Chapter

Educational Data Mining

Volume 524 of the series Studies in Computational Intelligence pp 411-439

Date:

Data Mining and Social Network Analysis in the Educational Field: An Application for Non-Expert Users

  • Diego García-SaizAffiliated withDepartment of Mathematics, Statistics, and Computer Science, University of Cantabria
  • , Camilo PalazuelosAffiliated withDepartment of Mathematics, Statistics, and Computer Science, University of Cantabria
  • , Marta ZorrillaAffiliated withDepartment of Mathematics, Statistics, and Computer Science, University of Cantabria Email author 

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Abstract

With the increasing popularity of social networking services like Facebook, social network analysis (SNA) has emerged again. Undoubtedly, there is an inherent social network in any learning context, where teachers, learners, and learning resources behave as main actors, among which different relationships can be defined, e.g., “participate in” among blogs, students, and learners. From their analysis, information about group cohesion, participation in activities, and connections among subjects can be obtained. At the same time, it is well-known the need of tools that help instructors, in particular those involved in distance education, to discover their students’ behavior profile, models about how they participate in collaborative activities or likely the most important, to know the performance and dropout pattern with the aim of improving the teaching–learning process. Therefore, the goal of this chapter is to describe our e-learning Web Mining tool and the new services that it provides, supported by the use of SNA and classification techniques.

Keywords

Data mining Educational data mining Social network analysis Learning analytics