Service delivery using social network analysis in P2P supportive e-learning environment

2011 ◽  
Author(s):  
Ye Conghuan
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Learning Environment ◽  
Service Delivery ◽  
E Learning

Visualizing Co-Authorship Social Networks and Collaboration Recommendations With CNARe

2018 ◽  
pp. 173-188
Author(s):  
Michele A. Brandão ◽  
Matheus A. Diniz ◽  
Guilherme A. de Sousa ◽  
Mirella M. Moro
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Large Scale ◽  
Online Tool ◽  
Strength Of Ties ◽  
E Learning

Studies have analyzed social networks considering a plethora of metrics for different goals, from improving e-learning to recommend people and things. Here, we focus on large-scale social networks defined by researchers and their common published articles, which form co-authorship social networks. Then, we introduce CNARe, an online tool that analyzes the networks and present recommendations of collaborations based on three different algorithms (Affin, CORALS and MVCWalker). Through visualizations and social networks metrics, CNARe also allows to investigate how the recommendations affect the co-authorship social networks, how researchers' networks are in a central and eagle-eye context, and how the strength of ties behaves in large co-authorship social networks. Furthermore, users can upload their own network in CNARe and make their own recommendation and social network analysis.

Using Social Network Analysis to Understand Sense of Community in an Online Learning Environment

2008 ◽  
Vol 39 (1) ◽  
pp. 17-36 ◽  
Author(s):  
Demei Shen ◽  
Piyanan Nuankhieo ◽  
Xinxin Huang ◽  
Christopher Amelung ◽  
James Laffey
Keyword(s):  
Online Learning ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Learning Environment ◽  
Sense Of Community ◽  
Online Learning Environment

Social network analysis of a gamified e-learning course: Small-world phenomenon and network metrics as predictors of academic performance

2016 ◽  
Vol 60 ◽  
pp. 312-321 ◽  
Author(s):  
Luis de-Marcos ◽  
Eva García-López ◽  
Antonio García-Cabot ◽  
José-Amelio Medina-Merodio ◽  
Adrián Domínguez ◽  
...  
Keyword(s):  
Academic Performance ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Small World ◽  
E Learning ◽  
Network Metrics ◽  
Predictors Of Academic Performance

The Sense of e-Learning Community Index (SeLCI) for Computer Supported Collaborative e-Learning (CSCeL)

2011 ◽  
pp. 672-698
Author(s):  
Niki Lambropoulos
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Learning Community ◽  
Social Engagement ◽  
Sense Of Belonging ◽  
Skills Acquisition ◽  
Online Teachers ◽  
Community Identification ◽  
E Learning

The aim of this research is to shed light in collaborative e-learning communities in order to observe, analyse and support the e-learning participants. The research context is the Greek teachers’ e-learning community, started in 2003 as part of a project for online teachers’ training and aimed at enabling teachers to acquire new competencies. However, these aims were not met because of passive participation; therefore this study aimed to enhance the Greek teachers’ social engagement to achieve the new skills acquisition. Therefore, the initial sense of community identification was based on empathy; however, because it was inadequate to fully describe the context,, a Sense of E-Learning Community Index (SeLCI) was developed. The new SeLCI attributes were: community evolution; sense of belonging; empathy; trust; intensity characterised by e-learners’ levels of participation and persistence on posting; collaborative e-learning quality measured by the quality in Computer Supported Collaborative eLearning (CSCeL) dialogical sequences, participants’ reflections on own learning; and social network analysis based on: global cohesion anchored in density, reciprocity, cliques and structural equivalence, global centrality derived from in- and out-degree centrality and closeness; and local nodes and centrality in real time. Forty Greek teachers participated in the study for 30 days using Moodle and enhanced Moodle with to measure participation, local Social network Analysis and critical thinking levels in CSCeL. Quantitative, qualitative, Social Network Analysis and measurements produced by the tools were used for data analysis. The findings indicated that each of the SeLCI is essential to enhance participation, collaboration, internalisation and externalisation of knowledge to ensure the e-learning quality and new skills acquisition. Affective factors in CSCeL (sense of belonging, empathy and trust) were also essential to increase reciprocity and promote active participation. Community management, e-learning activities and lastly, the technology appear to affect CSCeL.

Social Network Analysis to Monitor Interactions in Virtual Learning Environment

2015 ◽  
Vol 13 (10) ◽  
pp. 3482-3487 ◽  
Author(s):  
A.S. Silva ◽  
S.R. Brito ◽  
N.L. Vijaykumar ◽  
C.A.J. Rocha ◽  
J.C.W.A. Costa ◽  
...  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Learning Environment ◽  
Virtual Learning ◽  
Virtual Learning Environment

scholarly journals Challenges in assessing the nature of effective collaboration in blended university courses

2020 ◽  
pp. 1-14
Author(s):  
Robert A Ellis ◽  
Ana-Maria Bliuc ◽  
Feifei Han
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Learning Environment ◽  
Learning Outcomes ◽  
Approaches To Learning ◽  
University Graduates ◽  
Analysis Techniques ◽  
Student Approaches To Learning ◽  
Effective Collaboration

The ability to collaborate effectively face-to-face and online represents a critical skill for university graduates. However, there are still challenges regarding how to accurately assess this skill through traditional student learning measures. To better understand the nature of effective collaboration of university students in blended courses, the current study drew on the student approaches to learning framework and social network analysis techniques. We examined how student approaches to inquiry, approaches to online learning technologies, perceptions of the blended learning environment, different learning outcomes and configurations of collaboration are related. The methodologies commonly used in student approaches to learning research identified deep and surface approaches to inquiry and technologies, positive and negative perceptions of the integration of the learning environment, and of online workload, which also showed logical alignment with relatively better and poorer academic achievement in the course. Based on approaches, perceptions, and learning outcomes, students were divided into groups orientated towards understanding versus reproducing learning. The social network analysis techniques revealed features of different configurations of collaborations by different groups of students and their choices as to whether and with whom to collaborate during the learning process. Nuanced differences were found amongst different configurations of collaborations. Implications for practice or policy: When assessing student experience of collaboration, social network analysis techniques may be able to describe nuanced differences amongst different collaborative configurations. To encourage students’ collaboration, assessment tasks involving a large proportion of mandatory collaborative activities should be considered. To help student improve experience of collaboration, teachers may consider pairing students with a reproducing learning orientation with those having a deep disciplinary understanding.

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