The Myth of 'Learning Styles'

A straightforward journalistic article from the Atlantic that does a decent job of explaining how and why learning styles theories simply don’t work, with a particular focus on VARK. The takeaways are that, yes, people do often prefer to learn in different ways but, no, accommodating their preferences has no effect on comprehension or recall, and, even if it did, it would be doing a disservice to learners to do so because life ain’t like that (with darker implications of teaching people to believe they need something they don’t, thereby actually reducing their capacity to learn). We’ve known this for a really long time. I am still shocked that (at least as recently as 5 years ago) up to 90% of teachers actually believe in the learning styles myth. Of all the people that should know better, teachers are pretty much at the top of the list.

The article points to a good range of recent reliable sources, including:

Another Nail in the Coffin for Learning Styles? Disparities among Undergraduate Anatomy Students’ Study Strategies, Class Performance, and Reported VARK Learning Styles

Learning style, judgements of learning, and learning of verbal and visual information.

The Scientific Status of Learning Styles Theories

Matching Learning Style to Instructional Method: Effects on Comprehension

Proviso: there’s nothing wrong, and everything right, about thinking of different ways to enable people to learn stuff, and learning styles theories all encourage people to do that. As a design tool, that serves as a reminder that there is seldom one best way to teach anything, I’m all in favour of anything that gets the creative juices flowing and that allows learning designers to take and apply different perspectives. This is the sort of thing that increases engagement, interest, and time on task. Even if it is barking mad or positively evil, as long as we don’t let on why we are doing it, any way we find to do this is probably fine. I could, for instance, imagine ways that a ‘men are from Mars, women are from Venus’ perspective could, despite the very unsavoury nonsense behind it, actually result in some diverse approaches to teaching that would benefit everyone, as long as we didn’t try to teach men one way and women another, of course, and as long as we didn’t let on that we had designed it with these thoughts in mind. Substitute whatever demographic divide, bias, bigotry, or preference you like – religion, weight, politics, sexuality, race, drinking habits, liking for cats or dogs, general level of fitness, whatever. As long as you keep it to yourself and only let it affect how you design your teaching, then do what works for you. There’s a slippery slope to be avoided here, and some complexities to be wary of, especially when it changes the content and intended outcomes – if, say, you chose religion as your discriminator, that does not mean you should teach both evolution and intelligent design, though there might be value in remembering that there might be a religious demographic that won’t readily accept any amount of evidence or argument, so you might want to think about how best to help them, just as you should think about how best to help people with any disability.  Let’s just keep it that way, though – a dirty little in-house secret about how we design our teaching by thinking (wrongly or not) about differences between our learners –  and stop inflicting the stupid notion on our students.

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E-Learn 2019, Call for Proposals: Due July 8

Call for Proposals: Due July 8

E-learn has been running since 1996 (originally under the name of WebNet) and is a great conference for researchers into online learning working in higher education and related fields. At its peak, it used to attract about a thousand delegates though this has mostly tailed off a bit in recent years, so it’s a big enough number to ensure diversity and quality, but small enough that you can get to meet many of them. This year the conference is in one of my favourite locations to visit, New Orleans, and it runs from November 4-7, 2019.

What I like most about the conference is its diversity. It typically attracts a great range from more technical to more educationally focused researchers, with a great spread of experience from student researchers to the most famous in the field. There are also usually a lot of other interested and interesting people involved, as this pie chart suggests:

E-learn attendees pie chart

If you want to submit a paper or poster, acceptance rates are about par for the course: it’s certainly not trivially easy to get a paper accepted, but it’s not fiendishly hard.

Disclaimer: I’m on the Executive Committee, have co-chaired it a couple of times, and have only missed about three of the conferences in the last 20 years so I’m obviously a fan!

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In-person vs online teaching

This is roughly the content of my 3 minute pitch to explain (some of) my research, that I gave at the OUNL research day in Heerlen, Netherlands yesterday. I was allowed one slide:

in-person vs self-paced online learning

This is (very roughly) what I said:

Mediaeval scholars were faced with the problem that knowledge (doctrine actually), often found in rare and expensive books, needed to be passed from the few to the many. Lecturing was an efficient solution, given the constraints of physics. Because everyone needed to be in the same place at the same time for this to work, we developed schools, universities, classes, courses, timetables and terms and semesters. We built resources like libraries.  We created organizational units to manage it all, like faculties and colleges. Above all,  for efficiency, we needed rules of behaviour and a natural power dynamic putting the lecturer in control for every moment of the learning activity in a classroom.

Learning (like most things) works best – by far – when learners are intrinsically motivated. It barely works at all when learners are amotivated. Self determination theory tells us that three things are needed for intrinsic motivation: support for autonomy, competence, and relatedness. The mediaeval solution was good for relatedness, but bad for competence (some found it too challenging, some not challenging enough) and terrible for autonomy. The chance of amotivation is thus very high. Many of our pedagogies, processes, and much of the art of teaching since then have been, in one way or another, attempts to deal with this one central problem.  The most common solution to the lack of intrinsic motivation that resulted was to apply externally regulated extrinsic motivation – rewards like grades and qualifications, rules of attendance,  punishments for non-compliance,  etc – which, self determination theory shows, is infallibly fatal to intrinsic motivation, making things far worse. How crazy is it that we have to force people to do the one thing that makes us most human, a drive to learn that is arguably stronger than sex or even the pursuit of food?   Good teachers using well considered teaching methods can usually overcome many of the issues, at least for many students much of the time. But that’s what good pedagogy means. It is highly situated in solving the innate problems of in-person teaching.

On the whole, for perfectly understandable reasons (much distance teaching evolved in an in-person context with which it had to interoperate) we have transferred those exact same pedagogies unthinkingly to open, self paced, self directed, distance learning. ‘Teaching is teaching’, advocates claim, and so they try, as much as possible, to replicate online what they do in a classroom. But the motivational problems faced by distance learners are almost the exact inverse of those of in-person learners. They have lots of autonomy – you can’t really take it away – and can take different paths and pacing to gain competence (e.g. rewinding or skipping videos, re-reading text, augmenting with other resources, etc), but tend to suffer from reduced relatedness, especially when learning truly independently, in a self paced modality. Given this mismatch and the lack of well evolved support and processes for this very different context, it is not surprising there is often a high rate of attrition, especially when teachers (lacking the closeness and authority or in-person colleagues) double down on rewards and punishments through grades, even to the extent of rewarding participation, thus making it even worse.  

