Obsolescence and decay

Koristka camera  All technologies require an input of energy – to be actively maintained – or they will eventually drift towards entropy. Pyramids turn to sand, unused words die, poems must be reproduced to survive, bicycles rust. Even apparently fixed digital technologies rely on physical substrates and an input of power to be instantiated at all. A more interesting reason for their decay, though, is that virtually no technologies exist in isolation, and virtually all participate in, and/or are participated in by other technologies, whether human-instantiated or mechanical. All are assemblies and all exist in an ecosystem that affects them, and which they affect. If parts of that system change, then the technologies on which they depend may cease to function even though nothing about those technologies has, in itself, altered.

Would a (film) camera for which film is no longer available still be a camera? It seems odd to think of it as anything else. However, it is also a bit odd to think of it as a camera, given that it must be inherent to the definition of a camera that it can take photos. It is not (quite) simply that, in the absence of film, it doesn’t work. A camera that doesn’t take photos because the shutter has jammed or the lens is missing is still a camera: it’s just a broken camera, or an incomplete camera. That’s not so obviously the case here. You could rightly claim that the object was designed to be a camera, thereby making the definition depend on the intent of its manufacturer. The fact that it used to be perfectly functional as a camera reinforces that opinion. Despite the fact that it cannot take pictures, nothing about it – as a self-contained object – has changed. We could therefore simply say it is therefore still a camera, just one that is obsolete, and that obsolescence is just another way that cameras can fail to work. This particular case of obsolescence is so similar to that of the missing lens that it might, however, make more sense to think of it as an instance of exactly the same thing. Indeed someone might one day make a film for it and, being pedantic, it is almost certainly possible to cut up a larger format film and insert it, at which point no one would disagree that it is a camera, so this is a reasonable way to think about it. We can reasonably claim that it is still a camera, but that it is currently incomplete.

Notice what we are doing here, though. In effect, we are supposing that a full description of a camera – ie. a device to take photos – must include its film, or at least some other means of capturing an image, such as a CCD. But, if you agree to that, where do you stop? What if the only film that the camera can take demands processing that is not? What if is is a digital camera that creates images that no software can render? That’s not impossible. Imagine (and someone almost certainly will) a DRM’d format that relies on a subscription model for the software used to display it, and that the company that provides that subscription goes out of business. In some countries, breaking DRM is illegal, so there would be no legal way to view your own pictures if that were the case. It would, effectively, be the same case as that of a camera designed to have no shutter release, which (I would strongly argue) would not be a camera at all because (by design) it cannot take pictures. The bigger point that I am trying to make, though, is that the boundaries that we normally choose when identifying an object as a camera are, in fact, quite fuzzy. It does not feel natural to think of a camera as necessarily including its film, let alone also including the means of processing that film, but it fails to meet a common-sense definition of the term without those features.

A great many – perhaps most – of our technologies have fuzzy boundaries of this nature, and it is possible to come up with countless examples like this. A train made for a track gauge that no longer exists, clothing made in a size that fits no living person, printers for which cartridges are no longer available, cars that fail to meet emissions standards, electrical devices that take batteries that are no longer made, and so on. In each case, the thing we tend to identify as a specific technology no longer does what it should, despite nothing having changed about it, and so it is difficult to maintain that it is the same technology as it was when it was created unless we include in our definition the rest of the assembly that makes it work. One particularly significant field in which this matters a great deal is in computing. The problem occurs in every aspect of computing: disk formats for which no disk drives exist, programs written for operating systems that are no longer available, games made for consoles that cannot be found, and so on. In a modern networked environment, there are so many dependencies all the way down the line that virtually no technology can ever be considered in isolation. The same phenomenon can happen at a specific level too. I am currently struggling to transfer my websites to a different technology because the company providing my server is retiring it. There’s nothing about my sites that has changed, though I am having to make a surprising number of changes just to keep them operational on the new system. Is a website that is not on the web still a website?

Whatever we think about whether it remains the same technology, if it does not do what the most essential definition of that technology claims that it must, then a digital technology that does not adapt eventually dies, even though its physical (digital) form might persist unchanged. This is because its boundaries are not simply its lines of code. This both stems from and leads to fact that technologies tend to evolve to ever greater complexity. It is especially obvious in the case of networked digital technologies, because parts of the multiple overlapping systems in which they must participate are in an ever-shifting flux. Operating systems, standards, protocols, hardware, malware, drivers, network infrastructure, etc can and do stop otherwise-unchanged technologies from working as intended, pretty consistently, all the time. Each technology affects others, and is affected by them. A digital technology that does not adapt eventually dies, even though (just like the camera) its physical (digital) form persists unchanged. It exists only in relation to a world that becomes increasingly complex thanks to the nature of the beast.

All species of technology evolve to become more complex, for many reasons, such as:

  • the adjacent possibles that they open up, inviting elaboration,
  • the fact that we figure out better ways to make them work,
  • the fact that their context of use changes and they must adapt to it,
  • the fact other technologies with which they are assembled adapt and change,
  • the fact that there is an ever-expanding range of counter-technologies needed to address their inevitable ill effects (what Postman described as the Faustian Bargain of technology),  which in turn create a need for further counter-technologies to curb the ill effects of the counter technologies,
  • the layers of changes and fixes we must apply to forestall their drift into entropy.

