Abstract
Finding groups of students with similar preferences enables to adjust e-learning systems according to their needs. Building models for each group can help in suggesting teaching paths and materials according to member requirements. In the paper, it is proposed to connect a cluster representation, in the form of the likelihood matrix, and frequent patterns, for building models of student groups. Such approach enables to get the detailed knowledge of group members’ features. The research is focused on individual traits, which are dominant learning style dimensions. The accuracy of the proposed method is validated on the basis of tests done for different clusters of real and artificial data.
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Zakrzewska, D. (2010). Student Groups Modeling by Integrating Cluster Representation and Association Rules Mining. In: van Leeuwen, J., Muscholl, A., Peleg, D., Pokorný, J., Rumpe, B. (eds) SOFSEM 2010: Theory and Practice of Computer Science. SOFSEM 2010. Lecture Notes in Computer Science, vol 5901. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11266-9_62
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DOI: https://doi.org/10.1007/978-3-642-11266-9_62
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