Comparative study of artificial neural network and statistical models for predicting student grade point averages

Abstract

This paper compares linear regression; stepwise polynomial regression; and fully-connected, single middle layer artificial neural network models with an index used by an admissions committee for predicting student GPAs in professional school. It also provides methods for implementing, interpreting, and evaluating artificial neural networks, including an optimization of model structure for simple neural networks. While the neural network identifies additional model structure over the regression models, none of the empirical methods was statistically significantly better than the practitioners' index.