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Predicting dropout from higher education: Evidence from Italy

Marco Delogu (), Raffaele Lagravinese, Dimitri Paolini and Giuliano Resce ()
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Dimitri Paolini: CRENoS & University of Bari IT, UCL BE

DEM Discussion Paper Series from Department of Economics at the University of Luxembourg

Abstract: We investigate whether machine learning (ML) methods are valuable tools for predicting students’ likelihood of leaving pursuit of higher education. This paper takes advantage of administrative data covering the entire population of Italian students enrolled in bachelor’s degree courses for the academic year 2013-2014. Our numerical findings suggest that ML algorithms, particularly random forest and gradient boosting machines, are potent predictors pointing to their use as early warning indicators. In addition, feature importance analysis highlights the role of the number of European Credit Transfer System (ECTS) obtained during the first year for predicting the likelihood of dropout. Accordingly, our analysis suggests that policies that aim to boost the number of ECTS gained during the early academic career may be effective in reducing drop-out rates at Italian universities.

Keywords: "Early warning system; Machine learning; Dropout; Italy" (search for similar items in EconPapers)
JEL-codes: C53 C55 I20 (search for similar items in EconPapers)
Date: 2020
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