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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2403.13819 |
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| _version_ | 1866913275709489152 |
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| author | Priulla, Andrea Albano, Alessandro D'Angelo, Nicoletta Attanasio, Massimo |
| author_facet | Priulla, Andrea Albano, Alessandro D'Angelo, Nicoletta Attanasio, Massimo |
| contents | This paper explores the influence of Italian high school students' proficiency in mathematics and the Italian language on their university enrolment 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 enrolment preferences. Furthermore, we investigate potential gender 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 enrolment 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 endeavours. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_13819 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A machine learning approach to predict university enrolment choices through students' high school background in Italy Priulla, Andrea Albano, Alessandro D'Angelo, Nicoletta Attanasio, Massimo Machine Learning Applications Other Statistics This paper explores the influence of Italian high school students' proficiency in mathematics and the Italian language on their university enrolment 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 enrolment preferences. Furthermore, we investigate potential gender 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 enrolment 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 endeavours. |
| title | A machine learning approach to predict university enrolment choices through students' high school background in Italy |
| topic | Machine Learning Applications Other Statistics |
| url | https://arxiv.org/abs/2403.13819 |