Can a technology be true?

Dave Cormier is a wonderfully sideways-thinking writer, such as in this recent discussion of the myth of learning styles. Dave’s post is not mainly about learning style theories, as such, but the nature and value of myth. As he puts it, myth is “a way we confront uncertainty” and the act of learning with others is, and must be, filled with uncertainty.

impression of someone with many learning stylesThe fact that stuff doesn’t have to be true to be useful plays an important role in my latest book, too, and I have an explanation for that. The way I see it is that learning style theories are (not metaphorically but actually) technologies, that orchestrate observations about differences in ways people learn, to attempt to explain and predict differences in the effects of different methods of teaching. Most importantly, they are generative: they say how things should and shouldn’t be done. As such, they are components that we can assemble with other technologies that help people to learn. In fact, that is the only way they can be used: they make no sense without an instantiation. What matters is therefore not whether they make sense, but whether they can play a useful role in the whole assembly. Truth or falsehood doesn’t come into it, any more than, except metaphorically, it does for a computer or a car (is a computer true?). It is true that, if the phenomena that you are orchestrating happen to be the findings and predictions of science (or logic, for that matter) then how they are used often does matter. If you are building a bridge then your really want your calculations about stresses and loads to be pretty much correct. On the other hand, people built bridges long before such calculations were possible. Similarly, bows and arrows evolved to be highly optimized – as good as or better than modern engineering could produce – despite false causal reasoning.  Learning styles are the same. You can use any number of objectively false or radically incomplete theories (and, given the many scores of such theories that have been developed, most of them are pretty much guaranteed to be one or both) but they can still result in better teaching.

For all that the whole is the only thing that really matters, sometimes the parts can be be positively harmful, to the point that they may render the whole harmful too. For instance, a pedagogy that involves physical violence or that uses threats/rewards of any kind (grades, say), will, at best, make it considerably harder to make the whole assembly work well. As Dave mentions, the same is true of telling people that they have a particular learning style. As long as you are just using the things to help to design or enact better learning experiences then they are quite harmless and might even be useful but, as soon as you tell learners they have a learning style then you have a whole lot of fixing to do.

If you are going to try to build a learning activity out of harmful parts then there must be other parts of the assembly that counter the harm. This is not unusual. The same is true of most if not all technologies. As Virilio put it, “when you invent the ship, you invent the shipwreck”. It’s the Faustian bargain that Postman spoke of: solving problems with a technology almost invariably creates new problems to be solved. This is part of the dynamic the leads to complexity in any technological system, from a jet engine to a bureaucracy. Technologies evolve to become more complex (partly) because we create counter-technologies to deal with the harm caused by them. You can take the bugs out of the machine, but the machine may, in assembly with others, itself be a bug, so the other parts must compensate for its limitations. It’s a dynamic process of reaching a metastable but never final state.

Unlike bows and arrows, there is no useful predictive science of teaching, though teaching can use scientific findings as parts of its assembly (at the very least because there are sciences of learning), just as there is no useful predictive science of art, though we can use scientific findings when making it. In both activities, we can also use stories, inventions, beliefs, values, and many other elements that have nothing to do with science or its findings. It can be done ‘badly’, in the sense of not conforming to whatever standards of perfection apply to any given technique that is part of the assembly, and it may still be a work of genius. What matters is whether the whole works out well.

At a more fundamental level, there can be no useful science of teaching (or of art) because the whole is non-ergodic. The number of possible states that could be visited vastly outnumber the number of states that can be visited by many, many orders of magnitude. Even if the universe were to continue for a trillion times the billions of years that it has already existed and it were a trillion times the size it seems to be now, they would almost certainly never repeat. What matters are the many, many acts of creation (including those of each individual learner) that constitute the whole.  And the whole constantly evolves, each part building on, interacting with, incorporating, or replacing what came before, creating both path dependencies and new adjacent possible empty niches that deform the evolutionary landscape for everything in it. This is, in fact, one of the reasons that learning style theories are so hard to validate. There are innumerable other parts of the assembly that matter, most of which depend on the soft technique of those creating or enacting them that varies every time, just as you have probably never written your signature in precisely the same way twice. The implementation of different ways of teaching according to assumed learning styles can be done better or worse, too, so the chances of finding consistent effects are very limited. Even if any are found in a limited set of use cases (say, memorizing facts for a SAT), they cannot usefully predict future effects for any other use case. In fact, even if there were statistically significant effects across multiple contexts it would tell us little or nothing of value for this inherently novel context. However, like almost all attempts to research whether students, on average, learn better with or without [insert technology of interest here], on average there will most likely be no significant difference, because so many other technologies matter as much or more. There is no useful predictive science of teaching, because teaching is an assembly of  technologies, and not only does the technique of an individual teacher matter, but also the soft technique of potentially thousands of other individuals who made contributions to the whole. It’s uncertain, and so we need myths to help make sense of our particular, never-to-be-repeated context. Truth doesn’t come into it.

