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<p>COVID-19 may have significantly exacerbated existing sociospatial inequalities, particularly in education. This study examines how school district segregation and family level factors have jointly shaped students’ academic outcomes during the pandemic in urban China, using Suzhou as a case study. Grounded in the conceptual frameworks of the geography of opportunity, contextual effects, and family social capital, we draw on primary survey data collected from 1091 families during the COVID-19 pandemic. A multilevel ordinal logistic regression model is employed to capture both household-level influences and school district-level disruptions. School districts were developed to reflect institutional hierarchy, using indicators for school type, administrative level, and academic composition. The results indicate that family based social capital, especially parental aspirations and educational level, was a strong predictor of academic success. Moreover, compared with neighbourhood segregation, district-level changes in school composition contributed to a widening of educational disparities. A cross-level interaction analysis revealed that while students with a migrant background were more vulnerable to neighbourhood instability, they demonstrated relative adaptability to changes in peer composition at school. These findings call for policies fostering more inclusive and equitable education in the postpandemic context.</p>

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Educational Mosaic: the roles of school district segregation and family features in shaping academic outcomes in urban China during the COVID-19

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月22日

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  • Yuqing Zhang,
  • Mengchuan Liu,
  • Hong Ni,
  • Jiayi Li,
  • Tiantian Chen,
  • Hyungchul Chung

摘要

COVID-19 may have significantly exacerbated existing sociospatial inequalities, particularly in education. This study examines how school district segregation and family level factors have jointly shaped students’ academic outcomes during the pandemic in urban China, using Suzhou as a case study. Grounded in the conceptual frameworks of the geography of opportunity, contextual effects, and family social capital, we draw on primary survey data collected from 1091 families during the COVID-19 pandemic. A multilevel ordinal logistic regression model is employed to capture both household-level influences and school district-level disruptions. School districts were developed to reflect institutional hierarchy, using indicators for school type, administrative level, and academic composition. The results indicate that family based social capital, especially parental aspirations and educational level, was a strong predictor of academic success. Moreover, compared with neighbourhood segregation, district-level changes in school composition contributed to a widening of educational disparities. A cross-level interaction analysis revealed that while students with a migrant background were more vulnerable to neighbourhood instability, they demonstrated relative adaptability to changes in peer composition at school. These findings call for policies fostering more inclusive and equitable education in the postpandemic context.