Article

Identifying at-risk students in massive open online courses

Authors:

Benjamin I. P. Rubinstein,

Rui ZhangAuthors Info & Claims

AAAI'15: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence

Pages 1749 - 1755

Published: 25 January 2015 Publication History

Abstract

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 identification 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 well-calibrated 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 effectiveness of our algorithms.

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Cited By

Hassan TEdmison BWilliams DCox II LLouvet MKnijnenburg BMcCrickard D(2024)Simplify, Consolidate, Intervene: Facilitating Institutional Support with Mental Models of Learning Management System UseProceedings of the ACM on Human-Computer Interaction10.1145/36870518:CSCW2(1-23)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3687051
Li YCui XZhang Z(2022)Dropout Rate Prediction for MOOC based on Inceptiontime ModelProceedings of the 7th International Conference on Distance Education and Learning10.1145/3543321.3543330(54-59)Online publication date: 20-May-2022
https://dl.acm.org/doi/10.1145/3543321.3543330
Prenkaj BVelardi PStilo GDistante DFaralli S(2020)A Survey of Machine Learning Approaches for Student Dropout Prediction in Online CoursesACM Computing Surveys10.1145/338879253:3(1-34)Online publication date: 28-May-2020
https://dl.acm.org/doi/10.1145/3388792
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cover image Guide Proceedings

AAAI'15: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence

January 2015

4331 pages

ISBN:0262511290

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Association for the Advancement of Artificial Intelligence

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AAAI Press

Publication History

Published: 25 January 2015

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Cited By

Hassan TEdmison BWilliams DCox II LLouvet MKnijnenburg BMcCrickard D(2024)Simplify, Consolidate, Intervene: Facilitating Institutional Support with Mental Models of Learning Management System UseProceedings of the ACM on Human-Computer Interaction10.1145/36870518:CSCW2(1-23)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3687051
Li YCui XZhang Z(2022)Dropout Rate Prediction for MOOC based on Inceptiontime ModelProceedings of the 7th International Conference on Distance Education and Learning10.1145/3543321.3543330(54-59)Online publication date: 20-May-2022
https://dl.acm.org/doi/10.1145/3543321.3543330
Prenkaj BVelardi PStilo GDistante DFaralli S(2020)A Survey of Machine Learning Approaches for Student Dropout Prediction in Online CoursesACM Computing Surveys10.1145/338879253:3(1-34)Online publication date: 28-May-2020
https://dl.acm.org/doi/10.1145/3388792
Prenkaj BStilo GMadeddu Ld'Aquin MDietze SHauff CCurry ECudre Mauroux P(2020)Challenges and Solutions to the Student Dropout Prediction Problem in Online CoursesProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3412172(3513-3514)Online publication date: 19-Oct-2020
https://dl.acm.org/doi/10.1145/3340531.3412172
Borrella ICaballero-Caballero SPonce-Cueto E(2019)Predict and InterveneProceedings of the Sixth (2019) ACM Conference on Learning @ Scale10.1145/3330430.3333634(1-9)Online publication date: 24-Jun-2019
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Xiong FZou KLiu ZWang H(2019)Predicting learning status in MOOCs using LSTMProceedings of the ACM Turing Celebration Conference - China10.1145/3321408.3322855(1-5)Online publication date: 17-May-2019
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Ding MYang KYeung DPong THsiao SCunningham JMcCarthy KLynch GBrooks CFerguson RHoppe U(2019)Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online CoursesProceedings of the 9th International Conference on Learning Analytics & Knowledge10.1145/3303772.3303795(135-144)Online publication date: 4-Mar-2019
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Wang YLaw NHemberg EO'Reilly UHsiao SCunningham JMcCarthy KLynch GBrooks CFerguson RHoppe U(2019)Using Detailed Access Trajectories for Learning Behavior AnalysisProceedings of the 9th International Conference on Learning Analytics & Knowledge10.1145/3303772.3303781(290-299)Online publication date: 4-Mar-2019
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Yao HLian DCao YWu YZhou T(2019)Predicting Academic Performance for College StudentsACM Transactions on Intelligent Systems and Technology10.1145/329908710:3(1-21)Online publication date: 7-May-2019
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Inkpen KChancellor SDe Choudhury MVeale MBaumer EBrewster SFitzpatrick GCox AKostakos V(2019)Where is the Human?Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems10.1145/3290607.3299002(1-9)Online publication date: 2-May-2019
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