On the importance of place

Distance learners and teachers in different kinds of spaceI had the great pleasure of being invited to the Open University of the Netherlands and, later in the day, to EdLab, Maastricht University a few weeks ago, giving a slightly different talk in each place based on some of the main themes in my most recent book, How Education Works. Although I adapted my slides a little for each audience, with different titles and a few different slides adjusted to the contexts, I could probably have used either presentation interchangeably. In fact, I could as easily have used the slides from my SITE keynote on which both were quite closely based (which is why I am not sharing them here). As well as most of the same slides, I used some of the same words, many of the same examples, and several of the same anecdotes. For the most part, this was essentially the same presentation given twice. Except, of course, it really, really wasn’t. In fact, the two events could barely have been more different, and what everyone (including me) learned was significantly different in each session.

This is highly self-referential. One of the big points of the book is that it only ever makes sense to consider the entire orchestration, including the roles that learners play in making sense of it all the many components of the assembly, designed for the purpose and otherwise. The slides, structure, and content did provide the theme and a certain amount of hardness, but what we (collectively) did with them led to two very different learning experiences. They shared some components and purposes, just as a car, a truck, and a bicycle share some of the same components and purposes, but the assemblies and orchestrations were quite different, leading to very different outcomes. Some of the variation was planned in advance, including an hour of conversation at the end of each presentation and a structure that encouraged dialogue at various points along the way: these were as much workshops as presentations. However, much of the variance occurred not due to any planning but because of the locations themselves. One of the rooms was a well-appointed conventional lecture theatre, the other an airy space with grouped tables, and with huge windows looking out on a busy and attractive campus. In the lecture theatre I essentially gave a lecture: the interactive parts were very much staged, and I had to devise ways to make them work. In the airy room, I had a conversation and had to devise ways to maintain some structure to the process, that was delightfully disrupted by the occasional passing road train and the very tangible lives of others going on outside, as well as an innately more intimate and conversational atmosphere enabled (not entailed) by the layout. Other parts of the context mattered too: the time of day, the temperature, the different needs and interests of the audience, the fact that one occurred in the midst of planning for a major annual event, and so on. All of this had a big effect on how I and others behaved, and on what and how people learned. From one perspective, in both talks, I was sculpting the available affordances and constraints to achieve my intended ends but, from another equally valid point of view, I was being sculpted by them. The creators and maintainers of the rooms and I were teaching partners, coparticipants in the learning process. Pedagogically, and despite the various things I did to assemble the missing parts in each, they were significantly different learning technologies.

The complexity of distance teaching

Train journeys are great contexts for uninterrupted reflection (trains teach too) so, sitting on the train on my journey back the next day, I began to reflect on what all of this means for my usual teaching practice, and made some notes on which this post is based (notebooks teach, too).  I am a distance educator by trade and, as a rule, with exceptions for work-based learning, practicums, co-ops, placements, and a few other limited contexts, distance educators rarely even acknowledge that students occupy a physical space, let alone do we adapt to it. We might sometimes encourage students to use things in their environments as part of a learning activity, but we rarely change our teaching on the fly as a result of the differences between those environments. As I have previously observed, the problem is exacerbated by the illusion that online systems are environments (in the sense of being providers of the context in which we learn) and that we believe we can observe what happens in them. They are not, and we cannot. They are parts of the learners’ own environments, and all we can (ethically) observe are interactions with our designed systems, not the behaviour of the learners within the spaces that they occupy. It is as hard for students to understand our context as it is for us to understand theirs, and that matters too. It makes it trickier to model ways of thinking and approaches to problem solving, for example, if the teacher occupies a different context.

This matters little for some of the harder elements of the teaching process. Information provision, resource design, planning, and at least some forms of assessment and feedback are at least as easy to do at a distance as not. We can certainly do those and make a point of doing them well, thereby providing a little counterbalance. However, facilitation, role modelling, guidance, supporting motivation, fostering networks, monitoring of learning, responsive adaptation, and many other significant teaching roles are more complex to perform because of how little is known about learning activities within an environment. As Peter Goodyear has put it, matter matters. The more that the designated teacher can understand that, the more effective they can be in helping learners to succeed.

