Improving Graduate Outcomes by Identifying Skills Gaps and Recommending Courses Based on Career Interests
Rahul Soni, Basem Suleiman, Sonit Singh
TL;DR
The paper tackles the challenge of helping students choose relevant courses amid a vast elective landscape by proposing a Course Recommendation System (CRS) that aligns curricula with industry needs using data analytics and machine learning. It advances a hybrid recommender framework that combines content-based and collaborative filtering, supported by a learning engine that leverages $k$-means clustering and keyword extraction methods such as TF-IDF and RAKE, integrated with a Retrieval-Augmented Generation (RAG) model for personalised guidance. The system ingests course data and job descriptions, uses Lightcast and Open Skills APIs for skill extraction, and employs Google Cloud RAG plus Firebase for scalable, real-time recommendations, with a frontend-backend architecture designed for usability and accessibility. Evaluations emphasize usability, performance, and user satisfaction, demonstrating the approach's potential to bridge the gap between university offerings and industry expectations and to enhance graduate outcomes through data-driven, industry-informed course selections.
Abstract
This paper aims to address the challenge of selecting relevant courses for students by proposing the design and development of a course recommendation system. The course recommendation system utilises a combination of data analytics techniques and machine learning algorithms to recommend courses that align with current industry trends and requirements. In order to provide customised suggestions, the study entails the design and implementation of an extensive algorithmic framework that combines machine learning methods, user preferences, and academic criteria. The system employs data mining and collaborative filtering techniques to examine past courses and individual career goals in order to provide course recommendations. Moreover, to improve the accessibility and usefulness of the recommendation system, special attention is given to the development of an easy-to-use front-end interface. The front-end design prioritises visual clarity, interaction, and simplicity through iterative prototyping and user input revisions, guaranteeing a smooth and captivating user experience. We refined and optimised the proposed system by incorporating user feedback, ensuring that it effectively meets the needs and preferences of its target users. The proposed course recommendation system could be a useful tool for students, instructors, and career advisers to use in promoting lifelong learning and professional progression as it fills the gap between university learning and industry expectations. We hope that the proposed course recommendation system will help university students in making data-drive and industry-informed course decisions, in turn, improving graduate outcomes for the university sector.