Canadian Experience with Vancouver 2010 and G8/G20 Events

DTIC Science & Technology

2011-06-01

discuss best practices and the prerogatives of major events C2 solutions. In section 6, we present the conclusion. 2 Complexity of the Command and Control...best practices for sharing information, standard operating procedure (SOPs) and response plans have been investigated through formal studies and an...and contributed to the deployment of an information sharing solution on Command Network. This solution was based on Microsoft SharePoint. The team

The Role of Student Learning Outcomes for Institutional Accountability and Transparency. Pathways to College Network Brief

ERIC Educational Resources Information Center

Pathways to College Network, 2012

2012-01-01

The national focus on college completion has prompted a number of ambitious education goals, numerous research- and practice-based initiatives, and a spotlight on the need to continually improve higher education access and success. This national dialogue has raised not only public and political will for postsecondary completion, but also a number…

Interim Reflections on the Corporate University and SME Academy Business Development Innovation and Its Diffusion

ERIC Educational Resources Information Center

Dealtry, Richard

2008-01-01

Purpose: The purpose of this paper is to reflect on and inform about learning points from ECUANET, a two-year duration best practice action research and transnational networking project as it approaches its final stage. Design/methodology/approach: The paper explicates the key positive and obfuscating dynamics that the project team have had to,…

Communities of Practice in a Voluntary Youth Organisation: Reaching for the Sky and Building Social Capital

ERIC Educational Resources Information Center

Chan, Bill; Short, Tom

2011-01-01

The study is situated within a national youth organisation called the Australian Air League Inc (Air League). We examine the recent progress of the Air League in South Australia, starting as a loose network of volunteers engaged in a sporadic array of activities, to become a learning community that worked collaboratively and then developed further…

An Instructional Model to Support Problem-Based Historical Inquiry: The Persistent Issues in History Network

ERIC Educational Resources Information Center

Brush, Thomas; Saye, John

2014-01-01

For over a decade, we have collaborated with secondary school history teachers in an evolving line of inquiry that applies research-based propositions to the design and testing of a problem-based learning framework and a set of wise practices that represent a professional teaching knowledge base for implementing a particular model of instruction,…

The Relative Influence of Formal Learning Opportunities versus Indicators of Professional Community on Changes in Science Teaching in Urban Schools

ERIC Educational Resources Information Center

McGee, Steven

2016-01-01

Previous research has shown that professional communities have the potential to be a powerful lever for continuous improvement in school settings. This research seeks to extend previous research by investigating the indicators of professional community that influence science teaching practice. This study took place in a network of urban…

Learning to Write in the Digital Age: ELLs' Literacy Practices in and out of Their Western Urban High School

ERIC Educational Resources Information Center

Pu, Jiang

2013-01-01

The definition of literacy is constantly changing and expanding. A sociocultural view of Literacy considers literacy to be multiple, multimodal, and multilingual as situated in and across the social and cultural contexts. As technology, new media and social network has reformed many aspects of writing, they provide ELLs (English language learners)…

The Ethical and Practical Implications of Systems Architecture on Identity in Networked Learning: A Constructionist Perspective

ERIC Educational Resources Information Center

Koole, Marguerite L.; Parchoma, Gale

2012-01-01

Through relational dialogue, learners shape their identities by sharing information about the world and how they see themselves in it. As learners interact, they receive feedback from both the environment and other learners which, in turn, helps them assess and adjust their self-presentations. Although learners retain choice and personal agency,…

Mission Possible: How the Secrets of the Success Academies Can Work in Any School

ERIC Educational Resources Information Center

Moskowitz, Eva; Lavinia, Arin

2012-01-01

Eva Moskowitz (the founder and CEO of the Success Charter Network in Harlem) and Arin Lavinia offer practical, classroom-tested ideas for dramatically improving teaching and learning. Moskowitz and Lavinia reveal how a charter school in the middle of Harlem, enrolling neighborhood children selected at random, emerged as one of the top schools in…

Implementing the UH Asynchronous Learning Network: Practices, Issues and Challenges

ERIC Educational Resources Information Center

Odin, Jaishree K.

2002-01-01

In spite of ten campuses spread over four islands, access to higher education at the University of Hawai'i (UH) is unevenly distributed across the state. In an effort to address the problem of access, the Alfred P. Sloan Foundation has funded the University of Hawai'i to develop online courses and programs. In this article, the author describes…

The Influence of Collective Asynchronous Discourse Elaborated Online by Pre-Service Teachers on Their Educational Interventions in the Classroom

ERIC Educational Resources Information Center

Allaire, Stéphane

2015-01-01

Networked learning communities are growing and they offer new opportunities for reflection on practice in education. Many authors have studied the processes followed and the contents produced by such communities. On the other hand, few have observed how collective asynchronous discourse can be enacted in the classroom. This objective was pursued…

Experience preferred: insights from our newest public health professionals on how internships/practicums promote career development.

PubMed

Hernandez, Kristen E; Bejarano, Sandra; Reyes, Francis J; Chavez, Margarita; Mata, Holly

2014-01-01

Universities offering undergraduate degrees in health promotion or health education and/or graduate degrees in public health typically require an internship, practicum, or fieldwork experience. This type of mentored experience is an important aspect of career development for the next generation of public health professionals and benefits not only the students but also the profession and the communities in which they work. This article provides perspectives from four public health professionals who have recently graduated from designated minority-serving institutions and highlights the ways in which internship, practicum, or fieldwork experiences have contributed to their career development. From a career development perspective, internships provide unique opportunities to develop professional networks, practice competencies learned in the classroom, gain experience in different environments, and share lessons learned with others in our field. The diversification of the public health research and practice workforce is increasingly recognized as crucial in building health equity. Internship programs that focus specifically on the academic and professional development of students underrepresented in public health provide experiences that meet or supplement academic requirements, and provide students with real-world experience and an expanded network of mentors and role models.

Randomized Trial of Reducing Ambulatory Malpractice and Safety Risk: Results of the Massachusetts PROMISES Project.