Because we are not so able to adapt our teaching to the context, distance learning (more accurately, distance teaching) mostly works because students are the most important teachers, and the pedagogies they add to the raw materials we provide do most of the heavy lifting. Given some shared resources and guided interactions, they are the ones who perform most of the kinds of orchestration and assembly that I added to my two talks in the Netherlands; they are the ones who both adapt and adapt to their spaces for learning. Those better able to do this in the first place tend to do better in the long run, regardless of subject interest or innate ability. This is reflected in the results. In my faculty and on average, more than 95% of our graduate students – who have already proven themselves to be successful learners and so are better able to teach themselves – succeed on any given course, in the sense of reaching the end and achieving a passing grade.  70% of our undergraduates, on the other hand, are the first in their family to take a degree. Many have taken years or even decades out of formal education, and many had poor experiences in school. On average, therefore, they typically have fewer skills in teaching themselves in an academic context (which is a big thing to learn about in and of itself) and we are not able to adapt our teaching to what we cannot perceive, so we are of little assistance either. Without the shared physical context, we can only guess and anticipate when and where they might be learning, and we seldom have the faintest idea how it occurs, save through sparse digital signals that they leave in discussion forums or submitted assignments, or coarse statistics based on web page views. In a few undergraduate core courses within my faculty it is therefore no surprise that the success rates are less than 30%, and (on average) only about half of all our students are successful, with rates that improve dramatically in more senior level courses. The vast majority of those who get to the end pass. Most who don’t succeed drop out. It doesn’t take many core courses with success rates of 30% to eliminate nearly 95% of students by the end of a program.

Teaching with a context

We can better deal with this if we let go of the illusion that we can be in control and, at the same time, find better ways to stay close: to make the learning process including the environment in which it occurs, as visible as possible. It is emphatically not about capturing digital traces and using analytics to reveal patterns. Though such techniques can have a place in helping to build a picture of how learners are responding to our deliberate acts of teaching, they are not even close to a solution for understanding learners in context. Most learning analytics and adaptive systems are McNamara Machines, blind to most of what matters.  There’s a huge risk that we start by measuring the easily measurable then wind up not just ignoring but implicitly denying that the things we cannot measure are important. Yes, it might help us to help students who are going to get to the end anyway to get better grades, but it tells us very little about (for instance) how they are learning, what obstacles they face, or how we could help them orchestrate their learning in the contexts in which they live.  Could generative AI help with that? I think it might. In conversation, an AI agent could ask leading questions, could recommend things to do with the space, could aggregate and report back on how and where students seem to be learning. Unlike traditional adaptive systems, generative AI can play an active discovery role and make broader connections that have not been scripted. However, this is not and should not be a substitute for an actual teacher: rather, it should mediate between humans, amplifying and feeding back, not guiding or informing.

For the most part, though, I think the trick is to use pedagogical designs that are made to support flexibility, that encourage learners to connect with the spaces live and people they share them with, that support them in understanding the impact of the environments they are in, and, as much as possible, to incorporate conduits that make it likely that participants will share information about their contexts and what they are doing in them, such as through reflective learning diaries, shared videos or audio, or introductory discussions intended to elicit that information. A good trick that I’ve used in the past, for example, is to ask students to send virtual postcards showing where they are and what they have been doing (nowadays a microblog post might serve a similar role). Similarly, it can be useful to start discussions that seek ideas about how to configure time and space for learning, sharing problems and solutions from the students themselves. Modelling behaviours can help: in my own communications, I try to reveal things about where I am and what I have been doing that provide some context and background story, especially when it relates to how I am changing as a result of our shared endeavours. Building social interaction opportunities into every inhabited virtual space would help a lot, making it more likely that students will share more of what they are doing and increasing awareness of both the presence and the non-presence (the difference in context) of others. Learning management systems are almost universally utter rubbish for that, typically relegating interactions to controlled areas of course sites and encouraging instrumental and ephemeral discussions that largely ignore context. We need more, more pervasively, and we need better.

None of this will replicate the rich, shared environments of in-person learning, and that is not the point. This is about acknowledging the differences in online and distance learning and building different orchestrations around them. On the whole, the independence of distance students is an extremely good thing, with great motivational benefits, not to mention convenience, much lower environmental harm, exploitable diversity, and many other valuable features that are hard to reproduce in person. When it works, it works very well. We just need to make it work better for those for whom that is not enough. To do that, we need to understand the whole assembly, not just the pieces we provide.

Sets, nets and groups revisited

Here are the slides from a talk I gave earlier today, hosted by George Siemens and his fine team of people at Human Systems. Terry Anderson helped me to put the slides together, and offered some great insights and commentary after the presentation but I am largely to blame for the presentation itself. Our brief was to talk about sets, nets and groups, the theme of our last book Teaching Crowds: learning and social media and much of our work together since 2007 but, as I was the one presenting, I bent it a little towards generative AI and my own intertwingled perspective on technologies and collective cognition, which is most fully developed (so far) in my most recent book, How Education Works: Teaching, Technology, and Technique. If you’re not familiar with our model of sets, nets, groups and collectives, there’s a brief overview on the Teaching Crowds website. It’s a little long in the tooth but I think it is still useful and will help to frame what follows.

