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

Identifying At-Risk Students for Early Interventions?A Time-Series Clustering Approach
Author(s):
M Sivashankar, M.sreedevi
Keywords:
Clustering, classiﬁcation, and association rules, Feature extraction or construction, Mining methods and algorithms, Time-Series analysis, LMS, predictive modeling
Abstract
the purpose of this study 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 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 illustrates 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
Article Details
Unique Paper ID: 146011 Publication Volume & Issue: Volume 4, Issue 11 Page(s): 1051 - 1057
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