Application of Deep Learning in College Physical Education Design under Flipped Classroom. (16th September 2022)

• Record Type: Journal Article
• Authors: Huang, Jun; Yu, Dian
• Other Names: Ullah Inam Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2022(2022)
• Publication Date: 2022-09-16
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2022/7368771
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : With the development of information technology, teaching reform has also undergone major changes. The 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 flipped 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, effectively transfer knowledge, and solve real problems. This design applies DL and convolutional neural network to the teaching design of physical gymnastics in colleges and universities. The application of the DL teaching model based on FC in the microcourse teaching of gymnastics in colleges and universities is studied and evaluated. The results show that the current utilization of microcourse teaching resources is too low. Interest-oriented teaching microcourses cannot improve students' interests. The 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. The poor quality of microvideos and the lack of supervision and reward mechanism in the course are the main reasons for the insufficient students' interest. The complexity of the videos and the liveliness of the discussions are the main problems of low resource utilization. The student's interest in learning is greatly improved after the application of the designed model, and the proportion increases to 82.4%. The effect on ordinary college students is the most obvious, and the effect of microvideo learning has been significantly promoted. Design mode has the most obvious improvement in improving learning efficiency and autonomous learning ability. The improvement of learning ability has increased from 18% to 72%, and the improvement of learning efficiency has increased from 39% to 82%. Meanwhile, students' interest in learning is stimulated, and the utilization of resources is improved.