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 |
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) |
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ET2020-41 |
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) |
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Multimodal Discussion Analysis |
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Collaborative Learning |
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Social Signal Processing |
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Knowledge Transfer |
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1st Author's Name |
Shota Yokoyama |
1st Author's Affiliation |
Nagaoka University of Technology (NUT) |
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Tessai Hayama |
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Nagaoka University of Technology (NUT) |
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Speaker |
Author-1 |
Date Time |
2020-12-12 14:10:00 |
Presentation Time |
20 minutes |
Registration for |
ET |
Paper # |
ET2020-41 |
Volume (vol) |
vol.120 |
Number (no) |
no.289 |
Page |
pp.37-42 |
#Pages |
6 |
Date of Issue |
2020-12-05 (ET) |