Tag Archives: writing

Learning is a process – Not a destination

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After dropping out of Reed College, Steve Jobs attended a calligraphy class simply because he was fascinated by the lettering on signs. A decade later, that impulsive detour paid off because he used his knowledge of spacing and serif styles to give the Macintosh the world’s first beautiful digital typography, transforming modern computing forever.

People like to draw straight lines between what they want, their objective and the way they get there. And why not, it’s efficient and logical. But sometimes the fastest route is not always the best one.   

Students often assume that the main reason for answering a question is to test their understanding and prove they were paying attention. And while there is merit in this, the real purpose is a little more nuanced.  What we can say with some certainly is that teachers don’t set questions because they are looking for the answer themselves, the goal is in some way to provoke a cognitive change in the student.

TL;DR – the short audio version

Questions are set because the process involved in producing an answer, researching, structuring, arguing, thinking and deciding what to write is where learning actually happens. In fact, the answer is little more than a by-product.  The problem is that somewhere along the way we have taught students to see the answer as the objective or destination. Complete the task, hand it in, get a good mark and move on. Once you see learning like this, the sensible thing to do is to find the fastest route to the answer.

Nobody does a jigsaw for the picture

A good performance is not an indication of good learning
To put this another way, a high score on a test does not prove learning has taken place. In their 2015 paper, Soderstrom and Bjork (Learning Versus Performance: An Integrative Review) pointed out that what we can observe while someone is learning, their performance, is an unreliable guide to whether anything has been learned. Some conditions boost performance in the moment but leave little behind, while others make things feel harder and slower yet produce far better long-term learning.

This distinction has clear implications for test performance. A student who crams the night before an exam, rereads their notes repeatedly, or memorises a model answer may score well the next day. Yet these strategies mostly improve short-term performance rather than long term learning. Similarly, exam techniques, such as spotting question patterns or eliminating unlikely options, can raise a score without deepening understanding of the subject.

Hypocrite I hear you cry – Given much of what I have written before about how to pass exams this may sound “slightly” hypercritical, but in my defence…… Professional exams are incredibly demanding, they require the individual to perform at a very high level often in a three-hour artificial environment. Under these conditions even the best students, those who have an excellent understanding of their subject can fail to perform. The exam techniques are a tool they can use to redress the balance. 

This does not mean these techniques are effective in terms of long terms memory and learning. I would argue they are necessary to win the game that students have been forced to play.

“Exam skills help good students avoid failure” SPS

Questions and answers in an AI world
Let’s return to why we set tests in the first place, and as already stated, it isn’t because we need more answers. The value lies in the process of getting there, the recall, the struggle, the mistakes and corrections. If students skip that process and avoid the effort it demands, very little is actually learned.

This brings us, as ever, to AI. If the answer is the destination, then a tool that produces a perfectly good essay in seconds is the ultimate shortcut, and from the student’s point of view, it works. Generative AI has turbocharged an issue that already existed, mistaking a good answers for genuine understanding. Students can hand in very high scoring script while learning next to nothing, and unfortunately, they may not even realise it. This is exactly what Richard Feynman was warning about where students fool themselves.

“The first principle is that you must not fool yourself, and you are the easiest person to fool.” Richard P. Feynman

This problem is only going to get worse, according a recent report 87% of 11 to 24-year-olds have already used AI tools, nearly two-thirds for homework.

What can we do?

Firstly, educate students as to how learning works. They need to understand the science, that effort, difficulty and retrieval help develop knowledge, and that leaning on AI shortcuts undermines their own skill development. Students who understand why the struggle matters are far more likely to choose it.

Secondly, build in controls and a change in assessment. When success is measured by an exam result which will open doors to new careers and higher salaries, even when you know something is bad for you, often the temptation can be just too great. Meaning that some students will still reach for the shortcut, however well informed they are. In these cases, we need controls that make shortcutting impossible during the essential learning phases, such as supervised, in-class work or tasks designed so that the thinking has to happen in the room. The aim isn’t to ban AI, but to protect the moments when effort itself is the essentIal.

