The artificial curriculum

Posted , updated on

evolving into a robot “Shaping the Future of Education: Exploring the Potential and Consequences of AI and ChatGPT in Educational Settings” by Simone Grassini is a well-researched, concise but comprehensive overview of the state of play for generative AI (GAI) in education. It gives a very good overview of current uses, by faculty and students, and provides a thoughtful discussion of issues and concerns arising. It addresses technical, ethical, and pragmatic concerns across a broad spectrum. If you want a great summary of where we are now, with tons of research-informed suggestions as to what to do about it, this is a very worthwhile read.

However, underpinning much of the discussion is an implied (and I suspect unintentional) assumption that education is primarily concerned with achieving and measuring explicit specified outcomes. This is particularly obvious in the discussions of ways GAIs can “assist” with instruction. I have a problem with that.

There has been an increasing trend in recent decades towards the mechanization of education: modularizing rather than integrating, measuring what can be easily measured, creating efficiencies, focusing on an end goal of feeding industry, and so on. It has resulted in a classic case of the McNamara Fallacy, that starts with a laudable goal of measuring success, as much as we are able, and ends with that measure defining success, to the exclusion anything we do not or cannot measure. Learning becomes the achievement of measured outcomes.

It is true that consistent, measurable, hard techniques must be learned to achieve almost anything in life, and that it takes sustained effort and study to achieve most of them that educators can and should help with. Measurable learning outcomes and what we do with them matter. However, the more profound and, I believe, the more important ends of education, regardless of the subject, are concerned with ways of being in the world, with other humans. It is the tacit curriculum that ultimately matters more: how education affects the attitudes, the values, the ways we can adapt, how we can create, how we make connections, pursue our dreams, live fulfilling lives, engage with our fellow humans as parts of cultures and societies.

By definition, the tacit curriculum cannot be meaningfully expressed in learning outcomes or measured on a uniform scale. It can be expressed only obliquely, if it can be expressed at all, in words. It is largely emergent and relational, expressed in how we are, interacting with one another, not as measurable functions that describe what we can do. It is complex, situated, and idiosyncratic. It is about learning to be human, not achieving credentials.

Returning to the topic of AI, to learn to be human from a blurry JPEG of the web, or autotune for knowledge, especially given the fact that training sets will increasingly be trained on the output of earlier training sets, seems to me to be a very bad idea indeed.

The real difficulty that teachers face is not that students solve the problems set to them using large language models, but that in so doing they bypass the process, thus avoiding the tacit learning outcomes we cannot or choose not to measure. And the real difficulty that those students face is that, in delegating the teaching process to an AI, their teachers are bypassing the teaching process, thus failing to support the learning of those tacit outcomes or, at best, providing an averaged-out caricature of them. If we heedlessly continue along this path, it will wind up with machines teaching machines, with humans largely playing the roles of cogs and switches in them.

Some might argue that, if the machines do a good enough job of mimicry then it really doesn’t matter that they happen to be statistical models with no feelings, no intentions, no connection, and no agency. I disagree. Just as it makes a difference whether a painting ascribed to Picasso is a fake or not, or whether a letter is faxed or delivered through the post, or whether this particular guitar was played by John Lennon, it matters that real humans are on each side of a learning transaction. It means something different for an artifact to have been created by another human, even if the form of the exchange, in words or whatever, is the same. Current large language models have flaws, confidently spout falsehoods, fail to remember previous exchanges, and so on, so they are easy targets for criticism. However, I think it will be even worse when AIs are “better” teachers. When what they seem to be is endlessly tireless, patient, respectful and responsive; when the help they give is unerringly accurately personal and targeted; when they accurately draw on knowledge no one human could ever possess, they will not be modelling human behaviour. The best case scenario is that they will not be teaching students how to be, they will just be teaching them how to do, and that human teachers will provide the necessary tacit curriculum to support the human side of learning. However, the two are inseparable, so that is not particularly likely. The worst scenarios are that they will be teaching students how to be machines, or how to be an average human (with significant biases introduced by their training), or both.

And, frankly, if AIs are doing such a good job of it then they are the ones who should be doing whatever it is that they are training students to do, not the students. This will most certainly happen: it already is (witness the current actors and screenwriters strike). For all the disruption that results, it’s not necessarily a bad thing, because it increases the adjacent possible for everyone in so many ways. That’s why the illustration to this post is made to my instructions by Midjourney, not drawn by me. It does a much better job of it than I could do.

