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Paper Abstract and Keywords
Presentation 2023-01-20 15:15
Multi-task Training with Joining-in-type Robot-assisted Language Learning System
Yu Zha, Tsuneo Kato, Seiichi Yamamoto, Akihiro Tamura (Doshisha Univ.) ET2022-60
Abstract (in Japanese) (See Japanese page) 
(in English) Introducing robots into language learning systems is effective, especially in motivating learners to engage in learning and allowing the learners to talk in a more realistic conversational environment. The joining-in-type robot-assisted language learning (JIT-RALL) system uses two robots (one as a teacher and one as a co-learner) to simulate a multi-party conversation, which can increase learning effects. However, previous JIT-RALL had single training tasks and a fixed process. In order to further increase learning effects, we introduced the well-ordered system approach from second language acquisition theory and proposed a multi-task training with JIT-RALL system. In this paper, we compared the learning effects of learners who participated in the previous single-task JIT-RALL system with those who participated in our proposed multi-task training with JIT-RALL system. We found that the learning effects of the learners who participated in our multi-task RALL system increased by 0.20 points.
Keyword (in Japanese) (See Japanese page) 
(in English) Joining-in-type robot-assisted language learning system / Multi-task training / Well-ordered system approach / Learning effects / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 348, ET2022-60, pp. 23-28, Jan. 2023.
Paper # ET2022-60 
Date of Issue 2023-01-13 (ET) 
ISSN 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 ET2022-60

Conference Information
Committee ET  
Conference Date 2023-01-20 - 2023-01-20 
Place (in Japanese) (See Japanese page) 
Place (in English) Hyogo College of Medicine and Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Embodies Knowledge and Skill Education, etc. 
Paper Information
Registration To ET 
Conference Code 2023-01-ET 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Multi-task Training with Joining-in-type Robot-assisted Language Learning System 
Sub Title (in English)  
Keyword(1) Joining-in-type robot-assisted language learning system  
Keyword(2) Multi-task training  
Keyword(3) Well-ordered system approach  
Keyword(4) Learning effects  
1st Author's Name Yu Zha  
1st Author's Affiliation Doshisha University (Doshisha Univ.)
2nd Author's Name Tsuneo Kato  
2nd Author's Affiliation Doshisha University (Doshisha Univ.)
3rd Author's Name Seiichi Yamamoto  
3rd Author's Affiliation Doshisha University (Doshisha Univ.)
4th Author's Name Akihiro Tamura  
4th Author's Affiliation Doshisha University (Doshisha Univ.)
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Speaker Author-1 
Date Time 2023-01-20 15:15:00 
Presentation Time 25 minutes 
Registration for ET 
Paper # ET2022-60 
Volume (vol) vol.122 
Number (no) no.348 
Page pp.23-28 
Date of Issue 2023-01-13 (ET) 

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