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

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.