The limits and limitations of business requirements

Athabasca University’s Digital Governance Committee recently got into a heated debate about whether and why we should support Zoom. It was a classic IT manageability vs user freedom debate and, as is often the way in such things, the suggested resolution was to strike up a working group/sub-committee of stakeholders to identify business requirements that the IT department could use to find an acceptable solution. This approach is eminently sensible, politically expedient, tried-and-tested, and profoundly inadequate.

horse-carAs Henry Ford (probably never) said, “if I’d asked people what they wanted they would have said ‘a better horse'”.

A design approach that starts by gathering business requirements situates the problem in terms of the current solution, which is comprised of layers of solutions to problems caused by other solutions. For simple ‘hygiene’ tech that serves a hard, well-defined business function – leave reporting, accounting, etc – as long as you do properly capture the requirements and don’t gloss over things that matter, that’s normally fine, because you’re just building cogs to make the existing machine work more smoothly. However, for very soft social technologies like meetings, with potentially infinite ways of using them (by which I mean purposes, techniques, ways of assembling them with other technologies, and so on), no list of requirements could even begin to scratch at the surface. The thing about soft technologies – meetings, writing, pencils, pedagogies, programmable computers, chisels, wheels, technologies of fire, groups, poetry, etc – is that they don’t so much solve problems as they create opportunities. They create adjacent possible empty niches. In other words, they are defined by the gaps they leave, much more than the gaps they fill. What happens as a result of them is fundamentally non-deducible. 

Solving different problems, creating different possibles

Meetings are assemblies of vast ranges of technologies and other phenomena, and they serve a vast number of purposes. Meetings are not just one technology but a container for an indefinitely large number of them. They are, though, by and large, solutions to in-person problems, many of which are constrained by physics, physiology, psychology, and other factors that do not apply or that apply differently online. Most webmeeting systems are attempts to replicate the same solutions or (more often) to replicate other webmeeting systems that have already done so, but they are doomed to be pale shadows of the original because there are countless things they cannot replicate, or can only replicate poorly. Among the phenomena that are the default in in-person meetings are, for example:

  • the immense salience brought about by travelling to a location, especially when it involves significant effort (lost in webmeetings);
  • the fact that it forces attention for a sustained period   (most webmeeting software and ways of using it makes inattention much easier);
  • the social bonding that we have evolved to feel in the presence of others (not well catered for in webmeeting software);
  • the focus and meaning that comes from the ‘eventness’ of the occasion (diluted in webmeetings);
  • the ability to directly work together on an issue or artefact (limited in some ways in webmeetings, though potential exists for collaborative construction of digital artefacts);
  • the inability to invisibly escape (easy in most webmeetings);
  • the microexpressions, postures, movements, smells, etc that support communication (largely lost in webmeetings);
  • the social bonding value of sharing food and drink (lost in webmeetings);
  • the blurred boundaries of entering and leaving, the potential to leave together (usually lost in webmeetings);
  • the bonding that occurs in having a shared physical experience, including adversities such as a room that is too hot, roadworks outside, wasps in the room, etc, as well as good things like the smell of good coffee or luxurious chairs (not remotely possible in webmeetings, apart from when the tech fails – but then the meeting fails too);
  • the support for nuances of verbal interaction – knowing when it’s OK to interrupt, being able to sigh, talk at once, etc, not to mention having immediate awareness of who is speaking (webmeetings mostly suck at this);
  • the ability to cluster with others – to sit next to people you know (or don’t know), for instance (rarely an option in most webmeetings, and nothing like as salient or rich in potential as its in-person counterpart even when allowed);
  • the salience of being in a space, with all the values, history, power relationships, and so on that it embodies, from who sits where to which room is chosen (hardly a shadow of this in most webmeetings);
  • the ability to stand up and walk around together (a motion-sickness-inducing experience in webmeetings);
  • the problems and benefits of both over-crowding and excessive sparsity (very different in webmeetings);
  • the means to seamlessly integrate and employ other technologies, including every digital technology as well as paper, dance, desks, chairs, whiteboards, pins, clothing, coffee, doors, etc, etc, etc. (webmeetings offer a tiny fraction of this);
  • and so on.

A few of these might be replicated in current or future webmeeting software, though usually only in caricature. Most simply cannot be replicated at all, even if we could meet as virtual personas in Star Trek’s holodecks. Of course there are also many things that we should be grateful are not replicated in online meetings: conspicuous body odour, badly designed meeting rooms, schedule conflicts, and so on, as well as the unwanted consequences of most of the phenomena above. These, too, are phenomena that the technologies of meetings are designed around.  In-person meetings are incredibly highly-evolved technologies, making use of technological and non-technological phenomena in immensely subtle ways, as well as having layers of counter-technology a kilometre deep, from social mores and manners to Roberts’ rules, from meeting tables to pens and note-taking strategies. Much of the time we don’t even notice that there are any technologies involved at all (as Danny Hillis quipped, ‘technology’ is anything invented after you were born).

Webmeetings, though, also have distinctive phenomena that can be exploited, such as:

  • the ease of entering and leaving (so breaks are easier to take, they don’t need to last a long time, people can dip in and out, etc);
  • the automation of scheduling and note-taking;
  • the means to record all that occurs;
  • the means to directly share digital tools;
  • the fact that people occupy different spaces (often with tools at their disposal that would be unavailable in a shared meeting space);
  • the captions for the hard of hearing;
  • the integrated backchannels of text chat.

These are different kinds of problem space with different adjacent possibles as well as different constraints. It therefore makes no sense to blindly attempt to replicate in-person meetings when the problems and opportunities are so different. We don’t (or shouldn’t) teach online in the same way we teach in the classroom, so why should we try to use meetings in the same way? For that matter, why have meetings at all?

Dealing with the hard stuff

Some constraints are quite easy to specify. If a matter under discussion needs to be kept private, say, that limits the range of options, albeit that, for such a soft technology as a meeting, privacy needs may vary considerably, and what works for one context may fail abysmally for another. Similarly for security, accessibility, learnability, compatibility, interoperability, cost, reliability, maintainability, longevity, and other basic hygiene concerns. There are normally hard constraints defining a baseline, but it is a fuzzy baseline that can be moved in different contexts for different people and different uses. No one wants unreliable, insecure, expensive, incompatible, unusable, buggy, privacy abusing software but most of us nonetheless use Microsoft products.

It is also not completely unreasonable to look for specific known business requirements that need to be met. However, there are enormous risks of duplicating solutions to non-existent problems. It is essential, therefore, to try to find ways of understanding the problems themselves, as much as possible in isolation from existing solutions. It would be a bad requirement to simply specify that people should be able to see and hear one another in real-time, for example: that is a technological solution based on the phenomena that in-person meetings use, not a requirement. It is certainly a very useful phenomenon that might be exploited in any number of ways (we know that because our ancestors have done it since before humans walked the planet) but it tells us little about why the phenomenon matters, or what it is about it that matters.

It would be better, perhaps, to ask people what is wrong with in-person meetings. It still situates the requirements in the current problem space, but it looks more closely at the source rather than the copy. It makes it easier to ask what purposes being able to see and hear one another during in-person meetings serve, what phenomena it provides, on what phenomena (including those provided by other technologies) it depends, and what depends on it. From that we may uncover the business requirements that seeing and hearing other people actually meet. However, it is incredibly tricky to ask such questions in the abstract: the problem space is vast, complex, diverse, and deeply bound up in what we are familiar with, not what is possible.

It might help to make the familiar unfamiliar, for instance, by holding in-person meetings wearing blindfolds, or silently, or to attempt to conduct a meeting using only sticky notes (approaches I have used in my own teaching about communication technologies, as it happens). This kind of exercise forcibly creates a new problem space so that people can wonder about what is lost, what is gained, reasons for doing things, and so on. If you do enough of that, you might start to uncover what matters, and (perhaps) some of the reasons we have meetings in the first place.

Exploring the adjacent possible

Perhaps most importantly, though, soft technologies are not just solutions to problems. Soft technologies are, first and foremost, creators of opportunities, the vast majority of which we will never begin to imagine. Soft technology design is therefore, and must be, a partnership between the person and the technology: it’s not just about creating a tool for a task but about having a conversation with that tool, asking what it can do for us and wondering where it might lead us. What’s interesting about the ubiquitous backchannel feature of webmeetings, for instance, is that it did not find its way into the software as a result of a needs assessment or analysis of business requirements. It was, instead, an early (and deeply imperfect) attempt at replicating what could be replicated of synchronous meetings before multimedia communication became possible. When designing early web conferencing systems, no one said ‘we need a way of typing so that others can see it’. They looked at what could be done and said ‘hey, we can use that’. The functionality persisted and has become nearly ubiquitous because it’s easy to implement and obviously useful. It’s an exaptation, though, not the product of a pre-planned intentional design process. It’s a side-effect of something else we did – a poor solution to an existing problem – that created new phenomena we could co-opt for other purposes. New adjacent possible empty niches emerged from it.

One way to explore such niches would be to give people the chance to play with a wide range of existing ways of addressing the same problem space. A lot of people have turned their attention to these issues, so it makes sense to mine the creativity of the crowd. There are systems like Discord or MatterMost, that represent a different category of hybrid asynchronous/synchronous tool, for instance, blurring the temporal boundaries. There are spatial metaphor systems with isometric interfaces like Spatial, or Ovice, which can allow more intuitive clustering, perhaps contributing to a greater sense of the presence of others, while enabling novel approaches to (say) voting, and so on. There are immersive systems that more literally replicate spaces, like Mozilla Hubs or OpenSim. I hold out little hope for those, but they do have some non-literal features – especially in ways they allow impossible spaces to be created – that are quite interesting. There are instant messengers like Telegram or Signal, that offer ambient awareness as well as conventional meeting support (MS Teams, reflecting its Skype origins, has that too). There are games and game-like environments like Gather or Minecraft, that create new kinds of world as well as providing real-time conferencing features. And there are much smarter webmeeting systems like Around (that largely solves almost all audio problems, that – crucially – can make the meeting a part of a user’s environment rather than a separate space for gathering, that rethinks text chat as a transient, person-focused act rather than a separate text-stream, that makes working together on a digital artefact a richly engaging process, that automatically sends a record to participants, and more).  And there’s a wealth of research-based systems that we have built over the past few decades, including many of my own, that do things differently, or that use different metaphors. Computer-supported collaborative argumentation tools, for instance, or systems that leverage social navigation (I particularly love Viégas’s and Donath’s ChatCircles from the late 1990s, for instance), and so on. They all make new problems, and all have flaws of one kind or another, but thinking about how and why they are different helps to focus on what we are trying to do in the first place.

