Predicting students academic performance using artificial neural network: a case study of an engineering course

Abstract

"The observed poor quality of graduates of some Nigerian Universities in recent times has been partly traced to inadequacies of the National University Admission Examination System. In this study an Artificial Neural Network (ANN) model for predicting the likely performance of a candidate being considered for admission into the university was developed and tested. Various factors that may likely influence the performance of a student were identified. Such factors as ordinary level subjects' scores and subjects' combination, matriculation examination scores, age on admission, parental background, types and location of secondary school attended and gender, among others, were then used as input variables for the ANN model. A model based on the Multilayer Perception Topology was developed and trained using data spanning five generations of graduates from an Engineering Department of University of Ibadan, Nigeria's first University. Test data evaluation shows that the ANN model is able to correctly predict the performance of more than 70% of prospective students.

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