A recreation of the famous New Yorker cartoon, "On the Internet no one knows you are a dog" showing a dog using a web browser - but it is a robot dog
A recreation of the famous New Yorker cartoon, “On the Internet no one knows you are a dog” – but it is a robot dog

The key new insight that appears for the first time in this presentation is that, rather than being a fundamental social form in their own right, groups consist of technological processes that make use of and help to engender/give shape to the more fundamental forms of nets and sets. At least, I think they do: I need to think and talk some more about this, at least with Terry, and work it up into a paper, but I haven’t yet thought through all the repercussions. Even back when we wrote the book I always thought of groups as technologically mediated entities but it was only when writing these slides in the light of my more recent thinking on technology that I paid much attention to the phenomena that they actually orchestrate in order to achieve their ends. Although there are non-technological prototypes – notably in the form of families – these are emergent rather than designed. The phenomena that intentional groups primarily orchestrate are those of networks and sets, which are simply configurations of humans and their relationships with one another. Modern groups – in a learning context, classes, cohorts, tutorial groups, seminar groups, and so on – are designed to fulfill more specific purposes than their natural prototypes, and they are made possible by technological inventions such as rules, roles, decision-making processes, and structural hierarchies. Essentially, the group is a purpose-driven technological overlay on top of more basic social forms. It seems natural, much as language seems natural, because it is so basic and fundamental to our existence and how everything else works in human societies, but it is an invention (or many inventions, in fact) as much as wheels and silicon chips.

Groups are among the oldest and most highly evolved of human technologies and they are incredibly important for learning, but they have a number of inherent flaws and trade-offs/Faustian bargains, notably in their effects on individual freedoms, in scalability (mainly achieved through hierarchies), in sometimes unhealthy power dynamics, and in limitations they place on roles individuals play in learning. Modern digital technologies can help to scale them a little further and refine or reify some of the rules and roles, but the basic flaws remain. However, modern digital technologies also offer other ways of enabling sets and networks of people to support one another’s learning, from blogs and mailing lists to purpose-built social networking systems, from Wikipedia and Academia.edu to Quora, in ways that can (optionally) integrate with and utilize groups but that differ in significant ways, such as in removing hierarchies, structuring through behaviour (collectives) and filtering or otherwise mediating messages. With some exceptions, however, the purposes of large-scale systems of this nature (which would provide an ideal set of phenomena to exploit) are not usually driven by a need for learning, but by a need to gain attention and profit. Facebook, Instagram, LinkedIn, X, and others of their ilk have vast networks to draw on but few mechanisms that support learning and limited checks and balances for reliability or quality when it does occur (which of course it does). Most of their algorithmic power is devoted to driving engagement, and the content and purpose of that engagement only matters insofar as it drives further engagement. Up to a point, trolls are good for them, which is seldom if ever true for learning systems. Some – Wikipedia, the Khan Academy, Slashdot, Stack Exchange, Quora, some SubReddits, and so on – achieve both engagement and intentional support for learning. However, they remain works in progress in the latter regard, being prone to a host of ills from filter bubbles and echo chambers to context collapse and the Matthew Effect, not to mention intentional harm by bad actors. I’ve been exploring this space for approaching 30 years now, but there remains almost as much scope for further research and development in this area as there was when I began. Though progress has been made, we have yet to figure out the right rules and structures to deal with a great many problems, and it is increasingly difficult to slot the products of our research into an increasingly bland, corporate online space dominated by a shrinking number of bland, centralized learning management systems that continue to refine their automation of group processes and structures and, increasingly, to ignore the sets and networks on which they rely.

