For a long time, computer science has been treated as a self-contained territory. A place of abstract logic and formal syntax, of clean problems and cleaner solutions. For students who already feel at home in technical spaces, this framing can feel natural, even reassuring. For many others, it feels like standing in front of a closed door, uncertain whether they are meant to knock, or whether the room inside was designed with them in mind at all.

The usual response is familiar. Start earlier. Simplify the material. Use visual programming. Go unplugged. Add more exercises. Push a little harder.

But what if the difficulty is not when we introduce coding, or even how early, but how narrowly?

Fun matters, of course. But fun is not the mechanism. What truly moves students is curiosity with direction: the feeling that something real is at stake, and that computing is one (often non-negotiable) way, among others, to act on it.

Learning Begins With Wonder

Imagine you are thirteen and someone says, “Today we’re learning to code.” You might brace yourself. Now imagine they say instead, “Let’s try to understand why bees are disappearing and whether we can use data to help us make sense of it.” Suddenly, coding is no longer the destination. It is a means. That shift, small as it seems, changes everything.

Over time, we discovered something quietly transformative. Interdisciplinary learning does not merely make computer science more accessible. It creates entirely different paths into the field, paths many students would never encounter if coding were presented as the starting point rather than the tool. Perhaps most strikingly, the students who began by saying they wanted nothing to do with coding were often the ones who profited the most by experiencing the broad nature of computing.

Interdisciplinary learning reverses the usual order of instruction. Instead of asking students to care about a tool before they know why it matters, it begins with questions that already hold meaning, and introduces computing as a way to explore them.

Motivation comes first. Abstraction follows. This is not about making computer science easier. It is about making it legible.

From Subjects to Expeditions

We designed what we call RockStartIT expeditions: short, immersive learning experiences in which students explore real-world questions using computational tools. Not here is loop in JAVA, now apply it someday. But here is a problem: what tools might help us think about it?

In Save the Bees, students move between biology, data analysis, and artificial intelligence, learning about bee health while building websites, analysing population data with SQL, and training simple image-recognition models. No prior coding experience is required.

In Search of Other Life, students become space explorers. They decode camera signals, program rocket trajectories, and transmit images across distances. Other expeditions involve sorting vegetables with algorithms or modelling climate change through code.

Every step invites exploration. You do not need to know what a for-loop is to begin. You need curiosity.

When Learning Feels Human

When coding is woven into biology, climate science, or space exploration, students are no longer asked to audition for a pre-existing identity. They can arrive as themselves and allow computing to meet them where they are.

What we see, again and again, is not just enjoyment, but a shift in how students see themselves. Those who once described themselves as “not techy” begin to act like people who use technology to think. Coding becomes less intimidating when it clearly serves a purpose beyond itself. Confidence grows not because the material is diluted, but because its relevance is unmistakable.

This aligns with what our research has shown more broadly: interest and confidence develop most reliably when learners can connect new skills to questions they already care about. Abstraction alone rarely ignites motivation. Meaning does.

We initially designed these experiences with girls in mind, given how often computer science classrooms fail to make them feel welcome. But the effect reaches much further. Interdisciplinary, problem-based learning supports anyone who has ever felt uncertain, underprepared, or out of place in a technical environment.

When classrooms allow for different starting points, something subtle shifts. Engagement deepens. Stereotypes loosen their hold. Curiosity begins to travel.

Not Less Computer Science, But More of It

There is a common worry that interdisciplinarity dilutes the discipline. Our experience suggests the opposite. When coding is embedded in meaningful contexts, students are willing to wrestle with harder ideas. They tolerate ambiguity. They persist longer. The discipline does not disappear; it gains depth.

What changes is the entry point. Instead of asking students to prove they belong before they can apply computing, we let the application be the reason they begin. This perspective also connects to something we explored in an earlier Thinkable Letters essay: the idea that interest in computer science does not simply fade with age, but reorganises. As students grow older, curiosity becomes more selective, confidence more fragile, and relevance more decisive. Interdisciplinary learning meets this shift with care. By anchoring coding in questions that already matter, it honours learners’ developmental need for meaning and agency, rather than mistaking hesitation for disengagement.

Why This Matters Now

If we want more young people to engage with computer science, especially those who do not already see themselves reflected in it, the answer is unlikely to be more of the same, but earlier. We need to reconsider what counts as a legitimate beginning.

