How do you measure something you can never actually see?
It’s a challenging question. Economists must infer consumer confidence from spending patterns. Astronomers must often confirm the existence of a distant planet from the tiny wobble it causes in a star. Psychologists build a picture of what’s going on in someone’s mind from what their patients say and do, not from the thoughts themselves.
None of these experts ever get to look directly at the thing they’re trying to understand. You face this challenge every day in your classroom. You can see a student’s homework, hear them talk through a problem, watch how they do on a test. What you can’t see is the learning itself, quietly taking shape.
This is what drives the questions at the center of learning science: What is a student truly coming to understand? What really caused their growth? And how do you measure something you can never observe directly?
“Learning science” definition
Learning science is the interdisciplinary study of how people learn. It draws together cognitive science, psychometrics, causal inference, learning theory, and the philosophy of science and mind.
Cognitive scientists study how kids think about a concept, like fractions or main ideas. Psychometricians build models that turn scattered evidence, like test responses, into a defensible claim about what a student knows. Researchers in causal inference ask not just what’s associated with growth, but what could cause it. When we put these perspectives to work together, the result is a field built to answer questions none of them could answer separately.
Learning science vs. the science of learning
You’ll often hear “learning science” and “the science of learning” used interchangeably, and it’s easy to see why: both are focused on understanding how people learn. There isn’t a hard and fast line between them, but there is a useful distinction.
What is the science of learning?
The science of learning is the body of research focused on how people acquire, retain, and apply knowledge and skills. In practice, you’ll often see it applied one domain at a time, in phrases like “the science of reading” or “the science of math.” When we talk about “the science of reading,” we’re talking about a real and valuable body of evidence based on science of learning research into how students move from decoding words to comprehension.
How are learning science and the science of learning connected?
The science of learning explains how learning happens, cognitively and developmentally. Learning science takes that evidence and puts it to work, adding the measurement, modeling, and causal reasoning that turns “here’s how learning happens” into something you can act on in your classroom.
Why the learning sciences matter
Every day, you make decisions about instruction, intervention, and support, probably with less clarity than you’d like. By bringing multiple disciplines together to work on the same problem, learning science helps turn evidence like assessment results and classroom observations into a clearer picture of how a student is truly growing. The goal isn’t simply to collect more data, but to help you make sense of the evidence and use it to support your students.
What are the core principles of learning science?
Learning science draws from a lot of disciplines, but a handful of core ideas run through the work itself:
- Learning can’t be observed directly. You can’t see understanding from inside a student’s mind. What you can see is a manifestation of that understanding, like student work, classroom discussion, and assessment results. Learning science uses that evidence without mistaking it for the understanding itself.
- Understanding matters more than performance alone. Rather than asking whether a student answered a question correctly, learning science asks deeper questions. What skills has the student developed? What concepts do they understand? Where might gaps in understanding still exist? This shift moves the conversation from performance alone to the learning that drives it.
- Theory, measurement, and practice must work together. Researchers who study how learning develops, statisticians who test those theories against real data, and educators who put it into practice can’t do this work alone. It takes real coordination across the work of people who use different languages, jargon, and priorities.
- Models should support your judgment, not replace it. No model, however good, can fully capture what’s happening for a student in your classroom. Learning science treats a model as one more source of information, meant to complement what you already know.
- Humility is part of the job. Learning is extraordinarily complex, so we should all be skeptical of any theory that claims to have it all figured out. Learning science treats every model as a work in progress, to be tested and refined as new evidence comes in.
Taken together, these ideas keep learning science honest and trustworthy. It’s useful and reliable enough to act on, while humble enough to keep getting better.
What learning science reveals about student growth
Learning science treats growth as more than a number changing over time. It asks what evidence we have to support that number, such as which concepts a student has mastered, which skills are still forming, and where a little extra support could make the biggest difference. When we think of growth this way, it stops being a single score and starts being a story, one you can use to plan what happens next.
The role of assessment in the learning process
If learning science is about understanding how students learn, assessment is where a lot of that evidence comes from.
It’s tempting to think of assessment as just a way to measure what a student already knows. But a good assessment can do more than just report a score. It can support learning while it’s still happening, inform your next instructional move, and help you see progress over time.
That’s especially true of interim assessments, which are given periodically throughout the year. Unlike a year-end test that tells you how learning turned out, an interim assessment tells you how learning is progressing, helping you make decisions in the moment. MAP® Growth™ is a good example. As an adaptive interim assessment, it helps you measure both achievement and growth over time, while giving you information you can use to shape instruction. It connects results to classroom decisions, so you can see where a student is, where they can go, and how to help them get there.
From a learning science perspective, assessment is one important source of evidence, not the whole picture. When viewed alongside other indicators of learning, it can help you better understand student growth and the knowledge and skills driving that progress.
Using assessment data to understand student learning
Assessment data is most valuable when it helps you understand learning, not just measure it. Results can reveal strengths, flag where extra support might help, and let you track growth over time.
Learning science encourages you to look past the score and ask what the results actually reveal about a student’s knowledge, skills, and understanding. Take the zone of proximal development (ZPD), for example. ZPD is the range between what a student can do alone and what they can do with the right support. Assessment data can help you find that “just right” zone and match students with work that will move them forward.