With that in mind, I see big potential benefits for generative AIs – the ultimate collectives – as supporters and enablers for crowds of people learning together. Generative AI provides us with the means to play with structures and adapt in hitherto impossible ways, because the algorithms that drive their adaptations are indefinitely flexible, the reified activities that form them are vast, and the people that participate in them play an active role in adjusting and forming their algorithms (not the underpinning neural nets but the emergent configurations they take). These are significant differences from traditional collectives, that tend to have one purpose and algorithm (typically complex but deterministic), such as returning search results or engaging network interactions.  I also see a great many potential risks, of which I have written fairly extensively of late, most notably in playing soft orchestral roles in the assembly that replace the need for humans to learn to play them. We tread a fine line between learning utopia and learning dystopia, especially if we try to overlay them on top of educational systems that are driven by credentials. Credentials used to signify a vast range of tacit knowledge and skills that were never measured, and (notwithstanding a long tradition of cheating) that was fine as long as nothing else could create those signals, because they were serviceable proxies. If you could pass the test or assignment, it meant that you had gone through the process and learned a lot more than what was tested. This has been eroded for some time, abetted by social media like Course Hero or Chegg that remain quite effective ways of bypassing the process for those willing to pay a nominal sum and accept the risk. Now that generative AI can do the same at considerably lower cost, with greater reliability, and lower risk, without having gone through the process, they no longer make good signifiers and, anyway (playing Devil’s advocate), it remains unclear to what extent those soft, tacit skills are needed now that generative AIs can achieve them so well.  I am much encouraged by the existence of George’s Paul LeBlanc’s lab initiative, the fact that George is the headliner chief scientist for it, its intent to enable human-centred learning in an age of AI, and its aspiration to reinvent education to fit. We need such endeavours. I hope they will do some great things.

Proctored exams have fallen to generative AI

A Turkish university candidate was recently arrested after being caught using an AI-powered system to obtain answers to the entrance exam in real-time.

Source: Student Caught Using Artificial Intelligence to Cheat on University Entrance Test Students wired up to a computer while taking their exams

A couple of years ago (and a few times since) I observed that proctored exams offer no meaningful defence against generative AI so I am a little surprised it has taken so long for someone to be caught doing this. I guess that others have been more careful.

The candidate used a simple and rather obvious set-up: a camera disguised as a shirt button that was used to read the questions, a router hidden in a hollowed-out shoe linking to a stealthily concealed mobile device that queried a generative AI (likely ChatGPT-powered) that fed the answers back verbally to an in-ear bluetooth earpiece. Constructing such a thing would take a little ingenuity but it’s not rocket science. It’s not even computer science. Anyone could do this. It would take some skill to make it work well, though, and that may be the reason this attempt went wrong. The candidate was caught as a result of their suspicious behaviour, not because anyone directly noticed the tech. I’m trying to imagine the interface, how the machine would know which question to answer (did the candidate have to point their button in the right direction?), how they dealt with dictating the answers at a usable speed (what if they needed it to be repeated? Did they have to tap a microphone a number of times?), how they managed sequence and pacing (sub-vocalization? moving in a particular way?). These are soluble problems but they are not trivial, and skill would be needed to make the whole thing seem natural.

It may take a little while for this to become a widespread commodity item (and a bit longer for exam-takers to learn to use it unobtrusively), but I’m prepared to bet that someone is working on it, if it is not already available. And, yes, exam-setters will come up with a counter-technology to address this particular threat (scanners? signal blockers? Forcing students to strip naked?) but the cheats will be more ingenious, the tech will improve, and so it will go on, in an endless and unwinnable arms race.

Very few people cheat as a matter of course. This candidate was arrested – exam cheating is against the law in Turkey – for attempting to solve the problem they were required to solve, which was to pass the test, not to demonstrate their competence. The level of desperation that led to them adopting such a risky solution to the problem is hard to imagine, but it’s easy to understand how high the stakes must have seemed and how strong the incentive to succeed must have been. The fact that, in most societies, we habitually inflict such tests on both children and adults, on an unimaginably vast scale, will hopefully one day be seen as barbaric, on a par with beating children to make them behave. They are inauthentic, inaccurate, inequitable and, most absurdly of all, a primary cause of the problem they are designed to solve. We really do need to find a better solution.

Note on the post title: the student was caught so, as some have pointed out,  it would be an exaggeration to say that this one case is proof that proctored exams have fallen to generative AI, but I think it is a very safe assumption that this is not a lone example. This is a landmark case because it provides the first direct evidence that this is happening in the wild, not because it is the first time it has ever happened.

At the end of this post a successful reader will be able to make better use of learning outcomes

Jennie Young nails it in this delightful little bit of satire about the misuse of learning outcomes in education, Forget the Magic of Discovery, It’s Learning Outcomes That Help Children Identify, Comprehend, and Synthesize Their Dreams.

