Application of Decision Tree in PE Teaching Analysis and Management under the Background of Big Data

Abstract

With the continuous development of physical education reform, the defects and deficiencies of physical education teaching in colleges and universities are increasingly exposed. The reform of the original physical education teaching thought, education system, teaching mode, and method has achieved little. At present, the research on the prediction of physical education teaching achievements is mainly aimed at the prediction of athletes and physical education teaching achievements or the prediction of the past data of college students. This paper studies the physical education under the decision tree under the background of big data and constructs the physical education management system. When the number of tests reaches 40, the qualified rate of long-distance running is 65.2%, that of basketball is 68.1%, and that of volleyball is 68.2%. The quality of physical education teaching determines the lifeline of the development of school physical education teaching. In the process of collecting and selecting teaching materials, this paper enriches teaching materials and teaching reflection, cultivates one’s own understanding, and improves the artistic appeal and creativity of teaching.

Analysis and prediction of tennis players' match performance with sentiment analysis

“…Lee et al [11] chose to use decision tree algorithm for business analysis and prediction, because decision tree algorithm is a user-friendly prediction tool, and users can easily interpret data without statistical knowledge or complex formulas.Marynowicz et al [12] used the white-box decision tree model to verify and predict the perceived load rating of players in youth football, examined the relationship between load rating and external measurement of youth football training intensity at the group and individual levels, compared the applicability of the individual model and the group model, and the results showed that,Individual model should be used to evaluate the players to external load response. In addition, the research shows that the decision tree model provides intuitive explanations for the research, bringing the possibility of visualization.Che et al [13] studied physical education under the decision tree under the background of big data, built a physical education management system, used the decision tree model to predict athletes and physical teaching results, and predicted according to the past data of college students, which promoted the continuous deepening of physical education reform. Inspired by the game prediction study of NBA basketball players, we considered whether the correlation between emotional state and game performance inferred from social media posts of professional tennis players could be observed in professional tennis players, and with the same idea in mind, we undertook this project.…”

A cohort study of factors influencing the physical fitness of preschool children: a decision tree analysis

“…The software IBM SPSS modeler was used to create a decision tree model (parameters to be set as follows: the maximum depth of the tree structure is 5, the minimum number of cases of nodes influencing the change in the physical fitness of toddlers is 100, the minimum number of instances of sub-nodes is 50, and the minimum change value of Gini coefficient is 0.0001), and a 10-level crossover was used to verify the accuracy of the model identification. The Chi-squared Automatic Interaction Detector (CHAID), also known as the chi-square automatic interaction detection algorithm, is a decision tree technique based on adjusted significance testing (Bonferroni test), uses the chi-squared test to evaluate the significance of variable grouping on the target variable and selects the splitting point that maximizes the chi-squared value as the optimal splitting point (21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33). Due to the characteristics of large sample size, multiple indicators, this study used the significance of chi-square tests to automatically determine the grouping variables and split values of the multivariate contingency table, thereby quickly and effectively identifying the main influencing factors.…”

“…as these methods lack deep data mining and decision analysis and are vulnerable to covariance problems (21,22). In recent years, scholars have used decision trees in data mining techniques to conduct functional explorations in many fields (23)(24)(25)(26)(27)(28)(29)(30)(31)(32). As an important classification technique in data mining, decision trees are capable of effectively identifying the main influencing factors of physical changes.…”

“…Additionally, decision trees utilize tree diagrams to illustrate the interaction between variables at different levels (21,28). Decision trees have been applied in beneficial explorations in the fields of physical fitness and exercise training, such as the decision-making of human muscle strength indicators (24), analysis of influencing factors in practical teaching of public physical education courses in universities (26), key influencing factor analysis of overweight and obesity in young children (28), as well as the analysis of factors influencing physical activity in children and adolescents during the COVID-19 pandemic (29).…”

Research on College Students' Physical Training Scheme Decision Based on Big Data Analysis

“…Currently, physical fitness training in schools is still based on traditional methods, with problems such as unscientific training methods, unprofessional training teams, unsystematic training plans, inaccurate training monitoring, advanced training methods, and ineffective training management, leading to low training efficiency and poor effectiveness, high incidence of injuries and illnesses, and serious impact on the generation of combat effectiveness [4]. With the rapid development of science and technology, using intelligent methods to solve the problems existing in physical training has become a future trend, which has great potential for improving the level of military physical training.…”