There is no such thing as a disembodied, abstract, decontextualized pedagogy – it is all about orchestrating technologies- so any solution must be as much about buildings tools and structures as it is about using techniques and methods. They are entirely inseparable.  A significant part of my current research is thus an attempt to design native online pedagogies, technologies, and other parts of educational systems (including credentialling) that don’t rely on reward and punishment; that are built for supporting learning in the complex, ever changing modern world that does exist, rather than for the indoctrination of mediaeval students.



George Siemens says he was wrong about networks. Well, not exactly wrong…

A characteristically smart and articulate post from George Siemens explaining why a view of the universe as nothing but networks all the way down – that he has supported in the past – is not sufficient to explain everything that matters. As George says, a systems view tends to be way more useful. It is important to observe that this is not in any way incommensurate with a network-oriented view because systems are entirely about networks, network theories play a very important role in modelling and understanding systems and, in fact, network theories are just a subset of systems theories anyway so, as George points out in this essay, he was not actually wrong in the past. It’s just that (perhaps – I present a counter view at the end) he could have been more right.

Not just one theory but many

There’s a great deal of diversity in systems theories, crossing many disciplinary areas, with different standards for rigour and explanatory power, and that’s part of their strength. They offer ways of talking about systems that are appropriate to their context. What is common to all systems theories is that they are anti-reductive, focused on relationships and interactions between things over time more than their constitutive elements, but there’s a host of different ways that broad approach can be applied.  Personally, I am particularly drawn to the field of self-organizing systems, which means an interest in the general areas of cybernetics, complex adaptive systems, autopoietic systems, signal/boundary systems, evolution, stigmergy, swarm intelligence, networks, etc, but there’s a lot of other helpful kinds of system theory. I have found Michael Moore’s much higher-level systems view of education, for example, to be really useful in my research and teaching, and approaches like systems dynamics can be very helpful to understand why systems that surround us constantly fail.  Systems models can, for instance, help to explain why incentive systems reduce motivation, or how -more generally – systems (once created) develop their own goals independently of and often in direct opposition to the people within them or their creators. Systems views are not always presented as such. One of the most life-changing books I have ever read, for instance, is Jane Jacobs’s The Death and Life of Great American Cities, which presents a rich and poetic systems view of what makes a city area thrive or fail, and has been hugely influential in driving the development of many cities around the world, though there’s far more to it than that. There’s barely a network to be found within it, and it doesn’t draw on any formal systems theories, but it certainly contains one. Though others have developed more network-oriented systems theories out of it (notably Christopher Alexander and Bill Hillier) the power of Jacobs’s systems theory is far more to do with the richness of her storytelling and her complex, multi-layered, deeply humane analysis of human systems. The level of detailed observation and depth of insight is similar in many ways to that of Charles Darwin, another preeminent and seminal systems thinker who did not label himself as such. Both Darwin and Jacobs do not simply show that – they show why and how, in wondrous and complex detail, everything affects everything else. Some systems can be useful but rather boring, especially when they are closed. Computers, for instance, are systems of interoperating parts and layers. They are complicated, for sure, but not (in themselves) complex. This makes them, as systems in themselves, a bit dull. Sitting by itself, notwithstanding interesting ways it can be programmed to adapt, a computer is essentially a closed system that behaves in predictable ways. However, the field of information systems is much more about human systems than computers, the field of computing as a whole is rich in invention, and the field of software development using computers is fully open and truly complex, full of unexpected and emergent behaviours, combining ideas, fields, groups, individuals, and models from all over the place. Connected together, they can do very interesting and sometimes unexpected things. Computers are (mostly) boring systems, but they are part of, and are used to enact or contain many much richer systems. Similar things can be said of legal systems, accounting systems, most machines, many organizations, and so on. It’s true of many systems in nature, too, such as metabolic pathways or neural connections. In themselves, they are (I simplify a little) systems of interacting mechanical processes following a set of simple rules. Things only get really interesting when you look at them as subsystems of other systems, interacting with other subsystems, whether creating something planned or emergent. Of course, it’s not just about things with lots of parts. Even simple, uncomplicated systems can be complex: the classic three body problem is a good illustration of this. It’s about how those parts are configured, and their openness to energy or information from the environment.

More is different

Systems theories that go beyond mere networks are necessary because more is different, as P.W Anderson famously demonstrated way back in 1972, and new laws, principles, patterns, and concerns occur at many different scales. Such laws and regularities are inherently unpredictable from the behaviour of their parts (see Kauffman’s Reinventing the Sacred or Humanity in a Creative Universe or even his older Investigations for a solid theoretical explanation of why this must be – it’s all about adjacent possibles) so, even if you can posit a theory that consists of networks from bottom to top, there’s limited value to be gained from doing so. It’s like string theory – if true, it probably does explain nearly everything in the whole universe but it’s not a lot of help with your shopping or filing your tax returns. Network theory strips a lot of what is meaningful from the system it models. There is a great deal that can be learned about learning from an understanding of the dynamics of networks, but they are of limited value in helping you to, say, construct a learning plan for yourself or others, or figure out why you are procrastinating about your homework right now.

Signals and boundaries

I tweeted a rather opaque response to George’s announcement of his article, in which I mentioned signals and boundaries. That’s worth unpicking a little. The central concept comes from John Holland’s brilliant eponymous (and, sadly, last) book, Signals and Boundaries. For any system that we choose to look at, we must choose which are the boundaries that matter to us, examine the signals that pass between what is at either side of those boundaries, and consider what tranformations occur within the boundaries (not necessarily how they occur), in order to understand it at an appropriate level. Though there are some consistent patterns at every scale (that Holland brilliantly reveals) we come to very different understandings depending on the boundaries we choose: the rules, the signals, the behaviour of the systems, etc are, qualitatively, profoundly different. For instance, consider the difference between anatomy and metabolic pathways in cells. You can’t have the former without the latter, but there is no conceivable way you could deduce the function or form of the heart by looking at enzymes in cells (of course, you could learn useful things about how the heart works by looking at metabolic pathways because they are subsystems or, a little more accurately, sub-sub-sub-subsystems of the heart).  Choosing boundaries is a process of black-boxing wherein, once a significant boundary is chosen, we treat the internal part as a kind of ‘program’ that processes the signals it receives and evokes responses. This is what I think George is getting at when he suggests that what makes systems different is that they embody rules. This is smarter than a simpler network view in a variety of ways. It makes it easier to focus on levels that matter, using context-appropriate vocabularies and meanings, in whatever combinations are significant; it allows us to more easily combine different scales/granularities of boundaried entity; it allows us to think more deeply about qualitative as well as quantitative differences in the signals; it allows us to think about not just networks but sets, or organizational structures, or whatever is appropriate; and (arguably most usefully) it makes it far simpler to think about processes (the ‘programs’) that drive it, and how they affect one another. It does all this without losing any of the value of looking at it as a network. 

