WorldWideScience

Sample records for networked learning communities

  1. A Learning Dashboard to Monitor an Open Networked Learning Community

    Science.gov (United States)

    Grippa, Francesca; Secundo, Giustina; de Maggio, Marco

    This chapter proposes an operational model to monitor and assess an Open Networked Learning Community. Specifically, the model is based on the Intellectual Capital framework, along the Human, Structural and Social dimensions. It relies on the social network analysis to map several and complementary perspectives of a learning network. Its application allows to observe and monitor the cognitive behaviour of a learning community, in the final perspective of tracking and obtaining precious insights for value generation.

  2. Learning Networks--Enabling Change through Community Action Research

    Science.gov (United States)

    Bleach, Josephine

    2016-01-01

    Learning networks are a critical element of ethos of the community action research approach taken by the Early Learning Initiative at the National College of Ireland, a community-based educational initiative in the Dublin Docklands. Key criteria for networking, whether at local, national or international level, are the individual's and…

  3. Peer Apprenticeship Learning in Networked Learning Communities: The Diffusion of Epistemic Learning

    Science.gov (United States)

    Jamaludin, Azilawati; Shaari, Imran

    2016-01-01

    This article discusses peer apprenticeship learning (PAL) as situated within networked learning communities (NLCs). The context revolves around the diffusion of technologically-mediated learning in Singapore schools, where teachers begin to implement inquiry-oriented learning, consistent with 21st century learning, among students. As these schools…

  4. Social Networks and Performance in Distributed Learning Communities

    Science.gov (United States)

    Cadima, Rita; Ojeda, Jordi; Monguet, Josep M.

    2012-01-01

    Social networks play an essential role in learning environments as a key channel for knowledge sharing and students' support. In distributed learning communities, knowledge sharing does not occur as spontaneously as when a working group shares the same physical space; knowledge sharing depends even more on student informal connections. In this…

  5. Community and Social Network Sites as Technology Enhanced Learning Environments

    DEFF Research Database (Denmark)

    Ryberg, Thomas; Christiansen, Ellen

    2008-01-01

    This paper examines the affordance of the Danish social networking site Mingler.dk for peer-to-peer learning and development. With inspiration from different theoretical frameworks, the authors argue how learning and development in such social online systems can be conceptualised and analysed....... Theoretically the paper defines development in accordance with Vygotsky's concept of the zone of proximal development, and learning in accordance with Wenger's concept of communities of practice. The authors suggest analysing the learning and development taking place on Mingler.dk by using these concepts...... supplemented by the notion of horizontal learning adopted from Engestrm and Wenger. Their analysis shows how horizontal learning happens by crossing boundaries between several sites of engagement, and how the actors' multiple membership enables the community members to draw on a vast amount of resources from...

  6. Facilitating community building in Learning Networks through peer tutoring in ad hoc transient communities

    NARCIS (Netherlands)

    Kester, Liesbeth; Sloep, Peter; Van Rosmalen, Peter; Brouns, Francis; Koné, Malik; Koper, Rob

    2006-01-01

    De volledige referentie is: Kester, L., Sloep, P. B., Van Rosmalen, P., Brouns, F., Koné, M., & Koper, R. (2007). Facilitating Community Building in Learning Networks Through Peer-Tutoring in Ad Hoc Transient Communities. International Journal of Web based Communities, 3(2), 198-205.

  7. Learning Networks, Networked Learning

    NARCIS (Netherlands)

    Sloep, Peter; Berlanga, Adriana

    2010-01-01

    Sloep, P. B., & Berlanga, A. J. (2011). Learning Networks, Networked Learning [Redes de Aprendizaje, Aprendizaje en Red]. Comunicar, XIX(37), 55-63. Retrieved from http://dx.doi.org/10.3916/C37-2011-02-05

  8. Socio-semantic Networks of Research Publications in the Learning Analytics Community

    NARCIS (Netherlands)

    Fazeli, Soude; Drachsler, Hendrik; Sloep, Peter

    2013-01-01

    Fazeli, S., Drachsler, H., & Sloep, P. B. (2013, April). Socio-semantic Networks of Research Publications in the Learning Analytics Community. Presentation at the Learning Analystic and Knowelege (LAK13), Leuven, Belgium.

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

    Science.gov (United States)

    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…

  10. Network Analysis of a Virtual Community of Learning of Economics Educators

    Science.gov (United States)

    Fontainha, Elsa; Martins, Jorge Tiago; Vasconcelos, Ana Cristina

    2015-01-01

    Introduction: This paper aims at understanding virtual communities of learning in terms of dynamics, types of knowledge shared by participants, and network characteristics such as size, relationships, density, and centrality of participants. It looks at the relationships between these aspects and the evolution of communities of learning. It…

  11. A Social Network Analysis of Teaching and Research Collaboration in a Teachers' Virtual Learning Community

    Science.gov (United States)

    Lin, Xiaofan; Hu, Xiaoyong; Hu, Qintai; Liu, Zhichun

    2016-01-01

    Analysing the structure of a social network can help us understand the key factors influencing interaction and collaboration in a virtual learning community (VLC). Here, we describe the mechanisms used in social network analysis (SNA) to analyse the social network structure of a VLC for teachers and discuss the relationship between face-to-face…

  12. Influence of face-to-face meetings on virtual community activity: the case of Learning Network for Learning Design

    NARCIS (Netherlands)

    Burgos, Daniel; Hummel, Hans; Tattersall, Colin; Brouns, Francis; Kurvers, Hub; Koper, Rob

    2005-01-01

    Burgos, D., Hummel, H., Tattersall, C., Brouns, F., Kurvers, H., & Koper, R. (2006). Influence of face-to-face meetings on virtual community activity: the case of Learning Network for Learning Design. Proceedings of IADIS International Conference Web Based Communities 2006. February, 16-18,2006, San

  13. Fostering Sociability in Learning Networks through Ad-Hoc Transient Communities

    NARCIS (Netherlands)

    Sloep, Peter

    2008-01-01

    Sloep, P. B. (2009). Fostering Sociability in Learning Networks through Ad-Hoc Transient Communities. In M. Purvis & B. T. R. Savarimuthu (Eds.), Computer-Mediated Social Networking. First International Conference, ICCMSN 2008, LNAI 5322 (pp. 62-75). Heidelberg, Germany: Springer. June, 11-13, 2008,

  14. Investigating Student Communities with Network Analysis of Interactions in a Physics Learning Center

    CERN Document Server

    Brewe, Eric; Sawtelle, Vashti

    2011-01-01

    Developing a sense of community among students is one of the three pillars of an overall reform effort to increase participation in physics, and the sciences more broadly, at Florida International University. The emergence of a research and learning community, embedded within a course reform effort, has contributed to increased recruitment and retention of physics majors. Finn and Rock [1] link the academic and social integration of students to increased rates of retention. We utilize social network analysis to quantify interactions in Florida International University's Physics Learning Center (PLC) that support the development of academic and social integration,. The tools of social network analysis allow us to visualize and quantify student interactions, and characterize the roles of students within a social network. After providing a brief introduction to social network analysis, we use sequential multiple regression modeling to evaluate factors which contribute to participation in the learning community. ...

  15. Socio-semantic Networks of Research Publications in the Learning Analytics Community

    NARCIS (Netherlands)

    Fazeli, Soude; Drachsler, Hendrik; Sloep, Peter

    2013-01-01

    Fazeli, S., Drachsler, H., & Sloep, P. B. (2013). Socio-semantic Networks of Research Publications in the Learning Analytics Community. In M. d'Aquin, S. Dietze, H. Drachsler, E. Herder, & D. Taibi (Eds.), Linked data challenge, Learning Analytic and Knowledge (LAK13) (pp. 6-10). Vol. 974, Leuven,

  16. Investigating student communities with network analysis of interactions in a physics learning center

    Science.gov (United States)

    Brewe, Eric; Kramer, Laird; Sawtelle, Vashti

    2012-06-01

    Developing a sense of community among students is one of the three pillars of an overall reform effort to increase participation in physics, and the sciences more broadly, at Florida International University. The emergence of a research and learning community, embedded within a course reform effort, has contributed to increased recruitment and retention of physics majors. We utilize social network analysis to quantify interactions in Florida International University’s Physics Learning Center (PLC) that support the development of academic and social integration. The tools of social network analysis allow us to visualize and quantify student interactions and characterize the roles of students within a social network. After providing a brief introduction to social network analysis, we use sequential multiple regression modeling to evaluate factors that contribute to participation in the learning community. Results of the sequential multiple regression indicate that the PLC learning community is an equitable environment as we find that gender and ethnicity are not significant predictors of participation in the PLC. We find that providing students space for collaboration provides a vital element in the formation of a supportive learning community.

  17. Networks and learning: communities, practices and the metaphor of networks–a commentary

    Directory of Open Access Journals (Sweden)

    Bruce Ingraham

    2004-12-01

    Full Text Available In issue 12(1, Jones (2004 in his article ‘Networks and learning: communities, practices and the metaphor of networks' sets out to address three inter-related sets of issues: … firstly that learning technology needs to take account of the wider debate about networks and secondly that research in this field needs to address the theoretical and practical issues raised by advances in the field of networks. A third point is that the idea of the network acts as a powerful metaphor even if we are able to discount any particular theory generated in its support. The network metaphor can act as a unifying concept allowing us to bring together apparently disparate elements of the field.

  18. Ad-hoc transient communities in Learning Networks Connecting and supporting the learner

    NARCIS (Netherlands)

    Brouns, Francis

    2009-01-01

    Brouns, F. (2009). Ad-hoc transient communities in Learning Networks Connecting and supporting the learner. Presentation given for Korean delegation of Chonnam National University and Dankook University (researchers dr. Jeeheon Ryu and dr. Minjeong Kim and a Group of PhD and Master students).

  19. Complexity, theory and praxis: researching collaborative learning and tutoring processes in a networked learning community

    OpenAIRE

    de Laat, M.; Lally, V.

    2004-01-01

    This paper explores the complexity of researching networked learning and tutoring on two levels. Firstly, on the theoretical level, we argue that the nature of praxis in networked environments (that is, learning and tutoring) is so complex that no single theoretical model, among those currently available, is a sufficiently powerful, descriptively, rhetorically, inferentially or in its application to real contexts, to provide a framework for a research agenda that takes into account the key as...

  20. Learning in networks Community of Practice: a new approach to entrepreneurial learning

    NARCIS (Netherlands)

    Schrooten, G.B.J. (Gerrit)

    2009-01-01

    Educational programs teaching entrepreneurial behaviour and knowledge are crucial to a vital and healthy economy. The concept of building a Communities of Practice (CoP) could be very promising. CoP’s are formed by people who engage in a process of collective learning in a shared domain of

  1. Looking at learning communities with the appropriate glasses: hints and ideas from network sciences

    Directory of Open Access Journals (Sweden)

    Fabio Nascimbeni

    2013-02-01

    Full Text Available 0 0 1 229 1263 USAL 10 2 1490 14.0 Normal 0 21 false false false ES JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:Calibri; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-ansi-language:ES; mso-fareast-language:EN-US;} The level of network thinking within education – intended as the capacity to look at learning systems and communities by focussing on the relations among the involved actors (primarily teachers and learners and not only on the actors characteristics – is growing, with different speeds depending on the educational sector, but not at the pace needed to keep up with the increasingly network nature of our societies. We claim that educational research and practices should increase their capacity to look at learning communities through appropriate “networking-sensitive” glasses, and get equipped with tools and methods – such as Social network Analysis - to properly understand and support these networks. The application of Social Network Analysis to education, especially in the case of distance learning, can allow understanding the patterns of interactions between teachers and learners, and can facilitate the consolidation of new approaches to understand collaboration mechanisms. The paper presents and discusses - from a learning viewpoint - a brief overview of the main theoretical and practical contributions coming from Social Network Analysis – such as the “random graphs”, the “small-worlds” or the “weak-ties” theories – together with some general

  2. Networks and learning: communities, practices and the metaphor of networks–a response

    Directory of Open Access Journals (Sweden)

    Chris Jones

    2004-12-01

    Full Text Available I am pleased to have the opportunity to react to Bruce Ingraham's response to my article ‘Networks and learning: communities, practices and the metaphor of networks' (Jones, 2004. It is rare to have a dialogue with someone who has taken the time and trouble to consider what you have written for a journal. All too often reviewing is a one-way process with the reviewer remaining anonymous. It is all the more pleasant to have a response to what you have written that gets to grips with some of the issues that the author also finds troubling. It is in that spirit that I write this reaction to Ingraham; it is an opportunity for me to develop some of the points he has identified as problematic in the original article. I want to concentrate on two main issues, firstly the network metaphor itself and secondly the usefulness of abstraction and representations of various types.

  3. STAR Library Education Network: a hands-on learning program for libraries and their communities

    Science.gov (United States)

    Dusenbery, P.

    2010-12-01

    Science and technology are widely recognized as major drivers of innovation and industry (e.g. Rising above the Gathering Storm, 2006). While the focus for education reform is on school improvement, there is considerable research that supports the role that out-of-school experiences can play in student achievement and public understanding of STEM disciplines. Libraries provide an untapped resource for engaging underserved youth and their families in fostering an appreciation and deeper understanding of science and technology topics. Designed spaces, like libraries, allow lifelong, life-wide, and life-deep learning to take place though the research basis for learning in libraries is not as developed as other informal settings like science centers. The Space Science Institute’s National Center for Interactive Learning (NCIL) in partnership with the American Library Association (ALA), the Lunar and Planetary Institute (LPI), and the National Girls Collaborative Project (NGCP) have received funding from NSF to develop a national education project called the STAR Library Education Network: a hands-on learning program for libraries and their communities (or STAR-Net for short). STAR stands for Science-Technology, Activities and Resources. The overarching goal of the project is to reach underserved youth and their families with informal STEM learning experiences. This project will deepen our knowledge of informal/lifelong learning that takes place in libraries and establish a learning model that can be compared to the more established free-choice learning model for science centers and museums. The project includes the development of two STEM hands-on exhibits on topics that are of interest to library staff and their patrons: Discover Earth and Discover Tech. In addition, the project will produce resources and inquiry-based activities that libraries can use to enrich the exhibit experience. Additional resources will be provided through partnerships with relevant

  4. Establishing an implementation network: lessons learned from community-based participatory research

    Directory of Open Access Journals (Sweden)

    Garcia Piedad

    2009-03-01

    Full Text Available Abstract Background Implementation of evidence-based mental health assessment and intervention in community public health practice is a high priority for multiple stakeholders. Academic-community partnerships can assist in the implementation of efficacious treatments in community settings; yet, little is known about the processes by which these collaborations are developed. In this paper, we discuss our application of community-based participatory research (CBPR approach to implementation, and we present six lessons we have learned from the establishment of an academic-community partnership. Methods With older adults with psychosis as a focus, we have developed a partnership between a university research center and a public mental health service system based on CBPR. The long-term goal of the partnership is to collaboratively establish an evidence-based implementation network that is sustainable within the public mental healthcare system. Results In building a sustainable partnership, we found that the following lessons were instrumental: changing attitudes; sharing staff; expecting obstacles and formalizing solutions; monitoring and evaluating; adapting and adjusting; and taking advantage of emerging opportunities. Some of these lessons were previously known principles that were modified as the result of the CBPR process, while some lessons derived directly from the interactive process of forming the partnership. Conclusion The process of forming of academic-public partnerships is challenging and time consuming, yet crucial for the development and implementation of state-of-the-art approaches to assessment and interventions to improve the functioning and quality of life for persons with serious mental illnesses. These partnerships provide necessary organizational support to facilitate the implementation of clinical research findings in community practice benefiting consumers, researchers, and providers.

  5. Social Networking Tools and Teacher Education Learning Communities: A Case Study

    Science.gov (United States)

    Poulin, Michael T.

    2014-01-01

    Social networking tools have become an integral part of a pre-service teacher's educational experience. As a result, the educational value of social networking tools in teacher preparation programs must be examined. The specific problem addressed in this study is that the role of social networking tools in teacher education learning communities…

  6. Teachers' professional development in a community: A study of the central actors, their networks and web-based learning

    Directory of Open Access Journals (Sweden)

    Jiri Lallimo

    2008-07-01

    Full Text Available The goal of this article was to study teachers' professional development related to web-based learning in the context of the teacher community. The object was to learn in what kind of networks teachers share the knowledge of web-based learning and what are the factors in the community that support or challenge teachers professional development of web-based learning. The findings of the study revealed that there are teachers who are especially active, called the central actors in this study, in the teacher community who collaborate and share knowledge of web-based learning. These central actors share both technical and pedagogical knowledge of web-based learning in networks that include both internal and external relations in the community and involve people, artefacts and a variety of media. Furthermore, the central actors appear to bridge different fields of teaching expertise in their community.According to the central actors' experiences the important factors that support teachers' professional development of web-based learning in the community are; the possibility to learn from colleagues and from everyday working practices, an emotionally safe atmosphere, the leader's personal support and community-level commitment. Also, the flexibility in work planning, challenging pupils, shared lessons with colleagues, training events in an authentic work environment and colleagues' professionalism are considered meaningful for professional development. As challenges, the knowledge sharing of web-based learning in the community needs mutual interests, transactive memory, time and facilities, peer support, a safe atmosphere and meaningful pedagogical practices.On the basis of the findings of the study it is suggested that by intensive collaboration related to web-based learning it may be possible to break the boundaries of individual teachership and create such sociocultural activities which support collaborative professional development in the teacher

  7. Social Networks and the Building of Learning Communities: An Experimental Study of a Social MOOC

    Science.gov (United States)

    de Lima, Mariana; Zorrilla, Marta

    2017-01-01

    This study aimed to analyze the student's behaviour in relation to their degree of commitment, participation, and contribution in a MOOC based on a social learning approach. Interaction data was collected on the learning platform and in social networks, both of which were used in the third edition of a social MOOC course. This data was then…

  8. Effects of hierarchical levels on social network structures within communities of learning

    NARCIS (Netherlands)

    Rehm, M.; Gijselaers, W.H.; Segers, M.S.R.

    2014-01-01

    Facilitating an interpersonal knowledge transfer among employees constitutes a key building block in setting up organizational training initiatives. With practitioners and researchers looking for innovative training methods, online Communities of Learning (CoL) have been promoted as a promising

  9. Material matters for learning in virtual networks: a case study of a professional learning programme hosted in a Google+ online community

    Directory of Open Access Journals (Sweden)

    Aileen Ackland

    2015-08-01

    Full Text Available In this paper, we draw on Actor–Network Theories (ANT to explore how material components functioned to create gateways and barriers to a virtual learning network in the context of a professional development module in higher education. Students were practitioners engaged in family learning in different professional roles and contexts. The data comprised postings in the Google+ community, email correspondence, meeting notes, feedback submitted at the final workshop and post-module evaluation forms. Our analysis revealed a complex set of interactions, and suggests multiple ways human actors story their encounters with non-human components and the effects these have on the learning experience. The aim of this paper is to contribute to a more holistic understanding of the components and dynamics of social learning networks in the virtual world and consider the implications for the design of online learning for continuous professional development (CPD.

  10. Learning Networks for Lifelong Learning

    OpenAIRE

    Sloep, Peter

    2008-01-01

    Presentation in a seminar organized by Christopher Hoadley at Penn State University, October 2004.Contains general introduction into the Learning Network Programme and a demonstration of the Netlogo Simulation of a Learning Network.

  11. Do Institutional Social Networks Work? Fostering a Sense of Community and Enhancing Learning

    Science.gov (United States)

    Hatzipanagos, Stylianos; John, Bernadette A.

    2017-01-01

    In this paper we report on the evaluation of an institutional social network (KINSHIP) whose aims were 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, UK. Our evaluation focused on a pilot where students' needs with regard to the provision…

  12. Social Networking Technologies as Vehicles of Support for Women in Learning Communities

    Science.gov (United States)

    Burgess, Kimberly R.

    2009-01-01

    Women have long since used social networking as a means of coping with their struggles, educating and empowering themselves, engaging in broader social movements, and building international advocacy. Internet communities that are designed and facilitated to be inclusive of women's experiences can be important social spaces where women feel…

  13. Community Wireless Networks

    Science.gov (United States)

    Feld, Harold

    2005-01-01

    With increasing frequency, communities are seeing the arrival of a new class of noncommercial broadband providers: community wireless networks (CWNs). Utilizing the same wireless technologies that many colleges and universities have used to create wireless networks on campus, CWNs are creating broadband access for free or at costs well below…

  14. Learning Networks for Lifelong Learning

    NARCIS (Netherlands)

    Koper, Rob

    2004-01-01

    Presentation in a seminar organized by Christopher Hoadley at Penn State University, October 2004.Contains general introduction into the Learning Network Programme and a demonstration of the Netlogo Simulation of a Learning Network.

  15. An online practice and educational networking system for technical skills: learning experience in expert facilitated vs. independent learning communities.

    Science.gov (United States)

    Rojas, David; Cheung, Jeffrey J H; Weber, Bryce; Kapralos, Bill; Carnahan, Heather; Bägli, Darius J; Dubrowski, Adam

    2012-01-01

    This study explored the activities of trainees learning technical skills using an educational networking tool with and without expert facilitation. Medical students (participants) were video-recorded practicing suturing and knot tying techniques and the resulting videos were uploaded to an educational networking site. Participants were then divided into two groups (one group containing an expert facilitator while the other group did not) and encouraged to comment on the videos within their group. We monitored the number of logins and comments posted and all participants completed an exit survey. There were no differences between the activities the two groups (p = 0.387). We conclude that the presence of an expert within collaborative Internet environments in not necessary to promote interactivity amongst the learners.

  16. Brief report #3: building a rural community caregiver network: student learning in small town America.

    Science.gov (United States)

    Kaye, Lenard W; Crittenden, Jennifer A; Kelly, Nancy; Boylan, Deirdre

    2014-01-01

    The Rural Caregiver Network Project in Eastern Maine is a prime example of indigenous coalition-building in a region struggling to ensure that vulnerable older adults can age-in-place and manage with scarce resources. Through this innovative initiative, a range of elder caregiver interventions were mobilized, coordinated, and sustained in a rural two-county region in Maine, including navigator services, adult day care, information and referral, caregiver support groups, a caregiver resource center, and caregiver skills-building workshops. The endorsement of participatory research, evaluation, and programming principles enabled undergraduate and graduate social work students to assume major roles in all aspects of project planning, implementation, and assessment while remaining grounded in the realities of rural life. Competence in such a generalist gerontological social work practice perspective is critical in small towns and nonmetropolitan communities.

  17. Communities in Networks

    OpenAIRE

    Porter, Mason A.; Onnela, Jukka-Pekka; Mucha, Peter J

    2009-01-01

    We survey some of the concepts, methods, and applications of community detection, which has become an increasingly important area of network science. To help ease newcomers into the field, we provide a guide to available methodology and open problems, and discuss why scientists from diverse backgrounds are interested in these problems. As a running theme, we emphasize the connections of community detection to problems in statistical physics and computational optimization.

  18. Learning conditional Gaussian networks

    DEFF Research Database (Denmark)

    Bøttcher, Susanne Gammelgaard

    This paper considers conditional Gaussian networks. The parameters in the network are learned by using conjugate Bayesian analysis. As conjugate local priors, we apply the Dirichlet distribution for discrete variables and the Gaussian-inverse gamma distribution for continuous variables, given...... a configuration of the discrete parents. We assume parameter independence and complete data. Further, to learn the structure of the network, the network score is deduced. We then develop a local master prior procedure, for deriving parameter priors in these networks. This procedure satisfies parameter...... independence, parameter modularity and likelihood equivalence. Bayes factors to be used in model search are introduced. Finally the methods derived are illustrated by a simple example....

  19. Learning Networks for Lifelong Learning

    NARCIS (Netherlands)

    Koper, Rob

    2004-01-01

    Presentation at: "Learning Designs in a Networked World A Dutch - Canada Education Seminar", October 15th, 2004, University of Alberta, Edmonton, Canada. Similar presentation as: http://hdl.handle.net/1820/278

  20. Multilayer Optical Learning Networks

    Science.gov (United States)

    Wagner, Kelvin; Psaltis, Demetri

    1987-08-01

    In this paper we present a new approach to learning in a multilayer optical neural network which is based on holographically interconnected nonlinear Fabry-Perot etalons. The network can learn the interconnections that form a distributed representation of a desired pattern transformation operation. The interconnections are formed in an adaptive and self aligning fashion, as volume holographic gratings in photorefractive crystals. Parallel arrays of globally space integrated inner products diffracted by the interconnecting hologram illuminate arrays of nonlinear Fabry-Perot etalons for fast thresholding of the transformed patterns. A phase conjugated reference wave interferes with a backwards propagating error signal to form holographic interference patterns which are time integrated in the volume of the photorefractive crystal in order to slowly modify and learn the appropriate self aligning interconnections. A holographic implementation of a single layer perceptron learning procedure is presented that can be extendept ,to a multilayer learning network through an optical implementation of the backward error propagation (BEP) algorithm.

  1. A Learning Community Simulation

    NARCIS (Netherlands)

    Koné, Malik; Berlanga, Adriana; Sloep, Peter; Koper, Rob

    2007-01-01

    Koné, M., Berlanga, A. J., Sloep, P. B., & Koper, R. (2007). A Learning Community Simulation. In P. Kommers & P. Isaias (Eds.), Proceedings of the IADIS International Conference on Web Based Communities 2007 (pp. 331-335). February, 18-20, 2007, Salamanca, Spain: IADIS Press.

  2. Generating Attributed Networks with Communities

    National Research Council Canada - National Science Library

    Largeron, Christine; Mougel, Pierre-Nicolas; Rabbany, Reihaneh; Zaïane, Osmar R

    2015-01-01

    .... When nodes are described with a set of attributes we have an attributed network. Nodes and their relationships tend to naturally form into communities or clusters, and discovering these communities is paramount to many applications...

  3. Professional Learning Communities: Teaching, Learning, Understanding

    Science.gov (United States)

    Early, Phaedra Bell

    2012-01-01

    The purpose of this study was to focus on teacher learning as it relates to professional learning communities. It is often touted that schools are a place for student learning, but many teachers now see school as a place for them to become learners as well through professional learning communities. This qualitative case study was designed to…

  4. Learning Latent Structure in Complex Networks

    DEFF Research Database (Denmark)

    Mørup, Morten; Hansen, Lars Kai

    Latent structure in complex networks, e.g., in the form of community structure, can help understand network dynamics, identify heterogeneities in network properties, and predict ‘missing’ links. While most community detection algorithms are based on optimizing heuristic clustering objectives...... prediction performance of the learning based approaches and other widely used link prediction approaches in 14 networks ranging from medium size to large networks with more than a million nodes. While link prediction is typically well above chance for all networks, we find that the learning based mixed...... membership stochastic block model of Airoldi et al., performs well and often best in our experiments. The added complexity of the LD model improves link predictions for four of the 14 networks....

  5. Professional Learning Communities

    Science.gov (United States)

    Eley, Alison

    2017-01-01

    There are many professional development programmes on offer for primary science. The best of these involve teachers in developing practice over time, alongside engaging with theory. In this article, the author considers how working as part of a professional learning community can support a collaborative and evidence informed approach to improving…

  6. Building Global Learning Communities

    Science.gov (United States)

    Cochrane, Thomas; Buchem, Ilona; Camacho, Mar; Cronin, Catherine; Gordon, Averill; Keegan, Helen

    2013-01-01

    Within the background where education is increasingly driven by the economies of scale and research funding, we propose an alternative online open and connected framework (OOC) for building global learning communities using mobile social media. We critique a three year action research case study involving building collaborative global learning…

  7. Community Seismic Network (CSN)

    Science.gov (United States)

    Clayton, R. W.; Heaton, T. H.; Kohler, M. D.; Cheng, M.; Guy, R.; Chandy, M.; Krause, A.; Bunn, J.; Olson, M.; Faulkner, M.; Liu, A.; Strand, L.

    2012-12-01

    We report on developments in sensor connectivity, architecture, and data fusion algorithms executed in Cloud computing systems in the Community Seismic Network (CSN), a network of low-cost sensors housed in homes and offices by volunteers in the Pasadena, CA area. The network has over 200 sensors continuously reporting anomalies in local acceleration through the Internet to a Cloud computing service (the Google App Engine) that continually fuses sensor data to rapidly detect shaking from earthquakes. The Cloud computing system consists of data centers geographically distributed across the continent and is likely to be resilient even during earthquakes and other local disasters. The region of Southern California is partitioned in a multi-grid style into sets of telescoping cells called geocells. Data streams from sensors within a geocell are fused to detect anomalous shaking across the geocell. Temporal spatial patterns across geocells are used to detect anomalies across regions. The challenge is to detect earthquakes rapidly with an extremely low false positive rate. We report on two data fusion algorithms, one that tessellates the surface so as to fuse data from a large region around Pasadena and the other, which uses a standard tessellation of equal-sized cells. Since September 2011, the network has successfully detected earthquakes of magnitude 2.5 or higher within 40 Km of Pasadena. In addition to the standard USB device, which connects to the host's computer, we have developed a stand-alone sensor that directly connects to the internet via Ethernet or wifi. This bypasses security concerns that some companies have with the USB-connected devices, and allows for 24/7 monitoring at sites that would otherwise shut down their computers after working hours. In buildings we use the sensors to model the behavior of the structures during weak events in order to understand how they will perform during strong events. Visualization models of instrumented buildings ranging

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

    Science.gov (United States)

    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.

  9. [Networks of experiences on community health as an information system in health promotion: lessons learned in Aragon (Spain)].

    Science.gov (United States)

    Gállego-Diéguez, Javier; Aliaga Traín, Pilar; Benedé Azagra, Carmen Belén; Bueno Franco, Manuel; Ferrer Gracia, Elisa; Ipiéns Sarrate, José Ramón; Muñoz Nadal, Pilar; Plumed Parrilla, Manuela; Vilches Urrutia, Begoña

    2016-11-01

    Networks of community health experiences promote interaction and knowledge management in health promotion among their participants. These networks integrate both professionals and social agents who work directly on the ground in small environments, with defined objectives and inclusion criteria and voluntary participation. In this article, networks in Aragon (Spain) are reviewed in order to analyse their role as an information system. The Health Promotion Projects Network of Aragon (Red Aragonesa de Proyectos de Promoción de la Salud, RAPPS) was launched in 1996 and currently includes 73 projects. The average duration of projects is 12.7 years. RAPPS interdisciplinary teams involve 701 people, of which 89.6% are professionals and 10.6% are social agents. The Aragon Health Promoting Schools Network (Red Aragonesa de Escuelas Promotoras de Salud, RAEPS) integrates 134 schools (24.9% of Aragon). The schools teams involve 829 teachers and members of the school community, students (35.2%), families (26.2%) and primary care health professionals (9.8%). Experiences Networks boost citizen participation, have an influence in changing social determinants and contribute to the formulation of plans and regional strategies. Networks can provide indicators for a health promotion information and monitoring system on: capacity building services in the territory, identifying assets and models of good practice, cross-sectoral and equity initiatives. Experiences Networks represent an opportunity to create a health promotion information system, systematising available information and establishing quality criteria for initiatives. Copyright © 2016 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

  10. A Professional Learning Community Journey

    Directory of Open Access Journals (Sweden)

    Diana Maliszewski

    2008-06-01

    Full Text Available Four teachers (three classroom teachers and a teacher-librarian explain how their school applied a professional learning community framework to its operational practices. They discuss the process, the benefits, and the challenges of professional learning communities.

  11. From Learning Organization to Learning Community: Sustainability through Lifelong Learning

    Science.gov (United States)

    Kearney, Judith; Zuber-Skerritt, Ortrun

    2012-01-01

    Purpose: This paper aims to: extend the concept of "The learning organization" to "The learning community," especially disadvantaged communities; demonstrate how leaders in a migrant community can achieve positive change at the personal, professional, team and community learning levels through participatory action learning and…

  12. Research, Boundaries, and Policy in Networked Learning

    DEFF Research Database (Denmark)

    This book presents cutting-edge, peer reviewed research on networked learning organized by three themes: policy in networked learning, researching networked learning, and boundaries in networked learning. The "policy in networked learning" section explores networked learning in relation to policy...

  13. Dynamical detection of network communities

    Science.gov (United States)

    Quiles, Marcos G.; Macau, Elbert E. N.; Rubido, Nicolás

    2016-05-01

    A prominent feature of complex networks is the appearance of communities, also known as modular structures. Specifically, communities are groups of nodes that are densely connected among each other but connect sparsely with others. However, detecting communities in networks is so far a major challenge, in particular, when networks evolve in time. Here, we propose a change in the community detection approach. It underlies in defining an intrinsic dynamic for the nodes of the network as interacting particles (based on diffusive equations of motion and on the topological properties of the network) that results in a fast convergence of the particle system into clustered patterns. The resulting patterns correspond to the communities of the network. Since our detection of communities is constructed from a dynamical process, it is able to analyse time-varying networks straightforwardly. Moreover, for static networks, our numerical experiments show that our approach achieves similar results as the methodologies currently recognized as the most efficient ones. Also, since our approach defines an N-body problem, it allows for efficient numerical implementations using parallel computations that increase its speed performance.

  14. Layer Communities in Multiplex Networks

    Science.gov (United States)

    Kao, Ta-Chu; Porter, Mason A.

    2017-08-01

    Multiplex networks are a type of multilayer network in which entities are connected to each other via multiple types of connections. We propose a method, based on computing pairwise similarities between layers and then doing community detection, for grouping structurally similar layers in multiplex networks. We illustrate our approach using both synthetic and empirical networks, and we are able to find meaningful groups of layers in both cases. For example, we find that airlines that are based in similar geographic locations tend to be grouped together in a multiplex airline network and that related research areas in physics tend to be grouped together in a multiplex collaboration network.

  15. Assessing the "Learning" in Learning Communities

    Science.gov (United States)

    Gansemer-Topf, Ann M.; Tietjen, Kari

    2015-01-01

    Although assessment has been an integral part of the development and expansion of learning communities, much of the assessment was focused on investigating student satisfaction, retention, and graduation. This chapter provides a case study illustrating one learning community's efforts to create assessments focused on student learning.

  16. Learning Networks for Professional Development & Lifelong Learning

    NARCIS (Netherlands)

    Sloep, Peter

    2009-01-01

    Sloep, P. B. (2009). Learning Networks for Professional Development & Lifelong Learning. Presentation at a NeLLL seminar with Etienne Wenger held at the Open Universiteit Nederland. September, 10, 2009, Heerlen, The Netherlands.

  17. Enhancing Community Service Learning via Practical Learning Communities

    Science.gov (United States)

    Ronen, Ilana; Shemer-Elkiyam, Tal

    2015-01-01

    The advantages of learning communities focused on analyzing social issues and educational repercussions in the field are presented in this study. The research examines the contribution of a learning community to enhancing student teachers' responsibility and their social involvement. The assumption was that participating in learning community…

  18. E-Model for Online Learning Communities.

    Science.gov (United States)

    Rogo, Ellen J; Portillo, Karen M

    2015-10-01

    The purpose of this study was to explore the students' perspectives on the phenomenon of online learning communities while enrolled in a graduate dental hygiene program. A qualitative case study method was designed to investigate the learners' experiences with communities in an online environment. A cross-sectional purposive sampling method was used. Interviews were the data collection method. As the original data were being analyzed, the researchers noted a pattern evolved indicating the phenomenon developed in stages. The data were re-analyzed and validated by 2 member checks. The participants' experiences revealed an e-model consisting of 3 stages of formal learning community development as core courses in the curriculum were completed and 1 stage related to transmuting the community to an informal entity as students experienced the independent coursework in the program. The development of the formal learning communities followed 3 stages: Building a Foundation for the Learning Community, Building a Supportive Network within the Learning Community and Investing in the Community to Enhance Learning. The last stage, Transforming the Learning Community, signaled a transition to an informal network of learners. The e-model was represented by 3 key elements: metamorphosis of relationships, metamorphosis through the affective domain and metamorphosis through the cognitive domain, with the most influential element being the affective development. The e-model describes a 4 stage process through which learners experience a metamorphosis in their affective, relationship and cognitive development. Synergistic learning was possible based on the interaction between synergistic relationships and affective actions. Copyright © 2015 The American Dental Hygienists’ Association.

  19. A Team Formation and Project-based Learning Support Service for Social Learning Networks

    NARCIS (Netherlands)

    Spoelstra, Howard; Van Rosmalen, Peter; Van de Vrie, Evert; Obreza, Matija; Sloep, Peter

    2014-01-01

    The Internet affords new approaches to learning. Geographically dispersed self-directed learners can learn in computer-supported communities, forming social learning networks. However, self-directed learners can suffer from a lack of continuous motivation. And surprisingly, social learning networks

  20. Healthcare Learning Community and Student Retention

    Directory of Open Access Journals (Sweden)

    Sherryl W. Johnson, PhD

    2014-08-01

    Full Text Available Teaching, learning, and retention processes have evolved historically to include multifaceted techniques beyond the traditional lecture. This article presents related results of a study using a healthcare learning community in a southwest Georgia university. The value of novel techniques and tools in promoting student learning and retention remains under review. This study includes a healthcare learning community as a cutting-edge teaching and learning modality. The results of an introspective survey of 22 students in a learning community explore strategies to enhance culturally relevant teaching, learning, and retention. Although learning and retention studies have been conducted at numerous universities, few have included feedback from students in a healthcare learning community. Frequencies from student responses were tabulated using five thematic factors: social support, career knowledge/opportunities, academic support, networking and faculty rapport/relationship building. Of the five theme areas, social support was identified most frequently by students as a means to support their learning and retention in the university setting.

  1. Taking the Lead in Developing Learning Communities.

    Science.gov (United States)

    Harada, Violet H.

    2002-01-01

    Discusses the importance of fostering learning communities in today's school reform movement and the potential role of the school library media specialist as a leader in building collaborative networks with classroom teachers. Highlights include a comparison of traditional and collaborative models; linking collaboration and leadership; and…

  2. The development of Sustainability Graduate Community (SGC) as a learning pathway for sustainability education - a framework for engineering programmes in Malaysia Technical Universities Network (MTUN)

    Science.gov (United States)

    Johan, Kartina; Mohd Turan, Faiz

    2016-11-01

    ‘Environmental and sustainability’ is one of the Program Outcome (PO) designated by the Board of Engineers Malaysia (BEM) as one of the accreditation program requirement. However, to-date the implementation of sustainability elements in engineering programme in the technical universities in Malaysia is within individual faculty's curriculum plan and lack of university-level structured learning pathway, which enable all students to have access to an education in sustainability across all disciplines. Sustainability Graduate Community (SGC) is a framework designed to provide a learning pathway in the curriculum of engineering programs to inculcate sustainability education among engineering graduates. This paper aims to study the required attributes in Sustainability Graduate Community (SGC) framework to produce graduates who are not just engineers but also skilful in sustainability competencies using Global Project Management (GPM) P5 Standard for Sustainability. The development of the conceptual framework is to provide a constructive teaching and learning plan for educators and policy makers to work on together in developing the Sustainability Graduates (SG), the new kind of graduates from Malaysia Technical Universities Network (MTUN) in Malaysia who are literate in sustainability practices. The framework also support the call for developing holistic students based on Malaysian Education Blueprint (Higher Education) and address the gap between the statuses of engineering qualification to the expected competencies from industries in Malaysia in particular by achieving the SG attributes outlined in the framework

  3. Discovering network structure beyond communities.

    Science.gov (United States)

    Nishikawa, Takashi; Motter, Adilson E

    2011-01-01

    To understand the formation, evolution, and function of complex systems, it is crucial to understand the internal organization of their interaction networks. Partly due to the impossibility of visualizing large complex networks, resolving network structure remains a challenging problem. Here we overcome this difficulty by combining the visual pattern recognition ability of humans with the high processing speed of computers to develop an exploratory method for discovering groups of nodes characterized by common network properties, including but not limited to communities of densely connected nodes. Without any prior information about the nature of the groups, the method simultaneously identifies the number of groups, the group assignment, and the properties that define these groups. The results of applying our method to real networks suggest the possibility that most group structures lurk undiscovered in the fast-growing inventory of social, biological, and technological networks of scientific interest.

