Comparative Study of Prediction Models for Final GPA Score: A Case Study of Rajabhat Rajanagarindra University

Abstract

Recently, the analysis of educational data has become important to all universities. Rajabhat Rajanagarindra University, Thailand, wanted to study and analyze the students' performance based on their personal background. Thus, this research aimed to compare prediction models for the level of the final grade point average (GPA) score of graduated students using the data from the Faculty of Education during the 2010 to 2012 academic years. Two decision tree (C4.5 and ID3) algorithms, plus Naïve Bayes and K-nearest neighbor data mining techniques were adopted to analyze the data according to the CRISP-DM process. Factors that were proposed to influence the graduation GPA include the student's gender, scholarship awarded, previous educational background, admission type, talent and province of high school. The analysis revealed that the Naïve Bayes algorithm gave the best overall accuracy of 43.18%. This could help predict the graduation GPA score of students in the future and support teachers to make educational advice for their students and to develop the student quality in the future.