# Recommender System for University Degree Selection:
## 8 Conclusions  
This study presents a significant advancement in educational technology by developing and evaluating a recommender system designed to predict standardised test results and recommend suitable undergraduate degrees based on a combination of academic and socio-demographic data. Our approach leverages advanced machine learning algorithms, specifically XGBoost, Random Forest, GLMNET, and KNN, to enhance students' decision-making.  
Firstly, integrating academic and socio-demographic variables, such as parental education, school type, and regional data, proved crucial in improving the accuracy and contextual relevance of the recommendations. This holistic approach addresses the limitations of previous systems that relied solely on academic performance data, offering a more comprehensive tool for student support.  
Our findings demonstrate that machine learning models, particularly XGBoost and Random Forest, exhibit a robust performance in predicting standardised test outcomes and recommending undergraduate degrees. These models significantly outperform simpler algorithms, highlighting the importance of employing sophisticated techniques to achieve a high accuracy in educational recommendations.  
Further optimisation techniques could be employed to address computational efficiency, especially given the complexity of integrating and processing large datasets. For instance, model compression techniques such as quantisation or pruning could reduce the computational load without significantly compromising the model's accuracy. Moreover, deploying the system on cloud-based platforms that offer scalable computing resources could help manage the computational demands of real-time data processing and model updates.  
Incorporating these strategies into future system iterations would enhance its real-time applicability and ensure it remains computationally efficient and scalable. By focusing on these areas, we can continue to improve the system's ability to provide accurate, timely, and contextually relevant recommendations to students, ultimately supporting better educational outcomes.  
In conclusion, the proposed recommender system supports students in making informed academic decisions and aligns with the broader educational objectives of reducing gender disparities and ensuring equal access to education. Future research can explore the integration of real-time data and the continuous improvement of recommendation algorithms to further refine the system's predictive capabilities. Expanding the system to include personalised learning paths and career counselling features could offer more excellent value to students and educational institutions.  
Overall, this study contributes to the ongoing efforts to harness the power of data science and machine learning in education, aiming to improve educational outcomes and promote equity in access to quality education.  
Conceptualisation, E.J.D.-D. and R.H.-N.; methodology, E.J.D.-D. and R.H.-N.; software, E.J.D.-D.; validation, E.J.D.-D. and R.H.-N.; formal analysis, E.J.D.-D.; data curation, E.J.D.-D.; writing--original draft preparation, E.J.D.-D. and R.H.-N.; writing--review and editing, E.J.D.-D. and R.H.-N.; visualisation, E.J.D.-D.; supervision, R.H.-N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by research grants PID2022-137849 OB-I00 funded by MICIU/AEI/10.13039/501100011033 and by the ERDF, EU. The APC was funded by Universidad del Magdalena, Colombia.