Using Social Network Analysis Metrics of Virtual Forums to Predict Performance in e-Learning Courses

2018 ◽  
Author(s):  
Henrique Lemos dos Santos ◽  
Cristian Cechinel ◽  
Ricardo Matsumura Araujo ◽  
Emanuel Marques Queiroga
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
E Learning ◽  
Network Analysis Metrics

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.

scholarly journals Using Social Network Analysis and Social Capital to Identify User Roles on Polarized Political Conversations on Twitter

2019 ◽  
Vol 5 (2) ◽  
pp. 205630511984874 ◽  
Author(s):  
Raquel Recuero ◽  
Gabriela Zago ◽  
Felipe Soares
Keyword(s):  
Social Capital ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Opinion Leaders ◽  
Information Flows ◽  
Network Metrics ◽  
Network Analysis Metrics

In this article, we discuss the roles users play in political conversations on Twitter. Our case study is based on data collected in three dates during the former Brazilian president Lula’s corruption trial. We used a combination of social network analysis metrics and social capital to identify the users’ roles during polarized discussions that took place in each of the dates analyzed. Our research identified four roles, each associated with different aspects of social capital and social network metrics: activists, news clippers, opinion leaders, and information influencers. These roles are particularly useful to understand how users’ actions on political conversations may influence the structure of information flows.

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 Media Metrics in an Academic Setup

2017 ◽  
pp. 116-130
Author(s):  
Tasleem Arif ◽  
Rashid Ali
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Present Status ◽  
The Internet ◽  
Collaboration Networks ◽  
Different Types ◽  
Network Metrics ◽  
Network Analysis Metrics

Social media is perhaps responsible for largest share of traffic on the Internet. It is one of the largest online activities with people from all over the globe making its use for some sort of activity. The behaviour of these networks, important actors and groups and the way individual actors influence an idea or activity on these networks, etc. can be measured using social network analysis metrics. These metrics can be as simple as number of likes on Facebook or number of views on YouTube or as complex as clustering co-efficient which determines future collaborations on the basis of present status of the network. This chapter explores and discusses various social network metrics which can be used to analyse and explain important questions related to different types of networks. It also tries to explain the basic mathematics behind the working of these metrics. The use of these metrics for analysis of collaboration networks in an academic setup has been explored and results presented. A new metric called “Average Degree of Collaboration” has been defined to quantify collaborations within institutions.

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