“When a measure becomes a target, it ceases to be a good measure” Marilyn Strathern, (Goodhart’s law)

Conclusion
Steve Jobs didn’t take that calligraphy class because he thought it would help him build the Macintosh, nor to enhance his career in some way by attending. Yet the process left something behind that paid off ten years later. That something was learning.

Performance was always an imperfect guide to learning, and now AI can help produce answers where there has been no learning at all. A polished essay or a high mark no longer tells us that thinking has taken place. Exam techniques do not lead to long term learning and it’s easy to see a contradiction between what I am writing here and the many posts I have made over the years in support of these skills. But I believe that tension is superficial. So long as assessment remains an artificial game, we should give students the right tools to do well even if that means in some cases little is learned.

What’s more important is that we protect the moments where effort is required. That means teaching students why the struggle matters and designing tasks where the thinking has to happen” in the room”. The aim isn’t to ban shortcuts, but to ensure students engage in the intellectual work that actually builds understanding.

Because in the end, AI might be able to give us all the answers but it should never be allowed to do our thinking for us.

The University of Life – Simulation

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Have you ever heard someone say they went to the University of Life. It’s a slightly cynical way of suggesting that life experience is a far better way of learning than formal education.

It is of course true that life can teach you things that can’t be learned in the classroom, but only if you pay attention. And many people go through life and don’t!

We have become very good at teaching people what they need to know but less good at explaining what that knowledge is for. And even when it is explained, it will probably seem abstract and of little use. Equally, if you can’t put the idea or theory into practice immediately, the experience and memory will be lost.

The problem is that the University of Life is not the best place to learn, it’s hard, confusing, and as a consequence neither efficient nor effective. It relies on the individual to analyse, reflect and convert the experience into something they can learn from. But what it does is expose the learner to the real world, one where information is incomplete, the environment is complex, uncertain and everything has a deadline.

TL;DR – the short audio version

Simulation – The bridge to reality
There is a need for a half-way house, something that can act as a bridge between theory and practice, scaffolding the learning experience into the “real-world”. One method designed to do just this is simulation.

While definitions vary, I like this one – A simulation is a technique (not a technology) to replace and amplify real experiences with guided ones, often “immersive” in nature, that evoke or replicate substantial aspects of the real world in a fully interactive fashion.

A genuine learning simulation places the student inside a realistic, evolving scenario where they have to make a decision, see a consequence that follows from it, and then choose what to do next in light of that consequence. In terms of contrast, it is more like a flight simulator than a case study.

I hate reality, but it is still the only place where I can get a decent steak. – Woody Allen.

The missing middle – Much of professional education has settled into a familiar pattern. Students are taught the knowledge they need and then assessed on whether they can recall, apply or analyse it. This model is effective at measuring learning, but provides little opportunity to practise using knowledge in the messy, uncertain conditions of the real world before being assessed on it.

Knowledge → Assessment

What is largely missing is the stage in between. This is where simulation becomes particularly powerful. It can give students the opportunity to move beyond answering questions about how something might work in practice and experience a version of it. This matters because competence is not simply knowing the right answer. It involves judgement, knowing what to do, when to do it, and why.

Knowledge → Application → Decision → Consequence → Reflection → Try again→ Assessment

Why do simulations work?
Simulations draw on several strands of learning theory that all share a core idea, knowledge is more likely to be retained when learned through active engagement in a realistic context rather than received passively and applied later. Simulation sits within the broader concept of experiential learning, alongside approaches such as project-based and problem-based learning. Kolb (David A. Kolb) provides us with a clearer understanding of what is happening, describing experiential learning as a continuous process in which individuals move through experience, reflection, conceptual understanding, and experimentation to refine their knowledge and skills.

