Thematic and Sentiment Analysis of Learners’ Feedback in MOOCs

Abstract

In recent years, sentiment analysis has gained popularity among researchers in various domains, including the education domain. Sentiment analysis can be applied to review the course comments in Massive Open Online Courses (MOOCs), which could enable course designers’ to easily evaluate their courses. The objective of this study is to explore the influential factors that affect the completion rate of MOOCs and unravel the sentiments of dropout learners by evaluating learners’ feedback. In the present study, sentiment analysis was performed using Python programming and NVivo tools on the feedback of the learners enrolled in three MOOCs entitled Introduction to Cyber Security, Digital Forensics and Development of Online Courses for SWAYAM, which was hosted on the SWAYAM platform (www.swayam.gov.in). Two instruments were used for data collection: (1) a structured questionnaire using a 5-point Likert scale was administrated using Google Forms — the questionnaires have also some additional open-ended questions — and (2) semi-structured interview schedules with the domain experts. The feedback was collected using Google Forms and a total of 324 responses were received between April 23, 2022 to May 31, 2022. The non-probability sampling method served as the sampling approach in the quantitative phase in this study. During analysis, the findings of the feedback uncovered important dimensions of some peculiar factors that may be responsible for retention of learners, i.e., content localisation, credit mobility and latest trend courses that were less explored in the earlier literature.