Identifying Hubs in Undergraduate Course Networks Based on Scaled Co-Enrollments
Gary Weiss, Nam Nguyen, Karla Dominguez, Daniel Leeds
Jul 02, 2021 14:10 UTC+2
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Session PS2
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Gather Town
Keywords: Graph mining, Network Analysis, Educational Data Mining, Data Science
Abstract:
This study uses eight years of undergraduate course enrollment data from a major university to form networks of courses based on student co-enrollments. The networks are analyzed to identify "hub" courses often taken with many other courses. Two notions of hubs are considered: one based on raw popularity and another on proportional likelihoods of co-enrollment with other courses. Network metrics are calculated to describe the course networks. Academic departments and high-level academic categories (e.g., humanities), 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.