Sun, Zhongtian and Harit, Anoushka and Yu, Jialin and Cristea, Alexandra I. and Shi, Lei (2021) 'A Brief Survey of Deep Learning Approaches for Learning Analytics on MOOCs.', Intelligent Tutoring Systems Athens, Greece / Virtual, 7-11 Jun 2021.
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.
|Item Type:||Conference item (Paper)|
|Full text:||(AM) Accepted Manuscript|
Download PDF (245Kb)
|Publisher Web site:||https://doi.org/10.1007/978-3-030-80421-3_4|
|Publisher statement:||The final authenticated version is available online at https://doi.org/10.1007/978-3-030-80421-3_4|
|Date accepted:||13 March 2021|
|Date deposited:||13 April 2021|
|Date of first online publication:||09 July 2021|
|Date first made open access:||13 July 2021|
Save or Share this output
|Look up in GoogleScholar|