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Rank aggregation for course sequence discovery

Abstract:

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 stu...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted Manuscript

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Publisher copy:
10.1007/978-3-319-72150-7_12

Authors


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Institution:
University of Oxford
Division:
MPLS Division
Department:
Statistics
Role:
Author
ORCID:
0000-0002-8464-2152
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Grant:
W911NF-11-1-0332
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Grant:
FA9550-10-1-0569
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Grant:
N-0001-4121-0838
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Publisher:
Springer Verlag Publisher's website
Volume:
689
Pages:
139-150
Series:
International Conference on Complex Networks and their Applications VI
Publication date:
2017-11-27
Acceptance date:
2017-10-07
DOI:
ISSN:
1860-949X
Pubs id:
pubs:811702
URN:
uri:cab92454-eb54-4d7b-bdd2-1a41ad9a3991
UUID:
uuid:cab92454-eb54-4d7b-bdd2-1a41ad9a3991
Local pid:
pubs:811702
ISBN:
9783319721491

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