PubMed

Schiff, Gordon D; Reyes Nieva, Harry; Griswold, Paula; Leydon, Nicholas; Ling, Judy; Federico, Frank; Keohane, Carol; Ellis, Bonnie R; Foskett, Cathy; Orav, E John; Yoon, Catherine; Goldmann, Don; Weissman, Joel S; Bates, David W; Biondolillo, Madeleine; Singer, Sara J

2017-08-01

Evaluate application of quality improvement approaches to key ambulatory malpractice risk and safety areas. In total, 25 small-to-medium-sized primary care practices (16 intervention; 9 control) in Massachusetts. Controlled trial of a 15-month intervention including exposure to a learning network, webinars, face-to-face meetings, and coaching by improvement advisors targeting "3+1" high-risk domains: test result, referral, and medication management plus culture/communication issues evaluated by survey and chart review tools. Chart reviews conducted at baseline and postintervention for intervention sites. Staff and patient survey data collected at baseline and postintervention for intervention and control sites. Chart reviews demonstrated significant improvements in documentation of abnormal results, patient notification, documentation of an action or treatment plan, and evidence of a completed plan (all P<0.001). Mean days between laboratory test date and evidence of completed action/treatment plan decreased by 19.4 days (P<0.001). Staff surveys showed modest but nonsignificant improvement for intervention practices relative to controls overall and for the 3 high-risk domains that were the focus of PROMISES. A consortium of stakeholders, quality improvement tools, coaches, and learning network decreased selected ambulatory safety risks often seen in malpractice claims.

Can machine-learning improve cardiovascular risk prediction using routine clinical data?

PubMed Central

Kai, Joe; Garibaldi, Jonathan M.; Qureshi, Nadeem

2017-01-01

Background Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. Methods Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the ‘receiver operating curve’ (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins). Findings 24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723–0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739–0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755–0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755–0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759–0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm. Conclusions Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others. PMID:28376093

Can machine-learning improve cardiovascular risk prediction using routine clinical data?

PubMed

Weng, Stephen F; Reps, Jenna; Kai, Joe; Garibaldi, Jonathan M; Qureshi, Nadeem

2017-01-01

Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the 'receiver operating curve' (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins). 24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723-0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739-0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755-0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755-0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759-0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm. Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others.

Development of programmable artificial neural networks

NASA Technical Reports Server (NTRS)

Meade, Andrew J.

1993-01-01

Conventionally programmed digital computers can process numbers with great speed and precision, but do not easily recognize patterns or imprecise or contradictory data. Instead of being programmed in the conventional sense, artificial neural networks are capable of self-learning through exposure to repeated examples. However, the training of an ANN can be a time consuming and unpredictable process. A general method is being developed to mate the adaptability of the ANN with the speed and precision of the digital computer. This method was successful in building feedforward networks that can approximate functions and their partial derivatives from examples in a single iteration. The general method also allows the formation of feedforward networks that can approximate the solution to nonlinear ordinary and partial differential equations to desired accuracy without the need of examples. It is believed that continued research will produce artificial neural networks that can be used with confidence in practical scientific computing and engineering applications.

Multisensory visual servoing by a neural network.

PubMed

Wei, G Q; Hirzinger, G

1999-01-01

Conventional computer vision methods for determining a robot's end-effector motion based on sensory data needs sensor calibration (e.g., camera calibration) and sensor-to-hand calibration (e.g., hand-eye calibration). This involves many computations and even some difficulties, especially when different kinds of sensors are involved. In this correspondence, we present a neural network approach to the motion determination problem without any calibration. Two kinds of sensory data, namely, camera images and laser range data, are used as the input to a multilayer feedforward network to associate the direct transformation from the sensory data to the required motions. This provides a practical sensor fusion method. Using a recursive motion strategy and in terms of a network correction, we relax the requirement for the exactness of the learned transformation. Another important feature of our work is that the goal position can be changed without having to do network retraining. Experimental results show the effectiveness of our method.

The HSP, the QCN, and the Dragon: Developing inquiry-based QCN instructional modules in Taiwan

NASA Astrophysics Data System (ADS)

Chen, K. H.; Liang, W.; Chang, C.; Yen, E.; Lin, C.; Lin, G.

2012-12-01

High Scope Program (HSP) is a long-term project funded by NSC in Taiwan since 2006. It is designed to elevate the quality of science education by means of incorporating emerging science and technology into the traditional curricula in senior high schools. Quake-Catcher Network (QCN), a distributed computing project initiated by Stanford University and UC Riverside, encourages the volunteers to install the low-cost, novel sensors at home and school to build a seismic network. To meet both needs, we have developed a model curriculum that introduces QCN, earthquake science, and cloud computing into high school classrooms. Through professional development workshops, Taiwan cloud-based earthquake science learning platform, and QCN club on Facebook, we have worked closely with Lan-Yang Girl's Senior High School teachers' team to design workable teaching plans through a practical operation of seismic monitoring at home or school. However, some obstacles to learning appear including QCN installation/maintain problems, high self-noise of the sensor, difficulty of introducing earthquake sciences for high school teachers. The challenges of QCN outreach in Taiwan bring out our future plans: (1) development of easy, frequently updated, physics-based QCN-experiments for high school teachers, and (2) design of an interactive learning platform with social networking function for students.

Understanding the Convolutional Neural Networks with Gradient Descent and Backpropagation

NASA Astrophysics Data System (ADS)

Zhou, XueFei

2018-04-01

With the development of computer technology, the applications of machine learning are more and more extensive. And machine learning is providing endless opportunities to develop new applications. One of those applications is image recognition by using Convolutional Neural Networks (CNNs). CNN is one of the most common algorithms in image recognition. It is significant to understand its theory and structure for every scholar who is interested in this field. CNN is mainly used in computer identification, especially in voice, text recognition and other aspects of the application. It utilizes hierarchical structure with different layers to accelerate computing speed. In addition, the greatest features of CNNs are the weight sharing and dimension reduction. And all of these consolidate the high effectiveness and efficiency of CNNs with idea computing speed and error rate. With the help of other learning altruisms, CNNs could be used in several scenarios for machine learning, especially for deep learning. Based on the general introduction to the background and the core solution CNN, this paper is going to focus on summarizing how Gradient Descent and Backpropagation work, and how they contribute to the high performances of CNNs. Also, some practical applications will be discussed in the following parts. The last section exhibits the conclusion and some perspectives of future work.

Lessons Learned from Developing a Patient Engagement Panel: An OCHIN Report.

