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Paper Abstract and Keywords
Presentation 2021-03-03 16:15
Similar Problem Search Using Deep Learning for Supportintg Programming Education
Hiroki Yamamoto, Haruki Matsuo, Kentaro Okino, Yasutaka Kamei, Naoyasu Ubayashi (Kyushu Univ.) SS2020-36
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, the demand for programming education has been increasing, and classes on programming have been increasing. On the other hand, it is difficult to do a sufficient number of programming exercises problems within the limited class time. One possible solution is to support learning by similar problem search. If it is possible to search for similar problems that have not been tackled before, it would be an aid to programming exercises. In this study, we focused on similar problem search using deep learning and conducted similar problem search using as training data the datasets of programming problems used at Kyushu University and those used in competitive programming. As a result of the similar problem search using the two datasets as training data, similar problems were obtained more than 11.3% of the time. As a result of investigating which dataset is effective for training by changing the dataset used for training, we showed that more than 10.7% of similar problems can be obtained by using the same dataset as the program to be searched for training.
Keyword (in Japanese) (See Japanese page) 
(in English) Similar problem search / Deep Learning / Programming Education / Learning Support / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 407, SS2020-36, pp. 49-54, March 2021.
Paper # SS2020-36 
Date of Issue 2021-02-24 (SS) 
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)
Notes on Review This article is a technical report without peer review, and its polished version will be published elsewhere.
Download PDF SS2020-36

Conference Information
Committee SS  
Conference Date 2021-03-03 - 2021-03-04 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SS 
Conference Code 2021-03-SS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Similar Problem Search Using Deep Learning for Supportintg Programming Education 
Sub Title (in English)  
Keyword(1) Similar problem search  
Keyword(2) Deep Learning  
Keyword(3) Programming Education  
Keyword(4) Learning Support  
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1st Author's Name Hiroki Yamamoto  
1st Author's Affiliation Kyushu University (Kyushu Univ.)
2nd Author's Name Haruki Matsuo  
2nd Author's Affiliation Kyushu University (Kyushu Univ.)
3rd Author's Name Kentaro Okino  
3rd Author's Affiliation Kyushu University (Kyushu Univ.)
4th Author's Name Yasutaka Kamei  
4th Author's Affiliation Kyushu University (Kyushu Univ.)
5th Author's Name Naoyasu Ubayashi  
5th Author's Affiliation Kyushu University (Kyushu Univ.)
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Speaker Author-1 
Date Time 2021-03-03 16:15:00 
Presentation Time 25 minutes 
Registration for SS 
Paper # SS2020-36 
Volume (vol) vol.120 
Number (no) no.407 
Page pp.49-54 
#Pages 6 
Date of Issue 2021-02-24 (SS) 

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