Neural network model to predict electrical students' academic performance

Abstract

Students performance is very crucial to any educational institution particularly in the engineering field. This paper describes a neural network based model (NN model) for academic performance prediction of Electrical Engineering Degree students at the Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM), Malaysia. The study was conducted on student intakes from Matriculation entry level. The performance was measured based on their cumulative grade point average (CGPA) upon graduation. The students' results for fundamentals subjects at first semester are used as predictor variables (initial values) for predicting the expected (projected) final CGPA upon graduation using Artificial Neural Network (ANN). The outcomes of the study indicated that there appears to be a direct correlation between students' results for core subjects at semester one with the final overall academic performance irrespective of their gender. It can be ascertained that the analysis on strong students' abilities in engineering fundamentals contributed strongly in influencing the overall academic performance in Engineering. Based on the outcomes of this study, we believe that strategic interventions can be done during their study period to improve their final performance, which can be extracted from this prediction model.