Left-Right oscillate algorithm for community detection used in e-learning system
CISIM'12 Proceedings of the 11th IFIP TC 8 international conference on Computer Information Systems and Industrial Management
| Hi-index | 0.00 |
The growth of eLearning systems popularity motivates researchers to study these systems intensively. Users of eLearning systems form social networks through the different activities performed by them (sending emails, reading study materials, chat, taking tests, etc.). This paper focuses on searching of latent social networks from eLearning systems data. This data consists of students activity records where latent ties among actors are embedded. The social network studied in this paper is represented by groups of students who have similar contacts,and interact in similar social circles, where theinterest in performing similar tasks among users determines thegroups with similar interactions. Different methods of data clustering analysis wereapplied to these groups and the findings show the existence of latent ties among the group members. The second part of this paper focuses on social network visualization. Graphical representation of social network can describe its structure very efficiently. It can enable social network analysts to determine the network degree of connectivity. Analysts can easily determine individuals with a small or large amount of relationships and determine the amount of independent groups in a given network.