論文

2019年1月29日

Machine Learning Model for Analyzing Learning Situations in Programming Learning

2018 IEEE Conference on Big Data and Analytics, ICBDA 2018
  • Shota Kawaguchi
  • ,
  • Yoshiki Sato
  • ,
  • Hiroki Nakayama
  • ,
  • Ryo Onuma
  • ,
  • Shoichi Nakamura
  • ,
  • Youzou Miyadera

開始ページ
74
終了ページ
79
記述言語
英語
掲載種別
研究論文(国際会議プロシーディングス)
DOI
10.1109/ICBDAA.2018.8629776
出版者・発行元
Institute of Electrical and Electronics Engineers Inc.

In programming learning, students have individual difficulties, and teachers need to grasp those difficulties and provide appropriate support for the students. However, since it is a heavy burden for teachers, a method to automatically estimate the learning situations of students is required. In this research, we developed a method that adopts the development of a machine learning model as an approach to achieve this purpose. This machine learning model outputs the estimated learning situation when the source code editing history of new students is input. As a result of evaluating the developed method, it was possible to estimate the correct learning situations with high accuracy of 98%. The applicability of this learning situation estimation method in practical lessons was shown.

リンク情報
DOI
https://doi.org/10.1109/ICBDAA.2018.8629776
ID情報
  • DOI : 10.1109/ICBDAA.2018.8629776
  • SCOPUS ID : 85062785221

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