Chapter 15 Data Mining and Social Network Analysis in the Educational Field: An Application for Non-Expert Users Diego García-Saiz, Camilo Palazuelos and Marta Zorrilla 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 con- nections 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 edu- cation, to discover their students’ behavior profile, models about how they par- ticipate 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 Abbreviations API Application programming interface DM Data mining EDM Educational data mining D. García-Saiz  C. Palazuelos  M. Zorrilla (&) Department of Mathematics, Statistics, and Computer Science, University of Cantabria, Avenida de los Castros s/n 39005 Santander, Spain e-mail: marta.zorrilla@unican.es D. García-Saiz e-mail: diego.garcia@unican.es C. Palazuelos e-mail: camilo.palazuelos@unican.es A. Peña-Ayala (ed.), Educational Data Mining, Studies in Computational Intelligence 524, DOI: 10.1007/978-3-319-02738-8_15, Ó Springer International Publishing Switzerland 2014 411