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Placing Multiple Early Literacy Screening Instruments on a Common Risk Metric: A Machine Learning Approach
Many states now require districts to administer an early literacy screener in the primary grades, selecting from a list of state-approved screeners. Given each screener’s scale, benchmarks, and definition of risk, states may question how to compare results across screeners. This article presents a machine learning approach for placing multiple screeners on a common risk metric, defined by performance on the grade 3 reading assessment, so that states can aggregate results across districts using different tools.