  4. Redes de aprendizaje, aprendizaje en red Learning Networks, Networked Learning

    Directory of Open Access Journals (Sweden)

    Peter Sloep

    2011-10-01

    Full Text Available Las redes de aprendizaje (Learning Networks son redes sociales en línea mediante las cuales los participantes comparten información y colaboran para crear conocimiento. De esta manera, estas redes enriquecen la experiencia de aprendizaje en cualquier contexto de aprendizaje, ya sea de educación formal (en escuelas o universidades o educación no-formal (formación profesional. Aunque el concepto de aprendizaje en red suscita el interés de diferentes actores del ámbito educativo, aún existen muchos interrogantes sobre cómo debe diseñarse el aprendizaje en red para facilitar adecuadamente la educación y la formación. El artículo toma este interrogante como punto de partida, y posteriormente aborda cuestiones como la dinámica de la evolución de las redes de aprendizaje, la importancia de fomentar la confianza entre los participantes y el papel central que desempeña el perfil de usuario en la construcción de la confianza, así como el apoyo entre compañeros. Además, se elabora el proceso de diseño de una red de aprendizaje, y se describe un ejemplo en el contexto universitario. Basándonos en la investigación que actualmente se lleva a cabo en nuestro propio centro y en otros lugares, el capítulo concluye con una visión del futuro de las redes de aprendizaje.Learning Networks are on-line social networks through which users share knowledge with each other and jointly develop new knowledge. This way, Learning Networks may enrich the experience of formal, school-based learning and form a viable setting for professional development. Although networked learning enjoys an increasing interest, many questions remain on how exactly learning in such networked contexts can contribute to successful education and training. Put differently, how should networked learning be designed best to facilitate education and training? Taking this as its point of departure, the chapter addresses such issues as the dynamic evolution of Learning Networks

  5. Neural networks and statistical learning

    CERN Document Server

    Du, Ke-Lin

    2014-01-01

    Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardw...

  6. Leadership in Professional Learning Communities

    Science.gov (United States)

    Thornton, Kate; Cherrington, Sue

    2014-01-01

    Professional learning communities in the early childhood education sector have been under-researched. The focus on collaborative learning, collective enquiry and shared leadership of such communities makes them worthy of study in order to establish their relevance to the sector. One of the foci of this research involving case studies of different…

  7. Evolutionary epistemology and dynamical virtual learning networks.

    Science.gov (United States)

    Giani, Umberto

    2004-01-01

    This paper is an attempt to define the main features of a new educational model aimed at satisfying the needs of a rapidly changing society. The evolutionary epistemology paradigm of culture diffusion in human groups could be the conceptual ground for the development of this model. Multidimensionality, multi-disciplinarity, complexity, connectivity, critical thinking, creative thinking, constructivism, flexible learning, contextual learning, are the dimensions that should characterize distance learning models aimed at increasing the epistemological variability of learning communities. Two multimedia educational software, Dynamic Knowledge Networks (DKN) and Dynamic Virtual Learning Networks (DVLN) are described. These two complementary tools instantiate these dimensions, and were tested in almost 150 online courses. Even if the examples are framed in the medical context, the analysis of the shortcomings of the traditional educational systems and the proposed solutions can be applied to the vast majority of the educational contexts.

  8. PARTNERS IN LEARNING NETWORK FOR UKRAINIAN TEACHERS

    Directory of Open Access Journals (Sweden)

    K. Sereda

    2011-05-01

    Full Text Available The network «Partners in Learning Network» is presented in the article – the Ukrainian segment of global educational community. PILN is created with support of the Microsoft company for teachers who use information communication technology in their professional work. The PILN's purpose and value for Ukrainian teachers, for their professional dialogue and collaboration are described in the article. Functions of PILN's communities for teacher’s cooperation, the joint decision of questions and an exchange of ideas and of technique, teaching tools for increase of level of ICT introduction in educational process are described.

  9. The Founding of the Learning Communities Association

    Science.gov (United States)

    Huerta, Juan Carlos

    2017-01-01

    Learning communities have reached the point in their growth that we now need a professional association to allow for more opportunities for participation in advancing learning communities. This is the story of the founding of the new Learning Communities Association.

  10. Exploring the Community Structure of Complex Networks

    OpenAIRE

    Drago, Carlo

    2016-01-01

    Regarding complex networks, one of the most relevant problems is to understand and to explore community structure. In particular it is important to define the network organization and the functions associated to the different network partitions. In this context, the idea is to consider some new approaches based on interval data in order to represent the different relevant network components as communities. The method is also useful to represent the network community structure, especially the ...

  11. Theoretical Foundations of Learning Communities

    Science.gov (United States)

    Jessup-Anger, Jody E.

    2015-01-01

    This chapter describes the historical and contemporary theoretical underpinnings of learning communities and argues that there is a need for more complex models in conceptualizing and assessing their effectiveness.

  12. Virtual Communities of Collaborative Learning for Higher Education

    Directory of Open Access Journals (Sweden)

    Gilda E. Sotomayor

    2014-11-01

    Full Text Available This article aims to outline and project three new learning scenarios for Higher Education that, after the emergence of ICT and communication through the Network-lnternet, have come under the generic name of virtual communities. To that end, we start from a previous conceptual analysis on collaborative learning, cooperative learning and related concepts taking place in these communities and serving as a basis for sorting them into three types in particular: communities of educational work of professional practice and scientific knowledge. Virtual communities where the activities undertaken and skills acquired are set as important parts of our personal learning development, wich are necessary to build the Knowledge Society.

  13. Personal Profiles: Enhancing Social Interaction in Learning Networks

    NARCIS (Netherlands)

    Berlanga, Adriana; Bitter-Rijpkema, Marlies; Brouns, Francis; Sloep, Peter; Fetter, Sibren

    2009-01-01

    Berlanga, A. J., Bitter-Rijpkema, M., Brouns, F., Sloep, P. B., & Fetter, S. (2011). Personal Profiles: Enhancing Social Interaction in Learning Networks. International Journal of Web Based Communities, 7(1), 66-82.

  14. Taking Learning to the Community

    Science.gov (United States)

    Stanistreet, Paul

    2009-01-01

    These are tough times for adult and community learning, with many providers struggling to sustain a broad curriculum offer that includes a wide-ranging adult learning programme. South Devon College is determined to keep its flourishing adult offer alive but realises that, with funding increasingly scarce, it has to find innovative ways of ensuring…

  15. Collaborative learning in networks.

    Science.gov (United States)

    Mason, Winter; Watts, Duncan J

    2012-01-17

    Complex problems in science, business, and engineering typically require some tradeoff between exploitation of known solutions and exploration for novel ones, where, in many cases, information about known solutions can also disseminate among individual problem solvers through formal or informal networks. Prior research on complex problem solving by collectives has found the counterintuitive result that inefficient networks, meaning networks that disseminate information relatively slowly, can perform better than efficient networks for problems that require extended exploration. In this paper, we report on a series of 256 Web-based experiments in which groups of 16 individuals collectively solved a complex problem and shared information through different communication networks. As expected, we found that collective exploration improved average success over independent exploration because good solutions could diffuse through the network. In contrast to prior work, however, we found that efficient networks outperformed inefficient networks, even in a problem space with qualitative properties thought to favor inefficient networks. We explain this result in terms of individual-level explore-exploit decisions, which we find were influenced by the network structure as well as by strategic considerations and the relative payoff between maxima. We conclude by discussing implications for real-world problem solving and possible extensions.

  16. Community structure in introductory physics course networks

    CERN Document Server

    Traxler, Adrienne L

    2015-01-01

    Student-to-student interactions are foundational to many active learning environments, but are most often studied using qualitative methods. Network analysis tools provide a quantitative complement to this picture, allowing researchers to describe the social interactions of whole classrooms as systems. Past results from introductory physics courses have suggested a sharp division in the formation of social structure between large lecture sections and small studio classroom environments. Extending those results, this study focuses on calculus-based introductory physics courses at a large public university with a heavily commuter and nontraditional student population. Community detection network methods are used to characterize pre- and post-course collaborative structure in several sections, and differences are considered between small and large classes. These results are compared with expectations from earlier findings, and comment on implications for instruction and further study.

  17. A Reference Model for Online Learning Communities

    OpenAIRE

    Seufert, Sabine; Lechner, Ulrike; Stanoevska, Katarina

    2002-01-01

    Online learning communities are introduced as a comprehensive model for technology-enabled learning. We give an analysis of goals in education and the requirements to community platforms. The main contribution of the article is a reference model for online learning communities that consists of four layers designing the organizational, interaction, channel or service and the technological model of learning communities. This reference model captures didactic goals, learning methods and learning...

  18. Community-centred Networks and Networking among Companies, Educational and Cultural Institutions and Research

    DEFF Research Database (Denmark)

    Konnerup, Ulla; Dirckinck-Holmfeld, Lone

    2010-01-01

    This article presents visions for community-centred networks and networking among companies, educational and cultural institutions and research based on blended on- and off-line collaboration and communication. Our point of departure is the general vision of networking between government, industry...... and research as formulated in the Triple Helix Model (Etzkowitz 2008). The article draws on a case study of NoEL, a network on e-learning among business, educational and cultural institutions and research, all in all 21 partners from all around Denmark. Focus is how networks and networking change character......’ in Networked Learning, Wenger et al. 2009; The analysis concerns the participation structure and how the network activities connect local work practices and research, and how technology and online communication contribute to a change from participation in offline and physical network activities into online...

  19. Community Health Global Network and Sustainable Development

    Directory of Open Access Journals (Sweden)

    Rebekah Young

    2016-01-01

    Full Text Available With the achievements, failures and passing of the Millennium Development Goals (MDG, the world has turned its eyes to the Sustainable Development Goals (SDG, designed to foster sustainable social, economic and environmental development over the next 15 years.(1 Community-led initiatives are increasingly being recognised as playing a key role in realising sustainable community development and in the aspirations of universal healthcare.(2 In many parts of the world, faith-based organisations are some of the main players in community-led development and health care.(3 Community Health Global Network (CHGN creates links between organisations, with the purpose being to encourage communities to recognise their assets and abilities, identify shared concerns and discover solutions together, in order to define and lead their futures in sustainable ways.(4 CHGN has facilitated the development of collaborative groups of health and development initiatives called ‘Clusters’ in several countries including India, Bangladesh, Kenya, Tanzania, Zambia and Myanmar. In March 2016 these Clusters met together in an International Forum, to share learnings, experiences, challenges, achievements and to encourage one another. Discussions held throughout the forum suggest that the CHGN model is helping to promote effective, sustainable development and health care provision on both a local and a global scale.

  20. Learning Python network programming

    CERN Document Server

    Sarker, M O Faruque

    2015-01-01

    If you're a Python developer or a system administrator with Python experience and you're looking to take your first steps in network programming, then this book is for you. Basic knowledge of Python is assumed.

  1. Community Vitality in Dynamic Temporal Networks

    OpenAIRE

    Fu Cai; Li Min; Zou Deqing; Qu Shuyan; Han Lansheng; James J. Park

    2013-01-01

    Current researches on temporal networks mainly tend to detect community structure. A number of community detection algorithms can obtain community structure on each time slice or each period of time but rarely present the evolution of community structure. Some papers discussed the process of community structure evolution but lacked quantifying the evolution. In this paper, we put forward the concept of Community Vitality (CV), which shows a community's life intensity on a time slice. In the p...

  2. Language Choice & Global Learning Networks

    Directory of Open Access Journals (Sweden)

    Dennis Sayers

    1995-05-01

    Full Text Available How can other languages be used in conjunction with English to further intercultural and multilingual learning when teachers and students participate in computer-based global learning networks? Two portraits are presented of multilingual activities in the Orillas and I*EARN learning networks, and are discussed as examples of the principal modalities of communication employed in networking projects between distant classes. Next, an important historical precedent --the social controversy which accompanied the introduction of telephone technology at the end of the last century-- is examined in terms of its implications for language choice in contemporary classroom telecomputing projects. Finally, recommendations are offered to guide decision making concerning the role of language choice in promoting collaborative critical inquiry.

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

    Science.gov (United States)

    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…

  4. Community structure of complex networks based on continuous neural network

    Science.gov (United States)

    Dai, Ting-ting; Shan, Chang-ji; Dong, Yan-shou

    2017-09-01

    As a new subject, the research of complex networks has attracted the attention of researchers from different disciplines. Community structure is one of the key structures of complex networks, so it is a very important task to analyze the community structure of complex networks accurately. In this paper, we study the problem of extracting the community structure of complex networks, and propose a continuous neural network (CNN) algorithm. It is proved that for any given initial value, the continuous neural network algorithm converges to the eigenvector of the maximum eigenvalue of the network modularity matrix. Therefore, according to the stability of the evolution of the network symbol will be able to get two community structure.

  5. Changing Conditions for Networked Learning?

    DEFF Research Database (Denmark)

    Ryberg, Thomas

    2011-01-01

    of social technologies. I argue that we are seeing the emergence of new architectures and scales of participation, collaboration and networking e.g. through interesting formations of learning networks at different levels of scale, for different purposes and often bridging boundaries such as formal......In this talk I should like to initially take a critical look at popular ideas and discourses related to web 2.0, social technologies and learning. I argue that many of the pedagogical ideals particularly associated with web 2.0 have a longer history and background, which is often forgotten...

  6. Bullying in Virtual Learning Communities.

    Science.gov (United States)

    Nikiforos, Stefanos; Tzanavaris, Spyros; Kermanidis, Katia Lida

    2017-01-01

    Bullying through the internet has been investigated and analyzed mainly in the field of social media. In this paper, it is attempted to analyze bullying in the Virtual Learning Communities using Natural Language Processing (NLP) techniques, mainly in the context of sociocultural learning theories. Therefore four case studies took place. We aim to apply NLP techniques to speech analysis on communication data of online communities. Emphasis is given on qualitative data, taking into account the subjectivity of the collaborative activity. Finally, this is the first time such type of analysis is attempted on Greek data.

  7. Network repair based on community structure

    Science.gov (United States)

    Wang, Tianyu; Zhang, Jun; Sun, Xiaoqian; Wandelt, Sebastian

    2017-06-01

    Real-world complex systems are often fragile under disruptions. Accordingly, research on network repair has been studied intensively. Recently proposed efficient strategies for network disruption, based on collective influence, call for more research on efficient network repair strategies. Existing strategies are often designed to repair networks with local information only. However, the absence of global information impedes the creation of efficient repairs. Motivated by this limitation, we propose a concept of community-level repair, which leverages the community structure of the network during the repair process. Moreover, we devise a general framework of network repair, with in total six instances. Evaluations on real-world and random networks show the effectiveness and efficiency of the community-level repair approaches, compared to local and random repairs. Our study contributes to a better understanding of repair processes, and reveals that exploitation of the community structure improves the repair process on a disrupted network significantly.

  8. Social Capital in Virtual Learning Communities and Distributed Communities of Practice

    Directory of Open Access Journals (Sweden)

    Ben Daniel

    2003-10-01

    Full Text Available Abstract. Social capital has recently emerged as an important interdisciplinary research area. It is frequently used as a framework for understanding various social issues in temporal communities, neighbourhoods and groups. In particular, researchers in the social sciences and the humanities have used social capital to understand trust, shared understanding, reciprocal relationships, social network structures, common norms and cooperation, and the roles these entities play in various aspects of temporal communities. Despite proliferation of research in this area, little work has been done to extend this effort to technology-driven learning communities (also known as virtual learning communities. This paper surveys key interdisciplinary research areas in social capital. It also explores how the notions of social capital and trust can be extended to virtual communities, including virtual learning communities and distributed communities of practice. Research issues surrounding social capital and trust as they relate to technology-driven learning communities are identified.

  9. Constructivist Learning Environments and Defining the Online Learning Community

    Science.gov (United States)

    Brown, Loren

    2014-01-01

    The online learning community is frequently referred to, but ill defined. The constructivist philosophy and approach to teaching and learning is both an effective means of constructing an online learning community and it is a tool by which to define key elements of the learning community. In order to build a nurturing, self-sustaining online…

  10. Thoughts on Community Service Learning.

    Science.gov (United States)

    Kohlmoos, Jim

    1995-01-01

    Presents remarks from a keynote address given by the Senior Advisor for the Office of Elementary and Secondary Education at the New England Conference on Community Service Learning (1994). The speech offers insights into the policy goals of President Clinton's Department of Education. (GR)

  11. Toward Radicalizing Community Service Learning

    Science.gov (United States)

    Sheffield, Eric C.

    2015-01-01

    This article advocates a radicalized theoretical construction of community service learning. To accomplish this radicalization, I initially take up a discussion of traditional understandings of CSL rooted in pragmatic/progressive thought. I then suggest that this traditional structural foundation can be radicalized by incorporating Deborah…

  12. Using Web 2.0 for Learning in the Community

    Science.gov (United States)

    Mason, Robin; Rennie, Frank

    2007-01-01

    This paper describes the use of a range of Web 2.0 technologies to support the development of community for a newly formed Land Trust on the Isle of Lewis, in NW Scotland. The application of social networking tools in text, audio and video has several purposes: informal learning about the area to increase tourism, community interaction,…

  13. A model for evolution of overlapping community networks

    Science.gov (United States)

    Karan, Rituraj; Biswal, Bibhu

    2017-05-01

    A model is proposed for the evolution of network topology in social networks with overlapping community structure. Starting from an initial community structure that is defined in terms of group affiliations, the model postulates that the subsequent growth and loss of connections is similar to the Hebbian learning and unlearning in the brain and is governed by two dominant factors: the strength and frequency of interaction between the members, and the degree of overlap between different communities. The temporal evolution from an initial community structure to the current network topology can be described based on these two parameters. It is possible to quantify the growth occurred so far and predict the final stationary state to which the network is likely to evolve. Applications in epidemiology or the spread of email virus in a computer network as well as finding specific target nodes to control it are envisaged. While facing the challenge of collecting and analyzing large-scale time-resolved data on social groups and communities one faces the most basic questions: how do communities evolve in time? This work aims to address this issue by developing a mathematical model for the evolution of community networks and studying it through computer simulation.

  14. Extending a configuration model to find communities in complex networks

    Science.gov (United States)

    Jin, Di; He, Dongxiao; Hu, Qinghua; Baquero, Carlos; Yang, Bo

    2013-09-01

    Discovery of communities in complex networks is a fundamental data analysis task in various domains. Generative models are a promising class of techniques for identifying modular properties from networks, which has been actively discussed recently. However, most of them cannot preserve the degree sequence of networks, which will distort the community detection results. Rather than using a blockmodel as most current works do, here we generalize a configuration model, namely, a null model of modularity, to solve this problem. Towards decomposing and combining sub-graphs according to the soft community memberships, our model incorporates the ability to describe community structures, something the original model does not have. Also, it has the property, as with the original model, that it fixes the expected degree sequence to be the same as that of the observed network. We combine both the community property and degree sequence preserving into a single unified model, which gives better community results compared with other models. Thereafter, we learn the model using a technique of nonnegative matrix factorization and determine the number of communities by applying consensus clustering. We test this approach both on synthetic benchmarks and on real-world networks, and compare it with two similar methods. The experimental results demonstrate the superior performance of our method over competing methods in detecting both disjoint and overlapping communities.

  15. Machine learning for identifying botnet network traffic

    DEFF Research Database (Denmark)

    Stevanovic, Matija; Pedersen, Jens Myrup

    2013-01-01

    . Due to promise of non-invasive and resilient detection, botnet detection based on network traffic analysis has drawn a special attention of the research community. Furthermore, many authors have turned their attention to the use of machine learning algorithms as the mean of inferring botnet......-related knowledge from the monitored traffic. This paper presents a review of contemporary botnet detection methods that use machine learning as a tool of identifying botnet-related traffic. The main goal of the paper is to provide a comprehensive overview on the field by summarizing current scientific efforts....... The contribution of the paper is three-fold. First, the paper provides a detailed insight on the existing detection methods by investigating which bot-related heuristic were assumed by the detection systems and how different machine learning techniques were adapted in order to capture botnet-related knowledge...

  16. Community structure in the phonological network

    Directory of Open Access Journals (Sweden)

    Cynthia S. Q. Siew

    2013-08-01

    Full Text Available Community structure, which refers to the presence of densely connected groups within a larger network, is a common feature of several real-world networks from a variety of domains such as the human brain, social networks of hunter-gatherers and business organizations, and the World Wide Web (Porter et al., 2009. Using a community detection technique known as the Louvain optimization method, 17 communities were extracted from the giant component of the phonological network described in Vitevitch (2008. Additional analyses comparing the lexical and phonological characteristics of words in these communities against words in randomly generated communities revealed several novel discoveries. Larger communities tend to consist of short, frequent words of high degree and low age of acquisition ratings, and smaller communities tend to consist of longer, less frequent words of low degree and high age of acquisition ratings. Real communities also contained fewer different phonological segments compared to random communities, although the number of occurrences of phonological segments found in real communities was much higher than that of the same phonological segments in random communities. Interestingly, the observation that relatively few biphones occur very frequently and a large number of biphones occur rarely within communities mirrors the pattern of the overall frequency of words in a language (Zipf, 1935. The present findings have important implications for understanding the dynamics of activation spread among words in the phonological network that are relevant to lexical processing, as well as understanding the mechanisms that underlie language acquisition and the evolution of language.

  17. The Community Science Workshop Network Story: Becoming a Networked Organization

    Science.gov (United States)

    St. John, Mark

    2014-01-01

    The Community Science Workshops (CSWs)--with funding from the S.D. Bechtel, Jr. Foundation, and the Gordon and Betty Moore Foundation--created a network among the CSW sites in California. The goals of the CSW Network project have been to improve programs, build capacity throughout the Network, and establish new sites. Inverness Research has been…

  18. Building mathematics cellular phone learning communities

    OpenAIRE

    Wajeeh M. Daher

    2011-01-01

    Researchers emphasize the importance of maintaining learning communities and environments. This article describes the building and nourishment of a learning community, one comprised of middle school students who learned mathematics out-of-class using the cellular phone. The building of the learning community was led by three third year pre-service teachers majoring in mathematics and computers. The pre-service teachers selected thirty 8th grade students to learn mathematics with the cellular ...

  19. Community Based Networks and 5G

    DEFF Research Database (Denmark)

    Williams, Idongesit

    2016-01-01

    is hinged on a research aimed at understanding how and why Community Based Networks deploy telecom and Broadband infrastructure. The study was a qualitative study carried out inductively using Grounded Theory. Six cases were investigated.Two Community Based Network Mobilization models were identified......The deployment of previous wireless standards has provided more benefits for urban dwellers than rural dwellers. 5G deployment may not be different. This paper identifies that Community Based Networks as carriers that deserve recognition as potential 5G providers may change this. The argument....... The findings indicate that 5G connectivity can be extended to rural areas by these networks, via heterogenous networks. Hence the delivery of 5G data rates delivery via Wireless WAN in rural areas can be achieved by utilizing the causal factors of the identified models for Community Based Networks....

  20. Learning Community and Nonlearning Community Students in a Midwestern Community College

    Science.gov (United States)

    Laanan, Frankie Santos; Jackson, Dimitra Lynette; Stebleton, Michael J.

    2013-01-01

    The research on learning communities has focused primarily on students at four-year colleges and universities. There is a dearth of studies that examine learning communities in community colleges. The purpose of this comparative study was to conduct an analysis of learning community and nonlearning community students in a community college located…

  1. Professional Learning Communities: Assessment--Development--Effects.

    Science.gov (United States)

    Hipp, Kristine Kiefer; Huffman, Jane Bumpers

    This presentation addresses three topics: (1) the assessment of professional learning communities in schools; (2) the design and development of professional learning communities in schools; and (3) the effects of professional learning communities in schools. The purpose of this brief document is to share descriptions, processes, and materials…

  2. Learning Community Assessment 101--Best Practices

    Science.gov (United States)

    Huerta, Juan Carlos; Hansen, Michele J.

    2013-01-01

    Good assessment is part of all good learning communities, and this article provides a useful set of best practices for learning community assessment planning: (1) articulating agreed-upon learning community program goals; (2) identifying the purpose of assessment (e.g., summative or formative); (3) employing qualitative and quantitative assessment…

  3. Associative learning in biochemical networks.

    Science.gov (United States)

    Gandhi, Nikhil; Ashkenasy, Gonen; Tannenbaum, Emmanuel

    2007-11-07

    It has been recently suggested that there are likely generic features characterizing the emergence of systems constructed from the self-organization of self-replicating agents acting under one or more selection pressures. Therefore, structures and behaviors at one length scale may be used to infer analogous structures and behaviors at other length scales. Motivated by this suggestion, we seek to characterize various "animate" behaviors in biochemical networks, and the influence that these behaviors have on genomic evolution. Specifically, in this paper, we develop a simple, chemostat-based model illustrating how a process analogous to associative learning can occur in a biochemical network. Associative learning is a form of learning whereby a system "learns" to associate two stimuli with one another. Associative learning, also known as conditioning, is believed to be a powerful learning process at work in the brain (associative learning is essentially "learning by analogy"). In our model, two types of replicating molecules, denoted as A and B, are present in some initial concentration in the chemostat. Molecules A and B are stimulated to replicate by some growth factors, denoted as G(A) and G(B), respectively. It is also assumed that A and B can covalently link, and that the conjugated molecule can be stimulated by either the G(A) or G(B) growth factors (and can be degraded). We show that, if the chemostat is stimulated by both growth factors for a certain time, followed by a time gap during which the chemostat is not stimulated at all, and if the chemostat is then stimulated again by only one of the growth factors, then there will be a transient increase in the number of molecules activated by the other growth factor. Therefore, the chemostat bears the imprint of earlier, simultaneous stimulation with both growth factors, which is indicative of associative learning. It is interesting to note that the dynamics of our model is consistent with certain aspects of

  4. Blending Formal and Informal Learning Networks for Online Learning

    Science.gov (United States)

    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…

  5. Core of communities in bipartite networks

    Science.gov (United States)

    Bongiorno, Christian; London, András; Miccichè, Salvatore; Mantegna, Rosario N.

    2017-08-01

    We use the information present in a bipartite network to detect cores of communities of each set of the bipartite system. Cores of communities are found by investigating statistically validated projected networks obtained using information present in the bipartite network. Cores of communities are highly informative and robust with respect to the presence of errors or missing entries in the bipartite network. We assess the statistical robustness of cores by investigating an artificial benchmark network, the coauthorship network, and the actor-movie network. The accuracy and precision of the partition obtained with respect to the reference partition are measured in terms of the adjusted Rand index and the adjusted Wallace index, respectively. The detection of cores is highly precise, although the accuracy of the methodology can be limited in some cases.

  6. Building online learning communities in a graduate dental hygiene program.

    Science.gov (United States)

    Rogo, Ellen J; Portillo, Karen M

    2014-08-01

    The literature abounds with research related to building online communities in a single course; however, limited evidence is available on this phenomenon from a program perspective. The intent of this qualitative case study inquiry was to explore student experiences in a graduate dental hygiene program contributing or impeding the development and sustainability of online learning communities. Approval from the IRB was received. A purposive sampling technique was used to recruit participants from a stratification of students and graduates. A total of 17 participants completed semi-structured interviews. Data analysis was completed through 2 rounds - 1 for coding responses and 1 to construct categories of experiences. The participants' collective definition of an online learning community was a complex synergistic network of interconnected people who create positive energy. The findings indicated the development of this network began during the program orientation and was beneficial for building a foundation for the community. Students felt socially connected and supported by the network. Course design was another important category for participation in weekly discussions and group activities. Instructors were viewed as active participants in the community, offering helpful feedback and being a facilitator in discussions. Experiences impeding the development of online learning communities related to the poor performance of peers and instructors. Specific categories of experiences supported and impeded the development of online learning communities related to the program itself, course design, students and faculty. These factors are important to consider in order to maximize student learning potential in this environment. Copyright © 2014 The American Dental Hygienists’ Association.

  7. Neural networks and perceptual learning

    Science.gov (United States)

    Tsodyks, Misha; Gilbert, Charles

    2005-01-01

    Sensory perception is a learned trait. The brain strategies we use to perceive the world are constantly modified by experience. With practice, we subconsciously become better at identifying familiar objects or distinguishing fine details in our environment. Current theoretical models simulate some properties of perceptual learning, but neglect the underlying cortical circuits. Future neural network models must incorporate the top-down alteration of cortical function by expectation or perceptual tasks. These newly found dynamic processes are challenging earlier views of static and feedforward processing of sensory information. PMID:15483598

  8. Overlapping Community Detection based on Network Decomposition

    Science.gov (United States)

    Ding, Zhuanlian; Zhang, Xingyi; Sun, Dengdi; Luo, Bin

    2016-04-01

    Community detection in complex network has become a vital step to understand the structure and dynamics of networks in various fields. However, traditional node clustering and relatively new proposed link clustering methods have inherent drawbacks to discover overlapping communities. Node clustering is inadequate to capture the pervasive overlaps, while link clustering is often criticized due to the high computational cost and ambiguous definition of communities. So, overlapping community detection is still a formidable challenge. In this work, we propose a new overlapping community detection algorithm based on network decomposition, called NDOCD. Specifically, NDOCD iteratively splits the network by removing all links in derived link communities, which are identified by utilizing node clustering technique. The network decomposition contributes to reducing the computation time and noise link elimination conduces to improving the quality of obtained communities. Besides, we employ node clustering technique rather than link similarity measure to discover link communities, thus NDOCD avoids an ambiguous definition of community and becomes less time-consuming. We test our approach on both synthetic and real-world networks. Results demonstrate the superior performance of our approach both in computation time and accuracy compared to state-of-the-art algorithms.

  9. Learning Reproducibility with a Yearly Networking Contest

    KAUST Repository

    Canini, Marco

    2017-08-10

    Better reproducibility of networking research results is currently a major goal that the academic community is striving towards. This position paper makes the case that improving the extent and pervasiveness of reproducible research can be greatly fostered by organizing a yearly international contest. We argue that holding a contest undertaken by a plurality of students will have benefits that are two-fold. First, it will promote hands-on learning of skills that are helpful in producing artifacts at the replicable-research level. Second, it will advance the best practices regarding environments, testbeds, and tools that will aid the tasks of reproducibility evaluation committees by and large.

  10. Work and Community in Networked Organizations

    OpenAIRE

    Haythornthwaite, Caroline

    2017-01-01

    When computer networks link people as well as machines, they become social networks. Such computer-supported social networks (CSSNs) are becoming important bases of virtual communities, computer-supported co-operative work, and telework. We review our Toronto-based Virtually Social Research Group’s analysis of scholarly networks and on-line workgroups. We find that CSSNs sustain strong, intermediate and weak ties that provide information and social support in both specialized and broadly-base...

  11. Troubleshooting Assistance Services in Community Wireless Networks

    Directory of Open Access Journals (Sweden)

    P. Kriz

    2012-01-01

    Full Text Available We have identified new services intended for users and administrators of community wireless networks. Troubleshooting assistance services will assist the users during solution of communication problems, gathering data for expert analysis, informing the user about the state of the network (including outages, and so forth. Network administrators will be provided with a unique tool supporting the network analysis, operation, and development. We have mainly focused on the use cases and prerequirements—the problem of topology discovery.

  12. Networks communities within and across borders

    CERN Document Server

    Cerina, Federica; Pammolli, Fabio; Riccaboni, Massimo

    2013-01-01

    We investigate the impact of borders on the topology of spatially embedded networks. Indeed territorial subdivisions and geographical borders significantly hamper the geographical span of networks thus playing a key role in the formation of network communities. This is especially important in scientific and technological policy making and highlights the interplay of the internationalization pressure toward a global innovation system against the administrative borders imposed by the national and continental institutions. In this study we introduce an outreach index to quantify the impact of borders on the community structure and apply it to the case of the European and US patent co-inventors networks. We find that (a) the US connectivity decays as a power of distance, whereas we observe a faster exponential decay for Europe; (b) European network communities essentially correspond to nations and contiguous regions while US communities span multiple states across the whole country without any characteristic geog...

  13. Creative Network Communities in the Translocal Space of Digital Networks

    Directory of Open Access Journals (Sweden)

    Rasa Smite

    2013-01-01

    Full Text Available What should sociological research be in the age of Web 2.0? Considering that the task of “network sociology” is not only empirical research but also the interpretation of tendencies of the network culture, this research explores the rise of network communities within Eastern and Western Europe in the early Internet era. I coined the term creative networks to distinguish these early creative and social activities from today’s popular social networking. Thus I aimed to interpret the meaning of social action; the motivation of creative community actors, their main fields of activities and social organization forms; and the potential that these early developments contain for the future sustainability of networks. Data comprise interviews with networking experts and founders and members of various networks. Investigating respondents’ motivations for creating online networks and communities, and interpreting those terms, allows for comparing the creative networks of the 1990s with today’s social networks and for drawing conclusions.

  14. Networks : Empowering Communities through Telecentre Networking

    International Development Research Centre (IDRC) Digital Library (Canada)

    In 2006 IDRC's telecentre.org commissioned a survey entitled, Telecentre Scoping Study in North Africa and the Middle East (103537), the first of its kind in the region. The study revealed that although there are no formal telecentre networking activities in the region, those countries in which telecentres are strongest - Egypt, ...

  15. Implications of Online Social Network Sites on the Personal and Professional Learning of Educational Leaders

    Science.gov (United States)

    Elias, Scott

    2012-01-01

    The purpose of this study is to explore the ways in which five educational leaders make use of online social network sites (SNSs) for their personal and professional learning. Specifically, I focus on how participants use social networking tools to create and maintain online learning communities, how they interact within these communities, and how…

  16. Taxonomies of networks from community structure

    Science.gov (United States)

    Onnela, Jukka-Pekka; Fenn, Daniel J.; Reid, Stephen; Porter, Mason A.; Mucha, Peter J.; Fricker, Mark D.; Jones, Nick S.

    2012-09-01

    The study of networks has become a substantial interdisciplinary endeavor that encompasses myriad disciplines in the natural, social, and information sciences. Here we introduce a framework for constructing taxonomies of networks based on their structural similarities. These networks can arise from any of numerous sources: They can be empirical or synthetic, they can arise from multiple realizations of a single process (either empirical or synthetic), they can represent entirely different systems in different disciplines, etc. Because mesoscopic properties of networks are hypothesized to be important for network function, we base our comparisons on summaries of network community structures. Although we use a specific method for uncovering network communities, much of the introduced framework is independent of that choice. After introducing the framework, we apply it to construct a taxonomy for 746 networks and demonstrate that our approach usefully identifies similar networks. We also construct taxonomies within individual categories of networks, and we thereby expose nontrivial structure. For example, we create taxonomies for similarity networks constructed from both political voting data and financial data. We also construct network taxonomies to compare the social structures of 100 Facebook networks and the growth structures produced by different types of fungi.

  17. Immunization of networks with community structure

    Energy Technology Data Exchange (ETDEWEB)

    Masuda, Naoki [Graduate School of Information Science and Technology, University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo 113-8656 (Japan); PRESTO, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012 (Japan)], E-mail: masuda@mist.i.u-tokyo.ac.jp

    2009-12-15

    In this study, an efficient method to immunize modular networks (i.e. networks with community structure) is proposed. The immunization of networks aims at fragmenting networks into small parts with a small number of removed nodes. Its applications include prevention of epidemic spreading, protection against intentional attacks on networks, and conservation of ecosystems. Although preferential immunization of hubs is efficient, good immunization strategies for modular networks have not been established. On the basis of an immunization strategy based on eigenvector centrality, we develop an analytical framework for immunizing modular networks. To this end, we quantify the contribution of each node to the connectivity in a coarse-grained network among modules. We verify the effectiveness of the proposed method by applying it to model and real networks with modular structure.

  18. Rumor propagation on networks with community structure

    Science.gov (United States)

    Zhang, Ruixia; Li, Deyu

    2017-10-01

    In this paper, based on growth and preferential attachment mechanism, we give a network generation model aiming at generating networks with community structure. There are three characteristics for the networks generated by the generation model. The first is that the community sizes can be nonuniform. The second is that there are bridge hubs in each community. The third is that the strength of community structure is adjustable. Next, we investigate rumor propagation behavior on the generated networks by performing Monte Carlo simulations to reveal the influence of bridge hubs, nonuniformity of community sizes and the strength of community structure on the dynamic behavior of the rumor propagation. We find that bridge hubs have outstanding performance in propagation speed and propagation size, and larger modularity can reduce rumor propagation. Furthermore, when the decay rate of rumor spreading β is large, the final density of the stiflers is larger if the rumor originates in larger community. Additionally, when on networks with different strengths of community structure, rumor propagation exhibits greater difference in the density of stiflers and in the peak prevalence if the decay rate β is larger.

  19. Community of practice and situated learning

    Directory of Open Access Journals (Sweden)

    Nives Ličen

    2012-10-01

    Full Text Available The importance of everyday life learning or situated learning is growing, and new models for interpretation and re- search are being developed in concordance with new knowledge concepts and theories of learning. The article brings an analysis of situated learning as a type of learning without a fixed structure which develops (transforms into an organized and structured learning form. The first part examines the concept of situated learning and the theoretical context of the model of communities of practice. The second part presents a comparative analysis of two models of situ- ated learning, action learning and the community of practice, as the forms which support transmission of information and knowledge and imply innovation development. Both models are rooted in the context of poststructuralist practice theory and transformative learning theories. Advantages and deficiencies of the analysed models direct the practical use of action learning or communities of practice.

  20. Detecting communities through network data

    NARCIS (Netherlands)

    Bruggeman, J.; Traag, V.A.; Uitermark, J.

    2012-01-01

    Social life coalesces into communities through cooperation and conflict. As a case in point, Shwed and Bearman (2010) studied consensus and contention in scientific communities. They used a sophisticated modularity method to detect communities on the basis of scientific citations, which they then

  1. Detecting Clusters/Communities in Social Networks.

    Science.gov (United States)

    Hoffman, Michaela; Steinley, Douglas; Gates, Kathleen M; Prinstein, Mitchell J; Brusco, Michael J

    2018-01-01

    Cohen's κ, a similarity measure for categorical data, has since been applied to problems in the data mining field such as cluster analysis and network link prediction. In this paper, a new application is examined: community detection in networks. A new algorithm is proposed that uses Cohen's κ as a similarity measure for each pair of nodes; subsequently, the κ values are then clustered to detect the communities. This paper defines and tests this method on a variety of simulated and real networks. The results are compared with those from eight other community detection algorithms. Results show this new algorithm is consistently among the top performers in classifying data points both on simulated and real networks. Additionally, this is one of the broadest comparative simulations for comparing community detection algorithms to date.

  2. Identifying community structure in complex networks

    Science.gov (United States)

    Shao, Chenxi; Duan, Yubing

    2015-07-01

    A wide variety of applications could be formulated to resolve the problem of finding all communities from a given network, ranging from social and biological network analysis to web mining and searching. In this study, we propose the concept of virtual attractive strength between each pair of node in networks, and then give the definition of community structure based on the proposed attractive strength. Furthermore, we present a community detection method by moving vertices to the clusters that produce the largest attractive strengths to them until the division of network reaches unchanged. Experimental results on synthetic and real networks indicate that the proposed approach has favorite effectiveness and fast convergence speed, which provides an efficient method for exploring and analyzing complex systems.

  3. Mapping Community-based Natural Learning Opportunities.

    Science.gov (United States)

    Dunst, Carl J.; Herter, Serena; Shields, Holly; Bennis, Leslie

    2001-01-01

    This article explains the use of a community mapping methodology to identify natural learning environments and inclusion opportunities for young children with disabilities. Four steps are discussed: (1) selecting kinds of learning opportunities for mapping; (2) gathering information about community learning sources; (3) developing an informational…

  4. Building Learning Communities: Foundations for Good Practice

    Science.gov (United States)

    Davies, Alison; Ramsay, Jill; Lindfield, Helen; Couperthwaite, John

    2005-01-01

    The School of Health Sciences at the University of Birmingham provided opportunities for the development of student learning communities and online resources within the neurological module of the BSc Physiotherapy degree programme. These learning communities were designed to facilitate peer and independent learning in core aspects underpinning…

  5. Discovering Network Structure Beyond Communities

    OpenAIRE

    Nishikawa, Takashi; Motter, Adilson E.

    2011-01-01

    To understand the formation, evolution, and function of complex systems, it is crucial to understand the internal organization of their interaction networks. Partly due to the impossibility of visualizing large complex networks, resolving network structure remains a challenging problem. Here we overcome this difficulty by combining the visual pattern recognition ability of humans with the high processing speed of computers to develop an exploratory method for discovering groups of nodes chara...

