This paper presents a cyclical methodology for the continuous improvement of e-learning courses using data mining techniques applied to education. For this purpose, a specific data mining tool has been developed, which discovers relevant relationships between data about how students use a course. Unlike others data mining approaches applied to education, which focus on the student, this method is aimed professors and how to help them improve the structure and contents of an e-learning course by making recommendations. We also use a rule discovery algorithm without parameters in order to be easily used by non-expert users in data mining. The results of experimental tests performed on an online course are also presented, demonstrating the usefulness of the proposed methodology and algorithm. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
García, E., Romero, C., Ventura, S., & De Castro, C. (2006). Using rules discovery for the continuous improvement of e-learning courses. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4224 LNCS, pp. 887–895). Springer Verlag. https://doi.org/10.1007/11875581_106
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