Motivation and Self-Regulation as Mediators between Learning Environment and Engagement in Online Learning among Chinese University Students

Drawing on self-regulated learning theory and self-determination theory, this study examines the mediating roles of motivation and learning environment in the relationship between self-regulation and student engagement, while accounting for the influence of the online learning environment. Data were collected from 1521 Chinese university students enrolled in online courses and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results from the structural model indicate that the online learning environment strongly predicts self-regulation (β = 0.829, p < .001) and motivation (β = 0.301, p < .001). Self-regulation significantly influences motivation (β = 0.529, p < .001) and engagement (β = 0.299, p < .001), while motivation also exerts a significant effect on engagement (β = 0.330, p < .001). Engagement is substantially explained by the model (R² = 0.823), demonstrating high predictive power. Mediation analyses reveal significant indirect effects of self-regulation on engagement through motivation (β = 0.174, p < .001), as well as multiple indirect pathways linking the online learning environment to engagement via self-regulation and motivation (total indirect effect β = 0.492, p < .001). Predictive relevance was further supported by strong Q² values for engagement (Q² = 0.712). Overall, the findings confirm that motivation and engagement act as key psychological mechanisms through which self-regulation and learning environment quality translate into engagement in online learning. The study contributes to online learning research by empirically validating an integrated, mechanism-based model and offers practical implications for designing learning environments that foster self-regulation, motivation, and sustained student engagement.

Self-Regulated Learning; Motivation; Student Engagement; Learning Environment

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