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<p>Enhancing the physical quality of students in higher education institutions is crucial for ensuring the successful completion of their academic pursuits. This article begins by analyzing the fundamental algorithms of decision trees, subsequently building upon this theoretical foundation to develop an ID3 decision tree model. It employs the decision tree model for data classification, extraction, and other related processes. By integrating the C4.5 algorithm and further segmentation techniques, a more stable decision tree is derived, allowing for a comprehensive analysis of physical fitness test data. The study’s findings indicate that using rope skipping test data as an example, the analytical model maintains an error margin of 3%. Over four years of physical test data, there was a notable classification of 1% of students into the “excellent” category in 2020, with nearly 20% achieving “good” status. The proportions of “excellent” and “good” students in 2021 saw a significant rise from the previous year. Furthermore, the percentage of students categorized as “failing” in 2023 decreased by 0.88%. To enhance the effectiveness of physical education, reforms could focus on multi-faceted collaborative efforts, reinforcing the application of the ID3 decision tree model in analyzing physical fitness data and targeting interventions to boost physical fitness outcomes.</p>
</abstract>

Enhancing the physical quality of students in higher education institutions is crucial for ensuring the successful completion of their academic pursuits. This article begins by analyzing the fundamental algorithms of decision trees, subsequently building upon this theoretical foundation to develop an ID3 decision tree model. It employs the decision tree model for data classification, extraction, and other related processes. By integrating the C4.5 algorithm and further segmentation techniques, a more stable decision tree is derived, allowing for a comprehensive analysis of physical fitness test data. The study’s findings indicate that using rope skipping test data as an example, the analytical model maintains an error margin of 3%. Over four years of physical test data, there was a notable classification of 1% of students into the “excellent” category in 2020, with nearly 20% achieving “good” status. The proportions of “excellent” and “good” students in 2021 saw a significant rise from the previous year. Furthermore, the percentage of students categorized as “failing” in 2023 decreased by 0.88%. To enhance the effectiveness of physical education, reforms could focus on multi-faceted collaborative efforts, reinforcing the application of the ID3 decision tree model in analyzing physical fitness data and targeting interventions to boost physical fitness outcomes.

Information technology-assisted analysis of college students’ physical fitness test data and research on physical education teaching reforms

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Enhancing the physical quality of students in higher education institutions is crucial for ensuring the successful completion of their academic pursuits. This...

Information technology-assisted analysis of college students’ physical fitness test data and research on physical education teaching reforms

Data publikacji: 07 cze 2024

Zakres stron: -

Otrzymano: 04 mar 2024

Przyjęty: 04 maj 2024

DOI: https://doi.org/10.2478/amns-2024-1386

Słowa kluczowe
ID3 decision tree model, Physical test data analysis, Physical education reform, C4.5 algorithm

© 2024 Bing Xie, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Information technology-assisted analysis of college students’ physical fitness test data and research on physical education teaching reforms

Data publikacji: 07 cze 2024

Zakres stron: -

Otrzymano: 04 mar 2024

Przyjęty: 04 maj 2024

DOI: https://doi.org/10.2478/amns-2024-1386

Słowa kluczoweID3 decision tree model, Physical test data analysis, Physical education reform, C4.5 algorithm

© 2024 Bing Xie, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
ID3 decision tree model, Physical test data analysis, Physical education reform, C4.5 algorithm