Predicting Academic Self-Efficacy Based on Self-Directed Learning and Future Time Perspective.
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 A...
| Published in: | Psychological Reports Vol. 128; no. 4; pp. 2885 - 2906 |
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| Main Authors: | , , |
| Format: | Article |
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Sage Publications Inc.
Aug2025
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=185812011&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 185812011 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00332941 PSR jtl: Psychological Reports issn: 00332941 maglogo: Y pubinfo: dt: Aug2025 vid: 128 iid: 4 pid: 344 pub: Sage Publications Inc. artinfo: ui: 185812011 10.1177/00332941231191721 ppf: 2885 ppct: 21 formats: tig: atl: Predicting Academic Self-Efficacy Based on Self-Directed Learning and Future Time Perspective. aug: au: Karataş, Kasım Arpaci, Ibrahim Süer, Sedef affil: Department of Educational Sciences, 166263 Karamanoglu Mehmetbey University, Karaman, Turkey Department of Software Engineering, 450200 Bandirma Onyedi Eylul University, Balıkesir, Turkey Department of Educational Sciences, 37507 Dicle University, Diyarbakir, Turkey su: Time perspective Autodidacticism Machine learning Self-managed learning (Personnel management) Student teachers sug: subj: Time perspective Autodidacticism Machine learning Self-managed learning (Personnel management) Student teachers keyword: Academic self-efficacy future time perspective machine learning self-directed learning Academic self-efficacy future time perspective machine learning self-directed learning ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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