Arbeitspapier
Predicting Student Dropout: A Replication Study Based on Neural Networks
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.
- Language
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Englisch
- Bibliographic citation
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Series: CESifo Working Paper ; No. 9300
- Classification
-
Wirtschaft
- Subject
-
neural networks
student dropout
replication study
- Event
-
Geistige Schöpfung
- (who)
-
Buchhorn, Jascha
Wigger, Berthold U.
- Event
-
Veröffentlichung
- (who)
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Center for Economic Studies and ifo Institute (CESifo)
- (where)
-
Munich
- (when)
-
2021
- Handle
- Last update
-
10.03.2025, 11:44 AM CET
Data provider
This object is provided by:
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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Buchhorn, Jascha
- Wigger, Berthold U.
- Center for Economic Studies and ifo Institute (CESifo)
Time of origin
- 2021
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