Perhaps the best of all ways to explore those adjacent possible empty niches is to make them: not to engineer it according to a specification, but to tinker and play. I’ve written about this before (e.g. here and, paywalled, here, summarized by Stefanie Panke here). Tinkering as a research methodology is a process of exploration not of what exists but of what does not. It’s a journey into the adjacent possible, with each new creation or modification creating new adjacent possibles, a step by step means of reaching into and mapping the unknown. We don’t all have the capacity (in skills, time, or patience) to create software from scratch, but we can assemble what we already have. We can, for instance, try to add plugins to existing systems: it is seldom necessary to write your own WordPress plugin, for example, because tens of thousands of people have already done so. Or we can make use of frameworks to construct new systems: the Elgg system underpinning the Landing, for example, does require some expertise to build new components, but a lot can be achieved by assembling and/or modifying what others have built. Or, if standards are followed, we can assemble services as needed: there are standards like xcon, XMPP, Jabber, IRC, and so on that make this possible. And we don’t need to create software or hardware at all in order to dream. Hand-drawn mockups can create new possibilities to explore. Small steps into the unknown are better than no steps at all.

Stop looking for solutions

Webmeetings that attempt to replicate their in-person inspirations are unlikely to ever afford the flexibility of in-person meetings, because they have fewer phenomena to orchestrate and we are never going to be as adept at using them. The gaps they leave for us to fill are smaller, and our capacity to fill those gaps is less well-developed. However, digital systems can provide a great many new and different phenomena that, with creativity and inspiration, may meet our needs much better. Without the constraints of physical spaces we can invent a new physics of the digital. As long as we treat the problem as one of replicating meetings then it makes little difference what we choose: Zoom, Teams, Webex, Connect, BBB, Jitsi, whatever – the feature set may vary, there may be differences in reliability, security, cost, etc but any of them will do the job. The problem is that it is the wrong job. We already pay for and use at least three major systems for synchronous meetings at AU, as well as a bunch of minor ones, and that is nothing like enough. Those that begin to depart from the replication model – Around being my current favourite – are a step in the right direction, while those that double down on it (notably most immersive environments) are probably a step in the wrong direction. It is not about going forward or backward, though: it is about going sideways.

It is not too tricky to experiment in this particular field. For most digital systems we create our decisions normally haunt us for years or decades, because we become locked in to them with our data. Synchronous technologies can, with provisos, be swapped around and changed at will. Sure, there can be issues with recording and transcripts, there can be a training burden, contracts can be expensive and hard to escape, and tech support may be a little more costly but, for the most part, if we don’t like something then we can drop it and try something else. 

I don’t have a solution to choosing or making the right piece of software for AU’s needs, because there isn’t one. There are countless possible solutions, none of which will suit everyone, many of which will provide parts that might be useful to most people, and all of which will have parts or aspects that won’t. But I do know that the way to approach the problem is not to have meetings to determine business requirements. The solution is to find ways of discovering the adjacent possible, to seek inspiration, to look sideways and forwards instead of backwards. We don’t need simple problem-solving for this kind of situation (or rather, it is quite inadequate on its own): we need to find ways to dream, ways to wonder, ways to engage in the act of creation, ways to play.

 

Solving The Wrong Problems: Why Online Education Is and Must Be Different from In-Person Education – slides from my invited talk at ICEMI 2022

icemi22

These are the slides from my invited talk at the 11th International Conference on Education and Management Innovation (ICEMI 2022), June 11th. The talk went down well – at least, I was invited to repeat the performance at a workshop (where I gave a very similar presentation today – if you’ve seen one, you probably know the content of the other!) and to give a keynote later in the year.

It’s about how methods of teaching that solve problems for in-person teachers don’t apply online, and it provides a bit of advice on online-native approaches. I’ve talked quite a bit about this over the past decade so there’s not much new in it apart from minor refinements, though I have put a greater emphasis on what goes on outside the classroom in physical institutions because I’m increasingly thinking that this matters way more than we normally acknowledge. Notably, I discuss the ways that physical institutional structures and regulations provide significant teaching functions of their own, meaning that in-person teachers can be absolute rubbish or (in some subject areas or topics) even fail to turn up, and students can still learn pretty well. This helps to explain the bizarre phenomenon that, across much of in-person academia, professors and lecturers are not expected to learn how to teach (and many never do).

Here’s the abstract…

In-person educational institutions teach, at least as much as the individual teachers they employ. Students are taken out of their own environments and into that of the institution, signalling intent to learn. The physical environment is built for pervasive learning, from common rooms, to corridors, to campus cafes; students see one another learning, share learning conversations, learn from one another. Even the act of walking from classroom to classroom makes events within them more salient. Structures such as courses, timetables, semesters, and classes solve problems of teaching efficiently within the constraints of time and space but impose great constraints on how teaching occurs, and create multiple new pedagogical and management problems of their own. The institution’s regulations, expectations, and norms play a strong pedagogical role in determining how, and when learning occurs. Combined with other entrenched systems and tools like credentials, textbooks, libraries, and curricula, a great deal of the teaching process occurs regardless of teachers. What we most readily recognize as ‘good’ teaching overcomes the problems caused by these in-person environments, and exploits their affordances.

Online institutions have radically different problems to solve, and radically different affordances to exploit, so it makes no sense to teach or manage the learning process in the same ways. Online, students do not inhabit the environment of the institution: the institution inhabits the environment of the student. It is just one small part of the student’s physical and virtual space, shared with billions of other potential teachers (formal or not) who are a click, a touch, or a glance away. The institution is just a service, not the environment in which learning occurs. The student picks the time, the space, the pace, and virtually all the surrounding supports of the learning process. Teachers cannot actively control any of this, except through the use of rewards, punishments, and the promise of credentials, that force compliance but that are antagonistic to effective or meaningful learning. In this talk, I will discuss the implications of this inverted dynamic for pedagogy, motivation, digital system design, and organizational structures & systems for online learning.

 

 

 

The problematic metaphor of the environment in online learning (update: found a publisher!)

This is a preprint draft of a paper that has been translated by the exceptionally talented Junhong Xiao (he always gives the best and fastest feedback I’ve ever received on any of my work, and he does the translations) for publication in a forthcoming (likely August) edition of the open Journal of Distance Education in China. I’ll be touting it for publication in English so, if you’ve got an open journal that might want it or something like it, drop me a line: it’s a 10,000 word paper but I could shrink it to fit journal needs if that’s too long (thanks to editors of the OTESSA Journal for taking this on!). The paper is in fact mostly a mashup of a couple of two of my earlier blog posts – Nobody has ever learned anything at a distance, and no one ever goes to a distance institution  and A few thoughts on learning management systems, and on integrated learning environments and their implementation though it comes to some slightly different conclusions and emphasizes a few different things (and it has more references!).

I was reminded to share this because I attended an excellent and thought-provoking opening keynote yesterday by Martin Weller at the OTESSA 2022 conference, in which he discussed themes and ideas from his forthcoming AU Press book, Metaphors of Ed Tech. Martin takes a much broader (and really interesting) perspective on uses of metaphor than I use in this paper: I’m really looking forward to reading the book. This paper is largely focused on some of the more obvious spatial metaphors, notably that of the ‘environment’. I’m releasing it as CC-BY-NC so do as you wish with it but, if you do, please give credit both to me and to the Journal of Distance Education in China, where it will be published in Chinese (trans. Junhong Xiao).  Sorry for any weirdness caused by copy-and-paste from the original.

The problematic metaphor of the environment in online learning

Jon Dron, Athabasca University, jond@athabascau.ca

Abstract

In online educational systems, teachers often replicate pedagogical methods, and educational institutions replicate systems and structures used by their in-person counterparts, the only purpose of which was to solve problems created by having to teach in a physical environment. At the same time, a great deal of the development and use of learning technologies has focused on creating virtual learning environments that attempt to replicate features of their physical counterparts, thereby weakly replicating in software the problems that in-person teachers had to solve. This has led to a vicious circle of problem creation and problem solving that benefits no one. In this paper I argue that the term ‘environment’ is a dangerously misleading metaphor for the online systems we build to support learning, that leads to poor pedagogical choices and weak digital solutions. I propose an alternative metaphor of infrastructure and services that can enable more flexible, more learner-driven, and more digitally native ways of designing systems (including the tools, pedagogies, and structures) to support learning.

Keywords: online learning, learning environment, learning management system (LMS), Next Generation Digital Learning Environment (NGDLE), personal learning environment (PLE), learning infrastructure.

Introduction

Outside the walls of educational institutions, for those with adequate Internet access, intentional learning using online systems is almost certainly more popular than its in-person counterpart, as at least the first port of call for learning almost anything and, often, as the primary means through which it occurs. From Google Search to Wikipedia, from MOOCs to Twitter exchanges, from YouTube videos to Khan Academy tutorials, people with online access are swamped with  learning opportunities. However, many academics and students still see online education as a poor second-best to in-person learning (e.g. Protopsaltis & Baum, 2019; Bouygues, 2019; Tichavsky, Hunt, Driscoll, & Jicha, 2015). In this paper I will argue that the distinction between online and in-person learning is far less significant than it appears, because all learning is in-person and never online, and most learning that is labelled as ‘in-person’ actually occurs at a distance from the teacher. Problems emerge, however, when  institutional online teaching inadequately attempts to replicate features and forms of in-person teaching, many of which:

  1. Exist to solve problems caused by the distinctive physical, temporal, psychological, and economic limitations of material spaces, and
  2. Are successful mainly as a result of the features and forms of the physical setting, not as a result of intentional teaching.

