Castro, F., Vellido, A., Nebot, A., Mugica, F. (2007): Applying Data Mining Techniques to e-Learning Problems. In: Jain, L.C., Tedman, R.A. and Tedman, D.K. (eds.) Evolution of Teaching and Learning Paradigms in Intelligent Environment. Studies in Computational Intelligence, Springer-Verlag Berlin Heidelberg, Vol.62, ISBN 978-3-540-71973-1, Pp. 183-221
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, Intelligen Agents, and Inductive Reasoning, amongst to the type of Data Mining problem dealt with: clustering, classification, prediction, etc. Finally, from the standpoint of the e-learning practitioner, we provide a taxonomy of e-learning problems to which Data Mining techniques have been applied, including, for instance: Students' classification based on their learning performance; detection of irregular learning behaviors; e-learning system usage; and systemsadaptability to students requirements and capacities.