The same is true of most individual technologies of any complexity, ie. those that consist of many interacting parts and that interact with the world around them. They adapt because they must – internal and external pressures see to that – and, almost always, this involves adding rather than taking away parts of the assembly. This is true of ecosystems and even individual organisms, and the underlying evolutionary dynamic is essentially the same. Interestingly, it is the fundamental dynamic of learning, in the sense of an entity adapting to an environment, which in turn changes that environment, requiring other entities within that environment to adapt in turn, which then demands further adaptation to the ever shifting state of the system around it. This occurs at every scale, and every boundary. Evolution is a ratchet: at any one point different paths might have been taken but, once they have been taken, they provide the foundations for what comes next. This is how massive complexity emerges from simple, random-ish beginnings. Everything builds on everything else, becoming intricately interwoven with the whole. We can view the parts in isolation, but we cannot understand them properly unless we view them in relation to the things that they are connected with.

Amongst other interesting consequences of this dynamic, the more evolved technologies become, the more they tend to be comprised of counter-technologies. Some large and well-evolved technologies – transport systems, education systems, legal systems, universities, computer systems, etc – may consist of hardly anything but counter-technologies, that are so deeply embedded we hardly notice them any more. The parts that actually do the jobs we expect of them are a small fraction of the whole. The complex interlinking between counter-technologies starts to provide foundations on which further technologies build, and often feed back into the evolutionary path, changing the things that they were originally designed to counter, leading to further counter-technologies to cater for those changes. 

To give a massively over-simplified but illustrative example:

Technology: books.

Problem caused: cost.

Counter-technology: lectures.

Problem caused: need to get people in one place at one time.

Counter-technology: timetables.

Problem caused: motivation to attend.

Counter-technology: rewards and punishments.

Problem caused: extrinsic motivation kills intrinsic motivation.

Counter-technology: pedagogies that seek to re-enthuse learners.

Problem caused: education comes to be seen as essential to future employment but how do you know that it has been accomplished?

Counter-technology: exams provide the means to evaluate educational effectiveness.

Problem caused: extrinsic motivation kills intrinsic motivation.

Solution: cheating provides a quicker way to pass exams.

And so on.

I could throw in countless other technologies and counter-technologies that evolved as a result to muddy the picture, including libraries, loan systems, fines, courses, curricula, semesters, printing presses, lecture theatres, desks, blackboards, examinations, credentials, plagiarism tools, anti-plagiarism tools, faculties, universities, teaching colleges, textbooks, teaching unions, online learning, administrative systems, sabbaticals, and much much more. The end result is the hugely complex, ever shifting, ever evolving mess that is our educational systems, and all their dependent technologies and all the technologies on which they depend that we see today. This is a massively complex system of interdependent parts, all of which demand the input of energy and deliberate maintenance to survive. Changing one part shifts others, that in turn shift others, all the way down the line and back again. Some are harder and less flexible than others – and so have more effect on the overall assembly – but all contribute to change.

We have a natural tendency to focus on the immediate, the local, and the things we can affect most easily. Indeed, no one in the entire world can hope to glimpse more than a caricature of the bigger picture and, being a complex system, we cannot hope to predict much beyond the direct effects of what we do, in the context that we do them. This is true at every scale, from teaching a lesson in a classroom to setting educational policies for a nation. The effects of any given educational intervention are inherently unknowable in advance, whatever we can say about average effects. Sorry, educational researchers who think they have a solution – that’s just how it is. Anyone that claims otherwise is a charlatan or a fool. It doesn’t mean that we cannot predict the immediate future (good teachers can be fairly consistently effective), but it does mean that we cannot generalize what they do to achieve it.

One thing that might help us to get out of this mess would be, for every change we make, to think more carefully about what it is a counter-technology for,  and at least to glance at what the counter-technologies we are countering are themselves counter-technologies for. It might just be that some of the problems they solve afford greater opportunities to change than their consequences that we are trying to cope with. We cannot hope to know everything that leads to success – teaching is inherently distributed and inherently determined by its context – but we can examine our practice to find out at least some of the things that lead us to do what we do. It might make more sense to change those things than to adapt what we do to their effects.

 

A simple phishing scam

If you receive an unexpected email from what you might, at first glance, assume to me, especially if it is in atrocious English, don’t reply to it until you have looked very closely at the sender’s email address and have thought very carefully about whether I would (in a million years) ask you for whatever help it wants from you.

Being on sabbatical, my AU inbox has been delightfully uncrowded of late, so I rarely look at it until I’ve got a decent amount of work done most days, and occasionally skip checking it altogether, but a Skype alert from a colleague made me visit it in a hurry a couple of days back. I found a deluge of messages from many of my colleagues in SCIS, mostly telling me my identity had been stolen (it hadn’t), though a few asked if I really needed money, or wanted my groceries to be picked up. This would be a surprising, given that I live about 1000km away from most of them. All had received messages in poorly written English purporting to be from me, and at least a couple of them had replied. One – whose cell number was included in his sig – got a phishing text almost immediately, again claiming to be from me: this was a highly directed and malicious attack.

The three simple tricks that made it somewhat believable were:

  1. the fraudsters had created a (real) Gmail account using the username, jondathabascauca. This is particularly sneaky inasmuch as Gmail allows you to insert arbitrary dots into the name part of your email address, so they turned this into jond.athabasca.ca@gmail.com, which was sufficiently similar to the real thing to fool the unwary.

  2. the crooks simply copied and pasted the first part of my official AU page as a sig, which is pretty odd when you look at it closely because it included a plain text version of the links to different sections on the actual page (they were not very careful, and probably didn’t speak English well enough to notice), but again looks enough like a real sig to fool someone glancing at it quickly in the midst of a busy morning.

