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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

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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
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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Publisher:
Springer Verlag Publisher's website
Journal:
International Workshop on Complex Networks and their Applications Journal website
Volume:
689
Pages:
139-150
Series:
International Conference on Complex Networks and their Applications VI
Host title:
Studies in Computational Intelligence
Publication date:
2017-11-27
Acceptance date:
2017-10-07
DOI:
ISSN:
1860-949X
Source identifiers:
811702
ISBN:
9783319721491
Pubs id:
pubs:811702
UUID:
uuid:cab92454-eb54-4d7b-bdd2-1a41ad9a3991
Local pid:
pubs:811702
Deposit date:
2018-12-02

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