Using Data from Schools and Child Welfare Agencies to Predict Near-Term Academic Risks

Using Data from Schools and Child Welfare Agencies to Predict Near-Term Academic Risks

Published: Jul 06, 2020
Publisher: Regional Educational Laboratory Mid-Atlantic
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Authors

John Hotchkiss

Phil Killewald

This report provides information for administrators, researchers, and student support staff in local education agencies who are interested in identifying students who are likely to have near-term academic problems such as absenteeism, suspensions, poor grades, and low performance on state tests. The report describes an approach for developing a predictive model and assesses how well the model identifies at-risk students using data from two local education agencies in Allegheny County, Pennsylvania: a large local education agency and a smaller charter school network. It also examines which types of predictors— in-school variables (performance, behavior, and consequences) and out-of-school variables (human services involvement and public benefit receipt)—are individually related to each type of near-term academic problem to better understand why the model might flag students as at risk and how best to support these students.

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