  3. they  (apparently) only sent the phishing emails to other people listed on the same departmental bio pages, rightly assuming that all recipients would know me and so would be more likely to respond. The fact that the page still (inaccurately) lists me as school Chair probably probably means I was deliberately singled out.

As far as I know they have not extended the attacks further than to my colleagues in SCIS, but I doubt that this is the end of it. If they do think I am still the Chair of the school, it might occur to them that chairs tend to be known outside their schools too.

This is not identity theft – I have experienced the real thing over the past year and, trust me, it is far more unpleasant than this – and it’s certainly not hacking. It’s just crude impersonation that relies on human fallibility and inattention to detail, that uses nothing but public information from our website to commit good old fashioned fraud. Nonetheless, and though I was not an intended victim, I still feel a bit violated by the whole thing. It’s mostly just my foolish pride – I don’t so much resent the attackers as the fact that some of the recipients jumped to the conclusion that I had been hacked, and that some even thought the emails were from me. If it were a real hack, I’d feel a lot worse in many ways, but at least I’d be able to do something about it to try to fix the problem. All that I can do about this kind of attack is to get someone else to make sure the mail filters filter them out, but that’s just a local workaround, not a solution.

We do have a team at AU that deals with such things (if you have an AU account and are affected, send suspicious emails to phishing@athabascau.ca), so this particular scam should have been stopped in its tracks, but do tell me if you get a weird email from ‘me’.

What is it to be Bayesian? The (pretty simple) math modelling behind a Big Data buzzword | Aeon Videos

This is a great little (16 minute) video that intuitively explains Bayesian probability from a variety of perspectives, but especially in visual (geometric) terms. Very useful for pretty much anyone – this is a critical thinking skill that applies in many contexts – but especially for researchers or programmers struggling with the idea.

Originally posted at: https://landing.athabascau.ca/bookmarks/view/5278878/what-is-it-to-be-bayesian-the-pretty-simple-math-modelling-behind-a-big-data-buzzword-aeon-videos

Is China really the educational powerhouse that the PISA rankings suggest? (tl;dr: not even close)

Administered by the OECD, PISA is basically a set of tests, adapted to each country, that attempt to measure educational performance across a range of skills in order to rank educational systems around the world. The rankings really matter to many countries, and help to determine educational policies across the planet, being especially impactful when countries don’t do well. Often, a low PISA score triggers educational reform (not always ending well), but sometimes countries just stop playing the game. India, for instance, dropped out a decade ago after coming second from the bottom, complaining of lack of adaptation to the Indian context (which is totally fair – India is incredibly diverse, so one measure absolutely does not fit all) though it will be back again next year. There are many reasons to dislike PISA, but the one I want to highlight here is Goodhart’s Law that, when a measure becomes a target, it ceases to be a good measure.

This article – a report on an interview with Andreas Schleicher, OECD Director of Education and Skills (a very smart fellow) – provides some useful food for thought. Though it focuses on China as a case in point, the interview is not so much about China’s ‘success’ as it is about PISA and its limitations in general. Among Schleicher’s more interesting insights is the fact that China’s test results came solely from its four most highly developed and economically successful provinces. These are very unrepresentative of the whole. In fact, China replaced Guangdong in its submission this time round because it was blamed for poorer performance last time, suggesting that the Chinese government’s involvement with PISA is far more concerned with appearing effective on the International stage – on presenting a facade – than on actually improving learning. PISA is a test for countries, and some are quite happy to cheat on the test.

In fact, the biggest contributing factor to test results is, of course and as always, economic. Schleicher notes that, worldwide, the top 10% socioeconomically advantaged students have for at least 10 years consistently outperformed the 10% most disadvantaged students in reading by 141 score points, which equates to approximately three year’s worth of schooling. It is not news that by far the most productive way to improve the effectiveness of educational systems would be to diminish wealth inequalities. It is, though, worth noting that schools play a relatively small role in Chinese education, especially among more prosperous families, with vast amounts of (paid, private) tuition occurring outside schools. Similar extracurricular tuition patterns occur in several of the other highest ranking PISA countries, such as South Korea, Singapore, Japan, Hong Kong, and Taiwan. It is significant that, in these countries, test scores are extremely important in almost every way – economically, culturally, socially, and more – so there is a lot of teaching focused on test results at the expense of almost everything else.

It is also notable – and almost certainly a direct consequence of tests’ importance – that over 80% of Chinese students admit to cheating, which might be more than a minor contributor to the good results. In fairness, cheating rates for the US and Canada are also not too far short of that, correctly implying a serious endemic malaise with our educational systems worldwide (Goodhart’s Law, again), so this is just a relatively slight difference of degree, not of kind. Given the large amount of time spent learning outside school, the high levels of cheating, and the cherry-picking of top performing provinces, the implications are that, far from having a world-leading education system, teaching in China is actually really awful, on average. Among the things that can be gleaned from PISA results are that China performs very badly on productivity (points per hour of learning), and ranks 8th from bottom on life satisfaction for students. It is essentially a failure, by any reasonable measure. The PISA ranking is not quite a fiction but it is close. At least in the case of the high overall placing of China, it certainly fails to correctly measure the effectiveness of the educational system, if results are taken at face value.

