期刊名称：IEEE Transactions on Emerging Topics in Computing
印刷版ISSN：2168-6750
出版年度：2017
卷号：5
期号：1
页码：45-55
DOI：10.1109/TETC.2015.2504239
出版社：IEEE Publishing
摘要：The purpose of this paper is to identify at-risk online students earlier, more often, and with greater accuracy using time-series clustering. The case study showed that the proposed approach could generate models with higher accuracy and feasibility than the traditional frequency aggregation approaches. The best performing model can start to capture at-risk students from week 10. In addition, the four phases in student's learning process detected holiday effect and illustrate at-risk students' behaviors before and after a long holiday break. The findings also enable online instructors to develop corresponding instructional interventions via course design or student-teacher communications.
关键词：predictive modeling;Clustering;classification;association rules;feature extraction or construction;mining methods and algorithms;time-series analysis;LMS
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