College Physical Education Research Articles

Research on the Cultivation of Core Literacy of Physical Education Professionals in Colleges and Universities Based on the Background of Data Mining

Abstract In the context of the data-driven era, it is essential to explore practical ways to cultivate the core literacy of sports professionals in colleges and universities. This study aims to analyze the core literacy of physical education majors in depth by using data mining technology to seek a more scientific and systematic cultivation strategy. The study used the decision tree CART algorithm and the improved Apriori algorithm to analyze the physical fitness data of the students in the School of Physical Education of S University. The CART algorithm clustered the biological fitness data of male and female students, and it was found that male students’ primary physical fitness deficiencies were concentrated in the upper body strength and standing long jump events. In contrast, female students showed deficiencies in endurance and lower body explosive strength. The improved Apriori algorithm reveals the association rules between different physical fitness items, for example, there is a strong association between boys’ 50-meter running performance and pull-up performance. There are apparent differences in the influencing factors of physical fitness between male and female students, which need to be targeted to design training programs. It was found through association rule mining that specific physical testing programs significantly affect students’ physical fitness quality. This study provides a new path for cultivating core quality of physical education professionals based on data mining, which offers scientific basis and practical guidance for physical education in colleges and universities.

Inheritance and development of traditional sports culture in college physical education in the context of deep learning

Abstract This study focuses on the inheritance of traditional sports culture in physical education in colleges and universities, which is relatively weak in colleges and universities despite its rich connotation and national characteristics. The article adopts the back propagation algorithm in deep learning to shorten the distance between students and traditional sports culture and improve the inheritance effect. The inheritance critical value of traditional sports culture was calculated by exploring the inheritance effect of traditional sports culture from the three dimensions of cognition, emotion, and behavior, and combining the threshold value of information dissemination. The study further analyzed the inheritance value demand of different programs in colleges and universities based on students’ cognition of each traditional sports program. By comparing the inheritance scores of 20 colleges and universities, it reveals the differences in the effectiveness of different types of colleges and universities in the inheritance of traditional sports culture. The results show that students have the highest awareness of dragon boat as an item (0.6519), while the mean variance of betting plus and crossbow shooting are 47.245 and 17.862, respectively, indicating their importance in the inheritance needs of traditional sports programs in colleges and universities. Among the 20 studied universities, the 4th school had the highest classic sports culture inheritance effect score of 0.54. The study’s findings provide valuable data support and strategy suggestions for optimizing and enhancing the inheritance of traditional sports culture in universities.

The Application of Artificial Intelligence in College Physics Teaching

The Application of Artificial Intelligence in College Physics Teaching

The Impact of the New “3 3” Model of College Entrance Examination on College Physics Teaching

The Impact of the New “3 3” Model of College Entrance Examination on College Physics Teaching

An applied study of using deep learning technology to develop training programs for athletes in college physical education

Abstract This paper proposes the integration method of athlete training volume monitoring information based on deep learning, preprocessing the monitoring information using wavelet transform, obtaining the athlete training volume information after denoising and dimensionality reduction, combining with the theory of deep learning, utilizing convolutional neural network for feature extraction and integration of the processed information, and performing simulation test and analysis. The average Gini coefficient is 84.926, which proves the effectiveness of the method used in this paper. After exercising, the athletes’ weight and body fat rate were monitored to reduce a certain degree, and their lung capacity was improved. A deep learning algorithm generated personalized exercise program training achieved a heart rate interval of 94% for safe and effective workouts. The three stages of the test subjects’ training were tracked and analyzed, and the use of training monitoring can effectively promote the efficiency of training. The physical fitness program (P < 0.01) and physical fitness test (P < 0.05) in the first stage compared with the third stage showed significant improvement.