As a result, the systems we develop may not take full advantage of the medium, may not take advantage of the physical context of the students, and may attempt to solve problems that should not exist for those using them to learn because they are the result of in-person constraints. In many cases, online teaching may therefore actively militate against effective learning. Some of the problems may be solved using pedagogical adjustments in teaching and organizational changes at an institutional level, discussed in the first part of this paper. However, many emerge from the electronic systems that we use to teach, that poorly mimic the functions of their in-person counterparts in software. In this paper, I suggest that this is, to a significant extent, due to the misappropriation of spatial metaphors that cannot and should not be applied to online systems. I propose a different approach to the construction and conceptualization of tools for online learning, that better reflects the innate benefits of the medium, and that more fully supports the needs and circumstances of both online students and online teachers. I conclude by putting the pieces together and suggesting ways that, in combination, pedagogical, organizational, and digital changes may co-evolve to achieve the potential transformation of education that is afforded by digital networked devices.

In-person teaching

Although they may have physical or virtual windows to the world outside, the walls of the classroom provide clear boundaries that define the space and, because participants must be co-present,  the time in which activities intended to bring about learning occur. This is also true of most of the other buildings, rooms, and spaces that are provided by in-person institutions for students, including corridors, student accommodation, meeting rooms, common rooms, cafes, halls, quadrangles, staff offices, libraries, gymnasia, and examination halls. These spaces are not just support structures for the classroom, but active participants in the learning process (Dron. 2021). Even the act of physically walking to the classroom, especially with other people, creates a salience and value to the activity that is very different from that of clicking a link to an online resource. Most significantly, they are social spaces where learning happens as a result of direct and indirect interactions between learners (who are one another’s teachers) and, often, with their designated teachers. Simply seeing others learning makes a difference, as do the fliers and leaflets on the walls, and the spaces intended to support clubs and societies, where much academic discussion often occurs. Cafes, bars, and canteens are rich in learning dialogue. Student rooms, dormitories, and (especially) their kitchens, are powerful seed beds for learning, where much sense-making discussion occurs. Many universities provide purpose-built study areas. Even and perhaps especially, areas cordoned off for smokers provide an extremely fertile space where students from different subject areas and disciplines can and do share and construct their knowledge. Similarities in their design the world over speak to the fact that these are highly evolved spaces, supporting a learning process that extends far beyond the classroom.

These countless diverse learning opportunities in physical spaces, perhaps counter-intuitively, speak to the fact that there is a distance component to virtually all in-person education. Indeed, almost all learning is distance learning, in the sense of occurring somewhere and somewhen other than where and/or when deliberate, instrumental teaching occurred. People who learn with teachers in a physical space are almost always also interacting with other participants in the teaching role at a distance, usually in time and space, such as textbook authors, classroom designers, editors, illustrators, creators of timetables, and curriculum designers. And, for ‘in-person’ institutional learners, much of the learning itself also occurs at a distance, outside the classroom. This is most obvious in the form of assignments and homework but just as much learning can occur in conversation and interaction with others. Even when alone, if teaching works, sense-making connections always occur after the lesson is over, and continue to do so long after (sometimes decades after) the teaching event, almost never in the same place that the lesson originally occurred. In-person students do not have one teacher: they may have thousands. Weaknesses in in-person teachers can often be compensated for by these many other teachers, including the learners themselves and the institutions that provide the framework and resources for learning. This is amplified by that fact that, although credentials and grades are highly antagonistic to persistent intrinsic motivation (Kohn, 2011; Blum & Kohn, 2020; Ryan & Deci, 2017), they do encourage compliance. In search of good grades, students will therefore make use of whatever means they can – including those many other teachers as well as cheating or satisficing – to achieve the marks they seek. The physical environment of an in-person institution provides many supports to make this possible.

Online teaching

The in-person teacher, by default, controls learning in the classroom because it is a self-contained environment of which they are in charge for the duration of the lesson. Relinquishing control must be an active choice, or the result of an error. In contrast, the online teacher cannot, without a great deal of concerted effort, control the online student, any more than a writer of a book can control a reader. Online students can always choose when, where, with whom, how, for how long, and with what tools, media, and resources they learn (Dron & Anderson, 2014). It would therefore be surprising were online pedagogies to closely resemble their in-person counterparts, because they have different problems to solve. Most notably, without the requirement to share a single environment, with all the many rules, norms, structures, and constraints that entails, and without the need for the teacher to fill every moment of classroom time with learning activities, there should be no need for teachers to exercise the same level of control over their students.  However, online teaching evolved from in-person teaching, and online institutions must continue to interoperate with the in-person educational systems of which they are a part. As a result, many online teachers assume that they should dictate the learning process as much as their in-person counterparts and, usually, it becomes a partly self-fulfilling assumption through coercive methods like frequent grading, draconian scheduling, and tests. They consequently often make use of very similar pedagogies to those of their in-person colleagues, struggling to find simulacra or workarounds for the affordances of physical spaces that are no longer available, vainly believing that the learner is going to follow the path that they have determined for them and, too often, imagining that this is the sum total of the learning experience. To make matters worse, educational institutions impose other structures that are purely the result of constraints of teaching in physical classrooms, such as fixed-length (or multiples of fixed lengths) courses, deadlines, and perhaps most perniciously, the concept of failure. As any game-player or musician knows, failure is part of learning: it cannot ever be its end but, because of the constraints of having to run a course with co-present students and a beginning and end, failure becomes a potential outcome, not just part of the process. When all these factors are put together, the online student may have little more independence than their in-person counterpart but, at the same time, may lack the countless structures and forms of physical institutions that support in-person students. Rather than being immersed in learning opportunities, they must actively seek them within their own physical and virtual environments.

One obvious solution to this problem would be to create an online learning system that provides much of what is lost in translation from the physical environment, for example through a custom-made social media platform or an informal discussion area within a learning management system. However, this is not as easy or effective as it may seem, especially if it remains tightly coupled with other institutional policies, norms, and teaching methods. Partly, this is because of the too common focus on explicit outcomes and grading found in most institutional teaching together with failure by students and teachers to recognize the critical role of in-between spaces in learning. Thanks to the extrinsic coercion of marks and credentials,  if it makes no direct contribution to a grade, then it is seen as less valuable. Mainly, though, it is because it is not just there: students will not pass it on their way to somewhere else or be there for other reasons (like a need for rest or refreshment). They have to intentionally visit, typically with a purpose in mind. However, as the main value of it is its purposelessness (or, at least, that it supports a very broad set of purposes), that is rarely going to happen. Online systems are not environments in which students dwell: they are parts of their own environments.

This speaks to the central phenomenon around which this paper revolves: that nobody actually learns anything at a distance. We are always learning it where we are now. All learning is in-person learning, and it all takes place within a physical environment, part of which (but only a part) may include whatever technologies we might be using to talk with people, read, watch, listen, and learn from: books, computers, pens, emails, learning management systems (LMSs) and so on. Some of these may extend into other physical spaces occupied by other people, perhaps at other times, connected by online means. The broader learning environment is highly distributed in time and space, but learning itself only occurs locally. What we describe as ‘distance learning’ or ‘online learning’ is thus, in fact, nothing of the kind. It may involve distance or online teaching but the learning is always in-person. Online students exist, because the word ‘student’ only has meaning in relation to an online teacher, but online learners do not.

The promise of online learning environments

It is understandable that, when we teach in person, we have to occupy and make different uses of the same or similar environments like classrooms, labs, workshops, lecture theatres, and offices. There are huge financial, physical, and organizational constraints on making the environment fit the task, so it would normally be madness to build or even to substantially reconfigure a whole new classroom every time we wished to run a different class. Rooms may be built for flexibility, with moveable partitions and furniture, and that is much to be wished for, but there are physical limits such as walls and property boundaries that prevent this from going too far. Instead, our pedagogies and processes are normally made to fit the affordances and constraints of the classroom: they are another problem that our pedagogies have to solve, and/or an opportunity that our pedagogies can take advantage of. We may, sometimes, have some choice between classrooms that offer different facilities but, for the most part, our options are limited by what has already been built.

Online, there are countless tools available and, if none are suitable, it is not too hard to build them or to modify them to suit our needs, at least when compared with the costs of creating new physical spaces. There are few significant physical limits on how many can be used or how many people may use them: there are none of the limitations of physical space that constrain the use of buildings. Once they are built, moving between virtual tools just takes a tap of a screen or the click of a mouse or keyboard. It is even possible to use several of them at once, especially with a large high resolution monitor or more than one device. The learner’s environment may contain countless tools and systems, any of which may support learning, including physical books, instruments, and other people around them. And yet, for the most part, online teachers tend to make use of only a handful of possible tools: most consist of no more than a learning management system , email, and perhaps a webinar system.

There are many mutually reinforcing reasons that online teachers rarely provide the perfect application or combination of applications for the context of study:

  • Teachers’ lack of knowledge of the options (it takes time and effort to discover what’s available).
  • Teachers’ lack of skill in using them (most interesting tools have a learning curve, and that gets steeper in inverse proportion to the softness and diversity of the toolset, so most teachers don’t even know how to make the most of what they already have).
  • Lack of time and/or money for development (an application is just a shell for the content it contains and the interactions it supports, and it is not always as easy to add existing materials to a new tool as it might be in a physical space: for example, an in-person lecturer only needs to talk, whereas an online teacher must master the complexities of the hardware and software needed to record, edit, and share the same thing ).
  • Costs and difficulties in management (each tool adds costs in managing faults, configuration, accounting for use, performance, and security).
  • Cognitive load involved for learners in adapting to the metaphors, signposts, and methods needed to use the tool itself.

All of these are a direct consequence of the very diversity that would make us want to use different applications in the first place. This is a classic Faustian Bargain (Postman, 1998) in which the technology does what we want, and in the process creates new problems to solve.  Every digital system must establish rules of engagement that its users must learn, such as the ways that navigation occurs, the ways to make it perform its functions, the terminology it uses, and so on. In effect, every application invents its own metaphorical physics. That makes virtual systems harder to find out about, harder to learn, harder to develop, costlier to manage, and more difficult to navigate than the static, fixed facilities found in particular physical locations. They are all different, there are few if any universals, and any universal today may become a conditional tomorrow. In the case of cloud-hosted systems, the owners of which may unilaterally make changes to the software or configuration, this may be literally so.

