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.
- Sprache
-
Englisch
- Erschienen in
-
Series: CESifo Working Paper ; No. 9300
- Klassifikation
-
Wirtschaft
- Thema
-
neural networks
student dropout
replication study
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Buchhorn, Jascha
Wigger, Berthold U.
- Ereignis
-
Veröffentlichung
- (wer)
-
Center for Economic Studies and ifo Institute (CESifo)
- (wo)
-
Munich
- (wann)
-
2021
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Buchhorn, Jascha
- Wigger, Berthold U.
- Center for Economic Studies and ifo Institute (CESifo)
Entstanden
- 2021
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