Connecting Data and Equity: Exploring Human-Centered Data Practices
Over the past twenty years, the role of data in the classroom has increased in intensity and complexity. Teachers now have access to a wide variety of data from learning platforms, interim assessments, state tests, and formative measures - to name a few - that aim to support student learning. But accessing this barrage of testing data has not translated into more equitable outcomes for students (Barshay, 2022).
At Throughline Learning, we believe that data will be more useful when it is "human-centered", or generated and interpreted at the ground level. For example, we collect data about how students feel about the classrooms, lessons, opportunities, and interactions that they experience day-to-day. We use that data to help teachers understand how diverse groups of students are experiencing schooling, discuss unintentional biases, and develop asset-based action plans to improve both the student experience and student achievement.
Recently, we partnered with MassCUE, building on the success of their annual Datapalooza event, to discuss how our approach connects principles of equity with data routines. Across five meetings with a cohort of thoughtful Massachusetts educators, we generated powerful takeaways for making data more human-centered in schools.
Shift from FOR students to WITH students
When it comes to reviewing data, we sometimes curate pages and pages (or slides and slides) of notes, data points, and artifacts, an experience not unlike viewing a child as an organism under a microscope. This sheer amount of information can feel overwhelming, often leading to more questions than answers. It sounds simultaneously revolutionary and mundane to realize that the best next step may be taking a moment, looking up from the data mountain, and talking to students directly about how they are interpreting their progress.
For instance, one of our teachers was initially dismayed by what she interpreted as low scores on our Student Experience Survey. When she took the opportunity to ask her students what they meant by their responses, she was pleasantly surprised:
Student Survey Item
|Initial Teacher Interpretation||What Students Said When Asked||New Teacher Interpretation|
|I can learn the hardest topics in my class.||I need to revisit "academic mindset" with some students.||"I know that I still have a lot to learn so I answered in the middle!"||Students understand that, although a unit might be done, their learning isn't.|
|My teachers help me when I need it.||Students don't feel like I provide the support they need.||"We have to try things ourselves first, and then only ask if we get stuck."||Students have internalized our classroom norms & are trying to model productive struggle.|
This realization underscored how necessary it is to develop a holistic approach to data collection. We are often better able to interpret data when students - and families - are part of the analysis process and we can contextualize data points.
Empathize with the humans behind the numbers
When we hear the phrase "data-driven decisions", it's easy to default to numbers. But what does 10% mean in the context of a district of thousands of students? What does an n size of 15 imply without understanding the composition of a classroom? We practice centering experiences and people, not percentages.
Each teacher participant brought robust slide decks of data that showed average proficiency levels and growth on various assessments. But when they connected individual data with individual students, a clearer picture of strength and resiliency emerged.
On the surface, one student was not showing growth. When these numbers were layered on his school experience, his teacher saw things more holistically. This elementary school child had never had a stable schooling experience, moving to 11 different schools in his short educational career. Instead of focusing on progress reports and state tests, his teacher prioritized storytelling and collective action. She recognized that no one had initially reached out to her to detail the student's experience, a cycle that she could stop perpetuating. She worked to bring his parent, past and current teachers, and the student himself to the table, and documented his full schooling career, interests, strengths, and needs, as well as her recommendations for next steps.
This approach centered the why behind the numbers and inspired the teacher to take more informed action instead of spinning her wheels in frustration. When the student moved schools again, the teacher's portfolio followed him, equipping his new school district with meaningful information and disrupting a pattern.
An Essential, Collective Responsibility
Examples like these remind us that we all hold some power and responsibility for ensuring that data-driven decisions are both relational and student-centered. This is essential work, and can ground us during a time when so many things feel out of our control. Discussing data with students and getting to know them better allows teachers to combine quantitative assessment data with student experiences and perspectives - a combination that has powerful implications for student outcomes.
We are grateful to our participants, and to MassCUE, for investing in this learning opportunity. Having such a wide range of experiences and stakeholder roles resulted in sessions full of different perspectives, probing questions, and new insights to benefit our students. The open dialogue and learning mindsets within our cohort led to more meaningful data conversations and next steps. We look forward to building on this work with more partners.
Is your school team ready to think about student-centered data protocols? Email Maeve Murray from our team to learn more about Throughline Learning's liberatory data workshops and professional learning communities.