Interdisciplinary learning does not replace computer science. It reframes it as a way of understanding the world, not just a subject to master. And when students encounter coding in this way, something clicks. Not because it is flashy or simplified, but because it feels human.

When coding helps explain why bees are dying, how climates shift, or what signals travel through space, it stops being a gatekeeper. It becomes a way of thinking.

And that, far more than any syntax, is the real superpower.

Computer science has quietly become a foundational discipline. Not in the sense of replacing others, but in the way mathematics once did: as an extension of thought itself. It sits beneath almost every human endeavour now, supporting, shaping, and amplifying what we are able to imagine and execute. Science, art, medicine, archaeology, linguistics, music; each is increasingly carried forward by some strand of computation, whether we name it or not.

Not having access to these skills is no longer a neutral gap. It is a constraint on agency. It means having ideas you cannot test, observations you cannot explore, systems you cannot build, and questions you cannot follow all the way through. Creativity without the ability to execute becomes fragile. Vision without tools turns into regret, not because the ideas weren’t good, but because the world now moves through channels you cannot enter.

This will only intensify with AI. Those who can shape, adapt, and direct these systems will not just use technology; they will quietly build the world the rest of us have to live in. Others will still have ideas, often brilliant ones, but fewer ways to act on them. The gap will not be about intelligence or imagination. It will be about leverage.

What’s striking is how universal this leverage is. An archaeologist can use image recognition to reconstruct fragments of the past. A linguist can use AI to fill gaps in damaged or destroyed texts. A biologist can trace invisible patterns across ecosystems. A musician can explore structures no human hand could calculate alone. These are not “technical applications” in the narrow sense. They are extensions of curiosity.

Computer science, in this light, is not a career choice. It is a literacy. A way of turning wonder into action. And without it, even the most beautiful ideas risk remaining exactly that, beautiful, unrealised, and quietly lost to time.

For readers interested in the research behind these observations: Authentic interdisciplinary online courses for alternative pathways into computer science, Journal of Systems and Software, 2024.

“Children are not things to be molded, but are people to be unfolded.”

— Jess Lair

That idea, that learners grow in complexity, not just in raw ability, captures our findings better than any graph ever could. There is a stubborn story we tell about computer science education. That interest is something you either catch early or miss forever. That motivation declines because some students simply “aren’t suited for it.” And that gender explains most of what goes wrong.

Much of contemporary CS education is built on this assumption. Educators repeat the mantra that it’s never too early to start STEM, quietly implying that interest, confidence, and future trajectories are largely decided in childhood. Miss the window, and it closes. What is dangerous is that the early-start narrative is somehow comforting because it shifts responsibility away from later educational design, that is usually more difficult. Anyhow, our data tells a different story.

What Actually Happens to Interest

We looked at how more than 400 students, aged ten to eighteen, relate to computer science. What we found does not resemble a clean decline. Yes, attitudes fluctuate during adolescence. Yes, there is a noticeable dip in interest, confidence, and future intentions around mid-adolescence. But what changes is not whether students are capable or curious.

What changes is how they evaluate the subject and themselves in relation to it.

Underneath the surface variation, a stable core remained visible across age groups and genders: enjoyment of problem-solving, belief in one’s own ability, and the capacity to imagine a future connection to CS. That core does not disappear with age. What shifts is which parts of it carry the most weight.

Younger students tend to approach computer science as a broad, fascinating and playfull field. Their engagement is exploratory and generous. They are curious first and critical later. Older students become more selective. They ask tougher questions: Is this meaningful to me? Can I actually do this well? Does this fit who I’m becoming?Interest narrows, not because learners are weaker, but because they are more discerning.

The Adolescent Dip Isn’t A Loss of Ability

This transformation becomes especially visible during adolescence, a period shaped by identity formation, self-reflection, and social pressure. In our analysis, age mattered more than gender in shaping attitudes toward computer science. Both girls and boys showed rising interest up to early adolescence, followed by a marked dip between roughly fourteen and sixteen. That dip is often interpreted as disengagement. It isn’t. It’s a developmental transition.

Girls, Boys, and Timing

Gender differences did appear, but they were smaller than expected. The more striking pattern was timing. Girls’ self-efficacy began to decline earlier. Boys followed a couple of years later. This mirrors broader developmental research: adolescence reshapes confidence, identity, and perceived belonging, often earlier and more sharply for girls. The implication is not to “fix” girls or education for girls. It is to stop designing learning environments as if development didn’t exist.

