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Tensor factorization for student modeling and performance prediction in unstructured domain

Sahebi, S and Lin, YR and Brusilovsky, P (2016) Tensor factorization for student modeling and performance prediction in unstructured domain. Proceedings of the 9th International Conference on Educational Data Mining, EDM 2016. 502 - 506.

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Abstract

© 2016 International Educational Data Mining Society. All rights reserved. We propose a novel tensor factorization approach, Feedback-Driven Tensor Factorization (FDTF), for modeling student learning process and predicting student performance. This approach decomposes a tensor that is built upon students’ attempt sequence, while considering the quizzes students select to work with as its feedback. FDTF does not require any prior domain knowledge, such as learning resource skills, concept maps, or Q-matrices. The proposed approach differs significantly from other tensor factorization approaches, as it explicitly models the learning progress of students while interacting with the learning resources. We compare our approach to other state-of-the-art approaches in the task of Predicting Student Performance (PSP). Our experiments show that FDTF performs significantly better compared to baseline methods, including Bayesian Knowledge Tracing and a state-of-the-art tensor factorization approach.


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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Sahebi, Sshs106@pitt.eduSHS106
Lin, YRYURULIN@pitt.eduYURULIN0000-0002-8497-3015
Brusilovsky, Ppeterb@pitt.eduPETERB0000-0002-1902-1464
Date: 1 January 2016
Date Type: Publication
Journal or Publication Title: Proceedings of the 9th International Conference on Educational Data Mining, EDM 2016
Page Range: 502 - 506
Schools and Programs: Dietrich School of Arts and Sciences > Intelligent Systems
School of Information Sciences > Information Science
Refereed: Yes
Date Deposited: 05 Aug 2016 13:16
Last Modified: 02 Oct 2019 16:55
URI: http://d-scholarship.pitt.edu/id/eprint/29129

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