Education data analysis is tricky. Many of us teachers have heard that “data tells a story.” But we know better than anyone that it’s never the full story.
Too often, we narrowly think of “data” as test scores and spreadsheets. In fact, this is what gets many of us to panic: a nightmare of “I’m not a math person!” meets “Please don’t punish me for my test scores!” But in schools, data is everywhere, all the time. It lives in the moments between the numbers, in the hallway interactions, in the emails to families.
Teachers, we never stop gathering data. So let’s expand its definition. Each day, we have:
- Assessment data, like MAP® Growth™ or your district’s benchmark test, that tells us what a student knows—but not how they got there.
- Formative data, which shows up in exit tickets, group work, daily do-nows, ungraded quizzes, and more.
- Ecosystem data, like when you notice if a student comes late every day because their bus route is over an hour long. Or if they’re also enrolled in language services. Or if they’ve moved schools twice this year.
- Observational data that lives in your teacher gut: what you see in the hallway or hear during a conference, or even how quickly a student packs up their materials at the end of class.
- Situational data, which matters, too. Did a student do poorly on a quiz right after a fire drill? Or did they ace a test the same week they had a breakthrough with the school’s counselor?
Some of that data is messy. Some of it is deeply human. But together? It’s what makes you—not the algorithm—the expert. Because no matter your data training, you bring essential context to every education data analysis.
The case of the problem student
Picture this: You’re teaching seventh-grade math. It’s in-service week, and you’ve just stocked up on new dry-erase markers when your sixth-grade colleague pops in to warn you about Elijah. “Good luck with that one,” she says.
From what you’ve heard, Elijah has behavior issues, low motivation, and a serious case of the “I don’t cares” when it comes to math. You begin the school year and Elijah confirms these rumors. He’s… challenging.
A month into the year, when your class takes their first round of MAP Growth, you get a surprise. Elijah’s math scores are way above grade level. You take a peek at last year’s scores and, yep: high numbers. So what’s the problem? Why is he rowdy in class, leaving homework undone and chronically arriving with zero supplies?
As you speak with Elijah about his scores, you notice he’s clamming up. It’s clear he’s uncomfortable talking about it. With some well-phrased questions, you get to the bottom of it: Elijah shares that, a few years ago, another kid in his class did well and started being pulled out of his larger class for small-group work. He’s afraid he’ll suffer the same antisocial fate.
You begin to suspect that Elijah doesn’t have a “math problem.” He has a boredom-plus-FOMO problem.
Elijah has spent years finishing work early, getting restless, and getting in trouble. When an adaptive test like MAP® Growth™ meets him at his level, he lights up, but he doesn’t want his high aptitude to redefine his friendships. You devise a plan where Elijah can strategically tackle some advanced work within your classroom, stretching his muscles while remaining with his peers. A blend of a digital supplemental curriculum and some well-placed eighth-grade materials ensures that Elijah can stay engaged—and stay inside your classroom.
Data didn’t hand you the solution. It just nudged you to ask better questions.
Introducing Data Detectives
Like great athletes or musicians, teachers can’t always explain how they do what they do. Anecdotes like Elijah’s are a little art and a little science, just like the work of Sherlock Holmes, Miss Marple, and Hercule Poirot. We created our Data Detectives series because we believe educators deserve to see themselves in the data solution—not just as interpreters of charts but as the storytellers and sleuths they’ve always been!
Each article in this ongoing series follows educators navigating a rich ecosystem of data, using both quantitative information and human instinct to make meaningful change and crack the case. Let’s meet our first three in the series.
1. The case of the informational text
Summary: A sixth-grade teacher notices her students struggle with nonfiction texts until another teacher reveals their extracurricular activity may be behind a surprising reading boost.
Best for: Teachers who need a little help imagining how literacy might live across subjects, and who believe engagement might be the missing puzzle piece.
Clue: An exotic class pet might be the culprit.
2. The case of differentiation bandwidth
Summary: A math teacher sees low scores in one instructional area, but instead of jumping to fix the bottom group, he finds an untapped opportunity.
Best for: Teachers who feel like they’re stuck only targeting the extremes and who are curious about how a small shift in grouping might unlock big growth.
Clue: It’s not always the lowest performers who need your attention.
3. The case of the early reader
Summary: A second-grade teacher realizes a student’s confident voice masks their decoding difficulties and must bring the family into the solution.
Best for: Teachers who’ve ever had to gently correct a family’s well-meaning assumptions or who want to make early literacy data more actionable.
Clue: Try clicking “play” on a simple audio file.
Twist: You were the detective the whole time
Remember how Sherlock’s degree was in chemistry? Teachers, you don’t need a degree in data science—and you don’t need to overhaul your whole practice—to approach data differently. Start small. Try one of these:
- Get a histogram view. After your next assessment, look for the “middle group,” not just who’s above or below benchmark. Depending on your school’s RTI approach, your students in need of extra support might already have their needs met.
- Be curious. Do you ever ask your students “What do you notice? What do you wonder?” In your next PLC meeting, sit side by side with another teacher. Each pull up your own MAP Growth Class Profile report. Talk about it and share an observation. That’s it. Believe it or not, that’s talking about data!
- Lean on quick reviews. Use the “Instructional areas” tab in MAP Growth to drive a warm-up, not a unit plan. What skill needs 10 minutes of review?
- Note surprises. Identify the student whose scores surprised you—high or low—and ask, “What context might I be missing?”
- Keep a MAP Growth testing day diary. On your next MAP testing day, keep a diary. What’s your mood? How did you introduce the test? What did you eat? Now imagine what your students’ diaries would look like. How does that affect the integrity of the data, and how can you take steps to stabilize those factors?
Sometimes, just talking about data differently can lead to new ideas—and remind you that you already know how to do this. You just need space (and a little inspiration) to trust your instincts again.
Your trusted sidekick in education data analysis
At NWEA, we’re here to make education data analysis make sense. Our Data Detectives series is growing, and while we work on more stories to add to it, you can explore practical strategies for applying assessment data to instructional choices by visiting our archive of posts on using assessment data. We’ve rounded up real-life applications, educator voices, and ideas to help you get the most out of your data—especially MAP Growth.
Because when teacher instinct meets good education data analysis? That’s where the magic happens and the mystery is solved…at least until the next page-turner.
