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Article
Identifying At-Risk Students for Early Interventions—A Time-Series Clustering Approach
IEEE Transactions on Emerging Topics in Computing
  • Jui-Long Hung, Boise State University
  • Morgan C. Wang, University of Central Florida
  • Shuyan Wang, University of Southern Mississippi
  • Maha Abdelrasoul, Old Dominion University
  • Yaohang Li, Old Dominion University
  • Wu He, Old Dominion University
Document Type
Article
Publication Date
1-1-2017
Abstract

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.

Citation Information
Jui-Long Hung, Morgan C. Wang, Shuyan Wang, Maha Abdelrasoul, et al.. "Identifying At-Risk Students for Early Interventions—A Time-Series Clustering Approach" IEEE Transactions on Emerging Topics in Computing (2017)
Available at: http://works.bepress.com/andy_hung/33/