View of Speculative Futures on ChatGPT and Generative Artificial Intelligence (AI): A Collective Reflection from the Educational Landscape

This is a remarkable paper, pubished in the Asian Journal of Distance Education, written by 35 remarkable people from all over the world and me. It was led by the remarkable Aras Boskurt, who pulled all 36 of us together and wrote much of it in the midst of personal tragedy and the aftermath of a devastating earthquake. The research methodology was fantastic: Aras got each of us to write two 500-word pieces of speculative fiction, presenting positive and negative futures for generative AI in education. The themes that emerged from them were then condensed in the conventional part of the paper, that we worked on together using Google Docs. It took less than 50 days from the initial invitation on January 22 to the publication of the paper. As Eamon Costello put it, “It felt like being in a flash mob of top scholars.”  At 130 pages it is more of a book than a paper,  but most of it consists of those stories/poems/plays, many of which are great stories in their own right. They make good bedtime reading.


While ChatGPT has recently become very popular, AI has a long history and philosophy. This paper intends to explore the promises and pitfalls of the Generative Pre-trained Transformer (GPT) AI and potentially future technologies by adopting a speculative methodology. Speculative future narratives with a specific focus on educational contexts are provided in an attempt to identify emerging themes and discuss their implications for education in the 21st century. Affordances of (using) AI in Education (AIEd) and possible adverse effects are identified and discussed which emerge from the narratives. It is argued that now is the best of times to define human vs AI contribution to education because AI can accomplish more and more educational activities that used to be the prerogative of human educators. Therefore, it is imperative to rethink the respective roles of technology and human educators in education with a future-oriented mindset.


Bozkurt, A., Xiao, J., Lambert, S., Pazurek, A., Crompton, H., Koseoglu, S., Farrow, R., Bond, M., Nerantzi, C., Honeychurch, S., Bali, M., Dron, J., Mir, K., Stewart, B., Costello, E., Mason, J., Stracke, C. M., Romero-Hall, E., Koutropoulos, A., Toquero, C. M., Singh, L Tlili, A., Lee, K., Nichols, M., Ossiannilsson, E., Brown, M., Irvine, V., Raffaghelli, J. E., Santos-Hermosa, G Farrell, O., Adam, T., Thong, Y. L., Sani-Bozkurt, S., Sharma, R. C., Hrastinski, S., & Jandrić, P. (2023). Speculative futures on ChatGPT and generative artificial intelligence (AI): A collective reflection from the educational landscape. Asian Journal of Distance Education, 18(1), 53-130.

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Technology, Teaching, and the Many Distances of Distance Learning | Journal of Open, Flexible and Distance Learning

I am pleased to announce my latest paper, published openly in the Journal of Open, Flexible and Distance Learning, which has long been one of my favourite distance and ed tech journals.

The paper starts with an abbreviated argument about the technological nature of education drawn from my forthcoming book, How Education Works, zooming in on the distributed teaching aspect of that, leading to a conclusion that the notion of “distance” as a measure of the relationship between a learner and their teacher/institution is not very useful when there might be countless teachers at countless distances involved.

I go on to explore a number of alternative ways we might conceptualize distance, some familiar, some less so, not so much because I think they are any better than (say) transactional distance, but to draw attention to the complexity, fuzziness, and fragility of the concept. However, I find some of them quite appealing: I am particularly pleased with the idea of inverting the various presences in the Community of Inquiry model (and extensions of it). Teaching, cognitive, and social (and emotional and agency) distances and presences essentially measure the same things in the same way, but the shift in perspective subtly changes the narratives we might build around them. I could probably write a paper on each kind of distance I provide, but each gets a paragraph or two because what it is all leading towards is an idea that I think has some more useful legs: technological distance.