PubMed

Arkind, Jill; Likumahuwa-Ackman, Sonja; Warren, Nate; Dickerson, Kay; Robbins, Lynn; Norman, Kathy; DeVoe, Jennifer E

2015-01-01

There is renewed interest in patient engagement in clinical and research settings, creating a need for documenting and publishing lessons learned from efforts to meaningfully engage patients. This article describes early lessons learned from the development of OCHIN's Patient Engagement Panel (PEP). OCHIN supports a national network of more than 300 community health centers (CHCs) and other primary care settings that serve over 1.5 million patients annually across nearly 20 states. The PEP was conceived in 2009 to harness the CHC tradition of patient engagement in this new era of patient-centered outcomes research and to ensure that patients were engaged throughout the life cycle of our research projects, from conception to dissemination. Developed by clinicians and researchers within our practice-based research network, recruitment of patients to serve as PEP members began in early 2012. The PEP currently has a membership of 18 patients from 3 states. Over the past 24 months, the PEP has been involved with 12 projects. We describe developing the PEP and challenges and lessons learned (eg, recruitment, funding model, creating value for patient partners, compensation). These lessons learned are relevant not only for research but also for patient engagement in quality improvement efforts and other clinical initiatives. © Copyright 2015 by the American Board of Family Medicine.

Mortality risk score prediction in an elderly population using machine learning.

PubMed

Rose, Sherri

2013-03-01

Standard practice for prediction often relies on parametric regression methods. Interesting new methods from the machine learning literature have been introduced in epidemiologic studies, such as random forest and neural networks. However, a priori, an investigator will not know which algorithm to select and may wish to try several. Here I apply the super learner, an ensembling machine learning approach that combines multiple algorithms into a single algorithm and returns a prediction function with the best cross-validated mean squared error. Super learning is a generalization of stacking methods. I used super learning in the Study of Physical Performance and Age-Related Changes in Sonomans (SPPARCS) to predict death among 2,066 residents of Sonoma, California, aged 54 years or more during the period 1993-1999. The super learner for predicting death (risk score) improved upon all single algorithms in the collection of algorithms, although its performance was similar to that of several algorithms. Super learner outperformed the worst algorithm (neural networks) by 44% with respect to estimated cross-validated mean squared error and had an R2 value of 0.201. The improvement of super learner over random forest with respect to R2 was approximately 2-fold. Alternatives for risk score prediction include the super learner, which can provide improved performance.

Blending Formal and Informal Learning Networks for Online Learning

ERIC Educational Resources Information Center

Czerkawski, Betül C.

2016-01-01

With the emergence of social software and the advance of web-based technologies, online learning networks provide invaluable opportunities for learning, whether formal or informal. Unlike top-down, instructor-centered, and carefully planned formal learning settings, informal learning networks offer more bottom-up, student-centered participatory…

Immersive Environments - A Connectivist Approach

NASA Astrophysics Data System (ADS)

Loureiro, Ana; Bettencourt, Teresa

We are conducting a research project with the aim of achieving better and more efficient ways to facilitate teaching and learning in Higher Level Education. We have chosen virtual environments, with particular emphasis to Second Life® platform augmented by web 2.0 tools, to develop the study. The Second Life® environment has some interesting characteristics that captured our attention, it is immersive; it is a real world simulator; it is a social network; it allows real time communication, cooperation, collaboration and interaction; it is a safe and controlled environment. We specifically chose tools from web 2.0 that enable sharing and collaborative way of learning. Through understanding the characteristics of this learning environment, we believe that immersive learning along with other virtual tools can be integrated in today's pedagogical practices.

Building a community of practice for sustainability: strengthening learning and collective action of Canadian biosphere reserves through a national partnership.

PubMed

Reed, Maureen G; Godmaire, Hélène; Abernethy, Paivi; Guertin, Marc-André

2014-12-01

Deliberation, dialogue and systematic learning are now considered attributes of good practice for organizations seeking to advance sustainability. Yet we do not know whether organizations that span spatial scales and governance responsibilities can establish effective communities of practice to facilitate learning and action. The purpose of this paper is to generate a framework that specifies actions and processes of a community of practice designed to instill collective learning and action strategies across a multi-level, multi-partner network. The framework is then used to describe and analyze a partnership among practitioners of Canada's 16 UNESCO biosphere reserves, and additional researchers and government representatives from across Canada. The framework is a cycle of seven action steps, beginning and ending with reflecting on and evaluating present practice. It is supported by seven characteristics of collaborative environmental management that are used to gauge the success of the partnership. Our results show that the partnership successfully built trust, established shared norms and common interest, created incentives to participate, generated value in information sharing and willingness to engage, demonstrated effective flow of information, and provided leadership and facilitation. Key to success was the presence of a multi-lingual facilitator who could bridge cultural differences across regions and academia-practitioner expectations. The project succeeded in establishing common goals, setting mutual expectations and building relations of trust and respect, and co-creating knowledge. It is too soon to determine whether changes in practices that support sustainability will be maintained over the long term and without the help of an outside facilitator. Copyright © 2014 Elsevier Ltd. All rights reserved.

The Application of Observational Practice and Educational Networking in Simulation-Based and Distributed Medical Education Contexts.

PubMed

Welsher, Arthur; Rojas, David; Khan, Zain; VanderBeek, Laura; Kapralos, Bill; Grierson, Lawrence E M

2018-02-01

Research has revealed that individuals can improve technical skill performance by viewing demonstrations modeled by either expert or novice performers. These findings support the development of video-based observational practice communities that augment simulation-based skill education and connect geographically distributed learners. This study explores the experimental replicability of the observational learning effect when demonstrations are sampled from a community of distributed learners and serves as a context for understanding learner experiences within this type of training protocol. Participants from 3 distributed medical campuses engaged in a simulation-based learning study of the elliptical excision in which they completed a video-recorded performance before being assigned to 1 of 3 groups for a 2-week observational practice intervention. One group observed expert demonstrations, another observed novice demonstrations, and the third observed a combination of both. Participants returned for posttesting immediately and 1 month after the intervention. Participants also engaged in interviews regarding their perceptions of the usability and relevance of video-based observational practice to clinical education. Checklist (P < 0.0001) and global rating (P < 0.0001) measures indicate that participants, regardless of group assignment, improved after the intervention and after a 1-month retention period. Analyses revealed no significant differences between groups. Qualitative analyses indicate that participants perceived the observational practice platform to be usable, relevant, and potentially improved with enhanced feedback delivery. Video-based observational practice involving expert and/or novice demonstrations enhances simulation-based skill learning in a group of geographically distributed trainees. These findings support the use of Internet-mediated observational learning communities in distributed and simulation-based medical education contexts.

