Abstract
In multidimensional data processing, one of the important data structures is a higher-order tensor or a multidimensional array. In general, the processing related to the higher-order tensor is so complicated that we have been developing understanding support tools for it using 3D puzzles from the viewpoint of making students interested. However, although these tools have been tried by students in graduation studies and other some occasions, their introduction into lectures was one issue. Therefore, in this study, we developed a new programming exercise material for the higher-order tensor, which is supposed to be used in data science subjects, by using a 3D puzzle. This learning material is also composed of Microsoft Teams, and students can access the material remotely to learn programming. In this paper, several students actually tried this material. As a result, it was found that the students themselves could create and submit assignment reports by viewing explanatory videos and performing exercises. From this, it is expected that this material will be able to introduce to data science courses, including online use.
Highlights
The higher-order tensor is one of the typical data structures that handle multidimensional data such as big data processing
Did you understand that the higher-order tensors dealt with here are multidimensional arrays? Question 2
In this research, we have developed learning material that assumes programming exercises related to higher-order tensors in data science subjects
Summary
The higher-order tensor (multidimensional array) is one of the typical data structures that handle multidimensional data such as big data processing. A. Puzzle Problem and Its Solution In this research, we develop learning materials for programming exercises of higher-order tensors. The third-order tensor used to express the 3D puzzle is transformed into three types of 1-mode matrix unfolding A(1) , 2-mode matrix unfolding A(2) , and 3-mode matrix unfolding A(3) ,whose number of rows are I 1 , I 2 and I 3 , respectively This learning material contains an exercise to restore the original cube form from the map of the light bulb positions created by the matrix unfolding. The rTensor can be downloaded and used from the site called the Comprehensive R Archive Network (CRAN) In this learning material, the above-mentioned functions 1) to 4) are used to implement the operations defined in Definitions 1 to 4, respectively. Details about this material will be given
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