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

研究成果: 雜誌貢獻文章同行評審

9 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
頁(從 - 到)670-696
頁數27
期刊Journal of Educational Computing Research
57
發行號3
DOIs
出版狀態已發佈 - 6月 1 2019
對外發佈

ASJC Scopus subject areas

  • 教育
  • 電腦科學應用

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