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Sequence Clustering Techniques in Educational Data Mining

Sequence Clustering Techniques in Educational Data Mining
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Author(s): Qi Guo (Medical Council of Canada, Canada), Ying Cui (University of Alberta, Canada), Jacqueline P. Leighton (University of Alberta, Canada) and Man-Wai Chu (University of Alberta, Canada)
Copyright: 2021
Pages: 17
Source title: Handbook of Research on Modern Educational Technologies, Applications, and Management
Source Author(s)/Editor(s): Mehdi Khosrow-Pour D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-7998-3476-2.ch005

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Abstract

Digital technology has profound impacts on modern education. Digital technology not only greatly improves access to quality education, but it also can automatically save all the interactions between students and computers in log files. Clustering of log files can help researchers better understand students and improve the learning program. One challenge associated with log file clustering is that log files are sequential in nature, but traditional cluster analysis techniques are designed for cross-sectional data. To overcome this problem, several sequence clustering techniques are proposed recently. There are three major categories of sequence clustering techniques: Markov chain clustering, sequence distance clustering, and sequence feature clustering. The purpose of this chapter is to introduce these sequence clustering techniques and discuss their potential advantages and disadvantages.

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