Learning outcomes do have their uses. They are very useful tools when designing learning activities, courses, and programs. Done well, they help guide and manage the process, and they are especially helpful in teams as a way to share intentions and establish boundaries, which can also be handy when thinking about how they fit into a broader program of study, or how they mesh with other learning activities elsewhere. They can perform a useful role in assessment. I find them especially valuable when I’m called upon to provide a credential because, rather than giving marks to assignments that I force students to do, I can give marks for learning outcomes, thereby allowing students to select their own evidence of having met them. It’s a great way to encourage participation in a learning community without the appallingly controlling, inauthentic, but widespread practice of giving marks for discussion contributions because such contributions can be very good evidence of learning, but there are other ways to provide it. It also makes it very easy to demonstrate to others that course outcomes have been met, it makes it easy for students to understand the marks they received,  it helps to avoid over-assessment and, especially if students are involved in creating or weighting the outcomes themselves, it empowers them to take control of the assessment process. Coming up with the evidence is also a great reflective exercise in itself, and a chance to spot any gaps before it makes a difference to the marks. Learning outcomes can also help teachers as part of how they evaluate the success of an educational intervention, though it is better to harvest outcomes than to just measure achievement of ones that are pre-specified because, if teaching is successful, students always learn more than what we require them to learn. However, they should never be used in a managerial process as objective, measurable ways of monitoring performance because that is simply not what they do.

They can have some limited value for students when initially choosing a learning activity, course, or program, or (with care and support) for evaluating their own success. However, they should seldom if ever be the first things students see because you could hardly be more boring or controlling than to start with “at the end of this course you will …”. And they should seldom if ever be used to  constrain or hobble teaching or learning because, as Young’s article makes beautifully clear, learning is an adventure into the unknown that should be full of surprises, for learners and for teachers. That said, there are a few kinds of learning outcome (that I have been thinking about including in my own courses for many years but have yet to work up the nerve to implement) that might be exceptions. For example…

At the end of this course a successful student will be able to:

  • feel a sense of wonder and excitement about [subject];
  • feel a passionate need to learn more about [subject];
  • teach their teacher about [subject];
  • enthusiastically take the course again and learn something completely different the second time around;
  • learn better;
  • do something in [subject] that no one has ever done before;
  • use what they have learned to make the world a better place;
  • explain [subject] to their teacher’s grandmother in a way that she would finally understand;
  • laugh uncontrollably at a joke that only experts in the field would get;
  • tell an original good joke that only experts in the field would get and that would make them laugh;
  • at a dinner party, even when slightly tipsy, convince an expert in the field that they are more of an expert;
  • design and deliver a better course than this on [subject].

I would totally enrol on this course.


The importance of a good opening line

This post asks the question,

How does the order of questions in a test affects how well students do?

The answer is “significantly.”

The post points to a paywalled study that shows, fairly conclusively, that starting with simpler questions in a typical academic quiz (on average) improves the overall results and, in particular, the chances of getting to the end of a quiz at all.  The study includes both an experimental field study using a low-stakes quiz, and a large-scale correlational study using a PISA dataset. Some of the effect sizes are quite large: about a 50% increase in non-completions for the hard-to-easy condition compared with the easy-to-hard condition, and a about a 25% increase in time on task for the easy-to-hard condition, suggesting students stick at it more when they have gained confidence earlier on. The increase in marks for the easy-to-hard condition compared with the hard-to easy condition is more modest when non-completions are excluded, but enough to make the difference between a pass and a fail for many students.

I kind-of knew this already but would not have expected it to make such a big difference.  It is a good reminder that, of course, objective tests are not objective. A quiz is a kind of interactive story with a very definite beginning, middle, and end, and it makes a big difference which parts of the story happen when, especially the beginning. Quizzes are like all kinds of learning experience: scaffolding helps, confidence matters, and motivation is central.  You can definitely put someone off reading a story if it has a bad first paragraph. Attitude makes all the difference in the world, which is one very good reason that such tests, and written exams in general, are so unfair and weak at discriminating capability, and why I have always done unreasonably well in such things: I generally relish the challenge. The authors reckon that adaptive quizzes might be one answer, and would especially benefit weaker students by ramping up the difficulty slowly, but warn that they may make things worse for more competent students who would experience the more difficult questions sooner. That resonates with my experience, too.

I don’t give marks for quizzes in any of my own courses and I allow students to try them as often as they wish but, even so, I have probably caused motivational harm by randomizing formative questions. I’m going to stop doing that in future. Designated teachers are never the sole authors of any educational story but, whenever they exert control, their contributions can certainly matter, at small scales and large. I wonder, how many people have had their whole lives changed for the worse by a bad opening line?

Source: It’s a question of order – 3-Star learning experiences


And now in Chinese: 在线学习环境:隐喻问题与系统改进. And some thoughts on the value of printed texts.

Warm off the press, and with copious thanks and admiration to Junhong Xiao for the invitation to submit and the translation, here is my paper “The problematic metaphor of the environment in online learning” in Chinese, in the Journal of Open Learning. It is based on my OTESSA Journal paper, originally published as “On the Misappropriation of Spatial Metaphors in Online Learning” at the end of 2022 (a paper I am quite pleased with, though it has yet to receive any citations that I am aware of).

