With the development of information technology, teaching reform has also undergone major changes. e traditional college physical education teaching method cannot meet the needs of the majority of students, and the physical education teaching mode continues to be reformed. Microcourse is the most intuitive form of deep integration of information technology and physical education. From the perspective of the ipped classroom (FC), the physical education model has gradually changed from teacher centered to student centered. Deep learning (DL) emphasizes that learners have the ability to actively construct knowledge, e ectively transfer knowledge, and solve real problems. is design applies DL and convolutional neural network to the teaching design of physical gymnastics in colleges and universities.e application of the DL teaching model based on FC in the microcourse teaching of gymnastics in colleges and universities is studied and evaluated. e results show that the current utilization of microcourse teaching resources is too low. Interest-oriented teaching microcourses cannot improve students' interests. e proportion of students who are interested is relatively small, and more than 50% of students are not interested. Teachers generally believe that the current gymnastics microcourse needs further optimization and improvement. e poor quality of microvideos and the lack of supervision and reward mechanism in the course are the main reasons for the insu cient students' interest. e complexity of the videos and the liveliness of the discussions are the main problems of low resource utilization. e student's interest in learning is greatly improved after the application of the designed model, and the proportion increases to 82.4%. e e ect on ordinary college students is the most obvious, and the e ect of microvideo learning has been signi cantly promoted. Design mode has the most obvious improvement in improving learning e ciency and autonomous learning ability. e improvement of learning ability has increased from 18% to 72%, and the improvement of learning e ciency has increased from 39% to 82%. Meanwhile, students' interest in learning is stimulated, and the utilization of resources is improved.
In order to solve the problem, the psychological identification of athletes in professional competition pressure is difficult. This paper first analyzes the sources of athletes’ psychological pressure based on the hierarchical clustering method, and then divides the weights of the sources of psychological pressure, quantificationally scores them and constructs an identification model of athletes’ psychological pressure. Then, the clustering process is optimized based on the K-Means algorithm, and its effectiveness is verified. Finally, the psychological stress of 10 players in a football club was analyzed. The results show that the model effectively and reasonably reflects the influence of pressure sources on the athletes’ competitive state during the competition, which provides a basis for the decision-making of relief about athletes’ stress.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.