In a rational world we would not simply incorporate AI into teaching as we have always taught. It makes no more sense to let it replace teachers than it does to let it replace students. We really need to rethink what and why we are teaching in the first place. Unfortunately, such reinvention is rarely if ever how technology works. Technology evolves by assembly with and in the context of other technology, which is how come we have inherited mediaeval solutions to indoctrination as a fundamental mainstay of all modern education (there’s a lot more about such things in my book, How Education Works if you want to know more about that). The upshot will be that, as we integrate rather than reinvent, we will keep on doing what we have always done, with a few changes to topics, a few adjustments in how we assess, and a few “efficiencies”, but we will barely notice that everything has changed because students will still be achieving the same kinds of measured outcomes.

I am not much persuaded by most apocalyptic visions of the potential threat of AI. I don’t think that AI is particularly likely to lead to the world ending with a bang, though it is true that more powerful tools do make it more likely that evil people will wield them. Artificial General Intelligence, though, especially anything resembling consciousness, is very little closer today than it was 50 years ago and most attempts to achieve it are barking in the wrong forest, let alone up the wrong tree. The more likely and more troubling scenario is that, as it embraces GAIs but fails to change how everything is done, the world will end with a whimper, a blandification, a leisurely death like that of lobsters in water coming slowly to a boil. The sad thing is that, by then, with our continued focus on just those things we measure, we may not even notice it is happening. The sadder thing still is that, perhaps, it already is happening.

Originally posted at: https://landing.athabascau.ca/bookmarks/view/19390937/the-artificial-curriculum

Look what just arrived on my doorstep! #howeducationworks from @au_press is now available in print and e-book formats

Posted , updated on

Photo of hard copies of How Education Works

Hard copies and e-book versions of How Education Works are now available, and they are starting to turn up in bookstores. The recommended retail price is CAD$40 but Amazon is selling the Kindle version for a bit less.

Here are a few outlets that are selling it (or order it from your local independent bookstore!):

AU Press (CA)

Barnes & Noble (US)

Blackwells (UK)

Amazon (CA)

Amazon (JP)

University of Chicago Press (US)

Indigo (CA)

Booktopia (AU)

For those wanting to try before they buy or who cannot afford/do not want the paper or e-book versions, you can read it for free online, or download a PDF of the whole book.

The publishers see this as mainly targeted at professional teachers and educational researchers, but those are far from the only audiences I had in mind as I was writing it. Apart from anything else, one of the central claims of the book is that literally everyone is a teacher.  But it’s as much a book about the nature of technology as it is about education, and as much about the nature of knowledge as it is about how that knowledge is acquired. If you’re interested in how we come to know stuff, how technologies work, or how to think about what makes us (individually and collectively) smart, there’s something in the book for you. It’s a work of philosophy as much as it is a book of practical advice, and it’s about a way of thinking and being at least as much as it is about the formal practice of education. That said, it certainly does contain some ideas and recommendations that do have practical value for educators and educational researchers. There’s just more to it than that.

I cannot begin to express how pleased I am that, after more than 10 years of intermittent work, I finally have the finished article in my hands. I hope you get a chance to read it, in whatever format works for you! I’ll end this post with a quote, that happens to be the final paragraph of the book…

“If this book has helped you, however slightly, to think about what you know and how you have come to know it a little differently, then it has been a successful learning technology. In fact, even if you hold to all of your previous beliefs and this book has challenged you to defend them, then it has worked just fine too. Even if you disagreed with or misunderstood everything that I said, and even if you disliked the way that I presented it, it might still have been an effective learning technology, even though the learning that I hoped for did not come about. But I am not the one who matters the most here. This is layer upon layer of technology, and in some sense, for some technology, it has done what that technology should do. The book has conveyed words that, even if not understood as I intended them to be, even if not accepted, even if rabidly disagreed with, have done something for your learning. You are a different person now from the person you were when you started reading this book because everything that we do changes us. I do not know how it has changed you, but your mind is not the same as it was before, and ultimately the collectives in which you participate will not be the same either. The technology of print production, a spoken word, a pattern of pixels on a screen, or dots on a braille reader has, I hope, enabled you, at least on occasion, to think, criticize, acknowledge, recognize, synthesize, and react in ways that might have some value in consolidating or extending or even changing what you already know. As a result of bits and bytes flowing over an ether from my fingertips to whatever this page might be to you, knowledge (however obscure or counter to my intentions) has been created in the world, and learning has happened. For all the complexities and issues that emerge from that simple fact, one thing is absolutely certain: this is good.”