Learning management systems

The learning management system (LMS) addresses all of these problems, to some extent. Almost every LMS essentially automates the functions, though not exactly the form, of traditional classrooms. Indeed, they are typically seen as environments, or are referred to as ‘platforms’, underlining the physical metaphors that inform them. In some parts of the world people prefer to use the term ‘managed learning environment’ (MLE), and the LMS/MLE is, in most vocabularies, the most dominant representative of a larger category of systems usually described as virtual learning environments (VLEs) that also includes things like MOOs (multi-user dungeons, object oriented), immersive learning environments, and simpler web-based teaching systems that replicate aspects of physical teaching such as Google Classroom or Microsoft Classroom. The use of spatial metaphors for the names of such systems reflects a deep-held belief or tacit assumption that the virtual systems can provide the boundaries within which actions occur, in ways that tend to be seen as analogous to those of physical spaces. In a few limited contexts, notably through immersive systems, this belief may be partly justified,. However, it matters that even the most immersive system occurs in a physical space. For instance:

  • when participants leave the immersive system they exit into different rooms, losing the natural opportunities for incidental or continuing chat that are innate to physical spaces;
  • participants are at the mercy of dropped network connections, glitches, and issues with the machines that run the immersive environment, leading to potentially quite different experiences for different participants;
  • different participants experience different temperatures, background sounds, smells, and opportunities for interruption in their own physical environments.

LMSs differ from physical environments to a much greater extent than immersive systems, in ways I will describe over the rest of this section. This is not a trivial issue of nomenclature. I will be arguing that the misconception that they are meaningfully analogous to physical classrooms lies at the heart of many weaknesses and failings in both the design of the tools and their use, reinforcing the belief that online teaching closely resembles in-person teaching, and blinding us to essential differences between the two.

The building metaphor

Creators of early LMSs and VLEs back in the 1990s (including the author) based their designs on the functions and entities found in a traditional university because that was the context from which they sprang, and that was the context in which they had to fit. In the eyes of its designer, an LMS could be thought of as a big university building with rather uniform classrooms. It may have extensions built onto it using plugins or standards such as LTI (the learning tools interoperability standard), and it may have a few doors and gateways (mainly in the form of hyperlinks) linking it circuitously or in jury-rigged fashion to other similarly weakly connected ‘buildings’  such as ‘places’ to register, to seek support, to talk to an advisor, to complain, to find books, and so on. For the most part, though, its fundamental organizational metaphor is that of a university, college, or school.

 

The LMS is, however, an impoverished school. It has no metaphorical corridors, halls, common rooms, canteens, yards, libraries or any of the other parts of a typical university environment where students gather to (amongst other things) learn. Students rarely get to even be aware of other classrooms beyond those they are in. Some teachers may give classrooms informal-sounding names like ‘the learning cafe’ but it is still just another classroom that works in the same way as the rest. Students teleport from one classroom to the next because what happens in between is not perceived by the designers as a useful classroom function to be automated or perhaps, more charitably, they could not figure out how to automate that. But there are other differences that are, perhaps, even more pernicious, to which we turn next.

Centralized code bases

In a physical environment, every object is discrete, occupying its own space. Physical classrooms can and do change – new furnishings, equipment, and so on – but the effects are local to that particular space, and they seldom prevent teaching from occurring across an institution.  Learning management systems, on the other hand, re-use the exact same code to generate all of the virtual classrooms of which they consist. Instead of a number of courses occupying the same physical spaces, and there being many such spaces to choose from, every course gets its own instantiation of a single centrally hosted toolset. There may be options to switch features on and off within any given course instantiation, options to configure each component differently, and a choice between components may be offered, but everyone gets exactly the same set of features, determined by the developer and the system administrator. This means that one set of features has to suit everyone. If, say, a teacher wants a discussion component that does things the default discussion component does not support, then it has to be installed or integrated in the centralized code base. While the LMS may technically support this – through plugins, LTI integrations, OKI components, and so on – system administrators are usually rightly reluctant or unable to allow it. Every component is another potential source of failure or (often) security holes, incurs management costs, uses system resources, creates a significant maintenance burden, and increases the complexity of the system for everyone. To allow unfettered installation of alternative components would be completely unmanageable. As a result, most available features must be a compromise, that can be bent to suit the needs of (typically) thousands of courses and teachers, but that are unlikely to be an ideal fit with any of them. Unlike the physical classroom, changes to the underlying application affect everyone, at once. When the LMS goes down, it takes the whole institution with it, and when changes are made, they are made for everyone, often affecting hundreds or thousands of courses and tens of thousands of students.

This is particularly problematic in cloud-based systems where administrators are not even part of the same organization, and where the system must support hundreds or thousands of institutions. Few of us who teach using cloud-based systems have not experienced difficulties when the systems on which our courses run change without warning or consultation, disabling or altering things that disrupt the design, sometimes rendering it inoperable. Even when they work, the fact that they use a single code base limits the potential for customization. Because most LMSs based their designs on what was presumed to occur in an average university, they rarely fit well with any actual university, because virtually no universities are average. Sometimes, the problems may be relatively minor. For example, Blackboard calls its organization elements  ‘courses’, whereas many other names for such things are common, including modules, units, and papers, and ‘course’ may refer to what others around the world might call a ’program’. Even this may disrupt and cause confusion (Dron, 2006). Other problems can run deeper, to which we turn next.

Reified roles

The typical LMS is a very controlled environment where everyone has a programmatically enforced role (typically at least partially reflecting traditional educational roles), that may vary according to the ‘room’ in question, but that are far less fluid than those in physical spaces. There are strong hierarchies, and limited opportunities for moving between them. Some of those hierarchies are native to the online learning system: the system administrator, for instance, has far more power than anyone in a physical university to determine how learning happens, like an architect with the power to move walls, change the decor, add extensions, and so on, at will. The programmers of the system are almost god-like in their command of its metaphorical physics. But the ways that they give teachers (or learning designers, or administrators) control, as designers, directors, and regulators of the classroom, are perhaps the most pernicious. In a classroom a teacher may lead, and that is the default, but they may and usually should choose to at least share leadership with their students, often fluidly and in response to how students are learning. In an LMS, a teacher (or someone playing that role) must lead,

Tools such as discussion forums may seem to be more egalitarian, but teachers’ power to control events in them is usually far greater than that of their in-person colleagues, often including the means to delete unwanted messages, prevent replies, stop conversation threads stone dead, and many other things that would be superhuman capabilities in a physical space. In a physical classroom, a determined enough student can always make themselves heard. In an LMS, the teacher can silence them. There is thus less of the soft flexibility found within in-person classrooms that allows for conversational pedagogies that adapt to the interests and needs of learners. At the same time, though, it should be (though too rarely is) remembered that the teacher’s power is confined to a small part of the learner’s own environment, not to a whole classroom. In practice, teachers still tend to treat the forum as an analogue of the classroom and, recognizing the value of dialogue in such contexts, often resort to coercion to make it happen online: marks for discussion contributions are far more common than in in-person settings, even among experienced online teachers. This combination of hard, role-based digital authority and hard, reward-based pedagogical authority is fundamentally different from its physical analogue. It creates both a social and a power distance that compounds what is already a less immediate relationship between student and teacher.

Within the LMS the teacher sees things that students cannot, and controls things that the students may not. A teacher configures the space, and determines with some precision how it will be used. With a lot of effort and (usually) high risk to the security and stability of the system, it can be made to behave differently, but it almost never is, because doing so usually involves promoting students to roles with similar capabilities to that of the teacher. In many cases, especially when it involves the use of plugins or other tools that extend across the system, this cannot be localized, so the risks to every user of the system must be considered. This is beyond the capabilities or rights of most teachers, and so it usually falls to system administrators, reinforcing their already substantial power to affect the teaching process.

Functional design

An LMS is typically built along functional lines. Rather than attempting to be a precise mirror of the in-person context, its functions are mostly based on loose, superficial observations of the things that teachers and students seem to do in physical classrooms, analysed to their component parts. Mostly, they are structured by teaching functions: presenting, discussing, assessing, guiding, and so on. For instance, in most LMSs, if you want to talk with someone, you normally need to go to a separate discussion area inside the classroom or, metaphorically,  to leave a note on the teacher’s desk in the form of a direct message. Unlike a physical classroom, dialogue is seldom possible everywhere. The same is true if you want to take a test, or to share your work with others: it rarely occurs within the context of learning, but in a separate screen, often separated from its context by a hierarchical set of links. Indeed, in many architectures, it will be handled by a different component than the rest, with its own tables in the database and its own distinctive interface.

Similarly, lectures are either literally that (video recordings of lectures) or (more usefully, from a learning perspective), text and images to be read on screen. This results from the erroneous assumption that the only function of lectures is information transmission, which is perhaps their least useful role, given that we have known for almost a century that it is far more effective to read a book (Greene, 1928). Lectures can and do have value as physical and temporal signposts, as motivators to pay attention, as events that demand attendance and thus have greater salience than simple reading, as well as providing opportunities to engage with others, sometimes within but always outside the lecture hall. Online, there is seldom a chance for students to even put up a metaphorical hand to question the teacher, and ‘joining’ a lecture is no more salient than clicking a link to a Facebook post. There are limited opportunities to be aware of what other students are doing, including for the teacher (although teachers do usually have access to system logs that offer an impoverished caricature of what students are doing, albeit one that is blind to anything they do beyond clicking and tapping keys on a machine). Much of the ‘space’ may as well be unpopulated, given the little students see of one another. Learning resources are normally static and designed in advance, and so the teacher cannot nimbly adjust to student reactions to them. Notices can usually only be pinned on the ‘wall’ by teachers, often with names such as ‘announcements’, further emphasizing the controlling nature of the teacher-student relationship. Classroom timetables are embodied in software despite the fact that a rigid and unforgiving timetable makes little sense in a medium that supports learning anywhere, any time. Some LMSs may allow you to break up the content differently, but it is still another timetable; just a timetable without dates. It is always the teacher (or one to whom the role is delegated) who sets the order, pacing and content.

