Explainable machine learning unveils the key role of cooperation ability in school bullying and its gender-differentiated impact on cooperative atmosphere - Summary - MDSpire

Explainable machine learning unveils the key role of cooperation ability in school bullying and its gender-differentiated impact on cooperative atmosphere

  • By

  • Yawen Lv

  • Ziwen Zhou

  • Jinguo Li

  • Fang Fang

  • Hongyu Wang

  • June 4, 2026

  • 0 min

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Objective:

To examine the association between school bullying and social-emotional skills, with a specific focus on cooperation ability and its contextual expression in the school environment.

Key Findings:
  • 50.8% of adolescents experienced bullying, highlighting the prevalence of the issue.
  • Cooperation Ability showed the highest predictive importance among social-emotional dimensions, indicating its critical role.
  • XGBoost was the most effective model for predicting Cooperation Atmosphere, with specific performance metrics.
  • Top predictors included school belonging, competitive climate, and peer support, which are essential for understanding the context.
  • Gender-specific analysis revealed different predictive patterns for males and females, suggesting tailored interventions.
Interpretation:

The study suggests that school bullying is closely associated with poorer cooperation-related functioning and identifies key modifiable factors for targeted interventions.

Limitations:
  • The study is based on cross-sectional data, limiting causal inferences and the ability to establish temporal relationships.
  • Findings may not be generalizable beyond the specific demographic of Chinese adolescents, which could affect the applicability of results.
Conclusion:

The findings provide evidence for educators and policymakers to develop gender-specific strategies for improving school climate and reducing bullying, while also suggesting areas for future research.

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