Abstract—Tensor decomposition is used in a wide range of
research fields; however, its theory is difficult to understand.
Therefore, basic education is essential when using it in
programming. Currently, there are few Japanese universities
that provide education on tensor decomposition; however, some
overseas universities have already conducted it, and online
learning materials are also substantial. Therefore, in this paper,
we have developed online learning materials for basics and
programming exercises of higher-order singular value
decomposition (HOSVD), which is one of tensor decomposition,
for the purpose of increasing the learning materials for tensor
decomposition education. Our learning material is created on
Microsoft Teams, and students can access this material channel
and work on exercises on demand while watching explanatory
videos including CG animation. As a result of the trial of this
learning material, it was found that the students who used it can
generally understand the processes related to tensor
decomposition and can perform basic programming of them.
Index Terms—Tensor decomposition, online learning materials, HOSVD, 3D puzzle, R language.
S. Abe is with Kumamoto College, National Institute of Technology, Koshi, Japan (e-mail: firstname.lastname@example.org).
A. Ishida is with the Faculty of Liberal Arts, Kumamoto College, National Institute of Technology, Koshi, Japan (e-mail: email@example.com).
J. Murakami and N. Yamamoto are with the Faculty of Electronics and Information Systems Engineering, Kumamoto College, National Institute of Technology, Koshi, Japan (e-mail: firstname.lastname@example.org, email@example.com).
Cite: Shota Abe, Akio Ishida, Jun Murakami, and Naoki Yamamoto, "Development of Online Learning Materials for Tensor Data Processing Exercises," International Journal of Information and Education Technology vol. 12, no. 3, pp. 194-202, 2022.Copyright © 2022 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).