This is a chapter by me and Terry Anderson for Springer’s new Handbook of Open, Distance, and Digital Education that updates and refines our popular (1658 citations, and still rising, for the original paper alone) but now long-in-the-tooth ‘three generations’ model of distance learning pedagogy. We have changed the labels for the pedagogical families this time round to ones that I think are more coherent, divided according to their epistemological underpinnings: the objectivist, the subjectivist, and the complexivist. and we have added some speculations about whether further paradigms might have started to emerge in the 11 years since our original paper was published. Our main conclusion, though, is that no single pedagogical paradigm will dominate in the foreseeable future: that we are in an era of great pedagogical diversity, and that this diversity will only increase as time goes by.
The three major paradigms
Objectivist: previously known as ‘behaviourist/cognitivist’, what characterizes objectivist pedagogies is that they are both defined by assumptions of an objective external reality, and driven by (usually teacher-defined) objectives. It’s a paradigm of teaching, where teachers are typically sages on the stage using methods intended to achieve effective learning of defined facts and skills. Examples include behaviourism, learning styles theories, brain-based approaches, multiple intelligence models, media theories, and similar approaches where the focus is on efficient transmission and replication of received knowledge.
Subjectivist: formerly known as ‘social constructivist’, subjectivist pedagogies are concerned with – well – subjects: they are concerned with the personal and social co-construction of knowledge, recognizing its situated and always unique nature, saying little about methods but a lot about meaning-making. It’s a paradigm of learning, where teachers are typically guides on the side, supporting individuals and groups to learn in complex, situated contexts. Examples include constructivist, social constructivist, constructionist, and similar families of theory where the emphasis is as much on the learners’ growth and development in a human society as it is on what is being learned.
Complexivist: originally described as ‘connectivist’ (which was confusing and inaccurate), complexivist pedagogies acknowledge and exploit the complex nature of our massively distributed cognition, including its richly recursive self-organizing and emergent properties, its reification through shared tools and artefacts, and its many social layers. It’s a paradigm of knowledge, where teachers are fellow learners, co-travellers and role models, and knowledge exists not just in individual minds but in our minds’ extensions, in both other people and what we collectively create. Examples include connectivism, rhizomatic learning, distributed cognition, cognitive apprenticeship, networks of practice, and similar theories (including my own co-participation model, as it happens). We borrow the term ‘complexivist’ from Davis and Sumara, whose 2006 book on the subject is well worth reading, albeit grounded mainly in in-person learning.
No one paradigm dominates: all typically play a role at some point of a learning journey, all build upon and assemble ideas that are contained in the others (theories are technologies too), and all have been around as ways of learning for as long as humans have existed.
Beyond these broad families, we speculate on whether any new pedagogical paradigms are emerging or have emerged within the 12 years since we first developed these ideas. We come up with the following possible candidates:
Theory-free: this is a digitally native paradigm that typically employs variations of AI technologies to extract patterns from large amounts of data on how people learn, and that provides support accordingly. This is the realm of adaptive hypermedia, learning analytics, and data mining. While the vast majority of such methods are very firmly in the objectivist tradition (the models are trained or designed by identifying what leads to ‘successful’ achievement of outcomes) a few look beyond defined learning products into social engagement or other measures of the learning process, or seek open-ended patterns in emergent collective behaviours. We see the former as a dystopic trend, but find promise in the latter, notwithstanding the risks of filter bubbles and systemic bias.
Hologogic: this is a nascent paradigm that treats learning as a process of enculturation. It’s about how we come to find our places in our many overlapping cultures, where belonging to and adopting the values and norms of the sets to which we belong (be it our colleagues, our ancestors, our subject-matter peers, or whatever) is the primary focus. There are few theories that apply to this paradigm, as yet, but it is visible in many online and in-person communities, and is/has been of particular significance in collectivist cultures where the learning of one is meaningless unless it is also the learning of all (sometimes including the ancestors). We see this as a potentially healthy trend that takes us beyond the individualist assumptions underpinning much of the field, though there are risks of divisions and echo chambers that pit one culture against others. We borrow the term from Cumbie and Wolverton.
Bricolagogic: this is a free-for-all paradigm, a kind of meta-pedagogy in which any pedagogical method, model, or theory may be used, chosen for pragmatic or personal reasons, but in which the primary focus of learning is in choosing how (in any given context) we should learn. Concepts of charting and wayfinding play a strong role here. This resembles what we originally identified as an emerging ‘holistic’ model, but we now see it not as a simple mish-mash of pedagogical paradigms but rather as a pedagogic paradigm in its own right.
Another emerging paradigm?
I have recently been involved in a lengthy Twitter thread, started by Tim Fawns on the topic of his recent paper on entangled pedagogy, which presents a view very similar indeed to my own (e.g. here and here), albeit expressed rather differently (and more eloquently). There are others in the same thread who express similar views. I suggested in this thread that we might be witnessing the birth of a new ‘entanglist’ paradigm that draws very heavily on complexivism (and that could certainly be seen as part of the same family) but that views the problem from a rather different perspective. It is still very much about complexity, emergence, extended minds, recursion, and networks, and it negates none of that, but it draws its boundaries around the networked nodes at a higher level than theories like Connectivism, yet with more precision than those focused on human learning interactions such as networks of practice or rhizomatic learning. Notably, it leaves room for design (and designed objects), for meaning, and for passion as part of the deeply entangled complex system of learning in which we all participate, willingly or not. It’s not specifically a pedagogical model – it’s broader than that – though it does imply many things about how we should and should not teach, and about how we should understand pedagogies as part of a massively distributed system in which designated teachers account for only a fraction of the learning and teaching process. The title of my book on the subject (that has been under review for 16 months – grrr) sums this up quite well, I think: “How Education Works”. The book has now (as of a few days ago) received a very positive response from reviewers and is due to be discussed by the editorial committee at the end of this month, so I’m hoping that it may be published in the not-too-distant future. Watch this space!
Here’s the chapter abstract:
Building on earlier work that identified historical paradigm shifts in open and distance learning, this chapter is concerned with analyzing the three broad pedagogical paradigms – objectivist, subjectivist, and complexivist – that have characterized learning and teaching in the field over the past half century. It goes on to discuss new paradigms that are starting to emerge, most notably in “theory-free” models enabled by developments in artificial intelligence and analytics, hologogic methods that recognize the many cultures to which we belong, and a “bricolagogic,” theory-agnostic paradigm that reflects the field’s growing maturity and depth.
Dron J., Anderson T. (2022) Pedagogical Paradigms in Open and Distance Education. In: Zawacki-Richter O., Jung I. (eds) Handbook of Open, Distance and Digital Education. Springer, Singapore. https://doi.org/10.1007/978-981-19-0351-9_9-1
This Scientific American article tells the tale of one of the genesis stories of complexity science, this one from 1952, describing what, until relatively recently, was known as the Fermi-Pasta-Ulam (FPU) problem (or ‘paradox’, though it is not in fact a paradox). It is now more commonly known as the Fermi-Pasta-Ulam-Tsingdou (FPUT) problem, in recognition of the fact that it was only discovered thanks to the extraordinary work of Mary Tsingou, who wrote the programs that revealed what, to Fermi, Pasta, and Ulam, was a very unexpected result.
