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Applying Data Mining Techniques to e-Learning Problems

  • Chapter
Evolution of Teaching and Learning Paradigms in Intelligent Environment

Part of the book series: Studies in Computational Intelligence ((SCI,volume 62))

Abstract

This chapter aims to provide an up-to-date snapshot of the current state of research and applications of Data Mining methods in e-learning. The cross-fertilization of both areas is still in its infancy, and even academic references are scarce on the ground, although some leading education-related publications are already beginning to pay attention to this new field. In order to offer a reasonable organization of the available bibliographic information according to different criteria, firstly, and from the Data Mining practitioner point of view, references are organized according to the type of modeling techniques used, which include: Neural Networks, Genetic Algorithms, Clustering and Visualization Methods, Fuzzy Logic, Intelligent agents, and Inductive Reasoning, amongst others. From the same point of view, the information is organized according to the type of Data Mining problem dealt with: clustering, classification, prediction, etc.

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Authors and Affiliations

  1. Dept. Llenguatges i Sistemes Informatics, LSI, Universitat Politècnica de Catalunya, C. Jordi Girona 1-3, 8034, Campus Nord, Barcelona, España

    Félix Castro, Alfredo Vellido & Àngela Nebot

  2. Instituto Latinoamericano de la Comunicación Educativa (ILCE), Calle del Puente 45, 14380, México, D F, México

    Francisco Mugica

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  1. Félix Castro
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  2. Alfredo Vellido
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  3. Àngela Nebot
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  4. Francisco Mugica
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Editors and Affiliations

  1. School of Electrical and Information Engineering, University of South Australia, Adelaide, Mawson Lakes Campus, South Australia, 5095, Australia

    Lakhmi C. Jain (Foundation Director of the KES Centre) (Foundation Director of the KES Centre)

  2. School of Medicine, Griffith University Medicine and Oral Health Centre, PMB 50, GCMC, 9726, Brisbane, Queensland, Australia

    Raymond A. Tedman

  3. Kings Christian College, 68 Gemvale Road, Reedy Creek, Queensland, 4227, Australia

    Debra K. Tedman

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Castro, F., Vellido, A., Nebot, À., Mugica, F. (2007). Applying Data Mining Techniques to e-Learning Problems. In: Jain, L.C., Tedman, R.A., Tedman, D.K. (eds) Evolution of Teaching and Learning Paradigms in Intelligent Environment. Studies in Computational Intelligence, vol 62. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71974-8_8

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