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Student Dropout Prediction

Francesca del Bonifro 1, 2 Maurizio Gabbrielli 1, 2 Giuseppe Lisanti 1 Stefano Zingaro 1, 2
2 FOCUS - Foundations of Component-based Ubiquitous Systems
CRISAM - Inria Sophia Antipolis - Méditerranée , DISI - Dipartimento di Informatica - Scienza e Ingegneria [Bologna]
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.
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Submitted on : Friday, October 30, 2020 - 4:28:45 PM
Last modification on : Tuesday, November 3, 2020 - 3:30:15 AM
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Francesca del Bonifro, Maurizio Gabbrielli, Giuseppe Lisanti, Stefano Zingaro. Student Dropout Prediction. Artificial Intelligence in Education, pp.129-140, 2020, ⟨10.1007/978-3-030-52237-7_11⟩. ⟨hal-02983978⟩



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