Using Predicted Academic Performance to Identify At-Risk Students in Public Schools

Ed Working Papers has released a new paper by Ishtiaque Fazlul, Cory Koedel, and Eric Parsons focused on ways to use data to identify at-risk students. This paper offers a promising look at new ways to identify students early so that they can receive timely intervention.

Measures of student disadvantage—or risk—are critical components of equity-focused education policies. However, the risk measures used in contemporary policies have significant limitations, and despite continued advances in data infrastructure and analytic capacity, there has been little innovation in these measures for decades. The authors have developed a new measure of student risk for use in education policies, which they call Predicted Academic Performance (PAP). PAP is a flexible, data-rich indicator that identifies students at risk of poor academic outcomes. It blends concepts from emerging “early warning” systems with principles of incentive design to balance the competing priorities of accurate risk measurement and suitability for policy use. PAP is more effective than common alternatives at identifying students who are at risk of poor academic outcomes and can be used to target resources toward these students—and students who belong to several other associated risk categories—more efficiently.

For more, see: