Identifying At-Risk Students for Early Interventions—A Time-Series Clustering Approach

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


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.