There appear to be two distinct patterns among those countries that consistently achieve high PISA results, that appear to divide along broadly cultural lines. The first group includes the likes of China, South Korea, Japan, and Taiwan (all quite notable examples of what Hofsteder describes as collectivist cultures), with high levels of out-of-school tuition, a strong educational emphasis on test scores, and great personal penalties for failure. These countries seem to achieve their high ranking by a very strong focus on passing the tests, with high penalties for failure and great significance for success. As a consequence, their educational systems cannot be seen as standalone causes but, rather, as creators of problems that have to be overcome by other means (most notably in the form of extra-curricular assistance that funds a booming personal tuition economy).  Standard bearers for the other main pattern are Finland and Estonia, as well as Switzerland, and Canada (though the latter two devolve educational responsibility to canton/province, so they are less consistently successful in the rankings). In Hofstede’s terms, these are more individualist societies. In this group, test scores (slightly) tend to be seen as a measure of only one of several consequences of teaching, rather than being the primary motivation for doing it. I am certainly culturally biased, but I cannot help but think this is a better way of going about the process: education is for society, much more than for the individual, and certainly not for economic gain, so it must be understood across many dimensions of value. Whether they agree with me or not, I am almost certain that most educators everywhere would like to think that education is about much more than achieving good test scores. It is only a matter of degree, though. Education in all countries I am aware of relies on extrinsic motivation, and there are large pockets of excellence in the first group and large pockets of awfulness in the second. Averages are a stupid way to evaluate a whole country’s educational system, and they conceal great diversity. The boundaries are also blurred. Estonia, for instance, that is singled out in the article as a success story due to its rapid rise through the rankings, actually also makes extensive use of extra tuition in the form of ‘long day groups’ that take place in schools after curricular instruction. Estonia is no worse than most other countries in this regard, and in some ways superior because such long day groups take the place of at least some of the homework that is widely required in many countries, despite a singular lack of evidence that (on average) it has more than a tiny effect on learning. At least Estonia’s approach involves a modicum of good education theory and evidence to support it.

Overall, I think the main thing that is revealed by the PISA process is that average test scores are, for the most part, an extremely poor means of comparing education systems. Given that it is useful for a government to know how their policies are working, there does need to be some way for them to observe how schools are doing, but it would seem more sensible to rely on trained inspectors reviewing schools, their teaching, the work of children, etc, than on test scores. At the very least they should be considering signs of happiness, motivation, community, and social achievement at least as much as academic achievement. However, Goodhart’s Law would cause its usual harm if such things became the dominant measures of success, and more than the lightest of inspections would normally cause more harm than good. I experienced something not too far removed from this (in the form of OFSTED inspections) in the UK as a parent and school governor back in the 1990s. The results were not pretty. For about a year leading up to them teachers’ workloads were massively strained by the need to report on everything, students suffered, resentments piled up, everyone suffered. Though OFSTED reports did sometimes lead to improvements in particularly bad schools, the effects on the vast majority of schools (and especially on teachers) were disastrous, often radically disrupting work, increasing stress levels beyond reasonable bounds, and leading to more than a few resignations and early retirements from the best, most dedicated teachers who could barely cope with the workloads at the best of times. They were forced to become bureaucrats, which is a role to which teachers tend to be very poorly suited. It was (and, I believe, may still be) beyond stupid, despite best intentions.

What is really needed is something more collegial, that is focused on improvement rather than judgment, that celebrates and builds on success rather than amplifying failure, where everyone involved in the process benefits and no one suffers. The whole point (as far as I understand it) is to improve what we do, not to blame those who fail. Appreciative Inquiry is a good start. Simple things like peer observation (with no penalties, no judgments, just formative commentary) can be more than adequate for the most part at a local level, and are beneficial to both observer and observed. Maybe – if someone thinks it necessary – inspectors (volunteers, perhaps, from the teaching profession) could look at samples of student work from further afield with a similarly positive, formative attitude. It might not provide numbers to compare but, if there were enough of a culture of sharing across the whole sector, and if inspectors came from across the geographical and cultural spectrum, it ought to be good enough to improve practice, and to spread good ideas around, so the intent would be achieved. Governments could receive reports on what actually matters – that things are getting better – rather than on what does not (that things are bad, according to some unreliable measurement that compares nothing of any real value to educators, students, or society). Teaching is a deeply soft technology that cannot be reductively simplified to a relationship of entailment. It can, though, as a lived, creative, social process, be improved. This should be the goal of all teachers, and of all those who can influence the process, including governments. PISA only achieves such results in a tiny minority of extreme cases. For the most part, it actively militates against them because it substitutes education – in all its rich complexity – for test scores. These are not even a passable proxy. They are a gross distortion, an abhomination that can trivially be turned to evil, self-serving purposes without in any way improving learning. Schleicher fully understands this. I wish that the people who his organization serves did too.

Originally posted at: https://landing.athabascau.ca/bookmarks/view/5209267/is-china-really-the-educational-powerhouse-that-the-pisa-rankings-suggest-tldr-not-even-close

Excellent news: Twitter Makes A Bet On Protocols Over Platforms.

Well this is good news! Of course, the road to Hell is paved with good intentions and there is much that could go wrong in between plan and execution, but it seems that Twitter is recommitting itself to openness, standards, and the use of protocols for a federated social Web (see also https://twitter.com/biz/status/1204784388107636737 and https://twitter.com/jack/status/1204766078468911106 for the announcements by Twitter’s founders). It is a bit worrying that Twitter wants to help invent a new protocol when there are plenty of established ones that already exist (ActivityPub, OpenSocial, FOAF, XMPP, OStatus, OpenID, OAuth, PubSubHubbub, Zot, Diaspora, etc, etc). Also, there is already a pretty serviceable Twitter competitor in the form of Mastodon, that does most of what they seem to want to do. However, the fact that they are thinking about protocols rather than platforms at all is very heartening. The world needs much much more of this.

