Machine Learning Model for Analyzing Learning Situations in Programming Learning

Shota Kawaguchi, Yoshiki Sato, Hiroki Nakayama, Ryo Onuma, Shoichi Nakamura, Youzou Miyadera

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2018 IEEE Conference on Big Data and Analytics, ICBDA 2018
出版社Institute of Electrical and Electronics Engineers Inc.
ページ74-79
ページ数6
ISBN(電子版)9781538671283
DOI
出版ステータスPublished - 2019 1 29
イベント2018 IEEE Conference on Big Data and Analytics, ICBDA 2018 - Langkawi, Kedah, Malaysia
継続期間: 2018 11 212018 11 22

出版物シリーズ

名前2018 IEEE Conference on Big Data and Analytics, ICBDA 2018

Conference

Conference2018 IEEE Conference on Big Data and Analytics, ICBDA 2018
CountryMalaysia
CityLangkawi, Kedah
Period18/11/2118/11/22

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems and Management

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