Prediction of Pre-Service Teachers' Academic Self-Efficacy through Machine Learning Approaches

Autor/in	Yildiz, Hatice
Titel	Prediction of Pre-Service Teachers' Academic Self-Efficacy through Machine Learning Approaches
Quelle	In: African Educational Research Journal, 11 (2023) 1, S.32-44 (13 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2354-2160
Schlagwörter	Preservice Teachers; Academic Achievement; Self Efficacy; Artificial Intelligence; Prediction; Beliefs; Learner Engagement; Burnout; Foreign Countries; Measures (Individuals); Intention; Student Attitudes; Turkey; Maslach Burnout Inventory + Suchen Sie Ihr Suchwort? Schulleistung; Self-efficacy; Selbstwirksamkeit; Künstliche Intelligenz; Vorhersage; Belief; Glaube; Burn out (Psychology); Burnout-syndrom; Burnout-Syndrom; Ausland; Messdaten; Schülerverhalten; Türkei
Abstract	The aim of this study was to investigate the extent to which pre-service teachers' belief in academic engagement, student burnout, and proactive strategies predicts academic self-efficacy through machine learning approach. The study group consisted of 446 pre-service teachers at Sivas Cumhuriyet University, Faculty of Education. The Academic Self-Efficacy Scale, Academic Involvement Scale, Maslach Burnout Inventory-Student Scale, and Proactive Strategy Scale were used for data collection. In data analysis, two different machine learning approaches were used; linear regression and artificial neural networks (ANNs). As a result of the regression analysis, a positive, and significant relationship was found between the academic self-efficacy of pre-service teachers, their academic engagement, and proactive strategy. Also, there was a negative and significant relationship between pre-service teachers' academic self-efficacy and academic burnout. Considering the results of the regression analysis, academic engagement, academic burnout, and proactive strategy together explained 38% of academic self-efficacy. When the ANNs results were examined, it was seen that these three variables explained 77% of academic self-efficacy. Therefore, it was understood that ANNs perform better than multiple regression in predicting academic self-efficacy. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01