Twitter, as it evolved in its first couple of years, was brilliant. What made it great was that it could act as a highly efficient social bookmarking system *plus* commentary *plus* folksonomy, *plus* instant messaging, *plus* social networking, all through one incredibly simple, flexible, open field.  It was, in part, a descendant of social bookmarking systems that people like me developed in the 90s, but there were no predetermined fields for URLs (you could have more than one, or none at all); there were no predetermined categories; the tags (#hashtags) were trivially easy to include, without separate fields (this is what makes it highly supportive of social sets, in which the topic matters more than the person); and it had the lowest threshold social networking (especially through @mentions), again without the need for separate fields. It was a single small text box that did everything, and that could be used to share more or less anything with more or less anyone but, thanks to its size,  was primarily used to connect to other things. Part of what made this so cool is that #hashtags and @mentions were not designed into Twitter at the start, but emerged memetically from practice: the system evolved (at first) through a collective design process. Twitter’s implementation of such things in software ingeniously used automation to make the overall system even softer and more flexible than it was before. It was generous in what it shared, too, so a flourishing ecosystem grew around it, at least for the first few years. You could use pretty much any Twitter data to which you had access in any way you liked. It was a very simple, very powerful component, a tool rather than an environment or platform. In retrospect I wish we had used Twitter as a model when developing the Landing, rather than the kitchen sink approach that we settled for.

Twitter is widely viewed as a competitor to Facebook – increasingly even by the company itself – though it was (and still is, to an extent) a very different animal. Facebook has tried to emulate all of Twitter’s features as a subset of its own horrible evil mess, but completely misses the point. The strength of Twitter is that it (still) does one simple thing very well: it is primarily a hub that makes the rest of the Web more connected, rather than (like Facebook) sucking everything into it. However, that one simple thing is as soft and open to countless, unprestatable uses as an elastic band, a screwdriver, or good old fashioned email.  Jack Dorsey’s announcement of the new move itself is a classic example of this, creating a long-form announcement from short tweets. Beyond simply connecting stuff, people have used it to write novels, coordinate social protests, conduct personal conversations, influence elections, and thousands of other things. It is a very soft, very human-driven tool.

For a few years it was very open, and it seemed to be getting more so, but it lost its way after that and became much more the self-contained platform we see today, pulling a lot of features into its core, closing off many ways of connecting with and using it, and increasingly hardening things that should have stayed soft, notably in its algorithmic placing and sorting of tweets. Though its old character limit was frustrating at times, it was actually a very good idea to set such severe boundaries because it ensured that Twitter remained as a connecting hub, rather than a self-contained site. The new higher character limit is still somewhat constraining, but it makes longer-form conversations increasingly possible – especially when combined with the easy upload of video, files, images, etc – thus drawing people to stay more at the hub, rather than to visit the things that it connects. It has become more and more a social media platform, increasingly isolated, increasingly its own bubble, increasingly driven by the popularity contests and narcissism amplifiers that seldom end well. Twitter’s announcement, I hope, marks a reversal of this pattern. I hope (though don’t expect) that they get the Mastodon gang on board. I will watch with great interest, whatever happens.

Originally posted at: https://landing.athabascau.ca/bookmarks/view/5140548/excellent-news-twitter-makes-a-bet-on-protocols-over-platforms

E-Learn 2019 presentation – X-literacies: beyond digital literacy

Here are  my slides from E-Learn 2019, in New Orleans. The presentation was about the nature of technologies and their roles in communities (groups, networks, sets, whatever), their highly situated nature, and their deep intertwingling with culture. In general it is an argument that literacies (as opposed to skills, knowledge, etc) might most productively and usefully be seen as the hard techniques needed to operate the technologies that are required for any given culture. As well as clarifying the term and using it in the same manner as the original term “literacy”, this implies there may be an indefinitely large range of literacies because we are all members of an indefinitely large number of overlapping cultures. All sorts of possibilities and issues emerge from this perspective.

Abstract: Dozens, if not hundreds, of literacies have been identified by academic researchers, from digital- to musical- to health- to network- literacy, as well as combinatorial terms like new-, multi-, 21st Century-, and media-literacy. Proponents seek ways to support the acquisition of such literacies but, if they are to be successful, we must first agree what we mean by ‘literacy’. Unfortunately, the term is used in many inconsistent and incompatible ways, from simple lists of skills to broad characteristics or tendencies that are either ubiquitous or meaninglessly vague. I argue that ‘literacy’ is most usefully thought of as the set of learned techniques needed to participate in the technologies of a given culture. Through use and application of a culture’s techniques, increasing literacy also leads to increasing knowledge of the associated facts and adoption of the values that come with that culture. Literacy is thus contextually situated, mutates over time as a culture and its technologies evolve, and participates in that co-evolution. As well as subsuming and eliminating much of the confusion caused by the proliferation of x-literacies, this opens the door to more accurately recognizing the literacies that we wish to use, promote and teach for any given individual or group.

 

Social Media Has Not Destroyed a Generation   – Scientific American

Well this is not a surprise. It turns out that social media and cellphone use have little to no effect on the mental well-being of teenagers. And, having just hung out with more than 10,000 young people in Vancouver, I’d say that they seem to be doing pretty well,

(if the video does not display, visit www.youtube.com/watch?v=ZdyBpfYvxs4).

