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

Year 2014, Volume: 1 , 68 - 72, 31.05.2014

Sonja Isljamovıc , Milija Suknovıc

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. 

Keywords

Educational data mining, student success, artificial neural network, university education

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