Slides from my ICEEL ’24 Keynote: “No Teacher Left Behind: Surviving Transformation”

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Here are the slides from from my keynote at the 8th International Conference on Education and E-Learning in Tokyo yesterday. Sadly I was not actually in Tokyo for this but the online integration was well done and there was some good audience interaction. I am also the conference chair (an honorary title) so I may be a bit biased, but I think it’s a really good conference, with an increasingly rare blend of both the tech and the pedagogical aspects of the field, and some wonderfully diverse keynotes ranging in subject matter from the hardest computer science to reflections on literature and love (thanks to its collocation with ICLLL, a literature and linguistics conference). My keynote was somewhere in between, and deliberately targeted at the conference theme, “Transformative Learning in the Digital Era: Navigating Innovation and Inclusion.”

the technological connectome, represented in the style of 1950s children's booksAs my starting point for the talk I introduced the concept of the technological connectome, about which I have just written a paper (currently under revision, hopefully due for publication in a forthcoming issue of the new Journal of Open, Distance, and Digital Education), which is essentially a way of talking about extended cognition from a technological rather than a cognitive perspective. From there I moved on to the adjacent possible and the exponential growth in technology that has, over the past century or so, reached such a breakneck rate of change that innovations such as generative AI, the transformation I particularly focused on (because it is topical), can transform vast swathes of culture and practice in months if not in weeks. This is a bit of a problem for traditional educators, who are as unprepared as anyone else for it, but who find themselves in a system that could not be more vulnerable to the consequences. At the very least it disrupts the learning outcomes-driven teacher-centric model of teaching that still massively dominates institutional learning the world over, both in the mockery it makes of traditional assessment practices and in the fact that generative AIs make far better teachers if all you care about are the measurable outcomes.

The solutions I presented and that formed the bulk of the talk, largely informed by the model of education presented in How Education Works, were mostly pretty traditional, emphasizing the value of community, and of passion for learning, along with caring about, respecting, and supporting learners. There were also some slightly less conventional but widely held perspectives on assessment, plus a bit of complexivist thinking about celebrating the many teachers and acknowledging the technological connectome as the means, the object and the subject of learning, but nothing Earth-shatteringly novel. I think this is as it should be. We don’t need new values and attitudes; we just need to emphasize those that are learning-positive rather than the increasingly mainstream learning-negative, outcomes-driven, externally regulated approaches that the cult of measurement imposes on us.

Post-secondary institutions have had to grapple with their learning-antagonistic role of summative assessment since not long after their inception so this is not a new problem but, until recent decades, the two roles have largely maintained an uneasy truce. A great deal of the impetus for the shift has come from expanding access to PSE. This has resulted in students who are less able, less willing, and less well-supported than their forebears who were, on average, far more advantaged in ability, motivation, and unencumbered time simply because fewer were able to get in. In the past, teachers hardly needed to teach. The students were already very capable, and had few other demands on their time (like working to get through college), so they just needed to hang out with smart people, some of whom who knew the subject and could guide them through it in order to know what to learn and whether they had been successful, along with the time and resources to support their learning. Teachers could be confident that, as long as students had the resources (libraries, lecture notes, study time, other students) they would be sufficiently driven by the need to pass the assessments and/or intrinsic interest, that they could largely be left to their own devices (OK, a slight caricature, but not far off the reality).

Unfortunately, though this is no longer even close to the norm,  it is still the model on which most universities are based.  Most of the time professors are still hired because of their research skills, not teaching ability, and it is relatively rare that they are expected to receive more than the most perfunctory training, let alone education, in how to teach. Those with an interest usually have opportunities to develop their skills but, if they do not, there are few consequences. Thanks to the technological connectome, the rewards and punishments of credentials continue to do the job well enough, notwithstanding the vast amounts of cheating, satisficing, student suffering, and lost love of learning that ensues. There are still plenty of teachers: students have textbooks, YouTube tutorials, other students, help sites, and ChatGPT, to name but a few, of which there are more every day. This is probably all that is propping up a fundamentally dysfunctional system. Increasingly, the primary value of post-secondary education comes to lie in its credentialling function.

