Unveiling learner experience in MOOC reviews

Jingya Liu, Zhao Du, Qiao Zhong, Fang Wang and Shan Wang

International Journal of Networking and Virtual Organisations, 2024, vol. 31, issue 2, 147-167

Abstract: The surge of learner enrolment in massive open online courses (MOOCs) has led to a wealth of learner-generated data, such as online course reviews that document learner experience. To unveil learner experience with MOOCs, this research uses machine learning methods to extract prominent topics from MOOC reviews and assess the sentiments expressed by learners within them. Furthermore, this research investigates the cooccurrence of the topics using association rule mining. The findings reveal six central topics discussed in MOOC reviews, such as "instructor", "design", "material", "assignment", "platform", and "experience". Notably, most learners express positive sentiments in their reviews. The sentiment indicated in reviews of skill-seeking MOOCs is higher than that in reviews of knowledge-seeking MOOCs. Furthermore, the association rule mining identifies four meaningful association rules. The findings offer valuable insights for MOOC instructors to enhance course design and for platform operators to ensure the long-term viability and success of MOOC platforms.

Keywords: online learning; MOOC; massive open online course; course review; learning experience; text mining; sentiment analysis; topic cooccurrence analysis; machine learning; skill-seeking; knowledge-seeking. (search for similar items in EconPapers)
Date: 2024
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