Understanding MOOC Reviews: Text Mining using Structural Topic Model
- https://doi.org/10.2991/hcis.k.211118.001How to use a DOI?
- MOOC course reviews; programming courses; learner dissatisfaction; structural topic model; text mining
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.
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- 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
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 - https://doi.org/10.2991/hcis.k.211118.001 ID - Chen2021 ER -