Predicting Academic Self-Efficacy Based on Self-Directed Learning and Future Time Perspective

Abstract

The purpose of this study was to investigate the relationship between teacher candidates’ academic self-efficacy, self-directed learning, and future time perspective. A dual-stage analytical approach, utilizing both traditional structural equation modeling (SEM) and Machine Learning Classification Algorithms, was employed to test the proposed hypotheses. The study included a sample of 879 teacher candidates. The SEM analysis revealed that self-directed learning had a significant positive effect on academic self-efficacy. Furthermore, future time perspective was found to significantly predict academic self-efficacy. The combined endogenous constructs accounted for a substantial portion of the explained variance. Additionally, the study employed LMT and Multiclass classifiers from Machine Learning algorithms to predict academic self-efficacy. In summary, the findings of this study suggest that self-directed learning and future time perspective are significant factors in predicting teacher candidates’ academic self-efficacy. The study utilized both traditional SEM and Machine Learning algorithms to provide a comprehensive analysis of the relationships between these variables.