Vygotsky’s (Lev Semyonovich Vygotsky) concept of the Zone of Proximal Development provides another piece of the jigsaw. He explains that learning is most powerful when the student is working in the Zone of Proximal Development, the middle space between something they can already do and what they can’t do without support. When they are in this zone, stretched but not overwhelmed, these are the ideal conditions for growth. This leads us nicely into a more recent piece of research specifically relating to simulation. Simulation-Based Learning in Higher Education: A Meta-Analysis (2020)

This meta-analysis of 145 simulation based studies found a large positive effect on the development of complex skills (g = 0.85). The benefits extended beyond knowledge acquisition to skills such as critical thinking, problem solving, diagnosing, decision-making, communication and management. Interestingly, these are the skills most in demand by employers. The authors conclude that simulations are among the most effective ways of developing complex skills across higher education.  Simulations work best as structured learning environments where scaffolding provides support throughout the experience, with the level of guidance adapting to the needs and experience of the learner.

The role of AI – AI could provide some of the scaffolding traditionally supplied by a facilitator, for example, by questioning decisions, providing prompts, giving feedback, adjusting the level of support, role playing stakeholders and, crucially, prompting reflection. Importantly however, the research doesn’t demonstrate that AI can replace a human facilitator. This means its role is one of support rather than autonomy.  

Too much of what is called ‘education’ is little more than an expensive isolation from reality. Thomas Sowell, economist, and social theorist.

Conclusion
Perhaps the answer isn’t to choose between the classroom and the University of Life, but to bring the best of both together. Traditional education gives students the knowledge and conceptual frameworks, real life provides the complexity, uncertainty and consequences that turn that knowledge into professional capability. Simulation can provide the missing bridge between the two.

For students – this creates a safe place to practise. They can make decisions, get things wrong, receive feedback, reflect on what happened and try again. That opportunity to learn from mistakes, rather than simply being tested on whether they can avoid them, may be one of simulation’s greatest advantages.

For teachers – simulation offers something equally valuable. It moves the teacher from being primarily the source of knowledge towards becoming a designer, facilitator and coach of learning. Rather than having to create every possible example or anticipate every question, teachers can observe how students actually think, and intervene where their reasoning needs support. AI may be able to provide some of the routine scaffolding by role playing a difficult client, asking the awkward follow-up question, adjusting the difficulty level and prompting the student to reflect. What it can’t do, at least not yet, is replace the judgement of a human facilitator who knows when to push and when to let the student sit with the discomfort of getting it wrong.

The University of Life is still the ultimate simulation – it’s just a terrible classroom!

Want to learn more?

Here are a few examples of what is happening in the real world.

  • University of Gloucestershire (FutureSim) – A new £2.5 million investment exploring new ways learning, training and preparing people for future careers.
  • Aston University – A £4.8 million to expand simulation and laboratory facilities for students studying medicine, pharmacy, nursing, optometry and audiology.
  • Coventry University Business Simulation Suite – Aims to provide and support skill-based training through the use of simulation games, role-play activities, and scenario-based learning.
  • Hubro Simulations – Learning by doing with business simulation, where students learn through experience by running their own virtual companies.
  • SimVenture – Online business simulation designed for professional education.
  • InvestUp challenge – Real-time market simulation to build investment intelligence and commercial awareness, giving students an early professional edge.

The Antidote for an AI snake bite – Metacognition

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I first wrote about metacognition back in 2019 – the skill of thinking about your own thinking. Since then, much has changed, I have learned a bit more but crucially AI has come along and is having a significant impact on much of what we do.

It’s now woven into how we write, make decisions and even remember, and with that comes the danger of AI quietly eroding our ability to develop metacognitive skills. Taking the path of least resistance and delegating the hard work to AI leaves us exposed, and ultimately dependant.

As important as metacognition is for managing our own thinking, it’s indispensable when working with AI. It’s what allows us to evaluate the output rather than simply accept it.

TL;DR – the short audio version

Metacognition
Meta comes from Greek, meaning “beyond” or “about.” Metadata for example is data about data, so “metacognition” is thinking about thinking, you’re not solving the problem, you’re watching yourself solve it. Think of it like this – when a chef makes a sauce, he is cooking. He tastes as he goes, deciding if it needs a little more salt, that pause to check if it’s all going to plan, is the meta part. Metacognition works the same way – you’re not just thinking, you’re pausing to ask questions about what you’re doing e.g. does that make sense, what’s the evidence for this, what should I try next?

