Use of convolutive neural networks to predict university dropout. A quantitative research on the degree courses of the Department of Education of “Roma Tre” University

Abstract

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.