Modeling students’ academic performance using artificial neural network

Abstract

Artificial Neural Network has been discovered as a better alternative to traditional models and that is why a model based on the Multilayer Perceptron algorithm was developed in this study. The appropriate number of hidden neurons that best modeled the academic performance of students was determined by the developed Network algorithm. Test data evaluation showed that Network Architecture 17-80 -1 was chosen among the numerous developed network architectures because of its model performances. The chosen network architecture gave the minimum value of Mean Square Error (MSE = 0.0718), minimum value of Network Information Criteria (NIC = 0.0743), maximum value of R- Square (R2=0.8975) and maximum value of Adjusted Network Information Criteria (ANIC= 0.8931). It was equally observed that there were patterns in the movement of hidden neurons against the model evaluation criteria. As the number of the hidden neurons appreciates the value of both MSE and NIC decreases down the plot, while that of ^-Square and ^MCvalues appreciate down the plot. The network was able to model the research problem with acceptable values judging from the model checking criteria considered in this work. Also the order of contribution of the predictor variables to the model was determined.