Abstract
Academic self-efficacy and boredom were identified as key predictors of learning engagement in online learning. However, there has been little research designed to examine the mediating role of boredom in the relationship between academic self-efficacy and online learning engagement. To address this gap in knowledge, the present study utilizes social cognitive theory, control-value theory, and the self-system process model to examine the following: (1) the impact of academic self-efficacy on three sub-dimensions of learning engagement in online learning; and (2) whether four sub-dimensions of boredom mediate the relationships between academic self-efficacy and the three sub-dimensions of learning engagement in online learning. Data were collected from 528 university students (Mage = 19.77, SDage = 1.24) who voluntarily completed questionnaires assessing academic self-efficacy, boredom, and learning engagement. The results of the structural equation modeling indicated the following findings: (1) academic self-efficacy can predict online learning engagement; (2) affective boredom mediates the relationship between academic self-efficacy and behavioral and cognitive engagement; (3) cognitive boredom mediates the relationship between academic self-efficacy and cognitive engagement; (4) motivational boredom mediates the relationship between academic self-efficacy and behavioral and emotional engagement; and (5) physiological boredom mediates the relationship between academic self-efficacy and behavioral, emotional, and cognitive engagement. Finally, this study supports the notion that academic self-efficacy can influence learning engagement by addressing boredom in online learning. It also offers significant theoretical and practical implications for promoting students’ release from boredom and enhancing their engagement in online education.
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Lu, Q., & Rameli, M. R. M. (2023). How Academic Self-Efficacy Influences Online Learning Engagement: The Mediating Role of Boredom. International Journal of Interactive Mobile Technologies, 17(24), 115–135. https://doi.org/10.3991/IJIM.V17I24.44759
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