Among the many open problems in the learning process, students dropout is one of the most complicated and negative ones, both for the student and the institutions, and being able to predict it could help to alleviate its social and economic costs. To address this problem we developed a tool that, by exploiting machine learning techniques, allows to predict the dropout of a first-year undergraduate student. The proposed tool allows to estimate the risk of quitting an academic course, and it can be used either during the application phase or during the first year, since it selectively accounts for personal data, academic records from secondary school and also first year course credits. Our experiments have been performed by considering real data of students from eleven schools of a major University.
Student dropout prediction / Del Bonifro F.; Gabbrielli M.; Lisanti G.; Zingaro S.P.. - ELETTRONICO. - 12163:(2020), pp. 129-140. (Intervento presentato al convegno 21st International Conference on Artificial Intelligence in Education, AIED 2020 tenutosi a mar nel 2020) [10.1007/978-3-030-52237-7_11].
Student dropout prediction
Del Bonifro F.Methodology
;Gabbrielli M.Writing – Review & Editing
;Lisanti G.Conceptualization
;Zingaro S. P.
Primo
Methodology
2020
Abstract
Among the many open problems in the learning process, students dropout is one of the most complicated and negative ones, both for the student and the institutions, and being able to predict it could help to alleviate its social and economic costs. To address this problem we developed a tool that, by exploiting machine learning techniques, allows to predict the dropout of a first-year undergraduate student. The proposed tool allows to estimate the risk of quitting an academic course, and it can be used either during the application phase or during the first year, since it selectively accounts for personal data, academic records from secondary school and also first year course credits. Our experiments have been performed by considering real data of students from eleven schools of a major University.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