Many thanks, too, to Junhong for sending me the printed version that arrived today, smelling deliciously of ink. I hardly ever read anything longer than a shopping bill on paper any more but there is something rather special about paper that digital versions entirely lack. The particular beauty of a book or journal written in a language and script that I don’t even slightly understand is that, notwithstanding the ease with which I can translate it using my phone, it largely divorces the medium from the message. Even with translation tools my name is unrecognizable to me in this: Google Lens translates it as “Jon Delong”. Although I know it contains a translation of my own words, it is really just a thing: the signs it contains mean nothing to me, in and of themselves. And it is a thing that I like, much as I like the books on my bookshelf.

I am not alone in loving paper books, a fact that owners of physical copies of my most recent book (which can be read online for free but that costs about $CAD40 on paper) have had the kindness to mention, e.g. here and here. There is something generational in this, perhaps. For those of us who grew up knowing no other reading medium than ink on paper, there is comfort in the familiar, and we have thousands (perhaps millions) of deeply associated memories in our muscles and brains connected with it, made more precious by the increasing rarity with which those memories are reinforced by actually reading them that way. But, for the most part, I doubt that my grandchildren, at least, will lack that. While they do enjoy and enthusiastically interact with text on screens, from before they have been able to accurately grasp them they have been exposed to printed books, and have loved some of them as much as I did at the same ages.

It is tempting to think that our love of paper might simply be because we don’t have decent e-readers, but I think there is more to it than that. I have some great e-readers in many sizes and types, and I do prefer some of them to read from, for sure: backlighting when I need it, robustness, flexibility, the means to see it in any size or font that works for me, the simple and precise search, the shareable highlights, the lightness of (some) devices, the different ways I can hold them, and so on, make them far more accessible. But paper has its charms, too. Most obviously, something printed on a paper is a thing to own whereas, on the whole, a digital copy tends to just be a licence to read, and ownership matters. I won’t be leaving my e-books to my children. The thingness really matters in other ways, too. Paper is something to handle, something to smell. Pages and book covers have textures – I can recognize some books I know well by touch alone. It affects many senses, and is more salient as a result. It takes up room in an environment so it’s a commitment, and so it has to matter, simply because it is there; a rivalrous object competing with other rivalrous objects for limited space. Paper comes in fixed sizes that may wear down but will never change: it thus keeps its shape in our memories, too. My wife has framed occasional pages from my previously translated work, elevating them to art works, decoupled from their original context, displayed with the same lofty reverence as pages from old atlases. Interestingly, she won’t do that if it is just a printed PDF: it has to come from a published paper journal, so the provenance matters. Paper has a history and a context of its own, beyond what it contains. And paper creates its own context, filled with physical signals and landmarks that make words relative to the medium, not abstractions that can be reflowed, translated into other languages, or converted into other media (notably speech). The result is something that is far more memorable than a reflowable e-text. Over the years I’ve written a little about this here and there, and elsewhere, including a paper on the subject (ironically, a paper that is not available on paper, as it happens), describing an approach to making e-texts more memorable.

After reaching a slightly ridiculous peak in the mid-2000s, and largely as a result of a brutal culling that occurred when I came to Canada nearly 17 years ago, my paper book collection has now diminished to easily fit in a single and not particularly large free-standing IKEA shelving unit. The survivors are mostly ones I might want to refer to or read again, and losing some of them would sadden me a great deal, but I would only (perhaps) run into a burning building to save just a few, including, for instance:

  • A dictionary from 1936, bound in leather by my father and used in countless games of Scrabble and spelling disputes when I was a boy, and that was used by my whole family to look up words at one time or another.
  • My original hardback copy of the Phantom Tollbooth (I have a paperback copy for lending), that remains my favourite book of all time, that was first read to me by my father, and that I have read myself many times at many ages, including to my own children.
  • A boxed set of the complete works of Narnia, that I chose as my school art prize when I was 18 because the family copies had become threadbare (read and abused by me and my four siblings), and that I later read to my own children. How someone with very limited artistic skill came to win the school art prize is a story for another time.
  • A well-worn original hardback copy of Harold and the Purple Crayon (I have a paperback copy for lending) that my father once displayed for children in his school to read, with the admonition “This is Mr Dron’s book. Please handle with care” (it was not – it was mine).
  • A scribble-filled, bookmark-laden copy of Kevin Kelly’s Out of Control that strongly influenced my thinking when I was researching my PhD and that still inspires me today. I can remember exactly where I sat when I made some of the margin notes.
  • A disintegrating copy of Storyland, given to me by my godmother in 1963 and read to me and by me for many years thereafter. There is a double value to this one because we once had two copies of this in our home: the other belonged to my wife, and was also a huge influence on her at similar ages.