Connectivism as a systems theory

In fact, though George is a little dismissive of his most famous and widely cited article on the subject, a lot of this kind of systems perspective appears within it. He talks of ecologies (archetypal systems) quite a bit, explicitly mentions systems theories as playing a foundational role in setting the agenda for the theory he expounds, spends a fair bit of time on chaos theory and self-organization (both explicitly systems fields involving systems theories), and even, as he discusses the implications towards the end, explicitly refers to connectivism as “a systems view of learning”. Though not explicitly mentioned, the theory also draws quite a bit on the field of socially distributed cognition, which is essentially a systems view of knowledge. So, though George may have meandered off the path a bit along the way and got caught up in trying to make everything look like a network from time to time, the version of Connectivism that most people adopt is based on this paper, which is and has always been about a systems theory, rather than a network theory. Even its central message supports this view. Of the eight most oft-quoted principles at the centre of the essay, only three are explicitly about connections. The rest are concerned with processes, axioms, and attitudes that relate to what’s inside the black boxes (the network nodes). These might, charitably, be seen as supportive of networks, but are far more to do with how to learn in and as part of a self-organizing complex adaptive system rather than how the network itself embodies learning. That’s a big part of what makes it useful: we need such theories to make sense of the changing context in which we find ourselves, in which older theories (especially those embedded in a view of education as a formal process involving a teacher) seem inadequate. It also prevents it from being a complete theory of learning – there are other theories and models that take a different systems view (or even, perhaps, a non-systems view) that may be more appropriate, at least in combination with it, to some circumstances – but that’s no bad thing. In fact, it is kind of implied in one of the central axioms of the theory itself: “Learning and knowledge rests in diversity of opinions“.  This has been one of mine.

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Premature optimism

Despite careful future-proofing and a structure that was deliberately built to evolve over time so that it would remain current, my elderly Social Computing course has pretty much reached the end of its useful life, so I have started to revise and refactor it. While doing so, I came across this 2007 article that I had bookmarked for the course. The headline of the article was “Checkmate? MySpace, Bebo and SixApart To Join Google OpenSocial (confirmed).” The answer to the question posed in the headline was, as we now know, a very resounding NO.

Involving every social network of note, apart from Facebook, in a consortium, as well as having the support of many other huge industry players, OpenSocial seemed to me, and to almost everyone else in the field, to be the beginning of something amazing. At the time, I blogged about this article thus:

This is probably the biggest thing ever to happen in the world of social software.
MySpace, Bebo and SixApart are in on the deal that already includes Orkut, Salesforce, LinkedIn, Ning, Hi5, Plaxo, Friendster, Viadeo and Oracle (yes, Oracle). As the article says, checkmate for Facebook, but it can’t be long before they join in.
I can hardly wait to start playing.
The range of possible educational uses is staggeringly large. Maybe not as big as the invention of the Web itself, but potentially as transforming. I think that we have just seen the start of a new era.

I couldn’t have been more wrong. Facebook did not join in at all and it was anything but killed off by the consortium. In fact, the Evil One took the precise opposite course, ruthlessly locking more and more and more in, sucking in more and more from other systems, and giving less and less back, until they pretty much owned the space. Facebook always fought dirtier, with a more single minded focus on one and only one thing (building a social network), regardless of the consequences or moral imperatives, than anyone else. Of the list of prominent OpenSocial members back in 2007, a few just about limp along, but many are dead, including Google’s own Orkut and Google Plus. Friendster died, had a brief revamp as a gaming network then died again. MySpace and Hi5 limp along miserably. Ning did what seemed to be a really bad thing by completely closing its (originally beautiful, elegant, crowd-sourced, evolving) system and converting it into a paid social media hosting service, but it still survives in that role. SixApart is an empty shell company doing nothing of note. The rest barely register, then or now.

A fair number of social software companies fought back by trying to use Facebook’s own evil strategies, mostly without much success – I was particularly sad that Twitter, in particular, slowly removed most means to use its data in any meaningful or useful way outside the application.  A number of them survived by being what Terry Anderson and I call sets rather than networks, thereby avoiding head-on confrontation by not being perceived as social networks. Though many had a social network, that wasn’t their primary role. Many of these remain hugely successful – indeed, YouTube remains perhaps the only centralized social medium to resoundingly beat Facebook in user numbers, though Wikipedia comes pretty close by some measures. Reddit continues to thrive largely unaffected by the evil giant, and the set-oriented face of Twitter continues to do pretty well, even if its social networking side waned long ago. Though too seldom recognized as social media in commentaries on the subject, the success of Amazon and eBay is largely down to their clever use of social software: they are vast, and support vast communities. There are also lots of systems that are doing comfortably in their non-competing niches, such as Pinterest, Tumblr, Medium, and many others. A few vertical social networks, with specific foci (LinkedIn being by far the biggest and most successful example), continue to to very well: in my own fields of education and technology,, GitHub, ResearchGate and StackOverflow are doing fine, for instance (though they tend to be quite set-oriented, which helps), and various MOOC providers and MOOC-ish providers like the Khan Academy are thriving through the use of social software. A few did too well and got taken over by bigger companies, including by the evil cousins Facebook (WhatsApp and Instagram) and Microsoft (GitHub and LinkedIn). This is tragic.

OpenSocial is not exactly dead: there is still a group working on it within the W3C and there are a few implementations still available in minor social systems like MySpace and Hi5. However, it has not progressed significantly since 2013, and Apache closed down Shindig, the main reference implementation, several years ago. Other related standards, like OpenID, OAuth and even the venerable RSS are still going strong but slowly decaying, albeit that they have an enormous momentum that won’t make them easy to kill for a long time to come.