“Those who know how to think need no teachers.” – Mahatma Gandhi

Flavell and the fluency illusion – Metacognition was coined by the developmental psychologist John Flavell in the 1970s, he made an important distinction.

  • Cognition – the actual knowledge and skills you’re acquiring (formulas, theories etc)
  • Metacognition – your knowledge about that knowledge, your ability to judge accurately what you understand, what you don’t, and what to do about the gap

“Metacognition refers to one’s knowledge concerning one’s own cognitive processes” – John Flavell

Flavell broke it down even further:

  • Metacognitive knowledge – understanding how you learn best.
  • Metacognitive regulation – planning, monitoring, and adjusting your approach as you go.
  • Metacognitive experience – the gut feeling of “I’ve got this” or “I’m lost”, which, crucially, is often wrong.

That last point, the gut feeling being wrong, is where most students come unstuck. Psychologists call it the fluency illusion. Re-reading a chapter, highlighting a paragraph – these feel like learning because the material is familiar and easy to process. But familiarity is not the same as retrieval. You recognise the answer when it’s in front of you but that’s a completely different mental operation to producing it from memory in the exam under pressure. The result is a systematic and predictable overconfidence, students walk out of revision sessions feeling as if they are ready, and walk out of the exam hall feeling like they weren’t.

Why does it matter – Research suggests metacognitive skill is often a better predictor of exam success than IQ, because it’s not about how much a student knows but how well they manage their own learning process. That said, it only helps if it leads to action, noticing confusion is only useful if the student actually changes course rather than just being aware of the problem.

“Metacognition is the ability to, at any moment, step off the stage and sit down in the director’s chair.” – Ujjwal Ganesh, author The Observation Effect

The Antidote to AI
In a previous blog I highlighted the problems with Khanmigo, Khan Academy’s AI tutor. It struggled because it was built like a virtual Socrates, and needed students to ask good questions to prove its worth. The trouble was that many students simply replied “IDK” (I Don’t Know). Not because they were being difficult, but because if you don’t have the metacognitive skills to think through what you know and what you don’t, you can’t ask useful questions. This is the quiet danger of a world with answers on tap. AI can hand you correct information in seconds, but it cannot give you a sense of what you personally still need to learn. That judgement remains yours.

“Thinking: the talking of the soul with itself.”  – Plato

It’s also the skill we need most in order to work with AI properly, to check whether its answers are actually correct, to judge whether they’re useful for what we’re trying to do, and to push our understanding to a higher level. In other words, we need to apply metacognition to AI’s output the same way we apply it to our own thinking, not just to ask “do I understand this?” but “is this actually right, and is it what I needed?” Metacognition does two jobs here. It protects (The antidote) stopping AI from doing our thinking for us. It also helps us get better at using AI by teaching us to question, test, and push back. Same skill, two uses.

Conclusion
Metacognition has always been very important in terms of learning, helping us regulate our own thinking, finding the gap between what we assume we know and what we actually know, which helps us deepen our understanding. That job hasn’t gone away. If anything, it matters even more. But AI makes it doubly important, it’s not just about regulating our own thinking, but our thinking about AI’s thinking.  

Nobody will wake up having forgotten how to think. It’s a slow transfer of judgement, question by question, until the habit of checking simply isn’t there anymore. AI hasn’t changed what metacognition is, It’s just made it a skill we can no longer afford to leave undeveloped, for our own thinking, and now, for everything we borrow from someone, or something, else.

Want to learn more – Metacognition: An Important Skill for Modern Times | Brendan Conway-Smith

Mastery Learning – The case for jumping from A to C

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Firstly, let me say that I am a huge fan of Mastery Learning, after all, what’s not to like. In simple terms all that is required is for the student to fully understand a topic before moving onto the next. They must master ‘A’ before moving to ‘B’, and ‘B’ before ‘C’. To skip ahead would of course be mad. To those who have not thought too much about this before, you might assume this is how all learning works, until you reflect on your own schooling, where progression was based on age, and not understanding!

TL;DR – the short audio version

But it’s not the only way to learn. Much of what we learn is not linear, in fact knowledge might be better thought of as a web, that you can come at from many directions rather than a ladder.

