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[2602.17972] Student Flow Modeling for School Decongestion via Stochastic Gravity Estimation and Constrained Spatial Allocation
Summary
This article presents a computational framework for modeling student flow patterns to address school congestion in low- and middle-income countries, emphasizing data-driven policy simulations.
Why It Matters
School congestion significantly impacts educational equity and outcomes, particularly in developing regions. This research highlights the limitations of existing subsidy programs and proposes a data-driven approach to optimize student allocation, which is crucial for effective educational policy-making.
Key Takeaways
- School congestion affects learning outcomes and equity in education.
- Current subsidy programs often fail due to fragmented data systems.
- Geographic proximity influences school choice more than tuition costs.
- Slot capacity is a more critical constraint than subsidy amounts.
- Computational modeling can enhance resource allocation in education.
Computer Science > Machine Learning arXiv:2602.17972 (cs) [Submitted on 20 Feb 2026] Title:Student Flow Modeling for School Decongestion via Stochastic Gravity Estimation and Constrained Spatial Allocation Authors:Sebastian Felipe R. Bundoc, Paula Joy B. Martinez, Sebastian C. Ibañez, Erika Fille T. Legara View a PDF of the paper titled Student Flow Modeling for School Decongestion via Stochastic Gravity Estimation and Constrained Spatial Allocation, by Sebastian Felipe R. Bundoc and 3 other authors View PDF HTML (experimental) Abstract:School congestion, where student enrollment exceeds school capacity, is a major challenge in low- and middle-income countries. It highly impacts learning outcomes and deepens inequities in education. While subsidy programs that transfer students from public to private schools offer a mechanism to alleviate congestion without capital-intensive construction, they often underperform due to fragmented data systems that hinder effective implementation. The Philippine Educational Service Contracting program, one of the world's largest educational subsidy programs, exemplifies these challenges, falling short of its goal to decongest public schools. This prevents the science-based and data-driven analyses needed to understand what shapes student enrollment flows, particularly how families respond to economic incentives and spatial constraints. We introduce a computational framework for modeling student flow patterns and simulating policy scenarios....