A machine learning approach to predict university enrolment choices through students' high school background in Italy

arXiv:2403.13819v1 Announce Type: new
Abstract: 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 …

abstract arxiv courses cs.lg engineering influence italian italy language machine machine learning mathematics paper school science scientific stat.ap stat.ot stem students technology through type university