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Paper Abstract and Keywords
Presentation 2025-09-03 17:00
Estimation Method for Latent Attributes Using Multidimensional Item Response Theory Incorporating Q-Matrix
Kyosei Sato, Masaki Uto (UEC) ET2025-21
Abstract (in Japanese) (See Japanese page) 
(in English) Cognitive diagnostic models (CDMs) are mathematical frameworks for estimating learners' knowledge states. A key prerequisite for applying CDMs is the design of a Q-matrix, which specifies the relationship between test items and the knowledge or skills (referred to as attributes) they require. Since the estimation of knowledge states depends on the Q-matrix, it must be constructed accurately. However, it is often difficult to comprehensively enumerate all relevant latent attributes in advance due to their abstract nature. To address this issue, this study proposes a new model that infers unlisted latent attributes that were not captured in the initial Q-matrix. The proposed method is formulated as a model that integrates the Q-matrix into a multidimensional item response theory framework.
Keyword (in Japanese) (See Japanese page) 
(in English) Cognitive diagnostic model / multidimensional item response theory / Q-matrix / learning assessment / Bayesian statistics / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 171, ET2025-21, pp. 55-61, Sept. 2025.
Paper # ET2025-21 
Date of Issue 2025-08-27 (ET) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF ET2025-21

Conference Information
Committee ET  
Conference Date 2025-09-03 - 2025-09-03 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To ET 
Conference Code 2025-09-ET 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Estimation Method for Latent Attributes Using Multidimensional Item Response Theory Incorporating Q-Matrix 
Sub Title (in English)  
Keyword(1) Cognitive diagnostic model  
Keyword(2) multidimensional item response theory  
Keyword(3) Q-matrix  
Keyword(4) learning assessment  
Keyword(5) Bayesian statistics  
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Keyword(8)  
1st Author's Name Kyosei Sato  
1st Author's Affiliation The University of Electro-Communications (UEC)
2nd Author's Name Masaki Uto  
2nd Author's Affiliation The University of Electro-Communications (UEC)
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Speaker Author-1 
Date Time 2025-09-03 17:00:00 
Presentation Time 25 minutes 
Registration for ET 
Paper # ET2025-21 
Volume (vol) vol.125 
Number (no) no.171 
Page pp.55-61 
#Pages 7 
Date of Issue 2025-08-27 (ET) 

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