Predicting Math Proficiency Using Grades 1–3 Universal Screeners

• In Event: Predictors of Student Success in Mathematics Education

Abstract

Children’s math performance is strongly correlated with later life outcomes, but early gaps in math skills are stubbornly difficult to close. It is therefore important to identify student math needs early. Using Grade 1–3 student records from Kentucky public schools, the study finds that typically recommended cut scores for widely used early grade math screeners severely under-identify student needs in math. Using optimal cut scores estimated by the Classification and Regression Tree (CART) analysis, the likelihood of under identification of at-risk students decreases by an average of 16 percentage points and sensitivity improves by 28 percentage points. These improvements can be achieved without having to collect new data or administer new assessments.

Author

• Zeyu Xu, American Institutes for Research