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A Brief Survey of Deep Learning Approaches for Learning Analytics on MOOCs

Sun, Zhongtian; Harit, Anoushka; Yu, Jialin; Cristea, Alexandra I.; Shi, Lei

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Authors

Zhongtian Sun

Jialin Yu

Lei Shi



Contributors

Christos Troussas
Editor

Abstract

Massive Open Online Course (MOOC) systems have become prevalent in recent years and draw more attention, a.o., due to the coronavirus pandemic’s impact. However, there is a well-known higher chance of dropout from MOOCs than from conventional off-line courses. Researchers have implemented extensive methods to explore the reasons behind learner attrition or lack of interest to apply timely interventions. The recent success of neural networks has revolutionised extensive Learning Analytics (LA) tasks. More recently, the associated deep learning techniques are increasingly deployed to address the dropout prediction problem. This survey gives a timely and succinct overview of deep learning techniques for MOOCs’ learning analytics. We mainly analyse the trends of feature processing and the model design in dropout prediction, respectively. Moreover, the recent incremental improvements over existing deep learning techniques and the commonly used public data sets have been presented. Finally, the paper proposes three future research directions in the field: knowledge graphs with learning analytics, comprehensive social network analysis, composite behavioural analysis.

Citation

Sun, Z., Harit, A., Yu, J., Cristea, A. I., & Shi, L. (2021). A Brief Survey of Deep Learning Approaches for Learning Analytics on MOOCs. In A. Cristea, & C. Troussas (Eds.), . https://doi.org/10.1007/978-3-030-80421-3_4

Presentation Conference Type Conference Paper (Published)
Conference Name Intelligent Tutoring Systems
Start Date Jun 7, 2021
End Date Jun 11, 2021
Acceptance Date Mar 13, 2021
Online Publication Date Jul 9, 2021
Publication Date Jul 9, 2021
Deposit Date Apr 12, 2021
Publicly Available Date Apr 13, 2021
Publisher Springer
Pages 28-37
Series Title Lecture Notes in Computer Science
Series ISSN 0302-9743
DOI https://doi.org/10.1007/978-3-030-80421-3_4
Public URL https://durham-repository.worktribe.com/output/1139057

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