Machine Learning Model for Analyzing Learning Situations in Programming Learning

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2018 IEEE Conference on Big Data and Analytics, ICBDA 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages74-79
Number of pages6
ISBN (Electronic)9781538671283
DOIs
Publication statusPublished - 2019 Jan 29
Event2018 IEEE Conference on Big Data and Analytics, ICBDA 2018 - Langkawi, Kedah, Malaysia
Duration: 2018 Nov 212018 Nov 22

Publication series

Name2018 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

Fingerprint

Computer programming
Learning systems
Students
Machine learning
Learning model
Programming

Keywords

  • Education Support
  • Learning Situations Estimation
  • Machine Learning
  • Programming Learning
  • Source Code Editing History

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems and Management

Cite this

Kawaguchi, S., Sato, Y., Nakayama, H., Onuma, R., Nakamura, S., & Miyadera, Y. (2019). Machine Learning Model for Analyzing Learning Situations in Programming Learning. In 2018 IEEE Conference on Big Data and Analytics, ICBDA 2018 (pp. 74-79). [8629776] (2018 IEEE Conference on Big Data and Analytics, ICBDA 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICBDAA.2018.8629776

Machine Learning Model for Analyzing Learning Situations in Programming Learning. / Kawaguchi, Shota; Sato, Yoshiki; Nakayama, Hiroki; Onuma, Ryo; Nakamura, Shoichi; Miyadera, Youzou.

2018 IEEE Conference on Big Data and Analytics, ICBDA 2018. Institute of Electrical and Electronics Engineers Inc., 2019. p. 74-79 8629776 (2018 IEEE Conference on Big Data and Analytics, ICBDA 2018).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Kawaguchi, S, Sato, Y, Nakayama, H, Onuma, R, Nakamura, S & Miyadera, Y 2019, Machine Learning Model for Analyzing Learning Situations in Programming Learning. in 2018 IEEE Conference on Big Data and Analytics, ICBDA 2018., 8629776, 2018 IEEE Conference on Big Data and Analytics, ICBDA 2018, Institute of Electrical and Electronics Engineers Inc., pp. 74-79, 2018 IEEE Conference on Big Data and Analytics, ICBDA 2018, Langkawi, Kedah, Malaysia, 18/11/21. https://doi.org/10.1109/ICBDAA.2018.8629776
Kawaguchi S, Sato Y, Nakayama H, Onuma R, Nakamura S, Miyadera Y. Machine Learning Model for Analyzing Learning Situations in Programming Learning. In 2018 IEEE Conference on Big Data and Analytics, ICBDA 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 74-79. 8629776. (2018 IEEE Conference on Big Data and Analytics, ICBDA 2018). https://doi.org/10.1109/ICBDAA.2018.8629776
Kawaguchi, Shota ; Sato, Yoshiki ; Nakayama, Hiroki ; Onuma, Ryo ; Nakamura, Shoichi ; Miyadera, Youzou. / Machine Learning Model for Analyzing Learning Situations in Programming Learning. 2018 IEEE Conference on Big Data and Analytics, ICBDA 2018. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 74-79 (2018 IEEE Conference on Big Data and Analytics, ICBDA 2018).
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