Analysis of the psychological and physiological conditions of blockchain technology in college physical education

Abstract Blockchain technology, with its decentralization, traceability, and immutability, offers promising solutions across various sectors, including education. This study explores the application of blockchain in enhancing the security and personalization of educational resource sharing within university physical education (PE). We ensure secure resource sharing by developing a blockchain-based model for PE resource distribution and refining the consensus algorithm. Our research involved a 10-week experiment with 80 university students, measuring changes in psychological factors (e.g., anxiety, depression) and physiological parameters (e.g., lung capacity). Results indicated a notable improvement in students’ psychological well-being, with anxiety and depression levels dropping by an average of 15%, and a significant enhancement in physiological health, evidenced by an average increase in lung capacity of 2.11±1.00 ml/kg. These findings underscore the potential of blockchain technology to elevate university PE teaching practices and positively impact student health, highlighting its significant applicability and future potential in educational contexts.

The Construction of Wisdom Teaching Mode of Physical Education and Training in Colleges and Universities in the Internet Era

Abstract The rise of the intelligent teaching model, relying on artificial intelligence and digital information technology, provides students with personalized and autonomous learning pathways. The model encourages students to learn independently and develop personally by integrating online and offline learning resources, enhancing their innovation and practical skills. Educational innovation has focused on exploring how to effectively incorporate competent teaching resources to improve teaching efficiency and students’ physical education skills. This study designed a new smart teaching mode for physical training science using blended learning theory and artificial intelligence technology. The differences in students’ physical education performance, participation, and satisfaction between the traditional and intelligent teaching modes were analyzed by comparison. The students who used innovative teaching methods in the study were better than those who used conventional teaching methods in 100-meter sprint and basketball shooting accuracy, with an average improvement rate of 15%. According to the study, the intelligent teaching mode can effectively enhance students’ physical education learning efficiency and mastery of movement skills, paving the way for a new development direction in college physical education.

Innovative Research on Physical Education Models in Colleges and Universities

Abstract The physical health of college students has increasingly attracted widespread attention from all walks of life. As the new force of national development, the physical health of college students is related to whether the country can have enough strength to meet the difficulties and dangers on the road to wealth and strength. This paper explains whether the teaching of prescription teaching in college physical education is feasible, and then designs a sports prescription-assisted physical education system, which is divided into two modules, namely, physical fitness detection and physical fitness assessment. The article used the K-means algorithm and collected data from students’ fitness testing for data processing. Using the K-means algorithm, the data were clustered and divided into three categories: excellent, unqualified, and average. Based on the above method, the students with unqualified physical fitness were compared and analyzed in terms of teaching mode, and it was found that the students who used exercise prescription improved their sports performance and physical function more significantly. After the teaching experiment, the excellence rate in the three physical fitness tests of 13-minute run, standing long jump, and 90-meter run was 15%, 25%, and 16% higher in the exercise prescription teaching group compared with the traditional teaching group, respectively. The exercise prescription teaching mode has a better teaching effect than the traditional teaching mode, according to the results, and it has clear effects on students’ performance and physical fitness.

The Integration of Artificial Intelligence Technology in College Physical Education: Innovation and Challenges

The Integration of Artificial Intelligence Technology in College Physical Education: Innovation and Challenges

A study of stratified optimization of college physical education course effects under the perspective of cluster analysis

Abstract Due to the different physical fitness of college students, they have different acceptance abilities for college physical education courses, so a unified teaching method cannot be applied among many students. The article focuses on the student stratification model based on learning characteristics and takes the university physical education course as an example to design a university physical education teaching stratification method based on the improved K-means clustering algorithm with initialization denoising and center-of-mass optimization. Taking the physical education test data of the 2021~2023 sessions of a university as the research object, K-means clustering analysis was carried out on the 8-item physical fitness test data to realize the stratification of students’ learning situation. The experimental results show that the method of this paper clusters 14619 students into three layers, and the cluster centroid distances of Cluster 1~3 are 20.35, 16.97, and 18.39, respectively. The maximum standardized mutual information values are 6.820, 3.105, and 2.931, respectively, and the results of the stratification are reasonable and easy to interpret, and they can provide effective references for the development of personalized physical education for school students.