The team was attempting to simulate what happens to energy as it moves around atoms connected by chemical bonds. This is a classic non-linear problem that cannot be observed directly, and that cannot be solved by conventional reductive means (notwithstanding recent work that reveals statistical patterns in complex systems like urban travel patterns). It has to be implemented as a simulation in order to see what happens. Fermi, Pasta, and Ulam thought that, with enough iterations, it would reveal itself to be ergodic: that, given long enough, every state of a given energy of the system would be visited an equal number of times. Instead, thanks to Mary Tsingou’s work, they found that it was non-ergodic. Weird stuff happened, that could not be predicted. It was chaotic.
The discovery was, in fact, accidental. Initial results had shown the expected regularities then, one day, they left the program running for longer than usual and, instead of the recurring periodic patterns seen initially, it suddenly went haywire. It wasn’t a bug in the code. It was a phase transition, perhaps the first unequivocal demonstration of deterministic chaos. Though Fermi died and the paper was not actually published until nearly a decade later, it is hard to understate the importance of this ‘accidental’ discovery that deterministic systems are not necessarily ergodic. As Stuart Kauffman puts it, ‘non-ergodicity gives us history‘. Weather is non-ergodic. Evolution is non-ergodic. Learning is non-ergodic. We are non-ergodic. The universe is non-ergodic. Though there are other strands to the story that predate this work, more than anything else this marks the birth of a whole new kind of science – the science of complexity – that seeks to deal with the 90% or more of phenomena that matter to us, and that reductive science cannot begin to handle.
Here’s a bit of Tsingou’s work on the program, written for the MANIAC computer:
It was not until 2008 that Tsingou’s contribution was fully recognized. In the original paper she was thanked in a footnote but not acknowledged as a co-author. It is possible that, had it been published right away she might have received proper credit. However, it is at least as possible that she might not. The reasons for this are a mix of endemic sexism, and (relatedly) the low esteem accorded to computation at the time.
The relationship between these two factors runs deep. Historically, the word ‘computer’ originally referred to a job title. As scientists in the 19th Century amassed vast amounts of data that needed processing, there was far too much for an individual to handle. They figured out that tasks could be broken up into smaller pieces and farmed out in parallel to humans who could do the necessary rote arithmetic. Because women were much cheaper to hire, and computing was seen as a relatively unskilled (albeit very gruelling and cognitively demanding) role, computing therefore became a predominantly female occupation. From the 19th Century onwards into the mid 20th Century, all-women teams worked on astronomical data, artillery trajectories, and similar tasks, often performing extremely complex mathematical calculations requiring great precision and endurance, always for far less pay than they deserved or that a man would receive. Computers were victims of systematic gender discrimination from the very beginning.
The FPUT problem, however, is one that doesn’t lend itself to chunking and parallel computation: the output of one iteration of the computation is needed before you can calculate the next. Farming it out to human computers simply wouldn’t work. For work of this kind, you have to have a machine or it would take decades to come up with a solution.
In the first decade or so after digital computers were invented significant mathematical skill was needed to operate them. Because of their existing exploitation as human computers, there was, luckily enough, a large workforce of women with advanced math skills whose manual work was being obsoleted at the same time, so women played a significant role in the dawn of the industry. Mary Tsingou was not alone in making great contributions to the field.
By the 1970s that had changed a lot, not in a good way, but numbers slowly grew again until around the mid-1980s (a terrible decade in so many ways) when things abruptly changed for the worse.
Whether this was due to armies of parents buying PCs for their (male) children thanks to aggressive marketing to that sector, or highly selective media coverage, or the increasing recognition of the value of computing skills in the job market reinforcing traditional gender disparities, or something else entirely (it is in fact complex, with vast self-reinforcing feedback loops all the way down the line), the end result was a massive fall in women in the field. Today, less than 17% of students of computer science are women, while the representation of women in most other scientific and technical fields has grown considerably.
There’s a weirder problem at work here, though, because (roughly – this is an educated guess) less than 1% of computer science graduates ever wind up doing any computer science, unless they choose a career in academia (in which case the figure rises to very low single figures), and very few of them ever do more mathematics than an average greengrocer. What we teach in universities has wildly diverged from the skills that are actually needed in most computing occupations at an even sharper rate than the decline of women in the trade. We continue to teach it in ways that would have made sense in the 1950s, when it could not be done without a deep understanding of mathematics and the science behind digital computation, even though neither of these skills has much if any use at all for more than a minute fraction of our students when they get out into the real world. Sure, we have broadened our curriculum to include many other aspects of the field, but we don’t let students study them unless they also learn the (largely unnecessary in most occupations) science and math (a subject that suffers even lower rates of non-male participation than computing). Thinking of modern computing as a branch of mathematics is a bit like treating poetry as a branch of linguistics or grammar, and thinking of modern computing as a science is a bit like treating painting as a branch of chemistry. It’s not so much that women have left computing but that computing – as a taught subject – has left women.
Computing professionals are creative problem solvers, designers, architects, managers, musicians, writers, networkers, business people, artists, social organizers, builders, makers, teachers, or dreamers. The main thing that they share in common is that they work with computers. Some of them are programmers. A few (mostly those involved in designing machines and compilers) do real computer science. A few more do math, though rarely at more than middle school level, unless they are working on the cutting edge of a few areas like graphics, AI, or data science (in which case the libraries etc that would render it unnecessary have not yet been invented). The vast majority of computing professionals are using the outputs of this small elite’s work, not reinventing it. It it not surprising that there is enormous diversity in the field of computing because computers are universal machines, universal media, and universal environments, so they encompass the bulk of human endeavour. That’s what makes them so much fun. If you are a computing professional you can work with anyone, and you can get involved in anything that involves computers, which is to say almost everything. And they are quite interesting in and of themselves, partly because they straddle so many boundaries, and ideas and tools from one area can spark ideas and spawn tools in another.
If you consider the uses of computer applications in many fields, from architecture or design to medicine or media to art or music, there is a far more equal gender distribution. Computing is embedded almost everywhere, and it mostly demands very different skills in each of its uses. There are some consistent gaps that computing students could fill or, better, that computing profs could teach in the context they are used. Better use could be made of computers across the board with just a little programming or other technical skills. Unfortunately, those who create, maintain, and manage computers and their applications tend to mainly come out of computer science programs (at least in North America and some other parts of the world) so many are ill prepared for participating in all that richness, and computing profs tend to stick with teaching in computer science programs so the rest of the world has to figure out things they could help with for themselves.
