This is very cool – a library of articles and journals that have been censored in various regions, built inside Minecraft, that thereby evades censorship (for now). It lends a whole different meaning to ‘serious games’.
Originally posted at: https://landing.athabascau.ca/bookmarks/view/10846432/the-uncensored-library-%E2%80%93-reporters-without-borders
It has taken me over 14 years, with numerous setbacks along the way (some bizarre, some mundane), but, as of today, I am a Canadian citizen. I cried most of the way through the (Zoom) ceremony, and completely choked up singing the anthem.
I love Canada. I love that Canadian culture is guilty deep to its very heart (sorry), that caring for your neighbour is fundamental to our (yes, our!) identity, that line-ups last forever because everyone in front of you is having a fine and leisurely conversation with the person serving them, even in the biggest cities. I love waiting to cross a road on the corner of an empty street where a single truck heads nervously towards you half a kilometre away, but we all know the rules and it just works better that way. I love hockey, and the fact that the only riot I have ever seen here was when the Canucks (just, at the last possible moment) lost the Stanley Cup Final, but that half the city was up before work the next morning cleaning up the mess. I love the vast, vast land. I don’t think I’ll ever understand Tim Horton’s (really? Is that pale brown water actually coffee and what kind of doughnut doesn’t go bad for over a year?) but I love that it is so deeply embroiled with what it means to be Canadian that the awfulness of the coffee and doughnuts doesn’t matter. I love that there’s a hairdresser on every corner but that everyone’s haircut looks like it was done by their kids. I love the fact that diversity is a cause for celebration and delight, not division. I love plaid. I love that Canadians will describe themselves as German, or Scottish, or Polish, or pretty much anything (though almost never English) other than Canadian, even though the last person in their family to actually live elsewhere died a century ago. I love that I live in a city where over half the population was not born here. I love poutine, and mac n cheese, and beaver tails. I love that, wandering past my home in the very heart of a huge city are skunks, raccoons and coyotes, while seals, the occasional whale, beavers, and otters swim by, and giant bald eagles circle constantly overhead, eternally plagued by crows and seagulls trying to chase them from the sky. I love that everyone knows at least one person called Gord. I love that Canada is not America, and that’s part of the definition. I love that a gang of teenagers on a street corner will invariably wish you well and help you out rather than abuse you. I like maple syrup. I love maple leaves. I even like the anthem, though the words could do with far fewer gods and (in the French version) way less swords.
I could do without every second vehicle being a poorly driven truck or an SUV, the deep and unresolvable guilt at the horrific way indigenous people have been treated and continue to suffer, the mega-scale rape of the land that persists to this day, the many nonsensical practices and cultural quirks caught like diseases from south of the border, the fact that any prepared foodstuff contains more salt than food, and a few other things. But abhorring such things is quite Canadian, too.
I do miss the good-natured cruelty and friendly belligerence of old Blighty, the blithe disregard of rules, the bitingly dark humour, and cask beer in nearly every pub that won’t plaster you to the floor after a single cold, fizzy, strong yet peculiarly flavourless pint (and it’s an actual pint, where ‘pint’ means exactly the same thing literally everywhere). And, though a proper, salt-of-the-earth Canadian pub is a truly wonderful thing, I miss those English pubs – real pubs that smell of history and tobacco smoke (despite that smokers now smoke outside), where no one cares whether you are still working on your food or expects a tip for asking it. I miss the word ‘fuck’ used as a punctuation mark in every sentence, and ‘cunt’ used as a term of endearment. I miss the layer upon endless layer of the past that oozes out of every tiny cranny, and fills every little nook with tired ghosts that cling to the living. I miss the fact that people are bound together by mutual gloom or shared hatred of everyone else, but that there’s something chummy in it, a sort of kindness, a recognition of shared adversity that runs deep. And I miss the NHS.
But, on balance, I’d really much rather be here, and I am very proud indeed to be a citizen of the wonderful, apologetic, law-abiding, kindly mosaic that is Canada. It’s good to be Canadian, eh?
