Exploring the relationships between learners’ engagement, autonomy, and academic performance in an English language MOOC

Abstract

Language learner engagement, which is receiving increased attention, has predominantly focused on offline classroom contexts, while learner engagement in language Massive Open Online Courses (LMOOCs) remains under-explored. This study was conducted on a College English MOOC with the purpose of examining learner engagement and its relations with autonomy and academic performance in the LMOOC. In the first phase, the learning analytics (LA) approach was adopted. This involved collecting tracking data of 3673 learners from the learning management system (LMS) of the LMOOC platform. The data included three types of activities (videos watched, assignments submitted, and posts written) as indicators of learners’ online task engagement and scores on the final examination as a measure of their academic performance. In the second phase, 115 learners of the focal LMOOC responded to a survey that measured autonomy and three dimensions of engagement (behavioral, cognitive, and emotional engagement) typically explored in second language (L2) learning research. The results of the LA approach showed that all three indicators of online task engagement significantly predicted academic performance. The survey results indicated that only cognitive engagement among the three dimensions of engagement significantly predicted academic performance. While autonomy did not predict academic performance, cognitive engagement significantly mediated the relationship between autonomy and academic performance. Implications for LMOOC instructors, designers, and researchers are then addressed.

Keywords:

• Learner engagement
• online task engagement
• language MOOCs (LMOOCs)
• learning analytics approach
• academic performance

Disclosure statement

No potential conflict of interest was reported by the author

Notes

1 The use of structural equation modeling, which would have been a better option, was not permissible due to the small sample size. The current small sample size did not reach the lowest ratio of sample size to free parameters being 5:1, the rule of thumb advanced by Bentler and Chou (1987).

Additional information

Funding

This work was supported by The National Social Science Fund of China Fund [grant number 19BYY197].

Notes on contributors

Yuanlan Jiang

Yuanlan Jiang holds an MA in Applied Linguistics from Shantou University, China. Her research interests include individual differences, computer-assisted language learning, and second language acquisition.

Jian-E Peng

Jian-E Peng is a professor in the College of Liberal Arts, Shantou University, China. She holds a PhD from the University of Sydney. Her research interests include second language acquisition, research methodology, computer-assisted language learning, and teacher development. Her works include a book published by Multilingual Matters (2014), three book chapters, and some papers published in Language Learning, TESOL Quarterly, System, ELT Journal, Journal of English for Academic Purposes, Linguistics and Education, Journal of Multilingual and Multicultural Development, RELC Journal, Asia-Pacific Education Researcher, Sage Open, and Sustainability.