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2015

Identifying At-Risk Students in Massive Open Online Courses

4 years 1 days ago
Identifying At-Risk Students in Massive Open Online Courses
Massive Open Online Courses (MOOCs) have received widespread attention for their potential to scale higher education, with multiple platforms such as Coursera, edX and Udacity recently appearing. Despite their successes, a major problem faced by MOOCs is low completion rates. In this paper, we explore the accurate early identiļ¬cation of students who are at risk of not completing courses. We build predictive models weekly, over multiple offerings of a course. Furthermore, we envision student interventions that present meaningful probabilities of failure, enacted only for marginal students. To be effective, predicted probabilities must be both wellcalibrated and smoothed across weeks. Based on logistic regression, we propose two transfer learning algorithms to trade-off smoothness and accuracy by adding a regularization term to minimize the difference of failure probabilities between consecutive weeks. Experimental results on two offerings of a Coursera MOOC establish the effectivenes...
Jiazhen He, James Bailey, Benjamin I. P. Rubinstei
Added 27 Mar 2016
Updated 27 Mar 2016
Type Journal
Year 2015
Where AAAI
Authors Jiazhen He, James Bailey, Benjamin I. P. Rubinstein, Rui Zhang
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