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
With a possible audience of thousands, and without a clue they were there because the Zoom output was streamed to a different site (weird), I talked very fast about my experience of higher education for about 20 minutes at the THE UK Student Festival yesterday. The talk was recorded and will be used again for the THE Canada Student Festival later in the month. It’s a huge event – over 8000 enrolled (though not all attending every session) – with quite a lot of other keynotes and a great many other talks, panels, and discussions, aimed at helping students starting out in higher education. The audience was very different from those I normally talk to, and the (very sensible) strict 20-minute format gave me a lot less leeway than the usual hour allowed, so I found it interestingly challenging. These are the slides I used.
The brief I was given was not to preach, but to share my experience of higher education, as a student and as an educator. My personal agenda was to talk about why and how online learning is worth doing (especially at AU), so I tried (a bit clunkily) to aim my story in that direction. I failed to remember to mention some key things, I spent too long on others, and I suspect that the most memorable message that came through was, for in-person students living on campus, to get a kettle (it’s a great way to make lots of new friends fast) but, hopefully, my bigger message got through to some.
The essential point of my wild ramble was not that kettles are the solution to success in higher education, but that students and the rest of us should take ownership of our own education: it should be done by us, not to us. We should learn the way we want to learn, and we should learn what we want to learn; we should seek adventure and challenge rather than easy pickings; and we should hang out with interesting people and/or those we care about, because that’s how most learning happens, as well as being a large part of what makes it meaningful. I noted that we should focus on learning and should to try to ignore grades as much as possible, because grades destroy the love of doing something simply because we enjoy it. Essentially, my advice was about finding the things that intrinsically motivate us and reducing the effects of things that demotivate us. My own educational journey, and (I think) that of most committed educators, has largely followed that path: that’s how we thrived in a system not conducive to intrinsic motivation, and it’s the path we try to encourage our own students to take. I observed that it is much easier to own your own education when it is done online, at least if it is done in ways that take advantage of the medium, and not through a pale simulacrum of in-person teaching, because the teacher cannot be in control, the level of challenge is much more controllable, and there are way more people (online and in your own environment) who can support you.
The conference theme was ‘challenges of the digital’ so I thought it might be fun to reverse the problem, and to think instead about the challenges of in-person education. In this presentation I imagined a world in which in-person teaching had never been invented, and presented a case for doing so. In fairness, it was not a very good case! But I did have fun using some of the more exotic voice changing features of my Voicelive Play vocal processor (which I normally use for performing music), presenting some of the arguments against my suggestions in different voices using a much better mic than my usual (pretty good) Blue Yeti. I might not use the special effects again that often, but I was quite impressed with the difference the better microphone made.
My central points (mostly implicit until the end) were:
That the biggest challenge of the digital is all the baggage that we have inherited from in-person teaching, and our continuing need to interoperate with in-person institutions.
That pedagogies are neither universal nor neutral. They are solutions to problems of learning in a particular context, in assembly with countless constraints and possibilities provided by that context: people, tools, structures, methods, systems, and so on.
That solutions to learning in a physical context – at least in the one-to-many model of traditional education systems – inevitably lead to a very strong power imbalance between teacher and learner, where the teacher is in control of every moment that the teaching event occurs. This has many repercussions, not least of which being that needs for autonomy and competence support are very poorly addressed (though relatedness comes for free), so it is really bad for intrinsic motivation.
Thus, the pedagogies of physical spaces have to compensate for the loss of control and achievable challenge that they naturally entail.
That the most common approach – and, again, an almost inevitable (i.e. the shortest path) follow-on from teaching a lot of people at once – involves rewards and punishments, that massively impair or destroy intrinsic motivation to learn and, in most cases, actively militate against effective learning.
That the affordances of teaching everyone the same thing at once lead fairly naturally to credentials for having learned it, often achieved in ‘efficient’ ways like proctored exams that are incredibly bad for learning, and that greatly reinforce the extrinsic motivation that is already highly problematic in the in-person modality. The credentials, not the learning, become the primary focus.
