Analyzing University Enrollment Choices in Italy Using Machine Learning

Kernekoncepter

Proficiency in mathematics and Italian language significantly influences university enrollment choices, with gender disparities observed.

Resumé

This paper explores the impact of high school students' proficiency in math and Italian on their university enrollment choices, focusing on STEM courses. Gender differences are highlighted, along with the influence of socio-economic status and high school background. The study employs gradient boosting methodology to analyze the data comprehensively.
Structure:

Introduction:

Expansion of educational opportunities post-World War II.
Gender disparities in educational choices.

Literature Review:

Gender gap in STEM education.
Factors influencing educational choices.

Data:

Utilization of micro-data from INVALSI and ANS-U for analysis.

Methods:

Introduction to Gradient Boosting for predictive modeling.

Exploratory Analysis:

Descriptive statistics on student characteristics based on gender and enrollment choice.

Results:

Evaluation of two models predicting university enrollment and STEM enrollment.

Conclusions:

Insights into factors influencing university enrollment decisions.

Statistik

Females generally outperform males in reading test scores and academic achievements.
Females are underrepresented in STEM fields globally, with only 31% new enrollments at the bachelor's level being female (OECD, 2022).
High school curriculum significantly influences the probability of enrolling in STEM programs.

Citater

"Despite recent advancements in female educational attainment, gender horizontal segregation persists worldwide."
"Females require stronger signals of mathematical ability to choose STEM fields."

Vigtigste indsigter udtrukket fra

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

by Andrea Priul... kl. arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.13819.pdf

Dybere Forespørgsler

How can societal stereotypes be addressed to encourage more females to pursue STEM careers?

To address societal stereotypes and encourage more females to pursue STEM careers, several strategies can be implemented:

Promoting Female Role Models: Highlighting successful women in STEM fields can provide inspiration and challenge traditional gender norms. By showcasing diverse role models, young girls can envision themselves pursuing similar career paths.

Educational Initiatives: Implementing educational programs that promote gender equality in STEM education from an early age is crucial. Encouraging girls to participate in science fairs, coding clubs, and other hands-on activities can foster interest and confidence in STEM subjects.

Mentorship Programs: Establishing mentorship programs where female students are paired with women working in STEM professions can provide guidance, support, and valuable insights into potential career paths.

Addressing Biases: Educating teachers, parents, and peers about unconscious biases that may influence perceptions of gender roles in STEM is essential. Creating awareness about these biases can help mitigate their impact on educational choices.

Creating Inclusive Environments: Cultivating inclusive learning environments where all students feel supported and valued regardless of their gender is key. Encouraging collaboration, teamwork, and open discussions can help break down barriers for female students interested in pursuing STEM fields.

How do parental expectations play a role in shaping students' educational choices?

Parental expectations play a significant role in shaping students' educational choices by influencing their aspirations, motivations, and decisions regarding academic pursuits:

Expectations Impact Motivation: Parents' beliefs about their child's abilities and potential future success directly impact the student's motivation levels towards academic achievement.

Guidance on Career Paths: Parental expectations often guide students towards specific career paths based on family values or traditions.

Support System: Supportive parents who have high expectations for their children tend to create a conducive environment for academic success through encouragement and resources.

Pressure vs Encouragement: While some level of expectation can motivate students to excel academically, excessive pressure from unrealistic expectations may lead to stress or burnout.

5Communication: Open communication between parents and children regarding educational goals helps align expectations with the student's interests and capabilities.

How can machine learning models be further optimized to predict university enrollment choices accurately?

Machine learning models used for predicting university enrollment choices could be optimized through various techniques:
1Feature Engineering: Enhancing feature selection processes by identifying relevant variables that have the most significant impact on enrollment decisions could improve model accuracy.
2Ensemble Methods: Utilizing ensemble methods like Random Forests or stacking multiple models together could enhance predictive performance by leveraging different algorithms' strengths.
3Hyperparameter Tuning: Fine-tuning model parameters through grid search or Bayesian optimization techniques could optimize model performance by finding the best parameter combinations.
4Cross-Validation Techniques: Implementing advanced cross-validation strategies such as stratified k-fold cross-validation ensures robust model evaluation across different subsets of data
5Regularization Techniques: Applying regularization methods like Lasso or Ridge regression helps prevent overfitting while improving generalization capabilities of the model.
By incorporating these optimization strategies along with continuous refinement based on new data inputs will contribute significantly towards enhancing the accuracy of machine learning models predicting university enrollment choices effectively