Those of us who continue to dream of an open, distributed, social Web appear to lurk around the periphery. Mastodon continues to grow, Solid looks promising, though I would certainly not put any money on either of them coming to challenge the monoliths in any serious way. However, the biggest distributed social web system, by orders of magnitude, is sitting in front of us, hiding in plain view, and it is still growing very successfully. The open source WordPress powers about a third of all websites, and is rich in social features right out of the box. To put that in perspective, there are as many or more sites running on WordPress than there are sites of any description running on any of the major web servers (obviously, WordPress can run on any major web server). There are plugins to support most distributed standards and protocols, from WebMention to Solid, and much in between, and it supports basics like RSS, in-site collaboration, and public comments (including trackbacks and pingbacks) out of the box. There’s plentiful support, mainly through plugins or manual embedding, for mashups. Sure, it is a long way from the vision that many of us have of a fully distributed open social web, but it does much of the job well enough. And, yes, many WordPress sites are not particularly, if at all, social, but the majority of them have at least some support for engagement, and virtually all are an active part of the Web itself, linking to one another and other sites in many ways, including blogrolls and embedded feeds. A vast number, again most likely the majority, provide hooks or feeds into more than one of the monoliths which, though bad in itself, sneaks in distribution via the back door because the posts themselves remain independent and not locked in. My own site feeds its posts into Twitter, for example, and has the usual set of links that allow its pages to be shared via various social media. It also automatically sucks in a few of my RSS feeds from the Landing and elsewhere, so it is already a mashup. A fair number use BuddyPress, which explicitly overlays a social network onto the system, though they are all at the shallowest end of the long tail.

WordPress itself is inelegant from a software perspective (and it is built on similarly inelegant systems like PHP and MySQL) but, like the tools it is made from, it is very well evolved indeed. It just works. It is one of the most manageable server-based apps I have ever used and demands little skill of its users for authoring. It has an incredibly rich developer community that provides tens of thousands of themes and plugins that can be used to make it do almost anything. Its hybrid open/proprietary model is about the most sensible I have seen. Automattic (the company responsible for it) do try to sell you their hosted services, especially through the JetPack bundle of plugins that it comes with by default, but not objectionably, and they very actively support the open source code and its self-hosting users. Most of their services provide an acceptable free tier and, of course, you don’t have to use them at all as there are many alternatives available. Automattic make their money through providing high quality, convenient tools and services at fair prices, not by locking you in. The plugin marketplace is wide open, with a good balance of open source and commercial options that again provide plentiful choice, and there’s a lot more to be found outside the plugin site hosted by WordPress themselves. And, yes, there are even a couple of OpenSocial plugins, albeit feebly implementing a tiny subset of the standard. It’s not the future we all dreamed of, but it’s as good as it gets right now.

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A blast from my past: Google reimplements CoFIND

While searching for a movie using Google Search last night I got (for the first time that I can recall) the option to tag the result, as described in this article. I was pleased to discover that the tool they provide for this is virtually identical (albeit with a much slicker and more refined modern interface overhaul) to the CoFIND system that underpinned my PhD, that I built over 20 years ago now. You are presented with a list of tags, and can select one or more that describe the movie, and/or suggest your own, effectively creating a multi-dimensional rating system that other users can use to judge what the movie is like. When I rated the movie last night, for instance, popular tags presented to me included ‘terrible acting’, ‘bad writing’, ‘clichéed’, ‘boring’ and so on. Having seen the movie, I agree about the bad writing and clichés – it was at the terrible end of the scale – but actually think most of the acting was fairly good, and it was not very boring. What is interestingly different about this, compared with other tagging systems currently available, is that this kind of tag is fuzzy – it represents a value statement about the movie that exists on a continuum, not a simple categorization. The sorting algorithm for the list of tags presented to you appears (like my original CoFIND) to be based mainly on simple popularity though it is possible that (like CoFIND) it uses other metrics like tag age and perhaps even a user model as well. It’s vastly more useful and powerful than the typical thumbs-up/thumbs-down that Google normally provides. The feature has sadly not reappeared on subsequent movie searches, so I am guessing that Google is either still testing it or trying to build up a sufficient base of recommendations by occasionally showing it to people, before opening it up to everyone.

Just in case Google or anyone else has tried to patent this, and to assert my prior art, you can find a description and screenshots (p183 and p184) of my original CoFIND system in chapter 6 of my PhD thesis as well as in many papers before and since, not to mention in a fair few blog posts. It’s out there in the public domain for anyone to use. The interface of my system was, even by the standards of the day, pretty awful and not even a fraction as good as the one provided by Google, but those were different times: it did work in exactly the same way, though. As I developed it further, the interface actually became much worse. Over the course of a few years I experimented with quite a range of methods to get and display ratings/tags, including an ill-conceived Likert scale as well as a much more successful early use of tag clouds, all of which added complexity and reduced usability. Some of these later systems are described and discussed in my PhD too.  In its final, refactored, and heavily evolved form that postdates my PhD by several years, a version of Cofind (last modified 2007) is actually still available, that almost reverts to the the Google-style tag selection approach of the original, with the slight tweak that, in CoFIND, you can disagree about any particular tag use (for instance, if you don’t believe it to be inane then you can cast a vote against that tag).  The interface remains at least as awful as the original, though, and not a patch on Google’s. The main other differences, apart from interface variations, are that the nomenclature differs (I used ‘qualities’  rather than ‘tags), and that CoFIND could be used for anything with a URL, not just movies. If you’re interested, click on any resource link in the system and you’ll see my primitive, ugly, frame-based attempt to do very much the same as Google is doing for movies (nb. unless you are logged in you cannot add new qualities but, for authorized users, a field appears at the end that is just like Google’s). Though primarily intended to share and recommend educational resources, CoFIND was very flexible and was, over the years, used for a range of other purposes from comparing interface designs to discovering images and videos. It was always flaky, ugly, and unscalable, but it worked well enough for my research and teaching purposes, and (because it provides RSS feeds) it was my go-to tool for sharing interesting links right up until 2007, after which I reverted to more conventional but better-maintained tools like the Landing or WordPress. 

A little bit of CoFIND background

I’ve written a fair bit about CoFIND, formally and informally, but not for a few years now, so here’s a little background for anyone that might be interested, and to remind myself of a little of what I learned all those years ago in the light of what I know now.

An evolving, self-organizing, social bookmarking tool

I started my PhD research in 1997 with the observation that, even then, there was a vast amount of stuff to learn from that could be easily found on the Web, but that it was really difficult to find good stuff, let alone stuff that was actually useful to a particular learner at a particular stage in their development. Remember that this was before Google even started, so things were significantly worse then than they are now. Infoseek was as good as it got.

I had also observed that, in any group of learners, people would find different things and, between them, discover a much larger range of useful resources than any one learner (or teacher) could do alone, a fact that I use in my teaching to this day. These would likely (and, it turns out, in reality) be better than what a teacher could find alone because, though individual learners might be less able to distinguish low from high quality, they would know what worked for them and sufficient numbers of eyes would weed out the bad stuff as long as there was a mechanism for it. This was where I came in.

