District Systems to Support Equitable and High-Quality Teaching and Learning
Category: Policy, Politics, and Governance
School closures are often justified through seemingly neutral criteria such as enrollment or performance, but these metrics can unintentionally deepen educational disparities. This study uses a large urban district’s administrative data to simulate 5,040 closure scenarios, systematically varying seven policy design principles, including proximity, enrollment, seat utilization, building quality, academic performance, disproportionality safeguards, and ordering of schools considered. By comparing the equity, fiscal, and operational outcomes of each scenario, we reveal three key findings: (1) safeguards explicitly designed to prevent disproportionality improve fairness but reduce cost savings and seat reductions needed to balance capacity and demand; (2) common criteria like enrollment do little to advance either efficiency or equity; and (3) how schools are ranked for evaluation is a surprisingly powerful policy lever. This work contributes to the field by showing how simulation can equip district leaders to anticipate the trade-offs embedded in closure decisions, moving policy design toward proactive fairness.
Keywords
simulation, school closure, district leadership, explainable AI, education policy
Education level
Document Object Identifier (DOI)
10.26300/xt1x-1787
