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MOOC learners’ demographics, self-regulated learning strategy, perceived learning and satisfaction: A structural equation modeling approach
ARTICLE

Computers & Education Volume 132, Number 1, ISSN 0360-1315 Publisher: Elsevier Ltd

Abstract

Massive Open Online Courses (MOOCs) provide a great platform to study individual and group differences of learners in perceptions, motivations, and behaviors under self-directed learning context. This study examined the relationships, in particular, influential relationships, among MOOC learners' demographics, their self-regulated learning (SRL) strategy usage, perceived learning, and satisfaction. Participants were 4503 learners from 17 Coursera courses who responded to an online survey in 2018. Structural equation modeling showed that participants' age, gender, highest degree, and the number of online courses previously taken significantly predicted both goal setting and environment structuring usage. Previous experience with the course topics only predicted goal setting, not environment structuring. Gender, goal setting and environment structuring strategy usage predicted participants' perceived affective learning. Highest degree, the number of online courses previously took, goal setting, environment structuring strategy usage and perceived affective learning predicted participants' satisfaction with the course. Participants identified themselves with a "Latin America" culture had better environment structuring strategy usage than any other cultural group and higher perceived affective learning than the other cultural groups except for "Other". The results provided implications for researchers studying self-directed learning environments, differences in learning of learners with diverse backgrounds, and SRL behaviors, as well as for educators dealing with increasing SRL strategy usage, improving online learners’ satisfaction and teaching cross-culturally.

Citation

Li, K. (2019). MOOC learners’ demographics, self-regulated learning strategy, perceived learning and satisfaction: A structural equation modeling approach. Computers & Education, 132(1), 16-30. Elsevier Ltd. Retrieved November 13, 2019 from .

This record was imported from Computers & Education on March 15, 2019. Computers & Education is a publication of Elsevier.

Full text is availabe on Science Direct: http://dx.doi.org/10.1016/j.compedu.2019.01.003

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