| Literature DB >> 34567482 |
Min Liu1, Jianjun Bu1.
Abstract
In recent years, intelligent medical communication technology has developed rapidly, and the advancement of hardware technology has accelerated the development of software technology. Physical exercise breaks the limits of all aspects of the human body and requires scientific and reliable information. Industrial cameras realize collection and high-resolution image processing functions and use artificial intelligence to create conditions for collecting scientific data. With the powerful driving force of artificial intelligence, it is believed that physical education will develop to a new height. The intellectualization of physical education makes people expect that it will also bring some inevitable problems, but as long as we continue to overcome and improve, we believe that artificial intelligence will better serve physical education and benefit mankind. Artificial intelligence is the current trend of social development. This paper takes physical education as a breakthrough point to discuss the application of artificial intelligence in physical education. This paper uses the grey GM prediction model and questionnaire survey method to build an online education platform and develop an effective education model, which can integrate the country's best educational resources and reduce time and space constraints. Providing a relaxing learning environment for education, one can learn anytime, anywhere. In a typical physical education system, the average self-efficacy score is converted from 20.48 points to 68.37 points, and the average self-score of physical education in the integrated artificial intelligence environment is converted from 30.25 points to 75.54 points. The data show that the quality of education has greatly improved.Entities:
Mesh:
Year: 2021 PMID: 34567482 PMCID: PMC8460374 DOI: 10.1155/2021/4323043
Source DB: PubMed Journal: J Healthc Eng ISSN: 2040-2295 Impact factor: 2.682
Selection of commonly used teaching methods for college physical education teachers.
| Frequently used | Occasionally used | Uncommonly used | ||||
|---|---|---|---|---|---|---|
|
| % |
| % |
| % | |
| Lecture | 51 | 85.00 | 9 | 15.00 | 0 | 0.00 |
| Taught | 45 | 75.00 | 10 | 10.00 | 5 | 8.33 |
| Theoretical research | 26 | 43.33 | 26 | 46.33 | 8 | 13.34 |
| Seminar | 2 | 3.33 | 9 | 15.00 | 49 | 81.66 |
| Lecture style | 3 | 5.00 | 12 | 20.00 | 45 | 75.00 |
Figure 1The role of college physical education teachers in teaching.
The influence of the network technology on physical education graduate students in colleges and universities.
| Great influence | Big influence | Influential | Small influence | No effect | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
|
| % |
| % |
| % |
| % |
| % | |
| Learning philosophy | 37 | 41.11 | 28 | 31.11 | 18 | 20.00 | 7 | 7.78 | 0 | 0.00 |
| Learning attitude | 42 | 46.67 | 23 | 25.60 | 18 | 20.00 | 7 | 7.78 | 0 | 0.00 |
| Learning environment | 41 | 45.56 | 19 | 21.11 | 17 | 18.90 | 13 | 14.40 | 0 | 0.00 |
| Learning ability | 26 | 28.90 | 25 | 27.80 | 26 | 28.90 | 13 | 14.40 | 0 | 0.00 |
| Learning path | 26 | 28.90 | 24 | 26.70 | 20 | 22.22 | 10 | 11.11 | 0 | 0.00 |
Figure 2Influence of the network technology on sports graduate students in colleges and universities.
Figure 3Statistics table of the learning philosophy of postgraduates in physical education.