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

Autor/inn/en	Karatas, Kasim; Arpaci, Ibrahim; Yildirim, Yusuf
Titel	Predicting the Culturally Responsive Teacher Roles with Cultural Intelligence and Self-Efficacy Using Machine Learning Classification Algorithms
Quelle	In: Education and Urban Society, 55 (2023) 6, S.674-697 (24 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Karatas, Kasim)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1245
DOI	10.1177/00131245221087999
Schlagwörter	Prediction; Culturally Relevant Education; Teacher Role; Cultural Awareness; Self Efficacy; Artificial Intelligence; Classification; Algorithms + Suchen Sie Ihr Suchwort? Vorhersage; Lehrerrolle; Cultural identity; Kulturelle Identität; Self-efficacy; Selbstwirksamkeit; Künstliche Intelligenz; Classification system; Klassifikation; Klassifikationssystem; Algorithm; Algorithmus
Abstract	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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01