The only such mechanisms widely available at the time were simple rating systems. However, learners have very different learning needs, so I immediately realized that ‘thumbs-up’ or simple Likert scales would not work. This was not about finding the one ‘best’ solution for everyone, but was instead concerned with finding a range of alternatives to fill different ecological niches, and somehow discovering the most useful solution in that niche for a given learner at a given time.  My initial idea was to make use of a crowd, not an individual curator, and to employ a process closely akin to natural evolution to kill bad suggestions and promote good ones, in order to create an ecosystem of learning resources rather than a simple database. CoFIND was a series of software solutions that explored and extended this initial idea.

CoFIND was, on the face of it, what would eventually come to be called a social bookmarking system – a means for learners to find and to share Web resources (and, later, other things) with one another, along with a mechanism for other learners to recommend or critique them. It was by no means the first social bookmarking system, but it was certainly not a common genre at the time, and I don’t think such a dedicated system had ever been used in education before (for all such assertions, I stand to be corrected), though other means of sharing links, from simple web pages or wikis or discussion forums to purpose-built teacher-curated tools were not that uncommon. A lot of my early research involved learning about self-organization and complex systems, in particular focusing on evolution and stigmergy (self-organization through signs left in the environment). As well as the survival-of-the-fittest dynamic, evolution furnished me with many useful concepts that I made good use of, such as the importance of parcellation, the necessity of death, ways to avoid skyhooks, benefits of spandrels, ways to leverage chance (including extinction events), and various approaches to supporting speciation.  As a result of learning about stigmergy I independently developed what later came to be know as tag clouds. I don’t believe that mine were the first ever tag clouds – weighted lists of one sort or another had been around for a few years – but, though mine didn’t then use the name, they were likely the first uses of such things in educational software, and almost certainly the first with this particular theoretical model to support them (again, I am happy to be corrected).

A collaborative filter

The name CoFIND is an acronym for ‘collaborative filter in n-dimensions’. The n dimensions were substantiated through what we (my supervisors and I) called qualities. We went through a long list of possible names for these, and I was drawn for a while to calling them ‘values’, but (unfortunately) we never thought of ‘tags’ because the term was not in common use for this kind of purpose at the time. After a phase of calling them q-tags, I now call qualities by the much more accessible name of ‘fuzzy tags’. Fuzzy tags are not just binary classifications of a topic but tags that describe what we value, or don’t value, in a resource, and how much we value it. While people may sometimes disagree about binary classifications (conventional tags) it is always possible to have different opinions about the application of fuzzy tags: some may find something interesting, for instance, while others may not, and others may feel it to be quite interesting, or incredibly so. Fuzzy tags are to do with fuzzy sets, that have a continuum of grades of membership, which is where the name comes from. Different versions of CoFIND used different ways to establish the fuzziness of a tag – the Likert Scale used in a few mid-period versions was my failed attempt to make it explicit but this was a nightmare for people to actually use.  The first versions used the same kind of frequency-based weighting as Google’s movie tags, but that was a bit coarse – I was uncomfortable with the averaging effect and the unbridled Matthew Effect that threatened to keep early tags at the top of the list for all time, that I rather coarsely kept in check with a simple age-related weighting that was only boosted when they were used (the unfortunate side effect of which being that, if a system was not used for a few weeks, all the tags vanished in a huge extinction event, albeit that they could be revived if anyone ever used one of the dead ones again). The final version was a bit in-between, allowing an indefinitely large scale via simple up-down ratings, balanced with an algorithm that included a decaying but renewable novelty weighting that adjusted to the frequency of use of the system as a whole. This still had the peculiar effect of evening out/initializing all of the tags over time if no one used the system, but at least it caused fewer catastrophes.

‘Traditional’ collaborative filters simply discover whether things are likely to be more valued or less valued on a usually implicit single dimension (good-bad, liked-disliked, useful-useless, etc). CoFIND’s qualities/fuzzy tags allowed people to express in what ways they were better or worse – more interesting, less helpful, more complex, less funny, etc, just as Google’s movie tagging allows you to express what you like or dislike about a movie, not just whether you liked it or not. In many tag-based systems, people tend to use quite a few simple tags that are inherently fuzzy (e.g. Flickr photos tagged as ‘beautiful’) but they are seldom differentiated in the software from those that simply classify a resource as fitting a particular category, so they are rarely particularly helpful in finding stuff to help with, say, learning.

I was building CoFIND just as the field of collaborative filtering was coming out of its infancy, so the precise definition of the term had yet to be settled. At the time, a collaborative filter (then usually called an ‘automated collaborative filter’) was simply any system that used prior explicit and/or implicit preferences of a number of previous users (a usually anonymous crowd) to help make better recommendations and/or filter out weaker recommendations for the current users. The PageRank algorithm that still underpins Google Search would perhaps have then been described as a collaborative filter, as was one of its likely inspirations, PHOAKS (People Helping One Another Know Stuff), that mined Usenet newsgroups for links, taking them as an implicit recommendation within the newsgroup topic area. By this definition, CoFIND was in fact a semi-automated collaborative filter that combined explicit preferences with automated matching. Nowadays the term ‘collaborative filter’ tends to only apply to a specific subset of recommender systems that automatically predict future interests by matching individual patterns of behaviour with those of multiple others, whether by item (people who bought this also bought…) or user (people whose past or expressed preferences seem to be like yours also liked…). I think that, if I built CoFIND today, I would simply refer to it more generically as a recommender system, to avoid confusion.

Disembodied user models

Rather than a collaborative filter, back in the late 90s Peter Brusilovsky saw CoFIND as a new species of educational adaptive hypermedia, as it was perhaps the first (or at least one of the first) that worked on an open corpus rather than through a closed corpus of linked resources. However, he and I were both puzzled about where to find the user model, which was part of Peter’s definition of adaptive hypermedia. I didn’t feel that it needed one, because users chose the things that mattered to them at runtime. In retrospect, I think that the trick behind CoFIND, and what still distinguishes it from almost all other systems apart from this fairly new Google tool, is that it disembodied and exposed the user model. Qualities were, in essence, the things that would normally be invisibly stored in a user model, but I made them visible, in an extreme variant of what Judy Kay later described as scrutable adaptation.  In effect, a learner chose their own learner model at the time they needed it. The reasoning behind doing so was that, for learners, past behaviour is usually a poor predictor of future needs, mainly because 1) learning changes people (so past preferences may have little bearing on future preferences), and 2) learning is driven by a vast number of things other than taste or past actions: we often have a need for it thrust upon us by an extrinsic agency, like a teacher, or a legislative demand for a driving licence, for instance. Qualities (fuzzy tags) allow us to express the current value of something to us, in a form that we can leave behind without a lot of sticky residue, and that future users can use. In fact, later versions did tend to slightly emphasize similar things to those people had added, categorized, or rated (fuzzily tagged) earlier, but this was just a pragmatic attempt to make the system more valuable as a personal bookmark store, and therefore to encourage more use of it, rather than an attempt to build a full-blown collaborative filter in the modern sense of the word.