Mastery learning
Developed by Benjamin Bloom of Blooms taxonomy fame, Mastery Learning was driven by the 2-sigma problem – students receiving one-to-one tutoring outperformed classroom peers by two standard deviations, effectively moving from a grade C to a grade A. By fixing the standard rather than the time, he believed most students could reach levels of achievement traditionally reserved for the few.

This depended on formative assessment and corrective instruction. Low stake tests are used to identify knowledge gaps with the student needing to score around 80% – 90% before they can move on. If not, they receive “alternative” instruction rather than repetition. This continuous feedback loop was Bloom’s way of bringing classroom learning closer to that which can be achieved by one-to-one tutoring.  The word alternative is important here, the student does not simply go over the same material again, instead they are given a different explanation or re-taught with another method, for example pairing the student with those who have already reached mastery.

Sal Khan has adopted Mastery Learning in Khan Academy, here he makes a compelling case for its use with characteristic clarity – TED talk from 2018.

Jumping from A to C with no B
To help understand how we might pull off this magic trick, let’s look at the work of Lev Vygotsky, he developed the idea of the Zone of Proximal Development (ZPD). It makes a distinction between what a learner can do on their own, and what they can do with the right support. The gap between those two is not a barrier to cross, it’s where learning happens.

When a learner jumps to C with appropriate support, they do not completely ignore B they look back on it in the context of what they are about to learn in topic C. Because B now has a purpose, it tends to be learned and remembered more deeply than if it had been drilled in isolation.

The sequence flips: C with support → encounter B in context → C independently. The gap doesn’t have to be filled before you advance. It gets filled through advancing.

This approach is supported by Robert Bjork’s research on “desirable difficulties”, introducing challenge before a learner feels ready improves learning. Struggle is not always a sign something has gone wrong – it’s an opportunity for good learning.  

Horses for courses
This is not an argument to use the A – C approach over Mastery, there are situations where they can both be beneficial. It depends on context, for example early maths and reading benefit from attention to sequence, here Mastery should be used, but where personal developmental and social skills are the objective, the A-C approach might work best. Secondary school learners benefit from a blend, of both as well as project-based challenges, which helps develop problem solving skills. Adult professionals should be trusted to identify and fill in their own gaps, however where large amounts of knowledge and skills are required in a short period of time, as is the case with many high-stakes professional exams, Mastery is preferred.

Conclusion
The problem with Mastery Learning as an overarching model is that it presents a highly believable story as to how knowledge is actually acquired. Real learning, the kind that sticks and transforms, has always been messy. It involves confusion, premature exposure, partial understanding, backtracking, and sudden reorganisation. It might mean you are thrown into C before you feel ready and finding, to your surprise, that you manage.

Mastery is often described in terms of building strong foundations to support your future learning that sits on top. But knowledge is not a foundation, that once in place remains solid and strong, you never fully master anything, there are always gaps that over time without use will decay. Learning is less of a solid foundation and more like a piece of cheese, full of holes, and if left unattended, will only acquires more.

It is not always necessary to wait until you feel completely ready, in fact, the truth is many students never do. There are times when you just need to jump in, struggle, backfill, and push forward. The good news, you will develop a stronger and more resilient understanding of the subject – messy and illogical it might be, but its also hugely effective.

Sticky – The Science of Storytelling

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Long before writing, and even “classrooms,” people shared knowledge through the telling of stories. These stories conveyed essential lessons in survival and reflected the social norms of their time, handed down through generations.

To fulfil their purpose, they had to be memorable. What remains unclear is, did the story evolve to fit the brain’s natural ability to remember or did stories in some way shape our brains to make them easier to recall – a classic chicken-and-egg dilemma.

Regardless, it could be argued that stories were our first educational technology, influencing culture, guiding decisions, and ensuring knowledge was not lost.

If you don’t have time to read this month’s blog – listen to my AI alter ego summarise the key points.

Today, when we think of stories, we often associate them with novels, films, animations and more recently podcasts. At its core, they are simply a structured way of sharing events and information, with most following a familiar pattern. They begin by setting the scene, move into a middle phase where the story unfolds and end with some form of resolution that provides clarity or closure. This structure helps us make sense of experiences, maintain attention, communicate ideas, evoke emotions, and connect with others in meaningful ways. All of which help with recall.