Unfortunately, these wonderful young people are not to be confused with the very many utter creeps, idiots, paid lackeys of oil companies, bizarrely de-evolved evolution-deniers (not to mention climate-change deniers), and haters of all things decent who felt compelled to contribute to the live chat displayed alongside the YouTube video linked to above, as well as to far too many of the subsequent comments. This is what raw, unfiltered sets (the largely anonymous, non-networked social form that dominates on YouTube and many other social media) look like. The insane, the evil, and the stupid (often a mix of all three) have voices at least as loud as those who have something reasonable or human to say, and they have a platform where at least a few other people with ugly, broken souls will help them to feel validated, so they feel even more compelled to say the stupid, ugly, evil things they say. How dare they? Perhaps some of them are also children but, from many of the comments, I’d say that most have reached voting age. It’s not the kids that we need to worry about, apart from that they may be being brought up by such vile excuses for humanity, and that they have to learn to make sense of the stuff swamp of social media systems that enable such voices to be loudly heard.

When I hear Greta Thunberg talk it consistently brings tears to my eyes and sends shivers down my spine. She is astonishingly wonderful and deeply, deeply inspiring. She is brave, she is brilliant, she is right. She is not proposing anything apart from that politicians take action now on an unequivocal, plain to see, planet-wide threat, that is caused by problems that we know how to solve, and that demands political action. Yes, that will disrupt the lives of people that have profited from our collective madness – that is to say, most of us (but it is a hell of a lot less disruption than the alternative, at least for those not due to die any time soon). Yes, it is really difficult to make it happen. Yes, it means we will all have to change some of our ways, but that is no bad thing: our lives, and those of our children, and those of most of the living things on our planet, will be better as a result. And no, it is not her job to propose solutions, and she very deliberately does not try to do so, though she lives her life according to her convictions and does what she sees as necessary as an individual fighting the climate crisis. When she talks she simply states – with immense, infectious, intense passion – what is wrong, and demands that those who can fix it should do so. I am deeply humbled by this amazing teenager. We should all be.

Do buy the cheap, slim volume of her speeches, No One Is Too Small to Make a Difference. It is an inspiring book, and the proceeds will all go to charity.

Originally posted at: https://landing.athabascau.ca/bookmarks/view/4949999/social-media-has-not-destroyed-a-generation-%C2%A0-scientific-american

Causal understanding is not necessary for the improvement of culturally evolving technology (paywalled)

https://www.nature.com/articles/s41562-019-0567-9

I’ve been struggling a bit with writing a chapter on how we should research technologies, especially soft technologies, in the light of their innate complexity, the difficulties of identifying relevant boundaries, their situated nature, the impossibility of identifying all possible uses for any soft technology (and the immense importance of the role of the user in their enactment), and the fact that they are far from fixed, amongst other things. This (regretably paywalled) paper helps support the theoretical model I am developing. Using an experimental method, it shows that technologies can be developed, and can gain in sophistication and complexity over multiple generations, without any of its designers having an accurate or complete understanding of how they work. It is particularly interesting when viewed through a lens of distributed/situated/extended cognition because of the role the technology itself plays in its evolution, and it accords very well with Kauffman’s notion of the adjacent possible and Arthur’s theory of technological evolution.

From the abstract…

“Here we show that a physical artefact becomes progressively optimized across generations of social learners in the absence of explicit causal understanding. Moreover, we find that the transmission of causal models across generations has no noticeable effect on the pace of cultural evolution. The reason is that participants do not spontaneously create multidimensional causal theories but, instead, mainly produce simplistic models related to a salient dimension. Finally, we show that the transmission of these inaccurate theories constrains learners’ exploration and has downstream effects on their understanding. These results indicate that complex technologies need not result from enhanced causal reasoning but, instead, can emerge from the accumulation of improvements made across generations.”

Originally posted at: https://landing.athabascau.ca/bookmarks/view/4846373/causal-understanding-is-not-necessary-for-the-improvement-of-culturally-evolving-technology-paywalled

My learning style

I am a visual, aural, read/write, kinaesthetic, introvert, extravert, sensing, intuitive, analytic, thinking, feeling, judging, perceiving, independent, dependent, collaborative, competitive, participant, avoidant, wholist, analytic, verbalizing, imaging, visualizing, deductive, synthetic, expansive, serialist, holist, field-dependent, field-independent, intrinsically motivated, extrinsically motivated, impulsive, reflexive, convergent, divergent, levelling, sharpening, concrete-sequential, concrete-random, abstract-sequential, abstract-random, assimilating, exploring, adaptive, innovative, reproductive, experiencing, thinking, doing, reflective, directed, self-directed, undirected, application-directed, meaning-directed, deep, surface, strategic, apathetic, elaborative, impulsive, concrete, independent, self-assertive, cerebral,  affective, type 1, type 2, type 3, global, scanning, focusing, physical, logical, social, solitary, musical-rhythmic, interpersonal, intrapersonal, spatial, body, active, common sense, dynamic, imaginative, quadrant 1, quadrant 2, quadrant 3, quadrant 4, theorizing, organizing, humanitarian, legislative, judicial, executive, tactile, pragmatic, versatile learner.

My birth sign is Aquarius, and I was born in the Year of the Rat.

