This paper investigates how proficiency in mathematics and Italian tests in high school affect university enrolment choices in Italy. We distinguish between students from scientific and humanistic backgrounds, providing valuable insights into their enrolment choices. We employ gradient boosting methodology, adjusting for students' sociodemographic characteristics and previous educational attainment. Results shed light on the interplay between student performance, sex, and the type of high school attended in shaping enrolment choices.

Andrea Priulla, Alessandro Albano, Nicoletta D'Angelo, Massimo Attanasio (2024). Investigating the association between high school outcomes and university enrolment choices: a machine learning approach. In Proceedings of the Statistics and Data Science 2024 Conference.

Investigating the association between high school outcomes and university enrolment choices: a machine learning approach

Andrea Priulla
;
Alessandro Albano;Nicoletta D'Angelo;Massimo Attanasio
2024-06-01

Abstract

This paper investigates how proficiency in mathematics and Italian tests in high school affect university enrolment choices in Italy. We distinguish between students from scientific and humanistic backgrounds, providing valuable insights into their enrolment choices. We employ gradient boosting methodology, adjusting for students' sociodemographic characteristics and previous educational attainment. Results shed light on the interplay between student performance, sex, and the type of high school attended in shaping enrolment choices.
giu-2024
Settore SECS-S/01 - Statistica
Settore SECS-S/05 - Statistica Sociale
9788855096454
Andrea Priulla, Alessandro Albano, Nicoletta D'Angelo, Massimo Attanasio (2024). Investigating the association between high school outcomes and university enrolment choices: a machine learning approach. In Proceedings of the Statistics and Data Science 2024 Conference.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/640476
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