Supporting All Learners as They Graph & Analyze Data
Asking our learners to become "data literate" and all that entails is a big ask! Fortunately, we don't have to teach every aspect of data literacy all at once. By breaking the work into manageable pieces while still keeping the big picture in mind, we can help students steadily build their confidence and skills.
Universal Design for Learning (CAST, 2018) offers a helpful lens for thinking about how we support learner variability throughout that process. Rather than adding another initiative, many of the instructional moves encouraged through UDL simply strengthen practices that many teachers are already using.
In this three-part series, we are exploring how these practices can support all learners as they:
In the previous post, we focused on finding and organizing data. Here we'll continue with graphing and analyzing data.
Graphing is more than making a picture
Creating a graph can seem like a straightforward task. You begin with a table of organized information (most often numbers) and turn that information into a visual representation.
But consider how much thinking that transformation actually requires.
You need to determine which variables to add and where to add them, select an appropriate data visualization type based on your data and question, establish a scale, translate numbers into spatial positions, use symbols or colors consistently, and decide which features of the data deserve attention. In essence, graphing requires reorganizing information from one form into another—often on a flat two-dimensional surface—while also trying to understand what the resulting visualization means.
That is a significant cognitive task for any of us, especially our learners!
For students who experience challenges with visual-spatial integration, symbolic representation, number formation, or written expression, the demands may be even more noticeable (Sharma, 2019).
So let’s think about how we can be more intentional to make this accessible for all of our learners as creating a graph is much more than drawing a picture. Learners are translating information from one representation into another while making decisions about what the data should communicate. As teachers, we want to reduce unnecessary barriers while preserving the important thinking that graphing requires.
Below are a few strategies that can support all learners while strengthening their ability to graph and analyze data.
Use concrete representations to bridge to abstract graphs
One way to reduce the feeling of being overwhelmed is to provide a bridge between an organized data table and the final graph.
Take the time to represent data with concrete manipulatives (Dahlstrom & Wallace, 2022). Manipulatives can help learners make the jump from concrete to abstract, whether it's with our early elementary learners—when manipulatives are used often—or with our early high school learners. This step is all part of transference and translation, when learners compare something familiar, like manipulatives, to a new context, like a graph. Manipulatives also give learners a concrete model of an abstract concept, an access point they can be prompted to return to (Belenky & Nokes, 2009).
The important point is not simply that manipulatives make graphing easier. They help students see that a visualization is constructed from individual data points and that the position and arrangement of those points communicate meaning.
Move from instruction to prompting
Students often need support with the sequence of decisions involved in creating a visualization. UDL encourages us to guide information processing by making steps and important features more visible (CAST, 2018).
Initially, that instruction needs to be explicit:
Write time below the horizontal axis.
As students build familiarity, the same support can become a prompt:
What variable belongs on the x-axis?
Later, students will need only a broader reminder:
What decisions do you need to make before you begin graphing?
This progression matters.
Explicit instruction can help a learner successfully complete an unfamiliar task. Prompts can help the learner recall and apply a familiar process. Over time, those prompts can be reduced as the routine becomes internalized.
Simultaneous prompting has also been studied as an explicit, systematic practice for supporting individuals with moderate to severe disabilities as they learn chained tasks (Heinrich et al., 2016). Across the full spectrum of learners, progressing from instruction to prompting helps establish routines, elicits thinking, increases retention, and builds ownership of the skill.
I like to think of it as helping students know what to do rather than telling them what to do.
Use interactive graphing programs
Technology assists learners in many different ways, some of which may feel "too easy" for those of us who learned to spell, calculate, graph, and organize tables of data "the hard way."
Many interactive graphing programs (e.g., CODAP, DataClassroom, TinkerPlots, Tuva) allow students to manipulate variables, reorganize data points, change graph types, and observe how the representation changes. These tools have educational research behind them to support the challenges of learning data visualization techniques. Their designs are interactive, manipulable, and made specifically for graphing, rather than suggestive and automated like a spreadsheet program (e.g., Excel or Google Sheets).
Control, choice, and even the visual animation of random dots reorganizing themselves into a graph help learners become part of the graphing process rather than simply producing a finished product. Several of these programs also make it easy for learners to annotate parts of the graph where they notice patterns or points they want to communicate.
The question is not whether technology makes graphing easier or not. The question is: What thinking does the technology make possible?
Invite learners to compare different visualizations
Another way to deepen learners' understanding is to ask them to create more than one graph from the same dataset.
Rather than always telling learners what kind of graph to create, consider asking them to represent the same data in two different ways or create a class gallery of graphs. Together, discuss the strengths of each visualization and what each one makes easier—or more difficult—to notice.
These conversations help learners recognize that graphs are not simply finished products. They are tools for exploring data, noticing patterns, and answering questions.
Help learners see patterns—not just graphs
Once learners have created a graph, the next skill to reinforce is pattern recognition.
Many learners need support knowing where to look before they can begin making sense of what they see.
One simple strategy is to encourage active annotation of data visualizations. Ask learners to circle clusters, highlight unusual points, draw arrows toward trends, or write notes about patterns they notice. These annotations help make their thinking visible while also directing attention to the critical features and relationships within the graph (CAST, 2018).
Another helpful strategy is to activate prior knowledge before students begin analyzing the graph. Talk together about things like:
- What are the common patterns we have seen before?
- What does a cluster look like?
- What might an increasing trend or an outlier tell us?
Helping learners recall previously learned ideas gives them something familiar to connect to when interpreting an unfamiliar visualization.
Looking ahead
Once learners are creating graphs, the next skill to reinforce is pattern recognition and making sense of what those patterns mean. If they've chosen an appropriate graph type, recognizing and interpreting patterns can naturally follow.
Many of us already use manipulatives, prompts, technology, and discussion to support learners in other areas of instruction. UDL reminds us that these same practices can also strengthen students' ability to graph and analyze data. Rather than asking students to do something different, we're helping them access the thinking that graphing has always required.
In our next blog, we'll explore the final realm of data literacy: Interpret & Use Data.
What strategies or tools have you used to support learners as they graph, analyze, and make sense of data? We'd love to hear your ideas and learn from your experiences.
