From the Blog

No Data, No Equity: Using Data to Inform Instructional Practice

by Hayley Didriksen, Reed Dyer, and Arielle Sprotzer 

image of hayley reed and arielle

After reading this blog, don’t forget to check out our new resource: Using Data to Inform Instruction.

Flash back to the summer of 2018. We’re at an in-service teacher session; two hours are set aside for “data.” The first 75 minutes are spent reviewing high-stakes data from the previous spring’s state test; the last 45 minutes are set aside for non-academic data (such as student demographics, attendance, or behavior records). Although the school itself is held accountable for the high-stakes data year after year, it’s the non-academic data that gets everyone talking. For the first time as a whole faculty, the group is faced with the data that African-American students who make up only 50% of the overall population also make up over 80% of behavior incidents.

This in-service teacher session got intense. Teachers who otherwise might not have had a voice began to speak up. Underlying moral issues that had remained unspoken were on everyone’s radar and, as uncomfortable as it was, the staff began to wrestle with the power and privilege by which their system operated. A new journey for the staff began. 

Why Data Matters: An Essential Tool for Promoting Educational Equity

Data offers a tool for educators and school leaders to systematically monitor student learning, find answers to important questions, and analyze and reflect together on instructional practices, classroom environments, and school systems and policies. When data is at the center of classroom, school, and district improvement efforts, it can serve as a lever to ensure educational equity is the priority in decision-making processes. 

Data serves as a tool that we can use to create data-informed action plans, monitor progress toward goals, and make adjustments to practice based on thoughtful data review and reflection. Taking time to review and discuss different sources of data available to educators offers an opportunity not only to reflect on patterns in the data, but to identify and commit to making specific changes to practice.

Data disaggregation—that is, the practice of separating out and looking at data for different student subgroups or student characteristics—is an essential step to uncover hidden inequities in our classrooms and schools. Looking specifically at data for different subgroups (e.g., socio-economic status or gender) provides a strategy to help detect underlying trends, patterns, or insights that would not be observable in aggregated data sets.

The power of disaggregating data is clear. Consider the following theoretical example: Reviewing the results from a recent unit assessment in the aggregate form shows promise; 90% of students met or exceeded expectations. But when you disaggregate results by gender, you find that nearly all the students who exceeded expectations were female. This prompts the question: Why are female students performing at a higher level as compared to male students?

Only in asking such questions can we ever hope to answer them.

Data, Data Everywhere

While standardized testing data can serve as a valuable tool for understanding student achievement trends in our schools, they may not always provide educators with the real-time data necessary to make day-to-day decisions about how to adjust instruction to best meet the needs of students. There are, however, a wide range of other data that educators are collecting every day that can provide immediate and valuable information about students and that can  inform and influence how we teach, as well as where and what we review, readjust, and reteach. 

For example:

  • Classroom assessments: Formative and summative assessments, projects, essays, and presentations.
  • Classroom observation data: Feedback from colleagues, evaluators, and student participation data.
  • Feedback and surveys: Student, staff, and family feedback surveys; school climate surveys; exit tickets; student reflections; and lesson debriefs.
  • Non-academic data: Student demographics, attendance, behavior records, mobility, tardies, course-taking patterns, promotion and retention rates, and observations of behavior.

Not only can you be comprehensive in the types of data you look at, but disaggregation of that data must be a driving force to properly focus on educational equity. 

From Data to Action: Translating Data Into Instructional Change 

The goal here isn’t to look at cool spreadsheets, have a great data meeting, or even assign blame. The goal is to use data as a tool to improve student learning. We want to move data analysis and discussion to possible points of action, on whatever scale is appropriate. As we discuss implications, root causes, and possible responses to what we learn from data conversations, we can start to map out next steps to addressing the needs and opportunities we find within them. 

For example, if it is clear that students with Individualized Education Programs (IEPs) are not performing to the level of their peers, a school leadership team might be pressed to rethink schedules and support assignments for the following year. At the same time, grade-level teams might recognize the need to adjust assessment techniques over the coming units to better allow students to show what they actually know and can do. Additionally, an individual teacher might need to more deeply unpack the goals and accommodations in a certain student’s IEP in order to adjust instruction. 

Clear action steps will not always emerge from an initial data analysis. Often, the most immediate call to action will be a desire to dig deeper, to look at additional data, and try to answer any newly raised questions. Sometimes we just need to collect and process more data. 

Still, a move toward more equitable outcomes will truly begin only when we translate our ideas into action. If we want different results, we need to take different action. Perhaps the most important result of a data-centered professional culture is an openness to critique and change of our own practice—a willingness to try new things, not because it’s the newest fad or an administrator told us to, but because we have concluded from our data that new action is necessary. The beauty is that we can then use data to measure our own impact and adjust from there.

A final point to remember: Data does not provide the answers; it provides the right questions to ask. Don’t begin this journey thinking you are going to start with the solution to the problem. Instead, read the data, think about its implications, and make a commitment to challenge the imbalance of power and privilege.  

Excited to dig into data with your colleagues? Check out our new resource: Using Data to Inform Instruction!