Presentation | 2018-07-14 Development of a Machine Learning Model for Estimating Learning Situations Based on Source Code Editing History Shota Kawaguchi, Yoshiki Sato, Hiroki Nakayama, Shoichi Nakamura, Youzou Miyadera, |
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PDF Download Page | PDF download Page Link |
Abstract(in Japanese) | (See Japanese page) |
Abstract(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) |
Keyword(in English) | Programming Learning / Learning Situations Estimation / Machine learning / Source Code Editing History / Education Support |
Paper # | ET2018-27 |
Date of Issue | 2018-07-07 (ET) |
Conference Information | |
Committee | ET |
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Conference Date | 2018/7/14(1days) |
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. |
Chair | Yozo Miyadera(Tokyo Gakugei Univ.) |
Vice Chair | Ryo Takaoka(Yamaguchi Univ.) |
Secretary | Ryo Takaoka(Open Univ. of Japan) |
Assistant | Megumi Kurayama(National Inst. of Tech., Hakodate College) / Masaru Okamoto(Hiroshima City Univ.) |
Paper Information | |
Registration To | Technical Committee on Educational Technology |
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Language | JPN |
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.) |
Date | 2018-07-14 |
Paper # | ET2018-27 |
Volume (vol) | vol.118 |
Number (no) | ET-131 |
Page | pp.pp.47-52(ET), |
#Pages | 6 |
Date of Issue | 2018-07-07 (ET) |