Loading...
Predicting dropout from higher education with multinomial finite mixture models: empirical evidence from Italy
Forti, Marco
Bilancia, Massimo Cafarelli, Barbara del Gobbo, Emiliano Nigri, Andrea
Bilancia, Massimo
Cafarelli, Barbara
del Gobbo, Emiliano
Nigri, Andrea
Citations
Altmetric:
Abstract
The dropout phenomenon in higher education refers to students leaving their programs before completing their degrees. Despite recent improvements in graduation rates, Italy remains among the lowest in OECD countries, with a graduation rate of 45%, well below the OECD average of 69%. High dropout rates, particularly during the first two years of study, are influenced by both institutional factors, such as the decentralization of teaching and the expansion of degree programs, and student-specific factors, including academic performance and geographic distance from universities. Although reforms like the Bologna Process’s 3+2 model have aimed to improve retention, challenges persist. This paper employs multinomial logistic regression analysis, combined with finite mixture models, to explore the complex factors driving student dropout, offering insights for early intervention strategies and institutional reforms aimed at mitigating this issue.
Description
Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
Research Projects
Organizational Units
Journal Issue
Keywords
Conditions mixture models, Finite mixture models, Multinomial logistic regression, Expectation maximization (EM) algorithm, University dropout, Italy
Citation
Forti, Marco, Massimo Bilancia, Barbara Cafarelli, Emiliano del Gobbo, and Andrea Nigri. “Predicting Dropout from Higher Education with Multinomial Finite Mixture Models: Empirical Evidence from Italy.” Quality & Quantity, May 5, 1–15. 2025.
ISBN
URL
License
Attribution 4.0 International