Modelling, prediction and classification of student academic performance using artificial neural networks

Abstract

The conventional statistical evaluations are limited in providing good predictions of the university educational quality. This paper presents an approach with both conventional statistical analysis and neural network modelling/prediction of students’ performance. Conventional statistical evaluations are used to identify the factors that likely affect the students’ performance. The neural network is modelled with 11 input variables, two layers of hidden neurons, and one output layer. Levenberg–Marquardt algorithm is employed as the backpropagation training rule. The performance of neural network model is evaluated through the error performance, regression, error histogram, confusion matrix and area under the receiver operating characteristics curve. Overall, the neural network model has achieved a good prediction accuracy of 84.8%, along with limitations.