Moving on

I still believe that, in principle, this is an excellent approach and I have been a little disappointed that more people have not taken up the idea and improved on it. The big and, at the time, insurmountable obstacles that I hit were 1) that it demands a lot of its users to provide both tags and resources, with little obvious personal benefit, so it is unlikely to get a lot of use, 2) that the cold-start problem that affects most collaborative filters (it relies on many users to be useful but no one will use it until it is useful) is magnified exponentially by every one of those n dimensions so it really demands a big lot of users, and 3) that it is fiendishly hard to represent the complex ecological niches effectively in an interface, making the cognitive load unusably high. Google seems to have made good progress on the last point (an evolution enabled by improved web standards and browsers combined with a simplification of the process, which together are enough to reduce the cognitive load by a sizeable amount), and has plenty sufficient numbers of users to cope with the first and second points, at least with regard to movie recommendations. It remains challenging to see how this would work in an educational setting in anything less than the largest of MOOCs or the most passionately focused of user bases. However, I would love to see Google extend this mechanism to OERs, courses, and other educational resources, from Quora answers to Kahn Academy tutorials, because they do have the numbers, and it would work well. For the same reasons, it would also be great to see it applied to something like StackExchange or similar large-scale systems (Reddit perhaps) where people go to seek solutions to learning problems. I doubt that I will build a new version of CoFIND as such, but the ideas behind it should live on, I think, and it’s great to see them back on a system as big as Google Search, even if it is so far only experimental and, so far, just used to recommend movies.

A Universal Moral Code?

57 varieties It appears that there may be a universal moral code, at least across 60 very different cultures, at least according to this large metastudy of anthropological literature. The authors focus explicitly and exclusively on manifestations of cooperative behaviour, so the level of abstraction is fairly high. I’m not totally convinced that it constitutes anything as formulaic as a code, and it contributes little or nothing to philosophical or pragmatic debates about ethical behaviour, but it is nonetheless a very interesting discovery.

The seven moral behaviours/values that the authors hypothesized would be universal, based on their theory of morality-as-cooperation (a game-theory inspired model) are:

  • allocation of resources to kin (family values),
  • coordination to mutual advantage (group loyalty),
  • social exchange (reciprocity),
  • contest between hawks (showing bravery), and
  • doves (showing respect),
  • division (fairness), and
  • possession (property rights).

The hypothesis was confirmed by the analysis. Fascinatingly, one almost-exception was found relating to property rights. In Chuuk society, openly stealing from others is valorized as a form of bravery, albeit that other indicators show that property rights are normally respected by most Chuuk people most of the time, so all that this shows is that bravery is sometimes considered a more important moral value than respect for other people’s property. I suspect similar behaviours might be found among gangs in many cultures, where such actions may signify group loyalty, bravery, respect for other group members, and so on. Assuming that people generally behave well according to their social norms (which they manifestly do), moral issues are only ever a matter of deliberation when they come into conflict with one another so this is not so much an exception as a proof of the rule.

The authors wisely note the limitations of the study, which uses papers that were not originally intended to explore ethical issues, that only looks at 60 cultures, and that uses a methodology that is almost guaranteed to introduce bias, albeit that they took sensible precautions to limit the worst effects of this. They cannot claim that these are the only 7 universals, by any means: these are just the ones that they looked for. Nor can they even reliably claim universality, though this is a decent sample so any exceptions are likely to be quite exceptional, and the results do support their theory.  Because their focus is solely on cooperative strategies, there is nothing relating to pretty big ethical questions on which most societies are likely to agree, like whether it is OK to kill other people, or eat them, or lie to them, and so on. There’s still a lot of scope for variation in ethical beliefs and behaviours within this broad framework.

None-the-less, this provides a rare chunk of empirical evidence to support there being some universality to at least broad groups of moral behaviours and values. Mostly, and unsurprisingly given the game-theoretical basis of the model, the concerns addressed are what you might predict if you were thinking about how a complex society might develop methods of cooperation, given a few basic evolutionary assumptions about gene preservation, an innate urge to hang around with others of your species, and a limit on resources. Such patterns are likely to be innate simply due to the inevitable consequences of a large group of reproducing social animals living together with limited resources. This implies that we might see exactly the same universals in other social species in such circumstances too, at least in those with the capacity for complex thought like most mammals and higher avians. I can’t immediately think of any obvious real-life exceptions, though there are certainly differences in the significance and influence of each value in different social species. Also, at least some of the values may not translate well to truly eusocial creatures like naked mole rats, nor to species where territory or other forms of ownership mean very little (some fish, for instance) nor to those that do not normally spend a lot of time together (cats or octopuses, for instance).

There are potential conflicts between several of the values, the most obvious being bravery/respect, fairness/property rights, though it is possible to imagine conflicts between any or all of them, as the example of the Chuuk people illustrates. The fact that these might be universal ethical patterns does not imply that there are therefore any universal solutions to ethical dilemmas. Nor does universality have any bearing on the fact-value gap or the naturalistic fallacy: universality does not imply rightness. It does, though, provide a promising theoretical model that may be useful when imagining alien intelligences, including those we might one day design ourselves.

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Power, responsibility, maps and plans: some lessons from being a Chair

Empty chair

I’ve reached the end of my first week of not Chairing the School of Computing & Information Systems here at Athabasca University, which is now in the capable hands of the very wonderful Ali Dewan.

Along with quite a few people that I know, I am amazed that I stuck it out for over 3 years. I was a most reluctant Chair in the first place, because I’d been in middle management roles before and knew much of what to expect. It’s really not my kind of thing at all. Ideologically and temperamentally I loathe hierarchies but I’d rather be at the top or at the bottom if I have to be in one at all. However, with the help of some cajoling, I eventually convinced myself that being a Chair is essentially much the same as being a teacher, which is an activity that I both enjoy and can mostly do reasonably well. Like a teacher (at least one that does the job well), the job of a Chair is to help nurture a learning community, and to make it possible for those in that community to achieve what they most want to achieve with as few obstacles as possible. Like teaching, it is not at all about telling, but about listening, supporting, and helping others to orchestrate the process for themselves, not so much about leadership as followership, about being a supportive friend. It’s a bit about nudging and inspiring, too, of sharing the excitement of discovery and growth with other people. It’s a bit about challenging people to be who they want to be, collectively and individually. It’s a bit about solving problems, a bit about being a shoulder to cry on, a bit about being a punchbag for those needing to let off steam, an arbiter in disputes. It could be fun. And I could always give it up after a few months if it didn’t work out. That was what I convinced myself.

