Today, I’m sharing the final infographic in what has become for me a real passion project. This week, the last of six, it’s 24 ways to embed metacognition and self-regulation in your classroom.
Metacognition and self-regulation get cited a lot as the best bang for your buck in education, the highest impact for the lowest cost, largely off the back of the EEF’s teaching and learning toolkit. And that’s fair enough, as far as it goes. But low cost can often be conflated with low effort, and that just isn’t true. You might not need to buy any specialist kit or an off-the-shelf solution to do this well, but there is a cost. It’s just paid in thinking and hard work rather than money. It works when you work hard at it. It works when the strategies are embedded into students’ everyday practice rather than bolted on for a lesson. And that takes time.
Which is where I want to start, with a cricket net.
The bowling machine
A batter can look magnificent against a bowling machine. The deliveries arrive at the same pace, the same length, the same spot, over and over, and the batter grooves the shot until it is a thing of beauty. Then they walk out to face a real bowler, someone with a brain and a plan, who varies the pace, hides the ball, sets them up with three of one thing and then the thing they were not expecting, and the beautiful shot is nowhere to be found.
The net form was never match form. The practice that felt most productive, the smooth, repeatable, satisfying kind, turned out to be the practice that taught the least.
This is one of the uncomfortable things with the science of learning (Robert Bjork has spent a career on it), the conditions that make practice feel fluent are often the very conditions that stop it sticking. We trust the feeling of ease. We take smoothness as a sign that learning is happening. And we are frequently wrong. The struggle we instinctively try to design out is often the bit doing the work.
The bowling machine analogy has been a running theme in my head as I work with schools embracing AI in the classroom, because a large language model is, in one sense, the most accommodating bowling machine ever built. Ask it for an answer, and it delivers, with such verbosity, every, single, time. Ask it to explain, to summarise, to draft, and “hey presto!”, you get what you want, instantly and fluently. A student working alongside it can produce work that looks immaculate. The shot from the nets is beautiful. The question is whether anyone has actually learned to bat.
A smooth answer, arriving without effort, might feel like understanding, but it’s not. It is the appearance of understanding, and the appearance is exactly what the science warns us not to trust.
What game are you watching?
There is a second thing to consider, I think.
Watch a football match with a fan and then with a specialist coach, and look at the same ten seconds surrounding a goal, and they will tell you two entirely different stories. The supporter sees the goal, the obvious moment, the result. The coach sees the run that dragged a defender three yards out of position eight seconds earlier, not even by the player with the ball that scored the goal, the thing that made the result possible. They are watching the same footage. They are not seeing the same game. The difference isn’t attention. It is knowledge. The coach just knows what to look for.
There are obvious parallels here. A teacher monitoring a class is the coach, reading the room and not just the answers written, and able to do so only because they hold a model of what good looks like. But the second thing, related directly to metacognition and self-regulation, when we ask students to monitor their own understanding, to judge whether they have grasped something, to decide whether they are ready, they are not always best placed to know whether they are or whether they aren’t. And a novice cannot reliably do this, for precisely the reason the casual supporter cannot read the game. They do not yet hold the model. They do not know what good looks like, so they cannot tell how far they are from it. This is why self-assessment built on a feeling is unreliable, and why it has to be anchored to something external, a worked answer, a standard, a check, rather than left to the warm and misleading sense that it all seems fine.
What becomes valuable when answers are cheap
Vikram Singh left a comment on LinkedIn after sharing one of the infographics. He observed that so many of these strategies are really about making thinking visible rather than generating answers, and that this matters more and more as answers themselves become easy to come by. He wondered whether one of the unintended effects of AI might be to push us, as educators, to pay far closer attention to the thinking process than to the final product.
I think that is exactly right, and it is where the two ideas meet.
When a plausible answer is a few seconds away for any student who wants one, the answer stops telling us anything. What it cannot tell you is whether the thinking happened, and the thinking is the part that lasts, not that an answer was 12.2 cms. So the valuable thing, the thing worth looking at, becomes the process: how the student got there, what they considered, where they checked themselves. Reading the process takes a coach’s eye. It takes someone who knows what good thinking looks like, watching for it deliberately, in a way that marking a finished product can often struggle to do.
This week’s resource
That is what this week’s infographic aims to help with.
I have taken the work of Zimmerman, Flavell and Winne, with a bit of the thinking Olly Lewis and I have done on digital cognition, and broken it into 24 ways you can try to embed metacognitive and self-regulatory practice in your classroom.
The thread running through all of it is simple: knowledge first. You cannot monitor your understanding of something you do not yet understand, so every card ties back to real content, pairs self-judgement with an actual check, and (mostly) treats technology as a scaffold you fade rather than a crutch you keep hold of.
The digital cognition cards at the end are the ones closest to everything above. Using a calendar to own your own spacing. Building your own low-stakes quizzes. Managing the device as well as the mind. And being honest with students about the difference between a tool that helps them think and one that offloads it. Memory is the residue of thought, as we know. If you haven’t thought, well… That, in the end, is the whole point.
It is free, like all six in the series, to download, print and share.
And to next week
With this being the sixth and final infographic, it means the series, as a weekly thing, finishes here. But…
Next Wednesday, the 17th, all six come together into one free guide: Pedagogy First, Technology Second. Every one of the 144 strategies in a single place, and alongside each topic, the thinking behind it, how I arrived at the categories, and where technology can help, rather than leading it. As we often share, technology should enhance what we do, not replace it!
I’ve made it to try to help, and hopefully it can.
If you lead teaching and learning, I really hope you’ll find it useful, a shared, evidence-informed foundation you and your colleagues can run with. If you are in the classroom, it is a year of things to try, every one grounded and every one with a clear action attached. And it is free. Free to download, print and share across your school or your whole trust. No paywall, no catch. Just don’t try to pass it off as your own or rebrand it and sell it somewhere else. I would love it in as many hands as possible, so when it lands, please do share it.
I will say more on the day. For now, I hope this week’s resource is useful, and I would love to know how you get on with it.
Pedagogy first. Technology second. Always.