The end of an era? Midwifery conferences.

PubMed

Vilain, Annette Dalsgaard; Stewart, Sarah

2012-12-01

It has long been accepted that conferences are a useful mode of continuous professional development (CPD) (Russell 2010). Midwives welcome the chance to learn about recent practice developments, and the opportunity to network with each other in a face to face environment. However, barriers such as geographical isolation, time and financial constraints restrict midwives' ability to attend conferences (McIntosh 2007; Patterson and Davis 2007). At the same time, the effectiveness of conferences for CPD has been questioned (Guskey 2000). In these days of financial retrenchment, CPD has to be innovative and creative, offering ongoing support and learning in communities of practice that meet individual learning needs. The Virtual International Day of the Midwife (VIDM) is one such innovation. It is an annual 24 hour international synchronous online conference that celebrates the International Day of the Midwife on 5th May, and is freely open to all. Using the VIDM as a case study, this article discusses how online conferences may support and provide CPD for midwives.

Promoting Simulation Globally: Networking with Nursing Colleagues Across Five Continents.

PubMed

Alfes, Celeste M; Madigan, Elizabeth A

Simulation education is gaining momentum internationally and may provide the opportunity to enhance clinical education while disseminating evidence-based practice standards for clinical simulation and learning. There is a need to develop a cohesive leadership group that fosters support, networking, and sharing of simulation resources globally. The Frances Payne Bolton School of Nursing at Case Western Reserve University has had the unique opportunity to establish academic exchange programs with schools of nursing across five continents. Although the joint and mutual simulation activities have been extensive, each international collaboration has also provided insight into the innovations developed by global partners.

Understanding Classrooms through Social Network Analysis: A Primer for Social Network Analysis in Education Research.

PubMed

Grunspan, Daniel Z; Wiggins, Benjamin L; Goodreau, Steven M

2014-01-01

Social interactions between students are a major and underexplored part of undergraduate education. Understanding how learning relationships form in undergraduate classrooms, as well as the impacts these relationships have on learning outcomes, can inform educators in unique ways and improve educational reform. Social network analysis (SNA) provides the necessary tool kit for investigating questions involving relational data. We introduce basic concepts in SNA, along with methods for data collection, data processing, and data analysis, using a previously collected example study on an undergraduate biology classroom as a tutorial. We conduct descriptive analyses of the structure of the network of costudying relationships. We explore generative processes that create observed study networks between students and also test for an association between network position and success on exams. We also cover practical issues, such as the unique aspects of human subjects review for network studies. Our aims are to convince readers that using SNA in classroom environments allows rich and informative analyses to take place and to provide some initial tools for doing so, in the process inspiring future educational studies incorporating relational data. © 2014 D. Z. Grunspan et al. CBE—Life Sciences Education © 2014 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Constructing of Research-Oriented Learning Mode Based on Network Environment

ERIC Educational Resources Information Center

Wang, Ying; Li, Bing; Xie, Bai-zhi

2007-01-01

Research-oriented learning mode that based on network is significant to cultivate comprehensive-developing innovative person with network teaching in education for all-around development. This paper establishes a research-oriented learning mode by aiming at the problems existing in research-oriented learning based on network environment, and…

Maximum entropy methods for extracting the learned features of deep neural networks.

PubMed

Finnegan, Alex; Song, Jun S

2017-10-01

New architectures of multilayer artificial neural networks and new methods for training them are rapidly revolutionizing the application of machine learning in diverse fields, including business, social science, physical sciences, and biology. Interpreting deep neural networks, however, currently remains elusive, and a critical challenge lies in understanding which meaningful features a network is actually learning. We present a general method for interpreting deep neural networks and extracting network-learned features from input data. We describe our algorithm in the context of biological sequence analysis. Our approach, based on ideas from statistical physics, samples from the maximum entropy distribution over possible sequences, anchored at an input sequence and subject to constraints implied by the empirical function learned by a network. Using our framework, we demonstrate that local transcription factor binding motifs can be identified from a network trained on ChIP-seq data and that nucleosome positioning signals are indeed learned by a network trained on chemical cleavage nucleosome maps. Imposing a further constraint on the maximum entropy distribution also allows us to probe whether a network is learning global sequence features, such as the high GC content in nucleosome-rich regions. This work thus provides valuable mathematical tools for interpreting and extracting learned features from feed-forward neural networks.

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

PubMed

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

2014-05-01

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

Accelerating Learning By Neural Networks

NASA Technical Reports Server (NTRS)

Toomarian, Nikzad; Barhen, Jacob

1992-01-01

Electronic neural networks made to learn faster by use of terminal teacher forcing. Method of supervised learning involves addition of teacher forcing functions to excitations fed as inputs to output neurons. Initially, teacher forcing functions are strong enough to force outputs to desired values; subsequently, these functions decay with time. When learning successfully completed, terminal teacher forcing vanishes, and dynamics or neural network become equivalent to those of conventional neural network. Simulated neural network with terminal teacher forcing learned to produce close approximation of circular trajectory in 400 iterations.