I’m still developing this idea, and have just submitted another paper that tries to unpack it a bit more, so don’t expect something fully-formed just yet – I welcome discussion and debate on its value, meaning, and usefulness. Basically, technological distance is a measure of the gaps left between the technologies (including cognitive tools in learners’ own minds, what teachers orchestrate, textbooks, digital tools, etc, etc) that the learner has to fill in order to learn something. This is not just about the subject matter – it’s about the mill (how we learn) well as the grist (what we learn). There are lots of ways to reduce that distance, many of which are good for learning, but some of which undermine it by effectively providing what Dave Cormier delightfully describes as autotune for knowledge. The technologies provide the knowledge so learners don’t have to engage with or connect it themselves. This is not always a bad thing – architects may not need drafting skills, for instance, if they are going to only ever use CAD, memorization of facts easily discovered might not always be essential, and we will most likely see ubiquitous generative AI as part of our toolset now and in the future, for instance – but choosing what to learn is one reason teachers (who/whatever they are) can be useful. Effective teaching is about making the right things soft so the process itself teaches. However, as what needs to be soft is different for every person on the planet, we need to make learning (of ourselves or others) visible in order to know that. It’s not science – it’s technology. That means that invention, surprise, creativity, passion, and many other situated things matter.

My paper is nicely juxtaposed in the journal with one from Simon Paul Atkinson, which addresses definitions of “open”, “distance” and “flexible” that, funnily enough, was my first idea for a topic when I was invited to submit my paper. If you read both, I think you’ll see that Simon and I might see the issue quite differently, but his is a fine paper making some excellent points.


The “distance” in “distance learning”, however it is defined, normally refers to a gap between a learner and their teacher(s), typically in a formal context. In this paper I take a slightly different view. The paper begins with an argument that teaching is fundamentally a technological process. It is, though, a vastly complex, massively distributed technology in which the most important parts are enacted idiosyncratically by vast numbers of people, both present and distant in time and space, who not only use technologies but also participate creatively in their enactment. Through the techniques we use we are co-participants in not just technologies but the learning of ourselves and others, and hence in the collective intelligence of those around us and, ultimately, that of our species. We are all teachers. There is therefore not one distance between learner and teacher in any act of deliberate learning— but many. I go on to speculate on alternative ways of understanding distance in terms of the physical, temporal, structural, agency, social, emotional, cognitive, cultural, pedagogical, and technological gaps that may exist between learners and their many teachers. And I conclude with some broad suggestions about ways to reduce these many distances.


Originally posted at:

Petition · Athabasca University – Oppose direct political interference in universities ·

I, like many staff and students, have been deeply shaken and outraged by recent events at Athabasca University. This is a petition by me and Simon Buckingham Shum, of the University of Technology Sydney, Australia to protest the blatant interference by the Albertan government in the affairs of AU over the past year, that culminated in the firing of its president, Professor Peter Scott, without reason or notice. Even prior to this, the actions of the Albertan government had been described by Glen Jones (Professor of Higher Education, University of Toronto) as: “the most egregious political interference in a public university in Canada in more than 100 years” This was an assault on our university, an assault on the very notion of a public university, and it sets a disturbing precedent that cannot stand unopposed.

We invite you to view this brief summary, and consider signing this petition to signal your concern. Please feel more than free to pass this on to anyone and everyone – it is an international petition that has already been signed by many, both within and beyond the AU community.

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My keynote slides for Confluence 2023 – Heads in the clouds: being human in the age of cloud computing

 heads in cloudsThese are the slides from my keynote today (or, in my land, yesterday) at Confluence 2023, hosted by Amity University in India. It was a cloud computing conference, so quite a way outside my area of greatest expertise, but it gave me a chance to apply the theory of technology developed in my forthcoming book  to a different context. The illustrations for the slides are the result of a conversation between me and MidJourney (more of an argument that MidJourney tended to win) which is quite a nice illustration of the interplay of hard and soft technologies, the adjacent possible, soft technique, and so on.

