Title: Unveiling learner experience in MOOC reviews

Authors: Jingya Liu; Zhao Du; Qiao Zhong; Fang Wang; Shan Wang

Addresses: Business School, Central South University, No. 932, LuShan South Road, Yuelu District, Changsha, 410083, China ' School of Management, Beijing Sport University, No. 48, XinXi Road, Haidian District, Beijing, 100084, China ' School of Sports Engineering, Beijing Sport University, No. 48, XinXi Road, Haidian District, Beijing, 100084, China ' Lazaridis School of Business and Economics, Wilfrid Laurier University, 75 University Avenue West, Waterloo, Ontario, N2L 3C5, Canada ' Department of Finance and Management Science, University of Saskatchewan, 25 Campus Drive, Saskatoon, SK, S7N 2A5, Canada

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.

DOI: 10.1504/IJNVO.2024.142241

International Journal of Networking and Virtual Organisations, 2024 Vol.31 No.2, pp.147 - 167

Received: 13 Nov 2023
Accepted: 27 Aug 2024
Published online: 15 Oct 2024 *

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