Recently, college physical education has received a lot of attention. Traditional physical education and teaching are unable to meet the demand of students in today’s society and cannot attract students’ interest in sports. In this study, the challenges of college physical education classrooms are examined and the teaching objectives, contents, and teaching evaluation of the course are established to improve students’ deep learning (DL) and the effect and quality of college PE. The flipped classroom model based on DL is applied to PE teaching in colleges, and the influence of classroom teaching is explored. Moreover, a teaching experiment is conducted and the teaching effect before and after the experiment is compared. The results show that the
P
values of the three groups of students in the five items of 50 m running, sit-up/pull-up, 800 m/1000 m, sit and reach, and crossing direction change running are 0.003, 0.012, 0.024, 0.024, and 0.048, respectively. This indicates significant improvements in the physical quality of the three groups of students after the experiment. In addition, the three groups of students have significant differences in basketball technical and tactical application ability, DL ability, and autonomous learning ability after the experiment. This exploration integrates the concept of DL with the flipped classroom, providing a theoretical supplement for the design of flipped classrooms in college PE.
With the increased development of information technology, almost all the sectors have been developed. Age, educational qualifications, gender, and other factors have no bearing on acquiring knowledge in information technology.Most humans use mobile phones and other gadgets to make their lives easier. Machine Learning techniques are used to analyse the given data and aid in the classification or prediction of the dataset depending on the problem statement. It is significant to determine human behaviour analysis in the context of sports. In this research, the Deep Learning-Deep Belief Network (DL-DBN) algorithm is implemented with probability to analyse human behaviour in sports and implement a distributed probability model for classifying the behavior. The classification results have shown that the accuracy for strength training is both the maximum and the smallest, reaching 99% and 71%, respectively.
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