What matters to LMOOC learners: content and sentiment analyses of learner course reviews

AbstractThis article reports on the results of an investigation into what matters to learners of language Massive Open Online Courses (LMOOCs). The study conducted content and sentiment analyses of learner reviews of LMOOCs to investigate the key themes/subthemes in learner reviews and learners' emotional tendencies in the themes/subthemes as well as differences in learners' emotional tendencies detected in themes/subthemes. The dataset for this study included 8,671 learner reviews collected from 42 College English MOOCs hosted at a major MOOC platform in China. The content analysis identified four major themes—course, instructor, learning, and platform—and nine subthemes, revealing what learners were concerned about LMOOCs. The sentiment analysis detected both similarities and differences in learners' emotional tendencies expressed in different themes/subthemes, showing that learners as a whole held overwhelmingly positive attitudes towards the LMOOCs but had grave concerns about curriculum design, instructors' personal traits, and learning management system. These results yield a nuanced understanding of learners' likes and dislikes about LMOOCs and provide important implications for enhancing the design, delivery, and development of LMOOCs.Keywords: Language Massive Open Online Courses (LMOOCs)content analysissentiment analysiscourse review AcknowledgementsThis study is part of a larger research project on computer-assisted language learning supported by the National Social Science Fund of China (23AYY023) and the Start-Up Research Grant of Ningbo University. We would like to thank Xinyu Fu for her assistance in data collection.Disclosure statementNo potential conflict of interest was reported by the authors.