Early Detection of Students at Risk – Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods

Johannes Berens, Kerstin Schneider (), Simon Görtz, Simon Oster and Julian Burghoff

No 7259, CESifo Working Paper Series from CESifo

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.

Keywords: student attrition; machine learning; administrative student data; AdaBoost (search for similar items in EconPapers)
JEL-codes: C45 H42 I23 (search for similar items in EconPapers)
Date: 2018
New Economics Papers: this item is included in nep-big and nep-cmp
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Related works:
Working Paper: Early Detection of Students at Risk - Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods (2018)
Working Paper: Early Detection of Students at Risk - Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods (2018)
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