Incidentally…

It appears that 97% of American teachers actually believe in learning styles, by which I mean the belief that there are persistent traits describing how people learn that can be used to determine the best way to teach them. This is despite at least most, if not all, of the many scores of such theories existing somewhere between astrology and fairies in terms of evidence for their relevance or applicability in real life learning. Though there may be ever-shifting conditions under which we may at times prefer one or other of whatever learning styles the theory we like offers – this may be a source of the persisting appeal of the idea – there is no reliable evidence that this is in any way relevant to whether or not we will learn better or worse (whatever we think that means) when offered a learning experience that is tailored to that preference. It’s not by any means for want of trying – countless studies exist, and that’s not counting probably many more that never saw the light of day because they had only null results to report and so were not deemed worthy of publication – so the obvious conclusion to be drawn is that these theories are most likely false.

It wouldn’t be so worrying were it not that there is evidence that such beliefs are harmful to learners and, even if there were not, then the time, effort, and money put into trying to use them would be far better spent on things that actually might work.

In the extremely unlikely event that it were one day proven that an individual has a persistent style of learning that, when we teach to that style, consistently leads to improved learning (however we measure that), then it would be my duty as a teacher to try to teach them to learn in other ways, because here’s the thing: the real world in which we are and must be lifelong learners doesn’t come neatly packaged in ways that fit your learning style. We can all learn to learn in all the ways that I list above, and then some, and we can all become better and smarter by applying the right strategy at the right time. We therefore need to cultivate as many diverse learning strategies as we can, and learn when to use them. That’s just common sense which, as it happens and surprisingly enough, is itself a learning style, according to the 4MAT model.

Signals, boundaries, and change: how to evolve an information system, and how not to evolve it

primitive cell development

For most organizations there tend to be three main reasons to implement an information system:

  1.     to do things the organization couldn’t do before
  2.     to improve things the organization already does (e.g. to make them more efficient/cheaper/better quality/faster/more reliable/etc)
  3.     to meet essential demands (e.g. legislation, keep existing apps working, etc)

There are other reasons (political, aesthetic, reputational, moral, corruption/bribery/kickbacks, familiarity, etc) but I reckon those are the main ones that matter. They are all very good reasons.

Costs and debts

With each IT solution there will always be costs, both initial and ongoing. Because we are talking about technology, and all technologies evolve to greater complexity over time, the ongoing costs will inevitably escalate. It’s not optional. This is what is commonly described as the ‘technological debt’ but that is a horrible misnomer. It is not a debt, but the price we pay for the solutions we need. If we don’t do it, our IT systems decay and die, starved of their connections with the evolving business and global systems around them. It’s no more of a debt than the need to eat or receive medical care is a debt for living.

Thinking locally, not globally

When money needs to be saved in an organization, senior executives tend to look at the inevitably burgeoning cost of IT and see it as ripe for pruning. IT managers thus tend to be placed under extreme pressure to ‘save’ costs. IT managers might often be relieved about that because they are almost certainly struggling to maintain the customized apps already, unless they have carefully planned for those increased costs over years (few do). Sensibly (from their own local perspective, given what they have been charged with doing), they therefore tend to strip out customizations, then shift to baseline applications, and/or cloud-based services that offer financial savings or, at least, predictable costs, giving the illusion of control. Often, they wind up firing, repurposing, or not renewing contracts for development staff, support staff, and others with deep knowledge of the old tools and systems. This keeps the budget in check so they achieve the goals set for them.

Unfortunately, assuming that the organization continues to need to do what it has been doing up to that point, the unavoidable consequence is that things that computers used to do are now done by people in the workforce instead. When made to perform hard mechanical tasks that computers can and should do, people are invariably far more fallible, slow, inconsistent, and inefficient. Far more. They tend to be reluctant, too. To make things worse, these mundane repetitive tasks take time, and crowd out other, more important things that people need to do, such as the things they were hired for. People tend to get tired, angry, and frustrated when made to do mechanical things over which they have little agency, which reduces productivity much further than simply the time lost in doing them. To make matters even worse, there is inevitably going to be a significant learning curve, during which staff try to figure out how to do the work of machines. This tends to lead to inflated training budgets (usually involving training sessions that, as decades of research show, are rarely very effective and that have to be repeated), time to read documentation, and more time taken out of the working day. Creativity, ingenuity, innovation, problem-solving, and interaction with others all suffer. The organization as a whole consequently winds up losing many times more (usually by orders of magnitude) than they saved on IT costs, though the IT budget now looks healthy again so it is often deemed to be a success. This is like taking the wheels off a car then proudly pointing to the savings in fuel that result. Unfortunately, such general malaises seldom appear in budget reports, and are rarely accounted for at all, because they get lost in the work that everyone is doing. Often, the only visible signs that it has happened are that the organization just gets slower, less efficient, less creative, more prone to mistakes, and less happy. Things start to break, people start to leave, sick days multiply. The reputation of the organization begins to suffer.
 
This is usually the point that more radical large scale changes to the organization are proposed, again usually driven by senior management who (unless they listen very carefully to what the workforce is telling them) may well attribute the problems they are seeing to the wrong causes, like external competition. A common approach to the problem is to impose more austerity, thus delivering the killing blow to an already demoralized workforce. That’s an almost guaranteed disaster. Another common way to tackle it is to take greater risks, made all the more risky thanks to having just converted creative, problem-solving, inquisitive workers into cogs in the machine, in the hope of opening up new sources of revenue or different goals. When done under pressure, that seldom ends well, though at least it has some chance of success, unlike austerity. This vicious cycle is hard to escape from. I don’t know of any really effective way to deal with it once it has happened.