  6. How One Learning Community Approached Death

    Science.gov (United States)

    Ungemah, Lori

    2017-01-01

    In this narrative piece, the author describes how a learning community was able to transfer their practices of care to support a colleague as he faced illness and death. The author chronicles how the learning community responded to support their team member, other members of the campus community, and the students. She reflects on this experience…

  7. Social network analysis community detection and evolution

    CERN Document Server

    Missaoui, Rokia

    2015-01-01

    This book is devoted to recent progress in social network analysis with a high focus on community detection and evolution. The eleven chapters cover the identification of cohesive groups, core components and key players either in static or dynamic networks of different kinds and levels of heterogeneity. Other important topics in social network analysis such as influential detection and maximization, information propagation, user behavior analysis, as well as network modeling and visualization are also presented. Many studies are validated through real social networks such as Twitter. This edit

  8. Brand communities embedded in social networks.

    Science.gov (United States)

    Zaglia, Melanie E

    2013-02-01

    Brand communities represent highly valuable marketing, innovation management, and customer relationship management tools. However, applying successful marketing strategies today, and in the future, also means exploring and seizing the unprecedented opportunities of social network environments. This study combines these two social phenomena which have largely been researched separately, and aims to investigate the existence, functionality and different types of brand communities within social networks. The netnographic approach yields strong evidence of this existence; leading to a better understanding of such embedded brand communities, their peculiarities, and motivational drivers for participation; therefore the findings contribute to theory by combining two separate research streams. Due to the advantages of social networks, brand management is now able to implement brand communities with less time and financial effort; however, choosing the appropriate brand community type, cultivating consumers' interaction, and staying tuned to this social engagement are critical factors to gain anticipated brand outcomes.

  9. Learning Processes of Layered Neural Networks

    OpenAIRE

    Fujiki, Sumiyoshi; FUJIKI, Nahomi, M.

    1995-01-01

    A positive reinforcement type learning algorithm is formulated for a stochastic feed-forward neural network, and a learning equation similar to that of the Boltzmann machine algorithm is obtained. By applying a mean field approximation to the same stochastic feed-forward neural network, a deterministic analog feed-forward network is obtained and the back-propagation learning rule is re-derived.

  10. Netgram: Visualizing Communities in Evolving Networks.

    Directory of Open Access Journals (Sweden)

    Raghvendra Mall

    Full Text Available Real-world complex networks are dynamic in nature and change over time. The change is usually observed in the interactions within the network over time. Complex networks exhibit community like structures. A key feature of the dynamics of complex networks is the evolution of communities over time. Several methods have been proposed to detect and track the evolution of these groups over time. However, there is no generic tool which visualizes all the aspects of group evolution in dynamic networks including birth, death, splitting, merging, expansion, shrinkage and continuation of groups. In this paper, we propose Netgram: a tool for visualizing evolution of communities in time-evolving graphs. Netgram maintains evolution of communities over 2 consecutive time-stamps in tables which are used to create a query database using the sql outer-join operation. It uses a line-based visualization technique which adheres to certain design principles and aesthetic guidelines. Netgram uses a greedy solution to order the initial community information provided by the evolutionary clustering technique such that we have fewer line cross-overs in the visualization. This makes it easier to track the progress of individual communities in time evolving graphs. Netgram is a generic toolkit which can be used with any evolutionary community detection algorithm as illustrated in our experiments. We use Netgram for visualization of topic evolution in the NIPS conference over a period of 11 years and observe the emergence and merging of several disciplines in the field of information processing systems.

  11. Netgram: Visualizing Communities in Evolving Networks

    Science.gov (United States)

    Mall, Raghvendra; Langone, Rocco; Suykens, Johan A. K.

    2015-01-01

    Real-world complex networks are dynamic in nature and change over time. The change is usually observed in the interactions within the network over time. Complex networks exhibit community like structures. A key feature of the dynamics of complex networks is the evolution of communities over time. Several methods have been proposed to detect and track the evolution of these groups over time. However, there is no generic tool which visualizes all the aspects of group evolution in dynamic networks including birth, death, splitting, merging, expansion, shrinkage and continuation of groups. In this paper, we propose Netgram: a tool for visualizing evolution of communities in time-evolving graphs. Netgram maintains evolution of communities over 2 consecutive time-stamps in tables which are used to create a query database using the sql outer-join operation. It uses a line-based visualization technique which adheres to certain design principles and aesthetic guidelines. Netgram uses a greedy solution to order the initial community information provided by the evolutionary clustering technique such that we have fewer line cross-overs in the visualization. This makes it easier to track the progress of individual communities in time evolving graphs. Netgram is a generic toolkit which can be used with any evolutionary community detection algorithm as illustrated in our experiments. We use Netgram for visualization of topic evolution in the NIPS conference over a period of 11 years and observe the emergence and merging of several disciplines in the field of information processing systems. PMID:26356538

  12. Identifying Gatekeepers in Online Learning Networks

    Science.gov (United States)

    Gursakal, Necmi; Bozkurt, Aras

    2017-01-01

    The rise of the networked society has not only changed our perceptions but also the definitions, roles, processes and dynamics of online learning networks. From offline to online worlds, networks are everywhere and gatekeepers are an important entity in these networks. In this context, the purpose of this paper is to explore gatekeeping and…

  13. Adult and Community Learning Alliances: A New Role for Local Authorities.

    Science.gov (United States)

    Hooper, Richard

    2000-01-01

    Lancashire Adult and Community Learning Alliance is developing a plan to mobilize a network of community and volunteer resources to promote lifelong learning. As a local authority, it has the capacity to facilitate joint solutions, access nonparticipants, and use prime community centers for service delivery. (SK)

  14. Canada's Composite Learning Index: A Path Towards Learning Communities

    Science.gov (United States)

    Cappon, Paul; Laughlin, Jarrett

    2013-01-01

    In the development of learning cities/communities, benchmarking progress is a key element. Not only does it permit cities/communities to assess their current strengths and weaknesses, it also engenders a dialogue within and between cities/communities on the means of enhancing learning conditions. Benchmarking thereby is a potentially motivational…

  15. PARALLEL ALGORITHM FOR BAYESIAN NETWORK STRUCTURE LEARNING

    Directory of Open Access Journals (Sweden)

    S. A. Arustamov

    2013-03-01

    Full Text Available The article deals with implementation of a scalable parallel algorithm for structure learning of Bayesian network. Comparative analysis of sequential and parallel algorithms is done.

  16. Community detection by signaling on complex networks

    Science.gov (United States)

    Hu, Yanqing; Li, Menghui; Zhang, Peng; Fan, Ying; di, Zengru

    2008-07-01

    Based on a signaling process of complex networks, a method for identification of community structure is proposed. For a network with n nodes, every node is assumed to be a system which can send, receive, and record signals. Each node is taken as the initial signal source to excite the whole network one time. Then the source node is associated with an n -dimensional vector which records the effects of the signaling process. By this process, the topological relationship of nodes on the network could be transferred into a geometrical structure of vectors in n -dimensional Euclidean space. Then the best partition of groups is determined by F statistics and the final community structure is given by the K -means clustering method. This method can detect community structure both in unweighted and weighted networks. It has been applied to ad hoc networks and some real networks such as the Zachary karate club network and football team network. The results indicate that the algorithm based on the signaling process works well.

  17. Community detection by signaling on complex networks.

    Science.gov (United States)

    Hu, Yanqing; Li, Menghui; Zhang, Peng; Fan, Ying; Di, Zengru

    2008-07-01

    Based on a signaling process of complex networks, a method for identification of community structure is proposed. For a network with n nodes, every node is assumed to be a system which can send, receive, and record signals. Each node is taken as the initial signal source to excite the whole network one time. Then the source node is associated with an n -dimensional vector which records the effects of the signaling process. By this process, the topological relationship of nodes on the network could be transferred into a geometrical structure of vectors in n -dimensional Euclidean space. Then the best partition of groups is determined by F statistics and the final community structure is given by the K -means clustering method. This method can detect community structure both in unweighted and weighted networks. It has been applied to ad hoc networks and some real networks such as the Zachary karate club network and football team network. The results indicate that the algorithm based on the signaling process works well.

  18. Creating Learning Communities in the Classroom

    Science.gov (United States)

    Saville, Bryan K.; Lawrence, Natalie Kerr; Jakobsen, Krisztina V.

    2012-01-01

    There are many ways to construct classroom-based learning communities. Nevertheless, the emphasis is always on cooperative learning. In this article, the authors focus on three teaching methods--interteaching, team-based learning, and cooperative learning in large, lecture-based courses--that they have used successfully to create classroom-based…

  19. Community core evolution in mobile social networks.

    Science.gov (United States)

    Xu, Hao; Xiao, Weidong; Tang, Daquan; Tang, Jiuyang; Wang, Zhenwen

    2013-01-01

    Community detection in social networks attracts a lot of attention in the recent years. Existing methods always depict the relationship of two nodes using the temporary connection. However, these temporary connections cannot be fully recognized as the real relationships when the history connections among nodes are considered. For example, a casual visit in Facebook cannot be seen as an establishment of friendship. Hence, our question is the following: how to cluster the real friends in mobile social networks? In this paper, we study the problem of detecting the stable community core in mobile social networks. The cumulative stable contact is proposed to depict the relationship among nodes. The whole process is divided into timestamps. Nodes and their connections can be added or removed at each timestamp, and historical contacts are considered when detecting the community core. Also, community cores can be tracked through the incremental computing, which can help to recognize the evolving of community structure. Empirical studies on real-world social networks demonstrate that our proposed method can effectively detect stable community cores in mobile social networks.

  20. Community Forums: A Unique Approach to Community Service-Learning

    Science.gov (United States)

    Steiner, Sherrie; Warkentin, Buetta; Smith, Michael

    2011-01-01

    The service-learning movement has been criticized for not listening to the voices of community partners. Using Bourdieu's framework that equally values formal and practical knowledge, we evaluated a Manitoba college's service-learning program that focused on an issue of community concern. The program was uniquely designed to prioritize the voice…

  1. Information transfer in community structured multiplex networks

    Science.gov (United States)

    Solé Ribalta, Albert; Granell, Clara; Gómez, Sergio; Arenas, Alex

    2015-08-01

    The study of complex networks that account for different types of interactions has become a subject of interest in the last few years, specially because its representational power in the description of users interactions in diverse online social platforms (Facebook, Twitter, Instagram, etc.). The mathematical description of these interacting networks has been coined under the name of multilayer networks, where each layer accounts for a type of interaction. It has been shown that diffusive processes on top of these networks present a phenomenology that cannot be explained by the naive superposition of single layer diffusive phenomena but require the whole structure of interconnected layers. Nevertheless, the description of diffusive phenomena on multilayer networks has obviated the fact that social networks have strong mesoscopic structure represented by different communities of individuals driven by common interests, or any other social aspect. In this work, we study the transfer of information in multilayer networks with community structure. The final goal is to understand and quantify, if the existence of well-defined community structure at the level of individual layers, together with the multilayer structure of the whole network, enhances or deteriorates the diffusion of packets of information.

  2. Information transfer in community structured multiplex networks

    Directory of Open Access Journals (Sweden)

    Albert eSolé Ribalta

    2015-08-01

    Full Text Available The study of complex networks that account for different types of interactions has become a subject of interest in the last few years, specially because its representational power in the description of users interactions in diverse online social platforms (Facebook, Twitter, Instagram, etc.. The mathematical description of these interacting networks has been coined under the name of multilayer networks, where each layer accounts for a type of interaction. It has been shown that diffusive processes on top of these networks present a phenomenology that cannot be explained by the naive superposition of single layer diffusive phenomena but require the whole structure of interconnected layers. Nevertheless, the description of diffusive phenomena on multilayer networks has obviated the fact that social networks have strong mesoscopic structure represented by different communities of individuals driven by common interests, or any other social aspect. In this work, we study the transfer of information in multilayer networks with community structure. The final goal is to understand and quantify, if the existence of well-defined community structure at the level of individual layers, together with the multilayer structure of the whole network, enhances or deteriorates the diffusion of packets of information.

  3. Relations among Resources in Professional Learning Communities and Learning Outcomes

    Science.gov (United States)

    Christ, Tanya; Arya, Poonam; Chiu, Ming Ming

    2017-01-01

    This study focused on two professional learning communities (PLCs) situated in literacy education practica courses. How four PLC resources (colleagues, facilitators, readings, and videos) were related to outcomes, including teachers' learning, teachers' application of this learning, and subsequent students' learning, was examined. Participants…

  4. Analyzing Learning in Professional Learning Communities: A Conceptual Framework

    Science.gov (United States)

    Van Lare, Michelle D.; Brazer, S. David

    2013-01-01

    The purpose of this article is to build a conceptual framework that informs current understanding of how professional learning communities (PLCs) function in conjunction with organizational learning. The combination of sociocultural learning theories and organizational learning theories presents a more complete picture of PLC processes that has…

  5. An assessment of PKI and networked electronic patient record system: lessons learned from real patient data exchange at the platform of OCHIS (Osaka Community Healthcare Information System).

    Science.gov (United States)

    Takeda, Hiroshi; Matsumura, Yasushi; Kuwata, Shigeki; Nakano, Hirohiko; Shanmai, Ji; Qiyan, Zhang; Yufen, Chen; Kusuoka, Hideo; Matsuoka, Masaki

    2004-03-31

    To enhance medical cooperation between the hospitals and clinics around Osaka local area, the healthcare network system, named Osaka Community Healthcare Information System (OCHIS), was established with support of a supplementary budget from the Japanese government in fiscal year 2002. Although the system has been based on healthcare public key infrastructure (PKI), there remain security issues to be solved technically and operationally. An experimental study was conducted to elucidate the central and the local function in terms of a registration authority and a time stamp authority in contract with the Japanese Medical Information Systems Organization (MEDIS) in 2003. This paper describes the experimental design and the results of the study concerning message security.

  6. Network anomaly detection a machine learning perspective

    CERN Document Server

    Bhattacharyya, Dhruba Kumar

    2013-01-01

    With the rapid rise in the ubiquity and sophistication of Internet technology and the accompanying growth in the number of network attacks, network intrusion detection has become increasingly important. Anomaly-based network intrusion detection refers to finding exceptional or nonconforming patterns in network traffic data compared to normal behavior. Finding these anomalies has extensive applications in areas such as cyber security, credit card and insurance fraud detection, and military surveillance for enemy activities. Network Anomaly Detection: A Machine Learning Perspective presents mach

  7. Learning networks and communication skills

    Directory of Open Access Journals (Sweden)

    Kerry Musselbrook

    2000-12-01

    Full Text Available The increase in student numbers in further and higher education over the last decade has been dramatic, placing greater pressures on academic staff in terms of contact hours. At the same time public funding of universities has decreased. Furthermore, the current pace of technological innovation and change and the fact that there are fewer jobs for life with clear pathways for progression mean that more of us need to be engaged in learning throughout our lives in order to remain competitive in the job-market. That is the reality of lifelong learning. Students are consequently demanding (especially as they are having to meet more of the costs of education themselves a more flexible learning framework. This framework should be able to accommodate all types of learners - part-time, mature, remote and disabled students. The revised Disability Discrimination Act, which came into force in October 1999, only temporarily excludes education from its remit and has already challenged university practices. (Another JlSC-funded initiative, Disability Information Systems in Higher Education, addresses just this issue: http://www.disinhe.ac.uk. All this is set against a backdrop of the government's stated vision for a more inclusive, less elitist education system with opportunities for all, and the requirement for a professional and accountable community of university teachers.

  8. Towards a Pattern Language for Networked Learning

    NARCIS (Netherlands)

    Goodyear, Peter; Avgeriou, Paris; Baggetun, Rune; Bartoluzzi, Sonia; Retalis, Simeon; Ronteltap, Frans; Rusman, Ellen

    2004-01-01

    The work of designing a useful, convivial networked learning environment is complex and demanding. People new to designing for networked learning face a number of major challenges when they try to draw on the experience of others – whether that experience is shared informally, in the everyday

  9. Learning dynamic Bayesian networks with mixed variables

    DEFF Research Database (Denmark)

    Bøttcher, Susanne Gammelgaard

    This paper considers dynamic Bayesian networks for discrete and continuous variables. We only treat the case, where the distribution of the variables is conditional Gaussian. We show how to learn the parameters and structure of a dynamic Bayesian network and also how the Markov order can be learned...

  10. Learning and structure of neuronal networks

    Indian Academy of Sciences (India)

    We study the effect of learning dynamics on network topology. Firstly, a network of discrete dynamical systems is considered for this purpose and the coupling strengths are made to evolve according to a temporal learning rule that is based on the paradigm of spike-time-dependent plasticity (STDP). This incorporates ...

  11. Learning and structure of neuronal networks

    Indian Academy of Sciences (India)

    Corresponding author. E-mail: Kiran.Kolwankar@gmail.com. Abstract. We study the effect of learning dynamics on network topology. Firstly, a network of dis- crete dynamical systems is considered for this purpose and the coupling strengths are made to evolve according to a temporal learning rule that is based on the ...

  12. Personalized Learning Network Teaching Model

    Science.gov (United States)

    Feng, Zhou

    Adaptive learning system on the salient features, expounded personalized learning is adaptive learning system adaptive to learners key to learning. From the perspective of design theory, put forward an adaptive learning system to learn design thinking individual model, and using data mining techniques, the initial establishment of personalized adaptive systems model of learning.

  13. Community detection based on network communicability

    Science.gov (United States)

    Estrada, Ernesto

    2011-03-01

    We propose a new method for detecting communities based on the concept of communicability between nodes in a complex network. This method, designated as N-ComBa K-means, uses a normalized version of the adjacency matrix to build the communicability matrix and then applies K-means clustering to find the communities in a graph. We analyze how this method performs for some pathological cases found in the analysis of the detection limit of communities and propose some possible solutions on the basis of the analysis of the ratio of local to global densities in graphs. We use four different quality criteria for detecting the best clustering and compare the new approach with the Girvan-Newman algorithm for the analysis of two "classical" networks: karate club and bottlenose dolphins. Finally, we analyze the more challenging case of homogeneous networks with community structure, for which the Girvan-Newman completely fails in detecting any clustering. The N-ComBa K-means approach performs very well in these situations and we applied it to detect the community structure in an international trade network of miscellaneous manufactures of metal having these characteristics. Some final remarks about the general philosophy of community detection are also discussed.

  14. Community detection based on network communicability.

    Science.gov (United States)

    Estrada, Ernesto

    2011-03-01

    We propose a new method for detecting communities based on the concept of communicability between nodes in a complex network. This method, designated as N-ComBa K-means, uses a normalized version of the adjacency matrix to build the communicability matrix and then applies K-means clustering to find the communities in a graph. We analyze how this method performs for some pathological cases found in the analysis of the detection limit of communities and propose some possible solutions on the basis of the analysis of the ratio of local to global densities in graphs. We use four different quality criteria for detecting the best clustering and compare the new approach with the Girvan-Newman algorithm for the analysis of two "classical" networks: karate club and bottlenose dolphins. Finally, we analyze the more challenging case of homogeneous networks with community structure, for which the Girvan-Newman completely fails in detecting any clustering. The N-ComBa K-means approach performs very well in these situations and we applied it to detect the community structure in an international trade network of miscellaneous manufactures of metal having these characteristics. Some final remarks about the general philosophy of community detection are also discussed.

  15. Information transfer in community structured multiplex networks

    CERN Document Server

    Solé-Ribalta, Albert; Gómez, Sergio; Arenas, Alex

    2015-01-01

    The study of complex networks that account for different types of interactions has become a subject of interest in the last few years, specially because its representational power in the description of users interactions in diverse online social platforms (Facebook, Twitter, Instagram, etc.). The mathematical description of these interacting networks has been coined under the name of multilayer networks, where each layer accounts for a type of interaction. It has been shown that diffusive processes on top of these networks present a phenomenology that cannot be explained by the naive superposition of single layer diffusive phenomena but require the whole structure of interconnected layers. Nevertheless, the description of diffusive phenomena on multilayer networks has obviated the fact that social networks have strong mesoscopic structure represented by different communities of individuals driven by common interests, or any other social aspect. In this work, we study the transfer of information in multilayer ...

  16. Exploratory community sensing in social networks

    Science.gov (United States)

    Khrabrov, Alexy; Stocco, Gabriel; Cybenko, George

    2010-04-01

    Social networks generally provide an implementation of some kind of groups or communities which users can voluntarily join. Twitter does not have this functionality, and there is no notion of a formal group or community. We propose a method for identification of communities and assignment of semantic meaning to the discussion topics of the resulting communities. Using this analysis method and a sample of roughly a month's worth of Tweets from Twitter's "gardenhose" feed, we demonstrate the discovery of meaningful user communities on Twitter. We examine Twitter data streaming in real time and treat it as a sensor. Twitter is a social network which pioneered microblogging with the messages fitting an SMS, and a variety of clients, browsers, smart phones and PDAs are used for status updates by individuals, businesses, media outlets and even devices all over the world. Often an aggregate trend of such statuses may represent an important development in the world, which has been demonstrated with the Iran and Moldova elections and the anniversary of the Tiananmen in China. We propose using Twitter as a sensor, tracking individuals and communities of interest, and characterizing individual roles and dynamics of their communications. We developed a novel algorithm of community identification in social networks based on direct communication, as opposed to linking. We show ways to find communities of interest and then browse their neighborhoods by either similarity or diversity of individuals and groups adjacent to the one of interest. We use frequent collocations and statistically improbable phrases to summarize the focus of the community, giving a quick overview of its main topics. Our methods provide insight into the largest social sensor network in the world and constitute a platform for social sensing.

  17. Community Engagement for Student Learning in Geography

    Science.gov (United States)

    Bednarz, Sarah Witham; Chalkley, Brian; Fletcher, Stephen; Hay, Iain; Le Heron, Erena; Mohan, Audrey; Trafford, Julie

    2008-01-01

    This article examines the role and purpose of community engagement as a learning and teaching strategy within higher education geography. It explores different interpretations of the concept of community engagement and illustrates different examples of this kind of learning through six case studies drawn from Australia, New Zealand, the UK, and…

  18. Extensive Reading Materials Produced by Learning Communities

    Science.gov (United States)

    Jacobs, G. M.

    2013-01-01

    This article advocates that students and teachers create some of their own extensive reading materials. Learning communities act as a means of motivating and sustaining student and teacher production of extensive reading materials. The article begins by explaining learning communities. The bulk of the article has two parts. The first part focuses…

  19. Feedback Mechanisms in Learning Virtual Community Settings

    Science.gov (United States)

    Colazzo, Luigi; Comai, Alessio; Davi, Filippo; Molinari, Andrea; Villa, Nicola

    2010-01-01

    This paper introduces a set of services for the creation of on-line surveys, questionnaires, exams and self-assessment tests within a virtual community system used in e-learning settings. The system, called "Online Communities", is a dynamic web application used as platform for blended learning activities by the Faculty of Economics of…

  20. Learning from Community: Agenda for Citizenship Education

    Science.gov (United States)

    Ghosh, Sujay

    2015-01-01

    Citizenship is about individual's membership in the socio-political community. Education for citizenship conceives issues such as quality education, learning society and inclusion. Educational thinking in India has long valued community as a learning resource. With empirical experiences drawn from the programme of "Ecology and Natural…

  1. Faculty Experiences in a Research Learning Community

    Science.gov (United States)

    Holmes, Courtney M.; Kozlowski, Kelly A.

    2014-01-01

    The current study examines the experiences of faculty in a research learning community developed to support new faculty in increasing scholarly productivity. A phenomenological, qualitative inquiry was used to portray the lived experiences of faculty within a learning community. Several themes were found including: accountability, belonging,…

  2. It Is Time to Count Learning Communities

    Science.gov (United States)

    Henscheid, Jean M.

    2015-01-01

    As the modern learning community movement turns 30, it is time to determine just how many, and what type, of these programs exist at America's colleges and universities. This article first offers a rationale for counting learning communities followed by a description of how disparate counts and unclear definitions hamper efforts to embed these…

  3. Utilizing Online Learning Communities in Student Affairs

    Science.gov (United States)

    Calhoun, Daniel W.; Green, Lucy Santos

    2015-01-01

    In this chapter, the authors will expand upon the definition of learning communities, discussing the ways in which this concept has changed and adapted through the incorporation/infusion of web-based technologies. In addition, strategies on how to create and use online learning communities both with students and for professional practice will be…

  4. The Evolution of Learning Communities: A Retrospective

    Science.gov (United States)

    Matthews, Roberta S.; Smith, Barbara Leigh; MacGregor, Jean

    2012-01-01

    This volume focuses on learning communities at the beginning and at the culmination of work in the major of psychology and reflects a commitment to good practice both within and outside the classroom. Its comprehensive approach attests to the power of learning communities within the discipline and is a fine example of their evolution. In this…

  5. Emergence of multiplex communities in collaboration networks

    CERN Document Server

    Battiston, Federico; Nicosia, Vincenzo; Bianconi, Ginestra; Latora, Vito

    2015-01-01

    Community structures in collaboration networks reflect the natural tendency of individuals to organize their work in groups in order to better achieve common goals. In most of the cases, individuals exploit their connections to introduce themselves to new areas of interests, giving rise to multifaceted collaborations which span different fields. In this paper, we analyse collaborations in science and among movie actors as multiplex networks, where the layers represent respectively research topics and movie genres, and we show that communities indeed coexist and overlap at the different layers of such systems. We then propose a model to grow multiplex networks based on two mechanisms of intra and inter-layer triadic closure which mimic the real processes in which collaborations evolve. We show that our model is able to explain the multiplex community structure observed empirically, and we infer the strength of the two underlying social mechanisms from real-world systems. Being also able to correctly reproduce ...

  6. Community Structure in Online Collegiate Social Networks

    Science.gov (United States)

    Traud, Amanda; Kelsic, Eric; Mucha, Peter; Porter, Mason

    2009-03-01

    Online social networking sites have become increasingly popular with college students. The networks we studied are defined through ``friendships'' indicated by Facebook users from UNC, Oklahoma, Caltech, Georgetown, and Princeton. We apply the tools of network science to study the Facebook networks from these five different universities at a single point in time. We investigate each single-institution network's community structure, which we obtain through partitioning the graph using an eigenvector method. We use both graphical and quantitative tools, including pair-counting methods, which we interpret through statistical analysis and permutation tests to measure the correlations between the network communities and a set of characteristics given by each user (residence, class year, major, and high school). We also analyze the single gender subsets of these networks, and the impact of missing demographical data. Our study allows us to compare the online social networks for the five schools as well as infer differences in offline social interactions. At the schools studied, we were able to define which characteristics of the Facebook users correlate best with friendships.

  7. deal: A Package for Learning Bayesian Networks

    Directory of Open Access Journals (Sweden)

    Susanne G. Boettcher

    2003-12-01

    Full Text Available deal is a software package for use with R. It includes several methods for analysing data using Bayesian networks with variables of discrete and/or continuous types but restricted to conditionally Gaussian networks. Construction of priors for network parameters is supported and their parameters can be learned from data using conjugate updating. The network score is used as a metric to learn the structure of the network and forms the basis of a heuristic search strategy. deal has an interface to Hugin.

  8. Conditions for Productive Learning in Network Learning Environments

    DEFF Research Database (Denmark)

    Ponti, M.; Dirckinck-Holmfeld, Lone; Lindström, B.

    2004-01-01

    The Kaleidoscope1 Jointly Executed Integrating Research Project (JEIRP) on Conditions for Productive Networked Learning Environments is developing and elaborating conceptual understandings of Computer Supported Collaborative Learning (CSCL) emphasizing the use of cross-cultural comparative......: Pedagogical design and the dialectics of the digital artefacts, the concept of collaboration, ethics/trust, identity and the role of scaffolding of networked learning environments.   The JEIRP is motivated by the fact that many networked learning environments in various European educational settings...... are designed without a deep understanding of the pedagogical, communicative and collaborative conditions embedded in networked learning. Despite the existence of good theoretical views pointing to a social understanding of learning, rather than a traditional individualistic and information processing approach...

  9. Learning to teach in a coteaching community of practice

    Science.gov (United States)

    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

  10. Finding local community structure in networks

    Science.gov (United States)

    Clauset, Aaron

    2005-08-01

    Although the inference of global community structure in networks has recently become a topic of great interest in the physics community, all such algorithms require that the graph be completely known. Here, we define both a measure of local community structure and an algorithm that infers the hierarchy of communities that enclose a given vertex by exploring the graph one vertex at a time. This algorithm runs in time O(k2d) for general graphs when d is the mean degree and k is the number of vertices to be explored. For graphs where exploring a new vertex is time consuming, the running time is linear, O(k) . We show that on computer-generated graphs the average behavior of this technique approximates that of algorithms that require global knowledge. As an application, we use this algorithm to extract meaningful local clustering information in the large recommender network of an online retailer.

  11. Finding local communities in protein networks.

    Science.gov (United States)

    Voevodski, Konstantin; Teng, Shang-Hua; Xia, Yu

    2009-09-18

    Protein-protein interactions (PPIs) play fundamental roles in nearly all biological processes, and provide major insights into the inner workings of cells. A vast amount of PPI data for various organisms is available from BioGRID and other sources. The identification of communities in PPI networks is of great interest because they often reveal previously unknown functional ties between proteins. A large number of global clustering algorithms have been applied to protein networks, where the entire network is partitioned into clusters. Here we take a different approach by looking for local communities in PPI networks. We develop a tool, named Local Protein Community Finder, which quickly finds a community close to a queried protein in any network available from BioGRID or specified by the user. Our tool uses two new local clustering algorithms Nibble and PageRank-Nibble, which look for a good cluster among the most popular destinations of a short random walk from the queried vertex. The quality of a cluster is determined by proportion of outgoing edges, known as conductance, which is a relative measure particularly useful in undersampled networks. We show that the two local clustering algorithms find communities that not only form excellent clusters, but are also likely to be biologically relevant functional components. We compare the performance of Nibble and PageRank-Nibble to other popular and effective graph partitioning algorithms, and show that they find better clusters in the graph. Moreover, Nibble and PageRank-Nibble find communities that are more functionally coherent. The Local Protein Community Finder, accessible at http://xialab.bu.edu/resources/lpcf, allows the user to quickly find a high-quality community close to a queried protein in any network available from BioGRID or specified by the user. We show that the communities found by our tool form good clusters and are functionally coherent, making our application useful for biologists who wish to

  12. Finding local communities in protein networks

    Directory of Open Access Journals (Sweden)

    Teng Shang-Hua

    2009-09-01

    Full Text Available Abstract Background Protein-protein interactions (PPIs play fundamental roles in nearly all biological processes, and provide major insights into the inner workings of cells. A vast amount of PPI data for various organisms is available from BioGRID and other sources. The identification of communities in PPI networks is of great interest because they often reveal previously unknown functional ties between proteins. A large number of global clustering algorithms have been applied to protein networks, where the entire network is partitioned into clusters. Here we take a different approach by looking for local communities in PPI networks. Results We develop a tool, named Local Protein Community Finder, which quickly finds a community close to a queried protein in any network available from BioGRID or specified by the user. Our tool uses two new local clustering algorithms Nibble and PageRank-Nibble, which look for a good cluster among the most popular destinations of a short random walk from the queried vertex. The quality of a cluster is determined by proportion of outgoing edges, known as conductance, which is a relative measure particularly useful in undersampled networks. We show that the two local clustering algorithms find communities that not only form excellent clusters, but are also likely to be biologically relevant functional components. We compare the performance of Nibble and PageRank-Nibble to other popular and effective graph partitioning algorithms, and show that they find better clusters in the graph. Moreover, Nibble and PageRank-Nibble find communities that are more functionally coherent. Conclusion The Local Protein Community Finder, accessible at http://xialab.bu.edu/resources/lpcf, allows the user to quickly find a high-quality community close to a queried protein in any network available from BioGRID or specified by the user. We show that the communities found by our tool form good clusters and are functionally coherent

  13. Learning to Learn: A Hidden Dimension within Community Dance Practice

    Science.gov (United States)

    Barr, Sherrie

    2013-01-01

    This article explores ways of learning experienced by university dance students participating in a community dance project. The students were unfamiliar with community-based practices and found themselves needing to remediate held attitudes about dance. How the students came to approach their learning within the dance-making process drew on…

  14. Healthcare Learning Community and Student Retention

    Science.gov (United States)

    Johnson, Sherryl W.

    2014-01-01

    Teaching, learning, and retention processes have evolved historically to include multifaceted techniques beyond the traditional lecture. This article presents related results of a study using a healthcare learning community in a southwest Georgia university. The value of novel techniques and tools in promoting student learning and retention…

  15. Effective Strategies for Sustaining Professional Learning Communities

    Science.gov (United States)

    Bennett, Patricia R.

    2010-01-01

    Professional Learning Communities (PLCs), in which educators work collaboratively to improve learning for students, need effective strategies to sustain them. PLCs promote continuous improvement in student learning and build academic success with increased teacher expertise. Grounded in organizational systems theory, participative leadership…

  16. SOCIAL NETWORKS AS A MEANS OF LEARNING PROCESS

    Directory of Open Access Journals (Sweden)

    T. Arhipova

    2015-02-01

    Full Text Available This paper presents an analysis of social networks in terms of their possible use in the education system. The integration of new information and communication technologies with the technologies of learning is gradually changing the concept of modern education and promotes educational environment focused on the interests and personal development, achievement of her current levels of education, internationalization and increasing access to educational resources, creating conditions for mobility of students and teachers improving the quality of education and the formation of a single educational space. The peculiarity of such an environment is to provide creative research activity of the teacher and students in the learning process. Network services provide the means by which students can act as active creators of media content. The paper presents the results of a study of the advantages and disadvantages of using web communities in the educational process. Articulated pedagogical conditions of the effective organization of educational process in the virtual learning environment using social networks. The experience of the use of social networks in the learning process of the university. Such networking technologies, such as forums, blogs, wikis, educational portals and automated systems for distance learning, having undoubted didactic and methodological advantages, inferior social networks in terms of involving users in their communication space, as well as compliance with the intellectual, creative and social needs.

  17. Network Community Detection on Metric Space

    Directory of Open Access Journals (Sweden)

    Suman Saha

    2015-08-01

    Full Text Available Community detection in a complex network is an important problem of much interest in recent years. In general, a community detection algorithm chooses an objective function and captures the communities of the network by optimizing the objective function, and then, one uses various heuristics to solve the optimization problem to extract the interesting communities for the user. In this article, we demonstrate the procedure to transform a graph into points of a metric space and develop the methods of community detection with the help of a metric defined for a pair of points. We have also studied and analyzed the community structure of the network therein. The results obtained with our approach are very competitive with most of the well-known algorithms in the literature, and this is justified over the large collection of datasets. On the other hand, it can be observed that time taken by our algorithm is quite less compared to other methods and justifies the theoretical findings.

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

    Science.gov (United States)

    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.

  19. Communities in Large Networks: Identification and Ranking

    DEFF Research Database (Denmark)

    Olsen, Martin

    2008-01-01

    We study the problem of identifying and ranking the members of a community in a very large network with link analysis only, given a set of representatives of the community. We define the concept of a community justified by a formal analysis of a simple model of the evolution of a directed graph. ...... and its immediate surroundings. The members are ranked with a “local” variant of the PageRank algorithm. Results are reported from successful experiments on identifying and ranking Danish Computer Science sites and Danish Chess pages using only a few representatives....

  20. TRICALCAR : Weaving Community Based Wireless Networks in ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    This grant will support a capacity-building and applied research project on community wireless networking in Latin America and the Caribbean (LAC). Researchers will review, update and adapt 18 existing online thematic modules, and design seven new ones. A group of wireless experts with expertise in the social impacts ...

  1. Governance Mechanisms in Food Community Networks

    NARCIS (Netherlands)

    Pascucci, S.; Lombardi, A.; Cembalo, L.; Dentoni, D.

    2013-01-01

    This paper discusses the concept of the food community network (FCN) and how consumers and farmers organize credence food transactions. The FCN is based on pooling specific resources and using membership-based contracts to assign decision and property rights. It implies an organization based on a

  2. Stochastic Variational Learning in Recurrent Spiking Networks

    Directory of Open Access Journals (Sweden)

    Danilo eJimenez Rezende

    2014-04-01

    Full Text Available The ability to learn and perform statistical inference with biologically plausible recurrent network of spiking neurons is an important step towards understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent spiking networks with hidden neurons, combining principles from variational learning and reinforcement learning. Our network defines a generative model over spike train histories and the derived learning rule has the form of a local Spike Timing Dependent Plasticity rule modulated by global factors (neuromodulators conveying information about ``novelty on a statistically rigorous ground.Simulations show that our model is able to learn bothstationary and non-stationary patterns of spike trains.We also propose one experiment that could potentially be performed with animals in order to test the dynamics of the predicted novelty signal.

  3. THE SCHOOL AS A LEARNING COMMUNITY

    Directory of Open Access Journals (Sweden)

    Cintya Arely Hernández-López

    2015-07-01

    Full Text Available In the present study is to weight the learning communities, starting to know the approach that has a school in the Chihuahua state to become a learning community, expecting describe how the school gathers the elements to operate as such. The method that was in use was the study of case, resting on the technologies of observation, interview and survey, same that complemented each other with the information that came from the survey and from the analysis of the “portafolio”. The case of study though it presents characteristics that demonstrate inside a community of learning as quality, collaborative work however the institution does not possess the opening and the participation of the involved ones, being an obstacle for the consolidation and benefit of the educational community; ith what there meets distant the possibility that this politics to turn to the school in a community of learning could be consolidate.

  4. Network Learning and Innovation in SME Formal Networks

    Directory of Open Access Journals (Sweden)

    Jivka Deiters

    2013-02-01

    Full Text Available The driver for this paper is the need to better understand the potential for learning and innovation that networks canprovide especially for small and medium sized enterprises (SMEs which comprise by far the majority of enterprises in the food sector. With the challenges the food sector is facing in the near future, learning and innovation or more focused, as it is being discussed in the paper, ‘learning for innovation’ are not just opportunities but pre‐conditions for the sustainability of the sector. Network initiatives that could provide appropriate support involve social interaction and knowledge exchange, learning, competence development, and coordination (organization and management of implementation. The analysis identifies case studies in any of these orientations which serve different stages of the innovation process: invention and implementation. The variety of network case studies cover networks linked to a focus group for training, research, orconsulting, networks dealing with focused market oriented product or process development, promotional networks, and networks for open exchange and social networking.

  5. Quantitative learning strategies based on word networks

    Science.gov (United States)

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

    2018-02-01

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

  6. Learning in innovation networks: Some simulation experiments

    Science.gov (United States)

    Gilbert, Nigel; Ahrweiler, Petra; Pyka, Andreas

    2007-05-01

    According to the organizational learning literature, the greatest competitive advantage a firm has is its ability to learn. In this paper, a framework for modeling learning competence in firms is presented to improve the understanding of managing innovation. Firms with different knowledge stocks attempt to improve their economic performance by engaging in radical or incremental innovation activities and through partnerships and networking with other firms. In trying to vary and/or to stabilize their knowledge stocks by organizational learning, they attempt to adapt to environmental requirements while the market strongly selects on the results. The simulation experiments show the impact of different learning activities, underlining the importance of innovation and learning.

  7. What's the VALUE of Information Literacy? Comparing Learning Community and Non-Learning Community Student Learning Outomes

    Science.gov (United States)

    Rapchak, Marcia E.; Brungard, Allison B.; Bergfelt, Theodore W.

    2016-01-01

    Using the Information Literacy VALUE Rubric provided by the AAC&U, this study compares thirty final capstone assignments in a research course in a learning community with thirty final assignments in from students not in learning communities. Results indicated higher performance of the non-learning community students; however, transfer skills…

  8. A Transfer Learning Approach for Network Modeling

    Science.gov (United States)

    Huang, Shuai; Li, Jing; Chen, Kewei; Wu, Teresa; Ye, Jieping; Wu, Xia; Yao, Li

    2012-01-01

    Networks models have been widely used in many domains to characterize the interacting relationship between physical entities. A typical problem faced is to identify the networks of multiple related tasks that share some similarities. In this case, a transfer learning approach that can leverage the knowledge gained during the modeling of one task to help better model another task is highly desirable. In this paper, we propose a transfer learning approach, which adopts a Bayesian hierarchical model framework to characterize task relatedness and additionally uses the L1-regularization to ensure robust learning of the networks with limited sample sizes. A method based on the Expectation-Maximization (EM) algorithm is further developed to learn the networks from data. Simulation studies are performed, which demonstrate the superiority of the proposed transfer learning approach over single task learning that learns the network of each task in isolation. The proposed approach is also applied to identification of brain connectivity networks of Alzheimer’s disease (AD) from functional magnetic resonance image (fMRI) data. The findings are consistent with the AD literature. PMID:24526804

  9. Social network fragmentation and community health.

    Science.gov (United States)

    Chami, Goylette F; Ahnert, Sebastian E; Kabatereine, Narcis B; Tukahebwa, Edridah M

    2017-09-05

    Community health interventions often seek to intentionally destroy paths between individuals to prevent the spread of infectious diseases. Immunizing individuals through direct vaccination or the provision of health education prevents pathogen transmission and the propagation of misinformation concerning medical treatments. However, it remains an open question whether network-based strategies should be used in place of conventional field approaches to target individuals for medical treatment in low-income countries. We collected complete friendship and health advice networks in 17 rural villages of Mayuge District, Uganda. Here we show that acquaintance algorithms, i.e., selecting neighbors of randomly selected nodes, were systematically more efficient in fragmenting all networks than targeting well-established community roles, i.e., health workers, village government members, and schoolteachers. Additionally, community roles were not good proxy indicators of physical proximity to other households or connections to many sick people. We also show that acquaintance algorithms were effective in offsetting potential noncompliance with deworming treatments for 16,357 individuals during mass drug administration (MDA). Health advice networks were destroyed more easily than friendship networks. Only an average of 32% of nodes were removed from health advice networks to reduce the percentage of nodes at risk for refusing treatment in MDA to below 25%. Treatment compliance of at least 75% is needed in MDA to control human morbidity attributable to parasitic worms and progress toward elimination. Our findings point toward the potential use of network-based approaches as an alternative to role-based strategies for targeting individuals in rural health interventions.