They are also incredibly persuasive, and can become a vehicle for knowledge transfer, simply saying, “let’s take a moment, relax, I want to tell you a story” changes the mood in the room and opens the mind for a new experience.

If you’re still unsure about their power, Yuval Noah Harari provides a compelling example. He explains that money holds no inherent value, a banknote is simply paper, and digital currency just data. What makes money meaningful is the collective belief in its worth. This shared understanding allows it to function as a medium of exchange for goods, services, and influence.

He goes on to say….

Money is probably the most successful story ever told” he argues. “It has no objective value… but then you have these master storytellers: the big bankers, the finance ministers… and they come and they tell a very convincing story. ‘Look this piece of paper, it is actually worth 10 bananas’… and it works. Try doing that with a chimpanzee – it won’t work!” – Yuval Noah Harari, historian, public intellectual, and bestselling author

Why are stories sticky?
But what is happening in the brain when you hear a story or read one for yourself? Why do stories stay with us long after we’ve heard them, what makes them stick?

Cognitive rapport – When someone tells a story, something remarkable happens in the brain. Instead of just processing words, the listener’s brain begins to light up in multiple areas all at once. Stories create what researchers call neural coupling, the listener’s brain patterns start to mirror the storyteller’s, helping ideas flow more smoothly and making them easier to understand (Stephens et al., 2010).

“The human species thinks in metaphors and learns through stories.”  – Mary Catherine Bateson, writer, and cultural anthropologist

Emotional – Importantly, stories also stir emotion and when emotions are triggered, the amygdala and hippocampus work together to strengthen memory (Article McGaugh, 2013). In one test, a neutral learning event was given an emotional focus. Subjects were asked to memorise a list of words, a non-emotional task. They were then exposed to a brief, intense emotional experience e.g. Putting their arms into icy water (Cold Pressor Stress Test), which released stress hormones, epinephrine, and cortisol, telling the brain it is an important event. When tested weeks later, the individuals had forgotten the cold-water experience, but remembered the list of words!

“Storytelling is the most powerful way to put ideas into the world today.” – Robert McKee, author, lecturer, and story consultant

Structured – Stories give knowledge a shape and structure. A beginning, a challenge, and a resolution acting like mental scaffolding, allowing learners to slot new information into place. Structure also reduces cognitive load, (John Sweller 1988), and help create schemas, which are interconnected mental chunks of knowledge that are stored more easily in long term memory.

“Stories are memory aids, instruction manuals and moral compasses.” – Aleks Krotoski, broadcaster, journalist, and social psychologist

Engaging – And lastly, stories build a human connection, helping creat greater levels of engagement. Neuroscientist Paul J. Zak (2015) discovered that compelling narratives those with a strong dramatic storyline trigger the release of oxytocin, the neurochemical responsible for trust and empathy. In a learning context, this surge of empathy makes you more receptive to the message and strongly motivates, helping internalise the information and transforming simple facts into knowledge.

A word of caution – seductive details
However not all stories help us learn. The danger is that they include fascinating but irrelevant information known as “seductive details” (Harp & Mayer, 1998). This results in cognitive overload, causing the brain to waste resources processing the more interesting information at the expense of core principles. It can also break down that strong mental scaffolding, misdirecting the brain, to build a new organisational framework around the wrong idea. To avoid this, the detail in your narrative must directly support the learning objective, ensure the story integrates the facts, rather than just decorating them.

The final chapter
For educators, storytelling is not just a “nice extra” it’s a valuable tool and a natural way to help people learn. A well-told story draws attention, lowers resistance, and creates the sense that what follows is worth holding on to. Learners don’t just hear the information, they experience it, making knowledge far more memorable.
For learners, resist the urge to dismiss the story as a diversion from the important stuff, and instead listen with curiosity. They work on the mind in subtle ways connecting ideas, evoking emotions, and helping you see meaning, long after the classrrom door has closed. In this state, your brain does much of the hard work for you.

Want to know more?