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Predicting university enrollment choices in Italy: a machine learning analysis of high school background and gender differences.

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Title: Predicting university enrollment choices in Italy: a machine learning analysis of high school background and gender differences.
Authors: Priulla, Andrea1 (AUTHOR) andrea.priulla@unikore.it, Albano, Alessandro2 (AUTHOR) alessandro.albano@unipa.it, D'Angelo, Nicoletta2 (AUTHOR) nicoletta.dangelo@unipa.it, Attanasio, Massimo2 (AUTHOR) massimo.attanasio@unipa.it
Source: Higher Education (00181560). Feb2026, Vol. 91 Issue 2, p409-431. 23p.
Subject Terms: *College enrollment, *STEM education, *Education policy, *Secondary education, *Machine learning, Gender differences (Sociology), Boosting algorithms
Geographic Terms: Italy
Abstract: This paper explores the influence of Italian high school students' proficiency in mathematics and the Italian language on their university enrollment choices, specifically focusing on STEM (Science, Technology, Engineering, and Mathematics) courses. We distinguish between students from scientific and humanistic backgrounds in high school, providing valuable insights into their enrollment preferences. Furthermore, we investigate potential demographic differences in response to similar previous educational choices and achievements. The study employs gradient boosting methodology, known for its high predicting performance and ability to capture non-linear relationships within data, and adjusts for variables related to the socio-demographic characteristics of the students and their previous educational achievements. Our analysis reveals significant differences in the enrollment choices based on previous high school achievements. The findings shed light on the complex interplay of academic proficiency, gender, and high school background in shaping students' choices regarding university education, with implications for educational policy and future research endeavors. [ABSTRACT FROM AUTHOR]
Copyright of Higher Education (00181560) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: This paper explores the influence of Italian high school students' proficiency in mathematics and the Italian language on their university enrollment choices, specifically focusing on STEM (Science, Technology, Engineering, and Mathematics) courses. We distinguish between students from scientific and humanistic backgrounds in high school, providing valuable insights into their enrollment preferences. Furthermore, we investigate potential demographic differences in response to similar previous educational choices and achievements. The study employs gradient boosting methodology, known for its high predicting performance and ability to capture non-linear relationships within data, and adjusts for variables related to the socio-demographic characteristics of the students and their previous educational achievements. Our analysis reveals significant differences in the enrollment choices based on previous high school achievements. The findings shed light on the complex interplay of academic proficiency, gender, and high school background in shaping students' choices regarding university education, with implications for educational policy and future research endeavors. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Higher Education (00181560) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Italy
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              Text: Feb2026
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