I think it is about time that we relegated computer science to a minor (not unimportant) stream and got back into the real world – the one with women in it. There’s still a pressing need to bring more women into that minor stream: we need inspirations like Mary Tsingou, we could do worse than preferentially hiring more non-male professors, and we desperately need to shift the discriminatory culture surrounding (especially) mathematics but, if we can at least teach in a way that better represents the richness and diversity of the computing profession itself, it would be a good start.
Originally posted at: https://landing.athabascau.ca/bookmarks/view/10624709/some-thoughts-for-ada-lovelace-day
Why do we build digital learning systems to mimic classrooms?
It is understandable that, when we teach in person, we have to occupy and make different uses of the same or similar environments like classrooms, labs, workshops, lecture theatres, and offices. There are huge financial, physical, and organizational constraints on making the environment fit the task, so it would be madness to build a whole new classroom every time we wished to run a different class.
Online, we could build anything we like
But why do we do the same when we teach online? There are countless tools available and, if none are suitable, it is not too hard to build them or modify them to suit our needs. Once they are built, moving between them just takes a tap of a screen or the click of a mouse. Heck, you can even occupy several of them at once if you have a decent monitor or more than one device.
So why don’t we do this?
Here are a few of the more obvious reasons that using the perfect app for the context of study rarely happens:
Teachers’ lack of knowledge of the options (it takes time and effort to discover what’s available).
Teachers’ lack of skill in using them (most interesting tools have a learning curve, and that gets steeper in inverse proportion to the softness and diversity of the toolset, so most teachers don’t even know how to make the most of what they already have).
Lack of time and/or money for development (a real-life application is what it contains, not just the shell that contains it, and it is not always as easy to take existing stuff and put it in a new tool as it might be in a physical space).
Costs and difficulties in management (each tool adds costs in managing faults, configuration, accounting for use, performance, and security).
Cognitive load involved for learners in adapting to the metaphors, signposts, and methods needed to use the tool itself.
All of these are a direct consequence of the very diversity that would make us want to use different apps in the first place. This is a classic Faustian bargain in which the technology does what we want, and in the process creates new problems to solve. Every virtual system invents at least some of the dynamics of how people and things interact with it and within it. In effect, every app has its own physics. That makes them harder to find out about, harder to learn, harder to develop, costlier to manage, and more difficult to navigate than the static, fixed facilities found in particular physical locations. They are all different, there are few if any universals, and any universal today may become a conditional tomorrow. Gravity doesn’t necessarily work the same way in virtual systems.
And so we get learning management systems
The learning management system (LMS) kind of deals with all of these problems: poorly, harmfully, boringly, and painfully, but it does deal with them. Currently, most of the teaching at Athabasca University is through the open source Moodle LMS, lightly modified by us because our needs are not quite like others (self-pacing and all that). But Moodle is not special: in terms of what it does and how it does it, it is not significantly different from any other mainstream LMS – Blackboard, Brightspace, Canvas, Sakai, whatever.
Almost every LMS essentially automates the functions, though not exactly the form, of traditional classrooms. In other parts of the world people prefer to use the term ‘managed learning environment’ (MLE) for such things, and it is the most dominant representative of a larger category of systems usually described as virtual learning environments (VLEs) that also includes things like MOOs (multi-user dungeons, object oriented), immersive learning environments, and simpler web-based teaching systems that replicate aspects of classrooms such as Google Classroom or Microsoft’s gnarly bundle of hastily repurposed rubbish for teaching that I’m not sure even has a name yet. Notice the spatial metaphors in many of these names.
Little boxes made of ticky tacky
The people who originally designed LMSs back in the 90s (I did so myself) based their designs on the functions and entities found in a traditional university because that was their context, and that was where they had to fit. Metaphorically, an LMS or MLE is a big university building with rather uniform classrooms, with perhaps a yard where you can camp out with a few other systems (plugins, LTI hooks, etc) that conform to its requirements and that are allowed in to classrooms when invited, and a few doors and gateways (mainly hyperlinks) linking it circuitously or in jury-rigged fashion to other similarly weakly connected buildings (e.g. places to register, places to seek support, places to talk to an advisor, places to complain, places to find books, and so on). It doesn’t have metaphorical corridors, halls, common rooms, canteens, yards, libraries or any of the other things that normally make up a physical university. You rarely get to even be aware of other classrooms beyond those you are in. Some people (me in a past life) might give classrooms cute names like ‘the learning cafe’ but it’s still just another classroom. You teleport from one classroom to the next because what happens in corridors (really a big lot of incredibly important pedagogically useful stuff, as it happens) is not perceived by the designers as a useful classroom function to be automated or perhaps, more charitably, they just couldn’t figure out how to automate that.
It’s a very controlled environment where everyone has a programmatically enforced role (mostly reflecting traditional educational roles), that may vary according to the room, but that are far less fluid than those in physical spaces. There are strong hierarchies, and limited opportunities for moving between them. Some of those hierarchies are new: the system administrator, for instance, has way more power than anyone in a physical university to determine how learning happens, like an architect with the power to move walls, change the decor, add extensions, and so on, at will. The programmers of the system are almost god-like in their command of its physics. But the ways that they give teachers (or learning designers, or administrators) control, as designers, directors, and regulators of the classroom, are perhaps the most pernicious. In a classroom a teacher may lead (and, by default, usually does). In an LMS, a teacher (or someone playing that role) must lead. The teacher sees things that students cannot, and controls things that the students may not. A teacher configures the space, and determines with some precision how it will be used. With a lot of effort and risk, it can be made to behave differently, but it almost never is.
Functions are everything
An LMS is typically built along functional lines, and those functions are mostly based on loose, superficial observations of what teachers and students seem to do in physical classrooms. The metaphorical classrooms are weird, because they are structured by teaching (seldom learning) function rather than along pedagogical lines: for instance, if you want to talk with someone, you normally need to go to a separate enclosed area inside the classroom or leave a note on the teacher’s desk. Same if you want to take a test, or share your work with others. Another function, another space. Some have many little rooms for different things. Lectures are either literally that (video recordings) or (more usefully, from a learning perspective), text and images to be read on screen, based on the assumption that the only function of lectures is information transmission (it is so very, very much not – that’s its least useful and least effective role). There’s seldom a chance to put even put up your hand to question something. Notices can usually only be pinned on the wall by teachers. Classroom timetables are embodied in software because of course you need a rigid and unforgiving timetable in a medium that sells itself on enabling learning anywhere, any time. Some, including Moodle, will allow you to break up the content differently, but it’s still another timetable; just a timetable without dates. It’s still the teacher who sets the order, pacing and content.
It’s a high-tech classroom. There are often robots there that are programmed to make you behave in ways determined by those higher in the hierarchy (sometimes teachers, sometimes administrators, sometimes the programmers of the software). For instance, they might act as gatekeepers that prevent you from moving on to the next section before completing the current one, or they might prevent you submitting work before or after a specified date. They might mark your work. There are surveillance cameras everywhere, recording your every move, often only accessible to those with more powerful roles (though sometimes a robot or two might give you a filtered view of it).
