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February 1, 2022
Vol. 79
No. 4

Looking at Data Through an Equity Lens

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Educator teams need to take a more inquiry-based approach to data analysis to address inequitable norms and patterns.

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EquityAssessment
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If we want to support educators in using data to improve learning and teaching, we must reckon with the fact that data have not always been used to promote equity. In the United States, accountability pressures from the No Child Left Behind Act of 2002 led to a series of data-related actions that did not serve all students well. For example, although NCLB increased educators' access to information about students, it narrowed the field of what "data" were to standardized tests and easily graded assessments that put students into categories of proficiency. These assessments focused on literacy and mathematics at the expense of other academic content, social or emotional skills, or the arts. Practitioners and policymakers discounted other sources of data, such as observations of instruction, student work, and interviews, because they were harder to collect and analyze. Additionally, the passage of NCLB resulted in unintended consequences, such as teachers focusing on "bubble" students who were just at the cusp of proficiency, referring students who were not passing assessments to special education, and spending large amounts of class time preparing students to take standardized tests (Booher-Jennings, 2005).
As members of the Data Wise Project at the Harvard Graduate School of Education, we have always encouraged educators to avoid these data-analysis perils by taking an improvement-oriented approach, rather than one focused on accountability. The 19 school- and university-based educators who came together in 2005 to write Data Wise: A Step-By-Step Guide to Using Assessment Results to Improve Teaching and Learning (Harvard Education Press, 2005) were concerned that, without a clear process for doing otherwise, teachers would be pressured to use data in a way that disproportionately disadvantaged the students whom educational systems were already not serving well. To use data for improvement, educators need to look at data that help them plan instruction and provide supports to students for their next level of learning (Nelson, Slavit, & Deuel, 2012). This approach requires using data to understand student thinking and address misconceptions about content, widening the definition of data to include student work, classroom observations, and even school climate surveys and student interviews.
However, an improvement-oriented approach alone does not take a critical perspective on the policies, structures, and norms of schooling that created the disparate student outcomes in the first place (Tate, 1997). We at the Data Wise Project believe that educators must go a step further and use collaborative data inquiry to build more equitable schools—schools where each student has access to rigorous learning opportunities, student outcomes are not predictable by demographics, and students feel respected and celebrated for who they are. This means that every single student, regardless of race, ethnicity, gender, socioeconomic status, or any other dimension of identity, is provided with the support they need to be successful.
After reflecting on our own work, we are now more explicitly supporting educators to take an equity lens when engaging in collaborative data inquiry. Because systemic inequity is all around us and it is difficult to see clearly, we are determined to help educators sharpen their analyses.

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Taking an Equity Lens

The National Equity Project (n.d.) uses the metaphor of a lens to describe "the possibility of seeing our contexts in new and revealing ways" (p. 3). In other words, the glasses we wear can have a profound effect on what we see. In our daily life, there are very different lenses we would use if our goal was to read fine print, drive in brilliant sunlight, or watch a 3D-movie. Similarly, when engaging in collaborative data inquiry, it is helpful to put on a specialized pair of metaphorical glasses to explicitly focus on equity as we gather, analyze, and act upon data.
At the most basic level, taking an equity lens in Data Wise involves having a team ask itself probing questions at each step of the improvement process (Camacho, 2018). We have learned from colleagues that equity should be embedded within each step, rather than treated as a secondary consideration (Amante-Jackson, 2018). In Figure 1, we present these questions in alignment with the eight-step Data Wise Improvement Process and the ACE Habits of Mind, which form the foundation of Data Wise. These habits include a shared faculty commitment to actionassessment, and adjustment, intentional collaboration, and a relentless focus on evidence. Around the outside of the figure, we pose our equity lens questions, which ask us to consider how educators' beliefs shape their actions; who is included and who is left out; and what assumptions educators make when they draw conclusions about students.
February 2022 Bocala Figure 1
So that the equity lens drives the work instead of feeling like an add-on, it is also important for educators to ask themselves the equity questions as they are beginning a particular step. For example, in Step 3 of Data Wise, "Create Data Overview," educators compile relevant data to present to colleagues in a way that will ignite their curiosity about an area for improvement at the school. Before the team begins collecting and analyzing data, they should ask themselves: "Whose stories do we tell? Whose stories do we not tell?" As they think about different student subgroups, they may realize that they will need to disaggregate their data to make those students' stories more visible.
Or the team may decide that readily available data sources do not provide enough useful information about a particular subgroup: for example, educators might want to know whether their students of color feel connected with others in school, but this is not captured by standardized assessment results. They might decide to administer a new survey to capture students' perceptions of belonging. Having these conversations before they produce the data overview will ensure that they don't just default to using easily available data that does not challenge them to address important equity questions.
To see how this process looks in practice, let's take a closer look at two steps in the Data Wise Improvement Process and how they might be viewed through an equity lens for improvement.