No one who wants to teach wants this, but virtually all of those who teach in universities are the ones who succeeded in retaining their love of learning for its own sake despite it, so they find it hard to understand students who don’t. Too many (though, I believe, a minority) are positively hostile to their students as a result, believing that most students are lazy, willing to cheat, or to otherwise game the system, and they set up elaborate means of control and gotchas to trap them.  The majority who want the best for their students, however,  are also to blame, seeing their purpose as to improve grades, using “learning science” (which is like using colour theory to paint – useful, not essential) to develop methods that will, on average, do so more effectively. In fairness, though grades are not the purpose, they are not wrong about the need to teach the measurable stuff well: it does matter to achieve the skills and knowledge that students set out to achieve. However, it is only part of the purpose. Mostly, education is a means to less measurable ends; of forming identities, attitudes, values, ways of relating to others, ways of thinking, and ways of being. You don’t need the best teaching methods to achieve that: you just need to care, and to create environments and structures that support stuff like community, diversity, connection, sharing, openness, collaboration, play, and passion.

The keynote was recorded but I am not sure if or when it will be available. If it is released on a public site, I will share it here.

Forthcoming webinar, September 24, 2024 – How to be an Educational Technology: An Entangled Perspective on Teaching

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This is an announcement for an event I’ll be facilitating as part of TeachOnline’s excellent ongoing series of webinars. In it I will be discussing some of the key ideas of my open book, How Education Works, and exploring what they imply about how we should teach and, more broadly, how we should design systems of education. It will be fun. It will be educational. There may be music.

Here are the details:

Date: Tuesday, September 24, 2024

Time: 1:00 PM – 2:00 PM (Eastern Time) (find your time zone here)

Register (free of charge) for the event here

 

Source: How to be an Educational Technology: An Entangled Perspective on Teaching | Welcome to TeachOnline

On the importance of place

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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

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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.

Slides from my SITE keynote, 2024: The Intertwingled Teacher

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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

▶ I got air: interview with Terry Greene

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Since 2018, Terry Greene has been producing a wonderful series of podcast interviews with open and online learning researchers and practitioners called Getting Air. Prompted by the publication of How Education Works, (Terry is also responsible for the musical version of the book, so I think he likes it) this week’s episode features an interview with me.

I probably should have been better prepared. Terry asked some probing, well-informed, and sometimes disarming questions, most of which led to me rambling more than I might have done if I’d thought about them in advance. It was fun, though, drifting through a broad range of topics from the nature of technology to music to the perils of generative AI (of course).

I hope that Terry does call his PhD dissertation “Getting rid of instructional designers”.

Presentation – Generative AIs in Learning & Teaching: the Case Against

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Here are the slides from my presentation at AU’s Lunch ‘n’ Learn session today. The presentation itself took 20 minutes and was followed by a wonderfully lively and thoughtful conversation for another 40 minutes, though it was only scheduled for half an hour. Thanks to all who attended for a very enjoyable discussion! self portrait of chatGPT, showing an androgynous human face overlaid with circuits

The arguments made in this were mostly derived from my recent paper on the subject (Dron, J. (2023). The Human Nature of Generative AIs and the Technological Nature of Humanity: Implications for Education. Digital, 3(4), 319–335. https://doi.org/10.3390/digital3040020) but, despite the title, my point was not to reject the use of generative AIs at all. The central message I was hoping to get across was a simpler and more important one: to encourage attendees to think about what education is for, and what we would like it to be. As the slides suggest, I believe that is only partially to do with the objectives and outcomes we set out to achieve,  that it is nothing much at all to do with the products of the system such as grades and credentials, and that focus on those mechanical aspects of the system often creates obstacles to the achievement of it. Beyond those easily measured things, education is about the values, beliefs, attitudes, relationships, and development of humans and their societies.  It’s about ways of being, not just capacity to do stuff. It’s about developing humans, not (just) developing skills. My hope is that the disruptions caused by generative AIs are encouraging us to think like the Amish, and to place greater value on the things we cannot measure. These are good conversations to have.

▶ How Education Works, the audio book: now with beats

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My book has been set to music!

Many thanks to Terry Greene for converting How Education Works into the second in his inspired series of podcasts, EZ Learning – Audio Books with Beats. There’s a total of 15 episodes that can be listened to online, subscribed to with your preferred podcast app, or downloaded for later listening, read by a computer-generated voice and accompanied by some cool, soothing beats.

Terry chose a deep North American voice for the reader and Eaters In Coffeeshops Mix 1 by Eaters to accompany my book. I reckon it works really well. It’s bizarre, at first – the soothing robotic voice introduces weird pauses, mispronunciations, and curious emphases, and there are occasional voice parts in the music that can be slightly distracting – but you soon get used to it if you relax into the rhythm, and it leads to the odd serendipitous emphasis that enhances rather than detracts from the text. Oddly, in some ways it almost feels more human as a result. Though it can be a bit disconcerting at times and there’s a fair chance of being lulled to sleep by the gentle rhythm, I have a hunch that the addition of music might make it easier to remember passages from it, for reasons discussed in a paper I wrote with Rory McGreal, VIve Kumar, and Jennifer Davies a year or so ago.