Beginnings and ends
You can’t usually go back and visit when your course is over because someone decided it would be a good idea to set opening and closing enrolment dates and assumed that, when they were done, the learning was done (which of course it never is – it keeps on evolving long after explicit teaching and testing occurred). Again, it’s because physical classes are scheduled and terms come to an end because they must be, not because it makes pedagogical sense. And, like almost everything, you can override this default, but hardly anyone ever does, because it brings back those Faustian bargains, especially in manageability.
Dull caricatures of physical spaces
Basically, the LMS is an automated set of metaphorical classrooms that hardens many of the undesirable by-products of educational systems in software in brain-dead ways that have little to do with how best to teach, and that stretch the spatial metaphors that inform it beyond breaking point. Each bit of automation and each navigational decision hardens pedagogical choices. For all the cozy metaphors, programmers invent rather than replicate physics, in the process warping reality in ways that do no good and much harm. Classrooms solved problems of physics for in-person teaching and form part of a much larger structure that has evolved to teach reasonably well (including corridors, common rooms, canteens, and libraries, as it happens). Their more visible functions are only a part of that and, arguably, not the main part. There is much pedagogy embedded in the ways that physical universities, whether by accident or design, have evolved over centuries to support learning in every quadrangle and nook of a coffee shop. LMSs just focus on a limited subset of teaching roles, and empower the teacher in ways that caricature their already excessive dominance in the classroom (which only occurred because it had to, thanks to physics and the constraints it imposed).
LMSs are crap, but they contain recognizable semblances of their physical counterparts and just enough configurability and flexibility to more or less work as teaching tools, a bit, for everyone, almost no matter what their level of digital proficiency might be. They more or less solve the Faustian bargains listed earlier, but they do so by stifling what we wanted and should have been able to do in the first place with online tools, in the process creating new and quite horrific problems, as well as demolishing most of what makes physical universities work in the first place. It never has been true that virtual learning environments are learning environments – they are only ever parts of them – and there are places to escape from them, such as the Landing, other virtual systems, or even just plain old email, but then all those Faustian bargains come back to haunt us again. There has to be a better way.
Beyond the LMS
Cognisant of the issues, Athabasca University is now some way down the path to developing its own distinctive solutions to these problems, in a multi-year multi-million-dollar initiative known as (following the spatial metaphor) the Integrated Learning Environment (ILE). The ILE is not an application. It is an umbrella term for a lot of different, usually independent systems working together as one. Though some of the most interesting opportunities are still only loosely imagined, perhaps because they cause problems that are fiendishly hard to solve (e.g. how can we integrate systems that we build ourselves without creating risks for the rest of the ILE, and what happens when they need to be maintained?) a lot of progress is being made on the non-teaching foundations on which the rest depends (student admin systems, support tools, procedures, etc), as well as on the most visible and perhaps the biggest of its parts, BrightSpace, a proprietary commercial LMS that is meant to replace Moodle, for no obvious pedagogical or technical reasons (it’s no better). It might make economic sense. I don’t know, but I do know that open source software typically costs a fair bit to own, albeit because of the things that make it a much better idea (freedom, flexibility, ownership, etc). There is probably a fair bit of time and money being spent with Desire2Learn (makers of Brightspace) on the things that we spent a fair bit of time and money on many years ago to make Moodle a bit less classroom-like. The choice no doubt has something to do with how reliably and easily it can be made to work with some of the other proprietary commercial systems that someone has decided will make up the ILE. It bothers me greatly that we are not trying hard to choose open source solutions, for reasons that will become clearer in the rest of this post. However, (pedagogically speaking) all the mainstream LMSs are much of a muchness, making the same mistakes as one another in very similar ways, so it probably won’t wreck too much of what we already do within Moodle. But, on its own, it won’t move us much further forward and we could do it better. That’s what the ILE is supposed to do – to make the LMS just a part of a much larger teaching environment, intimately connected with the rest of what the university does for or with students, and extensible with new and better ways of learning, teaching, and assessing learning.
Lego bricks make poor metaphors
When we were first imagining the ILE, though the approach was admirably participative, engaging much of the university community, I was very worried by the things we were encouraged to focus on. It was all about the functionality, the usability, the design, the tools, the pedagogies, the business systems that supported them. Those things matter, for sure, and should be not be ignored, but they should and will change and grow all the time: in fact, part of the point of building this thing is to do just that. Using the city metaphor, pretty much all that we (collectively) considered were the spaces (the rooms, mainly), and the stuff that goes on inside them, much like LMS designers thought of universities as just collections of classrooms in which teaching functions were performed. Space and stuff are, not uncoincidentally, exactly what Stewart Brand identified long ago as inevitably being the fastest-changing, most volatile parts of any town or city (after site, structure, skin, and services). I’ve written a fair bit on the universality of this principle across all systems. It’s a solid structural principle that applies as much to ecosystems and educational systems as to cities. As Brand observes himself, drawing from O’Neill et al (1986), the larger, slower-changing elements of any system affect the smaller, faster-changing more than vice versa. This is for much the same reasons that path dependencies set in. It’s about the prior providing the context for what follows. Flexible things have to fit into the gaps left by less flexible, older, pre-existing things. In physical spaces, of course these tend to be bigger and/or slower, but the same is true in virtual spaces, where size seldom matters that much, but hardness (inflexibility, brittleness) really does. Though lip service was paid to the word ‘integrated’ in our discussions, I had the strong feeling that the kind of integration we had in mind was that of a Lego set. In fact, I think we were aiming to find a ‘Lego Athabasca University’ set, with assembly instructions and a picture on the box. The vendors who came to talk with us made much of how effectively they could do that, rather than how effectively they could make it possible for others to do that.
Metaphors matter. Lego bricks have to fit together tightly, in pre-specified ways, especially if you are following a plan. If you want to move them around, you have to dismantle a bit of the structure to fit them in. It’s difficult to integrate things that are not bricks, or that are made by different toy companies to work in different ways. At best you get what Brand calls ‘magazine architecture’, or ‘no road’ architecture, beautiful, fit for purpose, intricate and solid, but slow to learn. Lego is not a terrible way to build, compared with buying everything pre-assembled, but it could be improved.
Signals and boundaries
Drawing inspiration from John Holland’s brilliant last work, Signals & Boundaries, I tried to make the case that, instead, we should be focusing on the boundaries (the interfaces between the buildings and the rest of the city), and the signals that pass between them (the people, the messages, etc, the forms they take and how they move around). In Brand’s terms, I wanted us to be thinking about skin and services, and perhaps even structure, though site – Athabasca University – was a given. Though a few people nodded in agreement, I think it mainly fell on deaf ears. We wanted oven-ready solutions, not the infrastructure to enable those solutions. Though the city metaphor works well, because we are talking about human constructions, others would result in similar ways of thinking: cells in bodies, organisms in ecosystems, brains, termite mounds, and so on. All are organized by boundaries (at many levels of hierarchy) and the signals that pass between them.
