Monitoring Activity in E-Learning

2010 ◽  
pp. 111-130 ◽  
Author(s):  
E. Mazzoni ◽  
P. Gaffuri
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Tracking Data ◽  
Specific Data ◽  
Cultural Approach ◽  
Web Environment ◽  
Learning Contexts ◽  
E Learning ◽  
Individual Actions ◽  
Monitoring Activity

In this chapter the authors will focus on the monitoring of students’ activities in e-learning contexts. They will start from a socio-cultural approach to the notion of activity, which is conceived of as a context composed by actions, which, in turn, are composed by operations. Subsequently, the authors will propose a model for monitoring activities in e-learning, which is based on two principal measures. Firstly, they will take into consideration specific data collected through Web tracking, which they will elaborate further in order to obtain indicators that do not simply express frequencies, but that measure individuals’ actions within a Web environment. Secondly, the authors will suggest a possible application of social network analysis (SNA) to Web interactions occurring in collective discussions within Web environments. In the model that the authors will present, Web tracking data are considered as indicators of individual actions, whereas SNA indices concern two levels: collective indices referring to the activity carried out by groups and individual indices referring to the role that members play in collective e-learning activities.

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.

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.

Personalized Recommendation Research in E-Learning Systems

2013 ◽  
Vol 433-435 ◽  
pp. 603-606
Author(s):  
Bing Wu ◽  
Ping Ping Chen
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Learning Systems ◽  
Learning System ◽  
Personalized Recommendation ◽  
Important Research ◽  
Research Areas ◽  
Communication Efficiency ◽  
E Learning ◽  
Explicit Study

The purpose of this paper is to review the literatures which have made an explicit study on personalized recommendation in E-Learning systems. By identifying the important research areas, which are in different perspectives, firstly, filtering recommendation is introduced before the illustration of how it has been developed in E-Learning systems. Then personalized recommendation is proposed for E-Learning system. Although social network is the basic way to improve the communication efficiency with others in E-Learning system, previous studies pay less attention on this. Therefore social network analysis should be taken into consideration for the recommendation in E-Learning system for further research.

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