Predicting the Culturally Responsive Teacher Roles With Cultural Intelligence and Self-Efficacy Using Machine Learning Classification Algorithms

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dc.authorscopusid6505991402
dc.authorscopusid35728204400
dc.authorscopusid58466097600
dc.contributor.authorKarataş K.
dc.contributor.authorArpaci I.
dc.contributor.authorYildirim Y.
dc.date.accessioned2024-01-22T12:22:44Z
dc.date.available2024-01-22T12:22:44Z
dc.date.issued2023
dc.departmentKMÜen_US
dc.description.abstractThis study aimed to predict the culturally responsive teacher roles based on cultural intelligence and self-efficacy using machine learning classification algorithms. The research group consists of 415 teachers from different branches. The Bayes classifier (NaiveBayes), logistic-regression (SMO), lazy-classifier (KStar), meta-classifier (LogitBoost), rule-learner (JRip), and decision-tree (J48) were employed in the assessment of the predictive model. The results indicated that JRip rule-learner had a better performance than other classifiers in predicting the culturally responsive teachers based on six attributes used in the study. The JRip rule-learner classified the culturally responsive teachers as low, medium, or high with an accuracy of 99.76% (CCI: 414/415) [Kappa statistic: 0.996, Mean Absolute Error (MAE): 0.003, Root Mean Square Error (RMSE): 0.043, Relative Absolute Error (RAE): 0.663, Relative Squared Error (RRSE): 9.244]. The results indicated that all classifiers had an acceptable performance but JRip rule-learner had a better performance than the other classifiers in predicting the culturally responsive teachers. © The Author(s) 2022.en_US
dc.identifier.doi10.1177/00131245221087999
dc.identifier.endpage697en_US
dc.identifier.issn00131245
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85129265416
dc.identifier.scopusqualityQ2
dc.identifier.startpage674en_US
dc.identifier.urihttps://doi.org/10.1177/00131245221087999
dc.identifier.urihttps://hdl.handle.net/11492/8117
dc.identifier.volume55en_US
dc.identifier.wosWOS:000783886900001
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Sceince
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSAGE Publications Inc.en_US
dc.relation.ispartofEducation and Urban Societyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzkmusnmz
dc.subjectartificial intelligenceen_US
dc.subjectcultural intelligenceen_US
dc.subjectculturally responsive teachersen_US
dc.subjectmachine learningen_US
dc.subjectself-efficacyen_US
dc.titlePredicting the Culturally Responsive Teacher Roles With Cultural Intelligence and Self-Efficacy Using Machine Learning Classification Algorithmsen_US
dc.typeArticle

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