Research on the application of flipped classroom model in college physical education courses in the new media era

Research on the application of flipped classroom model in college physical education courses in the new media era

Multi-objective optimization model-based training program development for athletes in college physical education

Abstract Digital technology has been used in a large number of applications in all aspects of life and various fields. In the field of sports, digital training has been increasingly emphasized by frontline coaches, athletes, and sports researchers. In this paper, by building an objective optimization model, combining particle swarm optimization, and optimizing the computation process with the Kriging agent model, we formulate the most suitable training plan for college athletes and improve the scientific level of college athletes’ training. The multi-objective optimization training plan can accurately enhance the athletes’ physical fitness, physical quality, and sports skills, and the IRM and maximal power of bench press and half squat showed an increasing trend after the training. The body composition (body fat) and longitudinal jump (lower limb explosive power) improved to a certain extent before and after the training. There were significant differences in the sports skill evaluation of the 50-meter run and the three-stage continuous frog jump; the p-value was less than 0.05. The p-values were all less than 0.05. The scientific and practicality of the training program for college athletes based on the multi-objective optimization model is confirmed when the value falls below 0.05.

The Application of Multimedia Network Teaching Based on Big Data in Physical Education Teaching in Colleges and Universities

Abstract The integration of educational information technology presents novel opportunities for revolutionizing traditional pedagogical approaches in college physical education (PE). This study leverages big data analytics through the application of the OpenPose algorithm to meticulously map human postural features, thereby refining instructional design within the college PE framework. In parallel, to align pedagogical resources more precisely with the unique demands of college education, a decision tree algorithm is employed to derive comprehensive user profiles from a cohort of college students, which aids in the targeted recommendation of educational materials. Moreover, this research incorporates online teaching methodologies to develop and implement a big data-driven multimedia network teaching platform tailored specifically for college sports education. Empirical evidence underscores substantial benefits following the platform’s deployment. Post-implementation metrics revealed a marked increase in both the duration and frequency of students’ independent physical activities; the proportion of students engaging in 91-120 minutes of exercise escalated by 15%, and those exercising between three to five times per week saw an increase of 16%. Additionally, the platform significantly enhanced the PE performance of students. Users of the platform attained an average score of 89.29 in their assessments, surpassing the control group’s average of 83.23, who did not utilize the platform. Consequently, this multimedia network teaching platform emerges as a formidable enhancement to college physical education, delivering significant pedagogical advancements and fostering increased physical activity among students.

Research on the optimization model of personalized training for athletes based on machine learning in college physical education

Abstract We use deep learning to extract and identify the physiological indicators, sports, and data analysis parameters, providing scientific references for athletes’ personalized training. This paper aims to construct an optimization system for personalized training for athletes based on machine learning. Initially, we designed the system’s overall structure, hardware, and software scheme, and then we modeled the algorithms for measuring four physiological parameters. The NTC thermistor thermometry method was utilized to measure body temperature, the photoelectric volumetric method for determining blood oxygen and heart rate, and the ECG signal method for calculating blood pressure. The adaptive threshold method uses the human body’s steady-state recognition algorithm, based on a single triaxial acceleration sensor, to recognize steady-state motion states such as resting, running, and walking. After the construction of the system was completed, it was applied to the sports team of a university in Guangzhou, and a semester of personalized training optimization experiments for athletes was conducted. The system found a maximum error of 0.02 °C between the measured body temperature and the results from the medical temperature gun, ensuring stable performance during short-term body temperature changes like training. Except for B, C, D, and G errors of 1%, the rest of the athletes monitored by the system in this paper’s blood oxygen results are consistent with the oximeter. The accuracy of recognizing each sport is above 90%. In the eight swimming events that need to be assessed, the average score before the beginning of the semester was 65.43, and after the experiment, it was 87.14, an improvement of 21.71 points. The system in this paper has a great auxiliary effect on the optimization of personalized training for athletes in terms of physical function monitoring, sports status statistics, and training intensity setting, which provides new ideas and methods for the integration of personalized training for athletes with cutting-edge information technology.

