Understanding MOOC Reviews: Text Mining using Structural Topic Model

Xieling CHEN; Gary CHENG; Haoran XIE; Guanliang CHEN; Di ZOU

doi:10.2991/hcis.k.211118.001

Understanding MOOC Reviews: Text Mining using Structural Topic Model

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

^*Corresponding author for this work

Department of Computing and Decision Sciences

Research output: Journal Publications › Journal Article (refereed) › peer-review

12 Citations (Scopus)

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 problemsolving, 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 language	English
Pages (from-to)	55-65
Number of pages	11
Journal	Human-Centric Intelligent Systems
Volume	1
Issue number	3-4
Early online date	29 Nov 2021
DOIs	https://doi.org/10.2991/hcis.k.211118.001
Publication status	Published - 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.

Publisher Copyright:
© The Authors 2021.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 4 Quality Education

Keywords

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

Access to Document

10.2991/hcis.k.211118.001Licence: CC BY-NC

Cite this

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title = "Understanding MOOC Reviews: Text Mining using Structural Topic Model",

abstract = "Understanding the reasons for Massive Open Online Course (MOOC) learners{\textquoteright} 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{\textquoteright} main complaints about high-graded MOOCs include problemsolving, 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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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{\textquoteright}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. Publisher Copyright: {\textcopyright} The Authors 2021.",

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AB - 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 problemsolving, 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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ER -

Understanding MOOC Reviews: Text Mining using Structural Topic Model

Abstract

Bibliographical note

UN SDGs

Keywords

Access to Document

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