Data Mining and Social Network Analysis in the Educational Field: An Application for Non-Expert Users
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
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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 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 Learning analytics
Educational data mining Social network analysis
Abbreviations API DM EDM
Application programming interface Data mining 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: [email protected] D. García-Saiz e-mail: [email protected] C. Palazuelos e-mail: [email protected]
A. Peña-Ayala (ed.), Educational Data Mining, Studies in Computational Intelligence 524, DOI: 10.1007/978-3-319-02738-8_15, Springer International Publishing Switzerland 2014
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ElWM KDD LA LMS MOOC SNA SOA SOAP UC WSDL WS XML
D. García-Saiz et al.
E-learning web miner Knowledge discovery in databases Learning analytics Learning management system Massive open online course Social network analysis Service-oriented architecture Simple object access protocol University of Cantabria Web services description language Web service eXtended Markup Language
15.1 Introduction Since the late 1990s, the use of computer-based technologies has drastically changed learning and teaching processes in all academic levels, from elementary school to university. Nowadays, it is very frequent that teachers include in their subjects activities that require the use of Web 2.0 technologies in order to develop contents and social and communication skills. Collaborative activities, e.g., content search [1, 2], collaborative writing [3], and discussion forums [4], appear in many curricula independently of the educational field and level of the studies. Other tools frequently used, regardless of whether teaching is face-to-face or virtual, are the learning management systems (LMS), e.g., Moodle [5], Blackboard [6], or Shaka
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