Title: Research on learning experience influencing factors of information-based teaching MOOCs

Authors: Xin Zhang

Addresses: Graduate School of Education, Peking University, Beijing, China

Abstract: The growing importance of information technology in education reform has highlighted the need for more adaptable teaching methods. Traditional offline training, limited by varied resources and knowledge backgrounds, often fails to address the diverse needs of teachers. To address this, education departments are now advocating for online education, notably massive open online courses (MOOCs), which are renowned for their unique features and benefits. This study integrates latent Dirichlet allocation (LDA), BERTopic topic mining analysis, sentiment analysis, salience-valence analysis (SVA) analysis, and cluster analysis based on course reviews and reviewers' homepages in iCourse, a Chinese MOOC platform, to explore key factors driving positive learning experiences for distinct learner types. The findings offer insights into digital training mode and promotional strategies of teachers' information-based teaching ability, focusing on course organisation, content design, customisation for diverse learner profiles and utilising key opinion leaders (KOLs) for course promotion.

Keywords: learning experience; massive open online courses; MOOC; latent Dirichlet allocation; LDA; BERTopic; topic mining; sentiment analysis; salience-valence analysis; SVA; cluster; information-based teaching; text mining.

DOI: 10.1504/IJMLO.2025.147177

International Journal of Mobile Learning and Organisation, 2025 Vol.19 No.3, pp.299 - 320

Received: 24 Nov 2023
Accepted: 14 Feb 2024
Published online: 11 Jul 2025 *

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