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Evaluation of Clustering Methods for Adaptive Learning Systems
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Author(s): Wilhelmiina Hämäläinen (University of Eastern Finland, Finland), Ville Kumpulainen (University of Eastern Finland, Finland)and Maxim Mozgovoy (University of Aizu, Japan)
Copyright: 2015
Pages: 24
Source title:
Artificial Intelligence Applications in Distance Education
Source Author(s)/Editor(s): Utku Kose (Usak University, Turkey)and Durmus Koc (Usak University, Turkey)
DOI: 10.4018/978-1-4666-6276-6.ch014
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Abstract
Clustering student data is a central task in the educational data mining and design of intelligent learning tools. The problem is that there are thousands of clustering algorithms but no general guidelines about which method to choose. The optimal choice is of course problem- and data-dependent and can seldom be found without trying several methods. Still, the purposes of clustering students and the typical features of educational data make certain clustering methods more suitable or attractive. In this chapter, the authors evaluate the main clustering methods from this perspective. Based on the analysis, the authors suggest the most promising clustering methods for different situations.
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