A Statistical Analysis of Factors Affecting Higher Education Dropouts

• Published in: Social indicators research Vol. 156; no. 2-3; pp. 341 - 362
• Main Authors: Perchinunno, Paola, Bilancia, Massimo, Vitale, Domenico
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.08.2021; Springer Nature B.V
• Subjects: Classification; Colleges & universities; Data mining; Data science; Dropout Characteristics; Dropout Rate; Dropping out; Education; Educational systems; Higher education; Human Geography; Individual differences; Microeconomics; Original Research; Public Health; Quality of Life Research; School dropout programs; School dropouts; Social Sciences; Sociology; Statistical analysis; Italy; Data science; University careers; Dropout rates; Machine learning
• ISSN: 0303-8300; 1573-0921
• DOI: 10.1007/s11205-019-02249-y

One of the most significant indicators for assessing the quality of university careers is the dropout rate between the first and second year. Both literature on the subjects and the results that emerged from numerous specific investigations into the dropouts of the university system, showed the crucial importance of this junction between the first and the second year. Reasons for dropping out can be quite varied, ranging from incorrect and/or insufficient prospective student orientation, the willingness or need to find a job as quickly as possible, to a lack of awareness of not being able to cope with a particular course of study rather than another. In this paper we focus specifically on the problem of dropouts in Italy, addressing it from a dual point of view. At an aggregate level, the analysis deals with dropout rates in Italy between the first and second year, in order to identify the main trends and dynamics at the national level. Subsequently, we analyze individual-level data from the University of Bari Aldo Moro, aiming to identify the most important contributing factors. This individual-level approach has emerged over recent years, and is generally known as ‘Educational Data Mining’, focused on the development of ad hoc methods that can be used to discover regularities and new information within databases from contexts related to education. Using supervised classification methods, we are able to identify retrospectively the profile of students who are most likely to dropout.