Sports Faculty Department, Liaoning University, Shenyang, 110036, China.
Basic Teaching Department of Shandong Jiaotong University (Weihai Campus), Weihai, 264209, China.
Sci Rep. 2023 Sep 20;13(1):15628. doi: 10.1038/s41598-023-41496-5.
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).
大学生的体育行为受到多种因素的影响，体育学习兴趣和体育自主支持是潜在的心理特征因素，对大学生的体育行为有重要的影响价值。机器学习方法被广泛用于构建预测模型，并表现出高效性。为了了解体育学习兴趣和体育自主支持对大学生体育行为（体育锻炼水平）的影响，研究决定使用机器学习的相关方法构建预测模型，以找出它们之间的内在关系。本文从体育自主和体育学习兴趣两个方面总结了影响大学生体育行为（体育锻炼水平）的相关因素，并调查了大学生的人口统计学和社会学信息作为补充。研究通过构建机器学习算法的预测模型和比较方法来评估预测模型的水平，以确定最佳预测模型。结果表明，逻辑回归模型的预测准确率为 0.7288，召回率为 0.7590，F1 值为 0.7397；KNN 模型的预测准确率为 0.6895，召回率为 0.7596，F1 值为 0.7096；朴素贝叶斯模型的预测准确率为 0.7166，召回率为 0.6703，F1 值为 0.6864；LDA 模型的预测准确率为 0.7263，召回率为 0.7290，F1 值为 0.7265；支持向量机模型的预测准确率为 0.6563，召回率为 0.7700，F1 值为 0.6845；GBDT 模型的预测准确率为 0.6953，召回率为 0.7039，F1 值为 0.6989；决策树模型的预测准确率为 0.6872，召回率为 0.6507，F1 值为 0.6672。逻辑回归模型在体育学习兴趣和运动自主性支持的组合中表现最佳，这是由于其线性分类特性、更好的适应性、更高的计算效率以及更好的特征选择和异常值处理适应性。结论指出，当结合体育学习兴趣和体育自主支持来预测大学生的体育行为（体育锻炼等级）时，逻辑回归模型的预测水平最高，这也为提高大学生的体育行为（体育锻炼等级）提供了重要参考。