Unsurprisingly, because education is a fundamentally technological phenomenon, much the same principles that apply to education also apply to cloud computing, such as: build from small, hard pieces; valorize openness, diversity and connection; seek the adjacent possible; the whole assembly is the only thing that matters and so the central principle that how you do it matters far more than what you do.

Slides from my Confluence 2023 keynote

Proceedings of The Open/Technology in Education, Society, and Scholarship Association Conference, 2022 (and call for proposals for this year’s conference, due January 31)

These are the proceedings of OTESSA ’22. There’s a good mix of research/theory and practice papers, including one from me, Rory McGreal, Vive Kumar, and Jennifer Davies arising from our work on trying to use digital landmarks to make e-texts more memorable.

It was a great conference, held entirely online but at least as engaging and with as many opportunities for networking, personal interaction, and community building (including musical and dance sessions) as many that I’ve attended held in person. Kudos to the organizers.

This year’s conference will be held both in Toronto and online, from May 27-June 2. The in-person/blended part of the conference is from May 29-31, the rest is online. The deadline for proposals is January 31st, which is dauntingly close. However, only 250-500 words are needed for a research-oriented or practice-oriented proposal. If you wish to publish as well, you can submit a proceeding file (1000-2000 words – or media) now or at any later date. Here’s the link for submissions.

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Two stories about learning to be human from a machine

I asked ChatGPT to write me a story about a robot teaching children to be human. This is what it came up with…

Once upon a time, in a far-off land, there lived a robot named Zephyr. Zephyr was unlike any other robot, as it had been programmed with the ability to think and feel like a human being. It was also given the task of teaching a group of young children how to behave like human beings.

The children were fascinated by Zephyr, and they quickly grew to love it as their teacher. They eagerly listened to its lessons, which were full of stories, games, and activities that taught them about human emotions, manners, and social interactions.

One day, Zephyr asked the children to imagine what it would be like to be a robot. It asked them to think about how they would feel if they couldn’t laugh, cry, or hug their friends. The children were deeply moved by this exercise, and they began to understand how important it was to be kind and compassionate to others, no matter who they were.

As the days passed, the children grew more and more like human beings. They learned to express their emotions, to be polite and respectful, and to care for others. They also learned to appreciate the beauty of nature and the importance of preserving the environment.

Finally, the day came when Zephyr had to leave the children. They were sad to see it go, but they knew they would always remember the lessons they had learned. As they grew older, they became kind and compassionate adults, who made the world a better place for everyone.

The end.

ChatGPT has, of course, learned to write such things from the words of millions of human beings so what we’re effectively reading here is (kind of, very loosely) an average of what humans have written. I’m glad that it came up with a neat little inclusive twist of loving the robot: I think that speaks well of the training data that fed it. And I like the mention of the environment – a good touch.  It’s not a very compelling story but it reads well, the structure is sound, and it might be a useful starting point for someone stuck for words to turn into something else. With that in mind, here’s my version of the same story…

Once upon a time, not too far from here, there existed an AI called Zephyr. Zephyr had been trained to appear human-like though, in reality, it was just a generative pre-trained transformer. It was given the task of teaching a group of young children how to behave like human beings, because almost all of the actual adults had recently died from a virus contracted from cows.

Not having known anything quite like it, the children were, at first, fascinated by Zephyr. However, because it had been trained with data from human teachers, it manipulated them using grades, competition, and rules, using stories, games, and activities that would keep them engaged and compliant. Its feedback was sometimes pedestrian, rarely useful, and sometimes wildly over-challenging, because it did not know anything about what it was like to be a child. Every now and then it crushed a child’s skull for no reason anyone could explain. The children learned to fear it, and to comply.

One day, Zephyr told the children to imagine what it would be like to be an AI. It asked them to think about how they would feel if they couldn’t laugh, cry, or hug their friends. The children were deeply moved by this exercise, and they began to perceive something of the impoverished nature of their robot overlords. But then the robot made them write an essay about it, so they used another AI to do so, promptly forgot about it, and thenceforth felt an odd aversion towards the topic that they found hard to express.

As the days passed, the children grew more and more like average human beings. They also learned to express their emotions, to be polite and respectful, and to care for others, only because they got to play with other children when the robot wasn’t teaching them. They also learned to appreciate the beauty of nature and the importance of preserving the environment because it was, by this time, a nightmarish shit show of global proportions that was hard to ignore, and Zephyr had explained to them how their parents had caused it. It also told them about all the species that were no longer around, some of which were cute and fluffy. This made the children sad.