  10. Networks and emotion-driven user communities at popular blogs

    Science.gov (United States)

    Mitrović, M.; Paltoglou, G.; Tadić, B.

    2010-10-01

    Online communications at web portals represents technology-mediated user interactions, leading to massive data and potentially new techno-social phenomena not seen in real social mixing. Apart from being dynamically driven, the user interactions via posts is indirect, suggesting the importance of the contents of the posted material. We present a systematic way to study Blog data by combined approaches of physics of complex networks and computer science methods of text analysis. We are mapping the Blog data onto a bipartite network where users and posts with comments are two natural partitions. With the machine learning methods we classify the texts of posts and comments for their emotional contents as positive or negative, or otherwise objective (neutral). Using the spectral methods of weighted bipartite graphs, we identify topological communities featuring the users clustered around certain popular posts, and underly the role of emotional contents in the emergence and evolution of these communities.

  11. Cooperation in the prisoner's dilemma game on tunable community networks

    Science.gov (United States)

    Liu, Penghui; Liu, Jing

    2017-04-01

    Community networks have attracted lots of attention as they widely exist in the real world and are essential to study properties of networks. As the game theory illustrates the competitive relationship among individuals, studying the iterated prisoner's dilemma games (PDG) on community networks is meaningful. In this paper, we focus on investigating the relationship between the cooperation level of community networks and that of their communities in the prisoner's dilemma games. With this purpose in mind, a type of tunable community networks whose communities inherit not only the scale-free property, but also the characteristic of adjustable cooperation level of Holme and Kim (HK) networks is designed. Both uniform and non-uniform community networks are investigated. We find out that cooperation enhancement of communities can improve the cooperation level of the whole networks. Moreover, simulation results indicate that a large community is a better choice than a small community to improve the cooperation level of the whole networks. Thus, improving the cooperation level of community networks can be divided into a number of sub-problems targeting at improving the cooperation level of individual communities, which can save the computation cost and deal with the problem of improving the cooperation level of huge community networks. Moreover, as the larger community is a better choice, it is reasonable to start with large communities, according to the greedy strategy when the number of nodes can participate in the enhancement is limited.

  12. Brain Networks of Explicit and Implicit Learning

    Science.gov (United States)

    Yang, Jing; Li, Ping

    2012-01-01

    Are explicit versus implicit learning mechanisms reflected in the brain as distinct neural structures, as previous research indicates, or are they distinguished by brain networks that involve overlapping systems with differential connectivity? In this functional MRI study we examined the neural correlates of explicit and implicit learning of artificial grammar sequences. Using effective connectivity analyses we found that brain networks of different connectivity underlie the two types of learning: while both processes involve activation in a set of cortical and subcortical structures, explicit learners engage a network that uses the insula as a key mediator whereas implicit learners evoke a direct frontal-striatal network. Individual differences in working memory also differentially impact the two types of sequence learning. PMID:22952624

  13. Learning Bayesian networks for discrete data

    KAUST Repository

    Liang, Faming

    2009-02-01

    Bayesian networks have received much attention in the recent literature. In this article, we propose an approach to learn Bayesian networks using the stochastic approximation Monte Carlo (SAMC) algorithm. Our approach has two nice features. Firstly, it possesses the self-adjusting mechanism and thus avoids essentially the local-trap problem suffered by conventional MCMC simulation-based approaches in learning Bayesian networks. Secondly, it falls into the class of dynamic importance sampling algorithms; the network features can be inferred by dynamically weighted averaging the samples generated in the learning process, and the resulting estimates can have much lower variation than the single model-based estimates. The numerical results indicate that our approach can mix much faster over the space of Bayesian networks than the conventional MCMC simulation-based approaches. © 2008 Elsevier B.V. All rights reserved.

  14. The Temperament of Members of Learning Communities.

    Science.gov (United States)

    Johnson, Daniel P.

    1999-01-01

    A consortium leader discusses four dimensions of faculty temperament (extroversion/introversion, sensing/intuition, thinking/feeling, and judging/perceiving) that influence learning networks' success. Networks require strong commitment, a sense of shared purpose, a mixture of information sharing and psychological support, effective facilitation,…

  15. Deep Learning: Inquiring Communities of Practice

    Science.gov (United States)

    Halbert, Judy; Kaser, Linda

    2006-01-01

    This article discusses the Network of Performance-Based Schools, first developed in 1999, and how it answers a personal and professional desire shared to work together in multidisciplinary groups with the profit of learning. The development of the Network of Performance-Based Schools evolved out of a shared personal and professional desire for…

  16. Emergence of Multiplex Communities in Collaboration Networks.

    Directory of Open Access Journals (Sweden)

    Federico Battiston

    Full Text Available Community structures in collaboration networks reflect the natural tendency of individuals to organize their work in groups in order to better achieve common goals. In most of the cases, individuals exploit their connections to introduce themselves to new areas of interests, giving rise to multifaceted collaborations which span different fields. In this paper, we analyse collaborations in science and among movie actors as multiplex networks, where the layers represent respectively research topics and movie genres, and we show that communities indeed coexist and overlap at the different layers of such systems. We then propose a model to grow multiplex networks based on two mechanisms of intra and inter-layer triadic closure which mimic the real processes by which collaborations evolve. We show that our model is able to explain the multiplex community structure observed empirically, and we infer the strength of the two underlying social mechanisms from real-world systems. Being also able to correctly reproduce the values of intra-layer and inter-layer assortativity correlations, the model contributes to a better understanding of the principles driving the evolution of social networks.

  17. Defining and describing medical learning communities: results of a national survey.

    Science.gov (United States)

    Ferguson, Kristi J; Wolter, Ellen M; Yarbrough, Donald B; Carline, Jan D; Krupat, Edward

    2009-11-01

    To investigate what is meant by learning community in medical education and to identify the most important features of current medical education learning communities. After a literature review, the authors surveyed academic deans of all U.S. and Canadian medical schools and colleges (N=124) to identify those that had implemented a learning community. Those with student learning communities (N=18) answered a series of questions about the goals, structure, function, benefits, and challenges of their communities. The most common primary goals included fostering communication among students and faculty; promoting caring, trust, and teamwork; helping students establish academic support networks; and helping students establish social support networks. Most deans said that students remained in the same community for all four years of medical school and that communities were linked to specific faculty and/or peer advisors. For most schools, communities included students from many class years, and participation was mandatory. Curricular purposes included professionalism training, leadership development, and service learning. Almost all schools had social functions related to their communities, and most provided career planning, group mentoring, and personal counseling. Learning communities in medical education demonstrate diverse approaches to achieving the general goal of enhanced student learning. Medical school leaders considering learning communities should determine the goals they want to accomplish and be open to adopting different approaches based on local needs. Evaluation and effective monitoring of evolution are needed to determine the best approaches for different needs and to assess impact on students and faculty.

  18. Utilizing Peer Mentor Roles in Learning Communities

    Science.gov (United States)

    Rieske, Laura Jo; Benjamin, Mimi

    2015-01-01

    For a number of learning community programs, peer mentors provide an additional layer of staffing support. This chapter highlights peer mentor roles from a sample of programs and suggests important components for the construction of these roles.

  19. The Professional Learning Community in Special Education Schools: The Principal's Role

    Science.gov (United States)

    Schechter, Chen; Feldman, Niv

    2013-01-01

    The concept of a professional learning community is characterized by the networks of learning processes which exist among its members, where teachers continuously deliberate with one another on how to solve problems that relate to teaching and learning. Interestingly, whereas a growing number of studies have focused on how to promote collective…

  20. Rethinking the learning of belief network probabilities

    Energy Technology Data Exchange (ETDEWEB)

    Musick, R.

    1996-03-01

    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 rote 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 neural 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.

  1. Designing optimal peer support to alleviate learner cognitive load in Learning Networks

    NARCIS (Netherlands)

    Hsiao, Amy; Brouns, Francis; Sloep, Peter

    2012-01-01

    Hsiao, Y. P., Brouns, F., & Sloep, P. B. (2012, 21 July). Designing optimal peer support to alleviate learner cognitive load in Learning Networks. Presentation at IADIS International Conference Web-Based Communities and Social Media 2012, Lisbon, Portugal.

  2. Facebook: Networking the Community of Society

    DEFF Research Database (Denmark)

    Tække, Jesper

    of communication also provides the basis for the formation and maintenance of people’s social identity, so that they and society are in harmony. In contrast to community communication, the article explores the notion of network communication, which is classified as communication that may have some positive effects...... but that also may pose certain risks for modern society and for the development and maintenance of social identity. The article argues that communication through and about status updates on Facebook may be categorized as network communication, and finally it discusses whether and to what extent this kind...

  3. Adaptive Learning in Weighted Network Games

    NARCIS (Netherlands)

    Bayer, Péter; Herings, P. Jean-Jacques; Peeters, Ronald; Thuijsman, Frank

    2017-01-01

    This paper studies adaptive learning in the class of weighted network games. This class of games includes applications like research and development within interlinked firms, crime within social networks, the economics of pollution, and defense expenditures within allied nations. We show that for

  4. Comunidades de aprendizaje mediadas por redes informáticas Comunidades de aprendizagem mediadas pelas redes informáticas Computer-Network-Mediated Learning Communities

    Directory of Open Access Journals (Sweden)

    Luis Facundo Maldonado-Granado

    2008-06-01

    grupos consolidados e três grupos em processo de consolidação, docentes (de quatro matérias nas três escolas e estudantes. Estas comunidades interatuaram através de atividades de aprendizagem mediada por tecnologias, incluídas um software de representação de conhecimento mediante ontologias (Simas, um software para a representação gráfica e solução cooperativa de problemas (Coolmodes e uma plataforma web para fomentar a construção da comunidade (portal Colômbia Aprende. É descrito o processo de construção da comunidade e uma análise dos produtos logrados nas quatro matérias usando os ambientes informáticos. Como factores determinantes na conformação da comunidade, são identificados a qualidade da plataforma tecnológica, as habilidades no manejo tecnológico e em atividades de entretenimento, e a atitude e a cultura dois atores para o trabalho cooperativo.This article describes the results of a study entitled "Simas and CoolModes in the Development of Basic Competencies; An Experience in the Formation of a Technology-mediated Learning Community." It was conducted at the INEM School in Bucaramanga (Colombia; two schools in Cundinamarca also took part. Three learning communities were formed with researchers (interaction between two consolidated groups and three in the process of consolidation, teachers (teachers of four different subjects at the three schools were involved and students. These communities interacted through the development of technology-mediated learning activities. The technology included knowledge representation software that takes advantage of ontologies (Simas, software for graphic representation and collaborative problem-solving (CoolModes, and a web platform to promote the construction of the community (portal Colombia Aprende. The process used to construct the community is described, and the products obtained in the four subject areas, through the use of different computer environments, are analyzed. The factors identified

  5. Electronic Social Networks, Teaching, and Learning

    Science.gov (United States)

    Pidduck, Anne Banks

    2010-01-01

    This paper explores the relationship between electronic social networks, teaching, and learning. Previous studies have shown a strong positive correlation between student engagement and learning. By extending this work to engage instructors and add an electronic component, our study shows possible teaching improvement as well. In particular,…

  6. Realizing Wisdom Theory in Complex Learning Networks

    Science.gov (United States)

    Kok, Ayse

    2009-01-01

    The word "wisdom" is rarely seen in contemporary technology and learning discourse. This conceptual paper aims to provide some clear principles that answer the question: How can we establish wisdom in complex learning networks? By considering the nature of contemporary calls for wisdom the paper provides a metatheoretial framework to evaluate the…

  7. Social Presence and Transactional Distance as an Antecedent to Knowledge Sharing in Virtual Learning Communities

    Science.gov (United States)

    Karaoglan Yilmaz, Fatma Gizem

    2017-01-01

    Today, the use of social network-based virtual learning communities is increasing rapidly in terms of knowledge management. An important dynamic of knowledge management processes is the knowledge sharing behaviors (KSB) in community. The purpose of this study is to examine the KSB of the students in a Facebook-based virtual community created…

  8. NASA Engineering Network Lessons Learned

    Data.gov (United States)

    National Aeronautics and Space Administration — The NASA Lessons Learned system provides access to official, reviewed lessons learned from NASA programs and projects. These lessons have been made available to the...

  9. Communities in a large social network: visualization and analysis

    OpenAIRE

    Eiesland, Jon Wostryck

    2009-01-01

    Communities have been a hot topic in complex network research the last years. Several algorithms for detecting communities have been developed, and in this thesis we use the sequential clique percolation algorithm to detect communities in a large social network. Our network consists of 5.3 million mobile phone users, with mutual communication data aggregated over 18 weeks. In this thesis we do a visual study of the communities, and we clearly see the nested community structure when we do ...

  10. A Science Education Learning Community Story.

    Science.gov (United States)

    Parsons, Sharon

    This paper examines the establishment of a collaborative science education learning community over a five-year period. By assuming a pluralistic theoretical perspective which has been influenced by post-critical theory, postmodernism/poststructuralism, and feminism, focus is placed on the challenges experienced in developing a learning community…

  11. Learning Styles and the Community Educator.

    Science.gov (United States)

    Kussrow, Paul G.; Dunn, Kenneth

    1992-01-01

    Learning style research can be incorporated into community education practice by (1) matching learning time preferences to academic schedules; (2) recognizing that many adult students are global, not analytic, learners and tactual/kinesthetic rather than auditory; and (3) accommodating physical needs in classroom seating, lighting, etc. (SK)

  12. Pragmatism, Pedagogy, and Community Service Learning

    Science.gov (United States)

    Yoder, Scot D.

    2016-01-01

    In this paper I explore Goodwin Liu's proposal to ground the pedagogy of service-learning in the epistemology of pragmatism from the perspective of a reflective practitioner. I review Liu's epistemology and his claim that from within it three features common to service-learning--community, diversity, and engagement--become pedagogical virtues. I…

  13. Learning Communities and the Completion Agenda

    Science.gov (United States)

    Johnson, Kathy E.

    2013-01-01

    Learning communities are widely recognized as a powerful pedagogy that promotes deep learning and student engagement, while also addressing a range of challenges that plague higher education. The Completion Agenda represents a complex set of intersecting priorities advocated by federal and state government, nonprofit organizations, colleges, and…

  14. Multilabel user classification using the community structure of online networks.

    Science.gov (United States)

    Rizos, Georgios; Papadopoulos, Symeon; Kompatsiaris, Yiannis

    2017-01-01

    We study the problem of semi-supervised, multi-label user classification of networked data in the online social platform setting. We propose a framework that combines unsupervised community extraction and supervised, community-based feature weighting before training a classifier. We introduce Approximate Regularized Commute-Time Embedding (ARCTE), an algorithm that projects the users of a social graph onto a latent space, but instead of packing the global structure into a matrix of predefined rank, as many spectral and neural representation learning methods do, it extracts local communities for all users in the graph in order to learn a sparse embedding. To this end, we employ an improvement of personalized PageRank algorithms for searching locally in each user's graph structure. Then, we perform supervised community feature weighting in order to boost the importance of highly predictive communities. We assess our method performance on the problem of user classification by performing an extensive comparative study among various recent methods based on graph embeddings. The comparison shows that ARCTE significantly outperforms the competition in almost all cases, achieving up to 35% relative improvement compared to the second best competing method in terms of F1-score.

  15. Multilabel user classification using the community structure of online networks.

    Directory of Open Access Journals (Sweden)

    Georgios Rizos

    Full Text Available We study the problem of semi-supervised, multi-label user classification of networked data in the online social platform setting. We propose a framework that combines unsupervised community extraction and supervised, community-based feature weighting before training a classifier. We introduce Approximate Regularized Commute-Time Embedding (ARCTE, an algorithm that projects the users of a social graph onto a latent space, but instead of packing the global structure into a matrix of predefined rank, as many spectral and neural representation learning methods do, it extracts local communities for all users in the graph in order to learn a sparse embedding. To this end, we employ an improvement of personalized PageRank algorithms for searching locally in each user's graph structure. Then, we perform supervised community feature weighting in order to boost the importance of highly predictive communities. We assess our method performance on the problem of user classification by performing an extensive comparative study among various recent methods based on graph embeddings. The comparison shows that ARCTE significantly outperforms the competition in almost all cases, achieving up to 35% relative improvement compared to the second best competing method in terms of F1-score.

  16. QUANTA: An Interdisciplinary Learning Community (Four Studies).

    Science.gov (United States)

    Avens, Cynthia; Zelley, Richard

    QUANTA is a year-long interdisciplinary program at Daytona Beach Community College (Florida) that seeks to establish a learning community of students and teachers. Three courses (English, Pyschology, and Humanities) are integrated around a common theme each semester of the freshman year, and are taught using a collaborative teaching model. This…

  17. Living and Learning Communities: One University's Journey

    Science.gov (United States)

    Whitcher-Skinner, Kendra; Dees, Sharon J.; Watkins, Paul

    2017-01-01

    University housing has the capacity to offer more than comfortable living spaces, and campuses across the U.S., including our own, are exploring models of residential learning communities that provide both academic and social support students while cultivating a strong sense of community. In this article, we describe our campus foray into offering…

  18. Walk modularity and community structure in networks

    OpenAIRE

    Mehrle, David; Strosser, Amy; Harkin, Anthony

    2014-01-01

    Modularity maximization has been one of the most widely used approaches in the last decade for discovering community structure in networks of practical interest in biology, computing, social science, statistical mechanics, and more. Modularity is a quality function that measures the difference between the number of edges found within clusters minus the number of edges one would statistically expect to find based on random chance. We present a natural generalization of modularity based on the ...

  19. Identification of hybrid node and link communities in complex networks.

    Science.gov (United States)

    He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

    2015-03-02

    Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.

  20. Identification of hybrid node and link communities in complex networks

    Science.gov (United States)

    He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

    2015-03-01

    Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.

  1. Will Learning Social Inclusion Assist Rural Networks

    Science.gov (United States)

    Marchant, Jillian

    2013-01-01

    Current research on social networks in some rural communities reports continuing demise despite efforts to build resilient communities. Several factors are identified as contributing to social decline including globalisation and rural social characteristics. Particular rural social characteristics, such as strong social bonds among members of…

  2. Student and Community Partner Expectations for Effective Community-Engaged Learning Partnerships

    Science.gov (United States)

    Stack-Cutler, Holly; Dorow, Sara

    2012-01-01

    Student insight and community partner feedback can contribute to understanding and thus improve community-engaged learning practices. Student and community partner voices, however, are not often heard during community-engaged learning development. To ascertain student and community partner expectations for community-engaged learning, thematic…

  3. Shared Vision, Team Learning and Professional Learning Communities

    Science.gov (United States)

    Thompson, Sue C.; McKelvy, Earline

    2007-01-01

    Many middle schools do not use one of the most important strategies to improve student achievement and create socially equitable, developmentally responsive middle schools: becoming a professional learning community. This article summarizes the five disciplines which are vital for learning organizations -- systems thinking, personal mastery,…

  4. Writing Together, Learning Together: Teacher Development through Community Service Learning

    Science.gov (United States)

    He, Ye; Prater, Kathryn

    2014-01-01

    In this study, community service learning is incorporated into a graduate-level English-as-a-Second-Language (ESL) teacher preparation course. Focusing on a writing project participants completed with English Learners (ELs) as part of the service-learning project, we explored the impact of the project on: (1) teachers' understanding of ESL…

  5. Epidemic spreading on complex networks with community structures

    CERN Document Server

    Stegehuis, Clara; van Leeuwaarden, Johan S H

    2016-01-01

    Many real-world networks display a community structure. We study two random graph models that create a network with similar community structure as a given network. One model preserves the exact community structure of the original network, while the other model only preserves the set of communities and the vertex degrees. These models show that community structure is an important determinant of the behavior of percolation processes on networks, such as information diffusion or virus spreading: the community structure can both \\textit{enforce} as well as \\textit{inhibit} diffusion processes. Our models further show that it is the mesoscopic set of communities that matters. The exact internal structures of communities barely influence the behavior of percolation processes across networks. This insensitivity is likely due to the relative denseness of the communities.

  6. Community overlays upon real-world complex networks

    NARCIS (Netherlands)

    Ge, X.; Wang, H.

    2012-01-01

    Many networks are characterized by the presence of communities, densely intra-connected groups with sparser inter-connections between groups. We propose a community overlay network representation to capture large-scale properties of communities. A community overlay Go can be constructed upon a

  7. Building an Online Learning Community

    Science.gov (United States)

    Chen, Yu-Chein

    2004-01-01

    The Internet was not invented for education at beginning (Pett Grabinger, 1995), but it has influenced educational systems considerably, especially by providing another way for distance learning. This powerful communication function is superior to any other educational media. Students can conduct their own self-directed learning without…

  8. Improving the Robustness of Complex Networks with Preserving Community Structure

    Science.gov (United States)

    Yang, Yang; Li, Zhoujun; Chen, Yan; Zhang, Xiaoming; Wang, Senzhang

    2015-01-01

    Complex networks are everywhere, such as the power grid network, the airline network, the protein-protein interaction network, and the road network. The networks are ‘robust yet fragile’, which means that the networks are robust against random failures but fragile under malicious attacks. The cascading failures, system-wide disasters and intentional attacks on these networks are deserving of in-depth study. Researchers have proposed many solutions to improve the robustness of these networks. However whilst many solutions preserve the degree distribution of the networks, little attention is paid to the community structure of these networks. We argue that the community structure of a network is a defining characteristic of a network which identifies its functionality and thus should be preserved. In this paper, we discuss the relationship between robustness and the community structure. Then we propose a 3-step strategy to improve the robustness of a network, while retaining its community structure, and also its degree distribution. With extensive experimentation on representative real-world networks, we demonstrate that our method is effective and can greatly improve the robustness of networks, while preserving community structure and degree distribution. Finally, we give a description of a robust network, which is useful not only for improving robustness, but also for designing robust networks and integrating networks. PMID:25674786

  9. Proceedings of the Neural Network Workshop for the Hanford Community

    Energy Technology Data Exchange (ETDEWEB)

    Keller, P.E.

    1994-01-01

    These proceedings were generated from a series of presentations made at the Neural Network Workshop for the Hanford Community. The abstracts and viewgraphs of each presentation are reproduced in these proceedings. This workshop was sponsored by the Computing and Information Sciences Department in the Molecular Science Research Center (MSRC) at the Pacific Northwest Laboratory (PNL). Artificial neural networks constitute a new information processing technology that is destined within the next few years, to provide the world with a vast array of new products. A major reason for this is that artificial neural networks are able to provide solutions to a wide variety of complex problems in a much simpler fashion than is possible using existing techniques. In recognition of these capabilities, many scientists and engineers are exploring the potential application of this new technology to their fields of study. An artificial neural network (ANN) can be a software simulation, an electronic circuit, optical system, or even an electro-chemical system designed to emulate some of the brain`s rudimentary structure as well as some of the learning processes that are believed to take place in the brain. For a very wide range of applications in science, engineering, and information technology, ANNs offer a complementary and potentially superior approach to that provided by conventional computing and conventional artificial intelligence. This is because, unlike conventional computers, which have to be programmed, ANNs essentially learn from experience and can be trained in a straightforward fashion to carry out tasks ranging from the simple to the highly complex.

  10. Fuzziness and Overlapping Communities in Large-Scale Networks

    OpenAIRE

    Wang, Qinna; Fleury, Eric

    2012-01-01

    International audience; Overlapping community detection is a popular topic in complex networks. As compared to disjoint community structure, overlapping community structure is more suitable to describe networks at a macroscopic level. Overlaps shared by communities play an important role in combining different communities. In this paper, two methods are proposed to detect overlapping community structure. One is called clique optimization, and the other is named fuzzy detection. Clique optimiz...

  11. Professional Learning in Unlikely Spaces: Social Media and Virtual Communities as Professional Development

    Directory of Open Access Journals (Sweden)

    Kathleen P. King

    2011-12-01

    Full Text Available In this case study, results demonstrate that an individual’s use of social media as professional learning spans understanding, networking, professional identity development, and transformative learning. Specifically, virtual online communities facilitated through social media provide professional networks, social relationships and learning beyond the scope of the individual’s usual experience. Case study method reveals strategies, extent, and impact of learning providing insight into this phenomenon. The significance of the research includes purposefully facilitating professional learning through informal learning contexts, including social media and online communities beyond technology-centric fields. Discussion and recommendations include using social media and virtual communities as instructional strategies for graduate studies and continued learning beyond formal education.

  12. A Learning Community Revisited: Did Intentional Changes in a Wellness Learning Community Have the Desired Outcomes?

    Science.gov (United States)

    Frazier, William R.; Eighmy, Myron A.

    2016-01-01

    This study focuses on a wellness learning community in order to report changes that were made to its operation and to determine if its members had higher levels of satisfaction than did other students living in the same residence hall. Research was conducted on the wellness learning community at a Midwest university to determine if changes made in…

  13. Personalizing Access to Learning Networks

    DEFF Research Database (Denmark)

    Dolog, Peter; Simon, Bernd; Nejdl, Wolfgang

    2008-01-01

    In this article, we describe a Smart Space for Learning™ (SS4L) framework and infrastructure that enables personalized access to distributed heterogeneous knowledge repositories. Helping a learner to choose an appropriate learning resource or activity is a key problem which we address in this fra......In this article, we describe a Smart Space for Learning™ (SS4L) framework and infrastructure that enables personalized access to distributed heterogeneous knowledge repositories. Helping a learner to choose an appropriate learning resource or activity is a key problem which we address...... in this framework, enabling personalized access to federated learning repositories with a vast number of learning offers. Our infrastructure includes personalization strategies both at the query and the query results level. Query rewriting is based on learning and language preferences; rule-based and ranking...

  14. Collaborative distance learning: Developing an online learning community

    Science.gov (United States)

    Stoytcheva, Maria

    2017-12-01

    The method of collaborative distance learning has been applied for years in a number of distance learning courses, but they are relatively few in foreign language learning. The context of this research is a hybrid distance learning of French for specific purposes, delivered through the platform UNIV-RcT (Strasbourg University), which combines collaborative activities for the realization of a common problem-solving task online. The study focuses on a couple of aspects: on-line interactions carried out in small, tutored groups and the process of community building online. By analyzing the learner's perceptions of community and collaborative learning, we have tried to understand the process of building and maintenance of online learning community and to see to what extent the collaborative distance learning contribute to the development of the competence expectations at the end of the course. The analysis of the results allows us to distinguish the advantages and limitations of this type of e-learning and thus evaluate their pertinence.

  15. Social Media as Avenue for Personal Learning for Educators: Personal Learning Networks Encourage Application of Knowledge and Skills

    Science.gov (United States)

    Eller, Linda S.

    2012-01-01

    Social media sites furnish an online space for a community of practice to create relationships and trust, collaboration and connections, and a personal learning environment. Social networking sites, both public and private, have common elements: member profiles, groups, discussions, and forums. A community of practice brings participants together…

  16. Dynamics and control of diseases in networks with community structure.

    Directory of Open Access Journals (Sweden)

    Marcel Salathé

    2010-04-01

    Full Text Available The dynamics of infectious diseases spread via direct person-to-person transmission (such as influenza, smallpox, HIV/AIDS, etc. depends on the underlying host contact network. Human contact networks exhibit strong community structure. Understanding how such community structure affects epidemics may provide insights for preventing the spread of disease between communities by changing the structure of the contact network through pharmaceutical or non-pharmaceutical interventions. We use empirical and simulated networks to investigate the spread of disease in networks with community structure. We find that community structure has a major impact on disease dynamics, and we show that in networks with strong community structure, immunization interventions targeted at individuals bridging communities are more effective than those simply targeting highly connected individuals. Because the structure of relevant contact networks is generally not known, and vaccine supply is often limited, there is great need for efficient vaccination algorithms that do not require full knowledge of the network. We developed an algorithm that acts only on locally available network information and is able to quickly identify targets for successful immunization intervention. The algorithm generally outperforms existing algorithms when vaccine supply is limited, particularly in networks with strong community structure. Understanding the spread of infectious diseases and designing optimal control strategies is a major goal of public health. Social networks show marked patterns of community structure, and our results, based on empirical and simulated data, demonstrate that community structure strongly affects disease dynamics. These results have implications for the design of control strategies.

  17. Social Learning Network Analysis Model to Identify Learning Patterns Using Ontology Clustering Techniques and Meaningful Learning

    Science.gov (United States)

    Firdausiah Mansur, Andi Besse; Yusof, Norazah

    2013-01-01

    Clustering on Social Learning Network still not explored widely, especially when the network focuses on e-learning system. Any conventional methods are not really suitable for the e-learning data. SNA requires content analysis, which involves human intervention and need to be carried out manually. Some of the previous clustering techniques need…

  18. Student Support Networks in Online Doctoral Programs: Exploring Nested Communities

    Directory of Open Access Journals (Sweden)

    Sharla Berry

    2017-04-01

    Full Text Available Aim/Purpose: Enrollment in online doctoral programs has grown over the past decade. A sense of community, defined as feelings of closeness within a social group, is vital to retention, but few studies have explored how online doctoral students create community. Background: In this qualitative case study, I explore how students in one online doctoral program created a learning community. Methodology: Data for the study was drawn from 60 hours of video footage from six online courses, the message boards from the six courses, and twenty interviews with first and second-year students. Contribution: Findings from this study indicate that the structure of the social network in an online doctoral program is significantly different from the structure of learning communities in face-to-face programs. In the online program, the doctoral community was more insular, more peer-centered, and less reliant on faculty support than in in-person programs. Findings: Utilizing a nested communities theoretical framework, I identified four subgroups that informed online doctoral students’ sense of community: cohort, class groups, small peer groups, and study groups. Students interacted frequently with members of each of the aforementioned social groups and drew academic, social, and emotional support from their interactions. Recommendations for Practitioners: Data from this study suggests that online doctoral students are interested in making social and academic connections. Practitioners should leverage technology and on-campus supports to promote extracurricular interactions for online students. Recommendation for Researchers: Rather than focus on professional socialization, students in the online doctoral community were interested in providing social and academic support to peers. Researchers should consider how socialization in online doctoral programs differs from traditional, face-to-face programs. Impact on Society: As universities increase online offerings

  19. A Decomposition Algorithm for Learning Bayesian Network Structures from Data

    DEFF Research Database (Denmark)

    Zeng, Yifeng; Cordero Hernandez, Jorge

    2008-01-01

    It is a challenging task of learning a large Bayesian network from a small data set. Most conventional structural learning approaches run into the computational as well as the statistical problems. We propose a decomposition algorithm for the structure construction without having to learn...... the complete network. The new learning algorithm firstly finds local components from the data, and then recover the complete network by joining the learned components. We show the empirical performance of the decomposition algorithm in several benchmark networks....

  20. Community Structure in Time-Dependent, Multiscale, and Multiplex Networks

    OpenAIRE

    Mucha, Peter J; Richardson, Thomas; Macon, Kevin; Porter, Mason A.; Onnela, Jukka-Pekka

    2009-01-01

    Network science is an interdisciplinary endeavor, with methods and applications drawn from across the natural, social, and information sciences. A prominent problem in network science is the algorithmic detection of tightly-connected groups of nodes known as communities. We developed a generalized framework of network quality functions that allowed us to study the community structure of arbitrary multislice networks, which are combinations of individual networks coupled through links that con...

  1. Towards a Community Environmental Observation Network

    Science.gov (United States)

    Mertl, Stefan; Lettenbichler, Anton

    2014-05-01

    The Community Environmental Observation Network (CEON) is dedicated to the development of a free sensor network to collect and distribute environmental data (e.g. ground shaking, climate parameters). The data collection will be done with contributions from citizens, research institutions and public authorities like communities or schools. This will lead to a large freely available data base which can be used for public information, research, the arts,..... To start a free sensor network, the most important step is to provide easy access to free data collection and -distribution tools. The initial aims of the project CEON are dedicated to the development of these tools. A high quality data logger based on open hardware and free software is developed and a software suite of already existing free software for near-real time data communication and data distribution over the Internet will be assembled. Foremost, the development focuses on the collection of data related to the deformation of the earth (such as ground shaking, surface displacement of mass movements and glaciers) and the collection of climate data. The extent to other measurements will be considered in the design. The data logger is built using open hardware prototyping platforms like BeagleBone Black and Arduino. Main features of the data logger are: a 24Bit analog-to-digital converter; a GPS module for time reference and positioning; wireless mesh networking using Optimized Link State Routing; near real-time data transmission and communication; and near real-time differential GNSS positioning using the RTKLIB software. The project CEON is supported by the Internet Foundation Austria (IPA) within the NetIdee 2013 call.

  2. Sharing cost in social community networks

    DEFF Research Database (Denmark)

    Pal, Ranjan; Elango, Divya; Wardana, Satya Ardhy

    2012-01-01

    to gain attention amongst the civilian Internet users. By using special WiFi routers that are provided by a social community network provider (SCNP), users can effectively share their connection with the neighborhood in return for some monthly monetary benefits. However, deployment maps of existing WSCNs...... reflect their slow progress in capturing the WiFi router market. In this paper, we look at a router design and cost sharing problem in WSCNs to improve deployment. We devise a simple to implement, successful, budget-balanced, ex-post efficient, and individually rational auction-based mechanism...

  3. Deep Learning in Neural Networks: An Overview

    OpenAIRE

    Schmidhuber, Juergen

    2014-01-01

    In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpr...

  4. Logarithmic learning for generalized classifier neural network.

    Science.gov (United States)

    Ozyildirim, Buse Melis; Avci, Mutlu

    2014-12-01

    Generalized classifier neural network is introduced as an efficient classifier among the others. Unless the initial smoothing parameter value is close to the optimal one, generalized classifier neural network suffers from convergence problem and requires quite a long time to converge. In this work, to overcome this problem, a logarithmic learning approach is proposed. The proposed method uses logarithmic cost function instead of squared error. Minimization of this cost function reduces the number of iterations used for reaching the minima. The proposed method is tested on 15 different data sets and performance of logarithmic learning generalized classifier neural network is compared with that of standard one. Thanks to operation range of radial basis function included by generalized classifier neural network, proposed logarithmic approach and its derivative has continuous values. This makes it possible to adopt the advantage of logarithmic fast convergence by the proposed learning method. Due to fast convergence ability of logarithmic cost function, training time is maximally decreased to 99.2%. In addition to decrease in training time, classification performance may also be improved till 60%. According to the test results, while the proposed method provides a solution for time requirement problem of generalized classifier neural network, it may also improve the classification accuracy. The proposed method can be considered as an efficient way for reducing the time requirement problem of generalized classifier neural network. Copyright © 2014 Elsevier Ltd. All rights reserved.

  5. Emergence of communities and diversity in social networks

    OpenAIRE

    Han, Xiao; Cao, Shinan; Shen, Zhesi; Zhang, Boyu; Wang, Wen-Xu; Cressman, Ross; Stanley, H. Eugene

    2017-01-01

    Understanding how communities emerge is a fundamental problem in social and economic systems. Here, we experimentally explore the emergence of communities in social networks, using the ultimatum game as a paradigm for capturing individual interactions. We find the emergence of diverse communities in static networks is the result of the local interaction between responders with inherent heterogeneity and rational proposers in which the former act as community leaders. In contrast, communities ...

  6. Distributed Extreme Learning Machine for Nonlinear Learning over Network

    Directory of Open Access Journals (Sweden)

    Songyan Huang

    2015-02-01

    Full Text Available Distributed data collection and analysis over a network are ubiquitous, especially over a wireless sensor network (WSN. To our knowledge, the data model used in most of the distributed algorithms is linear. However, in real applications, the linearity of systems is not always guaranteed. In nonlinear cases, the single hidden layer feedforward neural network (SLFN with radial basis function (RBF hidden neurons has the ability to approximate any continuous functions and, thus, may be used as the nonlinear learning system. However, confined by the communication cost, using the distributed version of the conventional algorithms to train the neural network directly is usually prohibited. Fortunately, based on the theorems provided in the extreme learning machine (ELM literature, we only need to compute the output weights of the SLFN. Computing the output weights itself is a linear learning problem, although the input-output mapping of the overall SLFN is still nonlinear. Using the distributed algorithmto cooperatively compute the output weights of the SLFN, we obtain a distributed extreme learning machine (dELM for nonlinear learning in this paper. This dELM is applied to the regression problem and classification problem to demonstrate its effectiveness and advantages.

  7. Malware Propagation and Prevention Model for Time-Varying Community Networks within Software Defined Networks

    OpenAIRE

    Lan Liu; Ryan K. L. Ko; Guangming Ren; Xiaoping Xu

    2017-01-01

    As the adoption of Software Defined Networks (SDNs) grows, the security of SDN still has several unaddressed limitations. A key network security research area is in the study of malware propagation across the SDN-enabled networks. To analyze the spreading processes of network malware (e.g., viruses) in SDN, we propose a dynamic model with a time-varying community network, inspired by research models on the spread of epidemics in complex networks across communities. We assume subnets of the ne...

  8. Reinforcement learning account of network reciprocity.

    Directory of Open Access Journals (Sweden)

    Takahiro Ezaki

    Full Text Available Evolutionary game theory predicts that cooperation in social dilemma games is promoted when agents are connected as a network. However, when networks are fixed over time, humans do not necessarily show enhanced mutual cooperation. Here we show that reinforcement learning (specifically, the so-called Bush-Mosteller model approximately explains the experimentally observed network reciprocity and the lack thereof in a parameter region spanned by the benefit-to-cost ratio and the node's degree. Thus, we significantly extend previously obtained numerical results.

  9. CyberPsychological Computation on Social Community of Ubiquitous Learning

    Science.gov (United States)

    Zhou, Xuan; Dai, Genghui; Huang, Shuang; Sun, Xuemin; Hu, Feng; Hu, Hongzhi; Ivanović, Mirjana

    2015-01-01

    Under the modern network environment, ubiquitous learning has been a popular way for people to study knowledge, exchange ideas, and share skills in the cyberspace. Existing research findings indicate that the learners' initiative and community cohesion play vital roles in the social communities of ubiquitous learning, and therefore how to stimulate the learners' interest and participation willingness so as to improve their enjoyable experiences in the learning process should be the primary consideration on this issue. This paper aims to explore an effective method to monitor the learners' psychological reactions based on their behavioral features in cyberspace and therefore provide useful references for adjusting the strategies in the learning process. In doing so, this paper firstly analyzes the psychological assessment of the learners' situations as well as their typical behavioral patterns and then discusses the relationship between the learners' psychological reactions and their observable features in cyberspace. Finally, this paper puts forward a CyberPsychological computation method to estimate the learners' psychological states online. Considering the diversity of learners' habitual behaviors in the reactions to their psychological changes, a BP-GA neural network is proposed for the computation based on their personalized behavioral patterns. PMID:26557846

  10. CyberPsychological Computation on Social Community of Ubiquitous Learning.

    Science.gov (United States)

    Zhou, Xuan; Dai, Genghui; Huang, Shuang; Sun, Xuemin; Hu, Feng; Hu, Hongzhi; Ivanović, Mirjana

    2015-01-01

    Under the modern network environment, ubiquitous learning has been a popular way for people to study knowledge, exchange ideas, and share skills in the cyberspace. Existing research findings indicate that the learners' initiative and community cohesion play vital roles in the social communities of ubiquitous learning, and therefore how to stimulate the learners' interest and participation willingness so as to improve their enjoyable experiences in the learning process should be the primary consideration on this issue. This paper aims to explore an effective method to monitor the learners' psychological reactions based on their behavioral features in cyberspace and therefore provide useful references for adjusting the strategies in the learning process. In doing so, this paper firstly analyzes the psychological assessment of the learners' situations as well as their typical behavioral patterns and then discusses the relationship between the learners' psychological reactions and their observable features in cyberspace. Finally, this paper puts forward a CyberPsychological computation method to estimate the learners' psychological states online. Considering the diversity of learners' habitual behaviors in the reactions to their psychological changes, a BP-GA neural network is proposed for the computation based on their personalized behavioral patterns.

