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Educational Data Mining pp 411–439Cite as

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

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 524))

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.

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Notes

  1. 1.

    See http://congresoestilosdeaprendizaje.blogspot.com..

Abbreviations

API:

Application programming interface

DM:

Data mining

EDM:

Educational data mining

ElWM:

e-learning web miner

KDD:

Knowledge discovery in databases

LA:

Learning analytics

LMS:

Learning management system

MOOC:

Massive open online course

SNA:

Social network analysis

SOA:

Service-oriented architecture

SOAP:

Simple object access protocol

UC:

University of Cantabria

WSDL:

Web services description language

WS:

Web service

XML:

eXtended Markup Language

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Acknowledgements

The authors would like to thank the anonymous referees for their constructive comments, which led to a significant improvement of this paper. The authors are also deeply grateful to CEFONT, the department of the UC that is responsible for LCMS maintenance, for their help and collaboration. Likewise, the authors gratefully acknowledge the valuable collaboration of the instructors involved in the courses analyzed. This work is partially supported by the Ministry of Education, Culture, and Sport of the Government of Spain, under grant Beca de colaboración (2012–2013), and by the UC, under a Ph.D. studentship (2011–2015).

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Authors and Affiliations

  1. Department of Mathematics, Statistics, and Computer Science, University of Cantabria, Avenida de los Castros s/n, 39005, Santander, Spain

    Diego García-Saiz, Camilo Palazuelos & Marta Zorrilla

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  1. Diego García-Saiz
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  2. Camilo Palazuelos
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  3. Marta Zorrilla
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Correspondence to Marta Zorrilla .

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  1. Escuela Superior de Ingeniería Mecánica y Eléctrica, Zacatenco (ESIME-Z), World Outreach Light to the Nations Ministries (WOLNM), Instituto Politécnico Nacional (IPN), Gustavo A. Madero, Mexico City, Distrito Federal, Mexico

    Alejandro Peña-Ayala

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García-Saiz, D., Palazuelos, C., Zorrilla, M. (2014). Data Mining and Social Network Analysis in the Educational Field: An Application for Non-Expert Users. In: Peña-Ayala, A. (eds) Educational Data Mining. Studies in Computational Intelligence, vol 524. Springer, Cham. https://doi.org/10.1007/978-3-319-02738-8_15

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  • DOI: https://doi.org/10.1007/978-3-319-02738-8_15

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