Recommender System for University Degree Selection: A Socioeconomic and Standardised Test Data Approach

Abstract

This study introduces a novel recommender system that integrates academic performance and socio-demographic variables to provide personalised and contextually relevant recommendations for university degree selection. The system aims to optimise the alignment between students’ profiles and potential academic programmes by utilising advanced machine learning models, including XGBoost, Random Forest, GLMNET, and KNN. The research addresses a critical gap identified in the literature, where most existing systems rely solely on academic data, neglecting the significant impact of socioeconomic factors on educational decision-making. The proposed system demonstrates superior predictive accuracy through rigorous cross-validation and hyperparameter tuning compared to simpler models, such as linear regression. The results show that integrating socio-demographic data enhances the relevance of the recommendations, supporting students in making more informed choices. This approach contributes to educational equity by ensuring that guidance is tailored to each student’s unique circumstances, aligning with the sustainable development goal of quality education. The findings highlight the value of incorporating a comprehensive data-driven approach to improve educational outcomes and support more equitable decision-making processes.