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Paper Abstract and Keywords
Presentation 2018-07-14 14:55
Development of a Machine Learning Model for Estimating Learning Situations Based on Source Code Editing History
Shota Kawaguchi, Yoshiki Sato (Gakugei Univ.), Hiroki Nakayama (Waseda Univ.), Shoichi Nakamura (Fukushima Univ.), Youzou Miyadera (Gakugei Univ.) ET2018-27
Abstract (in Japanese) (See Japanese page) 
(in English) During the process of the programming course, teachers want to estimate the learning situation of students. Our findings show that the source code editing history is very important, as it reflects the process of trial and error by students. Therefore, this study aims to develop a method to estimate the detailed learning situation from the source code editing history regardless of compile error. To achieve this purpose, data correlating the source code editing history with the corresponding learning situation is applied to the machine learning method as training data. Moreover, we develop a machine learning model that outputs the estimated learning situation when new source code editing history is input. In this paper, we develop both a method and support tools for the learning situation estimation from learner's source code editing history data. As a result, the learner’s learning situation can be estimated with high accuracy, and the possibility of a learning situation estimation method in a practical lesson is shown.
Keyword (in Japanese) (See Japanese page) 
(in English) Programming Learning / Learning Situations Estimation / Machine learning / Source Code Editing History / Education Support / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 131, ET2018-27, pp. 47-52, July 2018.
Paper # ET2018-27 
Date of Issue 2018-07-07 (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)
Download PDF ET2018-27

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) Development of a Machine Learning Model for Estimating Learning Situations Based on Source Code Editing History 
Sub Title (in English)  
Keyword(1) Programming Learning  
Keyword(2) Learning Situations Estimation  
Keyword(3) Machine learning  
Keyword(4) Source Code Editing History  
Keyword(5) Education Support  
1st Author's Name Shota Kawaguchi  
1st Author's Affiliation Tokyo Gakugei University (Gakugei Univ.)
2nd Author's Name Yoshiki Sato  
2nd Author's Affiliation Tokyo Gakugei University (Gakugei Univ.)
3rd Author's Name Hiroki Nakayama  
3rd Author's Affiliation Waseda University (Waseda Univ.)
4th Author's Name Shoichi Nakamura  
4th Author's Affiliation Fukushima University (Fukushima Univ.)
5th Author's Name Youzou Miyadera  
5th Author's Affiliation Tokyo Gakugei University (Gakugei Univ.)
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Date Time 2018-07-14 14:55:00 
Presentation Time 25 
Registration for ET 
Paper # IEICE-ET2018-27 
Volume (vol) IEICE-118 
Number (no) no.131 
Page pp.47-52 
#Pages IEICE-6 
Date of Issue IEICE-ET-2018-07-07 

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