Exploration of Integrating “Technology+” Swimming Course Teaching for College Students
- DOI
- 10.2991/978-94-6463-242-2_37How to use a DOI?
- Keywords
- Swimming Course Teaching; “Technology+”; College Students; Deep Learning
- Abstract
Swimming is a compulsory course for survival, and in the university life where spare time is more adequate, more and more college students join swimming lessons, and then the teaching quality for swimming courses needs close attention. This article adopted the “technology+” deep learning method to improve and upgrade the teaching methods of swimming courses for college students, and compared the results with traditional teaching methods to obtain results. In the experimental results, the average teaching quality and average learning quality using deep learning methods were improved to 85 and 86 points, respectively. Compared with the upgraded double 79 points using traditional methods, it has increased by 6 points and 7 points respectively. The results indicated that the “technology+” method, which focuses on deep learning, can effectively improve the teaching quality of swimming courses and the quality of students’ learning.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Li Song AU - Dianyi Song AU - Xinxin Hao AU - Xiaoya Zhao PY - 2023 DA - 2023/09/22 TI - Exploration of Integrating “Technology+” Swimming Course Teaching for College Students BT - Proceedings of the 2023 4th International Conference on Artificial Intelligence and Education (ICAIE 2023) PB - Atlantis Press SP - 301 EP - 309 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-242-2_37 DO - 10.2991/978-94-6463-242-2_37 ID - Song2023 ER -