That support for autonomy and competence are naturally high in online learning, though support for relatedness is a mix of good and bad. There is no need for teachers being in control and, lacking most of the means of control available to in-person teachers, the only reliable way to regain it is through rewards and punishments which, as previously mentioned, are fatal to intrinsic motivation.
That the almost ubiquitous ways that distance educators inherit and use the pedagogies, methods, and structures of in-person learning – especially in the use of coercion through rewards and punishments (grades, credentials, etc) but also in schedules, fixed-length courses, inflexible learning outcomes, etc – are almost exactly the opposite of what its technologies can best support.
Towards the end, acknowledging that it is difficult to change such complex and deeply entangled systems (much though it is to be desired) I presented some ways of reducing the challenges of the physical in online teaching, and regaining that lost intrinsic motivation, that I summarized thus:
Let go (you cannot and should not control learning unless asked to do so), but stay close;
Make learning (not just its products) visible (and, in the process, better understand your teaching);
Make learning shared (cooperation and, where possible, collaboration built in from the ground up);
Don’t ever coerce (especially not through grades);
Care (for learners, for learning, for the subject).
It’s a theme that I have spoken and written of many, many times, but (apart from the last few slides) the way I presented it this time was new for me. I had fun pretending to be different people, and the audience seemed to like it, in a challenging kind of a way. There were some great questions at the end, not all of which I had time to answer, though I’m happy to continue the conversation here, or via Twitter.
There has been a lot of negative reaction of late to virtual proctors of online exams. Perhaps students miss the cheery camaraderie of traditional proctored exams, sitting silently in a sweaty room with pen and paper, doing one of the highest stakes, highest stress tasks of their lives, with someone scrutinizing their every nervous tic whose adverse judgment may destroy their hopes and careers, for the benefit of an invisible examiner whose motives and wishes are unclear but whose approval they dearly seek. Lovely. Traditional. Reassuring. A ritual for us all to cherish. It’s enough to bring a tear to the eye.
But exams cost a huge amount of money to host and to invigilate. It is even worse when one of the outcomes might, for the student or the invigilator, be death or disability due to an inconvenient virus.
I have a better solution.
Instead of costly invigilators and invigilation centres, all we need to do is to send out small (returnable, postage-paid) robots to students’ homes. A little robot sitting on the student’s desk or kitchen table as they sit their written exam (on paper, of course – tradition matters), recording every blink, watching their fingers writing on the paper, with 360 degree panoramic camera and the ability to zoom in on anything suspicious or interesting. Perhaps it could include microphones, infrared and microwave sensors, and maybe sensors to monitor skin resistance, pulse, etc, in order to look for nefarious activities or to call the ambulance if the student seems to be having a heart attack or stroke due to the stress. It could be made to talk, too. Perhaps it could offer spoken advice on the process, and alerts about the time left at carefully selected intervals. Students could choose the voice. It would also allow students to sit exams wherever and whenever they please: we are all in favour of student choice. With a bit of ingenuity it could scan what the students have written or drawn, and send it back to an examiner. Or, with a bit more ingenuity and careful use of AI, it could mark the paper on the spot, saving yet more money. Everyone wins.
It would be important to be student-centric in its design. It could, for instance, be made to look like a cute little furry animal with googly eyes to put students more at ease. Maybe it could make soothing cooing noises like a tribble, or like a cat purring. Conversely, it could be made to scuttle ominously around the desk and to appear like a spider with venomous-looking fangs, making gentle hissing noises, to remind students of the much lamented presence of in-person invigilators. Indeed, maybe it could be made to look like a caricature of a professor. More advanced models could emit bad smells to replicate invigilator farts or secret smoking habits. It could be made small and mobile, so that students could take it with them if they needed a bathroom break, during which it might play soothing muzak to put the student at ease, while recording everything they do. It would have to be tough, waterproof, and sterilizable, in order to cope with the odd frustrated student throwing or dunking it.
