Self-Regulated Learning as a Mediator of the Effect of Personalized Learning on Academic Achievement among Chinese University Students

Abstract

This study examined self-regulated learning as a mediator of the effect of personalized learning on academic achievement among graduate students in China. A quantitative cross-sectional survey design was employed. Data were collected from 450 graduate students in China through an electronic questionnaire, and 446 valid responses were retained after Mahalanobis distance screening. The research instrument consisted of 30 items measuring personalized learning, self-regulated learning, and perceived academic achievement. Data were analysed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The measurement model demonstrated satisfactory reliability and validity, with all outer loadings exceeding .70, Cronbach’s alpha values ranging from .930 to .937, composite reliability values ranging from .941 to .947, and AVE values ranging from .615 to .640. The structural model explained 65.4% of the variance in academic achievement and 51.6% of the variance in self-regulated learning. The results showed that personalized learning had a significant positive direct effect on academic achievement (β = .386, t = 8.101, p < .001) and self-regulated learning (β = .718, t = 25.018, p < .001). Self-regulated learning also significantly predicted academic achievement (β = .486, t = 10.138, p < .001). Mediation analysis confirmed that self-regulated learning significantly mediated the relationship between personalized learning and academic achievement (β = .349, t = 9.629, p < .001). The findings suggest that personalized learning improves graduate students’ academic achievement partly by enhancing students’ ability to regulate their learning process.