Not the magic algorithm: modelling and early-predicting students dropout through machine learning and multilevel approach
Sunday 7th June 2020
Cannistrà,M.; Masci, C.; Ieva, F.; Agasisti, T.; Paganoni, A.M.
According to OECD, almost 30 per cent of students leave tertiary education programs without obtaining a degree. This number measures a dead loss of human capital and a waste of public and private resources. This paper contributes to the existing knowledge about students dropout by combining a theoretical-based model with a data-driven approach to detect students who are more likely to leave university in the first year. We propose the use of multilevel statistical models and machine learning methods, applied to administrative data from a leading Italian university. The findings are encouraging, as the methodology is able to predict at-risk students very precisely. We provide evidence of the essential role of data relative to early performance (i.e. grades obtained in the first semester). Moreover, the selection of major strongly influences the probability of dropping out.