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Paper Abstract and Keywords
Presentation 2018-07-14 10:50
Pair formation optimization algorithm based on learning performance, attendance rate, and pair history of students -- An Application of hybrid of genetic algorithm and particle swarm optimization --
Satoshi V. Suzuki (Osaka Univ of Econ. and Law) ET2018-20
Abstract (in Japanese) (See Japanese page) 
(in English) Skills of collaboration with various people is increasingly required in the future society. The author focus on peer learning as one of the forms of collaborative learning to enhance such kind of skills. However, the methods to form pairs in the classroom has much room for improvement. Randomly forming pairs causes the variation of pair activity in wrong way and spends unnecessary time for lesson. Moreover, deciding the pair formation in advance for the class with many students who have a poor attendance record causes students with peer absent from the lesson. The author developed the pair formation optimization method using combinatorial optimization with genetic algorithm and particle swarm optimization. This method explores better pair formation as a solution based on learning performance and attendance rate of the students. Additionally, this method considers history of past pair formation to enable the students to learn with the different peer for each lesson. In this paper, the author compared this method with different combinatorial optimization algorithm and discuss research perspective for development of collaborative learning environment in the future.
Keyword (in Japanese) (See Japanese page) 
(in English) peer learning / pair formation / combinatorial optimization / genetic algorithm / particle swarm optimization / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 131, ET2018-20, pp. 7-12, July 2018.
Paper # ET2018-20 
Date of Issue 2018-07-07 (ET) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
Copyright
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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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Conference Information
Committee ET  
Conference Date 2018-07-14 - 2018-07-14 
Place (in Japanese) (See Japanese page) 
Place (in English) National Institute of Technology, Hakodate College 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Learning Analytics and Learning Data, etc. 
Paper Information
Registration To ET 
Conference Code 2018-07-ET 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Pair formation optimization algorithm based on learning performance, attendance rate, and pair history of students 
Sub Title (in English) An Application of hybrid of genetic algorithm and particle swarm optimization 
Keyword(1) peer learning  
Keyword(2) pair formation  
Keyword(3) combinatorial optimization  
Keyword(4) genetic algorithm  
Keyword(5) particle swarm optimization  
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1st Author's Name Satoshi V. Suzuki  
1st Author's Affiliation Osaka University of Economics and Law (Osaka Univ of Econ. and Law)
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Speaker
Date Time 2018-07-14 10:50:00 
Presentation Time 25 
Registration for ET 
Paper # IEICE-ET2018-20 
Volume (vol) IEICE-118 
Number (no) no.131 
Page pp.7-12 
#Pages IEICE-6 
Date of Issue IEICE-ET-2018-07-07 


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