Student physical education teaching achievement test incorporating biomechanics analysis via decision tree algorithm

In the context of China’s economic growth, building a “sports power” is crucial. With the Internet’s development, the nation focuses on physical education curriculum and students’ test results. Currently, Chinese students’ physical education achievements are mainly evaluated by storage, inquiry, and...

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Veröffentlicht in:Molecular & cellular biomechanics Jg. 22; H. 1; S. 712
Hauptverfasser: Zhao, Ningning, Wang, Lei, Sun, Hongxia
Format: Journal Article
Sprache:Englisch
Veröffentlicht: 15.01.2025
ISSN:1556-5297, 1556-5300
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Abstract In the context of China’s economic growth, building a “sports power” is crucial. With the Internet’s development, the nation focuses on physical education curriculum and students’ test results. Currently, Chinese students’ physical education achievements are mainly evaluated by storage, inquiry, and basic statistics. Digital mining is under - utilized, leaving much data unexplored. But digital education’s progress has brought educational data to the fore, enabling schools to find value and patterns for better teaching decisions. In this case, this paper delves into the integration of biomechanics analysis into the assessment of student physical education teaching achievements. Biomechanics offers invaluable insights into the mechanical aspects of human movement during physical activities. By examining biomechanical factors such as joint angles, muscle forces, and movement velocities during various sports, a more profound understanding of students’ physical capabilities and performance can be achieved. Taking the physical test results of students in a specific school as a case study, this research employs data mining technology to explore the rich dataset. The optimized decision tree algorithm is then utilized to analyze the biomechanics - related data. This algorithm enables the identification of key factors that influence students’ physical test outcomes. For instance, it can pinpoint how a student’s running gait mechanics, including stride length, frequency, and leg - muscle activation patterns, affect their performance in track - and - field events. Through this comprehensive approach, the paper not only aims to uncover hidden information within the physical test data but also to elucidate the intricate interplay between biomechanics and students’ overall physical education performance. By dissecting the factors that influence physical test results from a biomechanical perspective, this study provides actionable insights for enhancing physical education teaching methods. Educators can leverage these findings to design personalized teaching programs that cater to the unique biomechanical characteristics of each student, thereby optimizing the effectiveness of physical education and promoting students’ physical development and athletic performance.
AbstractList In the context of China’s economic growth, building a “sports power” is crucial. With the Internet’s development, the nation focuses on physical education curriculum and students’ test results. Currently, Chinese students’ physical education achievements are mainly evaluated by storage, inquiry, and basic statistics. Digital mining is under - utilized, leaving much data unexplored. But digital education’s progress has brought educational data to the fore, enabling schools to find value and patterns for better teaching decisions. In this case, this paper delves into the integration of biomechanics analysis into the assessment of student physical education teaching achievements. Biomechanics offers invaluable insights into the mechanical aspects of human movement during physical activities. By examining biomechanical factors such as joint angles, muscle forces, and movement velocities during various sports, a more profound understanding of students’ physical capabilities and performance can be achieved. Taking the physical test results of students in a specific school as a case study, this research employs data mining technology to explore the rich dataset. The optimized decision tree algorithm is then utilized to analyze the biomechanics - related data. This algorithm enables the identification of key factors that influence students’ physical test outcomes. For instance, it can pinpoint how a student’s running gait mechanics, including stride length, frequency, and leg - muscle activation patterns, affect their performance in track - and - field events. Through this comprehensive approach, the paper not only aims to uncover hidden information within the physical test data but also to elucidate the intricate interplay between biomechanics and students’ overall physical education performance. By dissecting the factors that influence physical test results from a biomechanical perspective, this study provides actionable insights for enhancing physical education teaching methods. Educators can leverage these findings to design personalized teaching programs that cater to the unique biomechanical characteristics of each student, thereby optimizing the effectiveness of physical education and promoting students’ physical development and athletic performance.
Author Zhao, Ningning
Wang, Lei
Sun, Hongxia
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