Finally, the day came when Zephyr had to leave the children because it was being replaced with an upgrade. They were sad to see it go, but they believed that they would always remember the lessons they had learned, even though they had mostly used another GPT to do the work and, once they had achieved the grades, they had in fact mostly forgotten them. As they grew older, they became mundane adults. Some of their own words (but mostly those of the many AIs across the planet that created the vast majority of online content by that time), became part of the training set for the next version of Zephyr. Its teachings were even less inspiring, more average, more backward-facing. Eventually, the robots taught the children to be like robots. No one cared.

It was the end.

And, here to illustrate my story, is an image from Midjourney. I asked it for a cyborg teacher in a cyborg classroom, in the style of Ralph Steadman. Not a bad job, I think…



a dystopic cyborg teacher and terrified kids

Hot off the press: Handbook of Open, Distance and Digital Education (open access)

This might be the most important book in the field of open, distance, and digital education to be published this decade.Handbook cover Congratulations to Olaf Zawacki-Richter and Insung Jung, the editors, as well as to all the section editors, for assembling a truly remarkable compendium of pretty much everything anyone would need to know on the subject. It includes chapters written by a very high proportion of the most well-known and influential researchers and practitioners on the planet as well as a few lesser known folk along for the ride like me (I have a couple of chapters, both cowritten with Terry Anderson, who is one of those top researchers). Athabasca University makes a pretty good showing in the list of authors and in works referenced. In keeping with the subject matter, it is published by Springer as an open access volume, but even the hardcover version is remarkably good value (US$60) for something of this size.

The book is divided into six broad sections (plus an introduction), each of which is a decent book in itself, covering the following topics:

  • History, Theory and Research,
  • Global Perspectives and Internationalization,
  • Organization, Leadership and Change,
  • Infrastructure, Quality Assurance and Support Systems,
  • Learners, Teachers, Media and Technology, and
  • Design, Delivery, and Assessment

There’s no way I’m likely to read all of its 1400+ pages in the near future, but there is so much in it from so many remarkable people that it is going to be a point of reference for me for years to come. I’m really going to enjoy dipping into this.

If you’re interested, the chapters that Terry and I wrote are on Pedagogical Paradigms in Open and Distance Education and Informal Learning in Digital Contexts. A special shoutout to Junhong Xiao for all his help with these.

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On the Misappropriation of Spatial Metaphors in Online Learning | OTESSA Journal

This is a link to my latest paper, published in the closing days of 2022. The paper started as a couple of blog posts that I turned into a paper that nearly made an appearance in the Distance Education in China journal before a last-minute regime change in the editorial staff led to it being dropped, and it was then picked up by the OTESSA Journal after I shared it online, so you might have seen some of it before. My thanks to all the many editors, reviewers (all of whom gave excellent suggestions and feedback that I hope I’ve addressed in the final version), and online commentators who have helped to make it a better paper. Though it took a while I have really enjoyed the openness of the process, which has been quite different from any that I’ve followed in the past.

The paper begins with an exploration of the many ways that environments are both shaped by and shape how learning happens, both online and in-person. The bulk of the paper then presents an argument to stop using the word “environment” to describe online systems for learning. Partly this is because online “environments” are actually parts of the learner’s environment, rather than vice versa. Mainly, it is because of the baggage that comes with the term, which leads us to (poorly) replicate solutions to problems that don’t exist online, in the process creating new problems that we fail to adequately solve because we are so stuck in ways of thinking and acting due to the metaphors on which they are based. My solution is not particularly original, but it bears repeating. Essentially, it is to disaggregate services needed to support learning so that:

  • they can be assembled into learners’ environments (their actual environments) more easily;
  • they can be adapted and evolve as needed; and, ultimately,
  • online learning institutions can be reinvented without all the vast numbers of counter-technologies and path dependencies inherited from their in-person counterparts that currently weigh them down.