The Lego set metaphor – whether deliberately or not – seems to have prevailed for now. A lot of old buildings are being slated for demolition and a lot of new virtual buildings are now being erected as part of this development, many of them chosen not because of problems with existing buildings but so that they can more easily connect together and live in the same cloud. This will very likely work, for now, but it is not cheap and it is not flexible, especially given the fact that most of it is not open so, like a rental property, we are not allowed to fix things, add utilities, change the walls, etc, and we are wholly dependent on the landlords being nice to us and each other (knowing that some – ahem, Microsoft – have a long history of abusing their tenants). Those buildings will age. We will find them cramped. Some will age faster than others, and will have to be modified to keep up, perhaps at high cost. Companies renting them might go out of business or change their terms so we might have to demolish the buildings and rent/make new ones. We will be annoyed at how they do things, usually without asking us. We will hate the landlords who dictate what we can do and how we can do it, and who will keep upping the rent while not doing what we ask. We will want more, and the only way to get it will be to build extensions, buy new brick sets, if it is not enough to pay someone to remodel the interiors (and it won’t be). Of course, because most of the big structural elements will not be open source, we will not be able to do that ourselves.
What the ILE really should be
The ILE is, I think, poorly named, because it should not be an environment at all. Following the building metaphor, the ILE is (or should be) more like the system that connects a lot of buildings, bringing them together into a coherent, safe, livable community. It’s infrastructure and services; it is the roads, the traffic signals, the doors, the sidewalks, the water pipes, the waste pipes, the electricity, the network cables; it is the services – fire, police, schools, traffic control, etc; it is all the many rules, standards, norms and regulations that make them work together to help make an environment in which people can live, work, play, and grow. It’s part of the environment – the part that makes it work – but it is not the environment itself. The environment itself is Athabasca University, not just the tools, processes, and systems that support its functions. That includes, most importantly, the people who are part of the university, or who are visitors to it, who are not just users of the environment or dwellers in its walls, but who are or should be the most significant and visible parts of it, just as trees are part of the environment of forests, not users of the forest. Those people live in physical as well as other virtual environments (social media, Word documents, websites, etc) that the ILE can connect together too, to make them a part of it, so the spatial metaphor gets weird at this point. The ILE makes environmental boundaries fuzzy, permeable, and shifting. It’s not an ILE, it’s an ILI – an integrated learning infrastructure.
If we focused on the connections and interfaces, and on how information and processes need to pass across them, and if we thought hard about the nature of those signals, then we could build a system that is resilient, that adapts, that lasts, that grows, that evolves, with parts that we can seamless replace or improve because the interfaces – the building facades, the mains pipes, the junction boxes, etc – will mostly stay the same, evolving slowly as they should. This is about strategy, not planning, a way of thinking about systems rather than a sequence of things to do.
Some of the key people involved in the process realize this. They are talking about standards, protocols, and projects to build interfaces between systems, and imagining future needs, though they are inevitably distracted by the process of renting Lego bricks, so I am not sure how much they will be able to stay focused on that. I hope they prevail over those who think they are building a set of classrooms and tightly connected admin offices out of self-contained interlocking bricks because our future depends on getting it right. We are aiming to grow. It just takes one critical piece in the Lego building to fail to support that, and the rest falls apart like a… well, like a pile of bricks.
Brand, S. (1997). How buildings learn. Phoenix Illustrated. https://www.penguinrandomhouse.ca/books/320919/how-buildings-learn-by-stewart-brand/9780140139969
Holland, J. H. (2012). Signals and Boundaries: Building Blocks for Complex Adaptive Systems. MIT Press. https://mitpress.mit.edu/books/signals-and-boundaries
O’Neill, R.V., DeAngelis, D.L, Waide, J. B., & Allen, T. F. H. (1986). A Hierarchical Concept of Ecosystems. Princeton University Press. http://www.gbv.de/dms/bs/toc/025157787.pdf
Postman, N. (1998). Five things we need to know about technological change. Denver, Colorado, 28. https://student.cs.uwaterloo.ca/~cs492/papers/neil-postman–five-things.html
I recently downloaded What Teacher Educators Should Have Learned From 2020. This is an open edited book, freely downloadable from the AACE site, for teachers of teachers whose lives were disrupted by the sudden move to emergency remote teaching over the past year or so. I’ve only skimmed the contents and read a couple of the chapters, but my first impressions are positive. Edited by Richard Ferdig and Kristine Pytash, It springs from the very active and engaged AACE SITE community, which is a good indicator of expertise and experience. It seems well organized into three main sections:
Social and Emotional Learning for Teacher Education.
Online Teaching and Learning for Teacher Education.
eXtended Reality (XR) for Teacher Education
I like the up-front emphasis on social and emotional aspects, addressing things like belongingness, compassion, and community, mainly from theoretical/model-oriented perspectives, and the other sections seem wisely chosen to meet practitioner needs. The chapters adopt a standardized structure:
What We Know.
Lessons Learned for Research.
Lessons Learned for Practice.
What You Should Read.
Again, this seems pretty sensible, maintaining a good focus on actionable knowledge and practical steps to be taken. It’s not quite a textbook, but it’s a useful teach-yourself resource with good coverage. I look forward to dipping into it a bit more deeply. I expect to find some good ideas, good practices, and good theoretical models to support my teaching and my understanding of the issues. And I’m really pleased that it is being released as an open publication: well done, AACE, for making this openly available.
But I do wonder a little about who else will read this.
Comfort zones and uncomfortable zones
The other day I was chatting with a neighbour who teaches a traditional hard science subject at one of the local universities, who was venting about the problems of teaching via Zoom. He knew that I had a bit of interest and experience in this area, so he asked whether I had any advice. I started to suggest some ways of rethinking it as a pedagogical opportunity, but he was not impressed. Even something as low-threshold and straightforward as flipping the classroom or focusing on what students do rather than what he has to tell them was a step too far. He patiently explained that he has classes with hundreds of students and fixed topics that they need to learn, and he really didn’t see it as desirable or even possible to depart from his well-tried lecture format. At least it would be too much work and he didn’t have the time for it. I did try to push back on that a bit and I may have mentioned the overwhelming body of research that suggests this might not be a wise move, but he was pretty clear and firm about this. What he actually wanted was for someone to make (or tell him how to make) the digital technology as easy and as comfortably familiar as the lecture theatre, and that would somehow make the students as engaged as he perceived them to normally be in his lectures, without notably changing how he taught. The problem was the darn technology, not the teaching. I bit my tongue at this point. I eventually came up with a platitude or two about trying to find different ways to make learning visible, about explicitly showing that he cares, about taking time to listen, about modelling the behaviour he wanted to see, about using the chat to good advantage, and about how motivation differs online and off, but I don’t think it helped. I suspect that the only things that really resonated with him were suggestions about how to get the most out of a webcam and a recommendation to get a better microphone.
