Name
Abstract | ||
|---|---|---|
With the increasing popularity of social networking services like Facebook or Twitter, social network analysis has emerged again. Discovering the underlying relationships between peopleas well as the reasons why they arise or the type of those interactionsand measuring their influence are examples of tasks that are becoming to be paramount in business. However, this is not the only field of application in which the use of social network analysis techniques might be appropriate. In this paper, we expose how social network analysis can be a tool of considerable utility in the educational context for addressing difficult problems, e.g., uncovering the students' level of cohesion, their degree of participation in forums, or the identification of the most influential ones. Furthermore, we show that the correct management of social behavior data, along with the use of the student activity, helps us build more accurate performance and dropout predictors. Our conclusions are drawn from the analysis of an e-learning course taught at the University of Cantabria for three consecutive academic years. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-40495-5_65 | ICCCI |
Keywords | DocType | Volume |
social network analysis, data mining, e-learning | Conference | 8083 |
ISSN | Citations | PageRank |
0302-9743 | 3 | 0.43 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Camilo Palazuelos | 1 | 12 | 3.22 |
| Diego García-Saiz | 2 | 57 | 10.32 |
| Marta E. Zorrilla | 3 | 51 | 16.05 |