What seems to be happening is not a loss of ability, but a shift in how students protect their emerging identity. Around mid-adolescence, learners become far more sensitive to signals of competence, belonging, and social comparison. Curiosity alone is no longer enough. Activities that once felt playful now carry reputational risk: being wrong feels public, being slow feels exposing, and being “into” something suddenly says something about who you are. For girls, this shift often arrives earlier, intersecting with earlier puberty and stronger stereotype pressure; for boys, it tends to surface later. In this phase, interest doesn’t disappear, it becomes conditional. Learners engage only where they feel safe enough, competent enough, and able to maintain a coherent sense of self. Interventions that ignore this reality misread withdrawal as apathy, when it is often a form of self-preservation. Once you see this, a lot of familiar patterns in CS education start to look different.

A Paradox: Older Students Change the Most

Here’s the finding that surprised even us. Despite reporting lower overall enthusiasm, older students showed the largest positive changes following short, well-designed computer science interventions. Their attitudes shifted more than those of younger students. Their resistance softened. Their future intentions became more open. Statements such as “I don’t want to deal with coding” weakened noticeably, while curiosity and willingness to re-engage reappeared.

This creates a paradox that standard educational narratives struggle to explain. Younger students enjoyed the courses more, but their underlying attitudes changed less. Older students were more critical, less easily impressed, yet more deeply affected.

So yes, younger learners had more fun. But older learners were more transformed. This is the paradox most narratives fail to explain:

being harder to impress does not mean being unreachable.

Why Early Exposure Is Not Enough

Early exposure certainly matters. It shapes familiarity, confidence, and access. That part of the story is true. But it is not the whole story. Early exposure, it turns out, is not a guarantee. And late engagement is not a lost cause. Our findings suggest something subtler. Interest development is not front-loaded. It is reactivable.

Interest does not crystallise once and for all in childhood. It remains responsive, especially when learning environments meet learners where they are developmentally.

Enthusiasm Is a Moment and That’s Okay

One of the biggest mistakes is treating enthusiasm as a personality trait. Enthusiasm is not a trait. It is not a promise of a career. It is not something you either “have” or “lack.” It is a momentary activation, when positive emotion, perceived value, and willingness to continue align. Those moments fluctuate. They fade. They return.

Short interventions cannot manufacture lifelong interest. But they can keep pathways open that would otherwise close quietly.

This distinction matters because many educational systems implicitly expect enthusiasm to be permanent. When it fluctuates, as it naturally does during adolescence, it is interpreted as failure. Our findings suggest a different reading: fluctuation is not a sign that interest is gone; it is a sign that it is reorganising.

Designing for Attunement, Not Accumulation

If interest shifts with age, then learning systems must shift too. Not by adding more content. Not by pushing harder. Not by optimising dashboards. But by modulating support: clearer when the challenge rises, lighter when autonomy grows, being present without dominating. Interest doesn’t need to be engineered. It needs room to reorganise.

This is where the implications extend beyond computer science education. What we observed is not a need for more stimulation, more persuasion, or more aggressive encouragement. It is a need for attunement. Learning environments that recognise when learners require clarity and when they require space. When guidance should be present and when it should recede.

The older students in our study did not need louder signals. They needed environments that respected their developmental shift, where support adapted in intensity without disappearing, and where autonomy was not confused with abandonment. They respond less to novelty and more to meaning. Less to enthusiasm imposed from outside, more to confidence rebuilt from within.

Interest, in this sense, behaves less like fuel and more like structure. It responds to how learning is framed, not just to what is taught. It reorganises as learners mature, and systems that fail to reorganise alongside it will always misinterpret adolescence as disengagement. The lesson here is not that enthusiasm declines. It is that it becomes more selective.

Seen this way, the problem is not that students lose interest in computer science. The problem is that many learning environments fail to adapt as interest changes form. If educational systems continue to assume that interest should look the same at sixteen as it did at ten, they will keep designing interventions that arrive either too early or too bluntly. But if we accept that interest is developmental, responsive, and revisitable, a different design space opens up, one focused not on accumulation, but on timing.

This is why the early-start narrative, while well-intentioned, is incomplete. It assumes a linear accumulation of interest, when what we observe instead is a series of reconfigurations. Interest can weaken, strengthen, fragment, and return, sometimes more than once.

The Quiet Conclusion

The real problem is not that students lose interest in computer science. It’s that we misread what interest looks like as they grow up. We treat early enthusiasm as success and adolescent hesitation as failure, judging impact by enjoyment, while overlooking that for older students, the most meaningful changes often happen quietly.

