Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/185457
Authors: 
Berens, Johannes
Schneider, Kerstin
Görtz, Simon
Oster, Simon
Burghoff, Julian
Year of Publication: 
2018
Series/Report no.: 
CESifo Working Paper 7259
Abstract: 
To successfully reduce student attrition, it is imperative to understand what the underlying determinants of attrition are and which students are at risk of dropping out. We develop an early detection system (EDS) using administrative student data from a state and a private university to predict student success as a basis for a targeted intervention. The EDS uses regression analysis, neural networks, decision trees, and the AdaBoost algorithm to identify student characteristics which distinguish potential dropouts from graduates. Prediction accuracy at the end of the first semester is 79% for the state university and 85% for the private university of applied sciences. After the fourth semester, the accuracy improves to 90% for the state university and 95% for the private university of applied sciences.
Subjects: 
student attrition
machine learning
administrative student data
AdaBoost
JEL: 
I23
H42
C45
Document Type: 
Working Paper
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