Identifying Hubs in Undergraduate Course Networks Based on Scaled Co-Enrollments: Extended Version
This work addresses practical course planning and student advising needs at universities, but it is incremental as it applies existing network analysis methods to educational data.
The study analyzed eight years of undergraduate course enrollment data to identify hub courses based on co-enrollment patterns, using network metrics to evaluate raw popularity and proportional likelihoods, with applications in predicting course demand and managing interdisciplinary enrollments.
Understanding course enrollment patterns is valuable to predict upcoming demands for future courses, and to provide student with realistic courses to pursue given their current backgrounds. This study uses undergraduate student enrollment data to form networks of courses where connections are based on student co-enrollments. The course networks generated in this paper are based on eight years of undergraduate course enrollment data from a large metropolitan university. The networks are analyzed to identify "hub" courses often taken with many other courses. Two notions of hubs are considered: one focused on raw popularity across all students, and one focused on proportional likelihoods of co-enrollment with other courses. A variety of network metrics are calculated to evaluate the course networks. Academic departments and high-level academic categories, such as Humanities vs STEM, are studied for their influence over course groupings. The identification of hub courses has practical applications, since it can help better predict the impact of changes in course offerings and in course popularity, and in the case of interdisciplinary hub courses, can be used to increase or decrease interest and enrollments in specific academic departments and areas.