These books proudly wear their history and their relationships with me and my loved ones in all their creases, coffee stains, scuffs, and tattered pages.pile of some of my favourite books  To a greater or lesser extent, the same is true of almost all of the other physical books I have kept. They sit there as a constant reminder of their presence – their physical presence, their emotional presence, their social presence and their cognitive presence – flitting by in my peripheral vision many times a day, connecting me to thoughts and inspirations I had when I read them and, often, with people and places connected with them. None of this is true of my e-books. Nor is it quite the same for other objects of sentimental value, except perhaps (and for very similar reasons) the occasional sculpture or picture, or some musical instruments. Much as I am fond of (say) baby clothes worn by my kids or a battered teddy bear, they are little more than aides memoires for other times and other activities, whereas the books (and a few other objects) latently embody the experiences themselves. If I opened them again (and I sometimes do) it would not be the same experience, but it would enrich and connect with those that I already had.

I have hundreds of e-books that are available on many devices, one of which I carry with me at all times, not to mention an Everand (formerly Scribd) account with a long history, not to mention a long and mostly lost history of library borrowing, and I have at least a dozen devices on which to read them, from a 4 inch e-ink reader to a 32 inch monitor and much in between, but my connection with those is far more limited and transient. It is still more limited for books that are locked to a certain duration through DRM (which is one reason they are the scum of the earth). When I look at my devices and open the various reading apps on them I do see a handful of book covers, usually those that I have most recently read, but that is too fleeting and volatile to have much value. And when I open them they don’t fall open on well-thumbed pages. The text is not tangibly connected with the object at all.

As well as smarter landmarks within them, better ways to make e-books more visible would help, which brings me to the real point of this post. For many years I have wanted to paper a wall or two with e-paper (preferably in colour) on which to display e-book covers, but the costs are still prohibitive. It would be fun if the covers would become battered with increasing use, showing the ones that really mattered, and maybe dust could settle on those that were never opened, though it would not have to be so skeuomorphic – fading would work, or glyphs. They could be ordered manually or by (say) reading date, title, author, or subject. Perhaps touching them or scanning a QR code could open them. I would love to get a research grant to do this but I don’t think asking for electronic wallpaper in my office would fly with most funding sources, even if I prettied it up with words like “autoethnography”, and I don’t have a strong enough case, nor can I think of a rigorous enough research methodology to try it in a larger study with other people. Well. Maybe I will try some time. Until the costs of e-paper come down much further, it is not going to be a commercially viable product, either, though prices are now low enough that it might be possible to do it in a limited way with a poster-sized display for a (very) few thousand dollars. It could certainly be done with a large screen TV for well under $1000 but I don’t think a power-hungry glowing screen would be at all the way to go: the value would not be enough to warrant the environmental harm or energy costs, and something that emitted light would be too distracting. I do have a big monitor on my desk, though, which is already doing that so it wouldn’t be any worse, to which I could add a background showing e-book covers or spines. I could easily do this as a static image or slideshow, but I’d rather have something dynamic. It shouldn’t be too hard to extract the metadata from my list of books, swipe the images from the Web or the e-book files, and show them as a backdrop (a screensaver would be trivial). It might even be worth extending this to papers and articles I have read. I already have Pocket open most of the time, displaying web pages that I have recently read or want to read (serving a similar purpose for short-term recollection), and that could be incorporated in this. I think it would be useful, and it would not be too much work to do it – most of the important development could be done in a day or two. If anyone has done this already or feels like coding it, do get in touch!

Slides from my SITE keynote, 2024: The Intertwingled Teacher

The Intertwingled Teacher 

UPDATE:  the video of my talk is now available at https://www.youtube.com/watch?v=ji0jjifFXTs  (slides and audio only) …

Photo of Jon holding a photo of Jon These are the slides from my opening keynote at SITE ‘24 today, at Planet Hollywood in Las Vegas. The talk was based closely on some of the main ideas in How Education Works.  I’d written an over-ambitious abstract promising answers to many questions and concerns, that I did just about cover but far too broadly. For counter balance, therefore, I tried to keep the focus on a single message – t’aint what you do, it’s the way that you do it (which is the epigraph for the book) – and, because it was Vegas,  I felt that I had to do a show, so I ended the session with a short ukulele version of the song of that name. I had fun, and a few people tried to sing along. The keynote conversation that followed was most enjoyable – wonderful people with wonderful ideas, and the hour allotted to it gave us time to explore all of them.