I have been slowly and painfully working on a manually performed audiobook of How Education Works but it is taking much longer than expected thanks to living on the flight path of a surprising number of float planes, being in a city built on a rain forest with a noisy gutter outside my window, having two very vocal cats, and so on, not to mention not having a lot of free time to work on it, so I am very pleased that Terry has done this. I won’t stop working on the human-read version – I think this fills a different and very complementary niche – but it’s great to have something to point people towards when they ask for an audio version.

The first season of Audio Books with Beats, appearing in the feed after the podcasts for my book chapters, was another AU Press book, Terry Anderson’s Theory and Practice of Online Learning which is also well worth a listen – those chapters follow directly from mine in the list of episodes. I hope and expect there will be more seasons to come so, if you are reading this some time after it was posted, you may need to scroll down through other podcasts until you reach the How Education Works. In case it’s hard to find, here’s a list of direct links to the episodes.

Acknowledgements, Prologue, introduction

Chapter 1: A Handful of Anecdotes About Elephants

Chapter 2:  A Handful of Observations About Elephants

Part 1: All About Technology

Chapter 3: Organizing Stuff to Do Stuff

Chapter 4: How Technologies Work

Chapter 5: Participation and Technique

Part II: Education as a Technological System

Chapter 6: A Co-Participation Model of Teaching

Chapter 7: Theories of Teaching

Chapter 8: Technique, Expertise, and Literacy

Part III: Applying the Co-Participation Model

Chapter 9: Revealing Elephants

Chapter 10: How Education Works

Epilogue

Originally posted at: https://landing.athabascau.ca/bookmarks/view/20936998/%E2%96%B6-how-education-works-the-audio-book-now-with-beats

Recording and slides from my ESET 2023 keynote: Artificial humanity and human artificiality

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Here are the slides from my keynote at ESET23 in Taiwan (I was online, alas, not in Taipei!).

I will try to remember to update this post with a link to the recording, when it is available.

Here’s a recording of the actual keynote.

The themes of my talk will be familiar to anyone who follows my blog or who has read my recent paper on the subject. This is about applying the coparticipation theory from How Education Works to generative AI, raising concerns about the ways it mimics the soft technique of humans, and discussing how problematic that will be if the skills it replaces atrophy or are never learned in the first place, amongst other issues.

This is the abstract:

We are participants in, not just users of technologies. Sometimes we participate as orchestrators (for instance, when choosing words that we write) and sometimes as part of the orchestration (for instance, when spelling those words correctly). Usually, we play both roles.  When we automate aspects of technologies in which we are just parts of the orchestration, it frees us up to be able to orchestrate more, to do creative and problem-solving tasks, while our tools perform the hard, mechanical tasks better, more consistently, and faster than we could ourselves. Collectively and individually, we therefore become smarter. Generative AIs are the first of our technologies to successfully automate those soft, open-ended, creative cognitive tasks. If we lack sufficient time and/or knowledge to do what they do ourselves, they are like tireless, endlessly flexible personal assistants, expanding what we can do alone. If we cannot draw, or draw up a rental agreement, say, an AI will do it for us, so we may get on with other things. Teachers are therefore scrambling to use AIs to assist in their teaching as fast as students use AIs to assist with their assessments.

For achieving measurable learning outcomes, AIs are or will be effective teachers, opening up greater learning opportunities that are more personalized, at lower cost, in ways that are superior to average human teachers.  But human teachers, be they professionals, other students, or authors of websites, do more than help learners to achieve measurable outcomes. They model ways of thinking, ways of being, tacit knowledge, and values: things that make us human. Education is a preparation to participate in human cultures, not just a means of imparting economically valuable skills. What will happen as we increasingly learn those ways of being from a machine? If machines can replicate skills like drawing, reasoning, writing, and planning, will humans need to learn them at all? Are there aspects of those skills that must not atrophy, and what will happen to us at a global scale if we lose them? What parts of our cognition should we allow AIs to replace? What kinds of credentials, if any, will be needed? In this talk I will use the theory presented in my latest book, How Education Works: Teaching, Technology, and Technique to provide a framework for exploring why, how, and for what purpose our educational institutions exist, and what the future may hold for them.

