ERIC Number: EJ1297760
Record Type: Journal
Publication Date: 2021-Apr
Pages: 15
Abstractor: As Provided
ISBN: N/A
ISSN: EISSN-1939-1382
EISSN: N/A
Analyzing Large Collections of Open-Ended Feedback From MOOC Learners Using LDA Topic Modeling and Qualitative Analysis
Nanda, Gaurav; Douglas, Kerrie A.; Waller, David R.; Merzdorf, Hillary E.; Goldwasser, Dan
IEEE Transactions on Learning Technologies, v14 n2 p146-160 Apr 2021
There is a large variation in background and purpose of massive open online course (MOOC) learners. To improve the overall MOOC learning experience, it is important to identify which MOOC characteristics are most important for learners. For this purpose, in this article, we analyzed about 150,000 open-ended learner responses from 810 MOOCs to three postcourse survey questions about their learning experience: (Q1) What was your most favorite part and why? (Q2) What your least favorite part and why? (Q3) How could the course be improved? We used the latent Dirichlet allocation topic model to identify prominent topics present in learner responses to each question. We determined the theme of each identified topic through qualitative analysis. Our results show that the following aspects of MOOCs can significantly impact the learning experience: quality of course content, accurate description of prerequisites and required time commitment in course syllabus, quality of assessment and feedback, meaningful interaction with peers and educators, engaging instructor and videos, accessibility of learning materials, and usability of platform.
Descriptors: Online Courses, Student Satisfaction, Course Evaluation, Educational Improvement, Preferences, Learning Experience, Educational Quality, Course Content, Prerequisites, Student Evaluation, Feedback (Response), Usability
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: National Science Foundation (NSF)
Authoring Institution: N/A
Grant or Contract Numbers: N/A