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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Veröffentlicht in:Scientific reports Jg. 15; H. 1; S. 19183 - 11
Hauptverfasser: Ma, Shumeng, Jia, Ning, Wei, Xiuchao, Zhang, Wanyi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 01.06.2025
Nature Publishing Group
Nature Portfolio
Schlagworte:
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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Zusammenfassung: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.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-04146-6