Path modeling of factors that predict self-regulated learning

Abstract

Students can encounter significant challenges when transitioning from high school to university. Students must possess the necessary skills to adjust to the self-directed learning atmosphere of the university, however frequently lack the ability to take responsibility for their own learning. This study employs path-modeling techniques to investigate and analyze the multifaceted relationships between various factors, that can predict self-regulated learning as they impact learners' academic achievements in higher education settings, as informed by an extensive review of existing literature. The population for this study were university undergraduates using a researcher-designed questionnaire for data collection. The data collected was modelled reflectively using partial least squares structural equation modelling (PLS-SEM). Results show that the measurement model assessment showed strong reliability and convergent validity of the latent constructs. However, only technology significantly predicted self-regulated learning as contributing to students' academic success in higher education. The findings from this study contribute significantly to understanding the nuanced pathways through which various learning indicators interact to predict students' self-regulation as influencing students' academic performance in the higher education space. Insights gained from the analysis offer valuable implications for relevant stakeholders aimed at fostering properly tailored conduct that enhances students' academic success in higher education.