Optimal evaluation policies to identify students with reading disabilities

Reading disabilities affect 10%–20% of students in the US. Untreated students fall behind their typically developing peers, leading to poor long-term outcomes. While instructional interventions can help, they are most effective when implemented early. Inexpensive screening tests can be used to monitor and flag at-risk students who may need expensive follow-up diagnostic evaluations that determine eligibility for intervention. However, conventional wisdom holds that the accuracy of these tests increase with grade level. Schools that do not have the capacity to do follow-up evaluations on every student flagged by screening are therefore believed to face an operational trade-off in allocating resources for evaluations, balancing the need for early intervention against budget constraints and legal obligations to honor direct parent or teacher requests. We examine how school administrators can choose evaluation policies to maximize benefits from intervention for students and ensure equitable allocation across diverse backgrounds. We model identification by optimizing over a time-dependent Bernoulli process which incorporates the screening test accuracy and the benefits from intervention at different grade levels. In collaboration with researchers from the Florida Center for Reading Research, we use longitudinal data from school districts across the state to empirically estimate these parameters and numerically solve for the optimal policies. Our study provides actionable insights for school administrators making resource allocation decisions and policy makers considering changes to laws governing the identification process. In this context, counter to conventional wisdom the screening test accuracy does not increase with grade level. To maximize the benefit to students under the current identification process, schools should simply evaluate as many students as their budget allows as early as possible. At existing budget levels, this policy also results in maximally equitable allocations. Changes to the identification process that ease legal obligations can increase benefits by up to 66% and decrease disparities by up to 100% without additional funding.