Enhancing Student Experience in Project Selection: A Personalized Recommendation Approach

Understanding students' academic profiles and skillsets is crucial for personalized guidance in higher education. In transnational education (TNE) programmes, large student cohorts and time zone differences often complicate the allocation process of final year projects. To address these challenges, a project recommendation framework was developed. Using latent semantic analysis (LSA), students' academic profiles are summarized into skillsets, which are then matched with project requirements. This framework was deployed in a TNE programme between Queen Mary University of London (QMUL) and Beijing University of Posts and Telecommunications (BUPT). Quantitative results show that 80% of students using the framework secured a project on the first day of the allocation process, compared to 64% in a previous cohort without the tool, effectively shortening the allocation timeline. Qualitative feedback indicates high student satisfaction, emphasizing the tool's ease of use and relevance, as well as its ability to help students identify projects aligned with their academic profiles and interests. These findings reflect the framework's potential to streamline project allocation, reduce administrative workload, and enhance student support in project allocation. Moreover, the student skillsets generated by the framework can support broader applications, including employability analysis, academic profiling, and strategic decision-making to enhance institutional processes and student outcomes.