Predicting dropout from higher education: Evidence from Italy

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.