Using Student Data for Equity

‍How can we use data in service of creating equitable learning experiences for all?

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As we look back on the last two years of trauma and interrupted teaching, we see drastic disproportionalities in student experience across lines of race and income. 

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Educators across the country know that this is not the time for stagnation, but rather to be biased toward action to make up for lost time with students. Educators are also feeling the intensity of the responsibility and a deep concern for the field around retention. With that, how do we work smarter, not harder to ensure students get what they need to get caught up? The answer is partially in data-informed instructional approaches.

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Data-informed instructional approaches are needed now more than ever: to pinpoint what students know and do not yet know, and to provide differentiated supports to meet students where they are in their pursuit of on-grade level excellence.  This comes with the responsibility to act ethically with data, in service of equity for all. 

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To create a vision for equity-centered data practices, we can look to the National Equity Project’s definition of equity and ask ourselves - what would it look like to use data in a way that promotes equity?‍

Educational equity means that each child receives what they need to develop to their full academic and social potential.

Working towards equity in schools involves:

1. Ensuring equally high outcomes for all participants in our educational system; removing the predictability of success or failures that currently correlates with any social or cultural factor;
2. Interrupting inequitable practices, examining biases, and creating inclusive multicultural school environments for adults and children; and
3. Discovering and cultivating the unique gifts, talents and interests that every human possesses. 

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Based on this definition, using data for equity would mean that the analysis and response to the data create conditions in which every student can develop to their full academic and social potential. Here are some equity-centered data practices we recommend in service of this mission: 

Inclusive Data Analysis

Educators should attend closely to the learning needs of all students. This means that data analysis should include an explicit review of student sub-groups to identify and respond to any disparities. The data analysis and action planning should be grounded in thoughtful attention to the needs of the most vulnerable students through questions such as - who is being served well based on the data? Who isn’t? What additional support may be needed? Who are the right people to intervene? 
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‍Conscious Data Analysis 

Educators should practice self-awareness by identifying and challenging personal bias. There are many types of bias that could impact the way data is used and interpreted - including negativity bias, availability bias, and confirmation bias. We recommend that educators identify biases that they lean toward in their analysis and challenge those biases throughout the data analysis process. 

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‍Contextualized Data Analysis

Educators should take context into account when analyzing data.  While reviewing the data, educators should take stock of what the data shows and doesn’t show, and embrace curiosity about the data by asking questions about it that could lead to deeper analysis and understanding of student learning. With this, we also recommend that educators talk to students to ask them how they’re feeling about what might be impeding their potential.

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‍Collaborative Data Analysis

Educators should work together to meet the needs of all students. As beautifully put by Shane Safir and Jamila Dugan, authors of Street Data, “We belong to a village, with children at the center.” To be in service to all students, we recommend that educators tap into their villages through collaborating with general educators, special educators, language and intervention teachers, and other stakeholders to ensure high-quality instruction across settings and to create routine opportunities to analyze and action plan around data together. 

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As we put forth efforts to address interrupted learning, we believe that equity-centered data analysis will foster an inclusive learning environment for all students.

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Relay offers a two-hour, highly interactive professional development session on the topic of Responding to Data Equitably. The PD is part of the Data Deep Dive Series and is designed to give educators guided practice with the equity-centered data practices through opportunities to see them in action and to apply them to provided case studies. If you are interested in scheduling Responding to Data Equitably, learn more here and reach out to professionaleducation@relay.edu.