Robot overlords

The LMS provides a high-tech classroom, populated by metaphorical robots.

Some of the robots may be programmed to attempt to force students to behave in ways determined by those higher in the hierarchy (sometimes teachers, sometimes administrators, sometimes the programmers of the software). For instance, adaptive systems might act as gatekeepers that prevent students from moving on to the next section of work before completing the current one, or they might prevent students from submitting work before or after a specified date (Martin, Chen, Moore, & Westine, 2020), or they might limit their access to a specified time period.

Some of the robots might even mark your work (Keuning, Jeuring, & Heeren, 2018). Human beings have grown up with other humans and therefore understand the context of the work, the motivations of the students, and the many different ways that things can go wrong, as well as creative and unexpected ways they can go right. Robots – even those that are employ deep learning and similar AI approaches – do not. While hard, mechanistic systems may be useful for providing feedback when students must play their role correctly in hard, mechanistic systems (in hard, ‘right answer’ subjects), those mechanistic skills are seldom the most important part of what they learn. Human teachers do not (or should not) just judge success or failure: they should model practice,  remedy misconceptions, provide encouragement, and so on.

There are metaphorical surveillance cameras everywhere, recording students’ every move (in very low resolution), often only accessible to those with more powerful roles, though sometimes a robot or two might give them a filtered view of it, such as through learning analytics traffic-light interfaces (Verbert, Duval, Klerkx, Govaerts, & Santos, 2013). Though the perpetrators of these tools may claim to have student interests in mind, and will often talk of ‘personalization‘ by way of justification, it is not personalization at all: it is system-enforced customization done to, not by the students (Kohn, 2015). These are all tools that are designed to enforce compliance: an attempt to embody in software the control that is demanded of an in-person teacher due to an accident of physics, not for any pedagogical purpose.

Beginnings and ends

The fundamental social form of the classroom that provides the primary metaphor of most LMSs is the formal group (Dron & Anderson, 2014). Formal groups are technological entities – inventions that are designed to address problems – at least as much as they are social. Among their many technological features are names, roles, procedures, rites of joining and leaving, rules of behaviour, schedules, beginnings and ends, almost all of which arise from the constraints of in-person learning, such as the need for people to be co-present, problems when people talk at once, limits to the capacity of classrooms, directionality of hearing and sight, and so on (Dron, 2016). Unsurprisingly, many of these features are embodied in code, not only in the reified roles already discussed but in processes of joining and processes of leaving.

A student cannot usually go back and visit when their course is over because most online courses have opening and closing enrolment dates. Perhaps their designers assumed that, when teaching was done, the learning was done which, of course, it never is. Learning keeps on evolving long after explicit teaching and testing occurred. Again, this is because physical classes are scheduled and terms come to an end because they must, not because it makes pedagogical sense. And, like almost everything, it is possible to override this default, but hardly anyone ever does, partly because it brings back those Faustian bargains, especially in manageability, but mainly because most people accept defaults (Kelly, 2009, Dron, 2006). LMSs embody enrolment technologies as much as they do teaching technologies and, in the process, they unnecessarily limit potential for learning.

Because the primary metaphor of almost all LMSs is the classroom, they can be a particularly poor fit with ways of teaching that have no classes, such as self-paced courses and MOOCs, individual projects, or flexible networked ways of learning such as those underpinned by Connectivist, or Rhizomatic models of learning. This is not to say that such uses are impossible. For example, assumptions about class schedules that are embedded in software (such as that all students must submit work by a certain deadline) can be disabled, or bypassed by setting a deadline in the far distant future, then manually informing students of when to submit their work. However, the fit with self-paced models of learning is typically poor. Among the many peculiarities that result are students who engage in discussions with ‘classmates’ who no longer have access to the provided forum, and the impossibility of collaboration when every student is at a different point in the course. More challengingly, and unlike teacher-paced courses in which the teacher can modify almost any aspect of the content or curriculum at will, knowing that the whole class will be affected in the same way, much confusion and even dismay can arise when changes are made to materials that may be in use by existing students.

Imperfect caricatures of physical spaces

In summary, most LMSs provide an automated set of metaphorical classrooms that harden many of the undesirable side-effects of educational systems in software, in ways that have little to do with how best to teach, and that inappropriately apply spatial metaphors in ways that conceal rather than illuminate their functions. Each bit of automation and each navigational decision hardens pedagogical choices, at least as much as the walls, doors, and physical limitations of physical spaces and, often, more. Programmers do not replicate physical classrooms but instead create or enlist new laws, new kinds of structure, and new kinds of hardened process that can be embodied in code. Classrooms solved problems of physics for in-person teaching and form part of a much larger structure that has evolved to teach reasonably well. LMSs just focus on a limited subset of teaching roles, and empower the teacher in ways that caricature their already excessive dominance in the classroom, that only occurred because of the nature of the physical space and the constraints it imposed.

LMSs leave much to be desired, but the metaphors on which they are based bear enough resemblance to physical reality to be readily understood by teachers and students. They usually provide just enough configurability and flexibility to more or less adequately work as teaching tools, for everyone, almost no matter what their level of digital proficiency might be. They more or less address the Faustian bargains listed earlier, albeit they normally do so by stifling what we wanted and should have been able to do in the first place with online tools, In the process they create new and quite extensive problems, as well as failing to replicate most of what makes physical universities work in the first place. Virtual learning environments are not like physical learning environments: they are only ever parts of them. There are other electronic ‘places’ to escape from them, such dedicated social media, or even just plain old email, but then all those Faustian bargains come back to haunt us again. They occupy space within the learner’s own physical environment, but it is rare for pedagogical designs to even acknowledge that, let alone to consider it in the design.

Improving the LMS

It is tempting, faced with these problems, to assume that they could be solved if only we made the LMS more closely resemble the physical environments on which it is modeled. However, this is a poor solution because, as we have already seen:

1)    physical environments create constraints and problems to solve that are unnecessary and avoidable in virtual systems; and

2)    it is not practical nor is it within our technical reach to replicate all the many incidental benefits of physical environments.

That said, there are lessons to be learned from physical spaces. Among many improvements that could be made would be:

1)    To make every part of the system at least potentially social: to allow synchronous and/or asynchronous dialogue to occur on every page or screen of the system. This is the default in all physical spaces: talking has to be prohibited if it is not wanted.

2)    To allow at least some parts of the system to be free of roles, or with more flexible roles, allowing all members of the system to create and share posts and resources using discretionary access control (so it is the poster’s responsibility to choose who can see it, and who can change it). Even in highly controlled physical environments, we choose what we reveal and to whom.

3)    To support social networking and the blurring of boundaries between areas, tools, and features of the site, so that courses are just one of many kinds of organizational unit, with selectively permeable boundaries through which others can pass, or with which they can overlap. Again, this is a default in physical spaces, that leak information through walls, floors, windows, and doors, that exhibit continuity of engagement when people enter or leave classrooms, that allow teachers to open doors to others, that admit a multiplicity of primary uses.

Though these improvements appear simple to achieve, adapting an existing mainstream LMS such as Blackboard, Canvas, Moodle, or BrightSpace to support them is fraught with difficulty.

By far the easiest of these improvements to make within an existing LMS is to make it more social. Achieving this within an existing course structure is a simple programming problem that can readily be solved in countless ways. In most LMSs, it could be built as a plugin. Existing architectures, in which courses and roles play a primary structural role, make it somewhat more difficult to extend such dialogue beyond the boundaries of the course. The metaphorical walls of a course are, for the most part, more of a barrier to engagement beyond it than those of a physical classroom because their metaphorical physics can be (and is) enforced in code. It is not, however, an insoluble problem. For example, a context-aware embeddable discussion system such as Disqus or Isso, hosted locally or remotely, could fairly easily be added.

Making the system free of roles is much more difficult because, in most LMSs, they underpin almost every function and structure of the system, and they cannot be made to work with an open, discretionary access- based model of permissions: the two approaches are, architecturally, mutually exclusive. One way of dealing with this would be to follow the lead of the Drupal content management system to support ‘organic’ groups: limited areas of the LMS where everyone has the same rights to create shared content or social areas, and where anyone can control who can see what they post. These areas could be as large or as small as desired but it would be difficult to make them extend beyond a course, or to encompass one or more courses. It would not be impossible, but to do it safely and reliably (without giving everyone a single, very powerful role) would require a major rewrite of the underlying LMS.

For all of the LMSs of which I am aware, the most difficult of all these improvements would be to blur the boundaries of the tools, features, and courses. The course is such a fundamental architectural unit of most, if not all, LMSs that changes to its operation would demand a significant redesign. It could be done, but it would not be the same kind of system any more.

It is for these reasons that, wishing to support all of these features and realizing the extreme difficulty of modifying the LMS without compromising some or all of its existing functionality, a group of us at Athabasca University created The Landing (Dron & Anderson, 2014), as a separate system to the LMS, linked only by a single sign-on and tenuous hyperlinks and, to a limited extent (only supporting public posts in either direction) RSS feeds. Further efforts to design deeper integration proved too difficult, for both technical and organizational reasons. Unfortunately, The Landing suffers from the same Faustian Bargain that besets all attempts to expand the range of systems available. The maintenance burden of a system with many thousands of users is too much to sustain for a system with very limited central support and even more limited funding. Pedagogically, the system fulfills an important need and so it has survived for more than 12 years but, technically and from a management perspective, its future is in jeopardy. Similar issues are playing out the world over. The more control and diversity that we enable, the more difficult and expensive it is to manage it.

Alternative approaches

Incremental improvements

Athabasca University is currently building an Integrated Learning Environment (ILE) that centres around very conventional elements of a institutional teaching system: an LMS, some relationship management tools, a student records system, an enrolment system, an examination management system, and so on. These are tightly integrated, but it is intended that the ILE will also embrace many other tools and systems that are far less institutionally bound, from the aforementioned Landing, to other social media (such as WordPress), to portfolio tools, to shared software repositories. This is an approach that starts with replicating existing structures and services by building a tightly managed administrative core, but that is intended to grow to support more open, diverse, and rich approaches to learning and teaching, co-evolving with methods and pedagogies that are more in keeping with the different problems and needs of distance learners. However, though it provides a managed approach to supporting change, this approach carries many risks.

