doi: 10.18178/ijiet.2023.13.3.1821
Analysis of Mathematics Scores in Achievement Exam of Information Technology College Students Using Covariance Structure Analysis
- Manuscript receivedSeptember 25, 2022
- revisedOctober 23, 2022
- acceptedDecember 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.
Keywords
- Covariance structure analysis
- achievement exam
- mathematics education
- R
- information course students
How to 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. https://doi.org/10.18178/ijiet.2023.13.3.1821
Copyright & License
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).