Perhaps it could offer stern spoken warnings if anomalies or abuses are found, and maybe connect itself to a human invigilator (I hear that they are cheaper in developing nations) who could control it and watch more closely. Perhaps it could be equipped with non-lethal weaponry to punish inappropriate behaviour if the warnings fail, and/or register students on an offenders database. It could be built to self-destruct if tampered with.
Though this is clearly something every university, school, and college would want, and the long-term savings would be immense, such technologies don’t come cheap. Quite apart from the hardware and software development costs, there would be a need for oodles of bandwidth and storage of the masses of data the robot would generate.
I have a solution to that, too: commercial sponsorship.
We could partner with, say, Amazon, who would be keen to mine useful information about the students’ surroundings and needs identified using the robot’s many sensors. A worn curtain? Stubborn stains? A shirt revealing personal interests? Send them to Amazon! Maybe Alexa could provide the voice for interactions and offer shopping advice when students stop to sharpen their pencils (need a better pencil? We have that in stock and can deliver it today!). And, of course, AWS would provide much of the infrastructure needed to support it, at fair educational prices. I expect early adopters would be described as ‘partners’ and offered slightly better (though still profitable) deals.
And there might be other things that could be done with the content. Perhaps the written answers could be analyzed to identify potential Amazon staffers. Maybe students expressing extremist views could be reported to the appropriate government agency, or at least added to a watch-list for the institution’s own use.
Naysayers might worry about hackers breaking into it or subverting its transmissions, or the data being sent to a country with laughable privacy laws, or the robot breaking down at a critical moment, or errors in handwriting recognition, but I’m sure that could be dealt with, the same as we deal with every other privacy, security, and reliability issue in IT in education. No problem. No sir. We have lawyers.
The details still need to be ironed out here and there, but the opportunities are endless. What could possibly go wrong? I think we should take this seriously. Seriously.
Brickit is what AI was made for. You take a picture of your pile of LEGO with your phone or tablet, then the app figures out what pieces you have, and suggests models you could build with it, including assembly plans. The coolest detail, perhaps, is that, having done so, it highlights the bricks you will need in the photo you took of your pile, so you can find them more easily. I’ve not downloaded it yet, so I’m not sure how well it works, but I love the concept.
The fan-made app is iOS only for now, but an Android version is coming in the fall. It’s free, but I’m guessing it may make money in future from in-app purchases giving access to more designs, options to purchase missing bricks, or something along those lines.
It would be cooler if it connected Lego enthusiasts so that they could share their MOCs (my own constructions) with others. I’m guessing it might use the LXFML format, which LEGO® itself uses to export designs from its (unsupported, discontinued, but still available) LEGO DIgital Designer app, so this ought to be easy enough. It would be even cooler if it supported a swap and share feature, so users could connect via the app to get hold of or share missing bricks. The fact that it should in principle be able to catalogue all your pieces would make this fairly straightforward to do. There are lots of existing sites and databases that share MOCs, such as https://moc.bricklink.com/pages/moc/index.page, or the commercial marketplace https://rebrickable.com/mocs/#hottest; there are brick databases like https://rebrickable.com/downloads/ that allow you to identify and order the bricks you need; there are even swap sites like http://swapfig.com/ (minifigures only); and, of course, there are many apps for designing MOCs or downloading others. However, this app seems to be the…er…missing piece that could make them much more useful.
Reviews suggest that it doesn’t always succeed in finding a model and might not always identify all the pieces. Also, I don’t think there’s a phone camera in the world with fine enough resolution to capture my son’s remarkably large LEGO collection. Even spreading the bricks out to take pictures would require more floor-space than any of us have in our homes. But what a great idea!
