Predicting Student Dropout: A Replication Study Based on Neural Networks

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