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Paper Abstract and Keywords
Presentation 2020-12-12 14:10
Multimodal Discourse Analysis for Encouraging Collaborative Learning in Small Group
Shota Yokoyama, Tessai Hayama (NUT) ET2020-41
Abstract (in Japanese) (See Japanese page) 
(in English) In group learning, effective knowledge transfer among learners is one of the most important factors in promoting cooperative learning among the learners. However, due to the difficulty in capturing the phenomenon of the knowledge transfer process, quantitative research and analysis have not been conducted. In this study, we investigated the process of learning knowledge transfer in small group discussions based on multimodal information analysis and statistically identified the multimodal features that are effective in knowledge transfer. As results, we found that 1) the amount of knowledge after group discussion was moderately correlated with superficial language, speech-turn, and behavioral features, 2) the relatively more knowledge-transferred group was identified with higher minimum intensity and more fearful facial expressions for the prelearned learners, 3) efficient knowledge transfer processes in group discussions was detected with prosody and facial expressions as features during speech, 4) the prelearned-learner recognition was performed with accuracy of higher than 0.9. Keywords Multimodal Dialog Analysis, Collaborative Learning, Social Signal Processing, Knowledge Transfer
Keyword (in Japanese) (See Japanese page) 
(in English) Multimodal Discussion Analysis / Collaborative Learning / Social Signal Processing / Knowledge Transfer / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 289, ET2020-41, pp. 37-42, Dec. 2020.
Paper # ET2020-41 
Date of Issue 2020-12-05 (ET) 
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)
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Conference Information
Committee ET  
Conference Date 2020-12-12 - 2020-12-12 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Sessions for Young Researchers (Young Researcher Awards Selection), etc. 
Paper Information
Registration To ET 
Conference Code 2020-12-ET 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Multimodal Discourse Analysis for Encouraging Collaborative Learning in Small Group 
Sub Title (in English)  
Keyword(1) Multimodal Discussion Analysis  
Keyword(2) Collaborative Learning  
Keyword(3) Social Signal Processing  
Keyword(4) Knowledge Transfer  
1st Author's Name Shota Yokoyama  
1st Author's Affiliation Nagaoka University of Technology (NUT)
2nd Author's Name Tessai Hayama  
2nd Author's Affiliation Nagaoka University of Technology (NUT)
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Date Time 2020-12-12 14:10:00 
Presentation Time 20 
Registration for ET 
Paper # IEICE-ET2020-41 
Volume (vol) IEICE-120 
Number (no) no.289 
Page pp.37-42 
#Pages IEICE-6 
Date of Issue IEICE-ET-2020-12-05 

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