What Counts as Learning?

TL;DR:

• Learnification = learning reduced to grades, tests, and checklists
  
  
• Real learning isn’t just measurable—it shows up in play, curiosity, and everyday life
  
  
• Research supports self-directed, interest-driven learning as more motivating and creative
  
  
• Moving beyond learnification helps us notice growth in all its forms

What Is Learnification?

Learnification.
It’s a word we may not recognize, but it captures something many parents and educators recognize right away.

Philosopher Gert Biesta uses it to describe what happens when education is reduced to measurable outputs—things like test scores, checklists, and performance targets.

When learning becomes “learnified,” we risk shrinking it down to whatever can be filed away in a gradebook.

If learnification is at the foundation of common practice in many schools, what other options do we have?

A Tale of Two Students

Picture two students.
One is preparing for next week’s math test, memorizing formulas, and practicing problems from the review sheet.

The other spends the afternoon outside, collecting rocks and sorting them by color, weight, and shape, then drawing up their own system for naming the categories.

Both are learning. But is it only counted when it comes home on a report card? Does it only matter if it earns a gold star or a letter grade? Is progress only real when it’s charted in neat boxes on a progress report?

So what really counts as learning?

Rethinking What We Count as Learning

We commonly define learning by what can be measured—grades, test scores, or content mastery within a prescribed curriculum. These metrics can become stand-ins for the learning process itself, reinforcing a narrow and often exclusionary understanding of what it means to know.

What if we started from a radically different framework: one in which learning is defined not by measurement, but by curiosity, and autonomy.

Viewing learning in this way invites reflection on how we define the concepts of education and learning. What would become possible if those definitions were suspended?

Learning as Conversation and Exploration

Curriculum theorist William Pinar describes curriculum as a “complicated conversation”—a process rooted in experience and relationships.

William Doll adds that learning is nonlinear and co-created, more like a winding path through the forest than a straight line down the highway. If you’ve ever watched a child start a project, get curious, and then follow that thread into something entirely new, you’ve seen what they mean.

What It Looks Like in Everyday Life

So what does this look like in real life?

• A child spends the day building a cardboard fort, adjusting the design when something collapses.
  
  
• Another bakes cookies, measuring ingredients and sharing them with friends.
  
  
• A group organizes a vote about how to divide up time in a shared space.
  
  

These aren’t distractions from “real” learning—they are learning. Planning, experimenting, negotiating, and reflecting are skills that last a lifetime.

Why Learnification Falls Short

The problem with learnification is that it shrinks our vision of what counts as learning, leaving out the bigger picture.

Children also learn how to navigate friendships, advocate for themselves, explore curiosities, and respond to the world around them—not because someone told them to, but because it matters to them.

What Research Shows

Thinkers like John Holt and Peter Gray have long argued that students learn best when their learning is curiosity led. Research in psychology and education echoes this.

• Studies on self-determination theory (Deci & Ryan) show that autonomy fuels deeper motivation.
  
  
• Peter Gray’s work in Free to Learn draws on decades of research about how play and curiosity support problem-solving and creativity.
  
  
• Sugata Mitra’s “Hole in the Wall” experiments demonstrated how children, left to explore freely, taught themselves computer literacy.
  
  
• More recent studies on project-based learning (Condliffe et al., 2017) also find that when students follow their interests, they build the kind of motivation and creativity that can’t be taught through worksheets alone.
  
  

Seeing the Full Picture

To value only what can be measured is to ignore much of what makes us human.

In expanding what we count as learning, we begin to notice the intelligence in laughter, the planning in play, and the deep thinking in casual conversation.

Think of the way a learner explains the rules of a game they invented, the focus they pour into building with LEGO or designing a science project, or the confidence they gain from helping cook dinner or leading a school club.

None of these moments show up on a report card, but they are powerful, enduring forms of learning.

Moving Beyond Learnification

The challenge—and the opportunity—is to move beyond learnification and notice these everyday sparks of curiosity for what they are: evidence of growth, creativity, and human potential.

References

• John Holt – How Children Learn: A classic on how curiosity and play drive real learning.
  
  
• Peter Gray – Free to Learn: Explores research on play, freedom, and self-directed education.
  
  
• Alfie Kohn – Punished by Rewards: A look at how grades and gold stars can sometimes get in the way of real motivation.
  
  
• Sugata Mitra – “Hole in the Wall” experiments: Research showing how children taught themselves computer literacy when given the freedom to explore.
  
  
• Condliffe et al. (2017) – Project-Based Learning: A study on how interest-driven projects improve engagement and long-term learning.
  
  
• William Pinar – What Is Curriculum Theory?: Introduces the idea of curriculum as a “complicated conversation.”
  
  
• William Doll – A Post-Modern Perspective on Curriculum: Describes learning as nonlinear and co-created, rather than scripted.
  
  
• Gert Biesta – Good Education in an Age of Measurement: Where the concept of learnification is explained.
  
  
• Deci & Ryan – Self-Determination Theory: Research showing how autonomy and intrinsic motivation drive deep learning.

Written by Chelsea Verrette