Abstract
This work pursues to find out patterns of characteristics and behaviors of students. Thus, it is presented an approach to mine repositories of student models (SM). The source information embraces students’ personal information and assessment of the use of a Web-based educational system (WBES) by students. In addition, the repositories reveal a profile composed by personal attributes, cognitive skills, learning preferences, and personality traits of a sample of students. The approach mines such repositories and produces several clusters. One cluster represents volunteers who tend to abandon. Another group clusters people who fulfill their commitments. It is concluded that: educational data mining (EDM) produces some findings to depict students that could be considered for authoring content and sequencing teaching-learning experiences.
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Peña-Ayala, A., Mizoguchi, R. (2011). Student Modeling by Data Mining. In: Nguyen, N.T., Trawiński, B., Jung, J.J. (eds) New Challenges for Intelligent Information and Database Systems. Studies in Computational Intelligence, vol 351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19953-0_21
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DOI: https://doi.org/10.1007/978-3-642-19953-0_21
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