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Iterative discriminant tensor factorization for behavior comparison in massive open online courses

Wen, X and Lin, YR and Liu, X and Brusilovsky, P and Pineda, JB (2019) Iterative discriminant tensor factorization for behavior comparison in massive open online courses. In: UNSPECIFIED.

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The increasing utilization of massive open online courses has significantly expanded global access to formal education. Despite the technology's promising future, student interaction on MOOCs is still a relatively under-explored and poorly understood topic. This work proposes a multi-level pattern discovery through hierarchical discriminative tensor factorization. We formulate the problem as a hierarchical discriminant subspace learning problem, where the goal is to discover the shared and discriminative patterns with a hierarchical structure. The discovered patterns enable a more effective exploration of the contrasting behaviors of two performance groups. We conduct extensive experiments on several real-world MOOC datasets to demonstrate the effectiveness of our proposed approach. Our study advances the current predictive modeling in MOOCs by providing more interpretable behavioral patterns and linking their relationships with the performance outcome.


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Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
CreatorsEmailPitt UsernameORCID
Wen, X
Lin, YRYURULIN@pitt.eduYURULIN0000-0002-8497-3015
Liu, X
Brusilovsky, Ppeterb@pitt.eduPETERB0000-0002-1902-1464
Pineda, JB
Date: 13 May 2019
Date Type: Publication
Journal or Publication Title: The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019
Page Range: 2068 - 2079
Event Type: Conference
DOI or Unique Handle: 10.1145/3308558.3313713
Schools and Programs: School of Computing and Information > Information Science
Refereed: Yes
ISBN: 9781450366748
Date Deposited: 04 Jun 2019 15:57
Last Modified: 03 Apr 2021 11:55


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