Working Toward a Shared Why

In Step 1 of the Data Wise Improvement Process, "Organizing for Collaborative Work," educators agree to meet as a team to tackle an area for improvement in their schools. These teams meet regularly over the school year to go through the steps of Data Wise. They could be grade-level or departmental teams of teachers, usually including a school administrator or an instructional specialist. In this step, educators learn practices such as setting norms or agreements about how they will work together, using a shared agenda and distributing facilitation roles (Boudett & City, 2014).
Beyond practices that help teams function more effectively, at the Data Wise Project we also ask educators to articulate a personal story about why they are in education, then work toward a shared Why that summarizes the team's common values. The individual Why consists of answering the questions: "Who am I? What do I believe about equity? How does that influence the work that I do?" When reflecting upon these questions, educators share stories of why they got into education and how their pathways were influenced by their identities, backgrounds, and experiences. Starting with a personal story can help educators build empathy and connect to one another's shared humanity (Ganz, 2011). We then ask team members to synthesize their individual Why stories into a "shared Why," which describes a common purpose fueled by similar beliefs and values. For example, a team may discover that they value belonging and connection, that they got into education to ensure that students have strong relationships with one another and with adults in their lives, and that they believe in the power of role models, especially those who come from the same community as their students. If challenged to describe their shared Why in a simple phrase, they might say their purpose is to "create a supportive learning community where everyone belongs."
As educators, we need to share why we care about equity because, as a field, we do not agree on a single vision for what an equitable system looks like. While some educators entered teaching to help students follow the typical pathway of educational success followed by career advancement, other teachers believe they should teach students to tear down the current structures that exist and redefine what it means to be successful (Dowd & Bensimon, 2015). Some teachers believe they should help students achieve high test scores and good grades, which are needed to succeed within the system as it is. Other teachers believe the system is fundamentally flawed, so they should teach students to abolish the system and work toward a different vision of freedom (Love, 2019).
This approach of connecting to an individual Why and a shared Why helps educators empathize with one another when engaging in difficult conversations about data, students, or instruction. When looking at data, educators become aware of inequitable patterns that fall along lines of identity markers such as race/ethnicity, gender, special education status, or social class. When discussing these issues and analyzing the inequities in the system that caused them, it is easy to become defensive, dismissive, or upset. But by starting with Why, educators can remember the connections they have and what values they share, even when conversations become uncomfortable or difficult.

Using Data Equitably

While educators should use data to identify students who need targeted supports, they must ensure that those labels are not used to deprive students of opportunities.

Celebrate Students' Strengths First

For our second example of looking at data through an equity lens, let's move to Step 4 of the Data Wise Improvement Process, when educators discuss evidence of student learning. Often in schools, data have been used to highlight gaps or problems, such as differences in standardized testing performance, usually with the intention of improving what is not working. However, with this approach, some student groups are frequently portrayed as "underperforming" or "at risk" (Tuck, 2009). While educators should use data to identify students who need targeted supports, they must ensure that those labels are not used to deprive students of opportunities. In the United States, for example, data have historically been used to rank and sort students by perceived "intelligence," ability, and merit (Lyons, 2020), and rigid tracking systems placed students on various pathways (e.g., vocational versus college-preparatory courses) that had consequences for their future career choices (Oakes, 1985).
In the Data Wise Project, we used the phrase "learner-centered problem" to describe what students were currently struggling with in their learning. But we realized that by only focusing on what students could not do, we were reinforcing deficit thinking. Educators who engage in deficit thinking have negative beliefs about students or students' communities, thereby limiting their expectations for what students might accomplish (Nelson & Guerra, 2014). Additionally, educators who are under accountability pressure to raise test scores or who work in punitive organizational conditions often engage in deficit thinking by placing blame for low performance on students, and not their own actions or systems (Lasater, Bengtson, & Albiladi, 2020).
To counteract these deficit-oriented tendencies, we now recommend using data to highlight students' strengths and assets before describing the next level in their development. This "asset-based perspective" can reinforce what is working, such as content students have mastered or skills they can build upon. This strategy also highlights goodness, strength, and resilience (Lawrence-Lightfoot & Davis, 1997), and it recognizes students have "funds of knowledge" that come from their experiences, families, and cultural backgrounds (González, Moll, & Amanti, 2005). In Data Wise, we now begin each learner-centered problem with a statement, grounded in the data, of what the students do well relevant to the focus of inquiry and what their next level of work should be. For example, an asset-oriented, learner-centered problem might be: "Students are able to organize their writing around a topic sentence and central argument, and they are still working on using textual evidence to support their claims." We teach educators to see these strengths as platforms from which to build to the next level of skill or learning.