A design approach that treats online systems as environments invariably makes the assumption that it is where everything associated with what goes on inside it happens, and (for online systems) this creates quite unnecessary restrictions on what can happen. Athabasca University’s design approach for its ILE was highly participative, engaging most of its teaching, technical, and administrative staff and asking for what they needed. However, inevitably, their requests were based on assumptions formed by their existing practices and, especially, by the existing environmental metaphors of the LMS and associated systems with which they were already familiar. In essence, they were asked what kinds of spaces they needed, and what kinds of stuff needed to be in those spaces for them to do what they currently do.  ‘Space’ and ‘stuff’ are what Stewart Brand (1997) describes as inevitably being the fastest-changing, most volatile parts of any physical building, after site (its physical limits), structure (what holds it up), skin (mainly the external walls), and services (electricity, gas, network wiring, etc). More abstractly, this is a solid structural principle that applies as much to ecosystems and educational systems as it does to buildings. As Brand himself observes, drawing from O’Neill, DeAngelis, Waide, & Allen (1986), the larger, slower-changing elements of any system affect the smaller, faster-changing more than vice versa. In physical spaces, these naturally tend to be bigger and/or more difficult to change, but the same is true in virtual spaces, where size seldom matters that much, but hardness (inflexibility, brittleness) has the same effect. The more difficult it is to make changes, the more an element of the system determines the behaviour of other elements in the system that interact with it. The ILE’s structure, skin and services have been designed based on needs determined by perceptions of the space and stuff within it, that were in turn very strongly determined by the LMS and other systems that went before, with all the inherited baggage that they inherited from in-person environments. Hence, the ILE’s fundamental design model is really no more than an extended LMS, and it inherits most of its weaknesses. The main way in which it differs is that it is designed to be extendable, but those extensions will still – in terms of how they are treated and used – be part of that same environment, with all the aforementioned problems that this entails.

Integrated learning infrastructures

I have argued that a better name for the system being developed at Athabasca University is not an ‘integrated learning environment’ but an ‘integrated learning infrastructure’ (ILI). In metaphorical terms, it should be like the utilities, services, and mechanisms that make an environment possible, but it should never be thought of as the environment itself.

Stripped to their essentials, digital systems intended to support the educational process provide services, consisting of tools that may be used to support learning, teaching, accreditation, and other roles and functions of an educational system.  Such services are many and various: discussions, presentations, file sharing, assignment submission & grading, quizzes, blogs, scheduling, wikis, bookmarking, real-time communications tools, enrolment systems, identity management systems, support systems, and much more. There is no good reason that these should be confined to loose approximations of their physical counterparts, nor is there any good reason that teachers or system administrators should be the only ones to control them, though it is important that each of them is owned by someone, otherwise the resulting free-for all would be difficult to manage. Microservice architectures that support such systems are quite mature, and widely implemented in different fields, if not so much within the educational sector. From the point of view of end users, these can be thought of as assemblable components, and the assemblies can be performed by anyone, including students. Ideally, it should be possible to integrate them with other applications and services offered beyond an institution, including on the desktop of individual students.

Ideally, it should be possible to assemble them into units with value in the system, that can themselves be assembled into other components. This provides a path for evolution from existing approaches because those units might include courses. There may be a need for additional services to support non-teaching functions associated with educational systems, such as administration or credentialling.

Such services are not so much environments as they are infrastructure that exists within and between the different environments that learners, teachers, administrators, and technicians occupy both virtually and in person. Non-exclusively, such infrastructure may minimally support needs such as:

  • Dialogue and interactions between participants
  • The presentation and curation of content
  • Assessment, formal and informal
  • Sharing of words, images, video, audio, and other document types
  • The formation of groups,  networks, and sets (social gatherings around shared interests or other commonalities)
  • Sharing of tools and resources
  • Etc.

What matters most is that all of these services can be combined in indefinitely many ways, by anyone.

This is not a new idea. In the early 2000s, the ELF (e-learning framework) and OKI (Open Knowledge Initiative) both attempted to provide ways to assemble services (ELF) or components (OKI) in many different ways. However, for the most part, both of these initiatives were firmly focused on building centralized systems that replicated the functions of an LMS, so they carried forward the assumption that what would be built from the components would be teaching environments; a better LMS, but still an LMS.

Around the same time as ELF and OKI were being developed, and driven by similar intents,  the notion of the personal learning environment (PLE) became popular, though with very many quite radically different interpretations (Martindale & Dowdy, 2010), ranging from institutionally controlled systems that were often described as ‘platforms’ (Yen et al, 2019) to collections of applications and services assembled by a learner on their own desktop in an ad hoc fashion (Wilson, 2008). Though some of the promoters of the concept saw the environment as extending beyond virtual systems, the vast majority of these interpretations considered only the digital tools, not the physical and social environment of the learner, nor the pedagogical and technical skills used by learners to create and manage those tools. Again, the ‘environment’ metaphor was inadequate and misleading. The PLE was also, for the most part, a concept, not a technology, though efforts were made in some circles to create standards for mashing up those tools, most notably through work on ELF which was, by some, seen as the VLE of the future (Wilson, 2005, cited in Martindale & Dowdy, 2010), and a number of systems were built that were described as PLEs, but that were essentially another kind of institutionally managed server, much like Athabasca Landing, referred to previously.  A more promising set of standards that did focus on the development of standards-based widgets that could be assembled by individuals as well as within an LMS or other system (Wilson, Sharples & Griffiths, 2008), failed to gain enough momentum, despite endorsement of the widget specification by the W3 Consortium, and implementations within all major operating systems. Meanwhile, the term ‘PLE’ itself became such an amorphous concept that even conversations about it were difficult to sustain, let alone useful implementations.

In more recent years, the Educause organization has vigorously promoted the Next Generation Digital Learning Environment (NGDLE), which is essentially very similar in purpose and approach to the earlier ELF initiative, but that:

1)    Takes into account the possibility of learners assembling their own digital toolsets;

2)    Incorporates developments in analytics and artificial intelligence, and

3)    that is largely agnostic to standards used for its implementation, although it does recommend standards and protocols such as xAPI, LTI, learning record stores, and Caliper to help bind them together (Brown, Dehoney, & Millichap, 2015).

Combining the best ideas from service-based systems and work on PLEs, the initiative shows promise. While, once again, the ‘environment’ metaphor fails to extend into the actual spaces that it is intended to be deployed, the initiative is a genuine move beyond the teacher-centric, classroom-inspired models of the LMS and towards a student-oriented service-provision model. There are now some implementations of the concept. For example, the OERu aggregates a wide assortment of open source tools systems providing services such as discussion, microblogging, blogging, wikis, social bookmarking, and so on, that can be used independently by students or as part of the university’s own system (Lane & Good, 2019). While these are still largely perceived as an environment composed of environments, the potential for such a design approach is to free us from the traditional classroom metaphors of the LMS.

Institutional teaching beyond virtual environments

A distance learner’s environment is never digital, though digital tools and services can comprise important parts of it. A learning environment is not just comprised of physical or virtual structures but also the social, pedagogical, organizational, personal, and other dynamic elements that determine how the parts of the structure evolve and interact. It is not just physical matter, or virtual systems, but also the people and what they do together. It is not just how teachers teach, but how learners teach themselves, and teach one another, and are taught by the countless teachers who create the websites, interactions, tools, and structures of the broader internet, and the many teachers who inhabit their own physical spaces, from family members to people in the street. How, therefore, should teachers in institutions teach, when they are just parts of someone else’s environment, co-players in the process, and what kinds of digital tools and systems will be needed to support that?

Perhaps one of the reasons that it is too easy to fall into the trap of thinking of the digital tools and systems as an environment is that it they are  an obvious class of things around which to put a boundary. However, an infrastructure is not just the digital tools but also the human-enacted methods, rules, protocols, and standards that accompany it. It is not just what we use, but the ways that we use it.  It is natural to focus mainly on the software and hardware when designing an online system to support learning, and thus to come to think of it as providing the learning environment itself. If, instead, we remember that we are only building tools to use in the learner’s own environment, and that we are just providers or curators, not controllers or managers of that environment, then a critical and oft overlooked design principle becomes clear: that online students are the primary orchestrators of their learning rather than, as in the physical classroom, their teachers.

An integrated learning infrastructure should therefore not attempt to replicate the form and structure of a traditional classroom, nor should it solely support teachers in assembling the tools needed for their teaching. Instead, the focus – both digitally and pedagogically – should be on making it possible for learners to assemble the services into their environments themselves, in order to avail themselves of the support they need, when they need it, for the purposes they intend. The processes, methods, techniques, tools, and structures that students bring with them are at least as important as those created by their teachers. An integrated learning infrastructure needs to support these aspects at least as much as the interconnections between software tools. Again, it is necessary to think of the environment as considerably more than just a set of digital components that it uses but one that includes the people, the spaces they inhabit, and the things that they do.  Pedagogically as well as technically, there may be a need to support students in making the best use of all of that, for instance to search well, to find people that can help them to learn, to organize their own learning process, but such support Is, again, a service on which students may draw, not a teacher-determined requirement. And those pedagogies themselves need to adapt: for example, those that rely on rewards and punishments to enforce compliance must be excised, while those that provide learners with autonomy should be amplified. New pedagogies will be needed that acknowledge the many teachers in a learner’s environment, that help them to traverse the complexity of it, to leverage the advantages and to avoid the pitfalls. Teachers will need to let go, but stay close.

