IJIET 2020 Vol.10(7): 488-493 ISSN: 2010-3689
doi: 10.18178/ijiet.2020.10.7.1412

Study about the Aptitude-Treatment Interaction between Learning Using the e-Learning System and Learning Type of Learner

Ryo Sugawara, Shun Okuhara, and Yoshikazu Sato

Abstract—The low completion rate of e-learning, which has been considered as a problem since its inception, is recently attracting renewed attention as a problem yet to be solved. This study confirms the assumption that the effect of e-learning varies with the learner’s learning type, and that a low e-learning completion rate results from the participation of learners who are unsuited to e-learning. It has been found that the differences between e-learning achievement rates can be classified into seven learning types. This suggests that e-learning may have little effect when the e-learning system does not match the learner's learning type.

Index Terms—e-Learning, act of learning, learning type, aptitude-treatment interaction.

Ryo Sugawara is with Meisei University, 2-1-1, Hodokubo, Hino, Tokyo, Japan (e-mail: ryo.sugawara@meisei-u.ac.jp). Shun Okuhara is with Fujita Health University, 1-98, Tarakugakubo, Kutsukake, Homei, Aichi, Japan (e-mail: sokuhara@fujita-hu.ac.jp). Yoshikazu Sato is with Kyushu University, 744, Motooka, nishi-ku, Fukuoka, Fukuoka, Japan (e-mail: ysato@artsci.kyushu-u.ac.jp).

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Cite:Ryo Sugawara, Shun Okuhara, and Yoshikazu Sato, "Study about the Aptitude-Treatment Interaction between Learning Using the e-Learning System and Learning Type of Learner," International Journal of Information and Education Technology vol. 10, no. 7, pp. 488-493, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3689 (Online)
  • Abbreviated Title: Int. J. Inf. Educ. Technol.
  • Frequency: Monthly
  • DOI: 10.18178/IJIET
  • Editor-in-Chief: Prof. Dr. Steve Thatcher
  • Executive Editor: Ms. Nancy Y. Liu
  • Abstracting/ Indexing: Scopus (Since 2019), INSPEC (IET), EBSCO, Electronic Journals Library, Google Scholar, Crossref, etc.
  • E-mail: ijiet@ejournal.net