A machine learning approach to predict university enrolment choices
  through students' high school background in Italy
Andrea Priulla Alessandro Albano Nicoletta D'Angelo Massimo Attanasio
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.