Abstract
With the increasing popularity of social networking services like Facebook or Twitter, social network analysis has emerged again. Discovering the underlying relationships between people—as well as the reasons why they arise or the type of those interactions—and 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.
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Palazuelos, C., García-Saiz, D., Zorrilla, M. (2013). Social Network Analysis and Data Mining: An Application to the E-Learning Context. In: Bǎdicǎ, C., Nguyen, N.T., Brezovan, M. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2013. Lecture Notes in Computer Science(), vol 8083. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40495-5_65
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DOI: https://doi.org/10.1007/978-3-642-40495-5_65
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