Design of Assessment Judging Model for Physical Education Professional Skills Course Based on Convolutional Neural Network and Few-Shot Learning.

In recent years, the promotion of quality education and the development of curriculum and teaching materials reform have put forward higher goals and requirements for professional skills in physical education. However, there are still many shortcomings in the nuclear assessment of physical education professional skills, such as lack of clarity in evaluation objectives, lack of scientific in evaluation indexes, lack of systematization in evaluation contents, lack of diversity in evaluation methods, lack of authority in evaluation results, and lack of timely prediction and analysis of students' mastery of classroom teaching skills, thus not giving good play to all the functions that the nuclear assessment should have, thus to a certain extent fettering the further enhancement of physical education. With the development of information technology, artificial intelligence, as a new technology, can guide the improvement of the assessment and judging mode of physical education professional skills courses, and is also an important guiding idea for physical education majors to meet the development demands of information-based society. Based on the analysis of the connotation and characteristics of deep learning, this paper points out the insufficiency of the assessment and evaluation of traditional physical education professional skills courses and proposes a method of assessment and evaluation of physical education professional skills courses based on convolutional neural networks and small sample learning. In the case of a small amount of data in the course assessment, we use a small number of samples to learn, and only need a small number of samples to learn quickly. Using the improvement measures under the teaching concept of deep learning, physical education personnel are required to truly change in terms of professional skills mastery and evaluation. We effectively implement improvement measures, promote the improvement of physical education professional skills, and realize the migration and innovation of sports knowledge and skills.

Should agility training for novice badminton players be physically or perceptually challenging?

BACKGROUND: Despite its well-known importance in sports, agility is ambiguously defined and lack of research. Shuttle Run (SR) challenges physical quickness and is commonly used to improve the on-court agility of badminton players. In contrast, Reactive Initiation Training (RIT) challenges perceptual quickness, merely demanding rapid initiation of step toward the direction of shuttlecock. METHODS: The current study explores to compare SR with RIT to determine the relative effectiveness of these training on improving the on-court agility of badminton. 20 novice badminton players were split in half to receive either RIT or SR on court for five days. Before and after training, the on-court agility test with and without anticipation was administered. RESULTS: The results showed that both training methods shortened the mean running time, however, only RIT additionally reduced the initiation time and its proportion on those time-consuming positions when agility was assessed without anticipation. CONCLUSIONS: Therefore, the agility training for novice badminton players should be more perceptually than physically challenging to avoid vain effort and unnecessary injuries.