A machine learning approach to predict university enrolment choices through students' high school background in Italy
This work addresses educational policy and research by providing insights into student enrollment patterns in Italy, but it is incremental as it applies an existing machine learning method to a new dataset.
This study tackled the problem of predicting university enrollment choices for Italian high school students, specifically focusing on STEM courses, by analyzing their mathematics and Italian language proficiency, high school background, and gender. The results revealed significant differences in enrollment choices based on previous high school achievements, using gradient boosting methodology.
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.