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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

TL;DR

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.

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 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.

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

TL;DR

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.

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 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.
Paper Structure (8 sections, 7 equations, 8 figures, 1 table)

This paper contains 8 sections, 7 equations, 8 figures, 1 table.

Table of Contents

  1. Introduction
  2. Literature review
  3. Data
  4. Methods
  5. Interpretability methods
  6. Exploratory analysis
  7. Results
  8. Conclusions

Figures (8)

  • Figure 1: Distributions of maths scores according to gender, the high school curriculum, and enrolment choice.
  • Figure 2: Distributions of Italian scores according to gender, the high school curriculum, and enrolment choice.
  • Figure 3: Bivariate distribution of maths and Italian test scores according to gender and high school curriculum.
  • Figure 4: ROC curves.
  • Figure 5: Relative influence of predictors.
  • ...and 3 more figures