The extent to which distance learning courses are impactful in the academic success of students is a controversial issue. This study investigates the academic success of students in the Faculty of Educational Sciences by estimating their graduation GPAs. Data are collected from different departments under the Faculty of Educational Sciences – including Computer and Instructional Technologies Education (CEIT), English Teaching (ELT), Preschool Teaching (ECE) and Psychological Counseling and Guidance (PDR) – and include the variables gender, department, university entry score, letter grades of the four distance learning courses, and course lecturers. 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.
academic success estimation, distance learning, classifier analysis, teacher candidates