Here is that bloated abstract:

Abstract: All of us are learning technologists, teaching others through the use of technologies, be they language, white boards, and pencils or computers, apps, and networks. We are all part of a vast, technology-mediated cognitive web in which a cast of millions – in formal education including teachers such as textbook authors, media producers, architects, software designers, system administrators, and, above all, learners themselves –  co-participates in creating an endless, richly entwined tapestry of learning. This tapestry spreads far beyond formal acts of teaching, far back in time, and far into the future, weaving in and helping to form not just the learning of individuals but the collective intelligence of the whole human race. Everyone’s learning journey both differs from and is intertwingled with that of everyone else. Education is an overwhelmingly complex and unpredictable technological system in which coarse patterns and average effects can be found but, except in the most rigid, invariant, minor details, of which individual predictions cannot be accurately made. No learner is average, and outcomes are always greater than what is intended. The beat of a butterfly’s wing in Timbuktu can radically affect the experience of a learner in Toronto. A slight variation in tone of voice can make all the difference between a life-transforming learning experience and a lifelong aversion to a subject. Beautifully crafted, research-informed teaching methods can be completely ineffective, while poor teaching, or even the absence of it, can result in profoundly affective learning. For all our efforts to understand and control it, education as a technological process is far closer to art than to engineering. What we do is usually far less significant than the idiosyncratic way that we do it, and how much we care for the subject, our students, and our craft is often far more important than the pedagogical methods we use. In this talk I will discuss what all of this implies for how we should teach, for how we understand teaching, and for how we research the massively intertwingled processes and tools of teaching. Along the way I will explain why there is no significant difference between measured outcomes of online or in-person learning, the futility of teaching to learning styles, the reason for the 2-sigma advantage of personal tuition, the surprising commonalities between behaviourist, cognitivist, constructivist models of learning and teaching, the nature of literacies, and the failure of reductive research methods in education. It will be fun

A conversation about generative AI with David Webster


A week or so ago, early (for me) on a Monday morning, Professor David Webster and I had a conversation about generative AI, which was recorded as the first of a podcast series on the topic, hosted by the University of Liverpool. Here is that podcast. In it we explore both the darker and the more optimistic aspects of genAI, in a pleasantly rambling discussion that, surprisingly, lasted for about an hour.

I hadn’t spoken with Dave for well over a decade, at a conference in Hawaii, long before we became full professors or got elevated to loftier roles in our respective institutions, but it felt like we were just continuing the conversations we had back then. The only thing missing was a cold beer, swaying palm trees, and the sound of ukuleles drifting in the warm breeze. Well, that and a 6.5 earthquake that took out the power for a day and that made the conference a lot more memorable than it otherwise might have been. This conversation was a lot less earth shattering but it was just as enjoyable.

New article from Gerald Ardito and me – The emergence of autonomy in intertwingled learning environments: a model of teaching and learning

Here is a paper from the Asia-Pacific Journal of Teacher Education by my friend Gerald Ardito and me that presents a slightly different way of thinking about teaching and learning. We adopt a broadly complexivist stance that sees environments not as a backdrop to learning but as a rich network of dynamic, interwingled relationships between the various parts (including parts played by people), mediated through technologies, enabling and enabled by autonomy. The model that we develop knits together a smorgasbord of theories and models, including Self-Determination Theory (SDT), Connectivism, an assortment of complexity theories, the extended version of Paulsen’s model of cooperative freedoms developed by me and Terry Anderson, Garrison & Baynton’s model of autonomy, and my own coparticipation theory, wrapping up with a bit of social network analysis of a couple of Gerald’s courses that puts it all into perspective. From Gerald’s initial draft the paper took years of very sporadic development and went through many iterations. It seemed to take forever, but we had fun writing it. Looking afresh at the finished article, I think the diagrams might have been clearer, we might have done more to join all the dots, and we might have expressed the ideas a bit less wordily, but I am mostly pleased with the way it turned out, and I am glad to see it finally published. The good bits are all Gerald’s, but I am personally most pleased with the consolidated model of autonomy visualized in figure 4, that connects my own & Terry Anderson’s cooperative freedoms, Garrison & Baynton’s model of autonomy, and SDT.

combining cooperative freedoms, autonomy, and SDT


Gerald Ardito & Jon Dron (2024) The emergence of autonomy in intertwingled learning environments: a model of teaching and learning, Asia-Pacific Journal of Teacher Education, DOI: 10.1080/1359866X.2024.2325746