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

Keywords

association rules; classification; Clustering; feature extraction or construction; LMS; mining methods and algorithms; predictive modeling; time-series analysis

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.

Publication Date

1-1-2017

Publication Title

IEEE Transactions on Emerging Topics in Computing

Volume

5

Issue

1

Number of Pages

45-55

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TETC.2015.2504239

Socpus ID

85027688738 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85027688738

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