Within the context in which he usually teaches, he is probably a very good teacher. He’s a likeable person who clearly cares a lot about his students, he knows a lot about his subject, and he knows how to make it appealing within the situation that he normally works. His courses, as he described them, are very conventional, relying a lot on the structure given to them by the industry-driven curriculum and the university’s processes, norms, and structures, and he fills his role in all that admirably. I think he is pretty typical of the vast majority of teachers. They’re good at what they do, comfortable with how they do it, and they just want the technology to accommodate them continuing to do so without unnecessary obstacles.
Unfortunately, technology doesn’t work that way.
The main reason it doesn’t work is very simple: technologies (including pedagogies) affect one another in complex and recursive ways, so (with some trivial exceptions) you can’t change one element (especially a large element) and expect the rest to work as they did before. It’s simple, intuitive, and obvious but unless you are already well immersed in both systems theories and educational theory, really taking it to heart and understanding how it must affect your practice demands a pretty big shift in weltanschauung, which is not the kind of thing I was keen to start while on my way to the store in the midst of a busy day.
To make matters worse, even if teachers do acknowledge the need to change, their assumption that things will eventually (maybe soon) return to normal means that they are – reasonably enough – not willing and probably not able to invest a lot of time into it. A big part of the reason for this is that, thanks to the aforementioned interdependencies, they are probably running round like blue-arsed flies just trying to keep things together, and filling their time with fixing the things that inevitably break in the process. Systems thrive on this kind of self-healing feedback loop. I guess teachers figure that, if they can work out how to tread water until the pandemic has run its course, it will be OK in the end.
Why in-person education works
The hallmark technologies (mandatory lectures, assignments, grades, exams, etc, etc) of in-person teaching are worse than awful but, just as a talented musician can make beautiful noises with limited technical knowledge and sub-standard instruments, so there are countless teachers who use atrocious methods in dreadful contexts but who successfully lead their students to learn. As long as the technologies are soft and flexible enough to allow them to paper over the cracks of bad tools and methods with good technique, talent, and passion, it works well enough for enough people enough of the time and can (with enough talent and passion) even be inspiring.
It would not work at all, though, without the massive machinery that surrounds it.
An institution (including its systems, structures, and tools) is itself designed to teach, no matter how bad the teachers are within it. The opportunities for students to learn from and with others around them, including other students, professors, support staff, administrators, and so on; the supporting technologies, including rules, physical spaces, structures, furnishings, and tools; the common rooms, the hallways, the smokers’ areas (best classrooms ever), the lecture theatres, the bars and the coffee shops; the timetables that make students physically travel to a location together (and thus massively increase salience); the notices on the walls; the clubs and societies; the librarians, the libraries, the students reading and writing within those libraries, echoing and amplifying the culture of learning that pervades them; the student dorms and shared kitchens where even more learning happens; the parties; even the awful extrinsic motivation of grades, teacher power, and norms and rules of behaviour that emerged in the first place due to the profound motivational shortcomings of in-person teaching. All of this and more conspires to support a basic level of at least mediocre (but good enough) learning, whether or not teachers teach well. It’s a massively distributed technology enacted by many coparticipants, of which designated teachers are just a part, and in which students are the lead actors among a cast of thousands. Online, those thousands are often largely invisible. At best, their presence tends to be highly filtered, channeled, or muted.
Why in-person methods don’t transfer well online
When most of that massive complex machinery is suddenly removed, leaving nothing but a generic interface better suited to remote business meetings than learning or, much worse, some awful approximation of all the evil, hard, disempowering technologies of traditional teaching wrapped around Zoom, or nightmarishly inhuman online proctoring systems, much of the teaching (in the broadest sense) disappears with it. Teaching in an institution is not just what teachers do. It’s the work of a community; of all the structures the community creates and uses; of the written and unwritten rules; of the tacit knowledge imparted by engagement in a space made for learning; of the massive preparation of schooling and the intricate loops that connect it with the rest of society; of attitudes and cultures that are shaped and reinforced by all the rest. It’s no wonder that teachers attempting to transfer small (but the most visible) parts of that technology online struggle with it. They need to fill the ever-widening gaps left when most of the comfortable support structures of in-person institutions that made it possible in the first place are either gone or mutated into something lean and hungry. It can be done, but it is really hard work.
More abstractly, a big part of the problem with this transfer-what-used-to-work-in-person approach is that it is a technology-first approach to the problem that focuses on one technology rather than the whole. The technology of choice in this case happens to be a set of pedagogical methods, but it is no different in principle than picking a digital tool and letting that decide how you will teach. Neither makes much sense. All the technologies in the assembly – including pedagogies, digital tools, regulations, designs, and structures – have to work together. No single technology has precedence, beyond the one that results from assembling the rest. To make matters worse, what-used-to-work-in-person pedagogies were situated solutions to the problems of teaching in physical classrooms, not universally applicable methods of teaching. Though there are some similarities here and there, the problems of teaching online are not at all the same as those of in-person teaching so of course the solutions are different. Simply transferring in-person pedagogies to an online context is much like using the paddles from a kayak to power a bicycle. You might move, but you won’t move far, you won’t move fast, you won’t move where you want to go, and it is quite likely to end in injury to yourself or others.
Such problems have, to a large extent, been adequately solved by teachers and institutions that work primarily online. Online institutions and organizations have infrastructure, processes, rules, tools, cultures, and norms that have evolved to work together, starting with the baseline assumption that little or none of the physical stuff will ever be available. Anything that didn’t work never made it to first base, or has not survived. Those that have been around a while might not be perfect, but they have ironed out most of the kinks and filled in most of the gaps. Most of my work, and that of my smarter peers, begins in this different context. In fact, in my case, it mainly involves savagely critiquing that context and figuring out ways to improve it, so it is yet another step removed from where in-person teachers are now.
OK, maybe I could offer a little advice or, at least, a metaphor
Roughly 20 years ago I did share a similar context. Working in an in-person university, I had to lead a team of novice online teachers from geographically dispersed colleges to create and teach a blended program with 28 new online courses. We built the whole thing in 6 months from start to finish, including the formal evaluations and approvals process. I could share some generic lessons from what I discovered then, the main one being to put most of the effort into learning to teach online, not into designing course materials. Put dialogue and community first, not structure. For instance, make the first thing students see in the LMS the discussion, not your notes or slides, and use the discussion to share content and guide the process. However, I’d mostly feel like the driver of a Model T Ford trying to teach someone to drive a Tesla. Technologies have changed, I have changed, my memory is unreliable.
