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<p>This study examines the relationship between student Self-Governance Networks (SGN) and Civic Participation Outcomes (CPO) using a network-based analytical framework. Data were collected from 237 student governance participants at a regional university in eastern China and analyzed using Social Network Analysis (SNA), Multivariate Regression (MR), and Machine Learning (ML) classification. The SGN displayed a modular network comprising 6 functional communities, including central administration, academic councils, disciplinary bodies, and interest-based organizations. Structural analysis revealed a hierarchical hub-and-spoke configuration, with the central student government serving as the primary bridge across communities, while peripheral groups remained comparatively isolated. Centrality measures varied systematically across roles, with formal leadership positions occupying structurally advantaged positions. Regression analyses controlling for demographics confirmed that network position significantly predicted civic engagement, with eigenvector centrality and the Community E–I Index emerging as consistent predictors across results. ML models, particularly XGBoost, achieved strong predictive accuracy (accuracy = 0.781, AUC-ROC = 0.842), indicating that network features reliably differentiate engaged from non-engaged students. These findings indicate that the civic benefits of governance participation are not uniformly distributed but depend on an individual’s structural position, with ties to central actors and cross-community linkages enhancing involvement. The study outlines theoretical contributions and practical implications for governance design and civic education.</p>

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Network based analysis of student self governance networks and predictive role in civic participation outcomes

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月27日

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  • Jing Liu,
  • Putu Kerti Nitiasih,
  • Made Hery Santosa,
  • Putu Nanci Riastini

摘要

This study examines the relationship between student Self-Governance Networks (SGN) and Civic Participation Outcomes (CPO) using a network-based analytical framework. Data were collected from 237 student governance participants at a regional university in eastern China and analyzed using Social Network Analysis (SNA), Multivariate Regression (MR), and Machine Learning (ML) classification. The SGN displayed a modular network comprising 6 functional communities, including central administration, academic councils, disciplinary bodies, and interest-based organizations. Structural analysis revealed a hierarchical hub-and-spoke configuration, with the central student government serving as the primary bridge across communities, while peripheral groups remained comparatively isolated. Centrality measures varied systematically across roles, with formal leadership positions occupying structurally advantaged positions. Regression analyses controlling for demographics confirmed that network position significantly predicted civic engagement, with eigenvector centrality and the Community E–I Index emerging as consistent predictors across results. ML models, particularly XGBoost, achieved strong predictive accuracy (accuracy = 0.781, AUC-ROC = 0.842), indicating that network features reliably differentiate engaged from non-engaged students. These findings indicate that the civic benefits of governance participation are not uniformly distributed but depend on an individual’s structural position, with ties to central actors and cross-community linkages enhancing involvement. The study outlines theoretical contributions and practical implications for governance design and civic education.