Originally posted at: https://landing.athabascau.ca/bookmarks/view/9558928/at-last-a-serious-use-for-ai-brickit
I am slowly getting used to the ugly abbreviation WFH that has emerged during the pandemic, though I don’t much like it because it’s not always accurate. Even in pandemic times I often work from my boat (WFB). In non-pandemic times I’ve worked from a tent (WFT), a library (WFL), a hotel room (WFHR), a park bench (WFPB), a conference (WFC), a plane (WFP), a bus (WF… OK, you get the picture), and much, much more. I have even worked at Athabasca University’s own buildings (Working from Work?) on rare occasions. But why do most of us in the trade so rarely use terms like learning from home when working from home (WFH) is so ubiquitous?
Terms like e-learning, online learning, distance learning, remote learning, and so on, are weird. Learning is never remote, electronic, online, or at a distance. There is more sense to terms like distance education, online education, remote teaching, and so on, because education and teaching describe relationships between people, and there are different ways that those relationships can be mediated, that do (or should) deeply affect the process. There is also a whole slew of intentional and implicit structures, systems, methods, and toolsets that are assumed when we prefix education with terms like distance or online. But why online or distance learning?
As teachers we are (rightly) taught that it’s not about the teaching, it’s about the learning. For at least the last 30 years or more we have, for instance, therefore been strongly encouraged to use the term ‘learning & teaching’ instead of ‘teaching & learning’ because learning must come first. I’ve corrected people myself for getting the order wrong, many times. Charitably, therefore, it might be that we are trying to draw attention to the fact that it’s about learning. But, if so, why distance or online?
I think something nasty has happened to the term ‘learning’ when it is used this way, because I think that what we actually mean by it is ‘teaching’. Some British English dialects take that dubious elision fully on board. When something nasty happens to someone as a consequence of something they have done that is perceived to be wrong, or even when some punishment is inflicted on them by someone else, it is common in some circles to say ‘that’ll learn yer’ (the ‘yer’ is important – don’t imagine the Queen saying in received pronunciation ‘that will learn you’ because it would be wrong). When I hear the phrase I imagine it being said with a snarl. It’s a cruel thing to say, though it can be used kind-of humorously, at least if, as many of my compatriots do, you appreciate a particularly crude form of Benny-Hillish shadenfreude (‘Ha ha, you fell flat on your face and hurt yourself. That’ll learn yer’).
Outside a subset of British and perhaps some other minor English vernaculars, learning is never something that we do to people. It’s something done by people, with what and with whom is around them (and that might include a teaching website, textbook, or course pack). So let’s stop calling people distance or online learners because it devalues and obscures what they are actually doing. They are not being learned at. They are being taught at a distance, and learning from home (or wherever they happen to be).
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
Over the last week I peripherally participated in an interesting exchange of views on Twitter between Jesse Stommel and Stephen Downes that raises some fascinating issues about the nature of online social spaces. It started with a plea from Jesse:
“Dear [insert company name], searching every mention of your company and jumping into conversations where you haven’t been tagged or invited is invasive. Stop doing that.”
“If I use a company name in a public forum, I expect they will take interest and maybe even reply. It’s a *public* forum. That’s how they work.”
What followed explored some fascinating territory, but the essence of the main arguments are (I skim the nuances), on Jesse’s side, that we have a reasonable expectation of being left alone during a private conversation in any public space and, on Stephen’s side, that there should be no expectation of privacy in a public digital space like Twitter, and that any claims to it tread on extremely dangerous ground. The central question is thus whether there are such things as private conversations on Twitter.
Stephen’s big concern is that, taken to its logical conclusion, laying claim to privacy on Twitter opens the door for outrages like the Proctorio vs Linkletter case, in which Proctorio claimed that “Mr. Linkletter infringed its copyright, circumvented technological protection measures, and breached confidence” by sharing one of its fully public (though not publicized) YouTube videos with students. YouTube quite closely resembles Twitter in its social structure (though little else), so it is a good analogy. Stephen is, I think rightly, concerned at ‘calling out’ individuals or organizations for invading ‘private’ conversations in public spaces because it implies the unilateral imposition of norms, rules of behaviour, and expectations by one individual or group on another, in a space that neither owns.