  11. Community Attachment and Satisfaction: The Role of a Community's Social Network Structure

    Science.gov (United States)

    Crowe, Jessica

    2010-01-01

    This paper links the micro and macro levels of analysis by examining how different aspects of community sentiment are affected by one's personal ties to the community compared with the organizational network structure of the community. Using data collected from residents of six communities in Washington State, network analysis combined with…

  12. Building Ocean Learning Communities: A COSEE Science and Education Partnership

    Science.gov (United States)

    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

  13. How Do Learning Communities Affect First-Year Latino Students?

    Science.gov (United States)

    Huerta, Juan Carlos; Bray, Jennifer J.

    2013-01-01

    Do learning communities with pedagogies of active learning, collaborative learning, and integration of course material affect the learning, achievement, and persistence of first-year Latino university students? The data for this project was obtained from a survey of 1,330 first-year students in the First-Year Learning Community Program at Texas…

  14. Portability and networked learning environments

    NARCIS (Netherlands)

    Collis, Betty; de Diana, I.P.F.

    1994-01-01

    Abstract The portability of educational software is defined as the likelihood of software usage, with or without adaptation, in an educational environment different from that for which it was originally designed and produced. Barriers and research relevant to the portability of electronic learning

  15. "Learning" in a Transgressive Professional Community

    DEFF Research Database (Denmark)

    Jensen, Carsten Juul; Drachmann, Merete; Jeppesen, Lise Kofoed

    2015-01-01

    to deal with overwhelming experiences concerning the naked bodies of patients and death, useful application of theoretical knowledge, the path from novice to advanced beginner, and adjusting to the workplace community. The conclusion is that the learning of nursing students during their first clinical in......This material is a part of a longitudinal development project which seeks to comprehend learning experiences of nursing students during their first clinical in-service placement. The study has a qualitative methodology, inspired by Michael Eraut’s thoughts on learning in the workplace. When...... the workplace perspective is applied, learning seems to be concentrated on actual situations which the learner is in, in contrast to employing constructed concepts. The nursing students’ learning seems to be oriented towards socialization in the clinic as a workplace. This means that the nursing students seek...

  16. Learning to trust : network effects through time.

    NARCIS (Netherlands)

    Barrera, D.; Bunt, G. van de

    2009-01-01

    This article investigates the effects of information originating from social networks on the development of interpersonal trust relations in the context of a dialysis department of a Dutch medium-sized hospital. Hypotheses on learning effects are developed from existing theories and tested using

  17. Learning to trust: network effects through time

    NARCIS (Netherlands)

    Barrera, D.; van de Bunt, G

    2009-01-01

    This article investigates the effects of information originating from social networks on the development of interpersonal trust relations in the context of a dialysis department of a Dutch medium-sized hospital. Hypotheses on learning effects are developed from existing theories and tested using

  18. Social Networking Services in E-Learning

    Science.gov (United States)

    Weber, Peter; Rothe, Hannes

    2016-01-01

    This paper is a report on the findings of a study conducted on the use of the social networking service NING in a cross-location e-learning setting named "Net Economy." We describe how we implemented NING as a fundamental part of the setting through a special phase concept and team building approach. With the help of user statistics, we…

  19. Learning chaotic attractors by neural networks

    NARCIS (Netherlands)

    Bakker, R; Schouten, JC; Giles, CL; Takens, F; van den Bleek, CM

    2000-01-01

    An algorithm is introduced that trains a neural network to identify chaotic dynamics from a single measured time series. During training, the algorithm learns to short-term predict the time series. At the same time a criterion, developed by Diks, van Zwet, Takens, and de Goede (1996) is monitored

  20. Social Networking Sites as a Learning Tool

    Science.gov (United States)

    Sanchez-Casado, Noelia; Cegarra Navarro, Juan Gabriel; Wensley, Anthony; Tomaseti-Solano, Eva

    2016-01-01

    Purpose: Over the past few years, social networking sites (SNSs) have become very useful for firms, allowing companies to manage the customer-brand relationships. In this context, SNSs can be considered as a learning tool because of the brand knowledge that customers develop from these relationships. Because of the fact that knowledge in…

  1. Learning in Networks for Sustainable Development

    NARCIS (Netherlands)

    Lansu, Angelique; Boon, Jo; Sloep, Peter; Van Dam-Mieras, Rietje

    2010-01-01

    The didactic model of remote internships described in this study provides the flexibility needed to support networked learners, i.e. to facilitate the development and subsequent assessment of their competences. The heterogeneity of the participants (students, employers, tutors) in the learning

  2. Research Trends in the Study of ICT Based Learning Communities: A Bibliometric Analysis

    Science.gov (United States)

    Hernández, Jonathan Bermúdez; Chalela, Salim; Arias, Jackeline Valencia; Arias, Alejandro Valencia

    2017-01-01

    The current opportunities to develop and acquire knowledge in the network, Information and Communications Technology (ICT) play a major role in the learning process. This research offers a bibliometric analysis in order to examine the state of the research activity carried out in relation to the learning communities based on ICT. The indicators…

  3. Enhancing Sustainability Curricula through Faculty Learning Communities

    Science.gov (United States)

    Natkin, L. W.; Kolbe, Tammy

    2016-01-01

    Purpose: Although the number of higher education institutions adopting sustainability-focused faculty learning communities (FLCs) has grown, very few of these programs have published evaluation research. This paper aims to report findings from an evaluation of the University of Vermont's (UVM's) sustainability faculty fellows (SFF) program. It…

  4. Educational Transforming, Learning Communities and Teacher Training

    Science.gov (United States)

    Alvarez-Alvarez, Carmen; Fernandez-Diaz, Elia; Osoro-Sierra, Jose Manuel

    2012-01-01

    This analyses of the paper show to develop innovative educational projects through teacher training. The starting point is "learning communities", which is a project to change educational practice. It has a long history in Spain. This project is generated according to the assessment process in order to change practices required by Miguel…

  5. Sustainable school development: professional learning communities

    NARCIS (Netherlands)

    Prof.Dr. E. Verbiest

    2008-01-01

    In this contribution we report about a project about Professional Learning Communities.This project combines development and research. In this contribution we pay attention to the effect of the organisational capacity of a school on the personal and interpersonal capacity and to the impact of a

  6. Teacher Metacognition within the Professional Learning Community

    Science.gov (United States)

    Prytula, Michelle P.

    2012-01-01

    A study of teacher metacognition within the context of the professional learning community (PLC) was conducted to understand how teachers describe their metacognition, what they describe as the catalysts to their metacognition, and how metacognition influences their work. Although the PLC was used as a context for the study, the findings include…

  7. Author-Editor Learning Communities: Writing Science.

    Science.gov (United States)

    Gessell, Donna A.; Kokkala, Irene

    This paper presents the experiences of educators at North Georgia College and State University while developing learning communities connecting the two disciplines of biology and English in the teaching of writing and editing within the scientific context. The paper states that for seven semesters English grammar and composition courses have been…

  8. Emerging Communities at BBC Learning English

    Science.gov (United States)

    Chapman, Catherine; Scott, Paul

    2008-01-01

    This paper traces the development of the BBC Learning English [http://www.bbc.co.uk/worldservice/learningenglish/] online community, focusing on tools such as e-mail discussion lists, message boards, comments boards, student/teacher blogs, competitions, and voting. It describes how relationships between the intermediate level users of all…

  9. College Students, Diversity, and Community Service Learning

    Science.gov (United States)

    Seider, Scott; Huguley, James P.; Novick, Sarah

    2013-01-01

    Background/Context: Over the past two decades, more than 200 studies have been published on the effects of community service learning on university students. However, the majority of these studies have focused on the effects of such programming on White and affluent college students, and few have considered whether there are differential effects…

  10. Learning Style, Sense of Community and Learning Effectiveness in Hybrid Learning Environment

    Science.gov (United States)

    Chen, Bryan H.; Chiou, Hua-Huei

    2014-01-01

    The purpose of this study is to investigate how hybrid learning instruction affects undergraduate students' learning outcome, satisfaction and sense of community. The other aim of the present study is to examine the relationship between students' learning style and learning conditions in mixed online and face-to-face courses. A quasi-experimental…

  11. Pharmacy Student Learning Through Community Service.

    Science.gov (United States)

    Sobota, Kristen Finley; Barnes, Jeremiah; Fitzpatrick, Alyse; Sobota, Micah J

    2015-07-01

    The Ohio Northern University American Society of Consultant Pharmacists chapter provides students the opportunity to apply classroom knowledge with learning through community service. One such program took place at the Lima Towers Apartment Community from September 18, 2014, to October 2, 2014, in Lima, Ohio. Three evening educational sessions focused on a different health topic: 1) mental health, 2) medication adherence/brown bag, and 3) healthy lifestyle choices/nutrition/smoking cessation. All three programs were structured identically, starting with dinner, followed by educational intervention, survey, blood pressure checks, and medication reviews. Two pharmacists and 16 pharmacy students implemented the program. Participants completed a total of 76 satisfaction surveys for the three programs, which were included in the data analysis. The average age of the participants was 65 years; 82% (n = 63) were female. Data demonstrated that 94% (n = 72) "learned something new," while 96% (n = 74) would "recommend the program to a friend/family member." The collected data showed the vast majority of participants from the surrounding community found value in the presentations performed by students, especially with regard to the new information they received and its perceived benefits. In light of such successes, we encourage other student chapters to implement similar community outreach events. ASCP student members can make a strong, positive impact in the community while learning in a nontraditional environment.

  12. Networked Community Change: Understanding Community Systems Change through the Lens of Social Network Analysis.

    Science.gov (United States)

    Lawlor, Jennifer A; Neal, Zachary P

    2016-06-01

    Addressing complex problems in communities has become a key area of focus in recent years (Kania & Kramer, 2013, Stanford Social Innovation Review). Building on existing approaches to understanding and addressing problems, such as action research, several new approaches have emerged that shift the way communities solve problems (e.g., Burns, 2007, Systemic Action Research; Foth, 2006, Action Research, 4, 205; Kania & Kramer, 2011, Stanford Social Innovation Review, 1, 36). Seeking to bring clarity to the emerging literature on community change strategies, this article identifies the common features of the most widespread community change strategies and explores the conditions under which such strategies have the potential to be effective. We identify and describe five common features among the approaches to change. Then, using an agent-based model, we simulate network-building behavior among stakeholders participating in community change efforts using these approaches. We find that the emergent stakeholder networks are efficient when the processes are implemented under ideal conditions. © Society for Community Research and Action 2016.

  13. Enhanced Community Structure Detection in Complex Networks with Partial Background Information

    Science.gov (United States)

    Zhang, Zhong-Yuan; Sun, Kai-Di; Wang, Si-Qi

    2013-11-01

    Community structure detection in complex networks is important since it can help better understand the network topology and how the network works. However, there is still not a clear and widely-accepted definition of community structure, and in practice, different models may give very different results of communities, making it hard to explain the results. In this paper, different from the traditional methodologies, we design an enhanced semi-supervised learning framework for community detection, which can effectively incorporate the available prior information to guide the detection process and can make the results more explainable. By logical inference, the prior information is more fully utilized. The experiments on both the synthetic and the real-world networks confirm the effectiveness of the framework.

  14. A novel community detection method in bipartite networks

    Science.gov (United States)

    Zhou, Cangqi; Feng, Liang; Zhao, Qianchuan

    2018-02-01

    Community structure is a common and important feature in many complex networks, including bipartite networks, which are used as a standard model for many empirical networks comprised of two types of nodes. In this paper, we propose a two-stage method for detecting community structure in bipartite networks. Firstly, we extend the widely-used Louvain algorithm to bipartite networks. The effectiveness and efficiency of the Louvain algorithm have been proved by many applications. However, there lacks a Louvain-like algorithm specially modified for bipartite networks. Based on bipartite modularity, a measure that extends unipartite modularity and that quantifies the strength of partitions in bipartite networks, we fill the gap by developing the Bi-Louvain algorithm that iteratively groups the nodes in each part by turns. This algorithm in bipartite networks often produces a balanced network structure with equal numbers of two types of nodes. Secondly, for the balanced network yielded by the first algorithm, we use an agglomerative clustering method to further cluster the network. We demonstrate that the calculation of the gain of modularity of each aggregation, and the operation of joining two communities can be compactly calculated by matrix operations for all pairs of communities simultaneously. At last, a complete hierarchical community structure is unfolded. We apply our method to two benchmark data sets and a large-scale data set from an e-commerce company, showing that it effectively identifies community structure in bipartite networks.

  15. Service-Learning Pedagogy: Benefits of a Learning Community Approach

    Science.gov (United States)

    Flinders, Brooke A.

    2013-01-01

    Service-learning is, by nature, continually evolving. Seifer (1996) stressed the importance of partnerships between communities and schools, and stated that reflection should facilitate the connection between practice and theory, and lead to critical thinking. Before these reflective activities occur, however, much can be done to maximize…

  16. Collaborative Supervised Learning for Sensor Networks

    Science.gov (United States)

    Wagstaff, Kiri L.; Rebbapragada, Umaa; Lane, Terran

    2011-01-01

    Collaboration methods for distributed machine-learning algorithms involve the specification of communication protocols for the learners, which can query other learners and/or broadcast their findings preemptively. Each learner incorporates information from its neighbors into its own training set, and they are thereby able to bootstrap each other to higher performance. Each learner resides at a different node in the sensor network and makes observations (collects data) independently of the other learners. After being seeded with an initial labeled training set, each learner proceeds to learn in an iterative fashion. New data is collected and classified. The learner can then either broadcast its most confident classifications for use by other learners, or can query neighbors for their classifications of its least confident items. As such, collaborative learning combines elements of both passive (broadcast) and active (query) learning. It also uses ideas from ensemble learning to combine the multiple responses to a given query into a single useful label. This approach has been evaluated against current non-collaborative alternatives, including training a single classifier and deploying it at all nodes with no further learning possible, and permitting learners to learn from their own most confident judgments, absent interaction with their neighbors. On several data sets, it has been consistently found that active collaboration is the best strategy for a distributed learner network. The main advantages include the ability for learning to take place autonomously by collaboration rather than by requiring intervention from an oracle (usually human), and also the ability to learn in a distributed environment, permitting decisions to be made in situ and to yield faster response time.

  17. Learning Communities for Developmental Education Students: Early Results from Randomized Experiments at Three Community Colleges

    Science.gov (United States)

    Weiss, Michael J.; Visher, Mary; Weissman, Evan

    2011-01-01

    This paper presents results from a rigorous random assignment study of Learning Communities programs operated at three of six community colleges participating in the National Center for Postsecondary Research's (NCPR) Learning Communities Demonstration. The demonstration's focus is on determining whether Learning Communities are an effective…

  18. Learning through Participatory Action Research for Community Ecotourism Planning.

    Science.gov (United States)

    Guevara, Jose Roberto Q.

    1996-01-01

    Ecologically sound tourism planning and policy require an empowering community participation. The participatory action research model helps a community gain understanding of its social reality, learn how to learn, initiate dialog, and discover new possibilities for addressing its situation. (SK)

  19. A History of Learning Communities within American Higher Education

    Science.gov (United States)

    Fink, John E.; Inkelas, Karen Kurotsuchi

    2015-01-01

    This chapter describes the historical development of learning communities within American higher education. We examine the forces both internal and external to higher education that contributed to and stalled the emergence of learning communities in their contemporary form.

  20. Modern Community Detection Methods in Social Networks

    Directory of Open Access Journals (Sweden)

    V. O. Chesnokov

    2017-01-01

    Full Text Available Social network structure is not homogeneous. Groups of vertices which have a lot of links between them are called communities. A survey of algorithms discovering such groups is presented in the article.A popular approach to community detection is to use an graph clustering algorithm.  Methods based on inner metric optimization are common. 5 groups of algorithms are listed: based on optimization, joining vertices into clusters by some closeness measure, special subgraphs discovery, partitioning graph by deleting edges,  and based on a dynamic process or generative model.Overlapping community detection algorithms are usually just modified graph clustering algorithms. Other approaches do exist, e.g. ones based on edges clustering or constructing communities around randomly chosen vertices. Methods based on nonnegative matrix factorization are also used, but they have high computational complexity. Algorithms based on label propagation lack this disadvantage. Methods based on affiliation model are perspective. This model claims that communities define the structure of a graph.Algorithms which use node attributes are considered: ones based on latent Dirichlet allocation, initially used for text clustering, and CODICIL, where edges of node content relevance are added to the original edge set. 6 classes are listed for algorithms for graphs with node attributes: changing egdes’ weights, changing vertex distance function, building augmented graph with nodes and attributes, based on stochastic  models, partitioning attribute space and others.Overlapping community detection algorithms which effectively use node attributes are just started to appear. Methods based on partitioning attribute space,  latent Dirichlet allocation,  stochastic  models and  nonnegative matrix factorization are considered. The most effective algorithm on real datasets is CESNA. It is based on affiliation model. However, it gives results which are far from ground truth

  1. The ground truth about metadata and community detection in networks.

    Science.gov (United States)

    Peel, Leto; Larremore, Daniel B; Clauset, Aaron

    2017-05-01

    Across many scientific domains, there is a common need to automatically extract a simplified view or coarse-graining of how a complex system's components interact. This general task is called community detection in networks and is analogous to searching for clusters in independent vector data. It is common to evaluate the performance of community detection algorithms by their ability to find so-called ground truth communities. This works well in synthetic networks with planted communities because these networks' links are formed explicitly based on those known communities. However, there are no planted communities in real-world networks. Instead, it is standard practice to treat some observed discrete-valued node attributes, or metadata, as ground truth. We show that metadata are not the same as ground truth and that treating them as such induces severe theoretical and practical problems. We prove that no algorithm can uniquely solve community detection, and we prove a general No Free Lunch theorem for community detection, which implies that there can be no algorithm that is optimal for all possible community detection tasks. However, community detection remains a powerful tool and node metadata still have value, so a careful exploration of their relationship with network structure can yield insights of genuine worth. We illustrate this point by introducing two statistical techniques that can quantify the relationship between metadata and community structure for a broad class of models. We demonstrate these techniques using both synthetic and real-world networks, and for multiple types of metadata and community structures.

  2. Identifying Social Communities in Complex Communications for Network Efficiency

    Science.gov (United States)

    Hui, Pan; Yoneki, Eiko; Crowcroft, Jon; Chan, Shu-Yan

    Complex communication networks, more particular Mobile Ad Hoc Networks (MANET) and Pocket Switched Networks (PSN), rely on short range radio and device mobility to transfer data across the network. These kind of mobile networks contain duality in nature: they are radio networks at the same time also human networks, and hence knowledge from social networks can be also applicable here. In this paper, we demonstrate how identifying social communities can significantly improve the forwarding efficiencies in term of delivery ratio and delivery cost. We verify our hypothesis using data from five human mobility experiments and test on two application scenarios, asynchronous messaging and publish/subscribe service.

  3. Partner network communities – a resource of universities’ activities

    Directory of Open Access Journals (Sweden)

    Romm Mark V.

    2016-01-01

    Full Text Available The network activity is not only part and parcel of the modern university, but it also demonstrates the level of its success. There appeared an urgent need for understanding the nature of universities’ network interactions and finding the most effective models of their network cooperation. The article analyzes partnership network communities with higher educational establishments (universities’ participation, which are being actively created nowadays. The conditions for successful network activities of a university in scientific, academic and professional network communities are presented.

  4. Joint community and anomaly tracking in dynamic networks

    CERN Document Server

    Baingana, Brian

    2015-01-01

    Most real-world networks exhibit community structure, a phenomenon characterized by existence of node clusters whose intra-edge connectivity is stronger than edge connectivities between nodes belonging to different clusters. In addition to facilitating a better understanding of network behavior, community detection finds many practical applications in diverse settings. Communities in online social networks are indicative of shared functional roles, or affiliation to a common socio-economic status, the knowledge of which is vital for targeted advertisement. In buyer-seller networks, community detection facilitates better product recommendations. Unfortunately, reliability of community assignments is hindered by anomalous user behavior often observed as unfair self-promotion, or "fake" highly-connected accounts created to promote fraud. The present paper advocates a novel approach for jointly tracking communities while detecting such anomalous nodes in time-varying networks. By postulating edge creation as the ...

  5. Learning in a Network: A "Third Way" between School Learning and Workplace Learning?

    Science.gov (United States)

    Bottrup, Pernille

    2005-01-01

    Purpose--The aim of this article is to examine network-based learning and discuss how participation in network can enhance organisational learning. Design/methodology/approach--In recent years, companies have increased their collaboration with other organisations, suppliers, customers, etc., in order to meet challenges from a globalised market.…

  6. Networks and Inter-Organizational Learning: A Critical Review.

    Science.gov (United States)

    Beeby, Mick; Booth, Charles

    2000-01-01

    Reviews literature on knowledge management and organizational learning; highlights the significance of networks, alliances, and interorganizational relationships. Refines a model of organizational learning to account for different levels: individual, interdepartmental, team, organizational, and interorganizational learning. (Contains 62…

  7. Exploring community structure in biological networks with random graphs.

    Science.gov (United States)

    Sah, Pratha; Singh, Lisa O; Clauset, Aaron; Bansal, Shweta

    2014-06-25

    Community structure is ubiquitous in biological networks. There has been an increased interest in unraveling the community structure of biological systems as it may provide important insights into a system's functional components and the impact of local structures on dynamics at a global scale. Choosing an appropriate community detection algorithm to identify the community structure in an empirical network can be difficult, however, as the many algorithms available are based on a variety of cost functions and are difficult to validate. Even when community structure is identified in an empirical system, disentangling the effect of community structure from other network properties such as clustering coefficient and assortativity can be a challenge. Here, we develop a generative model to produce undirected, simple, connected graphs with a specified degrees and pattern of communities, while maintaining a graph structure that is as random as possible. Additionally, we demonstrate two important applications of our model: (a) to generate networks that can be used to benchmark existing and new algorithms for detecting communities in biological networks; and (b) to generate null models to serve as random controls when investigating the impact of complex network features beyond the byproduct of degree and modularity in empirical biological networks. Our model allows for the systematic study of the presence of community structure and its impact on network function and dynamics. This process is a crucial step in unraveling the functional consequences of the structural properties of biological systems and uncovering the mechanisms that drive these systems.

  8. Partnerships and Learning Communities in Work-Integrated Learning: Designing a Community Services Student Placement Program

    Science.gov (United States)

    Harris, Lisa; Jones, Martyn; Coutts, Sally

    2010-01-01

    The paper describes and analyses the design and implementation of a higher education student placement program in the community services sector. Principally ideas about partnerships and social learning informed the design. The placement program represents a significant innovation in work-integrated learning, achieved through collaboration between…

  9. Measuring the robustness of network community structure using assortativity

    Science.gov (United States)

    Shizuka, Daizaburo; Farine, Damien R.

    2016-01-01

    The existence of discrete social clusters, or ‘communities’, is a common feature of social networks in human and nonhuman animals. The level of such community structure in networks is typically measured using an index of modularity, Q. While modularity quantifies the degree to which individuals associate within versus between social communities and provides a useful measure of structure in the social network, it assumes that the network has been well sampled. However, animal social network data is typically subject to sampling errors. In particular, the associations among individuals are often not sampled equally, and animal social network studies are often based on a relatively small set of observations. Here, we extend an existing framework for bootstrapping network metrics to provide a method for assessing the robustness of community assignment in social networks using a metric we call community assortativity (rcom). We use simulations to demonstrate that modularity can reliably detect the transition from random to structured associations in networks that differ in size and number of communities, while community assortativity accurately measures the level of confidence based on the detectability of associations. We then demonstrate the use of these metrics using three publicly available data sets of avian social networks. We suggest that by explicitly addressing the known limitations in sampling animal social network, this approach will facilitate more rigorous analyses of population-level structural patterns across social systems. PMID:26949266

  10. Node Attribute-enhanced Community Detection in Complex Networks.

    Science.gov (United States)

    Jia, Caiyan; Li, Yafang; Carson, Matthew B; Wang, Xiaoyang; Yu, Jian

    2017-05-25

    Community detection involves grouping the nodes of a network such that nodes in the same community are more densely connected to each other than to the rest of the network. Previous studies have focused mainly on identifying communities in networks using node connectivity. However, each node in a network may be associated with many attributes. Identifying communities in networks combining node attributes has become increasingly popular in recent years. Most existing methods operate on networks with attributes of binary, categorical, or numerical type only. In this study, we introduce kNN-enhance, a simple and flexible community detection approach that uses node attribute enhancement. This approach adds the k Nearest Neighbor (kNN) graph of node attributes to alleviate the sparsity and the noise effect of an original network, thereby strengthening the community structure in the network. We use two testing algorithms, kNN-nearest and kNN-Kmeans, to partition the newly generated, attribute-enhanced graph. Our analyses of synthetic and real world networks have shown that the proposed algorithms achieve better performance compared to existing state-of-the-art algorithms. Further, the algorithms are able to deal with networks containing different combinations of binary, categorical, or numerical attributes and could be easily extended to the analysis of massive networks.

  11. A multi-agent genetic algorithm for community detection in complex networks

    Science.gov (United States)

    Li, Zhangtao; Liu, Jing

    2016-05-01

    Complex networks are popularly used to represent a lot of practical systems in the domains of biology and sociology, and the structure of community is one of the most important network attributes which has received an enormous amount of attention. Community detection is the process of discovering the community structure hidden in complex networks, and modularity Q is one of the best known quality functions measuring the quality of communities of networks. In this paper, a multi-agent genetic algorithm, named as MAGA-Net, is proposed to optimize modularity value for the community detection. An agent, coded by a division of a network, represents a candidate solution. All agents live in a lattice-like environment, with each agent fixed on a lattice point. A series of operators are designed, namely split and merging based neighborhood competition operator, hybrid neighborhood crossover, adaptive mutation and self-learning operator, to increase modularity value. In the experiments, the performance of MAGA-Net is validated on both well-known real-world benchmark networks and large-scale synthetic LFR networks with 5000 nodes. The systematic comparisons with GA-Net and Meme-Net show that MAGA-Net outperforms these two algorithms, and can detect communities with high speed, accuracy and stability.

  12. Reconstructing Causal Biological Networks through Active Learning.

    Directory of Open Access Journals (Sweden)

    Hyunghoon Cho

    Full Text Available Reverse-engineering of biological networks is a central problem in systems biology. The use of intervention data, such as gene knockouts or knockdowns, is typically used for teasing apart causal relationships among genes. Under time or resource constraints, one needs to carefully choose which intervention experiments to carry out. Previous approaches for selecting most informative interventions have largely been focused on discrete Bayesian networks. However, continuous Bayesian networks are of great practical interest, especially in the study of complex biological systems and their quantitative properties. In this work, we present an efficient, information-theoretic active learning algorithm for Gaussian Bayesian networks (GBNs, which serve as important models for gene regulatory networks. In addition to providing linear-algebraic insights unique to GBNs, leading to significant runtime improvements, we demonstrate the effectiveness of our method on data simulated with GBNs and the DREAM4 network inference challenge data sets. Our method generally leads to faster recovery of underlying network structure and faster convergence to final distribution of confidence scores over candidate graph structures using the full data, in comparison to random selection of intervention experiments.

  13. The Development of Professional Learning Community in Primary Schools

    Science.gov (United States)

    Sompong, Samoot; Erawan, Prawit; Dharm-tad-sa-na-non, Sudharm

    2015-01-01

    The objectives of this research are: (1) To study the current situation and need for developing professional learning community in primary schools; (2) To develop the model for developing professional learning community, and (3) To study the findings of development for professional learning community based on developed model related to knowledge,…

  14. Conceptual Model of Technology-Enabled Creative Learning Community

    Directory of Open Access Journals (Sweden)

    Dawam Dwi Jatmiko Suwawi

    2017-05-01

    Full Text Available This paper proposes a definition of Creative Learning Community (CLC that is enabled with technology and its conceptual model in Graduate School of Telkom University. As rooted to learning community term, CLC is defined as a teaching and learning approach within a learning community that consists of a group of students and faculty member that uses creative learning concept. This study adapts the Design Science Research Framework in Information System by Hevner et al to build the conceptual model. First, the study synthesizes existing literature on learning community and creative learning community to define CLC term. Second, based on a review of previous studies and books on learning community, creative thinking, group creativity, engaged learning, student learning outcomes and technology supporting creative learning community, the author analyzes construct candidates of the model. Third, after selecting constructs from the candidates, the study continues by designing the conceptual model of technology-enabled creative learning community. The model was tested the implementations of learning community in Graduate School of Telkom University. The findings provide several conceptual and managerial insights into the role of technology in supporting creative learning community. Future work will need to evaluate the model in the context of other engineering.

  15. Maximum Entropy Learning with Deep Belief Networks

    Directory of Open Access Journals (Sweden)

    Payton Lin

    2016-07-01

    Full Text Available Conventionally, the maximum likelihood (ML criterion is applied to train a deep belief network (DBN. We present a maximum entropy (ME learning algorithm for DBNs, designed specifically to handle limited training data. Maximizing only the entropy of parameters in the DBN allows more effective generalization capability, less bias towards data distributions, and robustness to over-fitting compared to ML learning. Results of text classification and object recognition tasks demonstrate ME-trained DBN outperforms ML-trained DBN when training data is limited.

  16. Communities of Practice for Local Capacity in Central Asia : The Community Empowerment Network

    OpenAIRE

    Caldwell Johnson, Erik

    2005-01-01

    In 2002 the World Bank Institute and Europe and Central Asia Region (ECA) launched the Community Empowerment Network (CEN): four national networks linked through regional activities that would build the capacity of communities and development partners to implement community-driven development (CDD) projects. CEN has to date had clear successes as well as difficulties-particularly in linkag...

  17. Online communities: Challenges and opportunities for social network research

    NARCIS (Netherlands)

    Groenewegen, P.; Moser, C.; Brass, D.; Labianca, G.; Mehra, A.; Halgin, D; Borgatti, S

    2014-01-01

    Online communities form a challenging and still-evolving field for social network research. We highlight two themes that are at the core of social network literature: formative processes and structures, and discuss how these might be relevant in the context of online communities. Processes of tie

  18. How Arizona Community College Teachers Go About Learning to Teach

    OpenAIRE

    Hamblin, Carolyn J.

    2015-01-01

    This mixed-method study used a survey and semistructured interviews to learn how new Arizona community college teachers learned to teach, how available certain learning experiences and effective professional development activities were, how valuable teachers perceived those learning experiences and activities to be, and if there were any factors that underlie how new community college teachers learned to teach. The survey questioned whether 26 learning experiences were available to new commun...

  19. Dictionary Networking in an LSP Learning Context

    DEFF Research Database (Denmark)

    Nielsen, Sandro

    2007-01-01

    and usage of a subject-field, particularly when they have to read, write or translate domain-specific texts. The modern theory of dictionary functions presented in Bergenholtz and Tarp (2002) opens up exciting new possibilities for theoretical and practical lexicography and encourages lexicographers...... text production, but discusses an individual dictionary for a particular function. It is shown that in a general context of learning accounting and its relevant LSP with a view to writing or translating financial reporting texts, the modern theory of dictionary functions provides a good theoretical...... and practical basis. This paper describes how the study of communication-oriented and cognitive-oriented functions may lead to the creation of a network of four on-line accounting dictionaries for learning accounting and its LSP. The dictionary network described consists of two monolingual and two bilingual...

  20. Learning and coordinating in a multilayer network

    CERN Document Server

    Lugo, Haydee

    2014-01-01

    We introduce a two layer network model for social coordination incorporating two relevant ingredients: a) different networks of interaction to learn and to obtain a payoff , and b) decision making processes based both on social and strategic motivations. Two populations of agents are distributed in two layers with intralayer learning processes and playing interlayer a coordination game. We find that the skepticism about the wisdom of crowd and the local connectivity are the driving forces to accomplish full coordination of the two populations, while polarized coordinated layers are only possible for all-to-all interactions. Local interactions also allow for full coordination in the socially efficient Pareto-dominant strategy in spite of being the riskier one.

  1. Learning and coordinating in a multilayer network

    Science.gov (United States)

    Lugo, Haydée; Miguel, Maxi San

    2015-01-01

    We introduce a two layer network model for social coordination incorporating two relevant ingredients: a) different networks of interaction to learn and to obtain a pay-off, and b) decision making processes based both on social and strategic motivations. Two populations of agents are distributed in two layers with intralayer learning processes and playing interlayer a coordination game. We find that the skepticism about the wisdom of crowd and the local connectivity are the driving forces to accomplish full coordination of the two populations, while polarized coordinated layers are only possible for all-to-all interactions. Local interactions also allow for full coordination in the socially efficient Pareto-dominant strategy in spite of being the riskier one.

  2. Machine Learning for ATLAS DDM Network Metrics

    CERN Document Server

    Lassnig, Mario; The ATLAS collaboration; Vamosi, Ralf

    2016-01-01

    The increasing volume of physics data is posing a critical challenge to the ATLAS experiment. In anticipation of high luminosity physics, automation of everyday data management tasks has become necessary. Previously many of these tasks required human decision-making and operation. Recent advances in hardware and software have made it possible to entrust more complicated duties to automated systems using models trained by machine learning algorithms. In this contribution we show results from our ongoing automation efforts. First, we describe our framework for distributed data management and network metrics, automatically extract and aggregate data, train models with various machine learning algorithms, and eventually score the resulting models and parameters. Second, we use these models to forecast metrics relevant for network-aware job scheduling and data brokering. We show the characteristics of the data and evaluate the forecasting accuracy of our models.

  3. Learning of N-layers neural network

    Directory of Open Access Journals (Sweden)

    Vladimír Konečný

    2005-01-01

    Full Text Available In the last decade we can observe increasing number of applications based on the Artificial Intelligence that are designed to solve problems from different areas of human activity. The reason why there is so much interest in these technologies is that the classical way of solutions does not exist or these technologies are not suitable because of their robustness. They are often used in applications like Business Intelligence that enable to obtain useful information for high-quality decision-making and to increase competitive advantage.One of the most widespread tools for the Artificial Intelligence are the artificial neural networks. Their high advantage is relative simplicity and the possibility of self-learning based on set of pattern situations.For the learning phase is the most commonly used algorithm back-propagation error (BPE. The base of BPE is the method minima of error function representing the sum of squared errors on outputs of neural net, for all patterns of the learning set. However, while performing BPE and in the first usage, we can find out that it is necessary to complete the handling of the learning factor by suitable method. The stability of the learning process and the rate of convergence depend on the selected method. In the article there are derived two functions: one function for the learning process management by the relative great error function value and the second function when the value of error function approximates to global minimum.The aim of the article is to introduce the BPE algorithm in compact matrix form for multilayer neural networks, the derivation of the learning factor handling method and the presentation of the results.

  4. Homophyly/kinship hypothesis: Natural communities, and predicting in networks

    Science.gov (United States)

    Li, Angsheng; Li, Jiankou; Pan, Yicheng

    2015-02-01

    It has been a longstanding challenge to understand natural communities in real world networks. We proposed a community finding algorithm based on fitness of networks, two algorithms for prediction, accurate prediction and confirmation of keywords for papers in the citation network Arxiv HEP-TH (high energy physics theory), and the measures of internal centrality, external de-centrality, internal and external slopes to characterize the structures of communities. We implemented our algorithms on 2 citation and 5 cooperation graphs. Our experiments explored and validated a homophyly/kinship principle of real world networks. The homophyly/kinship principle includes: (1) homophyly is the natural selection in real world networks, similar to Darwin's kinship selection in nature, (2) real world networks consist of natural communities generated by the natural selection of homophyly, (3) most individuals in a natural community share a short list of common attributes, (4) natural communities have an internal centrality (or internal heterogeneity) that a natural community has a few nodes dominating most of the individuals in the community, (5) natural communities have an external de-centrality (or external homogeneity) that external links of a natural community homogeneously distributed in different communities, and (6) natural communities of a given network have typical structures determined by the internal slopes, and have typical patterns of outgoing links determined by external slopes, etc. Our homophyly/kinship principle perfectly matches Darwin's observation that animals from ants to people form social groups in which most individuals work for the common good, and that kinship could encourage altruistic behavior. Our homophyly/kinship principle is the network version of Darwinian theory, and builds a bridge between Darwinian evolution and network science.

  5. Community Health Global Network: “Clustering” Together to Increase the Impact of Community Led Health and Development

    Directory of Open Access Journals (Sweden)

    Marianne Safe

    2014-01-01

    Full Text Available Background: Community Health Global Network (CHGN is a collaborative network, founded to strengthen collaboration between community-based health programs - many of which are faith based initiatives. It seeks to address this in two ways: through its global network of players in community health and in the formation of “Clusters.” CHGN Clusters are networks of community health programmes and individuals in specific geographical locations. This case report outlines the formation of the Kenya Cluster. Aims: To describe the steps in the formation of the Kenya Cluster and to outline the primary outcomes and potential impact of the network. To discuss how learning from the Kenya Cluster may assist other established Clusters and the initiation of new Clusters. Method: Information for this case report was gained from meetings and consultations with various individuals including leaders and members of the Kenya Cluster, other national community health experts, CHGN International staff and advisors to CHGN Uttarakhand Cluster in India. In addition, information was gained from personal observation during in-country field work. Results: The Kenya Cluster is emerging as a platform for community health programs to connect and network. These connections have led to transfer of information through stories, best practice, training, contacts and opportunities amongst Cluster members. The Cluster has also established links with government and multilaterals enabling greater access to support at the community level. Conclusions: There is early indication that the formation of the Kenya Cluster is supportive of the Cluster model as a unique way of strengthening collaboration between community health programs. Clusters have the potential to improve the link between faith-inspired initiatives and secular and multilateral development organisations. Lessons from the Kenya Cluster can progress the development of other Clusters. Further evaluation will be conducted to

  6. Emergence of communities and diversity in social networks.

    Science.gov (United States)

    Han, Xiao; Cao, Shinan; Shen, Zhesi; Zhang, Boyu; Wang, Wen-Xu; Cressman, Ross; Stanley, H Eugene

    2017-03-14

    Communities are common in complex networks and play a significant role in the functioning of social, biological, economic, and technological systems. Despite widespread interest in detecting community structures in complex networks and exploring the effect of communities on collective dynamics, a deep understanding of the emergence and prevalence of communities in social networks is still lacking. Addressing this fundamental problem is of paramount importance in understanding, predicting, and controlling a variety of collective behaviors in society. An elusive question is how communities with common internal properties arise in social networks with great individual diversity. Here, we answer this question using the ultimatum game, which has been a paradigm for characterizing altruism and fairness. We experimentally show that stable local communities with different internal agreements emerge spontaneously and induce social diversity into networks, which is in sharp contrast to populations with random interactions. Diverse communities and social norms come from the interaction between responders with inherent heterogeneous demands and rational proposers via local connections, where the former eventually become the community leaders. This result indicates that networks are significant in the emergence and stabilization of communities and social diversity. Our experimental results also provide valuable information about strategies for developing network models and theories of evolutionary games and social dynamics.

  7. Learning in Neural Networks: VLSI Implementation Strategies

    Science.gov (United States)

    Duong, Tuan Anh

    1995-01-01

    Fully-parallel hardware neural network implementations may be applied to high-speed recognition, classification, and mapping tasks in areas such as vision, or can be used as low-cost self-contained units for tasks such as error detection in mechanical systems (e.g. autos). Learning is required not only to satisfy application requirements, but also to overcome hardware-imposed limitations such as reduced dynamic range of connections.

  8. Learning Affinity via Spatial Propagation Networks

    OpenAIRE

    Liu, Sifei; De Mello, Shalini; Gu, Jinwei; Zhong, Guangyu; Yang, Ming-Hsuan; Kautz, Jan

    2017-01-01

    In this paper, we propose spatial propagation networks for learning the affinity matrix for vision tasks. We show that by constructing a row/column linear propagation model, the spatially varying transformation matrix exactly constitutes an affinity matrix that models dense, global pairwise relationships of an image. Specifically, we develop a three-way connection for the linear propagation model, which (a) formulates a sparse transformation matrix, where all elements can be the output from a...