Our findings suggest something uncomfortable: enjoyment and impact are not the same. Younger students may enjoy activities more, but older students are often the ones whose attitudes shift more deeply. They are harder to impress, but more capable of genuine reorientation when learning environments respect their developmental stage.

From this perspective, a dip in interest is not a warning sign but a developmental signal. Interest does not follow a straight line; it fragments, reorganizes, and, under the right conditions, returns. Adolescent disengagement is not a verdict—it is a request for relevance, autonomy, and dignity.

This way of thinking is also why

Thinkable exists. It grew out of the discomfort with designing learning as if interest must be captured once and then preserved. Thinkable is built on a different assumption: that thinking develops in phases, that curiosity changes form, and that meaningful engagement comes from environments that know when to step closer and when to step back.

If that view of learning resonates, you’ll probably feel at home there.

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For more details read our research article Measuring Computer Science Enthusiasm: A Questionnaire-Based Analysis of Age and Gender Effects on Students’ Interest

Most of us are never explicitly taught how to think. Instead, we are taught what to know. We learn facts, formulas, and procedures, and we practice applying them to well-defined problems with clear boundaries and expected answers. This works well for building foundational knowledge. But it leaves an important gap.

A few years ago, I started noticing something that kept bothering me. Children do well in school. They learn quickly. They solve exercises, pass tests, and follow instructions. And yet, when problems become less clear, when there is no single correct answer, when different considerations pull in different directions, many of them suddenly feel lost. Not because they are incapable. But because no one ever taught them how to use their smart brains effectively and think structurally in such situations.

For a long time, this doesn’t show. School is carefully structured. Learning happens in small steps. Subjects are separated. Problems are simplified. Uncertainty is reduced as much as possible. Progress is measurable, feedback is immediate, and success is clearly defined. In this environment, children can perform very well without ever needing explicit thinking strategies.

This works surprisingly well. Until it doesn’t.

Real life does not stay small and well-structured. At some point, problems become complex, open-ended, and interdisciplinary. They cross subject boundaries. They involve uncertainty, trade-offs, and consequences that unfold over time. Suddenly, knowing the right formula or recalling the right fact is no longer enough. What matters is whether you can make sense of the situation as a whole.

This is where many learners struggle. Not because they lack intelligence or motivation, but because they lack tools for thinking.

Thinking methods, often called mental models, only reveal their true value when problems are complex enough to overwhelm intuition. They help us slow down, structure our reasoning, and look at the same situation from more than one perspective. When we change how we look at a problem, hidden assumptions become visible. Options appear that were invisible before.

In life and in business, the people who make better decisions are often not the ones who know the most. They are the ones with fewer blind spots. They see more of the problem and therefore make fewer costly mistakes. This is why thinking methods matter.

Thinkable grew out of a deep enthusiasm for this kind of thinking, and for the particular cognitive satisfaction that comes with real understanding. That moment when confusion turns into structure. When complexity becomes navigable. When something suddenly clicks.

This experience is powerful. It builds confidence, curiosity, and a sense of intellectual independence. And I believe children should experience it early, not only later in advanced education or professional life.

Thinking better does not mean thinking harder, and it does not mean filling children with more information. It means giving them simple, reliable processes that help guide their thinking across different dimensions of a problem and from multiple perspectives. Different problems require different approaches. Learning to recognise which thinking process fits a situation, and how to use it, is a foundational life skill. Over time, this becomes what we often call wisdom. Not knowing everything, but using what you know well.

At Thinkable, the focus is not on expanding how much information learners can access. Schools already do that. The focus is on expanding the range of thinking processes they can draw on. We work with real, interdisciplinary problems and meet children in domains they already care about, using curiosity as the starting point.

When children experience that better thinking leads to better understanding, motivation follows naturally.

Thinkable exists to make this kind of interconnected experience accessible. Especially in a world where information is abundant and answers are easy to generate, the ability to think clearly, reason carefully, and decide responsibly becomes one of the most important skills we can help the next generation develop.

In our Thinkable Letters, we share new courses and notify you as they emerge, reflect on research insights about thinking and learning, and regularly offer small thinking challenges you can explore together with your children, simple prompts designed to spark curiosity, conversation, and deeper understanding.

If you want learning to prepare your child not just to answer questions, but to ask better ones, you’re very welcome here.

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Thinkable.Space

Learning that strengthens thinking, judgment, and intellectual independence