Improving MOOCs Using Information From Discussion Forums: An Opinion Summarization and Suggestion Mining Approach

Abstract

Discussion forums are integral to MOOCs and a useful resource for collaborative learning. In this paper we examine if the posts made on forums by participants can also provide meaningful information to assess and improve the effectiveness of MOOCs. We present an empirical approach that uses posts in MOOC forums to summarize participants’ opinion towards aspects of a course and identify suggestions for improving a course. Specifically, our approach: (1) detects participants’ attitude towards aspects of a course (e.g. professor, lecture, assignment) at a context or sentence level, (2) extracts suggestions for course improvement, which is a novel space in MOOC learning analytics, and (3) aggregates and displays the results visually. The study used lexicon and rule-based approach and was able to identify aspect-based sentiments and suggestions related to course design elements with a good overall score (0.41 kappa score). By summarizing opinions from a vast amount of textual data on forums our approach allows instructors to improve their course and thereby student engagement and learning.

Keywords

• Discussion forums
• MOOC
• sentiment analysis
• suggestion mining
• visualization