Tools that involve engagement with others – the means to share, the means to discuss, the means to work together, schedule meetings, and so on – are connection points in learners’ environments that cannot usually be completely controlled by any one of them, because of the need to at least agree protocols through which to engage and, in many cases, the systems which they will use to interact . One way to deal with this problem is to make a decision to use a  small range of tools, ideally in consultation with students. A better approach is to use tools that give students a choice of toolset, using protocols or standards such as SMTP, Jabber,  iCal, WebMention, ActivityStreams, or NNTP. However, few new standards have gained traction in recent years thanks to the dominance of closed social media monoliths intent on locking users in to their systems, so this may unnecessarily limit the range of systems that may be used. Another approach, commonly used in Connectivist approaches to learning, is to aggregate what learners provide themselves, using standards like RSS or Atom, or proprietary APIs offered by tool providers, or mailing systems to collect what students have shared elsewhere. If that is impossible, even simple copy-and-paste by human beings (students, teachers, or others) may be sufficient to connect multiple systems: not everything in an ILI needs to be implemented in software. For example, student blogs may be shared through flexible  technologies such as email and messaging apps, then copied by themselves or by their teachers into shared wikis. One  interesting benefit of such approaches is that they can support both diversity and manageability, inasmuch as the management burden may be shared by the participants rather than taken on by a single teacher or institution. Students may choose which tools they use, rather than having them chosen by the teacher. This is the principle used by Connectivist MOOCs (Downes, 2008), in which one site aggregates the shared artefacts created in many different learner-managed systems.

A learner’s environment consists of much more than the digital tools and systems offered by an institution. While, to a large extent, much of this environment may be unknowable to their designated teachers, there is much value to those who seek to support student learning in discovering how they are learning, and what constitutes their learning environment. Learning – the process, the tools, and the ways of learning, and not just the products – must be made visible if teachers, including other students, are to help learners to learn (Hattie, 2013). Much use can be made of pedagogical approaches such as shared learning diaries or blogging, and some careful use may even be made of automated systems that indicate presence, or that record traces of visits, as long as their role is to provide support for understanding student learning, and not to provide the teacher with means to control of the student. Beyond individual courses, there may be much pedagogical value in encouraging learners to share their learning experience through media such as blogs, microblogs, and other online tools, which may (as long as means are available for the student to control their privacy as needed) be aggregated and shared across their whole distributed, diffuse environment. Rather than replicating the necessarily closed and time-limited nature of the classroom, the artefacts of learning and the relationships that are developed in the process may persist indefinitely. Connectivist MOOCs provide a useful model for this. For example, Cormier (2014) talks of ‘Zombie MOOCs’ in which learning and interaction persist long after the course itself is over.

Bringing about such changes at an institutional level requires both bottom-up and top-down support. Teacher’s pedagogies are normally  more malleable than digital tools, because they can adapt rapidly to any tools: they are, in Brand’s terms (Brand, 1997), the ‘stuff’. However, they are therefore also the most constrained by the structures into which they must slot, and the least able to significantly impact things at structural level.  A single teacher, or even a small group of teachers pressing for change is therefore unlikely to sway either institutional policy or the design of the LMS because, as we have seen, one LMS must address the needs of all, so anything that changes it must suit everyone.

From the top down, replacing the LMS with an integrated learning infrastructure is a necessary step towards breaking out of the vicious loops that prevent the pedagogies and structures from evolving. At first, an ILI will naturally resemble the LMS it replaces, because its boundaries will continue to be largely determined by the less flexible layers above it: the institutional forms and structures such as courses, credentials, legislation, and teachers’ employment contracts. It is important to remember that the LMS was originally designed not just to replicate classroom behaviours but to fit into the larger, slower-changing structures and systems of institutions, and that significant changes in how we teach will not occur unless those structures and systems also evolve. They create the boundaries within which the ILI operates and, to a large extent, are not just containers of it, but part of it. An infrastructure is not just the digital tools but also the human-enacted methods, rules, protocols, and standards that accompany it. It is not just what we use, but the ways that we use it.  However, unlike the LMS, in an ILI those boundaries will be malleable. This opens up opportunities for the structure, skin, and services to in turn change.

The opportunities for change may not be taken, at first, at least in part because the signals (such as qualified students, their credentials, and so on) that pass in and out of the boundaries of the university will go to and from governments, employers, and other institutions that may not be prepared for radical change, even if the institution itself is committed to it. If, say, other institutions insist on grade point averages for standardized courses, then it will be difficult to completely avoid providing them, or something that is recognizably equivalent.  However, the adjacent possible empty niches (Kauffman, 2019) that an ILI supports will inevitably be filled by those who see the opportunities it entails, from courses whose lengths are pedagogically determined, to integration of lifelong and workplace learning, to new forms of credentials and learning. Perhaps, if enough institutions start to adopt such practices, we may break free of the insular single-institution model of education altogether. Out of this may grow a truly learner- and learning-driven future, in which learners draw on services from multiple educational providers, leading to a vast participative system in which institutions meld or blend to offer support for learning not just any time and any place, but every time and every place.

References

Blum, S. D., & Kohn, A. (2020). Ungrading: Why rating students undermines learning (and what to do instead). West Virginia University Press.

Bouygues, H. L. (2019). Does Educational Technology Help Students Learn? https://reboot-foundation.org/does-educational-technology-help-students-learn/

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Brown, M., Dehoney, J., & Millichap, N. (2015). The next generation digital learning environment. A Report on Research. ELI Paper. Louisville, CO: Educause April, 5(1), 1-13.

Cormier, D. (2014). Community learning – the zombie resurrection. http://davecormier.com/edblog/2014/05/25/community-learning-the-zombie-resurrection/

Downes, S. (2008). Places to Go: Connectivism & Connective Knowledge. Innovate, 5(1). http://www.innovateonline.info/pdf/vol5_issue1/Places_to_Go-__Connectivism_&_Connective_Knowledge.pdf

Dron, J.  (2006). Any color you like, as long as it’s Blackboard®.  In. Hawaii: AACE.

Dron, J., & Anderson, T. (2014). Teaching crowds: Learning & Social Media. AU Press. http://teachingcrowds.ca

Dron, J. (2016). P-learning’s unwelcome legacy. TD Tecnologie Didattiche, 24(1), 72-81. http://www.tdjournal.itd.cnr.it/article/view/891

Dron, J. (2021). Educational technology: what it is and how it works. AI & SOCIETY, 1-12. https://doi.org/10.1007/s00146-021-01195-z

Greene, E. B. (1928). The relative effectiveness of lecture and individual reading as methods of college teaching. Genetic Psychology Monographs.

Hattie, J. (2013). Visible Learning: A Synthesis of Over 800 Meta-Analyses Relating to Achievement. Taylor & Francis.

Kauffman, S. A. (2019). A World Beyond Physics: The Emergence and Evolution of Life. Oxford University Press.

Kelly, K. (2009). Triumph of the Default. http://www.kk.org/thetechnium/archives/2009/06/triumph_of_the.php

Keuning, H., Jeuring, J., & Heeren, B. (2018). A systematic literature review of automated feedback generation for programming exercises. ACM Transactions on Computing Education (TOCE), 19(1), 1-43.

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Lane, D. C., & Good, C.  (2019). OERu’s delivery model for changing times: An Open Source NGDLE.  In World Conference on Online Learning, Dublin, Ireland. https://docs. oeru. org/s/fXQk2rJbzWCk8ia.

Martin, F., Chen, Y., Moore, R. L., & Westine, C. D. (2020). Systematic review of adaptive learning research designs, context, strategies, and technologies from 2009 to 2018. Educational Technology Research and Development, 68(4), 1903-1929.

Martindale, T., & Dowdy, M. (2010). Personal learning environments. Emerging technologies in distance education, 177, 193.

O’Neill, R.V., DeAngelis, D.L, Waide, J. B., & Allen, T. F. H. (1986). A Hierarchical Concept of Ecosystems. Princeton University Press.

Postman, N. (1998). Five things we need to know about technological change. Denver, Colorado, 28.  https://student.cs.uwaterloo.ca/~cs492/papers/neil-postman–five-things.html

Protopsaltis, S., & Baum, S. (2019). Does Online Education Live Up to its Promise? A Look at the Evidence and Implications for Federal Policy.

Ryan, R. M., & Deci, E. L. (2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford Publications.

Tichavsky, L. P., Hunt, A. N., Driscoll, A., & Jicha, K. (2015). “It’s Just Nice Having a Real Teacher”: Student Perceptions of Online versus Face-to-Face Instruction. International Journal for the Scholarship of Teaching and Learning, 9:2. http://digitalcommons.georgiasouthern.edu/ij-sotl/vol9/iss2/2/

Verbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J. L. (2013). Learning analytics dashboard applications. American Behavioral Scientist, 57(10), 1500-1509.

Wilson, S. (2008). Patterns of personal learning environments. Interactive learning environments, 16(1), 17-34.

Wilson, S., Sharples, P., & Griffiths, D.  (2008). Distributing education services to personal and institutional systems using Widgets.  In Proc. Mash-Up Personal Learning Environments-1st Workshop MUPPLE 8 (pp. 25-33).

Yen, C.-J., Tu, C.-H., Sujo-Montes, L. E., Harati, H., & Rodas, C. R. (2019). Using personal learning environment (PLE) management to support digital lifelong learning. International Journal of Online Pedagogy and Course Design (IJOPCD), 9(3), 13-31.

English version of my 2021 paper, “Technology, technique, and culture in educational systems: breaking the iron triangle”

Technology, technique, and culture in educational systems: breaking the iron triangle

This is the (near enough final) English version of my journal paper, translated into Chinese by Junhong Xiao and published last year (with a CC licence) in Distance Education in China. (Reference: Dron, Jon (2021).  Technology, technique, and culture in educational systems: breaking the iron triangle (translated by Junhong Xiao). Distance Education in China, 1, 37-49. DOI:10.13541/j.cnki.chinade.2021.01.005).

The underlying theory is the same as that in my paper Educational technology: what it is and how it works (Reference: Dron, J. Educational technology: what it is and how it works. AI & Soc 37, 155–166 (2022). https://doi.org/10.1007/s00146-021-01195-z direct link for reading, link to downloadable preprint) but this one focuses more on what it means for ways we go about distance learning. It’s essentially about ways to solve problems that we created for ourselves by solving problems in the context of in-person learning that we inappropriately transferred to a distance context.

