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In this work, we adapt the rank aggregation framework for the discovery ofoptimal course sequences at the university level. Each student provides apartial ranking of the courses taken throughout his or her undergraduatecareer. We compute pairwise rank comparisons between courses based on the orderstudents typically take them, aggregate the results over the entire studentpopulation, and then obtain a proxy for the rank offset between pairs ofcourses. We extract a global ranking of the courses via several state-of-theart algorithms for ranking with pairwise noisy information, includingSerialRank, Rank Centrality, and the recent SyncRank based on the groupsynchronization problem. We test this application of rank aggregation on 15years of student data from the Department of Mathematics at the University ofCalifornia, Los Angeles (UCLA). Furthermore, we experiment with the aboveapproach on different subsets of the student population conditioned on finalGPA, and highlight several differences in the obtained rankings that uncoverhidden pre-requisites in the Mathematics curriculum.

Rank Aggregation for Course Sequence Discovery