The relationship between measurement and evaluation in physical education teaching based on intelligent analysis and sensor data mining

Abstract

Assessing the effectiveness of physical education instruction, students’ learning, and the feedback received from the teaching process are all vital components of the physical education teaching process in colleges and universities. Improving the quality of physical education instruction in these settings is essential. With its ability to drive the digital revolution of physical education in schools, intelligent technology is bringing about significant changes in the field of education and drawing attention from people from all walks of life. To assess intelligent technology’s impact on physical education instruction in a scientific manner, this study utilizes the latest intelligent analysis and sensing data mining to design an intelligent physical education measurement and evaluation model, which utilizes GPS positioning, built-in maps, and gravity sensing to provide real-time feedback on the trajectory, distance, and time of the movement, and then calculates the real-time and average speed of the movement, as different students’ body postures to achieve the the same effect when the required speed is not the same, this paper randomly selected students with different BMI index for empirical analysis. The experimental results show that the principal components of the factor analysis extracted four common factors with a cumulative contribution rate of 69.5%, and the test-retest reliability of the four dimensions is 0.665–0.862.