PREDICTING STUDENTS’ ACADEMIC PERFORMANCE USING ARTIFICIAL NEURAL NETWORK : A CASE STUDY FROM FACULTY OF ORGANIZATIONAL SCIENCES

Abstract

University students’ retention and performance in higher education are important issues for educational institutions, educators, and students. Educational data mining is focused on developing models and methods for exploring data collected from educational environments in order to better understand and improve educational process. Analyzing and determining patterns among indicators of academic success (study grade point average) and their correlation to students’ personal, high school, admission data can present be a good foundation in process to adapt and improve the curriculum of higher education institutions, according to the students’ characteristics. In this paper we use different artificial neural network algorithms in order to find the best suited model for prediction of students' success at the end of their studies. Additionally, we identified which factors had the crucial influence on overall students’ success. Data were collected from the graduated students of Faculty of Organizational Sciences, University of Belgrade.