The relationship between adolescents’ self-regulated learning and academic achievement: an interrelated mediation model of academic emotions: evidence from a nationwide sample in China

Introduction: In the 21st century, self-regulated learning (SRL) plays a vital role in the cultivation of high-quality talent. Grounded in self-regulated learning theory and attachment theory, this study aims to systematically examine the relationship between adolescents' SRL and academic achievement, considering the roles of academic emotions and teacher-student relationships (TSR). Additionally, the study investigates whether a recursive pathway exists from academic emotions back to SRL. Methods: = 88,149 students) and employs structural equation modeling (SEM) to analyze the relationships among SRL, academic emotions, TSR, and academic achievement. Academic emotions were included as a mediating variable, and TSR as a moderating variable. Results: The results indicate that: (1) adolescents' levels of SRL are significantly and positively associated with their academic achievement; (2) three types of academic emotions (e.g., positive high-arousal emotions) significantly mediate the relationship between SRL and academic achievement; and (3) TSR moderate both the first stage of the mediation pathway ("SRL → academic emotions → academic achievement") and the direct effect of SRL on academic achievement. Discussion: Overall, the findings support the cognition-emotion cyclical model, suggesting that students can enhance SRL through positive academic emotions, while appropriate SRL strategies can in turn foster positive academic emotions, forming a virtuous cycle that strengthens students' autonomy in the learning process. This study extends Pintrich's SRL model by empirically validating a recursive loop between SRL and academic emotions. Furthermore, it highlights a "dual empowerment" path of emotional regulation and metacognitive monitoring through optimized TSR, offering vital policy insights for fostering high-order autonomous learning in the AI era.