Utilizzo delle reti neurali convolutive per la predizione dell’abbandono universitario. Una ricerca quantitativa sui corsi di laurea del Dipartimento di Scienze della Formazione dell’Università «Roma Tre»

The level of dropout in the scenery of European education is one of the major issues to be faced in a near future. In 2017, an average of 10.6% of young people (aged 18-24) in the EU-28 were early leavers from education and training according to Eurostat’s statistics. The main aim of this research is to predict, as early as possible, which student will dropout in a Higher Education context. The administrative data of approximately 6000 students enrolled from 2009 in the Education Department at Rome Tre University had been used to train the Convolutional Neural Networks (CNN). Then, the trained network provides a probabilistic model that indicates, for each student, the probability of dropping out. We used several types of CNNs, and their variants, in order to build the most accurate model for the dropout prediction. The accuracy of the obtained models ranged from 67.1% for the students at the beginning of the first year up to 88.7% for the students at the end of the second year of their academic career.