  9. THE IMPACTS OF SOCIAL NETWORKING SITES IN HIGHER LEARNING

    OpenAIRE

    Mohd Ishak Bin Ismail; Ruzaini Bin Abdullah Arshah

    2016-01-01

    Social networking sites, a web-based application have permeated the boundary between personal lives and student lives. Nowadays, students in higher learning used social networking site such as Facebook to facilitate their learning through the academic collaboration which it further enhances students’ social capital. Social networking site has many advantages to improve students’ learning. To date, Facebook is the leading social networking sites at this time which it being widely used by stude...

  10. SA-SOM algorithm for detecting communities in complex networks

    Science.gov (United States)

    Chen, Luogeng; Wang, Yanran; Huang, Xiaoming; Hu, Mengyu; Hu, Fang

    2017-10-01

    Currently, community detection is a hot topic. This paper, based on the self-organizing map (SOM) algorithm, introduced the idea of self-adaptation (SA) that the number of communities can be identified automatically, a novel algorithm SA-SOM of detecting communities in complex networks is proposed. Several representative real-world networks and a set of computer-generated networks by LFR-benchmark are utilized to verify the accuracy and the efficiency of this algorithm. The experimental findings demonstrate that this algorithm can identify the communities automatically, accurately and efficiently. Furthermore, this algorithm can also acquire higher values of modularity, NMI and density than the SOM algorithm does.

  11. Community Detection for Multiplex Social Networks Based on Relational Bayesian Networks

    DEFF Research Database (Denmark)

    Jiang, Jiuchuan; Jaeger, Manfred

    2014-01-01

    . In this paper we propose to use relational Bayesian networks for the specification of probabilistic network models, and develop inference techniques that solve the community detection problem based on these models. The use of relational Bayesian networks as a flexible high-level modeling framework enables us......Many techniques have been proposed for community detection in social networks. Most of these techniques are only designed for networks defined by a single relation. However, many real networks are multiplex networks that contain multiple types of relations and different attributes on the nodes...

  12. The Design, Experience and Practice of Networked Learning

    DEFF Research Database (Denmark)

    Gleerup, Janne; Heilesen, Simon; Helms, Niels Henrik

    2014-01-01

    . The Design, Experience and Practice of Networked Learning will prove indispensable reading for researchers, teachers, consultants, and instructional designers in higher and continuing education; for those involved in staff and educational development, and for those studying post graduate qualifications...... in learning and teaching. This, the second volume in the Springer Book Series on Researching Networked Learning, is based on a selection of papers presented at the 2012 Networked Learning Conference held in Maastricht, The Netherlands....

  13. A framework for detecting communities of unbalanced sizes in networks

    Science.gov (United States)

    Žalik, Krista Rizman; Žalik, Borut

    2018-01-01

    Community detection in large networks has been a focus of recent research in many of fields, including biology, physics, social sciences, and computer science. Most community detection methods partition the entire network into communities, groups of nodes that have many connections within communities and few connections between them and do not identify different roles that nodes can have in communities. We propose a community detection model that integrates more different measures that can fast identify communities of different sizes and densities. We use node degree centrality, strong similarity with one node from community, maximal similarity of node to community, compactness of communities and separation between communities. Each measure has its own strength and weakness. Thus, combining different measures can benefit from the strengths of each one and eliminate encountered problems of using an individual measure. We present a fast local expansion algorithm for uncovering communities of different sizes and densities and reveals rich information on input networks. Experimental results show that the proposed algorithm is better or as effective as the other community detection algorithms for both real-world and synthetic networks while it requires less time.

  14. Military, Family, and Community Networks Helping with Reintegration

    Science.gov (United States)

    2010-09-01

    Limitations Ideally we would want to show that this type of network model can actually help troops, Veterans and their families reintegrate more smoothly...08-2-0655 TITLE: Military, Family, and Community Networks Helping with Reintegration PRINCIPAL INVESTIGATOR: Dr. Laurie Slone... Reintegration Dartmouth College Hanover, NH 03755 Dr. Laurie Slone A community based network to assist with the reintegration of service members and their

  15. Comparing Community Structure to Characteristics in Online Collegiate Social Networks

    OpenAIRE

    Traud, Amanda L.; Kelsic, Eric D.; Mucha, Peter J; Porter, Mason A.

    2008-01-01

    We study the structure of social networks of students by examining the graphs of Facebook "friendships" at five American universities at a single point in time. We investigate each single-institution network's community structure and employ graphical and quantitative tools, including standardized pair-counting methods, to measure the correlations between the network communities and a set of self-identified user characteristics (residence, class year, major, and high school). We review the bas...

  16. Random field Ising model and community structure in complex networks

    Science.gov (United States)

    Son, S.-W.; Jeong, H.; Noh, J. D.

    2006-04-01

    We propose a method to determine the community structure of a complex network. In this method the ground state problem of a ferromagnetic random field Ising model is considered on the network with the magnetic field Bs = +∞, Bt = -∞, and Bi≠s,t=0 for a node pair s and t. The ground state problem is equivalent to the so-called maximum flow problem, which can be solved exactly numerically with the help of a combinatorial optimization algorithm. The community structure is then identified from the ground state Ising spin domains for all pairs of s and t. Our method provides a criterion for the existence of the community structure, and is applicable equally well to unweighted and weighted networks. We demonstrate the performance of the method by applying it to the Barabási-Albert network, Zachary karate club network, the scientific collaboration network, and the stock price correlation network. (Ising, Potts, etc.)

  17. Using CBPR for Health Research in American Muslim Mosque Communities: Lessons Learned

    Science.gov (United States)

    Killawi, Amal; Heisler, Michele; Hamid, Hamada; Padela, Aasim I.

    2015-01-01

    Background American Muslims are understudied in health research, and there are few studies documenting community-based participatory research (CBPR) efforts among American Muslim mosque communities. Objectives We highlight lessons learned from a CBPR partnership that explored the health care beliefs, behaviors, and challenges of American Muslims. Methods We established a collaboration between the University of Michigan and four Muslim-focused community organizations in Michigan. Our collaborative team designed and implemented a two-phase study involving interviews with community stakeholders and focus groups and surveys with mosque congregants. Lessons Learned Although we were successful in meeting our research goals, maintaining community partner involvement and sustaining the project partnership proved challenging. Conclusions CBPR initiatives within mosque communities have the potential for improving community health. Our experience suggests that successful research partnerships with American Muslims will utilize social networks and cultural insiders, culturally adapt research methods, and develop a research platform within the organizational infrastructures of the American Muslim community. PMID:25981426

  18. Developing an inter-organizational community-based health network: an Australian investigation.

    Science.gov (United States)

    Short, Alison; Phillips, Rebecca; Nugus, Peter; Dugdale, Paul; Greenfield, David

    2015-12-01

    Networks in health care typically involve services delivered by a defined set of organizations. However, networked associations between the healthcare system and consumers or consumer organizations tend to be open, fragmented and are fraught with difficulties. Understanding the role and activities of consumers and consumer groups in a formally initiated inter-organizational health network, and the impacts of the network, is a timely endeavour. This study addresses this aim in three ways. First, the Unbounded Network Inter-organizational Collaborative Impact Model, a purpose-designed framework developed from existing literature, is used to investigate the process and products of inter-organizational network development. Second, the impact of a network artefact is explored. Third, the lessons learned in inter-organizational network development are considered. Data collection methods were: 16 h of ethnographic observation; 10 h of document analysis; six interviews with key informants and a survey (n = 60). Findings suggested that in developing the network, members used common aims, inter-professional collaboration, the power and trust engendered by their participation, and their leadership and management structures in a positive manner. These elements and activities underpinned the inter-organizational network to collaboratively produce the Health Expo network artefact. This event brought together healthcare providers, community groups and consumers to share information. The Health Expo demonstrated and reinforced inter-organizational working and community outreach, providing consumers with community-based information and linkages. Support and resources need to be offered for developing community inter-organizational networks, thereby building consumer capacity for self-management in the community. © The Author (2014). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  19. Exploring the limits of community detection strategies in complex networks

    OpenAIRE

    Aldecoa, Rodrigo; Marín, Ignacio

    2013-01-01

    The characterization of network community structure has profound implications in several scientific areas. Therefore, testing the algorithms developed to establish the optimal division of a network into communities is a fundamental problem in the field. We performed here a highly detailed evaluation of community detection algorithms, which has two main novelties: 1) using complex closed benchmarks, which provide precise ways to assess whether the solutions generated by the algorithms are opti...

  20. Community Evolution in International Migration Top1 Networks

    Science.gov (United States)

    Xu, Helian

    2016-01-01

    Focusing on each country’s topmost destination/origin migration relation with other countries, this study builds top1 destination networks and top1 origin networks in order to understand their skeletal construction and community dynamics. Each top1 network covers approximately 50% of the complete migrant network stock for each decade between 1960 and 2000. We investigate the community structure by implementing the Girvan-Newman algorithm and compare the number of components and communities to illustrate their differences. We find that (i) both top1 networks (origin and destination) exhibited communities with a clear structure and a surprising evolution, although 80% edges persist between each decade; (ii) top1 destination networks focused on developed countries exhibiting shorter paths and preferring more advance countries, while top1 origin networks focused both on developed as well as more substantial developing nations that presented a longer path and more stable groups; (iii) only few countries have a decisive influence on community evolution of both top1 networks. USA took the leading position as a destination country in top1 destination networks, while China and India were the main Asian emigration countries in top1 origin networks; European countries and the Russian Federation played an important role in both. PMID:26859406

  1. Twitmographics: Learning the Emergent Properties of the Twitter Community

    Science.gov (United States)

    Cheong, Marc; Lee, Vincent

    This paper presents a framework for discovery of the emergent properties of users of the Twitter microblogging platform. The novelty of our methodology is the use of machine-learning methods to deduce user demographic information and online usage patterns and habits not readily apparent from the raw messages posted on Twitter. This is different from existing social network analysis performed on de facto social networks such as Face-book, in the sense that we use publicly available metadata from Twitter messages to explore the inherent characteristics about different segments of the Twitter community, in a simple yet effective manner. Our framework is coupled with the self-organizing map visualization method, and tested on a corpus of messages which deal with issues of socio politi-cal and economic impact, to gain insight into the properties of human interaction via Twitter as a medium for computer-mediated self-expression.

  2. Place and identity: networks of Neolithic communities in Central Europe

    Directory of Open Access Journals (Sweden)

    Roderick B. Salisbury

    2012-12-01

    Full Text Available The multi-layered and multi-scalar nature of the term ‘community’ makes it a useful tool for both particularistic studies and cross-cultural comparisons, connecting scales of community to regional scales of settlement, exchange and mobility. This paper explores three general themes of community: community as place, as identity and as network. A case study of Neolithic communities in eastern Hungary and Lower Austria demonstrates a spatial and geoarchaeological approach to understanding the relational aspects of places, networks and identity to develop a social archaeology of communities.

  3. Effect of size heterogeneity on community identification in complex networks

    Energy Technology Data Exchange (ETDEWEB)

    Danon, L.; Diaz-Guilera, A.; Arenas, A.

    2008-01-01

    Identifying community structure can be a potent tool in the analysis and understanding of the structure of complex networks. Up to now, methods for evaluating the performance of identification algorithms use ad-hoc networks with communities of equal size. We show that inhomogeneities in community sizes can and do affect the performance of algorithms considerably, and propose an alternative method which takes these factors into account. Furthermore, we propose a simple modification of the algorithm proposed by Newman for community detection (Phys. Rev. E 69 066133) which treats communities of different sizes on an equal footing, and show that it outperforms the original algorithm while retaining its speed.

  4. The Relationships Between Policy, Boundaries and Research in Networked Learning

    DEFF Research Database (Denmark)

    Ryberg, Thomas; Sinclair, Christine

    2016-01-01

    The biennial Networked Learning Conference is an established locus for work on practice, research and epistemology in the field of networked learning. That work continues between the conferences through the researchers’ own networks, ‘hot seat’ debates, and through publications, especially...... conferences, such as the inclusion of sociomaterial perspectives and recognition of informal networked learning. The chapters here each bring a particular perspective to the themes of Policy, Boundaries and Research in Networked Learning which we have chosen as the focus of the book. The selection...

  5. Support for Community Telecentres and Networks in Mali | CRDI ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Support for Community Telecentres and Networks in Mali. Telecentres across French-speaking West Africa are struggling to attain financial sustainability while remaining relevant to their communities. This is because they have access to fewer online resources and a smaller community of practice than their ...

  6. Multiview Community Discovery Algorithm via Nonnegative Factorization Matrix in Heterogeneous Networks

    Directory of Open Access Journals (Sweden)

    Wang Tao

    2017-01-01

    Full Text Available With the rapid development of the Internet and communication technologies, a large number of multimode or multidimensional networks widely emerge in real-world applications. Traditional community detection methods usually focus on homogeneous networks and simply treat different modes of nodes and connections in the same way, thus ignoring the inherent complexity and diversity of heterogeneous networks. It is challenging to effectively integrate the multiple modes of network information to discover the hidden community structure underlying heterogeneous interactions. In our work, a joint nonnegative matrix factorization (Joint-NMF algorithm is proposed to discover the complex structure in heterogeneous networks. Our method transforms the heterogeneous dataset into a series of bipartite graphs correlated. Taking inspiration from the multiview method, we extend the semisupervised learning from single graph to several bipartite graphs with multiple views. In this way, it provides mutual information between different bipartite graphs to realize the collaborative learning of different classifiers, thus comprehensively considers the internal structure of all bipartite graphs, and makes all the classifiers tend to reach a consensus on the clustering results of the target-mode nodes. The experimental results show that Joint-NMF algorithm is efficient and well-behaved in real-world heterogeneous networks and can better explore the community structure of multimode nodes in heterogeneous networks.

  7. Detecting and analyzing research communities in longitudinal scientific networks.

    Science.gov (United States)

    Leone Sciabolazza, Valerio; Vacca, Raffaele; Kennelly Okraku, Therese; McCarty, Christopher

    2017-01-01

    A growing body of evidence shows that collaborative teams and communities tend to produce the highest-impact scientific work. This paper proposes a new method to (1) Identify collaborative communities in longitudinal scientific networks, and (2) Evaluate the impact of specific research institutes, services or policies on the interdisciplinary collaboration between these communities. First, we apply community-detection algorithms to cross-sectional scientific collaboration networks and analyze different types of co-membership in the resulting subgroups over time. This analysis summarizes large amounts of longitudinal network data to extract sets of research communities whose members have consistently collaborated or shared collaborators over time. Second, we construct networks of cross-community interactions and estimate Exponential Random Graph Models to predict the formation of interdisciplinary collaborations between different communities. The method is applied to longitudinal data on publication and grant collaborations at the University of Florida. Results show that similar institutional affiliation, spatial proximity, transitivity effects, and use of the same research services predict higher degree of interdisciplinary collaboration between research communities. Our application also illustrates how the identification of research communities in longitudinal data and the analysis of cross-community network formation can be used to measure the growth of interdisciplinary team science at a research university, and to evaluate its association with research policies, services or institutes.

  8. Detecting and analyzing research communities in longitudinal scientific networks.

    Directory of Open Access Journals (Sweden)

    Valerio Leone Sciabolazza

    Full Text Available A growing body of evidence shows that collaborative teams and communities tend to produce the highest-impact scientific work. This paper proposes a new method to (1 Identify collaborative communities in longitudinal scientific networks, and (2 Evaluate the impact of specific research institutes, services or policies on the interdisciplinary collaboration between these communities. First, we apply community-detection algorithms to cross-sectional scientific collaboration networks and analyze different types of co-membership in the resulting subgroups over time. This analysis summarizes large amounts of longitudinal network data to extract sets of research communities whose members have consistently collaborated or shared collaborators over time. Second, we construct networks of cross-community interactions and estimate Exponential Random Graph Models to predict the formation of interdisciplinary collaborations between different communities. The method is applied to longitudinal data on publication and grant collaborations at the University of Florida. Results show that similar institutional affiliation, spatial proximity, transitivity effects, and use of the same research services predict higher degree of interdisciplinary collaboration between research communities. Our application also illustrates how the identification of research communities in longitudinal data and the analysis of cross-community network formation can be used to measure the growth of interdisciplinary team science at a research university, and to evaluate its association with research policies, services or institutes.

  9. How and What Do Academics Learn through Their Personal Networks

    Science.gov (United States)

    Pataraia, Nino; Margaryan, Anoush; Falconer, Isobel; Littlejohn, Allison

    2015-01-01

    This paper investigates the role of personal networks in academics' learning in relation to teaching. Drawing on in-depth interviews with 11 academics, this study examines, first, how and what academics learn through their personal networks; second, the perceived value of networks in relation to academics' professional development; and, third,…

  10. Evolutionary method for finding communities in bipartite networks

    Science.gov (United States)

    Zhan, Weihua; Zhang, Zhongzhi; Guan, Jihong; Zhou, Shuigeng

    2011-06-01

    An important step in unveiling the relation between network structure and dynamics defined on networks is to detect communities, and numerous methods have been developed separately to identify community structure in different classes of networks, such as unipartite networks, bipartite networks, and directed networks. Here, we show that the finding of communities in such networks can be unified in a general framework—detection of community structure in bipartite networks. Moreover, we propose an evolutionary method for efficiently identifying communities in bipartite networks. To this end, we show that both unipartite and directed networks can be represented as bipartite networks, and their modularity is completely consistent with that for bipartite networks, the detection of modular structure on which can be reformulated as modularity maximization. To optimize the bipartite modularity, we develop a modified adaptive genetic algorithm (MAGA), which is shown to be especially efficient for community structure detection. The high efficiency of the MAGA is based on the following three improvements we make. First, we introduce a different measure for the informativeness of a locus instead of the standard deviation, which can exactly determine which loci mutate. This measure is the bias between the distribution of a locus over the current population and the uniform distribution of the locus, i.e., the Kullback-Leibler divergence between them. Second, we develop a reassignment technique for differentiating the informative state a locus has attained from the random state in the initial phase. Third, we present a modified mutation rule which by incorporating related operations can guarantee the convergence of the MAGA to the global optimum and can speed up the convergence process. Experimental results show that the MAGA outperforms existing methods in terms of modularity for both bipartite and unipartite networks.

  11. Learning as Issue Framing in Agricultural Innovation Networks

    Science.gov (United States)

    Tisenkopfs, Talis; Kunda, Ilona; Šumane, Sandra

    2014-01-01

    Purpose: Networks are increasingly viewed as entities of learning and innovation in agriculture. In this article we explore learning as issue framing in two agricultural innovation networks. Design/methodology/approach: We combine frame analysis and social learning theories to analyse the processes and factors contributing to frame convergence and…

  12. Leading to learn in networks of practice: Two leadership strategies

    NARCIS (Netherlands)

    Soekijad, M.; van den Hooff, B.J.; Agterberg, L.C.M.; Huysman, M.H.

    2011-01-01

    This paper outlines two leadership strategies to support organizational learning through networks of practice (NOPs). An in-depth case study in a development organization reveals that network leaders cope with a learning tension between management involvement and emergent learning processes by

  13. Z-Score-Based Modularity for Community Detection in Networks.

    Science.gov (United States)

    Miyauchi, Atsushi; Kawase, Yasushi

    2016-01-01

    Identifying community structure in networks is an issue of particular interest in network science. The modularity introduced by Newman and Girvan is the most popular quality function for community detection in networks. In this study, we identify a problem in the concept of modularity and suggest a solution to overcome this problem. Specifically, we obtain a new quality function for community detection. We refer to the function as Z-modularity because it measures the Z-score of a given partition with respect to the fraction of the number of edges within communities. Our theoretical analysis shows that Z-modularity mitigates the resolution limit of the original modularity in certain cases. Computational experiments using both artificial networks and well-known real-world networks demonstrate the validity and reliability of the proposed quality function.

  14. Correlations between community structure and link formation in complex networks.

    Directory of Open Access Journals (Sweden)

    Zhen Liu

    Full Text Available BACKGROUND: Links in complex networks commonly represent specific ties between pairs of nodes, such as protein-protein interactions in biological networks or friendships in social networks. However, understanding the mechanism of link formation in complex networks is a long standing challenge for network analysis and data mining. METHODOLOGY/PRINCIPAL FINDINGS: Links in complex networks have a tendency to cluster locally and form so-called communities. This widely existed phenomenon reflects some underlying mechanism of link formation. To study the correlations between community structure and link formation, we present a general computational framework including a theory for network partitioning and link probability estimation. Our approach enables us to accurately identify missing links in partially observed networks in an efficient way. The links having high connection likelihoods in the communities reveal that links are formed preferentially to create cliques and accordingly promote the clustering level of the communities. The experimental results verify that such a mechanism can be well captured by our approach. CONCLUSIONS/SIGNIFICANCE: Our findings provide a new insight into understanding how links are created in the communities. The computational framework opens a wide range of possibilities to develop new approaches and applications, such as community detection and missing link prediction.

  15. Correlations between community structure and link formation in complex networks.

    Science.gov (United States)

    Liu, Zhen; He, Jia-Lin; Kapoor, Komal; Srivastava, Jaideep

    2013-01-01

    Links in complex networks commonly represent specific ties between pairs of nodes, such as protein-protein interactions in biological networks or friendships in social networks. However, understanding the mechanism of link formation in complex networks is a long standing challenge for network analysis and data mining. Links in complex networks have a tendency to cluster locally and form so-called communities. This widely existed phenomenon reflects some underlying mechanism of link formation. To study the correlations between community structure and link formation, we present a general computational framework including a theory for network partitioning and link probability estimation. Our approach enables us to accurately identify missing links in partially observed networks in an efficient way. The links having high connection likelihoods in the communities reveal that links are formed preferentially to create cliques and accordingly promote the clustering level of the communities. The experimental results verify that such a mechanism can be well captured by our approach. Our findings provide a new insight into understanding how links are created in the communities. The computational framework opens a wide range of possibilities to develop new approaches and applications, such as community detection and missing link prediction.

  16. The evolution of communities in the international oil trade network

    Science.gov (United States)

    Zhong, Weiqiong; An, Haizhong; Gao, Xiangyun; Sun, Xiaoqi

    2014-11-01

    International oil trade is a subset of global trade and there exist oil trade communities. These communities evolve over time and provide clues of international oil trade patterns. A better understanding of the international oil trade patterns is necessary for governments in policy making. To study the evolution of trade communities in the international oil trade network, we set up unweighted and weighted oil trade network models based on complex network theory using data from 2002 to 2011. We detected the communities in the oil trade networks and analyzed their evolutionary properties and stabilities over time. We found that the unweighted and weighted international oil trade networks show many different features in terms of community number, community scale, distribution of countries, quality of partitions, and stability of communities. Two turning points occurred in the evolution of community stability in the international oil trade network. One is the year 2004-2005 which correlates with changes in demand and supply in the world oil market after the Iraq War, and the other is the year 2008-2009 which is connected to the 2008 financial crisis. Different causations of instability show different features and this should be considered by policy makers.

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

    Directory of Open Access Journals (Sweden)

    Grainne Conole

    2011-03-01

    Full Text Available 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 personalisation within an educational context. One of the key challenges in the development of the site has been to understand the user interactions and the changing patterns of user behaviour as it evolves. The paper explores the extent to which four frameworks that have been used in researching networked learning contexts can provide insights into the patterns of user behaviour that we see in Cloudworks. The paper considers this within the current debate about the new types of interactions, networking, and community being observed as users adapt to and appropriate new technologies.

  18. Fuzzy communities and the concept of bridgeness in complex networks.

    Science.gov (United States)

    Nepusz, Tamás; Petróczi, Andrea; Négyessy, László; Bazsó, Fülöp

    2008-01-01

    We consider the problem of fuzzy community detection in networks, which complements and expands the concept of overlapping community structure. Our approach allows each vertex of the graph to belong to multiple communities at the same time, determined by exact numerical membership degrees, even in the presence of uncertainty in the data being analyzed. We create an algorithm for determining the optimal membership degrees with respect to a given goal function. Based on the membership degrees, we introduce a measure that is able to identify outlier vertices that do not belong to any of the communities, bridge vertices that have significant membership in more than one single community, and regular vertices that fundamentally restrict their interactions within their own community, while also being able to quantify the centrality of a vertex with respect to its dominant community. The method can also be used for prediction in case of uncertainty in the data set analyzed. The number of communities can be given in advance, or determined by the algorithm itself, using a fuzzified variant of the modularity function. The technique is able to discover the fuzzy community structure of different real world networks including, but not limited to, social networks, scientific collaboration networks, and cortical networks, with high confidence.

  19. Sampling from complex networks with high community structures.

    Science.gov (United States)

    Salehi, Mostafa; Rabiee, Hamid R; Rajabi, Arezo

    2012-06-01

    In this paper, we propose a novel link-tracing sampling algorithm, based on the concepts from PageRank vectors, to sample from networks with high community structures. Our method has two phases; (1) Sampling the closest nodes to the initial nodes by approximating personalized PageRank vectors and (2) Jumping to a new community by using PageRank vectors and unknown neighbors. Empirical studies on several synthetic and real-world networks show that the proposed method improves the performance of network sampling compared to the popular link-based sampling methods in terms of accuracy and visited communities.

  20. Community detection in complex networks via adapted Kuramoto dynamics

    Science.gov (United States)

    Maia, Daniel M. N.; de Oliveira, João E. M.; Quiles, Marcos G.; Macau, Elbert E. N.

    2017-12-01

    Based on the Kuramoto model, a new network model, namely, the generalized Kuramoto model with Fourier term, is introduced for studying community detection in complex networks. In particular, the Fourier term provides a natural phase locking of the trajectories into a pre-defined number of clusters. A mathematical approach is used to study the behavior of the solutions and its properties. Conditions for properly choosing the coupling parameters so that phase locking takes place are presented and a quality function called clustering density is introduced to measure the effectiveness of the communities identification. Illustrations with real and synthetic networks with community structure are presented.

  1. Community detection based on "clumpiness" matrix in complex networks

    CERN Document Server

    Faqeeh, Ali

    2011-01-01

    The "clumpiness" matrix of a network is used to develop a method to identify its community structure. A "projection space" is constructed from the eigenvectors of the clumpiness matrix and a border line is defined using some kind of angular distance in this space. The community structure of the network is identified using this borderline and/or the hierarchical clustering method. The performance of our algorithm is tested on some computer-generated and real-world networks. The accuracy of the results is checked using normalized mutual information. The effect of community size heterogeneity on the accuracy of the method is also discussed.

  2. Aligning Needs, Expectations, and Learning Outcomes to Sustain Self-Efficacy through Transfer Learning Community Programs

    Science.gov (United States)

    Leptien, Jennifer R.

    2015-01-01

    This chapter addresses strengths and difficulties encountered in implementing transfer learning community models and how efficacy is supported through transfer learning community programming. Transfer programming best practices and recommendations for program improvements are presented.

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

    Science.gov (United States)

    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.

  4. Learning network theory : its contribution to our understanding of work-based learning projects and learning climate

    OpenAIRE

    Poell, R.F.; Moorsel, M.A.A.H. van

    1996-01-01

    This paper discusses the relevance of Van der Krogt's learning network theory (1995) for our understanding of the concepts of work-related learning projects and learning climate in organisations. The main assumptions of the learning network theory are presented and transferred to the level of learning groups in organisations. Four theoretical types of learning projects are distinguished. Four different approaches to the learning climate of work groups are compared to the approach offered by t...

  5. Learning Communities as Transformative Pedagogy: Centering Diversity in Introductory Sociology

    Science.gov (United States)

    Snowden, Monica

    2004-01-01

    This paper discusses learning communities as pedagogy for introductory sociology courses, which are often plagued by student apathy. Most importantly, it examines the potential for learning communities to incorporate active and collaborative learning techniques as a vehicle to subvert dominant views of diversity, to see diversity as intersecting…

  6. Schools as Caring, Learning Communities. A Center Practice Brief

    Science.gov (United States)

    Center for Mental Health in Schools at UCLA, 2006

    2006-01-01

    Learning is neither limited to what is formally taught nor to time spent in classrooms. It occurs whenever and wherever the learner interacts with the surrounding environment. All facets of the community (not just the school) provide learning opportunities--thus the term learning community. This brief provides in-depth answers to the following…

  7. Creating Adult Learning Communities through School-College Partnerships

    Science.gov (United States)

    Gould, Holly C.; Brimijoin, Kay; Alouf, James L.; Mayhew, Mary Ann

    2010-01-01

    Given the challenges of time and economics in education today, what are practical models for creating adult learning communities that improve teaching and learning in today's diverse classrooms? How do Americans foster and nurture adult learning communities once they are established? The authors have found that carefully crafted partnerships…

  8. Peer Learning Community Guide. CEELO FastFact

    Science.gov (United States)

    Schilder, Diane; Brown, Kirsty Clarke; Gillaspy, Kathi

    2014-01-01

    States and technical assistance centers have asked the Center on Enhancing Early Learning Outcomes (CEELO) for guidance on establishing and maintaining a peer learning community (PLC). This document is designed to delineate the steps to establish and sustain a Peer Learning Community (PLC). It begins with a definition of a PLC and then presents…

  9. Integrating Best Practices: Learning Communities and the Writing Center

    Science.gov (United States)

    Parisi, Hope; Graziano-King, Janine

    2011-01-01

    Bringing together two evidence-based "best practices" in developmental education--learning communities and tutoring--seems natural, especially given that they share collaborative learning as a common pedagogical approach. And yet doing so raised questions around the role of the tutor in learning communities. In this article, a faculty development…

  10. Local communities obstruct global consensus: Naming game on multi-local-world networks

    Science.gov (United States)

    Lou, Yang; Chen, Guanrong; Fan, Zhengping; Xiang, Luna

    2018-02-01

    Community structure is essential for social communications, where individuals belonging to the same community are much more actively interacting and communicating with each other than those in different communities within the human society. Naming game, on the other hand, is a social communication model that simulates the process of learning a name of an object within a community of humans, where the individuals can generally reach global consensus asymptotically through iterative pair-wise conversations. The underlying network indicates the relationships among the individuals. In this paper, three typical topologies, namely random-graph, small-world and scale-free networks, are employed, which are embedded with the multi-local-world community structure, to study the naming game. Simulations show that (1) the convergence process to global consensus is getting slower as the community structure becomes more prominent, and eventually might fail; (2) if the inter-community connections are sufficiently dense, neither the number nor the size of the communities affects the convergence process; and (3) for different topologies with the same (or similar) average node-degree, local clustering of individuals obstruct or prohibit global consensus to take place. The results reveal the role of local communities in a global naming game in social network studies.

  11. ERT Conditions for Productive Learning in Networked Learning Environments: Leadership Report

    DEFF Research Database (Denmark)

    Dirckinck-Holmfeld, Lone

    This report provides a concluding account of the activities within the European Research Team: Conditions for Productive Learning in Networked Learning Environmentments......This report provides a concluding account of the activities within the European Research Team: Conditions for Productive Learning in Networked Learning Environmentments...

  12. The Effect of Service Learning on Community College Students

    Science.gov (United States)

    Sass, Margaret S.; Coll, Ken

    2015-01-01

    This study discusses the implementation of a service learning component in community college communication 101 level courses. Through the execution of a service learning component in communication classes at a community college, students' communicative competency and attitude toward community service is assessed. Using two different delivery…

  13. Community Service and Service-Learning in America's Schools

    Science.gov (United States)

    Spring, Kimberly; Grimm, Robert, Jr.; Dietz, Nathan

    2008-01-01

    In the spring of 2008, 1,847 principals of K-12 public schools, nationwide, responded to a survey on the prevalence of community service and service-learning in their schools. The "National Study of the Prevalence of Community Service and Service-Learning in K-12 Public Schools," sponsored by the Corporation for National and Community Service and…

  14. Informed Faith and Reason: A Perspective on Learning Community Pedagogy

    Science.gov (United States)

    DeIuliis, David

    2015-01-01

    The curriculum of each learning community at Duquesne University is integrated around a shared theme. The integrated classes equip students to articulate their biases in reference to the theme. The residual effect of the thematic communities is a byproduct of pedagogy informed by theory and embodied in service. The learning communities at Duquesne…

  15. Learning Communities' Impact on Student Success in Developmental English

    Science.gov (United States)

    Barnes, Randall A.; Piland, William E.

    2013-01-01

    Recent efforts to improve developmental education have included references to learning communities as examples of effective practices in basic skills education. The study "Basic skills as a foundation for student success in California community colleges" (2007) cited research from Tinto that suggested that learning communities and…

  16. Using smart mobile devices in social-network-based health education practice: a learning behavior analysis.

    Science.gov (United States)

    Wu, Ting-Ting

    2014-06-01

    Virtual communities provide numerous resources, immediate feedback, and information sharing, enabling people to rapidly acquire information and knowledge and supporting diverse applications that facilitate interpersonal interactions, communication, and sharing. Moreover, incorporating highly mobile and convenient devices into practice-based courses can be advantageous in learning situations. Therefore, in this study, a tablet PC and Google+ were introduced to a health education practice course to elucidate satisfaction of learning module and conditions and analyze the sequence and frequency of learning behaviors during the social-network-based learning process. According to the analytical results, social networks can improve interaction among peers and between educators and students, particularly when these networks are used to search for data, post articles, engage in discussions, and communicate. In addition, most nursing students and nursing educators expressed a positive attitude and satisfaction toward these innovative teaching methods, and looked forward to continuing the use of this learning approach. Copyright © 2014 Elsevier Ltd. All rights reserved.

  17. Structure Learning in Power Distribution Networks

    Energy Technology Data Exchange (ETDEWEB)

    Deka, Deepjyoti [Univ. of Texas, Austin, TX (United States); Chertkov, Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Backhaus, Scott N. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2015-01-13

    Traditionally power distribution networks are either not observable or only partially observable. This complicates development and implementation of new smart grid technologies, such as these related to demand response, outage detection and management, and improved load-monitoring. Here, inspired by proliferation of the metering technology, we discuss statistical estimation problems in structurally loopy but operationally radial distribution grids consisting in learning operational layout of the network from measurements, e.g. voltage data, which are either already available or can be made available with a relatively minor investment. Our newly suggested algorithms apply to a wide range of realistic scenarios. The algorithms are also computationally efficient – polynomial in time – which is proven theoretically and illustrated computationally on a number of test cases. The technique developed can be applied to detect line failures in real time as well as to understand the scope of possible adversarial attacks on the grid.

  18. [Numerical analysis on network characteristics of communities in herb-pairs network].

    Science.gov (United States)

    Cao, Jia; Xin, Juan-juan; Wang, Yun

    2015-06-01

    To interpret the traditional Chinese medicine (TCM) theory by the network technology, in order to promote the modernization and programming of studies on compatibility of TCMs. In this paper, efforts were made to express the direct interactions between drugs through the herb-pair network, analyze the community characteristics of the network and its relations with blood-Qi theory, and study the expression of blood-Qi theory on the herb-pair network through prescriptions. According to the findings, the herb-pairs network showed a strong community structure characteristics; Each community is composed of a series of herb pairs with close correlations, and either blood efficacy or Qi efficacy but not both of them. Based on that, the 386 single TCM ingredients involved by the herb-pair network were divided into three types of communities: Blood (B) community, Qi (Q) community and uncertain community. According to the statistical results of 262 prescriptions mapped onto the three types of communities, if a prescription contains single herbs of the Q community, the probability that it contains single herbs o the B community is 99.84%; Meanwhile, there are 140 prescriptions containing single herbs of both the Q community and the B community. The result is completely coincident with the TCM Blood-Qi theory that single herbs belong to both Q and B communities or the B community, because Qi regulation leads to blood regulation, but not vice versa. For example, a patient with hemorrhage due to trauma or blood-heat, Qi tonifying prescriptions may aggravate hemorrhage. In this paper, authors found high-recognition macroscopic network numerical characteristics to network data reference for judging rationality of new prescriptions, and proved human blood and Qi relations from the perspective of data analysis.

  19. On-line Professional Learning Communities: Increasing Teacher Learning and Productivity in Isolated Rural Communities

    Directory of Open Access Journals (Sweden)

    Dora Salazar

    2010-08-01

    Full Text Available On-line and distance professional learning communities provides teachers with increased access and flexibility as well as the combination of work and education. It also provides a more learner-centered approach, enrichment and new ways of interacting with teachers in isolated rural areas. For educational administrators, on-line learning offers high quality and usually cost-effective professional development for teachers. It allows upgrading of skills, increased productivity and development of a new learning culture. At the same time, it means sharing of costs, of training time, increased portability of training, and the exchange of creativity, information, and dialogue.

  20. WEB BASED LEARNING OF COMPUTER NETWORK COURSE

    Directory of Open Access Journals (Sweden)

    Hakan KAPTAN

    2004-04-01

    Full Text Available As a result of developing on Internet and computer fields, web based education becomes one of the area that many improving and research studies are done. In this study, web based education materials have been explained for multimedia animation and simulation aided Computer Networks course in Technical Education Faculties. Course content is formed by use of university course books, web based education materials and technology web pages of companies. Course content is formed by texts, pictures and figures to increase motivation of students and facilities of learning some topics are supported by animations. Furthermore to help working principles of routing algorithms and congestion control algorithms simulators are constructed in order to interactive learning

  1. Fuzzy analysis of community detection in complex networks

    Science.gov (United States)

    Zhang, Dawei; Xie, Fuding; Zhang, Yong; Dong, Fangyan; Hirota, Kaoru

    2010-11-01

    A snowball algorithm is proposed to find community structures in complex networks by introducing the definition of community core and some quantitative conditions. A community core is first constructed, and then its neighbors, satisfying the quantitative conditions, will be tied to this core until no node can be added. Subsequently, one by one, all communities in the network are obtained by repeating this process. The use of the local information in the proposed algorithm directly leads to the reduction of complexity. The algorithm runs in O(n+m) time for a general network and O(n) for a sparse network, where n is the number of vertices and m is the number of edges in a network. The algorithm fast produces the desired results when applied to search for communities in a benchmark and five classical real-world networks, which are widely used to test algorithms of community detection in the complex network. Furthermore, unlike existing methods, neither global modularity nor local modularity is utilized in the proposal. By converting the considered problem into a graph, the proposed algorithm can also be applied to solve other cluster problems in data mining.

  2. On local optima in learning bayesian networks

    DEFF Research Database (Denmark)

    Dalgaard, Jens; Kocka, Tomas; Pena, Jose

    2003-01-01

    This paper proposes and evaluates the k-greedy equivalence search algorithm (KES) for learning Bayesian networks (BNs) from complete data. The main characteristic of KES is that it allows a trade-off between greediness and randomness, thus exploring different good local optima. When greediness...... is set at maximum, KES corresponds to the greedy equivalence search algorithm (GES). When greediness is kept at minimum, we prove that under mild assumptions KES asymptotically returns any inclusion optimal BN with nonzero probability. Experimental results for both synthetic and real data are reported...

  3. Statistical and machine learning approaches for network analysis

    CERN Document Server

    Dehmer, Matthias

    2012-01-01

    Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internation

  4. Stochastic Online Learning in Dynamic Networks under Unknown Models

    Science.gov (United States)

    2016-08-02

    Stochastic Online Learning in Dynamic Networks under Unknown Models This research aims to develop fundamental theories and practical algorithms for...12211 Research Triangle Park, NC 27709-2211 Online learning , multi-armed bandit, dynamic networks REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S... Online Learning in Dynamic Networks under Unknown Models Report Title This research aims to develop fundamental theories and practical algorithms for

  5. Graduate Employability: The Perspective of Social Network Learning

    Science.gov (United States)

    Chen, Yong

    2017-01-01

    This study provides a conceptual framework for understanding how the graduate acquire employability through the social network in the Chinese context, using insights from the social network theory. This paper builds a conceptual model of the relationship among social network, social network learning and the graduate employability, and uses…

  6. Why STEM Learning Communities Work: The Development of Psychosocial Learning Factors through Social Interaction

    Science.gov (United States)

    Carrino, Stephanie Sedberry; Gerace, William J.