CosmoQuest Collaborative: Galvanizing a Dynamic Professional Learning Network

NASA Astrophysics Data System (ADS)

Cobb, Whitney; Bracey, Georgia; Buxner, Sanlyn; Gay, Pamela L.; Noel-Storr, Jacob; CosmoQuest Team

2016-10-01

The CosmoQuest Collaboration offers in-depth experiences to diverse audiences around the nation and the world through pioneering citizen science in a virtual research facility. An endeavor between universities, research institutes, and NASA centers, CosmoQuest brings together scientists, educators, researchers, programmers—and citizens of all ages—to explore and make sense of our solar system and beyond. Leveraging human networks to expand NASA science, scaffolded by an educational framework that inspires lifelong learners, CosmoQuest engages citizens in analyzing and interpreting real NASA data, inspiring questions and defining problems.The QuestionLinda Darling-Hammond calls for professional development to be: "focused on the learning and teaching of specific curriculum content [i.e. NGSS disciplinary core ideas]; organized around real problems of practice [i.e. NGSS science and engineering practices] … [and] connected to teachers' collaborative work in professional learning community...." (2012) In light of that, what is the unique role CosmoQuest's virtual research facility can offer NASA STEM education?A Few AnswersThe CosmoQuest Collaboration actively engages scientists in education, and educators (and learners) in science. CosmoQuest uses social channels to empower and expand NASA's learning community through a variety of media, including science and education-focused hangouts, virtual star parties, and social media. In addition to creating its own supportive, standards-aligned materials, CosmoQuest offers a hub for excellent resources and materials throughout NASA and the larger astronomy community.In support of CosmoQuest citizen science opportunities, CQ initiatives (Learning Space, S-ROSES, IDEASS, Educator Zone) will be leveraged and shared through the CQPLN. CosmoQuest can be present and alive in the awareness its growing learning community.Finally, to make the CosmoQuest PLN truly relevant, it aims to encourage partnerships between scientists and educators, and offer "just-in-time" opportunities to support constituents exploring emerging NASA STEM education, from diverse educators to the curious learner of any age.

An evaluation of a professional learning network for computer science teachers

NASA Astrophysics Data System (ADS)

Cutts, Quintin; Robertson, Judy; Donaldson, Peter; O'Donnell, Laurie

2017-01-01

This paper describes and evaluates aspects of a professional development programme for existing CS teachers in secondary schools (PLAN C) which was designed to support teachers at a time of substantial curricular change. The paper's particular focus is on the formation of a teacher professional development network across several hundred teachers and a wide geographical area. Evidence from a series of observations and teacher surveys over a two-year period is analysed with respect to the project's programme theory in order to illustrate not only whether it worked as intended, by why. Results indicate that the PLAN C design has been successful in increasing teachers' professional confidence and appears to have catalysed powerful change in attitudes to learning. Presentation of challenging pedagogical content knowledge and conceptual frameworks, high-quality teacher-led professional dialogue, along with the space for reflection and classroom trials, triggered examination of the teachers' own current practices.

Long-term fish monitoring in large rivers: Utility of “benchmarking” across basins

USGS Publications Warehouse

Ward, David L.; Casper, Andrew F.; Counihan, Timothy D.; Bayer, Jennifer M.; Waite, Ian R.; Kosovich, John J.; Chapman, Colin; Irwin, Elise R.; Sauer, Jennifer S.; Ickes, Brian; McKerrow, Alexa

2017-01-01

In business, benchmarking is a widely used practice of comparing your own business processes to those of other comparable companies and incorporating identified best practices to improve performance. Biologists and resource managers designing and conducting monitoring programs for fish in large river systems tend to focus on single river basins or segments of large rivers, missing opportunities to learn from those conducting fish monitoring in other rivers. We briefly examine five long-term fish monitoring programs in large rivers in the United States (Colorado, Columbia, Mississippi, Illinois, and Tallapoosa rivers) and identify opportunities for learning across programs by detailing best monitoring practices and why these practices were chosen. Although monitoring objectives, methods, and program maturity differ between each river system, examples from these five case studies illustrate the important role that long-term monitoring programs play in interpreting temporal and spatial shifts in fish populations for both established objectives and newly emerging questions. We suggest that deliberate efforts to develop a broader collaborative network through benchmarking will facilitate sharing of ideas and development of more effective monitoring programs.

Network congestion control algorithm based on Actor-Critic reinforcement learning model

NASA Astrophysics Data System (ADS)

Xu, Tao; Gong, Lina; Zhang, Wei; Li, Xuhong; Wang, Xia; Pan, Wenwen

2018-04-01

Aiming at the network congestion control problem, a congestion control algorithm based on Actor-Critic reinforcement learning model is designed. Through the genetic algorithm in the congestion control strategy, the network congestion problems can be better found and prevented. According to Actor-Critic reinforcement learning, the simulation experiment of network congestion control algorithm is designed. The simulation experiments verify that the AQM controller can predict the dynamic characteristics of the network system. Moreover, the learning strategy is adopted to optimize the network performance, and the dropping probability of packets is adaptively adjusted so as to improve the network performance and avoid congestion. Based on the above finding, it is concluded that the network congestion control algorithm based on Actor-Critic reinforcement learning model can effectively avoid the occurrence of TCP network congestion.

Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification.

PubMed

Sladojevic, Srdjan; Arsenovic, Marko; Anderla, Andras; Culibrk, Dubravko; Stefanovic, Darko

2016-01-01

The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%.

Coarse-coded higher-order neural networks for PSRI object recognition. [position, scale, and rotation invariant

NASA Technical Reports Server (NTRS)

Spirkovska, Lilly; Reid, Max B.

1993-01-01

A higher-order neural network (HONN) can be designed to be invariant to changes in scale, translation, and inplane rotation. Invariances are built directly into the architecture of a HONN and do not need to be learned. Consequently, fewer training passes and a smaller training set are required to learn to distinguish between objects. The size of the input field is limited, however, because of the memory required for the large number of interconnections in a fully connected HONN. By coarse coding the input image, the input field size can be increased to allow the larger input scenes required for practical object recognition problems. We describe a coarse coding technique and present simulation results illustrating its usefulness and its limitations. Our simulations show that a third-order neural network can be trained to distinguish between two objects in a 4096 x 4096 pixel input field independent of transformations in translation, in-plane rotation, and scale in less than ten passes through the training set. Furthermore, we empirically determine the limits of the coarse coding technique in the object recognition domain.

Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification

PubMed Central

Sladojevic, Srdjan; Arsenovic, Marko; Culibrk, Dubravko; Stefanovic, Darko

2016-01-01

The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%. PMID:27418923

Music Making as a Tool for Promoting Brain Plasticity across the Life Span

PubMed Central

Wan, Catherine Y.; Schlaug, Gottfried

2010-01-01

Playing a musical instrument is an intense, multisensory, and motor experience that usually commences at an early age and requires the acquisition and maintenance of a range of skills over the course of a musician's lifetime. Thus, musicians offer an excellent human model for studying the brain effects of acquiring specialized sensorimotor skills. For example, musicians learn and repeatedly practice the association of motor actions with specific sound and visual patterns (musical notation) while receiving continuous multisensory feedback. This association learning can strengthen connections between auditory and motor regions (e.g., arcuate fasciculus) while activating multimodal integration regions (e.g., around the intraparietal sulcus). We argue that training of this neural network may produce cross-modal effects on other behavioral or cognitive operations that draw on this network. Plasticity in this network may explain some of the sensorimotor and cognitive enhancements that have been associated with music training. These enhancements suggest the potential for music making as an interactive treatment or intervention for neurological and developmental disorders, as well as those associated with normal aging. PMID:20889966

Music making as a tool for promoting brain plasticity across the life span.

