Understanding MOOC Reviews: Text Mining using Structural Topic Model

Xieling CHEN, Gary CHENG*, Haoran XIE, Guanliang CHEN, Di ZOU

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review


Understanding the reasons for 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.
Original languageEnglish
Pages (from-to)55-65
JournalHuman-Centric Intelligent Systems
Issue number3-4
Early online date29 Nov 2021
Publication statusPublished - Dec 2021

Bibliographical note

The research described in this article has been supported by the One-off Special Fund from Central and Faculty Fund in Support of Research from 2019/20 to 2021/22 (MIT02/19-20), the Interdisciplinary Research Scheme of the Dean’s Research Fund 2019-20 (FLASS/DRF/IDS-2) and the Research Cluster Fund (RG 78/2019-2020R) of The Education University of Hong Kong, General Research Fund (No. 18601118) of Research Grants Council of Hong Kong SAR, and Lam Woo Research Fund (LWI20011) of Lingnan University, Hong Kong.


  • MOOC course reviews
  • programming courses
  • learner dissatisfaction
  • structural topic model
  • text mining


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