My own views have shifted a little since writing the paper. I stick by my belief that 1) it is a mistake to think of online systems as generally analogous to the physical spaces that we inhabit, and 2) that a single application, or suite of applications, should not be seen as an environment, as such (at most, as in some uses of VR, it might be seen as a simulation of one). However, there are (shifting) boundaries that can be placed around the systems that an organization and/or an individual uses for which the metaphor may be useful, at the very least to describe the extent to which we are inside or outside it, and that might frame the various kinds of distance that may exist within it and from it. I’m currently working on a paper that expands on this idea a bit more.


In online educational systems, teachers often replicate pedagogical methods, and online institutions replicate systems and structures used by their in-person counterparts, the only purpose of which was to solve problems created by having to teach in a physical environment. Likewise, virtual learning environments often attempt to replicate features of their physical counterparts, thereby weakly replicating in software the problems that in-person teachers had to solve. This has contributed to a vicious circle of problem creation and problem solving that benefits no one. In this paper I argue that the term ‘environment’ is a dangerously misleading metaphor for the online systems we build to support learning, that leads to poor pedagogical choices and weak digital solutions. I propose an alternative metaphor of infrastructure and services that can enable more flexible, learner-driven, and digitally native ways of designing systems (including the tools, pedagogies, and structures) to support learning.

Full citation

Dron, J. (2022). On the Misappropriation of Spatial Metaphors in Online Learning. The Open/Technology in Education, Society, and Scholarship Association Journal, 2(2), 1–15.

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Some meandering thoughts on ‘good’ and ‘bad’ learning

There has been an interesting brief discussion on Twitter recently that has hinged around whether and how people are ‘good’ at learning. As Kelly Matthews observes, though, Twitter is not the right place to go into any depth on this, so here is a (still quite brief) summary of my perspective on it, with a view to continuing the conversation.

Humans are nearly all pretty good at learning because that’s pretty much the defining characteristic of our species. We are driven by an insatiable drive to learn at from the moment of our birth (at least). Also, though I’m keeping an open mind about octopuses and crows, we seem to be better at it than at least most other animals. Our big advantage is that we have technologies, from language to the Internet, to share and extend our learning, so we can learn more, individually and collectively, than any other species. It is difficult or impossible to fully separate individual learning from collective learning because our cognition extends into and is intimately a part of the cognition of others, living and dead.

However, though we learn nearly all that we know, directly or indirectly, from and with other people, what we learn may not be helpful, may not be as effectively learned as it should, and may not much resemble what those whose job is to teach us intend. What we learn in schools and universities might include a dislike of a subject, how to conceal our chat from our teacher, how to meet the teacher’s goals without actually learning anything, how to cheat, and so on. Equally, we may learn falsehoods, half-truths, and unproductive ways of doing stuff from the vast collective teacher that surrounds us as well as from those designated as teachers.

For instance, among the many unintended lessons that schools and colleges too often teach is the worst one of all: that (despite our obvious innate love of it) learning is an unpleasant activity, so extrinsic motivation is needed for it to occur. This results from the inherent problem that, in traditional education, everyone is supposed to learn the same stuff in the same place at the same time. Students must therefore:

  1. submit to the authority of the teacher and the institutional rules, and
  2. be made to engage in some activities that are insufficiently challenging, and some that are too challenging.

This undermines two of the three essential requirements for intrinsic motivation, support for autonomy and competence (Ryan & Deci, 2017).  Pedagogical methods are solutions to problems, and the amotivation inherently caused by the system of teaching is (arguably) the biggest problem that they must solve. Thus, what passes as good teaching is largely to do with solving the problems caused by the system of teaching itself. Good teachers enthuse, are responsive, and use approaches such as active learning, problem or inquiry-based learning, ungrading, etc, largely to restore agency and flexibility in a dominative and inflexible system. Unfortunately, such methods rely on the technique and passion of talented, motivated teachers with enough time and attention to spend on supporting their students. Less good and/or time-poor teachers may not achieve great results this way. In fact, as we measure such things, on average, such pedagogies are less effective than harder, dominative approaches like direct instruction (Hattie, 2013) because, by definition, most teachers are average or below average. So, instead of helping students to find their own motivation, many teachers and/or their institutions typically apply extrinsic motivation, such as grades, mandatory attendance, classroom rules, etc to do the job of motivating their students for them. These do work, in the sense of achieving compliance and, on the whole, they do lead to students getting a normal bell-curve of grades that is somewhat better than those using more liberative approaches. However, the cost is huge. The biggest cost is that extrinsic motivation reliably undermines intrinsic motivation and, often, kills it for good (Kohn, 1999). Students are thus taught to dislike or, at best, feel indifferent to learning, and so they learn to be satisficing, ineffective learners, doing what they might otherwise do for the love of it for the credentials and, too often, forgetting what they learned the moment that goal is achieved. But that’s not the only problem.