PubMed

Wan, Catherine Y; Schlaug, Gottfried

2010-10-01

Playing a musical instrument is an intense, multisensory, and motor experience that usually commences at an early age and requires the acquisition and maintenance of a range of skills over the course of a musician's lifetime. Thus, musicians offer an excellent human model for studying the brain effects of acquiring specialized sensorimotor skills. For example, musicians learn and repeatedly practice the association of motor actions with specific sound and visual patterns (musical notation) while receiving continuous multisensory feedback. This association learning can strengthen connections between auditory and motor regions (e.g., arcuate fasciculus) while activating multimodal integration regions (e.g., around the intraparietal sulcus). We argue that training of this neural network may produce cross-modal effects on other behavioral or cognitive operations that draw on this network. Plasticity in this network may explain some of the sensorimotor and cognitive enhancements that have been associated with music training. These enhancements suggest the potential for music making as an interactive treatment or intervention for neurological and developmental disorders, as well as those associated with normal aging.

Cooperative Learning for Distributed In-Network Traffic Classification

NASA Astrophysics Data System (ADS)

Joseph, S. B.; Loo, H. R.; Ismail, I.; Andromeda, T.; Marsono, M. N.

2017-04-01

Inspired by the concept of autonomic distributed/decentralized network management schemes, we consider the issue of information exchange among distributed network nodes to network performance and promote scalability for in-network monitoring. In this paper, we propose a cooperative learning algorithm for propagation and synchronization of network information among autonomic distributed network nodes for online traffic classification. The results show that network nodes with sharing capability perform better with a higher average accuracy of 89.21% (sharing data) and 88.37% (sharing clusters) compared to 88.06% for nodes without cooperative learning capability. The overall performance indicates that cooperative learning is promising for distributed in-network traffic classification.

Using a multi-state Learning Community as an implementation strategy for immediate postpartum long-acting reversible contraception.

PubMed

DeSisto, Carla L; Estrich, Cameron; Kroelinger, Charlan D; Goodman, David A; Pliska, Ellen; Mackie, Christine N; Waddell, Lisa F; Rankin, Kristin M

2017-11-21

Implementation strategies are imperative for the successful adoption and sustainability of complex evidence-based public health practices. Creating a learning collaborative is one strategy that was part of a recently published compilation of implementation strategy terms and definitions. In partnership with the Centers for Disease Control and Prevention and other partner agencies, the Association of State and Territorial Health Officials recently convened a multi-state Learning Community to support cross-state collaboration and provide technical assistance for improving state capacity to increase access to long-acting reversible contraception (LARC) in the immediate postpartum period, an evidence-based practice with the potential for reducing unintended pregnancy and improving maternal and child health outcomes. During 2015-2016, the Learning Community included multi-disciplinary, multi-agency teams of state health officials, payers, clinicians, and health department staff from 13 states. This qualitative study was conducted to better understand the successes, challenges, and strategies that the 13 US states in the Learning Community used for increasing access to immediate postpartum LARC. We conducted telephone interviews with each team in the Learning Community. Interviews were semi-structured and organized by the eight domains of the Learning Community. We coded transcribed interviews for facilitators, barriers, and implementation strategies, using a recent compilation of expert-defined implementation strategies as a foundation for coding the latter. Data analysis showed three ways that the activities of the Learning Community helped in policy implementation work: structure and accountability, validity, and preparing for potential challenges and opportunities. Further, the qualitative data demonstrated that the Learning Community integrated six other implementation strategies from the literature: organize clinician implementation team meetings, conduct educational meetings, facilitation, promote network weaving, provide ongoing consultation, and distribute educational materials. Convening a multi-state learning collaborative is a promising approach for facilitating the implementation of new reimbursement policies for evidence-based practices complicated by systems challenges. By integrating several implementation strategies, the Learning Community serves as a meta-strategy for supporting implementation.

Applying Gradient Descent in Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Cui, Nan

2018-04-01

With the development of the integrated circuit and computer science, people become caring more about solving practical issues via information technologies. Along with that, a new subject called Artificial Intelligent (AI) comes up. One popular research interest of AI is about recognition algorithm. In this paper, one of the most common algorithms, Convolutional Neural Networks (CNNs) will be introduced, for image recognition. Understanding its theory and structure is of great significance for every scholar who is interested in this field. Convolution Neural Network is an artificial neural network which combines the mathematical method of convolution and neural network. The hieratical structure of CNN provides it reliable computer speed and reasonable error rate. The most significant characteristics of CNNs are feature extraction, weight sharing and dimension reduction. Meanwhile, combining with the Back Propagation (BP) mechanism and the Gradient Descent (GD) method, CNNs has the ability to self-study and in-depth learning. Basically, BP provides an opportunity for backwardfeedback for enhancing reliability and GD is used for self-training process. This paper mainly discusses the CNN and the related BP and GD algorithms, including the basic structure and function of CNN, details of each layer, the principles and features of BP and GD, and some examples in practice with a summary in the end.

node2vec: Scalable Feature Learning for Networks

PubMed Central

Grover, Aditya; Leskovec, Jure

2016-01-01

Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves. However, present feature learning approaches are not expressive enough to capture the diversity of connectivity patterns observed in networks. Here we propose node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks. In node2vec, we learn a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes. We define a flexible notion of a node’s network neighborhood and design a biased random walk procedure, which efficiently explores diverse neighborhoods. Our algorithm generalizes prior work which is based on rigid notions of network neighborhoods, and we argue that the added flexibility in exploring neighborhoods is the key to learning richer representations. We demonstrate the efficacy of node2vec over existing state-of-the-art techniques on multi-label classification and link prediction in several real-world networks from diverse domains. Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks. PMID:27853626

Outcomes of Interorganizational Networks in Canada for Chronic Disease Prevention: Insights From a Concept Mapping Study, 2015.

