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

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

VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy from Verein für Socialpolitik / German Economic Association

Abstract: High rates of student attrition in tertiary education are a major concern for universities and public policy, as dropout is not only costly for the students but also wastes public funds. To successfully reduce student attrition, it is imperative to understand which students are at risk of dropping out and what are the underlying determinants of dropout. We develop an early detection system (EDS) that uses machine learning and classic regression techniques to predict student success in tertiary education as a basis for a targeted intervention. The method developed in this paper is highly standardized and can be easily implemented in every German institution of higher education, as it uses student performance and demographic data collected, stored, and maintained by legal mandate at all German universities and therefore self-adjusts to the university where it is employed. The EDS uses regression analysis and machine learning methods, such as neural networks, decision trees and the AdaBoost algorithm to identify student characteristics which distinguish potential dropouts from graduates. The EDS we present is tested and applied on a medium-sized state university with 23,000 students and a medium-sized private university of applied sciences with 6,700 students. Our results indicate a prediction accuracy at the end of the 1st semester of 79% for the state university and 85% for the private university of applied sciences. Furthermore, accuracy of the EDS increases with each completed semester as new performance data becomes available. 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; early detection; administrative data; higher education; machine learning; AdaBoost (search for similar items in EconPapers)
JEL-codes: C45 H52 I23 (search for similar items in EconPapers)
Date: 2018
New Economics Papers: this item is included in nep-big and nep-cmp
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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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Persistent link: https://EconPapers.repec.org/RePEc:zbw:vfsc18:181544

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