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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 |
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| 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. |
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| 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 | 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 | IBISML2019-31 |
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| 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 |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 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 |
| #Pages | 8 |
| Date of Issue | 2020-01-02 (IBISML) |
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