PubMed

Willis, Cameron; Kernoghan, Alison; Riley, Barbara; Popp, Janice; Best, Allan; Milward, H Brinton

2015-11-19

We conducted a mixed methods study from June 2014 to March 2015 to assess the perspectives of stakeholders in networks that adopt a population approach for chronic disease prevention (CDP). The purpose of the study was to identify important and feasible outcome measures for monitoring network performance. Participants from CDP networks in Canada completed an online concept mapping exercise, which was followed by interviews with network stakeholders to further understand the findings. Nine concepts were considered important outcomes of CDP networks: enhanced learning, improved use of resources, enhanced or increased relationships, improved collaborative action, network cohesion, improved system outcomes, improved population health outcomes, improved practice and policy planning, and improved intersectoral engagement. Three themes emerged from participant interviews related to measurement of the identified concepts: the methodological difficulties in measuring network outcomes, the dynamic nature of network evolution and function and implications for outcome assessment, and the challenge of measuring multisectoral engagement in CDP networks. Results from this study provide initial insights into concepts that can be used to describe the outcomes of networks for CDP and may offer foundations for strengthening network outcome-monitoring strategies and methodologies.

Outcomes of Interorganizational Networks in Canada for Chronic Disease Prevention: Insights From a Concept Mapping Study, 2015

PubMed Central

Kernoghan, Alison; Riley, Barbara; Popp, Janice; Best, Allan; Milward, H. Brinton

2015-01-01

Introduction We conducted a mixed methods study from June 2014 to March 2015 to assess the perspectives of stakeholders in networks that adopt a population approach for chronic disease prevention (CDP). The purpose of the study was to identify important and feasible outcome measures for monitoring network performance. Methods Participants from CDP networks in Canada completed an online concept mapping exercise, which was followed by interviews with network stakeholders to further understand the findings. Results Nine concepts were considered important outcomes of CDP networks: enhanced learning, improved use of resources, enhanced or increased relationships, improved collaborative action, network cohesion, improved system outcomes, improved population health outcomes, improved practice and policy planning, and improved intersectoral engagement. Three themes emerged from participant interviews related to measurement of the identified concepts: the methodological difficulties in measuring network outcomes, the dynamic nature of network evolution and function and implications for outcome assessment, and the challenge of measuring multisectoral engagement in CDP networks. Conclusion Results from this study provide initial insights into concepts that can be used to describe the outcomes of networks for CDP and may offer foundations for strengthening network outcome-monitoring strategies and methodologies. PMID:26583571

Automated volumetric lung segmentation of thoracic CT images using fully convolutional neural network

NASA Astrophysics Data System (ADS)

Negahdar, Mohammadreza; Beymer, David; Syeda-Mahmood, Tanveer

2018-02-01

Deep Learning models such as Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in 2D medical image analysis. In clinical practice; however, most analyzed and acquired medical data are formed of 3D volumes. In this paper, we present a fast and efficient 3D lung segmentation method based on V-net: a purely volumetric fully CNN. Our model is trained on chest CT images through volume to volume learning, which palliates overfitting problem on limited number of annotated training data. Adopting a pre-processing step and training an objective function based on Dice coefficient addresses the imbalance between the number of lung voxels against that of background. We have leveraged Vnet model by using batch normalization for training which enables us to use higher learning rate and accelerates the training of the model. To address the inadequacy of training data and obtain better robustness, we augment the data applying random linear and non-linear transformations. Experimental results on two challenging medical image data show that our proposed method achieved competitive result with a much faster speed.

Learning may need only a few bits of synaptic precision

NASA Astrophysics Data System (ADS)

Baldassi, Carlo; Gerace, Federica; Lucibello, Carlo; Saglietti, Luca; Zecchina, Riccardo

2016-05-01

Learning in neural networks poses peculiar challenges when using discretized rather then continuous synaptic states. The choice of discrete synapses is motivated by biological reasoning and experiments, and possibly by hardware implementation considerations as well. In this paper we extend a previous large deviations analysis which unveiled the existence of peculiar dense regions in the space of synaptic states which accounts for the possibility of learning efficiently in networks with binary synapses. We extend the analysis to synapses with multiple states and generally more plausible biological features. The results clearly indicate that the overall qualitative picture is unchanged with respect to the binary case, and very robust to variation of the details of the model. We also provide quantitative results which suggest that the advantages of increasing the synaptic precision (i.e., the number of internal synaptic states) rapidly vanish after the first few bits, and therefore that, for practical applications, only few bits may be needed for near-optimal performance, consistent with recent biological findings. Finally, we demonstrate how the theoretical analysis can be exploited to design efficient algorithmic search strategies.

Biomimetic Hybrid Feedback Feedforward Neural-Network Learning Control.

PubMed

Pan, Yongping; Yu, Haoyong

2017-06-01

This brief presents a biomimetic hybrid feedback feedforward neural-network learning control (NNLC) strategy inspired by the human motor learning control mechanism for a class of uncertain nonlinear systems. The control structure includes a proportional-derivative controller acting as a feedback servo machine and a radial-basis-function (RBF) NN acting as a feedforward predictive machine. Under the sufficient constraints on control parameters, the closed-loop system achieves semiglobal practical exponential stability, such that an accurate NN approximation is guaranteed in a local region along recurrent reference trajectories. Compared with the existing NNLC methods, the novelties of the proposed method include: 1) the implementation of an adaptive NN control to guarantee plant states being recurrent is not needed, since recurrent reference signals rather than plant states are utilized as NN inputs, which greatly simplifies the analysis and synthesis of the NNLC and 2) the domain of NN approximation can be determined a priori by the given reference signals, which leads to an easy construction of the RBF-NNs. Simulation results have verified the effectiveness of this approach.

Organisational aspects and benchmarking of e-learning initiatives: a case study with South African community health workers.