Pre-conference background reading, including the book, articles, and blog posts on generative AI and education may be found linked from https://howeducationworks.ca

Preprint – The human nature of generative AIs and the technological nature of humanity: implications for education

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Here is a preprint of a paper I just submitted to MDPI’s Digital journal that applies the co-participation model that underpins How Education Works (and a number of my papers over the last few years) to generative AIs (GAIs). I don’t know whether it will be accepted and, even if it is, it is very likely that some changes will be required. This is a warts-and-all raw first submission. It’s fairly long (around 10,000 words).

The central observation around which the paper revolves is that, for the first time in the history of technology, recent generations of GAIs automate (or at least appear to automate) the soft technique that has, till now, been the sole domain of humans. Up until now, every technology we have ever created, be it physically instantiated, cognitive, organizational, structural, or conceptual, has left all of the soft part of the orchestration to human beings.

The fact that GAIs replicate the soft stuff is a matter for some concern when they start to play a role in education, mainly because:

  • the skills they replace may atrophy or never be learned in the first place. This is not even slightly like replacing hard skills of handwriting or arithmetic: we are talking about skills like creativity, problem-solving, critical inquiry, design, and so on. We’re talking about the stuff that GAIs are trained with.
  • the AIs themselves are an amalgam, an embodiment of our collective intelligence, not actual people. You can spin up any kind of persona you like and discard it just as easily. Much of the crucially important hidden/tacit curriculum of education is concerned with relationships, identity, ways of thinking, ways of being, ways of working and playing with others. It’s about learning to be human in a human society. It is therefore quite problematic to delegate how we learn to be human to a machine with (literally and figuratively) no skin in the game, trained on a bunch of signals signifying nothing but more signals.

On the other hand, to not use them in educational systems would be as stupid as to not use writing. These technologies are now parts of our extended cognition, intertwingled with our collective intelligence as much as any other technology, so of course they must be integrated in our educational systems. The big questions are not about whether we should embrace them but how, and what soft skills they might replace that we wish to preserve or develop. I hope that we will value real humans and their inventions more, rather than less, though I fear that, as long as we retain the main structural features of our education systems without significant adjustments to how they work, we will no longer care, and we may lose some of our capacity for caring.

I suggest a few ways we might avert some of the greatest risks by, for instance, treating them as partners/contractors/team members rather than tools, by avoiding methods of “personalization” that simply reinforce existing power imbalances and pedagogies designed for better indoctrination, by using them to help connect us and support human relationships, by doing what we can to reduce extrinsic drivers, by decoupling learning and credentials, and by doubling down on the social aspects of learning. There is also an undeniable explosion in adjacent possibles, leading to new skills to learn, new ways to be creative, and new possibilities for opening up education to more people. The potential paths we might take from now on are unprestatable and multifarious but, once we start down them, resulting path dependencies may lead us into great calamity at least as easily as they may expand our potential. We need to make wise decisions now, while we still have the wisdom to make them.

MDPI invited me to submit this article free of their normal article processing charge (APC). The fact that I accepted is therefore very much not an endorsement of APCs, though I respect MDPI’s willingness to accommodate those who find payment difficult, the good editorial services they provide, and the fact that all they publish is open. I was not previously familiar with the Digital journal itself. It has been publishing 4 articles a year since 2021, mostly offering a mix of reports on application designs and literature reviews. The quality seems good.

Abstract

This paper applies a theoretical model to analyze the ways that widespread use of generative AIs (GAIs) in education and, more broadly, in contributing to and reflecting the collective intelligence of our species, can and will change us. The model extends Brian Arthur’s insights into the nature of technologies as the orchestration of phenomena to our use by explaining the nature of humans participation in their enactment, whether as part of the orchestration (hard technique, where our roles must be performed correctly) or as orchestrators of phenomena (soft technique performed creatively or idiosyncratically). Education may be seen as a technological process for developing the soft and hard techniques of humans to participate in the technologies and thus the collective intelligence of our cultures. Unlike all earlier technologies, by embodying that collective intelligence themselves, GAIs can closely emulate and implement not only the hard technique but also the soft that, until now, was humanity’s sole domain: the very things that technologies enabled us to do can now be done by the technologies themselves. The consequences for what, how, and even whether we learn are profound. The paper explores some of these consequences and concludes with theoretically informed approaches that may help us to avert some dangers while benefiting from the strengths of generative AIs.

Originally posted at: https://landing.athabascau.ca/bookmarks/view/20512771/preprint-the-human-nature-of-generative-ais-and-the-technological-nature-of-humanity-implications-for-education