Research on Physical Education Teachers’ Role Change and Teaching Innovation Practices in Colleges and Universities in the Digital Era
Pubblicato online: 24 mar 2025
Ricevuto: 28 ott 2024
Accettato: 17 feb 2025
DOI: https://doi.org/10.2478/amns-2025-0724
Parole chiave
© 2025 Lihong Zheng et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Under the rapid development of the digital era, the personalized needs of college students for sports instruction are becoming more and more prominent. This requires college physical education teachers to change their roles and develop teaching innovations. Combining the characteristics and laws of college physical education courses in colleges and universities and based on the flipped classroom teaching model, this study designs an exercise prescription recommendation method based on ontological reasoning and similarity fusion calculation. The method, on the path of the construction of personalized exercise prescription intelligent recommendation system under artificial intelligence, uses ontological rule reasoning to determine the constraint space of exercise prescription parameters, as well as similarity fusion calculation of the parameters of high-quality cases of exercise prescription, to obtain the parameters of personalized exercise prescription within the constraint space which has its better effect and higher fitness. Finally, this paper analyzes the above-designed methods through experiments, and the experimental results show that the recommendation method using similarity fusion computation obtains relatively high ratings on most users compared to the traditional cooperation-based recommendation method, with an average value of 4.04, while the ResNet-EP and collaborative filtering recommendation methods have an average value of 3.75 and 3.10, respectively. This proves that the ontology-based reasoning of the exercise prescription recommendation model has high accuracy and effectiveness in recommending exercise effects, and has better performance compared to other algorithms, while it may be more suitable for exercise recommendation tasks in practical applications, and can provide users with a better exercise experience. In addition, the experimental data also showed that the pull-up performance and 1000 meters performance of the students in the experimental group who used the exercise prescription model under the flipped classroom teaching mode were significantly different from those of the students in the control group, with P<0.05, which proved that the method of recommending exercise prescription under the flipped classroom teaching mode was effective in promoting the improvement of students’ endurance quality and the quality of their upper limb strength, and verified the usability and validity of the method.