 

 

A decade of unwriting: the life history of "How Education Works"

Posted , updated on

How Education Works book coverAbout 10 years ago I submitted the first draft of a book called “How Learning Technologies Work” to AU Press. The title was a nod to David Byrne’s wonderful book, “How Music Works” which is about much more than just music, just as mine was about much more than learning technologies.

Pulling together ideas I had been thinking about for a few years, the book had taken me only a few months to write, mostly at the tail end of my sabbatical. I was quite pleased with it. The internal reviewers were positive too, though they suggested a number of sensible revisions, including clarifying some confusing arguments and a bit of restructuring. Also, in the interests of marketing, they recommended a change to the title because, though accurately describing the book’s contents, I was not using “learning technologies” in its mainstream sense at all (for me, poetry, pedagogies, and prayer are as much technologies as pots, potentiometers and practices), so it would appeal to only a small subset of its intended audience. They were also a bit concerned that it would be hard to find an audience for it even if it had a better title because it was at least as much a book about the nature of technology as it was a book about learning, so it would fall between two possible markets, potentially appealing to neither.

A few months later, I had written a new revision that addressed most of the reviewers’ recommendations and concerns, though it still lacked a good title. I could have submitted it then. However, in the process of disentangling those confusing arguments, I had realized that the soft/hard technology distinction on which much of the book rested was far less well-defined than I had imagined, and that some of the conclusions that I had drawn from it were just plain wrong. The more I thought about it, the less happy I felt.

And so began the first of a series of substantial rewrites. However, my teaching load was very high, and I had lots of other stuff to do, so progress was slow. I was still rewriting it when I unwisely became Chair of my department in 2016, which almost brought the whole project to a halt for another 3 years. Despite that, by the time my tenure as Chair ended, the book had grown to around double its original (not insubstantial) length, and the theory was starting to look coherent, though I had yet to make the final leap that made sense of it all.

By 2019, as I started another sabbatical, I had decided to split the book into two. I put the stuff that seemed useful for practitioners into a new book,  “Education: an owner’s manual”, leaving the explanatory and predictive theory in its own book, now grandiosely titled “How Education Works”, and worked on both simultaneously. Each grew to a few hundred pages.

Neither worked particularly well. It was really difficult to keep the theory out of the practical book, and the theoretical work was horribly dry without the stories and examples to make sense of it. The theory, though, at last made sense, albeit that I struggled (and failed) to give it a catchy name. The solution was infuriatingly obvious. In all my talks on the subject my catchphrase from the start had been “’tain’t what you do, it’s the way that you do it, that’s what gets results” (it’s the epigraph for the book), so it was always implicit that softness and hardness are not characteristics of all technologies, as such, nor even of their assemblies, but of the ways that we participate in their orchestration. Essentially, what matters is technique: the roles we play as parts of the orchestration or orchestrators of it. That’s where the magic happens.

But now I had two mediocre books that were going nowhere. Fearing I was about to wind up with two unfinished and/or unsellable books, about half way through my sabbatical I brutally slashed over half the chapters from both, pasted the remains together, and spent much of the time I had left filling in the cracks in the resulting bricolage.

I finally submitted “How Education Works: Teaching, Technology, and Technique” in the closing hours of 2020, accompanied by a new proposal because, though it shared a theme and a few words with the original, it was a very different book.

Along the way I had written over a million words, only around a tenth of which made it into what I sent to AU Press. I had spent the vast majority of my authoring time unwriting rather than writing the book and, with each word I wrote or unwrote, the book had written me, as much as I had written it. The book is as much a part of my cognition as a product of it.

And now, at last, it can be part of yours.

30 months after it was submitted – I won’t go into the reasons apart from to say it has been very frustrating –  the book is finally available as a free PDF download or to read on the Web. If all goes to plan, the paper and e-book versions should arrive June 27th, 2023, and can be pre-ordered now.

