Predicting Pre-Service Classroom Teachers' Civil Servant Recruitment Examination's Educational Sciences Test Scores Using Artificial Neural Networks

Autor/in	Demir, Metin
Titel	Predicting Pre-Service Classroom Teachers' Civil Servant Recruitment Examination's Educational Sciences Test Scores Using Artificial Neural Networks
Quelle	In: Educational Sciences: Theory and Practice, 15 (2015) 5, S.1169-1177 (9 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1303-0485
Schlagwörter	Preservice Teachers; Scores; Artificial Intelligence; Grade Point Average; Grades (Scholastic); Education Courses; Correlation; Teacher Competency Testing; Prediction; Foreign Countries; Turkey + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Notenspiegel; Fortbildungskurs; Korrelation; Vorhersage; Ausland; Türkei
Abstract	This study predicts the number of correct answers given by pre-service classroom teachers in Civil Servant Recruitment Examination's (CSRE) educational sciences test based on their high school grade point averages, university entrance scores, and grades (mid-term and final exams) from their undergraduate educational courses. This study was therefore designed by using a general survey model. The participants were 219 graduates of the departments of classroom teacher education from the education faculties of two different state universities. Artificial neural networks (ANNs) were used to predict the numbers of correct answers from the CSRE educational sciences test. As a result of different trials, the correlation between the predicted and actual numbers of correct answers was examined, and 10 ANN models were included in the study. Statistically, significant positive correlations were found between the numbers of correct answers predicted by the ANN and the students' actual correct answers in the CSRE. The highest loading was r = 0.63 (p < 0.01), and the lowest was r = 0.43 (p < 0.05). (As Provided).
Anmerkungen	Educational Consultancy, Ltd (EDAM). Kisikli Mh. Alemdag Cd. Yan Yol Sk., SBK Is Merkezi No:5 Kat:1, Uskudar-Istanbul, 34692 Turkey. Tel: +90-216-481-30-23; Fax: +90-216-481-31-36; e-mail: editor@edam.com.tr; Web site: http://www.estp.com.tr/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01