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Emotions, engagement, and self‐perceived achievement in a small private online course
Author(s) -
Ding Yan,
Zhao Ting
Publication year - 2020
Publication title -
journal of computer assisted learning
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.583
H-Index - 93
eISSN - 1365-2729
pISSN - 0266-4909
DOI - 10.1111/jcal.12410
Subject(s) - psychology , boredom , student engagement , mediation , context (archaeology) , social psychology , annoyance , mathematics education , computer science , paleontology , political science , law , biology , computer vision , loudness
Emotions are critical to learning. However, the function of emotions in the emerging context of massive open online courses (MOOCs) has been under‐researched. The present study complemented this line of research by modelling the relation between learner emotions, engagement with videos, engagement with assignments, and self‐perceived achievement in a small private online course—a localized instance of an extended MOOC. The results suggested that whereas enjoyment, excitement, boredom, and annoyance were all significant predictors of video engagement, only excitement and annoyance were significant predictors of assignment engagement. Excitement, in particular, was the strongest or stronger predictor of both types of engagement. In addition, both types of engagement predicted self‐perceived achievement, but video engagement predicted self‐perceived achievement via the mediation of assignment engagement. Implications for designing more effective MOOCs were proposed.