# Recommender System for University Degree Selection:
###### 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. recommendation system; learning analytics; machine learning Article Recommender System for University Degree Selection A Socioeconomic and Standardised Test Data Approach [PERSON], [PERSON]. context-specific recommendation systems that can offer more informed and personalised guidance, ultimately improving educational outcomes for a broader range of students. Moreover, career indecision in college students is predicted by a greater identity moratorium and diffusion, less maternal acceptance, and fewer years in college [4]. Consequently, a student satisfied with their professional choice is likely to perform better academically, thereby improving the efficiency of the study group. Data generated by former students regarding their career choices and academic performance can be used to create a decision-making framework that incorporates intrinsic factors of the learning process, such as standardised test results, and extrinsic factors, such as type of school and parent's education and occupation. In this context, our study proposes a machine-learning model to recommend university degrees for new students. By applying data science techniques to standardised high school and college test results and incorporating socioeconomic variables, the model predicts students' academic performance on standardised tests at the end of college. This allows for recommendations as to the career paths that will likely result in better performance. This work has practical applications for any student deciding which career to pursue and offers universities an advancement in student counselling by using records from former students to generate helpful information for future professionals. The remainder of this article is structured as follows: Section 2 reviews the relevant literature supporting our recommender system's development. Section 3 describes the theoretical framework, including the modelling approach for the recommender system. Section 4 presents the machine learning approach and strategies to prevent overfitting and ensure generalizability. Section 5 presents the results, highlighting the models' performance and the findings' practical implications. Section 6 discusses the results by comparing them with previous studies. Subsequently, Section 7 discusses the study's limitations and