PubMed

Reisach, Ulrike; Weilemann, Mitja

2016-06-01

South Africa desperately needs a comprehensive approach to fight HIV/AIDS. Education is crucial to reach this goal and Internet and e-learning could offer huge opportunities to broaden and deepen the knowledge basis. But due to the huge societal and digital divide between rich and poor areas, e-learning is difficult to realize in the townships. Community health workers often act as mediators and coaches for people seeking medical and personal help. They could give good advice regarding hygiene, nutrition, protection of family members in case of HIV/AIDS and finding legal ways to earn one's living if they were trained to do so. Therefore they need to have a broader general knowledge. Since learning opportunities in the townships are scarce, a system for e-learning has to be created in order to overcome the lack of experience with computers or the Internet and to enable them to implement a network of expertise. The article describes how the best international resources on basic medical knowledge, HIV/AIDS as well as on basic economic and entrepreneurial skills were benchmarked to be integrated into an e-learning system. After tests with community health workers, researchers developed recommendations on building a self-sustaining system for learning, including a network of expertise and best practice sharing. The article explains the opportunities and challenges for community health workers, which could provide information for other parts of the world with similar preconditions of rural poverty. © The Author(s) 2015.

Applications of Deep Learning in Biomedicine.

PubMed

Mamoshina, Polina; Vieira, Armando; Putin, Evgeny; Zhavoronkov, Alex

2016-05-02

Increases in throughput and installed base of biomedical research equipment led to a massive accumulation of -omics data known to be highly variable, high-dimensional, and sourced from multiple often incompatible data platforms. While this data may be useful for biomarker identification and drug discovery, the bulk of it remains underutilized. Deep neural networks (DNNs) are efficient algorithms based on the use of compositional layers of neurons, with advantages well matched to the challenges -omics data presents. While achieving state-of-the-art results and even surpassing human accuracy in many challenging tasks, the adoption of deep learning in biomedicine has been comparatively slow. Here, we discuss key features of deep learning that may give this approach an edge over other machine learning methods. We then consider limitations and review a number of applications of deep learning in biomedical studies demonstrating proof of concept and practical utility.

Using Remote Access to Scientific Instrumentation to Create Authentic Learning Activities in Pharmaceutical Analysis

PubMed Central

Albon, Simon P.; Cancilla, Devon A.; Hubball, Harry

2006-01-01

Objectives To pilot test and evaluate a gas chromatography-mass spectrometry (GCMS) case study as a teaching and learning tool. Design A case study incorporating remote access to a GCMS instrument through the Integrated Laboratory Network (ILN) at Western Washington University was developed and implemented. Student surveys, faculty interviews, and examination score data were used to evaluate learning. Assessment While the case study did not impact final examination scores, approximately 70% of students and all faculty members felt the ILN-supported case study improved student learning about GCMS. Faculty members felt the “live” instrument access facilitated more authentic teaching. Students and faculty members felt the ILN should continue to be developed as a teaching tool. Conclusion Remote access to scientific instrumentation can be used to modify case studies to enhance student learning and teaching practice in pharmaceutical analysis. PMID:17149450

Chinese Herbal Medicine Image Recognition and Retrieval by Convolutional Neural Network

PubMed Central

Sun, Xin; Qian, Huinan

2016-01-01

Chinese herbal medicine image recognition and retrieval have great potential of practical applications. Several previous studies have focused on the recognition with hand-crafted image features, but there are two limitations in them. Firstly, most of these hand-crafted features are low-level image representation, which is easily affected by noise and background. Secondly, the medicine images are very clean without any backgrounds, which makes it difficult to use in practical applications. Therefore, designing high-level image representation for recognition and retrieval in real world medicine images is facing a great challenge. Inspired by the recent progress of deep learning in computer vision, we realize that deep learning methods may provide robust medicine image representation. In this paper, we propose to use the Convolutional Neural Network (CNN) for Chinese herbal medicine image recognition and retrieval. For the recognition problem, we use the softmax loss to optimize the recognition network; then for the retrieval problem, we fine-tune the recognition network by adding a triplet loss to search for the most similar medicine images. To evaluate our method, we construct a public database of herbal medicine images with cluttered backgrounds, which has in total 5523 images with 95 popular Chinese medicine categories. Experimental results show that our method can achieve the average recognition precision of 71% and the average retrieval precision of 53% over all the 95 medicine categories, which are quite promising given the fact that the real world images have multiple pieces of occluded herbal and cluttered backgrounds. Besides, our proposed method achieves the state-of-the-art performance by improving previous studies with a large margin. PMID:27258404

Cooperative strategies for forest science management and leadership in an increasingly complex and globalized world: Proceedings of a workshop; 23- 26 August 1998; Quebec City, Quebec, Canada

Treesearch

Lane G. Eskew; David R. DeYoe; Denver P. Burns; Jean-Claude Mercier

1999-01-01

The purpose of this workshop was to develop organizational networks to help achieve best practices in management and leadership of forest research and foster continuous learning toward that goal through organizational benchmarking. The papers and notes herein document the presentations and discussions of the workshop.

On the Development and Use of Large Chemical Similarity Networks, Informatics Best Practices and Novel Chemical Descriptors towards Materials Quantitative Structure Property Relationships

ERIC Educational Resources Information Center

Krein, Michael

2011-01-01

After decades of development and use in a variety of application areas, Quantitative Structure Property Relationships (QSPRs) and related descriptor-based statistical learning methods have achieved a level of infamy due to their misuse. The field is rife with past examples of overtrained models, overoptimistic performance assessment, and outright…

Critical factors in recruiting health maintenance organization physicians.

PubMed

Fisher, N B; Smith, H L; Pasternak, D P

1993-01-01

What factors facilitate successful physician recruiting by health care organizations? Answers surfaced in a study of physician recruiting by a large HMO in the Southwest. Professional networking and word-of-mouth advertising appear to be the prominent means by which physicians learn of attractive staff positions. Successful recruiting also depends on a practice setting that fosters quality care, emphasis on patient care delivery, and collegial interaction.

Peer Learning Network: Implementing and Sustaining Cooperative Learning by Teacher Collaboration

ERIC Educational Resources Information Center

Miquel, Ester; Duran, David

2017-01-01

This article describes an in-service teachers', staff-development model "Peer Learning Network" and presents results about its efficiency. "Peer Learning Network" promotes three levels of peer learning simultaneously (among pupils, teachers, and schools). It supports pairs of teachers from several schools, who are linked…

The Integration of Personal Learning Environments & Open Network Learning Environments

ERIC Educational Resources Information Center

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

2012-01-01

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