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Paper Abstract and Keywords
Presentation 2020-01-10 13:40
Non-compensatory Temporal IRT with Local Variational Approximation
Hiroshi Tamano (NEC/SOKENDAI), Daichi Mochihashi (ISM) IBISML2019-31
Abstract (in Japanese) (See Japanese page) 
(in English) Knowledge tracing, the time varying extension of item response theory (IRT), is a task to trace learner’s
latent skill states to predict whether the learner can answer a new question correctly. Due to its educational domain,
knowledge tracing needs high interpretability of its prediction and explainability of its result. As for explainability,
explanation with human interpretable skills is necessary, which are usually given as conjunctive conditions. Such a
model is called a non-compensatory model in multi-dimensional IRT and explanation using non-compensatory item
response model is desired. To realize an interpretable and explainable knowledge tracing, we propose a probabilistic
model based on non-compensatory item response model combined with a linear dynamical system. Since it results
in a complicated posterior on the skill states of the learners, we approximate it using a local variational distribution.
We also show that our posterior adequately approximates the true posterior in artificial data, and our prediction
performance is better than two popular deep learning based knowledge tracing in ASSISTment Data.
Keyword (in Japanese) (See Japanese page) 
(in English) Educational Technology / IRT / Knowledge Tracing / Kalman Filter / Variational Approximation / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 360, IBISML2019-31, pp. 91-98, Jan. 2020.
Paper # IBISML2019-31 
Date of Issue 2020-01-02 (IBISML) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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 IBISML2019-31

Conference Information
Committee IBISML  
Conference Date 2020-01-09 - 2020-01-10 
Place (in Japanese) (See Japanese page) 
Place (in English) ISM 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Machine learning, etc. 
Paper Information
Registration To IBISML 
Conference Code 2020-01-IBISML 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Non-compensatory Temporal IRT with Local Variational Approximation 
Sub Title (in English)  
Keyword(1) Educational Technology  
Keyword(2) IRT  
Keyword(3) Knowledge Tracing  
Keyword(4) Kalman Filter  
Keyword(5) Variational Approximation  
1st Author's Name Hiroshi Tamano  
1st Author's Affiliation NEC Data Science Laboratories/The Graduate University for Advanced Studies, SOKENDAI (NEC/SOKENDAI)
2nd Author's Name Daichi Mochihashi  
2nd Author's Affiliation The Institute of Statistical Mathematics (ISM)
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Speaker Author-1 
Date Time 2020-01-10 13:40:00 
Presentation Time 25 minutes 
Registration for IBISML 
Paper # IBISML2019-31 
Volume (vol) vol.119 
Number (no) no.360 
Page pp.91-98 
Date of Issue 2020-01-02 (IBISML) 

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