How family structure influences middle-school students’ involvement in physical exercise and their academic achievement in China

Guo, Zhengmao; Qi, Changzhu; Yang, Jian; Xu, Yan; Li, Shouming

doi:10.1057/s41599-023-01636-8

Cited by 3 publications

(1 citation statement)

References 76 publications

(49 reference statements)

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“…The influencing factors of sports behavior are influenced by potential factors, including interest, autonomy and other factors 3 . Some studies have taken physical health as an important means to predict students’ academic performance and motor skill learning performance, and as an important educational means to interfere with students’ occupation 4 , 5 . In their research, they used some computer algorithms to establish a prediction model between physical fitness test indicators and academic performance or motor skill performance, so as to find out the factors that affect students’ prediction results based on the prediction results, and adjust and correct the actual problems, so as to make the improvement of students’ academic performance or motor skills more significant 6 .…”

Section: Introductionmentioning

confidence: 99%

Building a prediction model of college students’ sports behavior based on machine learning method: combining the characteristics of sports learning interest and sports autonomy

Liu,

Hou,

Emolyn

et al. 2023

Sci Rep

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College students’ sports behavior is affected by many factors, and sports learning interest and sports autonomy support are potential psychological characteristic factors, which have important influence value on college students’ sports behavior. Machine learning methods are widely used to construct prediction models and show high efficiency. In order to understand the impact of sports learning interest and sports autonomy support on college students’ sports behavior (physical exercise level), the research decided to use the relevant methods of machine learning to build a prediction model, so as to find the internal relationship between them. This paper summarizes the relevant factors that affect college students’ sports behavior (physical exercise level) from two aspects, namely, sports autonomy and sports learning interest, and surveys the demographic and sociological information of college students as a supplement. The research evaluates the level of the prediction model through the construction of the prediction model of the machine learning algorithm and the comparison method, so as to determine the optimal prediction model. The results show that the prediction accuracy of the logistic regression model is 0.7288, the recall rate is 0.7590, and F1 is 0.7397; The prediction accuracy of KNN model is 0.6895, the recall rate is 0.7596, and F1 is 0.7096; The prediction accuracy of naive Bayesian model is 0.7166, the recall rate is 0.6703, and F1 is 0.6864; the prediction accuracy of LDA model is 0.7263, the recall rate is 0.7290, and F1 is 0.7265; The prediction accuracy of the support vector machine model is 0.6563, the recall rate is 0.7700, and F1 is 0.6845; The prediction accuracy of GBDT model is 0.6953, the recall rate is 0.7039, and the F1 score is 0.6989; The prediction accuracy of the decision tree model is 0.6872, the recall rate is 0.6507, and F1 is 0.6672. The logistic regression model performs best in the combination of sports learning interest and motor autonomy support, due to the combination of its linear classification characteristics, better adaptability, high computational efficiency, and better adaptability to feature selection and outlier processing. The conclusion points out that the prediction level of logistic regression model is the highest when combining sports learning interest and sports autonomy support to predict college students’ sports behavior (sports exercise grade), which also provides an important reference for improving college students’ sports behavior (sports exercise grade).

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Section: Introductionmentioning

confidence: 99%

Building a prediction model of college students’ sports behavior based on machine learning method: combining the characteristics of sports learning interest and sports autonomy

Liu,

Hou,

Emolyn

et al. 2023

Sci Rep

View full text Add to dashboard Cite

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Family structure and children’s academic performance in China: the roles of economic capital, social capital, and cultural capital

Tang,

Jiang

2024

Asia Pacific Educ. Rev.

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The effectiveness of child and adolescent sports engagement in China: an analysis of China’s results for the 2016–2022 Global Matrix report cards on physical activity

Xiang,

Zhao,

Tengku Kamalden

et al. 2023

Humanit Soc Sci Commun

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China strives to become a leading sports nation. Through implemented policies and programs, Chinese children and adolescents have shown notable advancements and improvements in their engagement in physical activity in recent years, indicating an overall positive trend. However, some indicators exhibit a concerning “inversion phenomenon”. This study utilizes data from three surveys conducted between 2016 and 2022 by the Active Healthy Kids Global Alliance. By comparing current ratings with those from 2016 and 2018, both cross-sectionally and longitudinally, significant obstacles in promoting physical activity and health among Chinese children and adolescents are revealed, with persistently low scores. The 2022 Global Matrix 4.0 report ranks Chinese children and adolescents 53rd out of 57 surveyed countries, with an overall average physical activity score of D. Similarly, the average score for sources of influence ranks at 53rd. Consequently, the establishment of an integrated school, family, and social sports promotion mechanism, led by the government, becomes an imperative prerequisite for enhancing the levels of physical activity and overall health among children and adolescents.

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How family structure influences middle-school students’ involvement in physical exercise and their academic achievement in China

Cited by 3 publications

References 76 publications

Building a prediction model of college students’ sports behavior based on machine learning method: combining the characteristics of sports learning interest and sports autonomy

Building a prediction model of college students’ sports behavior based on machine learning method: combining the characteristics of sports learning interest and sports autonomy

Family structure and children’s academic performance in China: the roles of economic capital, social capital, and cultural capital

The effectiveness of child and adolescent sports engagement in China: an analysis of China’s results for the 2016–2022 Global Matrix report cards on physical activity

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