Name
Abstract | ||
|---|---|---|
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 the interest in performing similar tasks among users determines the groups with similar interactions. Different methods of data clustering analysis were applied 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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/CASoN.2009.34 | Fontainbleu |
Keywords | Field | DocType |
social network,social network visualization,network degree,latent tie,elearning systems popularity motivates,learning management system,elearning systems data,social network analyst,latent social network,similar social circle,similar contact,data visualization,pattern analysis,social networks,data mining,least squares approximation,data clustering,data consistency,information services,data analysis,computer science,electronic learning,data visualisation,computer networks,internet | Information system,Data science,Data mining,Social network,Computer science,Popularity,Artificial intelligence,The Internet,Organizational network analysis,World Wide Web,Data visualization,Learning Management,Student activities,Machine learning | Conference |
ISSN | ISBN | Citations |
2155-7047 | 978-1-4244-4613-1 | 4 |
PageRank | References | Authors |
0.49 | 10 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pavla Drazdilova | 1 | 9 | 2.40 |
| Katerina Slaninova | 2 | 11 | 2.13 |
| Jan Martinovic | 3 | 129 | 34.61 |
| Gamila Obadi | 4 | 8 | 1.29 |
| Václav Snasel | 5 | 1261 | 210.53 |