By investigating the status quo of the swimming training market in a certain area, we can obtain information on the current development of the swimming training market in a certain area and study the laws of the development of the market so as to provide a theoretical basis for the development of the market. This paper designs an evaluation algorithm suitable for swimming training based on the improved AlexNet network. The algorithm model uses a 3 × 3 size convolution kernel to extract features, and the pooling layer uses a nonoverlapping pooling strategy. In order to accelerate the network convergence, the model introduces batch normalization technology. The algorithm uses data augmentation technology to expand the data set, including rotation and random erasure, to a certain extent alleviating the problem of overfitting. The results of the study showed that there were no significant differences in fat, minerals, protein, body mass index, basal metabolic rate, and total energy expenditure in the body composition ratios of children in the convolutional neural network assessment group and the control group, while muscle and total body water were not significantly different. However, there are significant differences in fat-free body weight and muscle strength of various segments of the body, among which there are very significant differences in muscle strength of lower limbs in each segment of the body. There were no significant differences in minerals, body mass index, basal metabolic rate, total energy expenditure, and lower limb muscle strength in the body composition ratios of men and women in the convolutional neural network assessment group. There are significant differences in body weight, upper limb muscle strength, and trunk muscle strength. There were no significant differences in the proportions of body composition between men and women in the control group, except for fat and protein.
Objective. A retrospective cohort study was conducted to analyze the application value of Python programming in general education and comprehensive quality improvement of medical students. Methods. A retrospective analysis was made on the application value of Python programming in the general education classroom of medical students from September 2020 to July 2021 by undergraduate students majoring in anesthesia in grade 2020, imaging in grade 2019, clinical in grade 2020, and laboratory sciences in grade 2020 in our university. A hundred students who used Python programming in general education class were divided into study group and control group. The teaching satisfaction, medical knowledge and lifelong learning ability, clinical skills, medical service ability, disease prevention, health promotion ability, interpersonal communication ability, and information management and research ability were compared between the two groups. Results. In a comparison of teaching satisfaction between the two groups, the study group was very satisfied in 89 cases, satisfactory in 10 cases, and general in 1 case, and the satisfaction rate was 100.00%; the control group was very satisfied in 54 cases, satisfactory in 23 cases, general in 13 cases, and dissatisfied in 10 cases, and the satisfaction rate was 90.00%. The teaching satisfaction in the study group was higher than that in the control group, and the difference was statistically significant ( P < 0.05 ). Compared with the control group, medical knowledge ability (basic knowledge, general education, and professional knowledge) and lifelong learning ability (learning concept and professional learning attitude) in the research group were significantly higher than those in the research group ( P < 0.05 ). The scores of clinical skills (medical history analysis, basic diagnosis, treatment techniques, and disease analysis) and medical service ability (first aid ability, comprehensive analysis ability, and disease analysis ability) in the study group were significantly higher than those in the control group ( P < 0.05 ). In terms of the ability of disease prevention and health promotion, the scores of disease prevention (health guidance, health education, and self-care) and health promotion ability (cooperative participation in diagnosis and treatment, guidance of medical and health work, and rational use of health resources) in the study group were higher than those in the control group, and the difference was statistically significant ( P < 0.05 ). In the comparison of interpersonal communication ability, the scores of listening, expression, understanding, trust, medical terminology, and communication ability in the study group were higher than those in the control group, and the difference was statistically significant ( P < 0.05 ). Comparing information management with research ability, the scores of information management ability (searching information, screening information, and sorting information) and research ability (arrangement ability, planning ability, and execution ability) in the research group were higher than those in the control group, and the data difference was statistically significant ( P < 0.05 ). Conclusion. The application of the Python programming method in general education and comprehensive quality improvement of medical students can effectively improve medical students’ teaching satisfaction and medical knowledge such as lifelong learning ability, clinical skills, medical service ability, disease prevention, health promotion ability, interpersonal communication ability, and information management and research ability, which has a positive impact on the improvement of comprehensive quality.
To address the shortcomings of the existing comprehensive evaluation methods, the entropy TOPSIS (Technique for Oder Preference by Similarity to Ideal Solution) method was introduced into the comprehensive evaluation of classes students’ grades in college, and the results of year 2019∼2020 of 30 classes student’s major courses of 2 colleges within 2 semesters were used as an example for analysis. The study shows that the entropy TOPSIS method can not only effectively reflect the course differences but also avoid the subjectivity of weight setting and improve the rationality and objectivity of the comprehensive evaluation and ranking of students’ performance in classes, and it can be used as an objective evaluation tool for the external factors affecting students’ performance. At the same time, the internal factors affecting students’ performance are analyzed. It can be found that reasonable sleep time and the cultivation of good study motivation can help improve students’ academic performance. The results of this paper are of great theoretical value and technical reference value for scientific evaluation of student performance in universities.
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