Model-based clustering analysis of student data

Authors:
Mirwais Tanai;Jongwan Kim;Joong Hyuk Chang
Affiliations:
Dept. of Computer & Information Technology, Daegu University, Gyeongsan, Gyeongbuk, Korea;Dept. of Computer & Information Technology, Daegu University, Gyeongsan, Gyeongbuk, Korea;Dept. of Computer & Information Technology, Daegu University, Gyeongsan, Gyeongbuk, Korea
Venue:
ICHIT'11 Proceedings of the 5th international conference on Convergence and hybrid information technology
Year:
2011

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Abstract

Many students fail because of academic and financial difficulties and dissatisfaction with their instruction and academic environment. Educational data mining(EDM) community tries to find solutions for such problems by mining student's data. There are a wide variety of current methods popular within educational data mining but the topics of EDM research are changing. Discovery with models has recently gained widespread use in EDM and it was the second most common category of EDM research by 2008-2009. In this article an important sub method of discovery with models (model based cluster analysis) is addressed. We explain the rule of MBCA in EDM, MBCA algorithms, interests of MBCA, and an experimental comparison of MBCA algorithms, and attributes selection methods on student's data.