Ma Shumeng, Jia Ning, Wei Xiuchao, Zhang Wanyi
College of Education, Hebei Normal University, Shijiazhuang, 050024, China.
Qin Huangdao, No.1 Senior High School, Qinhuangdao, 066000, China.
Sci Rep. 2025 Jun 1;15(1):19183. doi: 10.1038/s41598-025-04146-6.
Negative academic emotions reflect the negative experiences that learners encounter during the learning process. This study aims to explore the effectiveness of machine learning algorithms in predicting high school students' negative academic emotions and analyze the factors influencing these emotions, providing valuable insights for promoting the psychological health of high school students. Based on the microsystem proposed in ecological systems theory, we comprehensively consider individual and school factors that affect students' negative academic emotions. We randomly selected 1,710 high school students from Hebei Province, China (742 males), who completed the Adolescent Resilience Scale, Multidimensional Multi-Attributional Causality Style Scale, Academic Self-Efficacy Questionnaire, Teacher Discipline Style Scale, and Academic Emotion Scale. We applied various machine learning models, such as logistic regression, naive Bayes, support vector machine, decision tree, random forest, gradient boosting decision tree, and adaptive boosting, to analyze the students' negative academic emotions. The results show that the random forest model had the best predictive performance, with an accuracy of 83.9%. Subsequently, the importance of variables was determined using the forward feature selection method. We concluded that the most important factors for predicting high school students' negative academic emotions are affect control, followed by ability attribution, luck attribution, background attribution, self-efficacy for learning behaviors, and self-efficacy for learning abilities. This study validates the applicability and value of machine learning models in predicting negative academic emotions, providing important insights for educational practice. When designing intervention strategies, attention should be given to the development of emotional control and attribution styles to help students better alleviate excessive negative academic emotions.
消极学业情绪反映了学习者在学习过程中遇到的负面经历。本研究旨在探讨机器学习算法在预测高中生消极学业情绪方面的有效性，并分析影响这些情绪的因素，为促进高中生的心理健康提供有价值的见解。基于生态系统理论中提出的微观系统，我们综合考虑影响学生消极学业情绪的个人和学校因素。我们从中国河北省随机选取了1710名高中生（742名男生），他们完成了青少年复原力量表、多维多归因因果风格量表、学业自我效能量表、教师纪律风格量表和学业情绪量表。我们应用了各种机器学习模型，如逻辑回归、朴素贝叶斯、支持向量机、决策树、随机森林、梯度提升决策树和自适应提升，来分析学生的消极学业情绪。结果表明，随机森林模型具有最佳的预测性能，准确率为83.9%。随后，使用前向特征选择方法确定变量的重要性。我们得出结论，预测高中生消极学业情绪的最重要因素是情感控制，其次是能力归因、运气归因、背景归因、学习行为自我效能感和学习能力自我效能感。本研究验证了机器学习模型在预测消极学业情绪方面的适用性和价值，为教育实践提供了重要见解。在设计干预策略时，应关注情绪控制和归因方式的发展，以帮助学生更好地缓解过度的消极学业情绪。
