Karataş Kasım, Arpaci Ibrahim, Süer Sedef
Department of Educational Sciences, Karamanoglu Mehmetbey University, Karaman, Turkey.
Department of Software Engineering, Bandirma Onyedi Eylul University, Balıkesir, Turkey.
Psychol Rep. 2025 Aug;128(4):2885-2905. doi: 10.1177/00332941231191721. Epub 2023 Jul 28.
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.
本研究的目的是调查职前教师的学术自我效能感、自主学习与未来时间观之间的关系。采用了一种双阶段分析方法，同时运用传统的结构方程模型（SEM）和机器学习分类算法来检验所提出的假设。该研究纳入了879名职前教师作为样本。结构方程模型分析表明，自主学习对学术自我效能感有显著的正向影响。此外，发现未来时间观能显著预测学术自我效能感。组合的内生构念解释了相当一部分的方差变异。此外，该研究运用机器学习算法中的LMT和多类分类器来预测学术自我效能感。总之，本研究结果表明，自主学习和未来时间观是预测职前教师学术自我效能感的重要因素。该研究同时运用了传统的结构方程模型和机器学习算法，以全面分析这些变量之间的关系。
