Constructing a predictive model of negative academic emotions in high school students based on machine learning methods

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 emotio...

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Vydáno v:	Scientific reports Ročník 15; číslo 1; s. 19183 - 11
Hlavní autoři:	Ma, Shumeng, Jia, Ning, Wei, Xiuchao, Zhang, Wanyi
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 01.06.2025 Nature Publishing Group Nature Portfolio
Témata:	631/477 631/477/2811 Academic Performance - psychology Adolescent Adolescents China Decision trees Emotions Female High school students Humanities and Social Sciences Humans Learning Learning algorithms Logistic Models Machine learning Machine learning algorithms Male multidisciplinary Negative academic emotions Negativism Prediction Prediction models Predictive Learning Models - standards Predictive Learning Models - statistics & numerical data Psychological Tests Random Forest Regression analysis Reproducibility of Results Resilience, Psychological Science Science (multidisciplinary) Secondary school students Secondary schools Self Efficacy Students Students - psychology System theory Systems Theory China Machine learning algorithms High school students Negative academic emotions Prediction
ISSN:	2045-2322, 2045-2322
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Abstract	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.
AbstractList	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.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. 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. Abstract 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.
ArticleNumber	19183
Author	Zhang, Wanyi Wei, Xiuchao Ma, Shumeng Jia, Ning
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Snippet	Negative academic emotions reflect the negative experiences that learners encounter during the learning process. This study aims to explore the effectiveness... Abstract Negative academic emotions reflect the negative experiences that learners encounter during the learning process. This study aims to explore the...
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SubjectTerms	631/477 631/477/2811 Academic Performance - psychology Adolescent Adolescents China Decision trees Emotions Female High school students Humanities and Social Sciences Humans Learning Learning algorithms Logistic Models Machine learning Machine learning algorithms Male multidisciplinary Negative academic emotions Negativism Prediction Prediction models Predictive Learning Models - standards Predictive Learning Models - statistics & numerical data Psychological Tests Random Forest Regression analysis Reproducibility of Results Resilience, Psychological Science Science (multidisciplinary) Secondary school students Secondary schools Self Efficacy Students Students - psychology System theory Systems Theory
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Title	Constructing a predictive model of negative academic emotions in high school students based on machine learning methods
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