Supporting All Learners as They Find & Organize Data
Sometimes an early challenge students encounter when working with data isn't creating a graph or drawing a conclusion. It's figuring out what data to collect in the first place and how to organize it so it can actually answer a question.
For experienced learners, these decisions may seem almost automatic. But for many students, deciding what information matters, what should be measured, and how to organize those measurements represents significant cognitive work. Too often, students encounter data collection through highly structured activities—a pre-labeled data table, explicit step-by-step directions, and little opportunity to make decisions for themselves. While those experiences have their place, they don't always prepare students for the kinds of decisions that authentic data work requires.
Fortunately, many of the instructional moves that help reduce barriers for learners are the very same moves that strengthen students' data literacy. Rather than thinking of Universal Design for Learning (UDL; CAST, 2018) as "one more thing" to add, we can view it as a framework that helps us become even more intentional about instructional practices many of us are already using.
This three-part series explores how UDL can support all learners as they:
We'll begin with the first step: finding and organizing data.
Finding and organizing data is about making decisions
Finding and organizing data is much more than filling in a table. Students need to determine what information is important, what should be measured or recorded, and how those choices will help answer the question they are investigating.
These decisions are often invisible to experts, but they can feel overwhelming to novice learners. Students may hesitate because they are worried about finding the "right" answer or because they simply aren't sure where to begin.
As teachers, our role isn't to remove those decisions—it is to make them more accessible. By intentionally designing opportunities for students to think through these choices, we help all learners develop the habits of mind needed for authentic data sensemaking.
Below are a few instructional moves that naturally support both learner variability and data literacy.
Help students connect data to meaningful questions
Students are more likely to engage deeply with data when the question they are investigating feels meaningful.
"Relevant" doesn't necessarily mean personally connected. It can also mean intellectually interesting. Can the data students are collecting help answer a question they genuinely wonder about? Can they see how the process of collecting and organizing data connects to something they already know—or something they might encounter beyond today's lesson? (See “What do we mean by “data”? for some other ideas.)
When students activate prior knowledge and recognize the purpose behind the data collection process, they are building on existing understanding rather than starting from scratch. This aligns with UDL's emphasis on supporting learners by connecting new learning to meaningful experiences (CAST, 2018).
Make decision-making visible
One of the most powerful ways to support learners is to provide opportunities to make decisions with appropriate levels of support.
Some students might choose between two investigable questions. Others might decide which variables to measure or how to organize information in a data table. As students gain confidence, those scaffolds can gradually be removed.
Rather than immediately telling students how data should be organized, consider inviting them to propose their own organizational systems and then discussing the strengths and limitations of different approaches afterward. These conversations help students recognize that organizing data is not simply following directions—it involves making thoughtful decisions that influence what patterns and relationships become visible.
Providing students with consistent routines can also reduce cognitive load while preserving opportunities for decision-making. For example, after students identify the question they are trying to answer, they can look for the words that represent measurable variables and use those to begin designing their data table. As learners revisit this routine across multiple investigations, they gradually internalize the process and become increasingly independent. (See some additional ideas here: What Can We Actually Claim from Our Data?).
Help students see data collection as a human endeavor
Students often think of data collection as simply following directions.
In reality, every dataset reflects a series of human decisions: what to measure, how to measure it, what counts, what doesn't, and how observations are recorded, etc. Data collection also includes the possibility of measurement error alongside the natural variability that exists in nearly every real-world dataset (California Mathematics Framework, 2023).
Taking time to discuss these decisions before and after collecting data helps students understand that data are created by people, not simply discovered.
These conversations also create opportunities to recognize and leverage the strengths that different learners bring to the process. One student may excel at carefully following a measurement protocol, contributing consistency across repeated trials. Another student who sometimes transposes numbers might initially struggle with recording measurements accurately but, with intentional routines for checking and verifying entries, can become especially attentive to data quality.
By making possible errors and sources of variability visible—not something to avoid, but something to understand—we help students develop stronger habits of critical thinking while reinforcing that careful data collection is an iterative process of reflection and improvement.
Looking ahead
Many teachers already use instructional practices like activating prior knowledge, offering meaningful choices, and encouraging reflection because they've learned these approaches help students engage more deeply with learning. UDL provides language for these practices, while data literacy reminds us why they matter when students are making sense of information.
Rather than asking, "What additional supports do my students need to learn data skills?" consider asking:
"Where are my students making decisions about data, and how might I make those moments more accessible and without taking away the thinking?"
Those moments often provide the richest opportunities to support learner variability while simultaneously strengthening data literacy.
We'd love to hear what you've noticed in your own classroom. What instructional moves have helped your students become more confident and thoughtful when finding and organizing data?
In our next post of the series, we'll explore how these same ideas extend to the second realm of data literacy: Graphing & Analyzing Data.