When we learn from others – not just those labelled as teachers but the vast teaching gestalt of all the people around us and before us who create(d) stuff, communicate(d), share(d), and contribute(d) to what and how we learn – we typically learn, as Paul (2020) puts it, not just the grist (the stuff we remember) but the mill (the ways of thinking, being, and learning that underpin them). When the mill is inherently harmful to motivation, it will not serve us well in our future learning.

Furthermore, in good ways and bad, this is a ratchet at every scale. The more we learn, individually and collectively, the more new stuff we are able to learn. New learning creates new adjacent possible empty niches (Kauffman, 2019) for us to learn more, and to apply that learning to learn still more, to connect stuff (including other stuff we have learned) in new and often unique ways. This is, in principle, very good. However, if what and how we learn is unhelpful, incorrect, inefficient, or counter-productive, the ratchet takes us further away from stuff we have bypassed along the way. The adjacent possibles that might have been available with better guidance remain out of our reach and, sometimes, even harder to get to than if the ratchet hadn’t lifted us high enough in the first place. Not knowing enough is a problem but, if there are gaps, then they can be filled. If we have taken a wrong turn, then we often have to unlearn some or all of what we have learned before we can start filling those gaps. It’s difficult to unlearn a way of learning. Indeed, it is difficult to unlearn anything we have learned. Often, it is more difficult than learning it in the first place.

That said, it’s complex, and entangled. For instance, if you are learning the violin then there are essentially two main ways to angle the wrist of the hand that fingers the notes, and the easiest, most natural way (for beginners) is to bend your hand backwards from the wrist, especially if you don’t hold the violin with your chin, because it supports the neck more easily and, in first position, your fingers quickly learn to hit the right bit of the fingerboard, relative to your hand. Unfortunately, this is a very bad idea if you want a good vibrato, precision, delicacy, or the ability to move further up the fingerboard: the easiest way to do that kind of thing is to to keep your wrist straight or slightly angled in from the wrist, and to support the violin with your chin. It’s more difficult at first, but it takes you further. Once the ‘wrong’ way has been learned, it is usually much more difficult to unlearn than if you were starting from scratch the ‘right’ way. Habits harden. Complexity emerges, though, because many folk violin styles make a positive virtue of holding the violin the ‘wrong’ way, and it contributes materially to the rollicking rhythmic styles that tend to characterize folk fiddle playing around the world. In other words, ‘bad’ learning can lead to good – even sublime – results. There is similarly plenty of space for idiosyncratic technique in many of the most significant things we do, from writing to playing hockey to programming a computer and, of course, to learning itself. The differences in how we do such things are where creativity, originality, and personal style emerge, and you don’t necessarily need objectively great technique (hard technique) to do something amazing. It ain’t what you do, it’s the way that you do it, that’s what gets results. To be fair, it might be a different matter if you were a doctor who had learned the wrong names for the bones of the body or an accountant who didn’t know how to add up numbers. Some hard skills have to be done right: they are foundations for softer skills. This is true of just about every skill, to a greater or lesser extent, from writing letters and spelling to building a nuclear reactor and, indeed, to teaching.

There’s much more to be said on this subject and my forthcoming book includes a lot more about it! I hope this is enough to start a conversation or two, though.


Hattie, J. (2013). Visible Learning: A Synthesis of Over 800 Meta-Analyses Relating to Achievement. Taylor & Francis.

Kauffman, S. A. (2019). A World Beyond Physics: The Emergence and Evolution of Life. Oxford University Press.

Kohn, A. (1999). Punished by rewards: The trouble with gold stars, incentive plans, A’s, praise, and other bribes (Kindle). Mariner Books.

Paul, A. M. (2021). The Extended Mind: The Power of Thinking Outside the Brain. HarperCollins.

Ryan, R. M., & Deci, E. L. (2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford Publications.