Open AccessThe Analysis of Student Colla borative Work Inside Social Learning Network Analysis Based on Degree and Eigenvector Centrality
Author(s) -
Andi Besse Firdausiah Mansur,
Norhaniza Yusof,
Ahmad Hoirul Basori
Publication year - 2016
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v6i5.pp2488-2498
Subject(s) - centrality , betweenness centrality , closeness , computer science , social network analysis , asynchronous communication , collaborative learning , work (physics) , value (mathematics) , node (physics) , ranking (information retrieval) , knowledge management , artificial intelligence , world wide web , machine learning , mathematics , social media , statistics , computer network , physics , mathematical analysis , quantum mechanics , thermodynamics
Social learning network analysis is a potential approach to analyze the behaviour of students in collaborative work. However, most of the previous works focus on asynchronous discussion forum as the learning activity. Very few of them are trying to analyze the students' collaborative work while using wiki e-learning. This paper proposes the degree centrality and eigenvector method for identifying the collaborative work of students while in wiki e-learning. The log data of the Moodle e-learning system is observed that records the students' activities and actions while using wiki. The result shows that there is a close similarity between the degree centrality and the eigenvector. The result also reveals the students who obtain high outdegree values. Furthermore, Agent_1 and Agent_12 represent the students who obtained high outdegree values, which mean these two nodes are acting as source providers that able to supply information and knowledge through the network. This result also strengthened by value of closeness and betweenness where Agent_1 and Agent_12 leading on this measurement. The high closeness value of Agent_1 and Agent_12 will lead into fast spreading information since they have fastest route and has the most direct route to the other node inside the network, thus collaborative work is easy to be initialized by these Agents. This work has successfully identified collaborative work of student. This finding is believed to bring enormous benefit on the e-learning system improvement in the future.