The MEI Study – Building System-Level Capacity for Equity Monitoring

• In Event: Equity by Design: Redesigning Principal Pathways for Systemic Change

Abstract

Districts often struggle to measure the impact of equity-centered initiatives. While tools like the CALL-OTL survey capture leadership practices inside schools, they do not account for systemic factors such as segregation, staffing inequities, or funding disparities that shape local implementation. The Mapping Educational Equity Indicators (MEI) study addresses this gap by providing access to publicly available, system-level data resources that help districts track the progress of initiatives like the ECPI.

Grounded in the Monitoring Educational Equity framework from the National Academies of Sciences, Engineering, and Medicine (2019), the MEI team cataloged more than 4,000 public education datasets from ECPI districts across seven states and Washington, D.C. The aim was to go beyond outcome metrics like test scores or graduation rates and include leading indicators of opportunity—such as access to certified teachers, inclusive climate, discipline practices, segregation patterns, and postsecondary readiness (Welner & Carter, 2013; Gutiérrez & Dixon-Román, 2011).

To structure this data scan, the team created the Metadata Megatable (MDMT), a cataloging tool aligned with FAIR principles (Findable, Accessible, Interoperable, Reusable) (Wilkinson et al., 2016; Bowers et al., 2023). Each dataset was coded based on its alignment with NASEM indicators, relevance to equity, and level of disaggregation by student demographics. This scan highlighted both the depth and limitations of public education data—such as missing data dictionaries, inconsistent school identifiers, and limited machine readability. Many datasets required customized access pathways that district data teams could not easily support, reflecting what Bowers and Choi (2023) call the “technical debt” of education data infrastructure. For the first time, MEI seeks to make this rich network of data resources available to districts through an open source system for analysis and visualization.

The MEI Finance team focused on mapping state and district datasets on revenues, expenditures, staffing costs, and resource allocations—alongside non-fiscal indicators like educator demographics, certification, and experience. Using a parallel coding scheme, the finance team assessed whether these data could support meaningful district-level analyses (Baker & Knight, 2025; Jackson et al., 2016). While many states had rich financial data resources, challenges remain in reporting district financial and educational resource data in ways that support district policy and practice. These challenges span reporting frameworks grounded in complicated accounting structures and reporting formats that pose challenges for data analysis.

The MEI study offers a model for an open-source infrastructure to examine how reforms—such as culturally responsive teaching, revised discipline policies, or resource reallocation—translate into improved opportunities to learn. With these tools, districts can explore whether investments align with equity goals, assess variation in access and inclusion, and strengthen accountability for equity commitments. By organizing thousands of disparate datasets into an accessible, equity-aligned system, MEI helps districts move from “equity talk” to evidence-informed action—building long-term analytic capacity for sustained system learning.

Authors

• Alex J. Bowers, Teachers College, Columbia University

• Christopher Saldaña, University of Wisconsin - Madison