Unlocking Deep Learning: How Self-Regulated Learning Shapes Chinese Students’ Online Course Experiences

The quality of online learning is generally acknowledged to be a crucial element in students’ academic achievement. Using a quantitative, cross-sectional paradigm, this study sought to analyze the link between university students perceived online course experiences and deep learning, with an emphasis on the mediating function of self-regulation through the adapted instruments of the Online Course Experience Questionnaire, Self-regulated Learning Questionnaire , and Deep Learning Scale . Employing a convenience sampling strategy, we collected data from an online questionnaire survey of 1,098 Chinese undergraduate students in November 2022. Structural equation modeling analysis indicated that students’ perceptions of good teaching in online course experience questionnaire (OCEQ) significantly predicted deep learning during online. Additionally, our research showed that the association between students’ opinions of effective instruction and deep learning strategies was mediated by self-regulation. Deep learning was found to be influenced indirectly by how clearly goals and standards were perceived, with self-regulated learning as the mediator. However, appropriate workload and assessment in OCEQ were neither directly nor indirectly related with deep learning. This study extends the understanding of student course experiences and provides actionable insights for educators to design strategies that foster deep learning in digital environments, ultimately enhancing the quality of online education systems.