Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/245481 
Year of Publication: 
2021
Series/Report no.: 
CESifo Working Paper No. 9300
Publisher: 
Center for Economic Studies and ifo Institute (CESifo), Munich
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.
Subjects: 
neural networks
student dropout
replication study
Document Type: 
Working Paper
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