# Recommender System for University Degree Selection:
## 6 Discussion  
Developing and evaluating a recommender system for predicting standardised test results and recommending suitable undergraduate degrees based on academic and socio-demographic data presents a significant advancement in educational technology. Our study leverages a combination of machine learning algorithms to enhance students' decision-making processes. This discussion compares our findings with those of previous studies and highlights the unique contributions of our research. To begin with, [PERSON] implemented collaborative filtering and ALS for elective course recommendations, achieving an accuracy of 86%. While that approach focused on course selection, our system extends to degree recommendations using a broader set of variables, including socio-demographic data, enhancing the recommendations' contextual relevance [38]. Similarly, [PERSON] and [PERSON] used a Random Forest regression model to classify universities based on student preferences and voting data. Although their system focused on graduate admissions, it parallels our use of Random Forest in predicting undergraduate degree suitability, highlighting the versatility and effectiveness of this algorithm [39]. Moreover, [PERSON] developed a hybrid recommendation system combining collaborative and content-based filtering with genetic optimisation to recommend courses. Their success in integrating multiple criteria underscores the importance of using diverse data points, a strategy we also employed to capture a comprehensive view of student profiles [40]. While the current system effectively uses advanced models to improve recommendation accuracy, there is room for further optimisation. For example, exploring deep learning techniques or ensemble methods could potentially enhance the system's performance. In addition, [PERSON] and [PERSON] applied probabilistic topic models for course and supervisor recommendations, focusing on the faculty's past publications and research interests. Our system similarly uses historical academic data but extends this to include socio-demographic factors, thus broadening the scope and potential impact of the recommendations [41]. Furthermore, [PERSON] proposed a framework for recommending academic programmes, courses, and instructors using collaborative filtering and non-negative matrix factorisation. While their system focused on optimising course schedules, our approach aimed at predicting standardised test results and recommending degrees, thereby supporting long-term academic planning [42]. Likewise, [PERSON] used institutional data and the Analytical Hierarchy Process (AHP) to predict course selections. Similarly, our study utilises institutional data but expands its use to recommend undergraduate degrees, thus providing more holistic support for academic decisions [43]. Besides, the AHP allows for a natural interpretation of the recommendation results. In our system, the explainability of the recommendations could be further refined to ensure that students fully understand the reasoning behind the suggestions. This could involve incorporating more sophisticated Explainable AI (XAI) techniques to make the decision-making process even more transparent and user-friendly. Additionally, [PERSON] and [PERSON] employed a personalised approach to course recommendation using ontology and sequential pattern mining. Our system also emphasises personalisation by incorporating socio-demographic factors, ensuring that recommendations align closely with individual student profiles [44]. Moreover, [PERSON] investigated supervised machine learning techniques for recommending undergraduate programmes using academic history and job market data. Our study similarly uses machine learning but focuses on standardised test predictions and degree recommendations, providing a comprehensive tool for student support [18]. Consequently, the average RMSE for the linear regression model was 72.1 during cross-validation.