Assessment data can also help you see growth through the lens of an individual student. Factor in that student’s starting point, how their skills have developed since, and what they might be ready for next, and you get a more complete picture of learning and growth. Combined with your own professional judgment and everything else you know about a student, assessment data becomes a real tool for deciding what to do next, not just a record of what already happened.
Bringing learning science into assessment practice
Learning science helps explain how learning develops over time. Paired with good assessment practice, it can help you gather real evidence of learning, make more informed instructional decisions, and build the conditions that support growth for every student. Below are a few recommendations for applying learning science principles to assessment practices.
For teachers
- Use formative checks to gather evidence of learning. Learning is a process, not an event. Frequent formative checks let you catch students while they are still thinking, so you can spot misconceptions and adjust before small gaps turn into bigger ones.
- Revisit key concepts. Learning rarely moves in a straight line. Circling back gives you another chance to check understanding, see whether students are ready to build on what they know, and catch anyone who needs more support.
- Monitor growth and set meaningful goals. Learning science views growth as a developmental process, and assessment data helps you track it from a student’s actual starting point. MAP Growth reports, like the Class Profile, can also help you spot patterns across your whole class and see where more support is needed and where students are ready for a bigger challenge. When students help set their own goals and can see their own progress, assessment starts to feel like part of learning instead of just a measure of it.
For school and district leaders
- Focus on growth alongside proficiency. While proficiency remains an important measure, examining growth alongside achievement can provide a more complete picture of student progress and help leaders recognize learning gains across a wide range of learners.
- Use assessment data to support decision-making. Data can back up decisions about interventions, advanced learning opportunities, program effectiveness, and where to put resources. Looking past a single score helps you spot students who could use more challenge or more support and make calls that track with the evidence.
- Build systems that reflect how learning actually occurs. Learning science reminds us that learning develops over time. Leaders can support that reality by creating systems that encourage ongoing use of assessment evidence, collaborative data conversations, and continuous improvement. When educators regularly examine evidence of learning together, assessment becomes a tool for understanding and supporting growth rather than simply evaluating outcomes.
How NWEA applies learning science in assessment design
For decades, we’ve worked to help you understand student learning and growth. That work is grounded in the same principles at the heart of learning science: seeing learning as something that develops over time, understanding growth matters as much as achievement, and knowing good decisions need good evidence.
MAP Growth reflects those principles. Instead of just checking whether a student hit a benchmark, it helps you understand where they are in their learning, how they’ve progressed, and where they can go. Those same principles shape how we think about growth measurement and reporting more broadly. By helping you see growth from a student’s starting point and spot patterns over time, assessment data becomes something you can use to understand and support learning, not just measure it.
How research drives innovation at NWEA
Behind every assessment, report, and growth measure is a mountain of research. We’re constantly studying how students learn and grow, and how you can best support them.
Our learning sciences team is a big part of that work. We have researchers, psychometricians, technologists, and experts from other fields working together on some of education’s hardest questions. How does learning actually develop? How do you measure growth? How do you turn evidence into something that helps a real student? Working across those boundaries is how research turns into something useful for you.
This research shapes everything from our MAP Growth norms and growth expectations to our reporting tools and instructional recommendations. Studying growth and learning at scale lets us spot patterns that can help you, the classroom experts, better understand achievement, progress, and paths for future learning.
Just as important, this work keeps our assessments evolving alongside advances in learning science, measurement, and educational practice. The goal is never simply to build a better test. It’s to give you the most useful evidence you can trust when it’s time to make a call about a student.
FAQs about learning science
Here are quick answers to some of the most common questions about learning science.
How is learning science influencing the future of assessment?
Learning science is pushing assessment past measuring achievement alone. More and more, assessments are built to reveal how learning develops, so you can understand growth, spot instructional opportunities, and make more precise decisions. As our understanding of learning keeps evolving, our assessments will offer even richer insights into a student’s progress and what’s driving it.
How does learning science help educators address unfinished learning?
Learning science encourages you to look past what a student missed and focus on what they know and can do right now. Using assessment evidence to spot strengths, gaps, and patterns lets you target instruction more precisely and meet students where they are, so unfinished learning can be addressed without losing forward momentum.
Can learning science benefit students at all achievement levels?
Yes. Learning science is about understanding how learning develops for every student, no matter where they currently stand. The same principles that help you spot a student who needs more support can just as easily help you spot one who’s ready for enrichment or a bigger challenge.
How can learning science help educators make sense of assessment data?
Learning science gives you a framework for reading results as evidence of learning and growth, not just a score. Instead of stopping at the number, you can use assessment data to spot patterns, understand strengths and areas for growth, and get a clearer sense of how learning is developing over time, turning data into something that informs your next decision.
Still learning, right alongside you
Learning science exists because understanding can’t be seen directly. It has to be inferred, carefully, from everything a student says and does. That’s why it takes so many different types of expertise—people who study how students actually think about a domain, people who build models that turn fragmented evidence into something you can trust, and people who are constantly looking at cause-and-effect relationships.
Learning is complicated, and the learning sciences team here at NWEA knows it. Nobody here claims to have it all figured out. We believe this kind of humility isn’t a weakness; it’s what keeps the work honest and dependable, always being tested, checked, and improved as new evidence comes in.
We’re learners ourselves, always asking questions, always testing our own assumptions, always looking for a better way. Because, ultimately, our job is to help you do what you already do best: help your students grow into the people who’ll solve tomorrow’s challenges.