It is still a book about technology at least as much as it is about education (very broadly defined), albeit that it is now firmly situated in the latter. It has to be both because among the central points I’m making are that we are part-technology and technology is part-us, that cognition is (in part) technology and technology is (in part) cognition, and that education is a fundamentally technological and thus fundamentally human activity. It’s all one complex, hugely distributed, recursive intertwingularity in which we and our technological creations are all co-participants in the cognition and learning of ourselves and one another.

During the 30 months AU Press has had the book I have noticed a thousand different ways the book could be improved, and I don’t love all of the edits made to it along the way (by me and others), but I reckon it does what I want it to do, and 10 years is long enough.

It’s time to start another.

A few places you can buy the book

AU Press (CA)

Barnes & Noble (US)

Blackwells (UK)

Amazon (CA)

Amazon (JP)

University of Chicago Press (US)

Indigo (CA)

Booktopia (AU)

Technological distance – my slides from OTESSA ’23

Posted , updated on

Technological Distance

Here are the slides from my talk today at OTESSA ’23. Technological distance is a way of understanding distance that fits with modern complexivist models of learning such as Connectivism, Heutagogy, Networks/Communities of Practice/Rhizomatic Learning, and so on. In such a model, there are potentially thousands of distances – whether understood as psychological, transactional, social, cognitive, physical, temporal, or whatever – so conventional views of distance as a gap between learner and teacher (or institution or other students) are woefully inadequate.

I frame technological distance as a gap between technologies learners have (including cognitive gadgets, skills, techniques, etc as well as physical, organization, or procedural technologies) and those they need in order to learn. It is a little bit like Vygotsky’s Zone of Proximal Development but re-imagined and extended to incorporate all the many technologies, structures, and people who may be involved in the teaching gestalt.

The model of technology that I use to explain the idea is based on the coparticipation perspective presented in my book that, with luck, should be out within the next week or two. The talk ends with a brief discussion of the main implications for those whose job it is to teach.

Thanks to MidJourney for collaborating with me to produce the images used in the slides.

people as interlocking cogs

Can a technology be true?

Posted , updated on

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

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

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

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

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

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

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

Posted , updated on

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

Abstract

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

Citation

Bozkurt, A., Xiao, J., Lambert, S., Pazurek, A., Crompton, H., Koseoglu, S., Farrow, R., Bond, M., Nerantzi, C., Honeychurch, S., Bali, M., Dron, J., Mir, K., Stewart, B., Costello, E., Mason, J., Stracke, C. M., Romero-Hall, E., Koutropoulos, A., Toquero, C. M., Singh, L Tlili, A., Lee, K., Nichols, M., Ossiannilsson, E., Brown, M., Irvine, V., Raffaghelli, J. E., Santos-Hermosa, G Farrell, O., Adam, T., Thong, Y. L., Sani-Bozkurt, S., Sharma, R. C., Hrastinski, S., & Jandrić, P. (2023). Speculative futures on ChatGPT and generative artificial intelligence (AI): A collective reflection from the educational landscape. Asian Journal of Distance Education, 18(1), 53-130. https://doi.org/10.5281/zenodo.7636568

Originally posted at: https://landing.athabascau.ca/bookmarks/view/17699638/view-of-speculative-futures-on-chatgpt-and-generative-artificial-intelligence-ai-a-collective-reflection-from-the-educational-landscape

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

Posted , updated on

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

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

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

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

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

Abstract

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

Reference

Originally posted at: https://landing.athabascau.ca/bookmarks/view/17293757/my-latest-paper-technology-teaching-and-the-many-distances-of-distance-learning-journal-of-open-flexible-and-distance-learning

Petition · Athabasca University – Oppose direct political interference in universities · Change.org

Posted

https://www.change.org/p/athabasca-university-oppose-direct-political-interference-in-universities

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

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

Originally posted at: https://landing.athabascau.ca/bookmarks/view/17102318/petition-%C2%B7-athabasca-university-oppose-direct-political-interference-in-universities-%C2%B7-changeorg

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

Posted , updated on

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

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

Slides from my Confluence 2023 keynote

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

Posted , updated on

https://conference.otessa.org/index.php/conference/issue/view/3

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

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

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

Originally posted at: https://landing.athabascau.ca/bookmarks/view/16754483/proceedings-of-the-opentechnology-in-education-society-and-scholarship-association-conference-2022-and-call-for-proposals-for-this-years-conference-due-january-31