Analysis of the achievement test using covariance structure analysis for mathematics education compatible with MCC

2023 Fiscal Year Final Research Report

• Project/Area Number: 21K13610
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 09050:Tertiary education-related
• Research Institution: National Institute of Technology, Kumamoto College
• Principal Investigator: Ishida Akio 熊本高等専門学校, リベラルアーツ系理数グループ, 助教 (80633619)
• Project Period (FY): 2021-04-01 – 2024-03-31
• Keywords: 共分散構造分析 / 探索的因子分析 / 確認的因子分析 / 学習到達度試験 / 国立高専 / 数学教育

Outline of Final Research Achievements

At National Institute of Technology, the Model Core Curriculum (MCC) has been clarified and the achievement test was being conducted to ensure the quality of education, and it is aimed at improving educational content and methods and encouraging students to develop an independent learning attitude. In this study, we focused on the fact that questions of the mathematics achievement test are asked for each learning content that corresponds to MCC, and we used covariance structure analysis to perform analysis without assuming latent variables,exploratory factor analysis and confirmatory factor analysis those are analysis assuming latent. As a result, we were able to clarify the causal relationship between the contents studied in the lower grades of National Institute of Technology by expressing them in a path diagram with numerical values, and we were able to identify highly relevant learning contents in the data used.

Free Research Field

教育工学

Academic Significance and Societal Importance of the Research Achievements

高専の教育の質の保証を目的とした「モデルコアカリキュラム」に対応した学習項目についての分析結果であり、それらの項目は高校数学と対応する項目が多いことから、数学の試験結果に対する分析方法の一つとして共分散構造分析により様々な分析が出来ることを明らかにした点において意義があるものであり、数学教育に活用できる結果であると考える。また、フリーソフトウェアである統計分析ソフトウェアRを利用したことで、他の高等専門学校や高等学校などでも本研究結果と同様の分析が比較的容易に実行できるため、教学IRの面でも社会的に貢献できたと考える。