Human-Centric Intelligent Systems

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Volume 1, Issue 3-4, December 2021, Pages 55 - 65

Understanding MOOC Reviews: Text Mining using Structural Topic Model

Authors
Xieling Chen1, Gary Cheng1, *, Haoran Xie2, Guanliang Chen3, Di Zou4
1Department of Mathematics and Information Technology, The Education University of Hong Kong, New Territories, Hong Kong SAR, China
2Department of Computing and Decision Sciences, Lingnan University, New Territories, Hong Kong SAR, China
3Faculty of Information Technology, Monash University, Clayton, Australia
4Department of English Language Education, The Education University of Hong Kong, New Territories, Hong Kong SAR, China
*Corresponding author. Email: chengks@eduhk.hk
Corresponding Author
Gary Cheng
Received 25 June 2021, Accepted 17 November 2021, Available Online 29 November 2021.
DOI
10.2991/hcis.k.211118.001How to use a DOI?
Keywords
MOOC course reviews; programming courses; learner dissatisfaction; structural topic model; text mining
Abstract

Understanding the reasons for Massive Open Online Course (MOOC) learners’ complaints is essential for MOOC providers to facilitate service quality and promote learner satisfaction. The current research uses structural topic modeling to analyze 21,692 programming MOOC course reviews in Class Central, leading to enhanced inference on learner (dis)satisfaction. Four topics appear more commonly in negative reviews as compared to positive ones. Additionally, variations in learner complaints across MOOC course grades are explored, indicating that learners’ main complaints about high-graded MOOCs include problem-solving, practices, and programming textbooks, whereas learners of low-graded courses are frequently annoyed by grading and course quality problems. Our study contributes to the MOOC literature by facilitating a better understanding of MOOC learner (dis)satisfaction using rigorous statistical techniques. Although this study uses programming MOOCs as a case study, the analytical methodologies are independent and adapt to MOOC reviews of varied subjects.

Copyright
© 2021 The Authors. Publishing services by Atlantis Press International B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Next Article In Issue>
Journal
Human-Centric Intelligent Systems
Volume-Issue
1 - 3-4
Pages
55 - 65
Publication Date
2021/11/29
ISSN (Online)
2667-1336
DOI
10.2991/hcis.k.211118.001How to use a DOI?
Copyright
© 2021 The Authors. Publishing services by Atlantis Press International B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Xieling Chen
AU  - Gary Cheng
AU  - Haoran Xie
AU  - Guanliang Chen
AU  - Di Zou
PY  - 2021
DA  - 2021/11/29
TI  - Understanding MOOC Reviews: Text Mining using Structural Topic Model
JO  - Human-Centric Intelligent Systems
SP  - 55
EP  - 65
VL  - 1
IS  - 3-4
SN  - 2667-1336
UR  - https://doi.org/10.2991/hcis.k.211118.001
DO  - 10.2991/hcis.k.211118.001
ID  - Chen2021
ER  -