2022/04/13 by Kasım Karataş, Ibrahim Arpaci, İbrahim Arpacı +2
Social Sciences · #Global Education and Multiculturalism #International Student and Expatriate Challenges #Teacher Professional Development and Motivation
paper · doi:10.1177/00131245221087999
crossref issued 2022/04/13 · crossref published 2022/04/13 · crossref published-online 2022/04/13 · openalex publication_date 2022/04/13 · crossref created 2022/04/13 · crossref published-print 2023/07/01 · openalex created_date 2025/10/10 · crossref deposited 2026/04/28 · crossref indexed 2026/07/31 · openalex updated_date 2026/07/31
This 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.