Recommending the right academic programs: An interest mining approach using BERTopic
It addresses the time-consuming and difficult task of matching personal interests to suitable academic programs for students and decision-makers, representing an incremental improvement through a novel application of existing methods.
This paper tackles the problem of helping prospective students select university programs by developing an information system that uses BERTopic to mine interest topics from course descriptions and recommends programs based on personal preferences. A case study with 80 programs and 5,000 courses showed over 98% alignment with user interests and 94% intent to reuse, with quantitative metrics like 98% program coverage and a personalization score of 0.77.
Prospective students face the challenging task of selecting a university program that will shape their academic and professional careers. For decision-makers and support services, it is often time-consuming and extremely difficult to match personal interests with suitable programs due to the vast and complex catalogue information available. This paper presents the first information system that provides students with efficient recommendations based on both program content and personal preferences. BERTopic, a powerful topic modeling algorithm, is used that leverages text embedding techniques to generate topic representations. It enables us to mine interest topics from all course descriptions, representing the full body of knowledge taught at the institution. Underpinned by the student's individual choice of topics, a shortlist of the most relevant programs is computed through statistical backtracking in the knowledge map, a novel characterization of the program-course relationship. This approach can be applied to a wide range of educational settings, including professional and vocational training. A case study at a post-secondary school with 80 programs and over 5,000 courses shows that the system provides immediate and effective decision support. The presented interest topics are meaningful, leading to positive effects such as serendipity, personalization, and fairness, as revealed by a qualitative study involving 65 students. Over 98% of users indicated that the recommendations aligned with their interests, and about 94% stated they would use the tool in the future. Quantitative analysis shows the system can be configured to ensure fairness, achieving 98% program coverage while maintaining a personalization score of 0.77. These findings suggest that this real-time, user-centered, data-driven system could improve the program selection process.