Abstract
The aim of this paper is to predict, on a purely algorithmic basis, students who are at risk of dropping out of university. Data used in this study originated from the University of Bari Aldo Moro, during 2013–16, and were provided by the Osservatorio Studenti-Didattica of Miur-Cineca. Data analysis is based solely on the information set available, for each student, inside the university information system. Predictions of individual dropouts have been carried out by means of suitable Machine Learning techniques, known as supervised classification algorithms.
This research work research has been carried out as part of the activities of the research program no. 13/08 of the Ionic Department of the University of Bari Aldo Moro, entitled: “Analysis of Business Intelligence aimed at intercepting abandonment by university students enrolled in the Ionic Department”, included in the program agreement with the Municipality of Taranto for the 2011/2013 period, to contribute to the consolidation of the Ionic University Facilities. We would like to thank the Rector of the University of Bari for authorizing the consultation, in an anonymous form and for scientific research purposes, of the data relating to the UniBA student population of the MIUR-Cineca Student-Didactic Observatory. Finally, we wish to thank the dott. Massimo Iaquinta, responsible of the U.O. Statistiche di Ateneo, Settore Servizi Istituzionali, for the support offered to the retrieval of data. The authors collaborated in equal parts to the writing of the present essay.
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Serra, A., Perchinunno, P., Bilancia, M. (2018). Predicting Student Dropouts in Higher Education Using Supervised Classification Algorithms. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10962. Springer, Cham. https://doi.org/10.1007/978-3-319-95168-3_2
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