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

Abstract

This study examines the direct and mediating relationships among students’ self-regulation, motivation, engagement, and academic success in online learning. Grounded in self-regulated learning theory and engagement frameworks, the proposed structural model posits that self-regulation predicts academic success both directly and indirectly through motivation and engagement. A quantitative cross-sectional design was employed, and data were collected from 1,521 undergraduate students enrolled in online courses at three public universities in Qinghai Province, China. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The measurement model demonstrated satisfactory reliability and validity (composite reliability = .915–.953; AVE = .604–.693). Structural results revealed that self-regulation had a significant direct effect on academic success (β = .355, p < .001), engagement significantly predicted academic success (β = .348, p < .001), and motivation also had a positive but smaller effect (β = .099, p < .001). Self-regulation strongly predicted engagement (β = .821, p < .001) and motivation (β = .699, p < .001). Mediation analysis indicated significant indirect effects through engagement (β = .285, p < .001) and motivation (β = .069, p < .001), confirming partial mediation. The model explained 71.6% of the variance in academic success (R² = .716), demonstrating substantial explanatory power. The findings underscore the central role of self-regulation in online academic achievement and highlight engagement as the strongest mediating mechanism linking self-regulation to success. The study contributes to the literature by integrating key learner variables within a unified predictive model and offers practical implications for enhancing online higher education practices.