Unfolding Sentimental and Behavioral Tendencies of Learners' Concerned Topics From Course Reviews in a MOOC

Sannyuya Liu, Xian Peng, Hercy N.H. Cheng, Zhi Liu, Jianwen Sun, Chongyang Yang

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


Course reviews, which is designed as an interactive feedback channel in Massive Open Online Courses, has promoted the generation of large-scale text comments. These data, which contain not only learners' concerns, opinions and feelings toward courses, instructors, and platforms but also learners' interactions (e.g., post, reply), are generally subjective and extremely valuable for online instruction. The purpose of this study is to automatically reveal these potential information from 50 online courses by an improved unified topic model Behavior-Sentiment Topic Mixture, which is validated and effective for detecting frequent topics learners discuss most, topics-oriented sentimental tendency as well as how learners interact with these topics. The results show that learners focus more on the topics about course-related content with positive sentiment, as well as the topics about course logistics and video production with negative sentiment. Moreover, the distributions of behaviors associated with these topics have some differences.

Original languageEnglish
Pages (from-to)670-696
Number of pages27
JournalJournal of Educational Computing Research
Issue number3
Publication statusPublished - Jun 1 2019
Externally publishedYes


  • behavior-sentiment topic mixture
  • behavioral and sentimental analytics
  • learning analytics
  • topic modeling

ASJC Scopus subject areas

  • Education
  • Computer Science Applications


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