Predicting student’s academic performance by applying data mining technique.

Abstract

The situation of students being forced to stop their studies is currently very popular at universities in Vietnam. This paper proposes a method for predicting students’ dropout based on the analysis of data from the university entrance scores, paper scores of subjects in the first three semesters and the current learning status of more than 555 students majoring in IT at Vinh University. Through these data, the Logistic Regression and Naïve Bayes data mining algorithms were applied to find a suitable model for predicting students’ dropout in the next courses. This study will help the university to give early warnings and supports to reduce the rate of students’ dropout in the next courses.