Thinking in systems

The way to avoid it in the first place is not to kill off and directly replace custom IT solutions with baseline alternatives. There are very good reasons for almost all of those customizations that have almost certainly not gone away: all those I mentioned at the start of the post don’t suddenly cease to apply. It is therefore positively stupid to simply remove them without an extremely deep, multifaceted analysis of how they are used and who uses them, and even then with enormous conservatism and care. However, you probably still want to get rid of them eventually anyway, because, as well as being an ever-increasing cost,  they have probably become increasingly out of line with how the organization and the world around it is evolving. Unless there has been a steady increase in investment in new IT staff (too rare), so much time is probably now spent keeping old systems going that there is no time to work on improvements or new initiatives. Unless more money can be put into maintaining them (a hard sell, though important to try) the trick is not to slash and burn, and definitely not to replace old customized apps with something different and less well-tailored, but to gently evolve towards whatever long-term solution seems sensible using techniques such as those I describe below. This has a significant cost, too, but it’s not usually as high, and it can be spread over a much longer period.
 

For example…

If you wish to move away from reliance on a heavily customized learning management system to a more flexible and adaptive learning ecosystem made of more manageable pieces, the trick is to, first of all, build connectors into and out of your old system (if they do not already exist), to expose as many discrete services as possible, and then to make use of plugin hooks (or similar) to seamlessly replace existing functions with new ones. The same may well need to be done with the new system, if it does not already work that way. This is the most expensive part, because it normally demands development time, and what is developed will have to be maintained, but it’s worth it. What you are doing, at an abstract level, is creating boundaries around parts that can be treated as distinct (functions, components, objects, services, etc) and making sure that the signals that pass between them can be understood in the same way by subsystems on either side of the boundary.

Open industry standards (APIs, protocols, etc) are almost essential here, because apps at both sides of the boundary need to speak the same language. Proprietary APIs are risky: you do not want to start doing this then have a vendor decide to change its API or its terms and conditions. It’s particularly dangerous to do this with proprietary cloud-based services, where you don’t have any control whatsoever over APIs or backends,  and where sudden changes (sometimes without even a notification that they are happening) are commonplace. It’s fine to use containers or virtual machines in the cloud – they can be replaced with alternatives if things go wrong, and can be treated much like applications hosted locally – and it’s fine to use services with very well defined boundaries, with standards-based APIs to channel the signals. It is also fine to build your own, as long as you control both sides of the boundary, though maintenance costs will tend to be higher.  It is not fine to use whole proprietary applications or services in the cloud because you cannot simply replace them with alternatives, and changes are not under your control. Ideally, both old and new systems should be open source so that you are not bound to one provider, you can make any changes you need (if necessary), and you can rely on having ongoing access to older versions if things change too fast.
 
Having done this, you have two main ways to evolve, that you can choose according to needs:

  1.  to gradually phase in the new tools you want and phase out the old ones you don’t want in the old system until, like the ship of Theseus, you have replaced the entire thing. This lets you retain your customizations and existing investments (especially in knowledge of those systems) for the longest time, because you can replace the parts that do not rely on them before tackling those that do. Meanwhile, those same fresh tools can start to make their appearance in whatever other new systems you are trying to build, and you can make a graceful, planned transition as and when you are ready. This is particularly useful if there is a great deal of content and learning already embedded in the system, which is invariably the case with LMSs. It means people can mostly continue to work the way they’ve always worked, while slowly learning about and transitioning to a new way of working.
  2.  to make use of some services provided by the old system to power the new one. For instance, if you have a well-established means of generating class lists or collecting assessment data that involves a lot of custom code, you can offer that as a service from the old tool to your new tool, rather than reimplementing it afresh straight away or requiring users to manually replace the custom functions with fallible human work. Eventually, once the time is right to move and you can afford it, you can then simply replace it with a different service, with virtually no disruption to anyone. This is better when you want a clean break, especially useful when the new system does things that the original could not do, though it still normally allows simultaneous operation for a while if needed, as well as the option to fall back to the old system in the event of a disaster.

There are other hybrid alternatives, such as setting up other systems to link both, so that the systems do not interact directly but via a common intermediary. In the case of an LMS migration, this might be a learning record store (LRS) or student record system, for instance. The general principle, though, is to keep part or all of the old system running simultaneously for however long it is needed, parcellating its tools and services, while slowly transitioning to the new. Of course, this does imply extra cost in the short term, because you now have to manage at least two systems instead of one. However, by phasing it this way you greatly reduce risk, spread costs over a timeframe that you control, and allow for changes in direction (including reversal) along the way, which is always useful. The huge costs you save are those that are hidden from conventional accounting – the time, motivation, and morale of the workforce that uses the system. As a useful bonus, this service-oriented approach to building your systems also allows you to insert other new tools and implement other new ideas with a greatly diminished level of risk, with fewer recurring costs, and without the one-time investment of having to deal with your whole monolithic codebase and data. This is great if you want to experiment with innovations at scale. Once you have properly modularized your system, you can grow it and change it by a process of assembly. It often allows you to offer more control to end users, too: for instance, in our LMS example you might allow individuals to choose between different approaches to a discussion forum, or content presentation, or to insert a research-based component without so many of the risks (security, performance, reliability, etc) normally associated with implementing less well-managed code.

Signals and boundaries

In essence, this is all about signals and boundaries. The idea is to identify and, if they don’t exist, create boundaries between distinct parts of systems, then to focus all your management efforts on the signals that pass across them. As long as the signals remain the same from both sides, what lies on either side of the boundaries can be isolated and replaced when needed. This happens to be the way that natural systems mainly evolve too, from organisms to ecosystems. It has done pretty good service for a good billion years or so.