In fact, I haven’t driven a car of any description in years. What I normally do now is, metaphorically, much closer to riding a bicycle, which I happen to do and enjoy a lot in real life too. A bike is a really smart, well-adapted, appropriate, versatile, maintainable, sustainable soft technology for getting around. The journey tends to be much more healthy and enjoyable, traffic jams don’t bother you, you can go all sorts of places cars cannot reach, and you can much more easily stop wherever you like along the way to explore what interests you. You can pretty much guarantee that you will arrive when and where you planned to arrive, give or take a few minutes. In the city, it’s often the fastest way to get around, once you factor in parking etc. It’s very liberating. It is true that more effort is needed to get from A to B, bad weather can be a pain, and it would not be the fastest or most comfortable way to reach the other side of the continent: sometimes, alternative forms of transport are definitely worth taking and I’m not against them when it’s appropriate to use them. And the bike I normally ride does have a little electric motor in one of the wheels that helps push me up hills (not much, but enough) but it doesn’t interfere with the joy (or most of the effort) of riding. I have learned that low-threshold, adaptable, resilient systems are often much smarter in many ways than high-tech platforms because they are part-human. They can take on your own smartness and creativity in ways no amount of automation can match. This is true of online learning tools as much as it is true of bicycles. Blogs, wikis, email, discussion forums, and so on often beat the pants off learning management systems, commercial teaching platforms, learning analytics tools or AI chatbots for many advanced pedagogical methods because they can become what you want them to be, rather than what the designer thought you wanted, and they can go anywhere, without constraint. Of course, the flip side is that they take more effort, sometimes take more time, and (without enormous care) can make it harder for all concerned to do things that are automated and streamlined in more highly engineered tools, so they might not always be the best option in all circumstances, any more than a bike is the best way to get up a snowy mountain or to cross an ocean.
Why you shouldn’t listen to my advice
It’s sad but true that most of what I would really like to say on the subject of online learning won’t help teachers on the ground right now, and it is actually worse than the help their peers could give them because what I really want to tell them is to change everything and to see the world completely differently. That’s pretty threatening, especially in these already vulnerable times, and not much use if you have a class to teach tomorrow morning.
The AACE book is more grounded in where in-person teachers are now. The chapter “We Need to Help Teachers Withstand Public Criticism as They Learn to Teach Online”, for example, delves into the issues well, in accessible ways that derive from a clear understanding of the context. However, the book cannot help but be an implicit (and, often, explicit) critique of how teachers currently teach: that’s implied in the title, and in the chapter structures. If you’re already interested enough in the subject and willing enough to change how you teach that you are reading this book in the first place, then this is great. You are 90% of the way there already, and you are ready to learn those lessons. One of the positive sides of emergency remote teaching has been that it has encouraged some teachers to reflect on their teaching practices and purposes, in ways that will probably continue to be beneficial if and when they return to in-person teaching. They will enjoy this book, and they may be the intended audience. But they are not the ones that really need it.
I would quite like to see (though maybe not to read) a different kind of book containing advice from beginners. Maybe it would have a title something like ‘What I learned in 2020’ or ‘How I survived Zoom.’ Emergency remote teachers might be more inclined to listen to the people who didn’t know the ‘right’ ways of doing things when the crisis began, who really didn’t want to change, who maybe resented the imposition, but who found ways to work through it from where they were then, rather than where the experts think (or know) they should be aiming now. It would no doubt annoy me and other distance learning researchers because, from the perspective of recognized good practice, much of it would probably be terrible but, unlike what we have to offer, it would actually be useful. A few chapters in the AACE book are grounded in concrete experience of this nature, but even they wind up saying what should have happened, framing the solutions in the existing discourse of the distance learning discipline. Most chapters consist of advice from experts who already knew the answers before the pandemic started. It is telling that the word ‘should’ occurs a lot more frequently than it should. This is not a criticism of the authors or editors of the book: the book is clear from the start that it is going to be a critique of current practice and a practical guidebook to the territory, and most of the advice I’ve seen in it so far makes a lot of sense. It’s just not likely to affect many of the ones who have no wish to change not just their practices but their fundamental attitudes to teaching. Sadly, that’s also true of this post which, I think, is therefore more of an explanation of why I’ve been staring into the headlights for most of the pandemic, rather than a serious attempt to help those in need. I hope there’s some value in that because it feels weird to be a (slight, minor, still-learning) expert in the field with very strong opinions about how online learning should work, but to have nothing useful to say on the subject at the one time it ought to have the most impact.
Read the book:
Ferdig, R.E. & Pytash, K.E. (2021). What Teacher Educators Should Have Learned From 2020. Association for the Advancement of Computing in Education (AACE). Retrieved March 22, 2021 from https://www.learntechlib.org/primary/p/219088/.
Martin Rees (UK Astronomer Royal) takes on complexity and emergence. This is essentially a primer on why complex systems – as he says, accounting for 99% of what’s interesting about the world – are not susceptible to reductionist science despite being, at some level, reducible to physics. As he rightly puts it, “reductionism is true in a sense. But it’s seldom true in a useful sense.” Rees’s explanations are a bit clumsy in places – for instance, he confuses ‘complicated’ with ‘complex’ once or twice, which is a rooky mistake, and his example of the Mandelbrot Set as ‘incomprehensible’ is not convincing and rather misses the point about why emergent systems cannot be usefully explained by reductionism (it’s about different kinds of causality, not about complicated patterns) – but he generally provides a good introduction to the issues.
These are well-trodden themes that most complexity theorists have addressed in far more depth and detail, and that usually appear in the first chapter of any introductory book in the field, but it is good to see someone who, from his job title, might seem to be an archetypal reductive scientist (he’s an astrophysicist) challenging some of the basic tenets of his discipline.
Perhaps my favourite works on the subject are John Holland’s Signals and Boundaries, which is a brilliant, if incomplete, attempt to develop a rigorous theory to explain and describe complex adaptive systems, and Stuart Kauffman’s flawed but stunning Reinventing the Sacred, which (with very patchy success) attempts to bridge science and religious belief but that, in the process, brilliantly and repeatedly proves, from many different angles, the impossibility of reductive science explaining or predicting more than an infinitesimal fraction of what actually matters in the universe. Both books are very heavy reading, but very rewarding.
This is my second post for today on the subject of boundaries and complex systems (yes, I am writing a paper!), this time pointing to a paper by Osberg, Biesta and Cilliers from 2008 that applies the concepts to knowledge and education. It’s a fascinating paper, drawing a theory of knowledge out of complex systems that the authors rather deftly fit with Dewey’s transactional realism and (far less compellingly) a bit of deconstructionism.
