Using Neural Network and Logistic Regression Analysis to Predict Prospective Mathematics Teachers' Academic Success upon Entering Graduate Education

Abstract

The ability to predict the success of students when they enter a graduate program is critical for educational institutions because it allows them to develop strategic programs that will help improve students' performances during their stay at an institution. In this study, we present the results of an experimental comparison study of Logistic Regression Analysis (LRA) and Artificial Neural Network (ANN) for predicting prospective mathematics teachers' academic success when they enter graduate education. A sample of 372 student profiles was used to train and test our model. The strength of the model can be measured through Logistic Regression Analysis (LRA). The average correct success rate of students for ANN was higher than LRA. The successful prediction rate of the back-propagation neural network (BPNN, or a common type of ANN was 93.02%, while the success of prediction of LRA was 90.75%.