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

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dc.authoridARPACI, Ibrahim/0000-0001-6513-4569
dc.contributor.authorKaratas, Kasim
dc.contributor.authorArpaci, Ibrahim
dc.contributor.authorSuer, Sedef
dc.date.accessioned2024-01-22T12:22:22Z
dc.date.available2024-01-22T12:22:22Z
dc.date.issued2023
dc.departmentKMÜen_US
dc.description.abstractThe 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.en_US
dc.identifier.doi10.1177/00332941231191721
dc.identifier.issn0033-2941
dc.identifier.issn1558-691X
dc.identifier.pmid37503547en_US
dc.identifier.pmid37503547
dc.identifier.scopus2-s2.0-85165953671
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1177/00332941231191721
dc.identifier.urihttps://hdl.handle.net/11492/7939
dc.identifier.wosWOS:001038720800001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Sceince
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSage Publications Incen_US
dc.relation.ispartofPsychological Reportsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzkmusnmz
dc.subjectAcademic self-efficacyen_US
dc.subjectfuture time perspectiveen_US
dc.subjectself-directed learningen_US
dc.subjectmachine learningen_US
dc.titlePredicting Academic Self-Efficacy Based on Self-Directed Learning and Future Time Perspectiveen_US
dc.typeArticle

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