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Stanford simulations: prioritizing students from low‑scoring census tracts increases access to high‑API schools
Summary
Researchers tested five assignment systems and found that adding an 'academic‑diversity' preference (priority for students from historically low‑CST census tracts) substantially increases historically underserved students' access to high‑API schools; adding a local/proximity preference on top of that had little cost to equitable access.
A team of researchers working with Stanford, MIT, Duke and others presented computer simulations to the Board showing how five different student‑assignment models would perform on two goals: reducing racially isolated schools and improving equitable student access to high‑API (Academic Performance Index) schools.
Muriel Niedle (Stanford) described two system families tested: local assignment (neighborhood first, restricted/unrestricted ranking) and lottery‑based assignments with optional priorities for local preference and an "academic‑diversity" preference…
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