Using social networks analysis for collaboration and team formation identification

Monclar, Rafael Studart; Oliveira, Jonice; Faria, Fabrício Firmino de; Ventura, Lucas; Souza, Jano Moreira de; Campos, María Luiza Machado

doi:10.1109/cscwd.2011.5960128

Cited by 18 publications

(4 citation statements)

References 13 publications

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“…Group (Team) formation is a very active research area. This research was started in the human resource management domain [41,59] and Social Networks [31,45]. However, as learning in groups is considered preparatory for real life working conditions, group formation has also become an essential issue in the educational research field .…”

Section: Background Knowledge and Related Workmentioning

confidence: 99%

Peer assessment and groups formation in massive open online courses

Haddadi

Bouarab-Dahmani

Guin

et al. 2018

Comp Applic In Engineering

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One of the major challenges facing Massive Open Online Courses (MOOCs) is assessing the learner performances beyond traditional automated assessment methods. This leads to a bottleneck problem due to the massiveness of course participants, especially in the context of problem solving. To tackle this issue, peer assessment has been proposed as an effective method. However, the validity of this process is still under discussion, suffers from a lack of credibility and has many weaknesses, particularly with regards to group formation. This paper develops a new method of peer assessment for MOOCs to improve the accuracy and exactitude of the learner grade. Our proposition is based on three main steps: the formation of learner groups, the assessment and synthesis of the results. First, the group definition process can use different elements of the learner model and enables to build heterogeneous groups. After, each learner is required to grade a small number of peer productions. Finally, a synthesis of the various grades is proposed using both data about the ability to assess of each learner and complexity of problems. To evaluate the proposed peer assessment process, we conducted an experimentation devoted to teaching Software Quality Assurance to beginners with computer science during the first university cycle.

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Section: Background Knowledge and Related Workmentioning

confidence: 99%

Peer assessment and groups formation in massive open online courses

Haddadi

Bouarab-Dahmani

Guin

et al. 2018

Comp Applic In Engineering

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“…radius, diameter, centrality, betweenness, shortest paths, density etc x Finding communities in the social network x Visualizing the whole or part of the social network Several works has been done on various social networks to analyse and discover various kinds of relationships and information [7][8][9] [10].…”

Section: Social Network Analysismentioning

confidence: 99%

Social Network Analysis Tools

Akhtar

2014

2014 Fourth International Conference on Communication Systems and Network Technologies

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Social networks like Facebook, Twitter, and Google+ are most visited domains on the Internet. They contain huge data about the users and the relationships among them. To analyze and mine useful information from these huge social network data, special graph based mining tools are required that can easily model the structure of the social networks. A number of such analysis tools are available with their own features and benefits. Choosing an appropriate tool for a particular task is difficult to decide. This work present a comparative analysis of four social network analysis tools-Networkx, Gephi, Pajek, IGraph based on platform, execution time, Graph types, algorithms complexity, input file format and graph features. (Abstract)

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“…The more people on the social networks increase the informational traces about different behavioral aspects. The impact of online discussion forums, crowdsourcing, intelligent networks, virtual cooperation, and smart technologies points toward observing and analyzing a mainstream platform in today's world [14]. The primary objective is to identify the nodes that are main source for the community's data flow.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Social Media Complex System using Community Detection Algorithms

Malik¹

2022

IJCDS

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The increasing significance of social networks has led to information propagation and community formation being an interesting domain in data science. The data gathered from big social networks exhibit different community structures. These communities attract various users who grow complex networks. The main goal is to identify the impacting nodes responsible for community data flow. The Twitter network edges are considered in the study, which plays a vital role in representing the activities and relationships developed among the community members. Different communities evolve when the network is analyzed using different community detection algorithms. The network statistics are used for analysis by calculating the weighted degree distribution of nodes in this study. The network is analyzed according to persistent clusters using community detection algorithms like the Spinglass, Walktrap, Fastgreedy, Leading Eigenvector, Multilevel, Edge Betweenness, and Label Propagation. It is found that these measures are very useful in community detection and observing the spread of information in social networks.

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Using social networks analysis for collaboration and team formation identification

Cited by 18 publications

References 13 publications

Peer assessment and groups formation in massive open online courses

Peer assessment and groups formation in massive open online courses

Social Network Analysis Tools

Analysis of Social Media Complex System using Community Detection Algorithms

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