Increase Sensemaking by Leaning into Playing with Data

Increasing sensemaking starts with giving students permission to play with data. When students explore data freely and actively—sorting, rearranging, visualizing different parts, annotating—they build deeper intuition about the dataset they are working with, which is transferable to the next time they work with data. This approach, which is a key part of traditional exploratory data analysis, is one of the most powerful (and underused) strategies for strengthening students’ data literacy in K-12 classrooms today. But it doesn’t have to be that way! Let’s explore…pun somewhat intended ;).

Increase Sensemaking by Leaning into Playing with Data: Why “Mucking About” Matters

Giving students time to “muck about” with data will likely feel messy at first, but it is foundational to genuine data sensemaking. Instead of viewing data as something static to decode (that someone else already knows the answer to), students learn that data is something they can interact with—move, question, break apart, and rebuild. This hands-on exploration helps them develop flexible thinking, an essential skill across science, social studies, math, and beyond.

When students play with data in low-stakes ways, they develop:

• Comfort making sense of unfamiliar datasets
• Curiosity that drives richer questions
• Confidence in noticing trends and unusual aspects
• Conceptual understanding of variability, distribution, and relationships

This strategy is also one of the reasons we surmise that the 2025 Science Scope article “How Can We Help Students Explore Data in Their Sensemaking?” became one of the top five most read articles of the year. Educators are recognizing that sensemaking doesn’t start with analysis — it starts with exploration.

What “Muck About” Looks Like in the Classroom

Exploring or playing with data is not chaotic guessing. It's structured freedom — an intentional first step that lowers the cognitive load so students can build understanding before they apply formal procedures. For a detailed description of the “muck about” classroom teaching strategy, we recommend reading the “How Can We Help Students Explore Data in Their Sensemaking?” Science Scope article.

But here are a few other ways that exploring and mucking about in data might look in your classroom:

1. Sorting and Resorting Data

Realizing that data is something that students can actively work with is a key part of building their comfort with exploratory data analysis. A great first step is getting students to sort or resort their data, rather than just take it at face value for how it appears in a data table. For example, students might categorize data by:

• Different categories of a particular variable/attribute included in the dataset
• Characteristics of the data values (e.g., size, type)
• Aspects of how the data were collected (e.g., time period)
• Features of the data values when graphed (e.g., “things that look similar”)

This helps students see the data as pieces to work with, rather than something static that they just need to look harder at..

2. Making Quick Visuals

No rulers. No templates. No one way to make it. No one-and-done graphing. Just:

• Quick “back of envelope” sketches on scratch paper
• Stacks of sticky notes lined up
• Dots on flipcharts or dry erase marks on whiteboards
• Manipulatives on a desk

These “rough draft” visuals help students realize that they can represent data meaningfully. AND that they can change their representations quickly to look at the data from a different perspective.

3. Asking Curiosity-Driven Questions

When it comes to data, students often think about the overarching or driving question for the unit. There is necessary work to help students narrow that down into a testable or investigative question to use data to explore. 

BUT there are also the important in-the-moment questions we want to encourage students to ask as they engage with the data. Students might ask:

• “Why are there so many small values?”
• “What happened here?”
• “Does this look normal?”
• “What would happen if we replaced this variable with this other one?”

This shifts them from “What does the graph look like?” to “What can I learn or make sense of about these data?” And that is where we want them to be sitting when they dig deeper into analyzing and interpreting the data.

How Playing with Data Supports Stronger Claims

Exploration is a stepping stone toward better reasoning.

Students who have time to play with data are more likely to:
✔ identify meaningful patterns
✔ explain variability rather than ignore it
✔ use evidence rather than opinion
✔ craft stronger, more nuanced claims

Instead of jumping straight to:
“The data shows X is on the horizontal axis,”
students begin saying:
“After exploring the data, I noticed A about these variables and B about these other variables…”

That shift is everything.

By the time students begin formal data interpretation or writing claims, they’ve already:

• interacted with the data
• noticed features common and unique across the values
• understood the variability within the dataset
• built intuition about what could matter when looking for and at patterns

This leads to clearer, stronger, more confident data-based conclusions — exactly what we want for sensemaking.

Why This Strategy Resonated Nationally

Dataspire’s “Muck About” work gained wide attention thanks to the article “How Can We Help Students Explore Data in Their Sensemaking?”, which became one of the top five most-read Science Scope articles in 2025.

Educators across the country shared that this approach:

• made data feel accessible
• supported multilingual learners
• reduced student anxiety
• helped them shift from answer-getting to meaning-making

You can explore the full article here:
👉 Read the full Science Scope article here

Great, I’ve Done “Muck About” Now What?

If you want to bring more sensemaking into your data lessons, and you have already done the full “Muck About” strategy in the article, try a simple three-step routine as a next step:

Step 1: Start with an unfamiliar but interesting data set

Something messy. Something real.

Step 2: Ask students to “play” for 5–10 minutes

Let them sort, sketch, rearrange, or highlight what stands out. Note, this goes much better with interactive data visualization tools like CODAP, DataClassroom, Tuva, etc. To see a list of suggestions visit: https://www.dataspire.org/blog/benefits-limitations-of-different-graphing-tools.

Step 3: Debrief using prompts like:

• What did you notice first?
• What patterns did you discover?
• What surprised you?
• What would you explore next?

This small shift opens the door to deeper analysis later.

Want to Dive Deeper into Data Sensemaking?

If you’d like additional context or strategies, you can read the full article here:
👉 Read the full Science Scope article here

It expands on:

• the pedagogical reasoning behind exploratory data analysis
• concrete classroom routines
• the cognitive steps students experience during early sensemaking

Still looking for more ideas? Check out our “Graph & Analyze Data” resources page or YouTube playlist.