Predicting Student Dropout: A Replication Study Based on Neural Networks

Abstract

Using neural networks, the present study replicates previous results on the prediction of student dropout obtained with decision trees and logistic regressions. For this purpose, multilayer perceptrons are trained on the same data as in the initial study. It is shown that neural networks lead to a significant improvement in the prediction of students at risk. Already after the first semester, potential dropouts can be identified with a probability of 95 percent.