Karatas, KasimArpaci, IbrahimSuer, Sedef2024-04-242024-04-2420230033-29411558-691Xhttps://doi.org/10.1177/00332941231191721https://hdl.handle.net/11468/18048The 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.eninfo:eu-repo/semantics/closedAccessAcademic Self-EfficacyFuture Time PerspectiveSelf-Directed LearningMachine LearningArticleWOS:0010387208000012-s2.0-851659536713750354710.1177/00332941231191721Q1N/A