Analyzing Large Collections of Open-Ended Feedback from MOOC Learners Using LDA Topic Modeling and Qualitative Analysis

Gaurav Nanda     Kerrie A. Douglas     David R. Waller     Hillary E. Merzdorf     Dan Goldwasser    
IEEE Transactions on Learning Technologies (IEEE TLT), 2021
[pdf]

Abstract

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.


Bib Entry

  @article{NDWMG_tlt_2021,
    author = "Gaurav Nanda and Kerrie A. Douglas and David R. Waller and Hillary E. Merzdorf and Dan Goldwasser",
    title = "Analyzing Large Collections of Open-Ended Feedback from MOOC Learners Using LDA Topic Modeling and Qualitative Analysis",
    booktitle = "IEEE Transactions on Learning Technologies (IEEE TLT)",
    year = "2021"
  }