Temporal Analysis in Massive Open Online Courses – Towards Identifying at-Risk Students Through Analyzing Demographical Changes

Lei Shi (), Bokuan Yang () and Armando Toda ()
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Lei Shi: Durham University
Bokuan Yang: University of Liverpool
Armando Toda: University of Sao Paulo

A chapter in Advances in Information Systems Development, 2020, pp 146-163 from Springer

Abstract: Abstract This chapter demonstrates a temporal analysis in Massive Open Online Courses (MOOCs), towards identifying at-risk students through analyzing their demographical changes. At-risk students are those who tend to drop out from the MOOCs. Previous studies have shown that how students interact in MOOCs could be used to identify at-risk students. Some studies considered student diversity by looking into subgroup behavior. However, most of them lack consideration of students’ demographical changes. Towards bridging the gap, this study clusters students based on both their interaction with the MOOCs (activity logs) and their characteristics and explores their demographical changes along the MOOCs progress. The result shows students’ demographical characteristics (membership of subgroups) changed significantly in the first half of the course and stabilized in the second half. Our findings provide insight into how students may be engaged in MOOCs and suggest the improvement of identifying at-risk students based on the temporal data.

Keywords: MOOCs; Clustering; Behavior patterns; Temporal analysis; Unsupervised machine learning; Learning analytics; Demographical characteristics (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-030-49644-9_9

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DOI: 10.1007/978-3-030-49644-9_9

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