Research on the Innovation of Smart Teaching Mode of University Physical Education Driven by Digital Technology

Abstract

Physical education teaching mode is the link between physical education teaching theory and physical education teaching practice, which has a direct impact on the quality and teaching effect of physical education teaching. In this paper, the recognition algorithm is applied to physical education, and a graph-convolutional neural network is constructed. In order to solve the problem that a graph convolutional neural network can only deal with non-motion graphs, using the Masked Attention mechanism, assigning different weights to neighboring nodes, extracting node features and features between neighboring nodes, thus the graph attention neural network, and introducing the YOLO model to predict the single and multiple postures and correct the students’ technical movements. Through the comparative experimental study in this paper, it was found that the p-values of the four dimensions of students’ attention to athletics, adverse interest in athletics learning, positive interest in athletics learning, and daily athletics exercise were all 0.000<0.05. In the two dimensions of positive interest in athletic learning and daily athletic exercise, the mean values of the students in the experimental group reached 29.11 and 32.89, respectively. In terms of comparing the results of the students in the two groups, the 50-meter Run p=0.021<0.05, Vertical Long Jump p=0.002<0.05, Seated Body Flexion and Sit-ups for girls p=0.000<0.05. Therefore, the data-intelligent physical education teaching model constructed in this paper can increase the degree of students’ interest in physical education, improve students’ participation in physical education classrooms, and optimize students’ physical education achievement and performance.

Keywords

• graph convolutional neural network
• graph attention network
• yolo model
• posture recognition
• physical education teaching
• 97p10