Before the team begins collecting and analyzing data, they should ask themselves: "Whose stories do we tell? Whose stories do we not tell?"

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Embed Equity into Improvement

As our work with the Data Wise Project has evolved, we've realized the importance of using an equity lens during collaborative data inquiry. The questions that we propose in Figure 1 can help educators examine the beliefs, assumptions, and behaviors that might be producing inequitable patterns. This work is already making a difference to educators: for example, they tell us that sharing personal Why stories and understanding their team's shared Why has helped them strengthen relationships and build trust to have the difficult conversations that they used to avoid. Other educators tell us that once they reflect upon whose stories are not being told, they begin looking for ways to show the diversity and variability within student groups by disaggregating data differently. We hope that these questions shift educators' thinking about the purposes of using data beyond needing to meet accountability targets or create narrowly focused improvement plans: Instead, we can use data to create the conditions needed to ensure that each student thrives.
Systemic inequity affects the expectations for what students can achieve, the way in which they are treated in classrooms, and how they see themselves in the curriculum. Considering systemic oppression might seem counterintuitive to educators who are coached to only focus on issues within their control, but it is necessary for educators to do both—to acknowledge the policies, structures, and norms of schooling that have led to inequities while also improving what is in their power to change. To do otherwise would be to remain silent about dynamics that routinely harm students who are most marginalized.

Reflect & Discuss

➛ Does your school pay close enough attention to equity issues when examining student data? If not, what stands in the way?

➛ How might coming to a “shared Why” or focusing on student strengths first help your team shed light on inequities within your school?

 

References

Amante-Jackson, D. (2018). Engaging our stories for leadership [Conference presentation]. Disruptive Equity Education Project / Data Wise Equity Workshop, Cambridge, MA.

Booher-Jennings, J. (2005). Below the bubble: "Educational Triage" and the Texas accountability system. American Educational Research Journal42(2), 231–268.

Boudett, K. P., & City, E. A. (2014). Meeting wise: Making the most of collaborative time for educators. Cambridge, MA: Harvard Education Press.

Camacho, C. (2018). Data Wise: Making equity more than a buzzword. The Data Wise Project. Unpublished paper.

Dowd, A. C., & Bensimon, E. M. (2015). Engaging the "race question": Accountability and equity in U.S. higher education. New York: Teachers College Press.

Ganz, M. (2011). Public narrative, collective action, and power. In S. Odugbemi, & L. Taeku (Eds.), Accountability through public opinion: From inertia to public action (pp. 273–289). Washington, D.C.: The International Bank for Reconstruction and Development / The World Bank.

González, N., Moll, L. C., & Amanti, C. (2005). Funds of knowledge: Theorizing practices in households, communities, and classrooms. Mahwah, NJ: Lawrence Erlbaum Associates.

Lasater, K., Bengtson, E., & Albiladi, W. S. (2020). Data use for equity?: How data practices incite deficit thinking in schools. Studies in Educational Evaluation69(5).

Lawrence-Lightfoot, S., & Davis, J. H. (1997). The art and science of portraiture. San Francisco, CA: Jossey-Bass.

Love, B. L. (2019). We want to do more than survive: Abolitionist teaching and the pursuit of educational freedom. Boston, MA: Beacon Press.

Lyons, S. (2020). We are part of the problem. Center for Assessment.

Nelson, S. W., & Guerra, P. L. (2014). Educator beliefs and cultural knowledge: Implications for school improvement efforts. Educational Administrative Quarterly50(1), 67–95.

Nelson, T. H., Slavit, D., & Deuel, A. (2012). Two dimensions of an inquiry stance toward student-learning data. Teachers College Record114(8), 1–42.

Oakes, J. (1985). Keeping track: How schools structure inequality. New Haven, CT: Yale University.

Tate, W. F. (1997). Chapter 4: Critical race theory and education: History, theory, and implications. Review of Research in Education22(1), 195–247.

Tuck, E. (2009). Suspending damage: A letter to communities. Harvard Educational Review79(3), 409–427.

Candice Bocala (candice_bocala@gse.harvard.edu) is a lecturer at the Harvard Graduate School of Education, where she is the faculty director for the RIDES Project.

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