Understanding the Determinants of Student Burnout in Blended Learning Environments

The negative phenomenon of student burnout, which may result in a considerably poor experience and low achievement, has drawn much attention in education and psychology. However, knowledge is scarce regarding the determinations of student burnout in blended learning environments. Accordingly, this study developed and validated a predictive model that explains the role of social support and self-regulated learning in predicting student burnout in blended learning environments. A comparative method using the Partial Least Squares Structural Equation Modelling (PLS-SEM) and machine learning algorithms was employed to validate the proposed model based on data gathered from 303 students. The PLS results demonstrated the negative role of perceived social support and self-regulated learning in alleviating burnout and the fully mediating role of self-regulated learning in the relationship between offline received social support and student burnout. Further, the applied algorithms predicted student burnout with an accuracy of more than 70% in most cases. The evidence from these findings provides the instructors and developers of courses in higher educational institutions with best practices to prevent and alleviate student burnout in blended learning.