Asset Framing: Putting Data Disaggregation in Context

Data Systems That Work
Asset Framing: Putting Data Disaggregation in Context

This blog is the second in a three-part series on asset framing – and how it can impact student success. Read the first post here.

Disaggregation sounds like a complicated word with no place in real-world conversations about improving education. But it’s actually an important tool that allows educators to understand how different groups of students fare in schools. Each of the strategies mentioned in our last blog – that allowed Chicago educators to dive deeper into data and pull out student strengths through their commitment to asset framing – were made possible by disaggregation.

Disaggregating data reveals disparities in academic performance between students of color and their white peers, or between students of differing socioeconomic status, that many describe as the achievement gap. The story, however, must not end with disaggregated data. To address disparities in achievement, disaggregated data must be used to inform better practice by putting inequities in education in proper context for practitioners.

How data is presented can create limiting narratives around achievement, especially for students who have been at a disadvantage. Data doesn’t come to life on its own so people who construct data (and the narratives surrounding it) must be aware of the potentially harmful biases they may be imposing on students. Take disaggregated discipline data for instance. Overrepresentation of black and brown students in suspension rates can be interpreted in a couple of ways: (1) those students are problem kids or (2) more work needs to be done to provide supports for the social emotional well-being of those students. Narratives – or how we interpret data – can present either challenges or solutions for students, depending on whether they are approached with an asset or a deficit frame. Keeping that in mind is critical for practitioners to meaningfully confront inequities using data.

In the Midwest, the University of Chicago Consortium – a research-practice partnership between the university researchers and Chicago Public Schools (CPS) – disaggregates data and uses it to emphasize keeping freshman high schoolers on track while also applying an equity lens. When reviewing disaggregated student data, teaching teams don’t place the entire burden of underperformance on students. If black male freshmen are less likely to receive a 3.0 GPA than their peers, CPS teaching teams aim to have thoughtful data-driven conversations. They discuss impacting factors like the number of years these students have had inexperienced teachers and whether those teachers see their students through a deficit lens. These conversations guide the solutions, through targeted supports and interventions, and joint parent/student conferences.

Disaggregation shines a bright light on numerous inequities in education – but proper context is key. Context allows those closest to students to make sense of the data and reveal gaps in achievement that aren’t reflective of natural ability but rather the opportunity to succeed. With access to the right information, and the right perspective, educators have the power to positively impact the futures of black and brown students who have been disadvantaged, and to ensure that they succeed.

Putting disaggregated data in proper context isn’t easy work, tune in next week for the third and final blog in our series to learn how the hard work of asset framing in data shapes opportunities for students. 

 

This blog post is also available as a story on Medium