Course recommendation system (front-end)

Abstract

This report explores the development of a personalised course recommendation system seamlessly integrated into a user-friendly front-end interface, designed to assist students in selecting courses aligned with their academic goals and interests. Motivated by the challenges students face in navigating their university journey, particularly in course selection, the project leverages machine learning and web development technologies to provide tailored guidance and support. The objectives encompass the creation of a robust recommendation system and an intuitive frontend interface, specifically tailored to the context of Nanyang Technological University (NTU). Drawing inspiration from successful implementations in various domains, the recommendation system aims to empower students in making informed academic decisions. A custom synthetic student dataset was crafted to capture the diverse and unique information required in course recommendation, aimed to closely emulate the complexities of real-world student interactions within an academic environment. The implementation of the recommendation model using the custom dataset yielded promising results, showcasing its efficacy in providing personalised course suggestions to students. This project not only addresses the importance and feasibility for personalised course recommendations but also lays the foundation for future research and innovation in the academic field, enabling educational institutions to enhance students' learning experiences and foster academic success.