Data plays a crucial role in shaping educational policies and practices, from informing curriculum by measuring test scores to evaluating and identifying inequities within lived community experiences. Yet, despite calls to build equitable data infrastructures, challenges and gaps still need to be addressed.

Fueled by inquiry, TC’s Alex Bowers, Professor of Education Leadership, and Yeonsoo Choi, a current Ph.D. student in Bowers’ Education Leadership Data Analytics Group, came together to analyze data through a new lens. They evaluated over 717 public education data sets through the FAIR (Findable, Accessible, Interoperable, and Reusable) principles, a set of data standards that put equity first, hoping to understand better how public educational data is used to address and inform needs and disparities in the public education system.

“We discovered that New York has over 717 public data sets, yet it was challenging to bring them all together,” shares Bowers. “FAIR aims to do just that by unifying data under such standards to identify challenges and successes across the public education system.”

The project was a departure from Bowers’ earlier study funded and facilitated by the BERC (Black Education Research Coalition) and TC’s Sonya Douglass, Founding Director of BERC, which previously evaluated data sets and data management practices across NYC.

“Educational researchers and practitioners alike have grappled with what it means to center equity in data reuse,” adds Choi. “This work contributes to the conversation by pointing to the importance of education data management and governance.”


Current Data Standards Pose Limitations

While current data management standards exist, they are rarely adhered to and pose a number of limitations. A recent analysis of the 2018 Statewide Longitudinal Data Systems (SLDS) survey administered by the National Center for Education Statistics revealed that only one-third of data systems aligned their data to the Common Education Data Standards

As a result, current datasets hold less value to critical stakeholders, like school administrators, policymakers, and communities at large. Bowers notes that this is a critical pitfall. 

“If we can't follow a set of cohesive data standards, it’s impossible to make the data sets talk to each other—to be interoperable. Without that, we fail to understand how our students access opportunities and resources.” 

In evaluating data under the current standards in New York City, Bowers and Choi found that many datasets lacked accessibility, requiring passwords and logins, while others lacked detail and common identifiers.

 “Without detailed documentation of data sources, analysis methods, and code, it can be difficult for others to validate or build on existing analyses,” adds Choi, noting that the FAIR standards allow for data organization across the board. 


Equitable, Interoperable Datasets Are Critical

So, why is equity so important? Bowers emphasizes that equitable data practices contribute to large and small successes across the board.

“It’s a systemic issue. When I talk about the importance of equitable data, people don’t always understand how and why it’s even relevant. Yet little do they realize that it impacts their schools, children, and communities.

For students and teachers, this might look like streamlined access to assignment grades, attendance, or information about higher education and career pathways. 

On a larger scale, for policymakers and community leaders, this might look like how school districts use and distribute funds along with information on characteristics and lived experiences of community members encompassing race/ethnicity, gender, economic status, disability status and more.

“Consistent equity audits under the FAIR framework will help examine what data are collected and how community members’ lived experiences and social identities are measured and translated into said data,” shares Bowers. “This ensures equity-focused data systems are held accountable to the communities from whom the data originate.”

Through simple changes like using distinct identifiers and citing in a standardized format, researchers can replicate data analyses to inform further critical studies. “If we have equitable data for communities to rely on, we can empower school administrators and educators to have more evidence-based conversations in schools to help address disparities,” notes Choi.


How Might FAIR Reshape the Future of Data Management?

While the FAIR framework alone may not be entirely enough to prompt equity-focused data reuse, Bowers adds that it’s certainly a “step in the right direction” and that broader sociocultural and political forces can help amplify the conversation.

“We can start by getting our local communities involved and engaged with equity assessments,” notes Bowers, something that he detailed during his presentation at this year’s Education Leadership Data Analytics (ELDA) 2023 Conference. “We hope to incite more disciplined, deeper conversations around how to bring about equitable education data governance and also invite various stakeholders to join these conversations,” adds Bowers. 

He’s currently in the process of meeting with fellow researchers, data analysts and even school policyholders and superintendents to share why FAIR is crucial to successful data outcomes.

“This research is a great example of how the meaningful work and conversations that we have at Teachers College can contribute to something greater,” concludes Bowers.