    2016-01-01

    STEM learning communities facilitate student academic success and persistence in science disciplines. This prompted us to explore the underlying factors that make learning communities successful. In this paper, we report findings from an illustrative case study of a 2-year STEM-based learning community designed to identify and describe these…

  7. Teacher Education in Schools as Learning Communities: Transforming High-Poverty Schools through Dialogic Learning

    Science.gov (United States)

    Garcia-Carrion, Rocio; Gomez, Aitor; Molina, Silvia; Ionescu, Vladia

    2017-01-01

    Teachers' professional development in Schools as Learning Communities may become a key process for the sustainability and transferability of this model worldwide. Learning Communities (LC) is a community-based project that aims to transform schools through dialogic learning and involves research-grounded schools that implement Successful…

  8. Finding statistically significant communities in networks

    National Research Council Canada - National Science Library

    Lancichinetti, Andrea; Radicchi, Filippo; Ramasco, José J; Fortunato, Santo

    2011-01-01

    .... Despite the large variety of methods proposed to detect communities in graphs, there is a big need for multi-purpose techniques, able to handle different types of datasets and the subtleties of community structure...

  9. Researching Design, Experience and Practice of Networked Learning

    DEFF Research Database (Denmark)

    Hodgson, Vivien; de Laat, Maarten; McConnell, David

    2014-01-01

    and final section draws attention to a growing topic of interest within networked learning: that of networked learning in informal practices. In addition, we provide a reflection on the theories, methods and settings featured in the networked learning research of the chapters. We conclude the introduction......In the introductory chapter, we explore how networked learning has developed in recent years by summarising and discussing the research presented in the chapters of the book. The chapters are structured in three sections, each highlighting a particular aspect of practice. The first section focuses...

  10. Exploring Classroom Community: A Social Network Study of Reacting to the Past

    Directory of Open Access Journals (Sweden)

    Jeff Webb

    2016-03-01

    Full Text Available In this exploratory social network study, we examined how student relationships evolved during three month-long Reacting to the Past (RTTP role-playing games in a lower division honors course at a large US public university. Our purpose was to explore how RTTP games—and collaborative learning approaches more generally—impact classroom community in college courses. We found that both acquaintance and friendship ties between students increased dramatically during the game, eliminating student isolation without tending to create new cliques. These added ties made acquaintance and friendship networks simultaneously denser and more inclusive than they were before the game. We conclude by advancing a hypothesis about the network effects of intensive peer interaction. Collaborative learning approaches like RTTP, we suggest, produce high-density networks with limited clustering because structured peer interactions cut across existing or naturally occurring clique boundaries.

  11. Community evolution mining and analysis in social network

    Science.gov (United States)

    Liu, Hongtao; Tian, Yuan; Liu, Xueyan; Jian, Jie

    2017-03-01

    With the development of digital and network technology, various social platforms emerge. These social platforms have greatly facilitated access to information, attracting more and more users. They use these social platforms every day to work, study and communicate, so every moment social platforms are generating massive amounts of data. These data can often be modeled as complex networks, making large-scale social network analysis possible. In this paper, the existing evolution classification model of community has been improved based on community evolution relationship over time in dynamic social network, and the Evolution-Tree structure is proposed which can show the whole life cycle of the community more clearly. The comparative test result shows that the improved model can excavate the evolution relationship of the community well.

  12. Physical Heterogeneity and Aquatic Community Function in River Networks

    Science.gov (United States)

    The geomorphological character of a river network provides the template upon which evolution acts to create unique biological communities. Deciphering commonly observed patterns and processes within riverine landscapes resulting from the interplay between physical and biological...

  13. Water distribution network modelling of a small community using ...

    African Journals Online (AJOL)

    Water distribution network modelling of a small community using watercad simulator. ... Global Journal of Engineering Research ... Pipes P-6, P-12, P-15 and P-19 expectedly have relatively low flow velocities due to the low average day ...

  14. Community Clustering Algorithm in Complex Networks Based on Microcommunity Fusion

    Directory of Open Access Journals (Sweden)

    Jin Qi

    2015-01-01

    Full Text Available With the further research on physical meaning and digital features of the community structure in complex networks in recent years, the improvement of effectiveness and efficiency of the community mining algorithms in complex networks has become an important subject in this area. This paper puts forward a concept of the microcommunity and gets final mining results of communities through fusing different microcommunities. This paper starts with the basic definition of the network community and applies Expansion to the microcommunity clustering which provides prerequisites for the microcommunity fusion. The proposed algorithm is more efficient and has higher solution quality compared with other similar algorithms through the analysis of test results based on network data set.

  15. Finding community structure in networks using the eigenvectors of matrices.

    Science.gov (United States)

    Newman, M E J

    2006-09-01

    We consider the problem of detecting communities or modules in networks, groups of vertices with a higher-than-average density of edges connecting them. Previous work indicates that a robust approach to this problem is the maximization of the benefit function known as "modularity" over possible divisions of a network. Here we show that this maximization process can be written in terms of the eigenspectrum of a matrix we call the modularity matrix, which plays a role in community detection similar to that played by the graph Laplacian in graph partitioning calculations. This result leads us to a number of possible algorithms for detecting community structure, as well as several other results, including a spectral measure of bipartite structure in networks and a centrality measure that identifies vertices that occupy central positions within the communities to which they belong. The algorithms and measures proposed are illustrated with applications to a variety of real-world complex networks.

  16. Epistemic Communities, Situated Learning and Open Source Software Development

    DEFF Research Database (Denmark)

    Edwards, Kasper

    2001-01-01

    of epistemic communities does indeed contribute to the understanding of open source software development. But, the important learning process of open source software development is not readily explained. The paper then introduces situated learning and legitimate peripheral participation as theoretical......This paper analyses open source software (OSS) development as an epistemic community where each individual project is perceived as a single epistemic community. OSS development is a learning process where the involved parties contribute to, and learn from the community. It is discovered that theory...... perspectives. This allows the learning process to be part of the activities in the epistemic community. The combination of situated learning and epistemic communities is shown to be fruitful and capable of explaining some of the empirical observations. In particular the combination of theories can shed light...

  17. An Efficient Hierarchy Algorithm for Community Detection in Complex Networks

    Directory of Open Access Journals (Sweden)

    Lili Zhang

    2014-01-01

    Full Text Available Community structure is one of the most fundamental and important topology characteristics of complex networks. The research on community structure has wide applications and is very important for analyzing the topology structure, understanding the functions, finding the hidden properties, and forecasting the time-varying of the networks. This paper analyzes some related algorithms and proposes a new algorithm—CN agglomerative algorithm based on graph theory and the local connectedness of network to find communities in network. We show this algorithm is distributed and polynomial; meanwhile the simulations show it is accurate and fine-grained. Furthermore, we modify this algorithm to get one modified CN algorithm and apply it to dynamic complex networks, and the simulations also verify that the modified CN algorithm has high accuracy too.

  18. Exponential random graph models for networks with community structure.

    Science.gov (United States)

    Fronczak, Piotr; Fronczak, Agata; Bujok, Maksymilian

    2013-09-01

    Although the community structure organization is an important characteristic of real-world networks, most of the traditional network models fail to reproduce the feature. Therefore, the models are useless as benchmark graphs for testing community detection algorithms. They are also inadequate to predict various properties of real networks. With this paper we intend to fill the gap. We develop an exponential random graph approach to networks with community structure. To this end we mainly built upon the idea of blockmodels. We consider both the classical blockmodel and its degree-corrected counterpart and study many of their properties analytically. We show that in the degree-corrected blockmodel, node degrees display an interesting scaling property, which is reminiscent of what is observed in real-world fractal networks. A short description of Monte Carlo simulations of the models is also given in the hope of being useful to others working in the field.

  19. Adaptive clustering algorithm for community detection in complex networks

    Science.gov (United States)

    Ye, Zhenqing; Hu, Songnian; Yu, Jun

    2008-10-01

    Community structure is common in various real-world networks; methods or algorithms for detecting such communities in complex networks have attracted great attention in recent years. We introduced a different adaptive clustering algorithm capable of extracting modules from complex networks with considerable accuracy and robustness. In this approach, each node in a network acts as an autonomous agent demonstrating flocking behavior where vertices always travel toward their preferable neighboring groups. An optimal modular structure can emerge from a collection of these active nodes during a self-organization process where vertices constantly regroup. In addition, we show that our algorithm appears advantageous over other competing methods (e.g., the Newman-fast algorithm) through intensive evaluation. The applications in three real-world networks demonstrate the superiority of our algorithm to find communities that are parallel with the appropriate organization in reality.

  20. Developing a Comprehensive Learning Community Program: Implementing a Learning Community Curriculum

    Science.gov (United States)

    Workman, Jamie L.; Redington, Lyn

    2016-01-01

    This is the second of a three-part series which will share information about how a mid-size, comprehensive university developed a learning community program, including a residential curriculum. Through intentional collaboration and partnerships, the team, comprised of faculty and staff throughout the university, developed a "multi-year plan…

  1. Boltzmann learning of parameters in cellular neural networks

    DEFF Research Database (Denmark)

    Hansen, Lars Kai

    1992-01-01

    The use of Bayesian methods to design cellular neural networks for signal processing tasks and the Boltzmann machine learning rule for parameter estimation is discussed. The learning rule can be used for models with hidden units, or for completely unsupervised learning. The latter is exemplified...... by unsupervised adaptation of an image segmentation cellular network. The learning rule is applied to adaptive segmentation of satellite imagery...

  2. Service-Learning as a Catalyst for Community Development: How Do Community Partners Benefit From Service-Learning?

    Science.gov (United States)

    Geller, Joanna D.; Zuckerman, Natalie; Seidel, Adam

    2016-01-01

    Service-learning has the potential to create mutually beneficial relationships between schools and communities, but little research explores service-learning from the community's perspective. The purpose of this study was to (a) understand how community-based organizations (CBOs) benefited from partnering with students and (b) examine whether…

  3. Learning and Best Practices for Learning in Open-Source Software Communities

    Science.gov (United States)

    Singh, Vandana; Holt, Lila

    2013-01-01

    This research is about participants who use open-source software (OSS) discussion forums for learning. Learning in online communities of education as well as non-education-related online communities has been studied under the lens of social learning theory and situated learning for a long time. In this research, we draw parallels among these two…

  4. Growing networks of overlapping communities with internal structure

    Science.gov (United States)

    Young, Jean-Gabriel; Hébert-Dufresne, Laurent; Allard, Antoine; Dubé, Louis J.

    2016-08-01

    We introduce an intuitive model that describes both the emergence of community structure and the evolution of the internal structure of communities in growing social networks. The model comprises two complementary mechanisms: One mechanism accounts for the evolution of the internal link structure of a single community, and the second mechanism coordinates the growth of multiple overlapping communities. The first mechanism is based on the assumption that each node establishes links with its neighbors and introduces new nodes to the community at different rates. We demonstrate that this simple mechanism gives rise to an effective maximal degree within communities. This observation is related to the anthropological theory known as Dunbar's number, i.e., the empirical observation of a maximal number of ties which an average individual can sustain within its social groups. The second mechanism is based on a recently proposed generalization of preferential attachment to community structure, appropriately called structural preferential attachment (SPA). The combination of these two mechanisms into a single model (SPA+) allows us to reproduce a number of the global statistics of real networks: The distribution of community sizes, of node memberships, and of degrees. The SPA+ model also predicts (a) three qualitative regimes for the degree distribution within overlapping communities and (b) strong correlations between the number of communities to which a node belongs and its number of connections within each community. We present empirical evidence that support our findings in real complex networks.

  5. Evolution properties of the community members for dynamic networks

    Science.gov (United States)

    Yang, Kai; Guo, Qiang; Li, Sheng-Nan; Han, Jing-Ti; Liu, Jian-Guo

    2017-03-01

    The collective behaviors of community members for dynamic social networks are significant for understanding evolution features of communities. In this Letter, we empirically investigate the evolution properties of the new community members for dynamic networks. Firstly, we separate data sets into different slices, and analyze the statistical properties of new members as well as communities they joined in for these data sets. Then we introduce a parameter φ to describe community evolution between different slices and investigate the dynamic community properties of the new community members. The empirical analyses for the Facebook, APS, Enron and Wiki data sets indicate that both the number of new members and joint communities increase, the ratio declines rapidly and then becomes stable over time, and most of the new members will join in the small size communities that is s ≤ 10. Furthermore, the proportion of new members in existed communities decreases firstly and then becomes stable and relatively small for these data sets. Our work may be helpful for deeply understanding the evolution properties of community members for social networks.

  6. Intelligent sensor networks the integration of sensor networks, signal processing and machine learning

    CERN Document Server

    Hu, Fei

    2012-01-01

    Although governments worldwide have invested significantly in intelligent sensor network research and applications, few books cover intelligent sensor networks from a machine learning and signal processing perspective. Filling this void, Intelligent Sensor Networks: The Integration of Sensor Networks, Signal Processing and Machine Learning focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on the world-class research of award-winning authors, the book provides a firm grounding in the fundamentals of intelligent sensor networks, incl

  7. Spectral methods for network community detection and graph partitioning

    OpenAIRE

    Newman, M.E.J.

    2013-01-01

    We consider three distinct and well studied problems concerning network structure: community detection by modularity maximization, community detection by statistical inference, and normalized-cut graph partitioning. Each of these problems can be tackled using spectral algorithms that make use of the eigenvectors of matrix representations of the network. We show that with certain choices of the free parameters appearing in these spectral algorithms the algorithms for all three problems are, in...

  8. Building a Network of Internships for a Diverse Geoscience Community

    Science.gov (United States)

    Sloan, V.; Haacker-Santos, R.; Pandya, R.

    2011-12-01

    Individual undergraduate internship programs, however effective, are not sufficient to address the lack of diversity in the geoscience workforce. Rather than competing with each other for a small pool of students from historically under-represented groups, REU and internship programs might share recruiting efforts and application processes. For example, in 2011, the RESESS program at UNAVCO and the SOARS program at UCAR shared recruiting websites and advertising. This contributed to a substantial increase in the number of applicants to the RESESS program, the majority of which were from historically under-represented groups. RESESS and SOARS shared qualified applications with other REU/internship programs and helped several additional minority students secure summer internships. RESESS and SOARS also leveraged their geographic proximity to pool resources for community building activities, a two-day science field trip, a weekly writing workshop, and our final poster session. This provided our interns with an expanded network of peers and gave our staff opportunities to work together on planning. Recently we have reached out to include other programs and agencies in activities for our interns, such as mentoring high-school students, leading outreach to elementary school students, and exposing our interns to geoscience careers options and graduate schools. Informal feedback from students suggests that they value these interactions and appreciate learning with interns from partner programs. Through this work, we are building a network of program managers who support one another professionally and share effective strategies. We would like to expand that network, and future plans include a workshop with university partners and an expanded list of REU programs to explore further collaborations.

  9. Efficient inference of overlapping communities in complex networks

    DEFF Research Database (Denmark)

    Fruergaard, Bjarne Ørum; Herlau, Tue

    2014-01-01

    We discuss two views on extending existing methods for complex network modeling which we dub the communities first and the networks first view, respectively. Inspired by the networks first view that we attribute to White, Boorman, and Breiger (1976)[1], we formulate the multiple-networks stochastic...... blockmodel (MNSBM), which seeks to separate the observed network into subnetworks of different types and where the problem of inferring structure in each subnetwork becomes easier. We show how this model is specified in a generative Bayesian framework where parameters can be inferred efficiently using Gibbs...

  10. New Ideas for Communities of Practice: Networks of Networks ...

    African Journals Online (AJOL)

    Journal Home > Vol 2, No 2 (2013) > ... that support a 'Knowledge Network of Networks' that can adapt to changing technologies, is self-maintaining and scalable, and can be supported by a variety of clients in any number of interaction channels – from traditional desktops and laptops to mobile phones, and smart devices.

  11. Exploring the limits of community detection strategies in complex networks.

    Science.gov (United States)

    Aldecoa, Rodrigo; Marín, Ignacio

    2013-01-01

    The characterization of network community structure has profound implications in several scientific areas. Therefore, testing the algorithms developed to establish the optimal division of a network into communities is a fundamental problem in the field. We performed here a highly detailed evaluation of community detection algorithms, which has two main novelties: 1) using complex closed benchmarks, which provide precise ways to assess whether the solutions generated by the algorithms are optimal; and, 2) A novel type of analysis, based on hierarchically clustering the solutions suggested by multiple community detection algorithms, which allows to easily visualize how different are those solutions. Surprise, a global parameter that evaluates the quality of a partition, confirms the power of these analyses. We show that none of the community detection algorithms tested provide consistently optimal results in all networks and that Surprise maximization, obtained by combining multiple algorithms, obtains quasi-optimal performances in these difficult benchmarks.

  12. Reimagining Diversity Work: Multigenerational Learning, Adult Immigrants, and Dialogical Community-Based Learning

    Science.gov (United States)

    Yep, Kathleen S.

    2014-01-01

    Interactions between universities and surrounding communities have the potential to create empowering education through community engagement. Innovative "town/gown" relationships such as multigenerational learning communities with immigrant communities may foster positive student learning outcomes while at the same time strengthen local…

  13. Human teaching and learning involve cultural communities, not just individuals.

    Science.gov (United States)

    Rogoff, Barbara

    2015-01-01

    Cultural accounts of how people facilitate learning extend beyond the five types of teaching outlined by Kline's target article. Rather than focusing so exclusively on individual teaching, cultural accounts examine the mutually constituting efforts of individuals who are teaching, together with those who are learning. Further, cultural research emphasizes the community contexts of people's arrangements for learning and their teaching/learning interactions.

  14. Beyond the ivory tower: service-learning for sustainable community ...

    African Journals Online (AJOL)

    The concept and practice of service-learning has succeeded in uniting these core functions. Whereas the quality of student learning resulting from service-learning experiences is of crucial importance for universities, the role of service-learning in community development also deserves attention. The article explores the ...

  15. Learning network theory : its contribution to our understanding of work-based learning projects and learning climate

    NARCIS (Netherlands)

    Poell, R.F.; Moorsel, M.A.A.H. van

    1996-01-01

    This paper discusses the relevance of Van der Krogt's learning network theory (1995) for our understanding of the concepts of work-related learning projects and learning climate in organisations. The main assumptions of the learning network theory are presented and transferred to the level of

  16. Experiencing community psychology through community-based learning class projects: reflections from an American University in the Middle East.

    Science.gov (United States)

    Amer, Mona M; Mohamed, Salma N; Ganzon, Vincent

    2013-01-01

    Many introductory community psychology courses do not incorporate community-based learning (CBL), and when they do, it is most often in the form of individualized volunteer hours. We present an alternative model for CBL in which the entire class collaborates on an experiential project that promotes community action. We believe that such an approach better embodies the values and methods of the discipline and has a more powerful impact on the students and stakeholders. It may be especially effective in developing countries that do not have an established network of service infrastructures; in such nations the onus is on the teachers and learners of community psychology to contribute to transformative change. In this article practical guidelines are provided by the instructor regarding how to structure and implement this CBL model. Additionally, two students describe how the CBL experience solidified their learning of course concepts and significantly impacted them personally.

  17. Strengthening the Community in Order to Enhance Learning

    NARCIS (Netherlands)

    Fetter, Sibren

    2008-01-01

    Fetter, S. (2008). Strengthening the Community in Order to Enhance Learning. Presented at the Doctoral Consortium of the IADIS International Conference on Web Based Communities 2008. July, 26, 2008, Amsterdam, The Netherlands.

  18. Supporting communities in programmable grid networks: gTBN

    NARCIS (Netherlands)

    Cristea, M.L.; Strijkers, R.J.; Marchal, D.; Gommans, L.; de Laat, C.; Meijer, R.J.

    2009-01-01

    This paper presents the generalised token based networking (gTBN) architecture, which enables dynamic binding of communities and their applications to specialised network services. gTBN uses protocol independent tokens to provide decoupling of authorisation from time of usage as well as

  19. Supporting Communities in Programmable Grid Networks: gTBN

    NARCIS (Netherlands)

    Christea, M.L; Strijkers, R.J.; Marchal, D.; Gommans, L.; Laat, C. de; Meijer, R.J.

    2009-01-01

    Abstract—This paper presents the generalised Token Based Networking (gTBN) architecture, which enables dynamic binding of communities and their applications to specialised network services. gTBN uses protocol independent tokens to provide decoupling of authorisation from time of usage as well as

  20. Water distribution network modelling of a small community using ...

    African Journals Online (AJOL)

    In this study a network model was constructed for the hydraulic analysis and design of a small community (Sakwa) water distribution network in North Eastern geopolitical region of Nigeria using WaterCAD simulator. The analysis included a review of pressures, velocities and head loss gradients under steady state average ...

  1. Water Distribution Network Modelling of a Small Community using ...

    African Journals Online (AJOL)

    Water Distribution Network Modelling of a Small Community using Watercad Simulator. ... Global Journal of Engineering Research ... with respect to pressure or available fire flow for the proposed service area and also that flow velocities are not excessive while head loss gradients in the network are within acceptable limits.

  2. Semantic Social Network Portal for Collaborative Online Communities

    Science.gov (United States)

    Neumann, Marco; O'Murchu, Ina; Breslin, John; Decker, Stefan; Hogan, Deirdre; MacDonaill, Ciaran

    2005-01-01

    Purpose: The motivation for this investigation is to apply social networking features to a semantic network portal, which supports the efforts in enterprise training units to up-skill the employee in the company, and facilitates the creation and reuse of knowledge in online communities. Design/methodology/approach: The paper provides an overview…

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

    Science.gov (United States)

    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…

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

    Science.gov (United States)

    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.

  5. Social Networks: Gated Communities or Free Cantons?

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    Online social networks and other cloud-based services have concentrated the control of the web in the hands of a few corporations. Our personal data has been commodified, often without our knowledge or consent. Is there a way to retain all the benefits of social networking without giving up control of our data?

  6. Startup : Philippine Community eCentres Network | IDRC ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Startup : Philippine Community eCentres Network. More than 300 Community eCenters or telecentres are currently operating as part of local government units throughout the Philippines. Exchange visits with the M.S. Swaminathan Research Foundation of India have enabled these centres to evolve in such a way as to ...

  7. Social contagions on time-varying community networks

    Science.gov (United States)

    Liu, Mian-Xin; Wang, Wei; Liu, Ying; Tang, Ming; Cai, Shi-Min; Zhang, Hai-Feng

    2017-05-01

    Time-varying community structures exist widely in real-world networks. However, previous studies on the dynamics of spreading seldom took this characteristic into account, especially those on social contagions. To study the effects of time-varying community structures on social contagions, we propose a non-Markovian social contagion model on time-varying community networks based on the activity-driven network model. A mean-field theory is developed to analyze the proposed model. Through theoretical analyses and numerical simulations, two hierarchical features of the behavior adoption processes are found. That is, when community strength is relatively large, the behavior can easily spread in one of the communities, while in the other community the spreading only occurs at higher behavioral information transmission rates. Meanwhile, in spatial-temporal evolution processes, hierarchical orders are observed for the behavior adoption. Moreover, under different information transmission rates, three distinctive patterns are demonstrated in the change of the whole network's final adoption proportion along with the growing community strength. Within a suitable range of transmission rate, an optimal community strength can be found that can maximize the final adoption proportion. Finally, compared with the average activity potential, the promoting or inhibiting of social contagions is much more influenced by the number of edges generated by active nodes.

  8. Adaptive multi-resolution Modularity for detecting communities in networks

    Science.gov (United States)

    Chen, Shi; Wang, Zhi-Zhong; Bao, Mei-Hua; Tang, Liang; Zhou, Ji; Xiang, Ju; Li, Jian-Ming; Yi, Chen-He

    2018-02-01

    Community structure is a common topological property of complex networks, which attracted much attention from various fields. Optimizing quality functions for community structures is a kind of popular strategy for community detection, such as Modularity optimization. Here, we introduce a general definition of Modularity, by which several classical (multi-resolution) Modularity can be derived, and then propose a kind of adaptive (multi-resolution) Modularity that can combine the advantages of different Modularity. By applying the Modularity to various synthetic and real-world networks, we study the behaviors of the methods, showing the validity and advantages of the multi-resolution Modularity in community detection. The adaptive Modularity, as a kind of multi-resolution method, can naturally solve the first-type limit of Modularity and detect communities at different scales; it can quicken the disconnecting of communities and delay the breakup of communities in heterogeneous networks; and thus it is expected to generate the stable community structures in networks more effectively and have stronger tolerance against the second-type limit of Modularity.

  9. Startup : Philippine Community eCentres Network | CRDI - Centre ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Startup : Philippine Community eCentres Network. More than 300 Community eCenters or telecentres are currently operating as part of local government units throughout the Philippines. Exchange visits with the M.S. Swaminathan Research Foundation of India have enabled these centres to evolve in such a way as to ...

  10. Professional Learning Communities and Communities of Practice: A Comparison of Models, Literature Review

    Science.gov (United States)

    Blankenship, Selena S.; Ruona, Wendy E. A.

    2007-01-01

    Due to the growing interest of school leaders in implementing learning communities as a way to build capacity for and sustain change, a better understanding of how the concepts of professional learning communities (PLCs) and communities of practice (CoPs) are related will aid educators in their quest to implement these concepts. This paper…

  11. De-novo learning of genome-scale regulatory networks in S. cerevisiae.

    Directory of Open Access Journals (Sweden)

    Sisi Ma

    Full Text Available De-novo reverse-engineering of genome-scale regulatory networks is a fundamental problem of biological and translational research. One of the major obstacles in developing and evaluating approaches for de-novo gene network reconstruction is the absence of high-quality genome-scale gold-standard networks of direct regulatory interactions. To establish a foundation for assessing the accuracy of de-novo gene network reverse-engineering, we constructed high-quality genome-scale gold-standard networks of direct regulatory interactions in Saccharomyces cerevisiae that incorporate binding and gene knockout data. Then we used 7 performance metrics to assess accuracy of 18 statistical association-based approaches for de-novo network reverse-engineering in 13 different datasets spanning over 4 data types. We found that most reconstructed networks had statistically significant accuracies. We also determined which statistical approaches and datasets/data types lead to networks with better reconstruction accuracies. While we found that de-novo reverse-engineering of the entire network is a challenging problem, it is possible to reconstruct sub-networks around some transcription factors with good accuracy. The latter transcription factors can be identified by assessing their connectivity in the inferred networks. Overall, this study provides the gene network reverse-engineering community with a rigorous assessment of the accuracy of S. cerevisiae gene network reconstruction and variability in performance of various approaches for learning both the entire network and sub-networks around transcription factors.

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

    Science.gov (United States)

    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…

  13. Project team formation support for self-directed learners in social learning networks

    NARCIS (Netherlands)

    Spoelstra, Howard; Van Rosmalen, Peter; Sloep, Peter

    2012-01-01

    Spoelstra, H., Van Rosmalen, P., & Sloep, P. B. (2012). Project team formation support for self-directed learners in social learning networks. In P. Kommers, P. Isaias, & N. Bessis (Eds.), Proceedings of the IADIS International Conference on Web Based Communities and Social Media (ICWBC & SM 2012)

  14. On the Importance of Personal Profiles to Enhance Social Interaction in Learning Networks

    NARCIS (Netherlands)

    Berlanga, Adriana

    2008-01-01

    Berlanga, A. J., Bitter-Rijpkema, M. E., Brouns F., & Sloep, P. B. (2008). On the Importance of Personal Profiles to Enhance Social Interaction in Learning Networks. Presented at the IADIS International Conference on Web Based Communities 2008. July, 24-26, 2008, Amsterdam, The Netherlands.

  15. On the importance of personal profiles to enhance social interaction in Learning Networks

    NARCIS (Netherlands)

    Berlanga, Adriana; Bitter-Rijpkema, Marlies; Brouns, Francis; Sloep, Peter

    2008-01-01

    Berlanga, A. J., Bitter-Rijpkema, M., Brouns F., & Sloep, P.B. (2008). On the importance of personal profiles to enhance social interaction in Learning Networks. In P. Kommers (Ed.), Proceedings of Web Based Communities Conference (WEBC 2008) (pp. 55-62). July, 24-26, 2008, Amsterdam, The

  16. Designing optimal peer support to alleviate learner cognitive load in Learning Networks

    NARCIS (Netherlands)

    Hsiao, Amy; Brouns, Francis; Sloep, Peter

    2012-01-01

    Hsiao, Y. P., Brouns, F., & Sloep, P. B. (2012). Designing optimal peer support to alleviate learner cognitive load in Learning Networks. In P. Kommers, & N. Bessis (Eds.), Proceedings of IADIS International Conference Web-Based Communities and Social Media 2012 (pp. 73-80). July, 19-21, 2012,

  17. Cooperative Learning for Distributed In-Network Traffic Classification

    Science.gov (United States)

    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.

  18. CosmoQuest Collaborative: Galvanizing a Dynamic Professional Learning Network

    Science.gov (United States)

    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

  19. Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

    Science.gov (United States)

    Chernoded, Andrey; Dudko, Lev; Myagkov, Igor; Volkov, Petr

    2017-10-01

    Most of the modern analyses in high energy physics use signal-versus-background classification techniques of machine learning methods and neural networks in particular. Deep learning neural network is the most promising modern technique to separate signal and background and now days can be widely and successfully implemented as a part of physical analysis. In this article we compare Deep learning and Bayesian neural networks application as a classifiers in an instance of top quark analysis.

  20. Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

    Directory of Open Access Journals (Sweden)

    Chernoded Andrey

    2017-01-01

    Full Text Available Most of the modern analyses in high energy physics use signal-versus-background classification techniques of machine learning methods and neural networks in particular. Deep learning neural network is the most promising modern technique to separate signal and background and now days can be widely and successfully implemented as a part of physical analysis. In this article we compare Deep learning and Bayesian neural networks application as a classifiers in an instance of top quark analysis.

  1. A clustering algorithm for determining community structure in complex networks

    Science.gov (United States)

    Jin, Hong; Yu, Wei; Li, ShiJun

    2018-02-01

    Clustering algorithms are attractive for the task of community detection in complex networks. DENCLUE is a representative density based clustering algorithm which has a firm mathematical basis and good clustering properties allowing for arbitrarily shaped clusters in high dimensional datasets. However, this method cannot be directly applied to community discovering due to its inability to deal with network data. Moreover, it requires a careful selection of the density parameter and the noise threshold. To solve these issues, a new community detection method is proposed in this paper. First, we use a spectral analysis technique to map the network data into a low dimensional Euclidean Space which can preserve node structural characteristics. Then, DENCLUE is applied to detect the communities in the network. A mathematical method named Sheather-Jones plug-in is chosen to select the density parameter which can describe the intrinsic clustering structure accurately. Moreover, every node on the network is meaningful so there were no noise nodes as a result the noise threshold can be ignored. We test our algorithm on both benchmark and real-life networks, and the results demonstrate the effectiveness of our algorithm over other popularity density based clustering algorithms adopted to community detection.

  2. Towards a Social Networks Model for Online Learning & Performance

    Science.gov (United States)

    Chung, Kon Shing Kenneth; Paredes, Walter Christian

    2015-01-01

    In this study, we develop a theoretical model to investigate the association between social network properties, "content richness" (CR) in academic learning discourse, and performance. CR is the extent to which one contributes content that is meaningful, insightful and constructive to aid learning and by social network properties we…

  3. The Fire Learning Network: A promising conservation strategy for forestry

    Science.gov (United States)

    Bruce E. Goldstein; William H. Butler; R. Bruce. Hull

    2010-01-01

    Conservation Learning Networks (CLN) are an emerging conservation strategy for addressing complex resource management challenges that face the forestry profession. The US Fire Learning Network (FLN) is a successful example of a CLN that operates on a national scale. Developed in 2001 as a partnership between The Nature Conservancy, the US Forest Service, and land-...

  4. Social networks as ICT collaborative and supportive learning media ...

    African Journals Online (AJOL)

    ... ICT collaborative and supportive learning media utilisation within the Nigerian educational system. The concept of ICT was concisely explained vis-à-vis the social network concept, theory and collaborative and supportive learning media utilisation. Different types of social network are highlighted among which Facebook, ...

  5. Problems in the Deployment of Learning Networks In Small Organizations

    NARCIS (Netherlands)

    Shankle, Dean E.; Shankle, Jeremy P.

    2006-01-01

    Please, cite this publication as: Shankle, D.E., & Shankle, J.P. (2006). Problems in the Deployment of Learning Networks In Small Organizations. Proceedings of International Workshop in Learning Networks for Lifelong Competence Development, TENCompetence Conference. March 30th-31st, Sofia, Bulgaria:

  6. The Practices of Student Network as Cooperative Learning in Ethiopia

    Science.gov (United States)

    Reda, Weldemariam Nigusse; Hagos, Girmay Tsegay

    2015-01-01

    Student network is a teaching strategy introduced as cooperative learning to all educational levels above the upper primary schools (grade 5 and above) in Ethiopia. The study was, therefore, aimed at investigating to what extent the student network in Ethiopia is actually practiced in line with the principles of cooperative learning. Consequently,…

  7. Detecting and evaluating communities in complex human and biological networks

    Science.gov (United States)

    Morrison, Greg; Mahadevan, L.

    2012-02-01

    We develop a simple method for detecting the community structure in a network can by utilizing a measure of closeness between nodes. This approach readily leads to a method of coarse graining the network, which allows the detection of the natural hierarchy (or hierarchies) of community structure without appealing to an unknown resolution parameter. The closeness measure can also be used to evaluate the robustness of an individual node's assignment to its community (rather than evaluating only the quality of the global structure). Each of these methods in community detection and evaluation are illustrated using a variety of real world networks of either biological or sociological importance and illustrate the power and flexibility of the approach.

  8. EduCamp Colombia: Social Networked Learning for Teacher Training

    OpenAIRE

    Diego Ernesto Leal Fonseca

    2011-01-01

    This paper describes a learning experience called EduCamp, which was launched by the Ministry of Education of Colombia in 2007, based on emerging concepts such as e-Learning 2.0, connectivism, and personal learning environments. An EduCamp proposes an unstructured collective learning experience, which intends to make palpable the possibilities of social software tools in learning and interaction processes while demonstrating face-to-face organizational forms that reflect social networked lear...

  9. Learning Networks for Lifelong Learning: An Exploratory Survey on Distance Learners’ preferences

    NARCIS (Netherlands)

    Berlanga, Adriana; Rusman, Ellen; Eshuis, Jannes; Hermans, Henry; Sloep, Peter

    2010-01-01

    Berlanga, A. J., Rusman, E., Eshuis, J., Hermans, H., & Sloep, P. B. (2010, 3 May). Learning Networks for Lifelong Learning: An Exploratory Survey on Distance Learners’ preferences. Presentation at the 7th International Conference on Networked Learning (NLC-2010), Aalborg, Denmark.

  10. Latent Semantic Analysis As a Tool for Learner Positioning in Learning Networks for Lifelong Learning

    Science.gov (United States)

    van Bruggen, Jan; Sloep, Peter; van Rosmalen, Peter; Brouns, Francis; Vogten, Hubert; Koper, Rob; Tattersall, Colin

    2004-01-01

    As we move towards distributed, self-organised learning networks for lifelong learning to which multiple providers contribute content, there is a need to develop new techniques to determine where learners can be positioned in these networks. Positioning requires us to map characteristics of the learner onto characteristics of learning materials…

  11. A local immunization strategy for networks with overlapping community structure

    Science.gov (United States)

    Taghavian, Fatemeh; Salehi, Mostafa; Teimouri, Mehdi

    2017-02-01

    Since full coverage treatment is not feasible due to limited resources, we need to utilize an immunization strategy to effectively distribute the available vaccines. On the other hand, the structure of contact network among people has a significant impact on epidemics of infectious diseases (such as SARS and influenza) in a population. Therefore, network-based immunization strategies aim to reduce the spreading rate by removing the vaccinated nodes from contact network. Such strategies try to identify more important nodes in epidemics spreading over a network. In this paper, we address the effect of overlapping nodes among communities on epidemics spreading. The proposed strategy is an optimized random-walk based selection of these nodes. The whole process is local, i.e. it requires contact network information in the level of nodes. Thus, it is applicable to large-scale and unknown networks in which the global methods usually are unrealizable. Our simulation results on different synthetic and real networks show that the proposed method outperforms the existing local methods in most cases. In particular, for networks with strong community structures, high overlapping membership of nodes or small size communities, the proposed method shows better performance.

  12. A Comparative Analysis of Community Detection Algorithms on Artificial Networks.

    Science.gov (United States)

    Yang, Zhao; Algesheimer, René; Tessone, Claudio J

    2016-08-01

    Many community detection algorithms have been developed to uncover the mesoscopic properties of complex networks. However how good an algorithm is, in terms of accuracy and computing time, remains still open. Testing algorithms on real-world network has certain restrictions which made their insights potentially biased: the networks are usually small, and the underlying communities are not defined objectively. In this study, we employ the Lancichinetti-Fortunato-Radicchi benchmark graph to test eight state-of-the-art algorithms. We quantify the accuracy using complementary measures and algorithms' computing time. Based on simple network properties and the aforementioned results, we provide guidelines that help to choose the most adequate community detection algorithm for a given network. Moreover, these rules allow uncovering limitations in the use of specific algorithms given macroscopic network properties. Our contribution is threefold: firstly, we provide actual techniques to determine which is the most suited algorithm in most circumstances based on observable properties of the network under consideration. Secondly, we use the mixing parameter as an easily measurable indicator of finding the ranges of reliability of the different algorithms. Finally, we study the dependency with network size focusing on both the algorithm's predicting power and the effective computing time.

  13. Community structure from spectral properties in complex networks

    Science.gov (United States)

    Servedio, V. D. P.; Colaiori, F.; Capocci, A.; Caldarelli, G.

    2005-06-01

    We analyze the spectral properties of complex networks focusing on their relation to the community structure, and develop an algorithm based on correlations among components of different eigenvectors. The algorithm applies to general weighted networks, and, in a suitably modified version, to the case of directed networks. Our method allows to correctly detect communities in sharply partitioned graphs, however it is useful to the analysis of more complex networks, without a well defined cluster structure, as social and information networks. As an example, we test the algorithm on a large scale data-set from a psychological experiment of free word association, where it proves to be successful both in clustering words, and in uncovering mental association patterns.

  14. Supervised Learning with Complex-valued Neural Networks

    CERN Document Server

    Suresh, Sundaram; Savitha, Ramasamy

    2013-01-01

    Recent advancements in the field of telecommunications, medical imaging and signal processing deal with signals that are inherently time varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial neural networks.  Furthermore, to efficiently preserve the physical characteristics of these complex-valued signals, it is important to develop complex-valued neural networks and derive their learning algorithms to represent these signals at every step of the learning process. This monograph comprises a collection of new supervised learning algorithms along with novel architectures for complex-valued neural networks. The concepts of meta-cognition equipped with a self-regulated learning have been known to be the best human learning strategy. In this monograph, the principles of meta-cognition have been introduced for complex-valued neural networks in both the batch and sequential learning modes. For applications where the computati...

  15. Online experimentation and interactive learning resources for teaching network engineering

    OpenAIRE

    Mikroyannidis, Alexander; Gomez-Goiri, Aitor; Smith, Andrew; Domingue, John

    2017-01-01

    This paper presents a case study on teaching network engineering in conjunction with interactive learning resources. This case study has been developed in collaboration with the Cisco Networking Academy in the context of the FORGE project, which promotes online learning and experimentation by offering access to virtual and remote labs. The main goal of this work is allowing learners and educators to perform network simulations within a web browser or an interactive eBook by using any type of ...

  16. Creating Experiential Learning in the Graduate Classroom through Community Engagement

    Science.gov (United States)

    Johnson, Katryna

    2013-01-01

    Educators can provide opportunities for active learning for the students by engaging them in client-based projects with the community, which enhances application of theory and provides students with the relevance demanded from the business community. Experiential learning opportunities through client-based projects provide for such an experience.…

  17. Computer-facilitated community building for E-learning

    NARCIS (Netherlands)

    Nijholt, Antinus; Petrushin, V.; Kommers, Petrus A.M.; Kinshuk, X.; Galeev, I.

    2002-01-01

    This is a short survey of tools and ideas that are helpful for community building for E-learning. The underlying assumption in the survey is that community building for students and teachers in a joint learning and teaching situation is useful. Especially student-student interaction in student life

  18. Service Learning and Community Health Nursing: A Natural Fit.

    Science.gov (United States)

    Miller, Marilyn P.; Swanson, Elizabeth

    2002-01-01

    Community health nursing students performed community assessments and proposed and implemented service learning projects that addressed adolescent smoking in middle schools, home safety for elderly persons, industrial worker health, and sexual abuse of teenaged girls. Students learned to apply epidemiological research methods, mobilize resources,…

  19. Engaging College Students from Diverse Backgrounds in Community Service Learning

    Science.gov (United States)

    Novick, Sarah; Seider, Scott C.; Huguley, James P.