On the bright side, I don’t think that I broke anything vital. I did help a couple of good things to happen, and I think that most of my staff were reasonably happy and empowered, a few of them more than before. One or two were probably less happy. But, in the grand scheme of it all, I left things much the same as or a little better than I found them, despite often strenuous efforts to bring about far more exciting changes. My tenure as Chair was, on the whole, not great, but not terrible. I have been wondering a bit about why that happened, and what I could or should have done differently, which is what the next part of this post is about.

Authority vs influence, responsibility vs power

One of my most notable discoveries (more accurately, rediscoveries) is that authority and responsibility barely, if at all, correlate with power and influence. In fact, for a middle management role like this, the precise inverse is true. One of the strange paradoxes of being in a position of more responsibility and authority has been that, in many ways, I feel that I’ve actually had considerably less capacity to bring about change, or to control my own life, than I had as a plain old professor.  It’s just possible that I may have overused the joke about a Chair being the one everyone gets to sit on, but it resonated with me. And this is not to contradict Uncle Ben’s sage advice to Spiderman – it may be true that with great power comes great responsibility, but that doesn’t mean that with great responsibility comes great power.

Partly the problem was just the myriad small but draining demands that had to be done throughout the course of a typical day (most of which were insufferably tedious and mostly mindless bureaucratic tasks that anyone else could do at least as well), as well as having to attend many more meetings, and to engage in a few much lengthier tasks like workload planning. It wore me down. I put a lot of things that were important to me, but that didn’t contribute to my role, to one side because there were too few chunks of uninterrupted time to do them. Blogging and sharing on social media, for instance.

Partly it was because I felt that my role was primarily to support those that reported to me – I had to do their bidding much more than they had to do mine. Instead of doing what I would intrinsically wish to do, much of the time I was trying to do what those that I supervised required of me. This was not just a result of my own views on leadership. I think a lot of it would have affected most people in the same position.

Partly it was because I often felt (with a little external reinforcement) that I must shut up and/or toe the line because I represented the School or the Dean or the University. Being the ‘face’ of the school meant that I often felt obliged to try to represent the opinions and demands of others, even when I disagreed with them. Often, I had to present a collective agenda, or that of an individual higher up the foodchain, rather than my own, whether or not I found it dull, mistaken, or pointless. Also, being a Chair puts you in some sensitive situations where a wrong step can easily lead to litigation, grievance proceedings, or (worse) very unhappy people. I’m not naturally tactful or taciturn, to say the least, so this was tricky at times. I sometimes stayed quiet when I might otherwise have spoken out.

The upshot of it is that, as a Chair, I was directly responsible both to my Dean and to the people I supervised (not to mention more or less directly to students, visitors, admins, tech staff, VPAs, etc, etc), and I consequently felt that I had very little control over my own life at all. Admittedly it was at least partly due to my very intentional approach to the role, but I think similar issues would emerge no matter what leadership style I had adopted. There’s a surprising amount of liberty in being at the bottom of a hierarchy, at least when (like all academics) you are expected – nay, actually required – to be creative, self-starting, and largely autonomous in your work. Academic freedom is a wonderful thing, and some of it is subdued when you move a little way up the scale.

Some compensations 

There have been plentiful compensations, of course. I wouldn’t have stayed this long if it had been uniformly awful. Being a Chair made some connections easier to make, within and beyond the university, and has helped me get to know my colleagues a lot better. And I have some great colleagues: it would have been much harder to manage had I not had such friendly, supportive, smart, creative, willing, and capable team to work with. I solved or at least made fair progress on a few problems, none huge but all annoying, and helped to lay the groundwork for some ongoing improvements. There were opportunities for creativity here and there. I will miss some of the ways I could help shape our values and systems simply thanks to being a Chair, rather than having to actually work at it. I’ll miss being the default person people came to with interesting ideas. I’ll miss the very small but not trivial stipend. I’ll miss being involved by default in most decisions that affect the school. I’ll miss the kudos. I’ll miss being a formal hub in a network, albeit a small one.

Not quite like teaching

In most ways I was right about the job being much like teaching. Most of the skills, techniques, goals, and patterns are very similar, but there’s one big difference that I had not thought enough about. On the whole, most actual teachers engage with learners over a fairly fixed period, or at least for a fixed project, and there is a clear beginning, middle, and end, with well defined rituals, rules, and processes to mark their passage. This is even true to an extent of more open forms of teaching like apprenticeship and mentorship. Although this in some ways relates to any kind of project, the fact that people, working together in a social group, are both the focus and the object of change, makes it fairly distinctive. I can’t think of many other human activities that are particularly similar to teaching in this regard, apart from perhaps some team sports or, especially, performing arts.

To be a teacher without a specific purpose in mind is a surprisingly different kind of activity, like producing an improvised play that has no script, no plot, no beginning, and no end. Although a teacher is responsible to their students, much as I was responsible to my staff, the responsibility is tightly delimited in time and in scope, so it remains quite manageable, for the most part. In retrospect, I think I should have planned it better. I probably should have set more distinct goals, milestones, tasks, sub-projects, etc. I should have planned for a very clear and intentional end, and set much firmer boundaries. It would not have been easy, though, as many goals emerged over the years, a lot changed when we got our new (and much upgraded) administration, and a lot depended on serendipity and opportunism. I had, at first, no idea how long I would stick with the role. Until quite some time into it, I had only a limited idea about what changes I might even be allowed to accomplish (not much, as it happens, with no budget, a freeze on course development, diminishing staff numbers, need to fit faculty plans, etc). It might have been difficult to plan too far ahead, though it would have been really useful to have had a map showing the directions we might have gone and the limits of the territory. I think there may be useful lessons to be learned from this about support for self-directed lifelong learning.