Teaching Core Literacy in College Physical Education and Health Courses Based on Multiple Data Integration

Abstract The study used cluster analysis and decision tree modeling to construct a multivariate data fusion physical fitness evaluation model, and also built a physical fitness core literacy evaluation model through factor analysis and fuzzy comprehensive evaluation method. These two evaluation models were subsequently applied to physical education and health teaching in colleges and universities to measure the quality of physical education teaching in colleges and universities and students’ physical health status. By analyzing the practice of physical education teaching in colleges and universities, it was found that the pull-up program had the worst performance in students’ physical fitness test, with a score of 287. In addition, the lung capacity and 50-meter dash programs contributed the most to students’ total performance, with scores of 1,291 and 1,245, respectively. From the perspective of physical education and health teaching quality, the scores of teaching situation, teaching management and biological education school effectiveness were 4.68, 4.01 and 4.52, respectively, and all of these evaluation grades were good. This study provided an accurate and scientific analysis of the overall quality of physical education and health teaching in universities and the health status of students.

Pedagogical Integration of Core Literacy in College Physical Education and Health Courses Based on Data Mining Techniques

Abstract At universities and colleges today, physical education is overly regimented and ignores students’ basic health literacy. This study aims to investigate this phenomenon by examining the state of physical education instruction in colleges and universities nowadays using data mining techniques. In order to retrieve information on the essential literacies of health and physical education courses at colleges and universities, the study first builds a data mining framework using data clustering analysis, association rule extraction, and decision tree algorithms. The teaching-coordination-level model of the core literacies of physical education and health courses is established utilizing the entropy power approach, and the development history of physical education is examined. To determine the best teaching technique for integrating the core literacies of physical education and health courses, an experiment was conducted using College A as an example. The findings indicate that following the trial, students in the experimental class had substantial improvements in their physical and health literacy scores, with the exception of physical fitness (P>0.05). In the previous 10 years, the mean value of the coupling coordination was between 0.6683. The integration of the essential literacies of physical education and health courses accounted for more than 90% of the total credit hours earned in the best colleges and universities. In addition to enhancing physical education abilities, the integration of health and physical education curricula with core literacy in colleges and universities supports the general development of students’ health literacy.

The Relationship and Effect Among Physical Literacy Attributes in University Physical Education During the Pandemic Quarantine Period

Purpose: This prospective cohort study investigated the impact of online and hybrid teaching of university-required physical education on students’ physical literacy-related correlates. To avoid the widespread pandemic, the university decided to offer courses online through video conferencing or via a hybrid mode. When compared with face-to-face instruction, these formats may intensely affect students’ physical literacy journey during early adulthood. Methods: A group of 1,738 students (42% males and 58% females) aged 18.52 (±1.29) responded to a three-phase online survey in various courses. Results: The structural equation modeling determined that motivation and physical activity levels were negatively related to physical competence and knowledge and understanding. Further repeated-measure analyses recognized the interaction effect of amotivation. Results indicated that participants in the online teaching mode may experience a learning predicament over those in the hybrid one. Conclusion: Practically, the physical literacy teaching strategies in nurturing motivation and physical competence should be reinforced.

Application of Diversified Teaching Strategies in University Physical Education Courses

Application of Diversified Teaching Strategies in University Physical Education Courses

The Ideological and Political Education in University Physical Education Courses: A New Concept of Physical Education

The Ideological and Political Education in University Physical Education Courses: A New Concept of Physical Education

The Strategy of “Sports for People” in University Physical Education in East China under the Concept of Integration of Sports and Education

The Strategy of “Sports for People” in University Physical Education in East China under the Concept of Integration of Sports and Education