Jesse’s counter-arguments are interesting, and subtle. He strongly rejects Stephen’s analogy with the Proctorio case because all he is doing is asserting his right to privacy, not abusing his market position or trying to cause harm. It’s just a request to be let alone, calling on what he sees as norms of politeness, not a demand that this should be enshrined in rules or legislation. He observes that, though Twitter is a public space, it has variegation that emerges because of (often tacit, seldom explicit) ways that many (not all) people use it, which in turn is supported by the ways that Twitter’s algorithms push some kinds of tweet more than others. For this particular case in point, he notes that the algorithm tends to broadcast initial tweets more than it does replies, so what follows in a set of replies could be assumed by its participants to be a less public conversation. In fact, as I understand his argument, Jesse thinks of it as a private conversation in a public space, analogous to having a private conversation in a public park where one might be inadvertently overheard, but it would be rude to deliberately listen in or contribute unless invited. If this were a true analogy then I might support it. But, if it is true, then so are quite a few other things, and that’s where it starts to get interesting.
I’ve been a Twitter user for approaching 15 years now and it has never occurred to me till now that any of my conversations might in any way be construed as private. They are sometimes personal, for sure, but definitely not private. Conversations are soft technologies that are flexible, mutable, and situated, and (without further clues like people quietly conversing in a corner) you need to read them in order to know whether you would be intruding on them, which means that they are simply not private. Without further reasons to assume privacy, it is just a conversation in public between two people to which other people are not invited.
So the crux of Jesse’s argument seems to be the notion that a happenstance of Twitter’s current implementation that makes some tweets less likely to be seen than others, combined with a set of norms relating to that, that may or may not be shared by others, allows one to claim that a conversation is not just personal but private.
The physics of online social spaces
Twitter is, as Stephen says and Jesse agrees, for the most part a completely public space (not counting direct messaging or constraints on tweets to only those you follow/are following) but, as the example of the relative prominence given to initial tweets compared with replies to them amply demonstrates, it does have a structure. It is just one that does not obey anything like the same physics as a physical space. You can achieve a measure of privacy in a public physical space because there has to be proximity in space and time in order to communicate at all, and there are limits to human voice projection, ability to hear, and ability to attend to multiple conversations at once. There are also visual clues that people are talking privately. Though there is variegation in structure, none of those limits apply in Twitter or, for that matter, most online social spaces.
Early in the conversation I chipped in to observe that one of the many differences between private conversations in physical space and Twitter exchanges is that tweets are persistent. They are a little like graffiti left in public spaces that continues to communicate long after the initial intent has passed, and may be happened upon at any time in the future in quite different contexts than those imagined by the graffiti artist. Jesse’s response to that was that there’s a difference between graffiti on a public building in five foot high letters and graffiti on a shady tree or in a tunnel. Again, his point is that there are parts of Twitter where there might be a reasonable expectation of relative privacy, where it would be rude to join the conversation. Though I agree that it is often possible to tell from reading a conversation whether you might be welcome or not (and yes, social norms apply to that), my big problem with Jesse’s argument is that proximity in Twitter-space is not just defined by relative position in a dialogue or likelihood of appearance in a Twitter feed, as he seems to imply.
Beyond its support for conversations between individuals, Twitter embodies two distinct but overlapping social forms: the network and the set. @mentions in Twitter combined with its ‘following’ functionality are the main drivers for the network form. If you follow someone or they mention you then your message becomes proximal to them. That’s a big part of Twitter’s physics, and it has no analogue in physical space. Thus, your conversation is very likely to be overheard by others because you are (metaphorically) standing right next to them and chipping your words in five foot letters in stone where they can and will be found, now and in the future. If you wanted to have a private conversation in a park then you wouldn’t stand less than a metre away from someone that you didn’t want to listen in and shout in their face. But that’s not all.
Hashtags and search terms are the main drivers for the set social form, which at least closely competes with if not exceeds the value of social networks in Twitter. When you use a hashtag or even a distinctive word (say, the name of a company or person) then your message becomes proximal to those who follow that hashtag or who have saved a search for that keyword. So you are not just standing right next to everyone in your social network, but to the potentially much larger social set of people who are interested in keywords that you use in your conversation. Again, you might not intend it, you might not even be able to see them, but you are shouting in their faces.
