Student Groups Modeling by Integrating Cluster Representation and Association Rules Mining

Authors:
Danuta Zakrzewska
Affiliations:
Institute of Computer Science, Technical University of Lodz, Lodz, Poland 90-924
Venue:
SOFSEM '10 Proceedings of the 36th Conference on Current Trends in Theory and Practice of Computer Science
Year:
2009

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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.