Data-Driven Decisions: Using Equity Theory to Highlight Implications for Underserved Students

This article highlights how data are currently being used to solve the what and not the why as it relates to achievement gaps for marginalized students in urban settings.

By using equity theory through a social justice lens, the authors intend to highlight how data are currently being used to solve the what and not the why as it relates to achievement gaps for marginalized students in urban settings.

School practitioners have been utilizing quantitative data, such as district and state achievement test scores, math and reading levels, and class assignments to determine the academic levels of students. While this information is useful, the authors will argue it does not tell the whole story. Specifically, the authors explain why these measures may not accurately reflect the knowledge level of underserved students and the areas that may be needed to create a holistic picture of the social and academic needs of individual children.