Maybe you do have a right to privacy in any public space, but that right does not overrule simple physics. You have to know the physics of that space in order to know what ‘private’ means within it. And the simple physics of Twitter means that ‘next to’ and ‘within hearing distance’ extends to anyone with an interest in you or what you are saying in the sentences you write. If you want different social physics that support privacy, then you need to take your conversation to a different space, because Twitter doesn’t work that way. You can ask for non-interference in a personal conversation, but not for privacy.
Designing better social physics
As it happens, we grappled a lot with issues of context and privacy exactly like this when we designed the social physics of the Landing. Its social physics are deliberately designed to make precisely those nooks and niches that Jesse wants to find in Twitter. The Landing starts with discretionary access control for every post and every profile field (we chose to build it using the Elgg framework because of its support for this). Like the much missed (and never hit) Google+ it also allows you to create circles, that are not just useful for following but, more significantly, for limiting access to particular individuals. Again, that came for free with Elgg, though we added some enhancements to forefront it, and to make it usable.
It’s not just about the content, though; it’s about presentation of self (we were influenced in this by Goffman’s dramaturgical analysis). We also therefore built a range of context-switching tools – notably tabbed profiles and pinboards (known internally as ‘sets’) – that allow you to present a completely different facade to different circles, groups, and sets of people. This is not just concerned with showing or hiding different fields and content, but with looking completely different and showing completely different stuff to different people. The public facade of my profile is not the same as the one displayed to my friends and, if I wished, I could present different facades to all the different circles or groups of people I follow or belong to. We’ve still not solved the temporal issue – like most social sites, the fundamental unit of communication is still persistent graffiti. In fact, to a large extent we wanted it that way, because it’s a site for collective learning, and so it has to have a collective memory though, like memories in brains, it would be useful to have short-term memories too. However, simply letting posts expire is not the solution, in part due to the many ways that digital content can be copied and archived but, more importantly, because forgetting is and must be an active process that cannot and should not be automated. My earlier CoFIND system did have a way to deal with that (memories had to be actively maintained by active interest and use by members or, though they would never be fully lost, they would be far less likely to be recalled) but we didn’t make much use of that idea on the Landing, save in isolated pockets, because it would have really irritated the many people or groups that engage intermittently (e.g. in iterations of paced courses).
Unfortunately, most of the Landing’s context-switching features are not even slightly intuitive (especially to those already familiar with the cruder social physics of popular social media) so most are very rarely used. Google+, with its massively simplified version of the same idea, probably failed at least in part for this reason. Such complexity can work, with the right membership. Slashdot, for instance, has an extraordinarily rich and ever-evolving social physics, and it has thrived for about 25 years, but the reasons for its success probably lie at least in part in its tagline ‘News for Nerds’. Its members are not phased by complex interfaces, and it is well-enough designed to work reasonably well if you don’t engage with all the features.
Perhaps a bigger issue, though, is that the richer social physics of both Slashdot and the Landing only work if you happen to be a member. For public posts, like this one, the physics are very much like those of Twitter or Facebook.
For now, the best bet is to use different social spaces for different aspects of your life but, thanks largely to Facebook’s single-minded and highly effective undermining of OpenSocial, there’s not a lot of ways to seamlessly move between them right now while retaining a rich and faceted identity. At least there’s still RSS, which is how come you might be reading this on the Landing (where it is originally posted) or at https://jondron.ca/ (which will automagically then push it to Twitter), but it’s not ideal.
It’s very challenging to design a digital space that is both richly supportive of human social needs and easy to use. The Landing is definitely not the solution, but the underlying idea – that people are richly faceted social beings who interact and present themselves differently to different people at different times – still makes sense to me. As the conversation between Jesse and Stephen shows, there is a need for support for that more than ever.