Manuscript received September 25, 2022; revised October 23, 2022; accepted December 5, 2022.
Abstract—In recent years, big data has been attracting
attention in various fields, and Japanese institutions of higher
education are also focusing on improving education based on
the analysis of data accumulated in schools. Therefore, as an
example of the application of big data analysis to mathematics
education, we considered the mathematics exam score data
accumulated at our college as big data and analyzed it. Another
major objective is to obtain some knowledges about
mathematics education for information engineering students.
The software used for the analysis is the freeware statistical
analysis software R. The analysis method used is covariance
structure analysis, which represents correlations and causal
relationships among observed variables as a well-fitting model.
As a result, two models with good fit indices were created for
the integrated data of multi-year exams, and it was found the
learning items that have a significant impact on the later
learning items in that data. Furthermore, it was also able to use
one of the models to analyze the characteristics of a single-year
data. It is thought that these results can contribute to
mathematics education for information engineering course
students.
Index Terms—Covariance structure analysis, achievement
exam, mathematics education, R, information course students
A. Ishida is with the Faculty of Liberal Arts, Kumamoto College, National
Institute of Technology, Koshi, Japan.
N. Yamamoto and J. Murakami are with the Faculty of Electronics and
Information Systems Engineering, Kumamoto College, National Institute of
Technology, Koshi, Japan.
*Correspondence: shida@kumamoto-nct.ac.jp (A.I.)
Cite: A. Ishida*, N. Yamamoto, and J. Murakami, "Analysis of Mathematics Scores in Achievement Exam of Information Technology College Students Using Covariance Structure Analysis," International Journal of Information and Education Technology vol. 13, no. 3, pp. 417-422, 2023.
Copyright © 2023 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).