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

Karataş K.Arpaci I.Yildirim Y.2024-01-222024-01-22202300131245https://doi.org/10.1177/00131245221087999https://hdl.handle.net/11492/8117This 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.enartificial intelligencecultural intelligenceculturally responsive teachersmachine learningself-efficacyPredicting the Culturally Responsive Teacher Roles With Cultural Intelligence and Self-Efficacy Using Machine Learning Classification AlgorithmsArticle556674697info:eu-repo/semantics/closedAccess2-s2.0-85129265416WOS:00078388690000110.1177/00131245221087999Q2Q4