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Bringing a Sharper Lens to Data Reviews: Part 1


Written by Dr. Tim Tasker with Adan Garcia & Jake Ramirez

Bringing a Sharper Lens to Data Reviews: Part 1

Data reviews are an important part of the instructional planning process. They offer educators an opportunity to not only reflect but also put values into practice.

Every student deserves teaching that prepares them for their next step and beyond, no matter who they are, where they come from, or how much money their family has. That means teachers must build on their experiences as strengths while providing the right mix of grade-level instruction and scaffolds. Robust data can indicate the extent to which that is happening and illuminate opportunities for continued focus.

Instructional planning is incredibly complex in a typical year. The global pandemic made the most recent school years even harder. Now, educators are piecing together incomplete data to figure out how to accelerate learning like never before. So, how can we make sure equity stays top of mind?

The choices ahead are complex and high-stakes. We’re here to help with a set of steps to sharpen the lens you bring to data reviews.

How to plan for data reviews

Considering fairness alongside data is not just about what you look at but also how you look at it. Data about student learning may appear to be objective indicators, but the way they are interpreted is always subjective. Such subjectivity leaves room for bias to shape the stories we perceive and what we believe.

Approach data reviews as a collaborative, team-based process that allows you to describe and reach a shared vision of student success. Data reviews offer the opportunity to draw on team members’ unique perspectives and skillsets to uncover and explore trends within classrooms and schools.

Researchers have dedicated much ink and many pixels to explaining the value that diversity brings to a team’s work, and the work of data reviews is no exception. For one thing, our own assumptions and misconceptions are much harder to recognize and manage when we are working alone. Reviewing data as a team helps ensure that multiple, diverse perspectives are brought to the data. It also offers reviewers support from others to both recognize and minimize the impact of their expectations.

Try taking these actions to prepare for a sharper review process.

a group of teachers review data about biliteracyAssemble your team 

Whether you are about to embark on a new data review with an existing team or you are seeking to convene a new team for this purpose, it is always important to reflect on who is and who should be at the table.

Team leaders can consider: who thinks about things in another way; who may want to join us but faces barriers (e.g., job responsibilities, lack of childcare) to participating; or who is most directly impacted by the decisions we may make, including students? Recognize that existing teams may fall prey to group-think or have established ways of working together that discourage diverging viewpoints. Both should be disrupted.

Embrace the benefits that having diverse stakeholders along with group dynamics that foster productive disagreement will bring to existing and emerging teams alike.

Identify the right time and place

Research shows that implicit biases are harder to recognize and manage when we are feeling stressed, overwhelmed, or distracted. Therefore, plan data meetings for a time and space where team members will feel most comfortable and have the attention the task requires. Even when the timing or location of a data review meeting is not ideal, it is still important to explicitly acknowledge the fact that biases will be harder to manage. In that case, invite team members to pay particular attention to the ways their biases could shape their own and others’ perceptions and interpretations.

Choose multiple data points 

Teachers’ and students’ school experiences are complex and diverse, and no single datapoint, measure, or method is capable of reflecting the full richness of those experiences. On their own, each datapoint offers only a very narrow window into what is happening in classrooms or schools.

Expand your frame by assembling multiple types of education data that will allow you to explore the various connections between them, like the connection between discipline and academic achievement. Consider how students’ experience of the school’s culture may drive their classroom engagement. Gather qualitative data (e.g., student focus groups) that can inform trends you have observed in quantitative data (e.g., state summative tests), and vice versa.

girl sits at a desk behind a barrier

Access identifiers 

Social identities and demographics powerfully shape how people experience the world. To determine how those group identities may be showing up in your data, ensure you will have access to the demographic information of the people the data represent. Individual level demographics tend to be more useful; however, group level aggregate scores can also provide useful insights.

For instance, knowing the percentage of students receiving free and reduced lunch in each school within a district would allow you to consider how the general socioeconomic status of each neighborhood community might be influencing the outcomes you care about. In focusing on demographic differences, however, be especially mindful to avoid deficit thinking about either individuals or groups.

Reflect on goals and goal-setting

We often review data for the purpose of either setting a goal or understanding whether we have met it. At the goal-setting stage, consider specifying “inclusive” and “equitable” criteria for measuring goals. Inclusive criteria help to ensure you do not exclude anyone (unintentionally or otherwise) and that data accurately reflect all of your population. Equitable criteria are important because goals can be achieved in ways that perpetuate, rather than disrupt, gaps.

For instance, a team of teachers can work to improve the overall average score on a particular test, while at the same time as the gaps between students of different identity groups widen. Two examples of how to do this are adding language to a goal that specifies that the full sample or population of students will be included or that there will be no significant gaps between relevant identity groups.

Looking Ahead

Now that you have assembled your team gathered data, and reflected on goals, you’re ready to conduct your data review. We’ll cover suggestions for how to use the time you have together in Part 2.

Until then, remember that improvement will always be a journey, an iterative process of incremental growth and change. Take your reflections about negative expectations and recommit to the personal work of addressing them. Support others to do the same.

Some things you can do:

  1. Plan team-based learning on students’ cultures, identities, and the things their families value.
  2. Participate in an affinity group.
  3. Add an expectations-focused goal to your individual development plan.
  4. Bolster your team’s stakeholder or community engagement.
  5. Regularly use a resource library to consider new perspectives.
  6. Want Support Implementing & Scaling Data-Driven Practice?

    We’ve helped instructional teams and leaders shift the ways they plan and collaborate for more than a decade, building the capacity of educators to meet and exceed ambitious goals. Get in touch if you want to learn more about opportunities to partner!

Tim Tasker is a proud data nerd with a passion for making evidence accessible to all stakeholders. As Director of Data & Evaluation, he supports program teams to develop customized evaluation strategies for their local contexts. He also designs tools for evaluating the school- and system-level conditions that enable effective teacher professional learning.

Tim holds a B.A. in Psychology from Northwestern University and later earned his Ph.D. in Community and Prevention Research from the University of Illinois at Chicago. He is a current Fellow with the Strategic Data Project at the Center for Education Policy Research at Harvard University.

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