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Predicting and structuring adolescent academic burnout: A multi-ecological framework via machine learning and network

Shuo Gong1, Yunjing Li1, Haijiang Li1

  • 1School of Psychology, Shanghai Normal University, No. 100 Guilin Rd. Xuhui District, Shanghai, 200234, China.

Acta Psychologica
|May 2, 2026
PubMed
Summary
This summary is machine-generated.

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This study used machine learning to analyze academic burnout in adolescents, finding that depressive symptoms and digital stressors are key factors. Targeted interventions considering these elements and relationships are recommended for better support.

Area of Science:

  • Psychology
  • Adolescent Development
  • Computational Social Science

Background:

  • Academic burnout is a significant issue for adolescents.
  • Traditional methods struggle to capture burnout's complex, multi-level nature.
  • A systems-informed approach is needed to understand and address burnout.

Purpose of the Study:

  • To investigate the multi-ecological structure of academic burnout using machine learning and network analysis.
  • To identify key predictors and their interrelationships within adolescent academic burnout.

Main Methods:

  • Employed machine learning (ML), including Support Vector Machine (SVM), to classify academic burnout.
  • Utilized SHapley Additive exPlanations (SHAP) to interpret ML model findings.
  • Applied network analysis to map the structural relationships between burnout indicators across ecological domains.
Keywords:
Academic burnoutAdolescentsDigital stressMachine learningNetwork analysis

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Main Results:

  • The Support Vector Machine model demonstrated strong classification performance (AUC = 0.830).
  • Depressive symptoms, digital stressors (e.g., social media vigilance), and resilience were identified as highly important predictors.
  • Network analysis revealed depressive symptoms as a central node, digital stressors as cross-contextually connected, and teacher-student relationships as a bridge between school and individual burnout.

Conclusions:

  • Machine learning and network analysis offer powerful tools for understanding complex phenomena like academic burnout.
  • Addressing academic burnout requires targeted, system-informed strategies focusing on core predictors and their interconnectedness.
  • Interventions should consider the central role of depressive symptoms and the bridging function of teacher-student relationships.