Predicting teachers' sense of efficacy: A multimodal analysis integrating SEM, deep learning, and ANN

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dc.authoridArpaci, Ibrahim/0000-0001-6513-4569
dc.contributor.authorArpaci, Ibrahim
dc.contributor.authorKaratas, Kasim
dc.contributor.authorGun, Feyza
dc.contributor.authorSuer, Sedef
dc.date.accessioned2025-01-12T17:19:36Z
dc.date.available2025-01-12T17:19:36Z
dc.date.issued2024
dc.departmentKaramanoğlu Mehmetbey Üniversitesi
dc.description.abstractThis study aims to investigate the predictive role of cultural intelligence, motivation to teach, and culturally responsive classroom management self-efficacy (CRCMSE) in teachers' sense of efficacy. The study utilized a combination of structural equation modeling (SEM), deep learning, and artificial neural network (ANN) to analyze data collected from 1061 preservice teachers. The SEM analysis indicated that cultural intelligence, motivation to teach, and CRCMSE significantly predicted the sense of efficacy of the teacher candidates, accounting for 59% of the variance. Additionally, the ANN model accurately predicted the teachers' sense of efficacy with 75.71% and 75.17% accuracy for training and testing, respectively. The sensitivity analysis revealed that CRCMSE played the most crucial role in predicting the preservice teachers' sense of efficacy. The deep learning model also predicted the sense of efficacy with an overall accuracy of 74.18%. The utilization of a multimodal analysis approach facilitated the identification of both linear and nonlinear relationships between the constructs. This study investigated the predictive role of cultural intelligence, motivation to teach, and CRCMSE in teachers' sense of efficacy. The study employed a combination of SEM, deep learning, and ANN to analyze the data. The multimodal analysis facilitated the identification of both linear and nonlinear relationships.
dc.identifier.doi10.1002/pits.23222
dc.identifier.endpage3389
dc.identifier.issn0033-3085
dc.identifier.issn1520-6807
dc.identifier.issue8
dc.identifier.scopus2-s2.0-85192218518
dc.identifier.scopusqualityQ2
dc.identifier.startpage3373
dc.identifier.urihttps://doi.org/10.1002/pits.23222
dc.identifier.urihttps://hdl.handle.net/11492/10101
dc.identifier.volume61
dc.identifier.wosWOS:001217228600001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Sceince
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofPsychology in The Schools
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20250111
dc.subjectclassroom management
dc.subjectcultural intelligence
dc.subjectmotivation to teach
dc.subjectself-efficacy
dc.titlePredicting teachers' sense of efficacy: A multimodal analysis integrating SEM, deep learning, and ANN
dc.typeArticle

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