I think this sits very firmly within the connectivist family of theories (Stephen Downes may disagree!) albeit from a slightly different perspective. The context is the realm of complex (mostly complex adaptive) systems but the notion of knowledge as an emergent and shifting phenomenon born of engagement – a process, not a product – and the significance of the connected whole in both enabling and embodying it all is firmly in the connectivist tradition. It’s a slightly different perspective but one that is well-grounded in theory and comes to quite a similar conclusion, aptly put:
“education (becoming educated) is no longer about understanding a finished universe, or even about participating in a finished and stable universe. It is the result, rather, of participating in the creation of an unfinished universe.“
The authors begin by defining what they describe as a ‘representational’ or ‘spatial’ epistemology that underpins most education. This is not quite as simplistic as it sounds – they include models and theories in this, at least. Their point is that education takes people out of ‘real life’ and therefore must rely on a means to represent ‘real life’ to do its job properly. I think this is pushing it a bit: yes, that is true of a fair amount of intentional teaching but there is a lot that goes on in education systems that is unintentional, or emerges as a by-product of interaction, or that happens in playgrounds, cafes, or common rooms, that is very different and is not just an incidental to the process but quite critical to it. To pretend that educational systems are nothing but the explicit things we intentionally do to people is, I think deliberately, creating a bit of a straw man. They make much the same point: I guess it is done to distinguish this from their solution, which is an ’emergentist’ epistemology.
The really interesting stuff for me comes from Cillier’s contribution (I’m guessing) on boundaries, which makes the simple and obvious point that complex systems (as opposed to complicated ones) are inherently incompressible, so any model we make of them is inaccurate: in leaving out the tiniest thing we make it impossible to make deterministic predictions, save in that we can create boundaries to focus on particular aspects we might care about and come up with probabalistic inferences (e.g. predicting the weather). Those boundaries are thus, of necessity, created (or, more accurately, negotiated), not discovered. They are value-laden. Thus:
“…models and theories that reduce the world to a system of rules or laws cannot be understood as pure representations of a universe that exists independently, but should rather be understood as valuable but provisional and temporary tools by means of which we constantly re-negotiate our understanding of and being in the world“
They go on…
“We need boundaries around our regularities before we can model or theorise them, before we can find their rules of operation, because rules make sense only in terms of boundaries. The point is that the setting of the boundary creates the condition of possibility for a rule or a law to exist. When a boundary is not naturally given, as is the case with natural complex systems, the rules that we ‘discover’ also cannot be understood as naturally given. Rules and ‘laws’ are not ‘real’ features of the systems we theorise about. Theories that attempt to reduce complexity to a system of rules or laws, like our models which do precisely this, therefore cannot be understood as pictures of reality.“
So, the rules that we find are pragmatic ones – they are tools, rather than pictures of reality, that help us to renegotiate our world and the meaning we make in and of it:
“From this perspective, knowledge is not about ‘the world’ as such, it is not about truth; rather, it is about what we can do in the world, how we can change it.…One could say ‘acquiring’ knowledge does not ‘solve’ problems for us: it creates problems for us to solve.”
At this point they come round to Dewey, whose transactional model is not about finding out about the world but leads to a constantly emerging and ever renegotiated state of being.
“…in acting, we create knowledge, and in creating knowledge, we learn to act in different ways and in acting in different ways we bring about new knowledge which changes our world, which causes us to act differently, and so on, unendingly. There is no final truth of the matter, only increasingly diverse ways of interacting in a world that is becoming increasingly complex.“
One of the more significant aspects of this, that is not dwelt on anything like enough in this paper but that forms a consistent subtext, is that this is a fundamentally social pursuit. This is a complex system not just of individuals negotiating an active relationship with the world, but of people doing it together, as part of a complex system that drives its own adaptation, at every scale and within every (overlapping, interpenetrating) boundary.
They continue with an, I think, unsuccessful attempt to align this perspective with postmodernist/poststructuralist/deconstructionist theory, claiming that Dillon’s differentiation between the radical relationality of complexity and poststructuralist theorists is illusory, because a complex system is always in a state of becoming without being, so it is much the same kind of thing. Whether or not this is true, I don’t think it adds anything significant to the arguments.
The paper rushes to a rather unsatisfactory conclusion – at last hitting the promised topic of the title – about the role of this emergentist epistemology in schooling:
“Acquisition is no longer the name of the game …. This means questions about what to present in the curriculum and whether these things should be directly presented or should be represented (such that children may acquire knowledge of these things most efficiently or effectively) are no longer relevant as curricular questions. While content is important, the curriculum is less concerned with what content is presented and how, and more with the idea that content is engaged with and responded to …. Here the content that is engaged is not pre-given, but emerges from the educative situation itself. With this conception of knowledge and the world, the curriculum becomes a tool for the emergence of new worlds rather than a tool for stabilisation and replication
This follows quite naturally and makes sense, but it diminishes the significance of a pretty obvious elephant in the room, which is that the educational institution itself is one of those boundaried systems that plays a huge role in and of itself, not to mention with other boundaried systems, regardless of the processes enacted within its boundaries. I think this is symptomatic of a big gap that the paper very much implies but barely attempts to address, which is that all of these complex systems involved processes, structures, rules, tools, objects, content (whatever that is!), media, and a host of other things are part of those complex systems. Knowledge is indeed a dynamic process, a state of becoming or of being, but it incorporates really a lot of things, only a limited number of which are in the minds of individuals. It’s not about people learning – it’s about that whole, massive, complex adaptive system itself.
This rather elderly paper by Paul Cilliers peters off to an unsatisfyingly vague and obvious conclusion, but it does have some quite useful clarifications and observations about the nature of boundaries as they relate to hierarchies, networks and complex systems in general. I particularly like:
“We often fall into the trap of thinking of a boundary as something that separates one thing from another. We should rather think of a boundary as something that constitutes that which is bounded. “
This simple observation leads to further thoughts on how we choose those boundaries and the (necessary) ways we create models that make use of them. The thing is, we are the creators of those boundaries, at least in any complex system – Cilliers mentions neural networks as a good example – so what we choose to model is always determined by us and, like any model, it is and must be a partial representation, not an analogue, of the impossible complexities of the world it models. In a very real sense, we shape our understanding of the world through the boundaries that we choose to (or are hard-wired to) consider significant and there are always other places to draw those boundaries that change the meaning of what we are observing. It makes the analysis of complex systems quite hard, because we can seldom see beyond the boundaries we create that simplify the complexity in them and we have a tendency to over-simplify: as he points out, even apparently clear hierarchies shift and interpenetrate one another. This is more than, though related to, categories and metaphors of the sort examined by the likes of Hofstadter or Lakoff.
Since this paper was written, John Holland has done some mind-bending and deeply thought-provoking work on signals and boundaries in complex systems that delves far deeper and that begins to address the problem head-on, but which I have been struggling to understand properly for many months: I’m pretty certain that Holland is onto something of staggering importance, if I could only grasp precisely what that might be! He is not the clearest of writers and he tends to leave a lot unsaid and assumed, that the reader has to fill in. It’s also complicated stuff – suffice to say, stochastic urns play a significant role. This paper by Cilliers is a good stab at the issue from a high altitude philosophical perspective that makes a few of the wicked and profound issues quite clear.