Not Just Data: Why Data Equity Matters in Education Program Evaluation

In program evaluation, evaluators make hundreds of decisions that impact millions of students who participate in educational programs outside of school every year. These decisions often reflect personal and structural biases, but centering data equity throughout the evaluation process can guide evaluators to make more equitable decisions. This study proposes a framework — Data Equity in Education Program Evaluation (DEEPE) — for embedding data equity decisions throughout the evaluation process. The framework includes three stages, each with a focal principle: (1) planning: center racism and the voices of marginalized students, (2) analysis: challenge categorization, and (3) recommendations: use asset-based narratives. I use a case study of an online upskilling and reskilling program to demonstrate how to apply DEEPE and how it compares to a “traditional” evaluation. The evaluations focus on students’ course and certificate completion rates in the program. The resulting insights show that centering data equity can lead to program evaluations that are more accurate, highlight deeper inequities, and push for integrating equity in future steps. As education continues to become increasingly data-driven, embedding data equity into evaluation processes will be critical to achieving educational equity.