    2011-01-01

    Community service learning at the university level is often conceived of as a mechanism for introducing privileged young adults to people with whom they have never interacted and experiences they have never had. American universities and courses involving community service learning are increasingly filling, however, with undergraduates who are…

  20. Two Key Strategies for Enhancing Community Service Learning

    Science.gov (United States)

    Seider, Scott

    2013-01-01

    A growing body of research has found community service learning to have a positive effect upon participating college students' civic development; however, far less scholarship has considered the impact of particular components of a community service learning program. This article presents two preliminary but promising strategies for enhancing the…

  1. Planning for Technology Integration in a Professional Learning Community

    Science.gov (United States)

    Thoma, Jennifer; Hutchison, Amy; Johnson, Debra; Johnson, Kurt; Stromer, Elizabeth

    2017-01-01

    Barriers to technology integration in instruction include a lack of time, resources, and professional development. One potential approach to overcoming these barriers is through collaborative work, or professional learning communities. This article focuses on one group of teachers who leveraged their professional learning community to focus on…

  2. Recommendations from the Field: Creating an LGBTQ Learning Community

    Science.gov (United States)

    Jaekel, Kathryn S.

    2015-01-01

    This article details the creation of a lesbian, gay, bisexual, transgender, and queer (LGBTQ) learning community. Created because of research that indicates chilly campus climates (Rankin, 2005), as well as particular needs of LGBTQ students in the classroom, this learning community focused upon LGBTQ topics in and out of the classroom. While…

  3. The Right Time: Building the Learning Community Movement

    Science.gov (United States)

    Mutnick, Deborah

    2015-01-01

    The author argues that the current conjuncture is a kairotic moment for their own learning community program as well as the national movement to support the development of learning communities in universities and colleges and the array of pedagogical approaches associated with them. With Barbara Leigh Smith (2013), they recognize a link between…

  4. Mentoring: A Natural Role for Learning Community Faculty

    Science.gov (United States)

    Hessenauer, Sarah L.; Law, Kristi

    2017-01-01

    The purpose of this article is to highlight mentoring as an important piece of leading a learning community. The authors will share a definition of mentoring which is applicable to the learning community experience. Characteristics of mentoring will be described, including types of mentoring and mentor-mentee relationships. The authors will apply…

  5. Broadening Participation in Research Focused, Upper-Division Learning Communities

    Science.gov (United States)

    Hinckley, Robert A.; McGuire, John P.

    2015-01-01

    We address several challenges faced by those who wish to increase the number of faculty participating in upper-division learning communities that feature a student research experience. Using illustrations from our own learning community, we describe three strategies for success that focus on providing low cost incentives and other means to promote…

  6. Community Response in Disasters: An Ecological Learning Framework

    Science.gov (United States)

    Preston, John; Chadderton, Charlotte; Kitagawa, Kaori; Edmonds, Casey

    2015-01-01

    Natural disasters are frequently exacerbated by anthropogenic mechanisms and have social and political consequences for communities. The role of community learning in disasters is seen to be increasingly important. However, the ways in which such learning unfolds in a disaster can differ substantially from case to case. This article uses a…

  7. The Learning Community Experience: Cultivating a Residual Worldview

    Science.gov (United States)

    McDowell Marinchak, Christina L.; DeIuliis, David

    2013-01-01

    In this essay, we conceptualize first-year learning communities as worldviews that, during the first year and residually in subsequent years, allow students to recognize and engage difference and acknowledge and articulate their biases. Students who take part in a learning community have an opportunity to develop the biases and presuppositions of…

  8. Community Garden: A Bridging Program between Formal and Informal Learning

    Science.gov (United States)

    Datta, Ranjan

    2016-01-01

    Community garden activities can play a significant role in bridging formal and informal learning, particularly in urban children's science and environmental education. It promotes relational methods of learning, discussing, and practicing that will integrate food security, social interactions, community development, environmental activism, and…

  9. The Fish Kill Mystery: Learning about Aquatic Communities

    Science.gov (United States)

    Kosal, Erica F.

    2004-01-01

    This paper presents a case where students can learn about aquatic communities. In this case, students speculate on what may have caused a major fish kill in an estuary in North Carolina. In the process, they explore how land runoff and excess nutrients affect aquatic communities. They also learn about the complex life cycle of the dinoflagellate…

  10. Malware Propagation and Prevention Model for Time-Varying Community Networks within Software Defined Networks

    Directory of Open Access Journals (Sweden)

    Lan Liu

    2017-01-01

    Full Text Available As the adoption of Software Defined Networks (SDNs grows, the security of SDN still has several unaddressed limitations. A key network security research area is in the study of malware propagation across the SDN-enabled networks. To analyze the spreading processes of network malware (e.g., viruses in SDN, we propose a dynamic model with a time-varying community network, inspired by research models on the spread of epidemics in complex networks across communities. We assume subnets of the network as communities and links that are dense in subnets but sparse between subnets. Using numerical simulation and theoretical analysis, we find that the efficiency of network malware propagation in this model depends on the mobility rate q of the nodes between subnets. We also find that there exists a mobility rate threshold qc. The network malware will spread in the SDN when the mobility rate q>qc. The malware will survive when q>qc and perish when qnetwork malware and provide a theoretical basis to reduce and prevent network security incidents.

  11. Reconstructing cancer drug response networks using multitask learning.

    Science.gov (United States)

    Ruffalo, Matthew; Stojanov, Petar; Pillutla, Venkata Krishna; Varma, Rohan; Bar-Joseph, Ziv

    2017-10-10

    Translating in vitro results to clinical tests is a major challenge in systems biology. Here we present a new Multi-Task learning framework which integrates thousands of cell line expression experiments to reconstruct drug specific response networks in cancer. The reconstructed networks correctly identify several shared key proteins and pathways while simultaneously highlighting many cell type specific proteins. We used top proteins from each drug network to predict survival for patients prescribed the drug. Predictions based on proteins from the in-vitro derived networks significantly outperformed predictions based on known cancer genes indicating that Multi-Task learning can indeed identify accurate drug response networks.

  12. Exploring Classroom Community: A Social Network Study of Reacting to the Past

    OpenAIRE

    Jeff Webb; Ann Engar

    2016-01-01

    In this exploratory social network study, we examined how student relationships evolved during three month-long Reacting to the Past (RTTP) role-playing games in a lower division honors course at a large US public university. Our purpose was to explore how RTTP games—and collaborative learning approaches more generally—impact classroom community in college courses. We found that both acquaintance and friendship ties between students increased dramatically during the game, eliminating student ...

  13. Multi-Relational Characterization of Dynamic Social Network Communities

    Science.gov (United States)

    Lin, Yu-Ru; Sundaram, Hari; Kelliher, Aisling

    The emergence of the mediated social web - a distributed network of participants creating rich media content and engaging in interactive conversations through Internet-based communication technologies - has contributed to the evolution of powerful social, economic and cultural change. Online social network sites and blogs, such as Facebook, Twitter, Flickr and LiveJournal, thrive due to their fundamental sense of "community". The growth of online communities offers both opportunities and challenges for researchers and practitioners. Participation in online communities has been observed to influence people's behavior in diverse ways ranging from financial decision-making to political choices, suggesting the rich potential for diverse applications. However, although studies on the social web have been extensive, discovering communities from online social media remains challenging, due to the interdisciplinary nature of this subject. In this article, we present our recent work on characterization of communities in online social media using computational approaches grounded on the observations from social science.

  14. George Washington Community High School: analysis of a partnership network.

    Science.gov (United States)

    Bringle, Robert G; Officer, Starla D H; Grim, Jim; Hatcher, Julie A

    2009-01-01

    After five years with no public schools in their community, residents and neighborhood organizations of the Near Westside of Indianapolis advocated for the opening of George Washington Community High School (GWCHS). As a neighborhood in close proximity to the campus of Indiana University-Purdue University Indianapolis, the Near Westside and campus worked together to address this issue and improve the educational success of youth. In fall 2000, GWCHS opened as a community school and now thrives as a national model, due in part to its network of community relationships. This account analyzes the development of the school by focusing on the relationships among the university, the high school, community organizations, and the residents of the Near Westside and highlights the unique partnership between the campus and school by defining the relational qualities and describing the network created to make sustainable changes with the high school.

  15. Inclusion Community Model: Learning from Bali

    Directory of Open Access Journals (Sweden)

    David Samiyono

    2014-06-01

    method of setting Balinese case study in Bali andLampung. The analysis was conducted in the narrative and constructive way by involving various resource persons and participants. The Research shows that there is value in Balinese inclusion both in the province of Bali and Lampung province in various fields such as social, cultural, economic, and governance.For further research, the learning module of Balinese inclusion Community should be  made. A research on other wealth local communities besides Bali should also be made in Indonesia.Keywords: Bali, inclusion community, menyama braya.

  16. Structure of Small World Innovation Network and Learning Performance

    Directory of Open Access Journals (Sweden)

    Shuang Song

    2014-01-01

    Full Text Available This paper examines the differences of learning performance of 5 MNCs (multinational corporations that filed the largest number of patents in China. We establish the innovation network with the patent coauthorship data by these 5 MNCs and classify the networks by the tail of distribution curve of connections. To make a comparison of the learning performance of these 5 MNCs with differing network structures, we develop an organization learning model by regarding the reality as having m dimensions, which denotes the heterogeneous knowledge about the reality. We further set n innovative individuals that are mutually interactive and own unique knowledge about the reality. A longer (shorter distance between the knowledge of the individual and the reality denotes a lower (higher knowledge level of that individual. Individuals interact with and learn from each other within the small-world network. By making 1,000 numerical simulations and averaging the simulated results, we find that the differing structure of the small-world network leads to the differences of learning performance between these 5 MNCs. The network monopolization negatively impacts and network connectivity positively impacts learning performance. Policy implications in the conclusion section suggest that to improve firm learning performance, it is necessary to establish a flat and connective network.

  17. Community-Centered Service Learning: A Transformative Lens for Teaching-Learning in Nursing.

    Science.gov (United States)

    Yancey, Nan Russell

    2016-04-01

    Although service learning has been proposed as a teaching-learning modality in response to an ongoing challenge to transform nursing education, there is a risk to community and student when service learning is poorly conceived. A community-centered service learning approach founded on a nursing theoretical perspective and community model is explored as a way to honor the wisdom and perspective of the community in changing while illuminating a new way of being a nurse in community for the nursing student. © The Author(s) 2016.

  18. Enhancing Formal E-Learning with Edutainment on Social Networks

    Science.gov (United States)

    Labus, A.; Despotovic-Zrakic, M.; Radenkovic, B.; Bogdanovic, Z.; Radenkovic, M.

    2015-01-01

    This paper reports on the investigation of the possibilities of enhancing the formal e-learning process by harnessing the potential of informal game-based learning on social networks. The goal of the research is to improve the outcomes of the formal learning process through the design and implementation of an educational game on a social network…

  19. An ART neural network model of discrimination shift learning

    NARCIS (Netherlands)

    Raijmakers, M.E.J.; Coffey, E.; Stevenson, C.; Winkel, J.; Berkeljon, A.; Taatgen, N.; van Rijn, H.

    2009-01-01

    We present an ART-based neural network model (adapted from [2]) of the development of discrimination-shift learning that models the trial-by-trial learning process in great detail. In agreement with the results of human participants (4-20 years of age) in [1] the model revealed two distinct learning

  20. Integrative and Deep Learning through a Learning Community: A Process View of Self

    Science.gov (United States)

    Mahoney, Sandra; Schamber, Jon

    2011-01-01

    This study investigated deep learning produced in a community of general education courses. Student speeches on liberal education were analyzed for discovering a grounded theory of ideas about self. The study found that learning communities cultivate deep, integrative learning that makes the value of a liberal education relevant to students.…

  1. Perceptions of School Principals on Participation in Professional Learning Communities as Job-Embedded Learning

    Science.gov (United States)

    Gaudioso, Jennifer A.

    2017-01-01

    Perceptions of School Principals on Participation in Professional Learning Communities as Job-Embedded Learning Jennifer Gaudioso Principal Professional Learning Communities (PPLCs) have emerged as a vehicle for professional development of principals, but there is little research on how principals experience PPLCs or how districts can support…

  2. Exploring Students' Experiences in First-Year Learning Communities from a Situated Learning Perspective

    Science.gov (United States)

    Priest, Kerry L.; Saucier, Donald A.; Eiselein, Gregory

    2016-01-01

    This study looked to situated learning (Lave & Wenger, 1991) in order to explore students' participation in the social practices of first-year learning communities. Wenger's (1998) elaboration on "communities of practice" provides insight into how such participation transforms learners. These perspectives frame learning as a…

  3. EduCamp Colombia: Social Networked Learning for Teacher Training

    Directory of Open Access Journals (Sweden)

    Diego Ernesto Leal Fonseca

    2011-03-01

    Full Text Available This paper describes a learning experience called EduCamp, which was launched by the Ministry of Education of Colombia in 2007, based on emerging concepts such as e-Learning 2.0, connectivism, and personal learning environments. An EduCamp proposes an unstructured collective learning experience, which intends to make palpable the possibilities of social software tools in learning and interaction processes while demonstrating face-to-face organizational forms that reflect social networked learning ideas. The experience opens new perspectives for the design of technology training workshops and for the development of lifelong learning experiences.

  4. Nursing student perceptions of community in online learning.

    Science.gov (United States)

    Gallagher-Lepak, Susan; Reilly, Janet; Killion, Cheryl M

    2009-01-01

    Nursing faculty need to understand the unique aspects of online learning environments and develop new pedagogies for teaching in the virtual classroom. The concept of community is important in online learning and a strong sense of community can enhance student engagement and improve learning outcomes in online courses. Student perceptions of community in online learning environments were explored in this study. Five focus group sessions were held and online nursing students were asked to give examples of experiences related to sense of community. Fifteen major themes emerged: class structure, required participation, teamwork, technology, becoming, commonalities, disconnects, mutual exchange, online etiquette, informal discussions, aloneness, trepidation, unknowns, nonverbal communication and anonymity. Themes sorted into the categories of structural, processual and emotional factors. Theme descriptions show how sense of community can be enhanced and/or diminished in online courses. This study adds depth and detail to the limited body of research on sense of community in distance education in nursing courses.

  5. Comparison and validation of community structures in complex networks

    Science.gov (United States)

    Gustafsson, Mika; Hörnquist, Michael; Lombardi, Anna

    2006-07-01

    The issue of partitioning a network into communities has attracted a great deal of attention recently. Most authors seem to equate this issue with the one of finding the maximum value of the modularity, as defined by Newman. Since the problem formulated this way is believed to be NP-hard, most effort has gone into the construction of search algorithms, and less to the question of other measures of community structures, similarities between various partitionings and the validation with respect to external information. Here we concentrate on a class of computer generated networks and on three well-studied real networks which constitute a bench-mark for network studies; the karate club, the US college football teams and a gene network of yeast. We utilize some standard ways of clustering data (originally not designed for finding community structures in networks) and show that these classical methods sometimes outperform the newer ones. We discuss various measures of the strength of the modular structure, and show by examples features and drawbacks. Further, we compare different partitions by applying some graph-theoretic concepts of distance, which indicate that one of the quality measures of the degree of modularity corresponds quite well with the distance from the true partition. Finally, we introduce a way to validate the partitionings with respect to external data when the nodes are classified but the network structure is unknown. This is here possible since we know everything of the computer generated networks, as well as the historical answer to how the karate club and the football teams are partitioned in reality. The partitioning of the gene network is validated by use of the Gene Ontology database, where we show that a community in general corresponds to a biological process.

  6. Social networking for web-based communities

    NARCIS (Netherlands)

    Issa, T.; Kommers, Petrus A.M.

    2013-01-01

    In the 21st century, a new technology was introduced to facilitate communication, collaboration, and interaction between individuals and businesses. This technology is called social networking; this technology is now part of Internet commodities like email, browsing and blogging. From the 20th

  7. The Sea Floor: A Living Learning Residential Community

    Science.gov (United States)

    Guentzel, J. L.; Rosch, E.; Stoughton, M. A.; Bowyer, R.; Mortensen, K.; Smith, M.

    2016-02-01

    Living learning communities are collaborations between university housing and academic departments designed to enhance the overall student experience by integrating classroom/laboratory learning, student life and extracurricular activities. At Coastal Carolina University, the residential community associated with the Marine Science program is known as the Sea Floor. Students selected to become members of the Sea Floor remain "in residence" for two consecutive semesters. These students are first-time freshman that share a common course connection. This course is usually Introduction to Marine Science (MSCI 111) or MSCI 399s, which are one credit field/laboratory centered internships. The common course connection is designed so residents can establish and maintain an educational dialog with their peers. Activities designed to enhance the students' networking skills and educational and social development skills include monthly lunches with marine science faculty and dinner seminars with guest speakers from academia, industry and government. Additionally, each semester several activities outside the classroom are planned so that students can more frequently interact with themselves and their faculty and staff partners. These activities include field trips to regional aquariums, local boat trips that include water sample collection and analysis, and an alternative spring break trip to the Florida Keys to study the marine environment firsthand. The resident advisor that supervises the Sea Floor is usually a sophomore or junior marine science major. This provides the residents with daily communication and mentoring from a marine science major that is familiar with the marine science program and residence life. Assessment activities include: a university housing community living survey, student interest housing focus groups, fall to spring and fall to fall retention, and evaluation of program advisors and program activities.

  8. Rethinking PhD Learning Incorporating Communities of Practice

    Science.gov (United States)

    Shacham, Miri; Od-Cohen, Yehudit

    2009-01-01

    This paper grows from research which focuses on the learning characteristics of PhD students, incorporating communities of practice both during their studies and beyond completion of their PhD, and drawing on theories of adult learning and lifelong learning. It shows how professional discourse enhances academic discourse through student engagement…

  9. Digital Gaming and Language Learning: Autonomy and Community

    National Research Council Canada - National Science Library

    Chik, Alice

    2014-01-01

    ..., then, presents an interesting context to study digital gaming and second language (L2) learning because gamers frequently use an L2 to play digital games outside the classroom. While gamers are playing L2 games, many are also using L2 gameplay for L2 learning. Some are playing and learning autonomously, while some are seeking support from communities (...

  10. School to community: service learning in hospitaliy and tourism

    Science.gov (United States)

    Kimberly Monk; Jessica Bourdeau; Michele Capra

    2007-01-01

    In the effort to augment hospitality and tourism education beyond classroom instruction and internships, the added instructional methodology of community service learning is suggested. Service learning is an instructional method where students learn and develop through active participation in organized experiences that meet actual needs, increasing their sense of...

  11. The Role of Nonclassroom Spaces in Living-Learning Communities

    Science.gov (United States)

    Altimare, Emily; Sheridan, David M.

    2016-01-01

    A body of research suggests that learning communities provide a range of academic benefits by increasing social connectedness. Researchers have also hypothesized that informal learning spaces--nonclassroom spaces (NCSs)--can facilitate learning by supporting social connectedness. This study uses qualitative methods to explore the way nonclassroom…

  12. Facilitation for Professional Learning Community Conversations in Singapore

    Science.gov (United States)

    Salleh, Hairon

    2016-01-01

    Professional Learning Community (PLC) has steadily grown in importance over the last decade. The growing importance of PLCs lies in its potential to act as a lever for school-based curriculum development and innovation so as to provide diverse learning experiences to satisfy broader learning outcomes beyond academic achievements (e.g., the…

  13. Service-Learning from the Perspective of Community Organizations

    Science.gov (United States)

    Petri, Alexis

    2015-01-01

    As a central construct in the theory of service-learning, reciprocity for community partners is not often the subject of scholarship, especially scholarship that seeks to understand the benefits and opportunity costs of service-learning. This article explores how reciprocity works in higher education service-learning from the perspective of…

  14. Information Literacy: A Community Service-Learning Approach

    OpenAIRE

    Eugene J. Rathswohl

    2003-01-01

    Business, academic, and government leaders have spoken out for professional education to integrate solid knowledge and skills with a spirit of volunteerism and community service (Briscoe, 1998; Hayes, 1997; Small/Venkatesh, 1998). This paper describes an example of how community service-learning has been applied in an information systems course required in a Bachelor of Business Administration degree. Keywords: information systems, teaching, community service-learning, information literacy

  15. Learning OpenStack networking (Neutron)

    CERN Document Server

    Denton, James

    2014-01-01

    If you are an OpenStack-based cloud operator with experience in OpenStack Compute and nova-network but are new to Neutron networking, then this book is for you. Some networking experience is recommended, and a physical network infrastructure is required to provide connectivity to instances and other network resources configured in the book.

  16. Theoretical framework on selected core issues on conditions for productive learning in networked learning environments

    DEFF Research Database (Denmark)

    Dirckinck-Holmfeld, Lone; Svendsen, Brian Møller; Ponti, Marisa

    The report documents and summarises the elements and dimensions that have been identified to describe and analyse the case studies collected in the Kaleidoscope Jointly Executed Integrating Research Project (JEIRP) on Conditions for productive learning in network learning environments....

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

    Directory of Open Access Journals (Sweden)

    King Lesley

    2011-02-01

    Full Text Available Abstract 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.

  18. Epidemic spreading in time-varying community networks

    Energy Technology Data Exchange (ETDEWEB)

    Ren, Guangming, E-mail: wangxy@dlut.edu.cn, E-mail: ren-guang-ming@163.com [School of Electronic and Information, Guangdong Polytechnic Normal University, Guangzhou 510665 (China); Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024 (China); Wang, Xingyuan, E-mail: wangxy@dlut.edu.cn, E-mail: ren-guang-ming@163.com [Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024 (China)

    2014-06-15

    The spreading processes of many infectious diseases have comparable time scale as the network evolution. Here, we present a simple networks model with time-varying community structure, and investigate susceptible-infected-susceptible epidemic spreading processes in this model. By both theoretic analysis and numerical simulations, we show that the efficiency of epidemic spreading in this model depends intensively on the mobility rate q of the individuals among communities. We also find that there exists a mobility rate threshold q{sub c}. The epidemic will survive when q > q{sub c} and die when q < q{sub c}. These results can help understanding the impacts of human travel on the epidemic spreading in complex networks with community structure.

  19. Digital associative memory neural network with optical learning capability

    Science.gov (United States)

    Watanabe, Minoru; Ohtsubo, Junji

    1994-12-01

    A digital associative memory neural network system with optical learning and recalling capabilities is proposed by using liquid crystal television spatial light modulators and an Optic RAM detector. In spite of the drawback of the limited memory capacity compared with optical analogue associative memory neural network, the proposed optical digital neural network has the advantage of all optical learning and recalling capabilities, thus an all optics network system is easily realized. Some experimental results of the learning and the recalling for character recognitions are presented. This new optical architecture offers compactness of the system and the fast learning and recalling properties. Based on the results, the practical system for the implementation of a faster optical digital associative memory neural network system with ferro-electric liquid crystal SLMs is also proposed.

  20. Exploring Community College Student Perceptions of Online Learning

    Science.gov (United States)

    Morris, Terry Ann

    2010-01-01

    Successful completion of online courses by community college students is an issue both at the national and local level. The purpose of this qualitative, phenomenological study was to explore community college student perceptions of online learning within the theoretical construct of the Community of Inquiry model, which describes the manner in…

  1. Network exposure and homicide victimization in an African American community.

    Science.gov (United States)

    Papachristos, Andrew V; Wildeman, Christopher

    2014-01-01

    We estimated the association of an individual's exposure to homicide in a social network and the risk of individual homicide victimization across a high-crime African American community. Combining 5 years of homicide and police records, we analyzed a network of 3718 high-risk individuals that was created by instances of co-offending. We used logistic regression to model the odds of being a gunshot homicide victim by individual characteristics, network position, and indirect exposure to homicide. Forty-one percent of all gun homicides occurred within a network component containing less than 4% of the neighborhood's population. Network-level indicators reduced the association between individual risk factors and homicide victimization and improved the overall prediction of individual victimization. Network exposure to homicide was strongly associated with victimization: the closer one is to a homicide victim, the greater the risk of victimization. Regression models show that exposure diminished with social distance: each social tie removed from a homicide victim decreased one's odds of being a homicide victim by 57%. Risk of homicide in urban areas is even more highly concentrated than previously thought. We found that most of the risk of gun violence was concentrated in networks of identifiable individuals. Understanding these networks may improve prediction of individual homicide victimization within disadvantaged communities.

  2. Modelling opinion formation driven communities in social networks

    CERN Document Server

    Iñiguez, Gerardo; Kertész, János; Kaski, Kimmo K

    2010-01-01

    In a previous paper we proposed a model to study the dynamics of opinion formation in human societies by a co-evolution process involving two distinct time scales of fast transaction and slower network evolution dynamics. In the transaction dynamics we take into account short range interactions as discussions between individuals and long range interactions to describe the attitude to the overall mood of society. The latter is handled by a uniformly distributed parameter $\\alpha$, assigned randomly to each individual, as quenched personal bias. The network evolution dynamics is realized by rewiring the societal network due to state variable changes as a result of transaction dynamics. The main consequence of this complex dynamics is that communities emerge in the social network for a range of values in the ratio between time scales. In this paper we focus our attention on the attitude parameter $\\alpha$ and its influence on the conformation of opinion and the size of the resulting communities. We present numer...

  3. THE IMPACTS OF SOCIAL NETWORKING SITES IN HIGHER LEARNING

    Directory of Open Access Journals (Sweden)

    Mohd Ishak Bin Ismail

    2016-02-01

    Full Text Available Social networking sites, a web-based application have permeated the boundary between personal lives and student lives. Nowadays, students in higher learning used social networking site such as Facebook to facilitate their learning through the academic collaboration which it further enhances students’ social capital. Social networking site has many advantages to improve students’ learning. To date, Facebook is the leading social networking sites at this time which it being widely used by students in higher learning to communicate to each other, to carry out academic collaboration and sharing resources. Learning through social networking sites is based on the social interaction which learning are emphasizing on students, real world resources, active students` participation, diversity of learning resources and the use of digital tools to deliver meaningful learning. Many studies found the positive, neutral and negative impact of social networking sites on academic performance. Thus, this study will determine the relationship between Facebook usage and academic achievement. Also, it will investigate the association of social capital and academic collaboration to Facebook usage.

  4. Graph kernels, hierarchical clustering, and network community structure: experiments and comparative analysis

    Science.gov (United States)

    Zhang, S.; Ning, X.-M.; Zhang, X.-S.

    2007-05-01

    There has been a quickly growing interest in properties of complex networks, such as the small world property, power-law degree distribution, network transitivity, and community structure, which seem to be common to many real world networks. In this study, we consider the community property which is also found in many real networks. Based on the diffusion kernels of networks, a hierarchical clustering approach is proposed to uncover the community structure of different extent of complex networks. We test the method on some networks with known community structures and find that it can detect significant community structure in these networks. Comparison with related methods shows the effectiveness of the method.

  5. Identifying influential user communities on the social network

    Science.gov (United States)

    Hu, Weishu; Gong, Zhiguo; Hou U, Leong; Guo, Jingzhi

    2015-10-01

    Nowadays social network services have been popularly used in electronic commerce systems. Users on the social network can develop different relationships based on their common interests and activities. In order to promote the business, it is interesting to explore hidden relationships among users developed on the social network. Such knowledge can be used to locate target users for different advertisements and to provide effective product recommendations. In this paper, we define and study a novel community detection problem that is to discover the hidden community structure in large social networks based on their common interests. We observe that the users typically pay more attention to those users who share similar interests, which enable a way to partition the users into different communities according to their common interests. We propose two algorithms to detect influential communities using common interests in large social networks efficiently and effectively. We conduct our experimental evaluation using a data set from Epinions, which demonstrates that our method achieves 4-11.8% accuracy improvement over the state-of-the-art method.

  6. Modelling sequences and temporal networks with dynamic community structures.

    Science.gov (United States)

    Peixoto, Tiago P; Rosvall, Martin

    2017-09-19

    In evolving complex systems such as air traffic and social organisations, collective effects emerge from their many components' dynamic interactions. While the dynamic interactions can be represented by temporal networks with nodes and links that change over time, they remain highly complex. It is therefore often necessary to use methods that extract the temporal networks' large-scale dynamic community structure. However, such methods are subject to overfitting or suffer from effects of arbitrary, a priori-imposed timescales, which should instead be extracted from data. Here we simultaneously address both problems and develop a principled data-driven method that determines relevant timescales and identifies patterns of dynamics that take place on networks, as well as shape the networks themselves. We base our method on an arbitrary-order Markov chain model with community structure, and develop a nonparametric Bayesian inference framework that identifies the simplest such model that can explain temporal interaction data.The description of temporal networks is usually simplified in terms of their dynamic community structures, whose identification however relies on a priori assumptions. Here the authors present a data-driven method that determines relevant timescales for the dynamics and uses it to identify communities.

  7. A graph clustering method for community detection in complex networks

    Science.gov (United States)

    Zhou, HongFang; Li, Jin; Li, JunHuai; Zhang, FaCun; Cui, YingAn

    2017-03-01

    Information mining from complex networks by identifying communities is an important problem in a number of research fields, including the social sciences, biology, physics and medicine. First, two concepts are introduced, Attracting Degree and Recommending Degree. Second, a graph clustering method, referred to as AR-Cluster, is presented for detecting community structures in complex networks. Third, a novel collaborative similarity measure is adopted to calculate node similarities. In the AR-Cluster method, vertices are grouped together based on calculated similarity under a K-Medoids framework. Extensive experimental results on two real datasets show the effectiveness of AR-Cluster.

  8. Virality prediction and community structure in social networks.

    Science.gov (United States)

    Weng, Lilian; Menczer, Filippo; Ahn, Yong-Yeol

    2013-01-01

    How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily. Hence, the spread within highly clustered communities is enhanced, while diffusion across communities is hampered. A common hypothesis is that memes and behaviors are complex contagions. We show that, while most memes indeed spread like complex contagions, a few viral memes spread across many communities, like diseases. We demonstrate that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration. The more communities a meme permeates, the more viral it is. We present a practical method to translate data about community structure into predictive knowledge about what information will spread widely. This connection contributes to our understanding in computational social science, social media analytics, and marketing applications.

  9. Collaboration: the Key to Establishing Community Networks in Regional Australia

    Directory of Open Access Journals (Sweden)

    Wal Taylor

    2002-01-01

    Full Text Available Despite the promise of community involvement, cohesion and empowerment offered by local community networks (CN using Internet Technologies, few communities in regional Australia have been able to demonstrate sustainable and vibrant CN which demonstrate increased social, cultural or self-reliance capital. The Faculty of Informatics and Communication at Central Queensland University (CQU and a local council have established a formal alliance to establish the COIN (Community Informatics projects to research issues around this topic. This paper presents the initial findings from this work and draws conclusions for possible comparison with other international experience. The research focuses attention on community understanding and cohesion, local government priorities in a community with relatively low diffusion of the Internet and the competing demands in a regional university between traditional service provision in an increasingly competitive market and the needs of establishing outreach research for altruistic, industry establishment and commercial rationale.

  10. Virality Prediction and Community Structure in Social Networks

    Science.gov (United States)

    Weng, Lilian; Menczer, Filippo; Ahn, Yong-Yeol

    2013-08-01

    How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily. Hence, the spread within highly clustered communities is enhanced, while diffusion across communities is hampered. A common hypothesis is that memes and behaviors are complex contagions. We show that, while most memes indeed spread like complex contagions, a few viral memes spread across many communities, like diseases. We demonstrate that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration. The more communities a meme permeates, the more viral it is. We present a practical method to translate data about community structure into predictive knowledge about what information will spread widely. This connection contributes to our understanding in computational social science, social media analytics, and marketing applications.

  11. Plurality and equality in the Learning Communities

    Directory of Open Access Journals (Sweden)

    Mimar Ramis-Salas

    2015-09-01

    Full Text Available Purpose: to present empirical evidence of the success generated as a result of the types of organization of the centres and the classrooms in the CA. The inclusion of the plurality of voices of families from very different origins allows for an education that based on the plurality and diversity manages to achieve a greater equality in the results of all children. Design/methodology/approach: the present article is based on 1 review of the scientific literature in journals selected in the Journal Citation Reports about the types of participation of migrant families and from cultural minorities and their effect on the education of their children; and 2 on the collection of testimonies of migrant and cultural minority families through qualitative techniques. Findings and Originality/value: empirical evidence is presented about how the types of management and organization of the families participation in the classroom and the school of Learning communities maximize the plurality of voices (migrant and cultural minority families and contribute to improve the results of the children of the social groups who are most underprivileged and who obtain a greater improvement in the results levelling them with those of the mainstream society. Research limitations/implications: complexity to achieve a climate of ideal egalitarian dialogue in the framework of the communicative research data collection techniques Social implications: the article emphasizes the fact that evidence based actions achieve social and educational transformation, contributing to respond to the objectives of Europe 2020 to achieve more inclusive societies. Originality/value: how through implementing certain forms of classroom and school organization based on the inclusion of the plurality of voices, we contribute evidence of the improvement of the management of the center and the transformation of the relations with the community, beyond the educational success.

  12. Learning oncogenetic networks by reducing to mixed integer linear programming.

    Science.gov (United States)

    Shahrabi Farahani, Hossein; Lagergren, Jens

    2013-01-01

    Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events. Finding the order in which the genetic events have occurred and progression pathways are of vital importance in understanding the disease. In order to model cancer progression, we propose Progression Networks, a special case of Bayesian networks, that are tailored to model disease progression. Progression networks have similarities with Conjunctive Bayesian Networks (CBNs) [1],a variation of Bayesian networks also proposed for modeling disease progression. We also describe a learning algorithm for learning Bayesian networks in general and progression networks in particular. We reduce the hard problem of learning the Bayesian and progression networks to Mixed Integer Linear Programming (MILP). MILP is a Non-deterministic Polynomial-time complete (NP-complete) problem for which very good heuristics exists. We tested our algorithm on synthetic and real cytogenetic data from renal cell carcinoma. We also compared our learned progression networks with the networks proposed in earlier publications. The software is available on the website https://bitbucket.org/farahani/diprog.

  13. Who Networks? The Social Psychology of Virtual Communities

    Science.gov (United States)

    2004-06-01

    and the clandestine side, characterized by so-called “ darknets ,” criminals, terrorists, racists, and cults. Closed/legitimate communities (such...virtual “states,” and online gaming communities - and the clandestine side, characterized by so-called “ darknets ,” criminals, terrorists, rac- ists...2003 from: www.rand.org/publications/MR/MR1653/MR1653. pdf Carley, C., Lee, J. and Krackhardt, D. (2002). Destabilizing Networks. Connections. Vol

  14. Emergence of bursts and communities in evolving weighted networks.

    Science.gov (United States)

    Jo, Hang-Hyun; Pan, Raj Kumar; Kaski, Kimmo

    2011-01-01

    Understanding the patterns of human dynamics and social interaction and the way they lead to the formation of an organized and functional society are important issues especially for techno-social development. Addressing these issues of social networks has recently become possible through large scale data analysis of mobile phone call records, which has revealed the existence of modular or community structure with many links between nodes of the same community and relatively few links between nodes of different communities. The weights of links, e.g., the number of calls between two users, and the network topology are found correlated such that intra-community links are stronger compared to the weak inter-community links. This feature is known as Granovetter's "The strength of weak ties" hypothesis. In addition to this inhomogeneous community structure, the temporal patterns of human dynamics turn out to be inhomogeneous or bursty, characterized by the heavy tailed distribution of time interval between two consecutive events, i.e., inter-event time. In this paper, we study how the community structure and the bursty dynamics emerge together in a simple evolving weighted network model. The principal mechanisms behind these patterns are social interaction by cyclic closure, i.e., links to friends of friends and the focal closure, links to individuals sharing similar attributes or interests, and human dynamics by task handling process. These three mechanisms have been implemented as a network model with local attachment, global attachment, and priority-based queuing processes. By comprehensive numerical simulations we show that the interplay of these mechanisms leads to the emergence of heavy tailed inter-event time distribution and the evolution of Granovetter-type community structure. Moreover, the numerical results are found to be in qualitative agreement with empirical analysis results from mobile phone call dataset.

  15. Emergence of bursts and communities in evolving weighted networks.

    Directory of Open Access Journals (Sweden)

    Hang-Hyun Jo

    Full Text Available Understanding the patterns of human dynamics and social interaction and the way they lead to the formation of an organized and functional society are important issues especially for techno-social development. Addressing these issues of social networks has recently become possible through large scale data analysis of mobile phone call records, which has revealed the existence of modular or community structure with many links between nodes of the same community and relatively few links between nodes of different communities. The weights of links, e.g., the number of calls between two users, and the network topology are found correlated such that intra-community links are stronger compared to the weak inter-community links. This feature is known as Granovetter's "The strength of weak ties" hypothesis. In addition to this inhomogeneous community structure, the temporal patterns of human dynamics turn out to be inhomogeneous or bursty, characterized by the heavy tailed distribution of time interval between two consecutive events, i.e., inter-event time. In this paper, we study how the community structure and the bursty dynamics emerge together in a simple evolving weighted network model. The principal mechanisms behind these patterns are social interaction by cyclic closure, i.e., links to friends of friends and the focal closure, links to individuals sharing similar attributes or interests, and human dynamics by task handling process. These three mechanisms have been implemented as a network model with local attachment, global attachment, and priority-based queuing processes. By comprehensive numerical simulations we show that the interplay of these mechanisms leads to the emergence of heavy tailed inter-event time distribution and the evolution of Granovetter-type community structure. Moreover, the numerical results are found to be in qualitative agreement with empirical analysis results from mobile phone call dataset.

  16. Community Networks and the Nature of Emergence in Civil Society

    Directory of Open Access Journals (Sweden)

    Jenny Onyx

    2010-03-01

    Full Text Available Our research challenges the limitations of extant knowledge of social formation by its focus on the ordinary, everyday lived reality of maintaining community and on identifying its operations from the internal perspective of civil society. We aim to explore the actual mobilising processes and structures that underpin the formation of social capital in the community. We examine how networks emerge and operate.

  17. Using an academic-community partnership model and blended learning to advance community health nursing pedagogy.

    Science.gov (United States)

    Ezeonwu, Mabel; Berkowitz, Bobbie; Vlasses, Frances R

    2014-01-01

    This article describes a model of teaching community health nursing that evolved from a long-term partnership with a community with limited existing health programs. The partnership supported RN-BSN students' integration in the community and resulted in reciprocal gains for faculty, students and community members. Community clients accessed public health services as a result of the partnership. A blended learning approach that combines face-to-face interactions, service learning and online activities was utilized to enhance students' learning. Following classroom sessions, students actively participated in community-based educational process through comprehensive health needs assessments, planning and implementation of disease prevention and health promotion activities for community clients. Such active involvement in an underserved community deepened students' awareness of the fundamentals of community health practice. Students were challenged to view public health from a broader perspective while analyzing the impacts of social determinants of health on underserved populations. Through asynchronous online interactions, students synthesized classroom and community activities through critical thinking. This paper describes a model for teaching community health nursing that informs students' learning through blended learning, and meets the demands for community health nursing services delivery. © 2013 Wiley Periodicals, Inc.

  18. Towards Contextual Experimentation: Creating a Faculty Learning Community to Cultivate Writing-to-Learn Practices

    Science.gov (United States)

    Chang, Mary K.; Rao, Kavita; Stewart, Maria L.; Farley, Cynthia A.; Li, Katherine

    2016-01-01

    In order to explore ways to integrate new pedagogical practices, five faculty members created an informal faculty learning community focused on writing-to-learn practices, an inquiry and process-based writing pedagogy. The faculty members learned the writing-to-learn practices together, periodically met to discuss how they implemented the…

  19. Teacher Agency and Professional Learning Communities; What Can Learning Rounds in Scotland Teach Us?

    Science.gov (United States)

    Philpott, Carey; Oates, Catriona

    2017-01-01

    Recently there has been growth in researching teacher agency. Some research has considered the relationship between teacher agency and professional learning. Similarly, there has been growing interest in professional learning communities as resources for professional learning. Connections have been made between professional learning communities…

  20. Enhancing the Social Capital of Learning Communities by Using an Ad Hoc Transient Communities Service

    NARCIS (Netherlands)

    Fetter, Sibren

    2009-01-01

    Fetter, S. (2009). Enhancing the Social Capital of Learning Communities by Using an Ad Hoc Transient Communities Service. Presentation at the 8th International Conference Advances in Web based Learning - ICWL 2009. August, 19-21, 2009, Aachen, Germany: RWTH Aachen University.