IJOEEC (International Journal of Eurasian Education and Culture) (ISSN: 2602-4047) Vol / Cilt: 5 Issue / Sayı: 11 Year / Yıl: 2020 2103 Özmutlu, M. & Akgün, E. (2020). Modelling and Estimation of Academic Success of Prospective Teachers with Distance Learning Courses, International Journal of Eurasian Education and Culture, Issue: 11, pp. (2103-2131). Article Type: Research Article MODELLING AND ESTIMATION OF ACADEMIC SUCCESS OF PROSPECTIVE TEACHERS WITH DISTANCE LEARNING COURSES Meltem ÖZMUTLU Specialist , Bahçeşehir University, Turkey, meltem.ozmutlu@es.bau.edu.tr ORCID: 0000-0002-3812-4610 Ergün AKGÜN Assist Prof., Bahçeşehir University, Turkey, ergun.akgun@de.bau.edu.tr ORCID: 0000-0002-7271-6900 Received:09.02.2020 Accepted: 15.11.2020 Published: 15.12.2020 ABSTRACT Distance education is a learning method that can be used at all levels of education. The main reason for this is that has facilities for both teachers and students. It is in an active position in the grade averages of the students, as in the courses taken face to face in distance education courses. The extent to which the courses taken by the distance education method play an active role in the academic success of the students is an important issue as well as controversial issue. Considering this situation, the academic achievements of the students of the Faculty of Educational Sciences were wondered within the scope of this study, and the process of estimating their graduation grade point averages was made. The data of the of the graduates of Computer Education and Instructional Technologies (CEIT), English Language Teaching (ELT), Preschool Education (ECE) and Guidance and Psychological Counseling within the Faculty of Educational Sciences were collected. From the data collected, students’ gender, department, university entrance score and letter grades of four courses they took jointly with distance education were used as variables. Interval calculations are made by taking students’ GPAs into account and two categorical variables, “succeeded” and “failed”, are derived. After sweeping and categorical differentiation procedures, the classification method was applied to the data set. The best performance was acquired from “Gaussion Naïve Bayes (Model 3.1)” with 82.8% within “Naïve Bayes Classifiers” among the performance segments with 15 techniques. These acquired performance values illustrate high levels of accurate estimation with the data from the study. Keywords: Classifier analysis, acedemic success estimation, distance learnin, naïve bayes.