Understanding Learners’ Perception of MOOCs Based on Review Data Analysis Using Deep Learning and Sentiment Analysis

Xieling CHEN, Fu Lee WANG*, Gary CHENG, Man-Kong CHOW, Haoran XIE

*Corresponding author for this work

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

4 Citations (Scopus)


Massive open online courses (MOOCs) have exploded in popularity; course reviews are important sources for exploring learners’ perceptions about different factors associated with course design and implementation. This study aims to investigate the possibility of automatic classification for the semantic content of MOOC course reviews to understand factors that can predict learners’ satisfaction and their perceptions of these factors. To do this, this study employs a quantitative research methodology based on sentiment analysis and deep learning. Learners’ review data from Class Central are analyzed to automatically identify the key factors related to course design and implementation and the learners’ perceptions of these factors. A total of 186,738 review sentences associated with 13 subject areas are analyzed, and consequently, seven course factors that learners frequently mentioned are found. These factors include: “Platforms and tools”, “Course quality”, “Learning resources”, “Instructor”, “Relationship”, “Process”, and “Assessment”. Subsequently, each factor is assigned a sentimental value using lexicon-driven methodologies, and the topics that can influence learners’ learning experiences the most are decided. In addition, learners’ perceptions across different topics and subjects are explored and discussed. The findings of this study contribute to helping MOOC instructors in tailoring course design and implementation to bring more satisfactory learning experiences for learners.
Original languageEnglish
Article number218
Number of pages17
JournalFuture Internet
Issue number8
Early online date25 Jul 2022
Publication statusPublished - 25 Jul 2022

Bibliographical note

Funding Information:
This research was funded by the Research Grants Council of the Hong Kong Special Adminstrative Region, China (UGC/FDS16/E01/19), 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 Dean’s Research Fund 2021/22 (FLASS/DRF/IDS-3) of The Education University of Hong Kong, and The Faculty Research Grants (DB22B4), Lingnan University.

Publisher Copyright:
© 2022 by the authors.


  • MOOCs
  • deep learning
  • learners’ perceptions
  • review data analysis
  • sentiment analysis


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