Lessons for learning and teaching

A curse of institutional learning can be the many scales of rigid structure it provides, that too often take agency away from learners and limit support for diversity. However, it also supports an individual learner’s agency to have a good map of the journey ahead, even if all that they are given is the equivalent of a bus route, showing only the fixed paths their learning will take. I have long grappled with the tensions and trade-offs between surfing the adjacent possible and following a planned learning path. I spent a lot of time in the late 1990s and early 2000s designing online systems that leveraged the crowd to allow learners to help one another to learn, but most of them only helped with finding what to do next, or to solve a current problem, not to chart a whole journey. Figuring out an effective way to plan ahead without sacrificing learner control was one of the big outstanding research problems left to be solved when I finished my PhD (in self-organized learning in networks) very many moons ago, and it still is. There are lots of ineffective ways that I and others have tried, of course. Obvious approaches like matching paths through collaborative filtering or similar techniques are a dead-end: there are way too many extraneous variables to confound it, way too much variation in start and end points to effectively cater for, even if you start with a huge dataset. This is not to mention the blind-leading-the-blind issues, the fact that learning changes people so past activity poorly predicts future behaviour, and the fact that there is often a narrative context that assumes specific prior activities have occurred and known future activities will follow. Using ontologies is even worse, because the knowledge map of a subject developed by subject experts is seldom if ever the best map for learning and may be among the worst. The most promising approaches I have seen, and that I had a doctoral student working on myself until he had to give up in the mid 2000s, mine the plans of many experts (e.g. by looking at syllabuses) to identify common paths and branches for a particular subject, combining them with whatever other information can be gleaned to come up with a good direction for a specific learner and learning need. However, there are plenty of issues with that, too, not least of which being the fact that institutional teaching assumes a very distinctive context, and suffers from a great many constraints (from having to be squashed into a standardized length to fitting preferred teaching patterns and schedules), that learners unhindered by such arbitrary concerns would neither want nor need. Many syllabuses are actually thoughtlessly copied from the same templates (e.g. from a professional association model syllabus), or textbooks, and may be awful in the same ways. And, again, narrative matters. If you took a chunk out of one of my courses and inserted it somewhere else it would often change its meaning and value utterly.

This is a problem I would dearly love to solve. Though I stand by my teaching approaches, one of the biggest perennial complaints about the tools and methods I tend to use is that it is easy to feel lost, especially if the helping hands of others are not around when needed. There are always at least a few students who would, as a matter of principle, rather be told what to do, how to do it, and where to go next. The majority would prefer to work in an environment that avoids the need for unnecessary decisions, such as where to upload a file, that have little to do with what they are trying to learn. My role (and that of my tutors, and the design of my courses) is to help them through all that, to relieve them of their dependency on being told what to do, and to help them at least understand why things are done the way they are done. However, that can result in quite inconsistent experiences if I or tutors let the ball slip for a moment. It can be hard for people who have been taught, often over decades, that teaching is telling, and that learning can reliably be accomplished by following a set of teacher-determined steps, to be set adrift to figure it out in their own ways.

It is made far worse by the looming threat of grades that, though eliminated in my teaching itself, still lie in wait at the end of the path as extrinsic targets. Students often find it hard to know in advance how they will meet the criteria, or even whether they have met them when they reach the end. I can and do tell them all of this, of course, usually repeatedly and in many ways and using many media, but the fact that at least some remain puzzled just proves the point: teaching is not telling. Again, a lot of manual social intervention is necessary. But that leads to the issue that following one of my courses demands a big leap of faith (mainly in me) that it will turn out OK in the end. It usually takes effort and time to build such trust, which is costly for all concerned, and is easily lost with a careless word or a missed message.  It would be really useful for my students to have a better map that allows them to plan detours and take more alternative transit options for themselves, especially with overlays to show recommended routes, warnings of steep hills and traffic, and real-time information about the whereabouts of people on their network and points of interest along the way. It would, of course, also be really handy to have a big ‘you are here’ label.  I would have really liked such a map when I started out as Chair.

Moving on

Leaving the Chair role behind still feels a little like stepping off a boat after a rough voyage, and either the land or my legs feel weird, I’m not sure which. As my balance returns, I am much looking forward to catching up with things I put to one side over the past 3 years. I’m happy to be getting back to doing more of what I do best, and I hope to be once more sharing more of my discoveries and cogitations in posts like this. It’s easier to move around with your feet on the ground than when you are sitting on a chair.


Google kills off Hangouts and Allo in a cull to its messaging apps

Well, this is rotten.

As the article suggests, the loss of Hangouts and Allo was pretty inevitable, given the large overlap in functionality between them and various Google apps (notably their Messages app), and that Google has a long history of dropping popular (usually ‘free’) tools on which people rely. But Google is only notable because it is so big. It has become an all-too-common feature of working in a centralized, cloud-based ecosystem that those of us who rely on tools created by the big few are completely at their mercy when they decide to make changes, or when they simply go under. The same is true whether we pay for it or not: Athabasca University’s use of O365, for instance, forces us to accept whatever ‘upgrades’ Microsoft choose to inflict on us (including loss of services, features, and functionality) despite the large amount of money it costs us to use it. But a very similar problem affects totally open and cool systems like the much missed Firefox Hello, that was ignominiously killed off a couple of years ago.

If everyone were using open standards, as I believe they should, then this would not be a really serious problem – we’d just switch to a different provider, using the same protocols, maybe with different apps and, perhaps, using different hosts. But, for most of us, that’s not how things work any more. Gone (I hope temporarily) are the days when the community established standards, and people wrote apps that used them. The Web couldn’t happen again today, nor email, nor SFTP, nor telnet, nor any number of critical protocols on which we all rely, whether we know it or not. It’s not that there is no need – the fact that people rely on things like Hangouts or Skype demonstrates a big demand, and it’s actually quite bizarre that a perfectly acceptable protocol (XMPP) is actively ignored for such things. It’s like the bad old days before TCP/IP glued everything together all over again, only worse, because the Internet has become a critical service, and it affects so many more people than before. Maybe the odd API has been standardized (WebRTC, for instance) but that’s not the same thing as a real protocol, and nothing like as universally useful.

Trouble is, if centralized organizations (mainly commercial companies) lock us into changes in ways we don’t like, or the service vanishes, or terms and conditions change to something unacceptable, we are royally screwed. This is the fundamental problem with the cloud, and why we should be very wary of using it. It’s not bad in principle – you can use the cloud to follow open standards if you shop around or rely on your own management skills – but it’s really bad when open standards are flagrantly ignored.

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Microsoft under GDPR microscope for Office 365 and OneDrive | Alphr

No big surprises here to anyone that has ever so much as glanced at Microsoft’s business model and historical abuse of its customers, but definitely a matter of concern for organizations (like Athabasca University) that rent their services from Microsoft, especially given the fact that hosting for O365 is in Trumpland. In brief, Microsoft has been secretly collecting user data from many apps (including the Office suite) without telling its users, thus failing Privacy 101.

We should not be using this service, and nor should anyone outside the US who cares about confidentiality or privacy. Those in the US who deal with sensitive data should also avoid it, but they’ve got bigger privacy problems to worry about than this.

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