Rank Aggregation for Course Sequence Discovery
- 3.9k Downloads
This work extends the rank aggregation framework for the setting of discovering optimal course sequences at the university level, and contributes to the literature on educational applications of network analysis. Each student provides a partial ranking of the courses taken throughout her or his undergraduate career. We build a network of courses by computing pairwise rank comparisons between courses based on the order students typically take them, and aggregate the results over the entire student population, to obtain a proxy for the rank offset between pairs of courses. We extract a global ranking of the courses via several state-of-the art algorithms for ranking with pairwise noisy information, including SerialRank, Rank Centrality, and the recent SyncRank based on the group synchronization problem. We test this application of rank aggregation on 15 years of student data from the Department of Mathematics at the University of California, Los Angeles (UCLA). Furthermore, we experiment with the above approach on different subsets of the student population conditioned on final GPA, and highlight several differences in the obtained rankings that uncover potential hidden pre-requisites in the Mathematics curriculum.
This work was supported by NSF grant DMS-1045536, UC Lab Fees Research Grant 12-LR-236660, ARO MURI grant W911NF-11-1-0332, AFOSR MURI grant FA9550-10-1-0569, NSF grant DMS-1417674, and ONR grant N-0001-4121-0838, and EPSRC grant EP/N510129/1.
- 2.Bowen, R.M.: Science and Engineering Indicators 2012. National Science Foundation, Online, Arlington VA (2012)Google Scholar
- 5.Dwork, C., Kumar, R., Naor, M., Sivakumar, D.: Rank aggregation methods for the web. In: Proceedings of the 10th International Conference on World Wide Web, pp. 613–622. ACM (2001)Google Scholar
- 6.Fogel, F., d’Aspremont, A., Vojnovic, M.: Serialrank: spectral ranking using seriation. In: Advances in Neural Information Processing Systems, pp. 900–908 (2014)Google Scholar
- 8.Negahban, S., Oh, S., Shah, D.: Iterative ranking from pair-wise comparisons. In: NIPS, pp. 2474–2482 (2012)Google Scholar
- 9.Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web. (1999)Google Scholar
- 10.Pei, J., Han, J., Wang, W.: Mining sequential patterns with constraints in large databases. In: Proceedings of the 11th International Conference on Information and Knowledge Management, pp. 18–25. ACM (2002)Google Scholar
- 11.Raman, K., Joachims, T.: Methods for ordinal peer grading. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1037–1046. ACM (2014)Google Scholar
- 12.Sayama, H.: Mapping the curricular structure and contents of network science courses. CoRR abs/1707.09570 (2017). http://arxiv.org/abs/1707.09570
- 14.Snyder, T.D., Dillow, S.A.: Digest of education statistics, 2012. National Center for Education Statistics (2013)Google Scholar
- 17.Xu, J., Xing, T., Van Der Schaar, M.: Personalized course sequence recommendations. IEEE Trans. Signal Process. 64(20), 5340–5352Google Scholar
- 19.Yang, J., Wang, W., Yu, P.S., Han, J.: Mining long sequential patterns in a noisy environment. In: Proceedings of the 2002 ACM SIGMOD International Conference on Management of Data, pp. 406–417. ACM (2002)Google Scholar