Here’s the abstract:
This paper presents arguments for a different way of thinking about how distance education should be designed. The paper begins by explaining education as a technological process, in which we are not just users of technologies for learning but coparticipants in their instantiation and design, implying that education is a fundamentally distributed technology. However, technological and physical constraints have led to processes (including pedagogies) and path dependencies in In-person education that have tended to massively over-emphasize the designated teacher as the primary controller of the process. This has resulted in the development of many counter technologies to address the problems this causes, from classrooms to grades to timetables, most of which have unnecessarily been inherited by distance education. By examining the different strengths and weaknesses of distance education, the paper suggests an alternative model of distance education that is more personal, more situated in communities and cultures, and more appropriate to the needs of learners and society.

I started working on a revised version of this (with a snappier title) to submit to an English language journal last year but got waylaid. If anyone is interested in publishing this, I’m open to submitting it!

Some thoughts for Ada Lovelace Day

This Scientific American article tells the tale of one of the genesis stories of complexity science, this one from 1952, describing what, until relatively recently, was known as the Fermi-Pasta-Ulam (FPU) problem (or ‘paradox’, though it is not in fact a paradox). It is now more commonly known as the Fermi-Pasta-Ulam-Tsingdou (FPUT) problem, in recognition of the fact that it was only discovered thanks to the extraordinary work of Mary Tsingou, who wrote the programs that revealed what, to Fermi, Pasta, and Ulam, was a very unexpected result. 

The team was attempting to simulate what happens to energy as it moves around atoms connected by chemical bonds. This is a classic non-linear problem that cannot be observed directly, and that cannot be solved by conventional reductive means (notwithstanding recent work that reveals statistical patterns in complex systems like urban travel patterns). It has to be implemented as a simulation in order to see what happens. Fermi, Pasta, and Ulam thought that, with enough iterations, it would reveal itself to be ergodic: that, given long enough, every state of a given energy of the system would be visited an equal number of times. Instead, thanks to Mary Tsingou’s work, they found that it was non-ergodic. Weird stuff happened, that could not be predicted. It was chaotic.

The discovery was, in fact, accidental. Initial results had shown the expected regularities then, one day, they left the program running for longer than usual and, instead of the recurring periodic patterns seen initially, it suddenly went haywire. It wasn’t a bug in the code. It was a phase transition, perhaps the first unequivocal demonstration of deterministic chaos. Though Fermi died and the paper was not actually published until nearly a decade later, it is hard to understate the importance of this ‘accidental’ discovery that deterministic systems are not necessarily ergodic. As Stuart Kauffman puts it, ‘non-ergodicity gives us history‘. Weather is non-ergodic. Evolution is non-ergodic. Learning is non-ergodic. We are non-ergodic. The universe is non-ergodic. Though there are other strands to the story that predate this work, more than anything else this marks the birth of a whole new kind of science – the science of complexity – that seeks to deal with the 90% or more of phenomena that matter to us, and that reductive science cannot begin to handle. 

Here’s a bit of Tsingou’s work on the program, written for the MANIAC computer:

Mary Tsingou's original algorithm design, drawn in freehand

It was not until 2008 that Tsingou’s contribution was fully recognized. In the original paper she was thanked in a footnote but not acknowledged as a co-author. It is possible that, had it been published right away she might have received proper credit. However, it is at least as possible that she might not. The reasons for this are a mix of endemic sexism, and (relatedly) the low esteem accorded to computation at the time.

The relationship between these two factors runs deep.  Historically, the word ‘computer’ originally referred to a job title.  As scientists in the 19th Century amassed vast amounts of data that needed processing, there was far too much for an individual to handle. They figured out that tasks could be broken up into smaller pieces and farmed out in parallel to humans who could do the necessary rote arithmetic.  Because women were much cheaper to hire, and computing was seen as a relatively unskilled (albeit very gruelling and cognitively demanding) role, computing therefore became a predominantly female occupation. From the 19th Century onwards into the mid 20th Century, all-women teams worked on astronomical data, artillery trajectories, and similar tasks, often performing extremely complex mathematical calculations requiring great precision and endurance, always for far less pay than they deserved or that a man would receive. Computers were victims of systematic gender discrimination from the very beginning. 

The FPUT problem, however, is one that doesn’t lend itself to chunking and parallel computation: the output of one iteration of the computation is needed before you can calculate the next. Farming it out to human computers simply wouldn’t work. For work of this kind, you have to have a machine or it would take decades to come up with a solution.

In the first decade or so after digital computers were invented significant mathematical skill was needed to operate them. Because of their existing exploitation as human computers, there was, luckily enough, a large workforce of women with advanced math skills whose manual work was being obsoleted at the same time, so women played a significant role in the dawn of the industry. Mary Tsingou was not alone in making great contributions to the field.

By the 1970s that had changed a lot, not in a good way, but numbers slowly grew again until around the mid-1980s (a terrible decade in so many ways) when things abruptly changed for the worse.

graph showing the huge drop in women in IT from the 1980s onwards

Whether this was due to armies of parents buying PCs for their (male) children thanks to aggressive marketing to that sector, or highly selective media coverage, or the increasing recognition of the value of computing skills in the job market reinforcing traditional gender disparities, or something else entirely (it is in fact complex, with vast self-reinforcing feedback loops all the way down the line), the end result was a massive fall in women in the field. Today, less than 17% of students of computer science are women, while the representation of women in most other scientific and technical fields has grown considerably.

There’s a weirder problem at work here, though, because (roughly – this is an educated guess) less than 1% of computer science graduates ever wind up doing any computer science, unless they choose a career in academia (in which case the figure rises to very low single figures), and very few of them ever do more mathematics than an average greengrocer. What we teach in universities has wildly diverged from the skills that are actually needed in most computing occupations at an even sharper rate than the decline of women in the trade. We continue to teach it in ways that would have made sense in the 1950s, when it could not be done without a deep understanding of mathematics and the science behind digital computation, even though neither of these skills has much if any use at all for more than a minute fraction of our students when they get out into the real world. Sure, we have broadened our curriculum to include many other aspects of the field, but we don’t let students study them unless they also learn the (largely unnecessary in most occupations) science and math (a subject that suffers even lower rates of non-male participation than computing). Thinking of modern computing as a branch of mathematics is a bit like treating poetry as a branch of linguistics or grammar, and thinking of modern computing as a science is a bit like treating painting as a branch of chemistry. It’s not so much that women have left computing but that computing – as a taught subject – has left women. 

Computing professionals are creative problem solvers, designers, architects, managers, musicians, writers, networkers, business people, artists, social organizers, builders, makers, teachers, or dreamers. The main thing that they share in common is that they work with computers. Some of them are programmers. A few (mostly those involved in designing machines and compilers) do real computer science. A few more do math, though rarely at more than middle school level, unless they are working on the cutting edge of a few areas like graphics, AI, or data science (in which case the libraries etc that would render it unnecessary have not yet been invented).  The vast majority of computing professionals are using the outputs of this small elite’s work, not reinventing it. It it not surprising that there is enormous diversity in the field of computing because computers are universal machines, universal media, and universal environments, so they encompass the bulk of human endeavour. That’s what makes them so much fun. If you are a computing professional you can work with anyone, and you can get involved in anything that involves computers, which is to say almost everything. And they are quite interesting in and of themselves, partly because they straddle so many boundaries, and ideas and tools from one area can spark ideas and spawn tools in another.

If you consider the uses of computer applications in many fields, from architecture or design to medicine or media to art or music, there is a far more equal gender distribution. Computing is embedded almost everywhere, and it mostly demands very different skills in each of its uses. There are some consistent gaps that computing students could fill or, better, that computing profs could teach in the context they are used. Better use could be made of computers across the board with just a little programming or other technical skills. Unfortunately, those who create, maintain, and manage computers and their applications tend to mainly come out of computer science programs (at least in North America and some other parts of the world) so many are ill prepared for participating in all that richness, and computing profs tend to stick with teaching in computer science programs so the rest of the world has to figure out things they could help with for themselves.

I think it is about time that we relegated computer science to a minor (not unimportant) stream and got back into the real world – the one with women in it. There’s still a pressing need to bring more women into that minor stream: we need inspirations like Mary Tsingou, we could do worse than preferentially hiring more non-male professors, and we desperately need to shift the discriminatory culture surrounding (especially) mathematics but, if we can at least teach in a way that better represents the richness and diversity of the computing profession itself, it would be a good start.

Originally posted at: https://landing.athabascau.ca/bookmarks/view/10624709/some-thoughts-for-ada-lovelace-day

A modest proposal for improving exam invigilation

There has been a lot of negative reaction of late to virtual proctors of online exams. Perhaps students miss the cheery camaraderie of traditional proctored exams, sitting silently in a sweaty room with pen and paper, doing one of the highest stakes, highest stress tasks of their lives, with someone scrutinizing their every nervous tic whose adverse judgment may destroy their hopes and careers, for the benefit of an invisible examiner whose motives and wishes are unclear but whose approval they dearly seek. Lovely. Traditional. Reassuring. A ritual for us all to cherish. It’s enough to bring a tear to the eye.

But exams cost a huge amount of money to host and to invigilate. It is even worse when one of the outcomes might, for the student or the invigilator, be death or disability due to an inconvenient virus.

I have a better solution.

photo of a toy robotInstead of costly invigilators and invigilation centres, all we need to do is to send out small (returnable, postage-paid) robots to students’ homes. A little robot sitting on the student’s desk or kitchen table as they sit their written exam (on paper, of course – tradition matters), recording every blink, watching their fingers writing on the paper, with 360 degree panoramic camera and the ability to zoom in on anything suspicious or interesting. Perhaps it could include microphones, infrared and microwave sensors, and maybe sensors to monitor skin resistance, pulse, etc, in order to look for nefarious activities or to call the ambulance if the student seems to be having a heart attack or stroke due to the stress. It could be made to talk, too. Perhaps it could offer spoken advice on the process, and alerts about the time left at carefully selected intervals. Students could choose the voice. It would also allow students to sit exams wherever and whenever they please: we are all in favour of student choice. With a bit of ingenuity it could scan what the students have written or drawn, and send it back to an examiner. Or, with a bit more ingenuity and careful use of AI, it could mark the paper on the spot, saving yet more money. Everyone wins.

It would be important to be student-centric in its design. It could, for instance, be made to look like a cute little furry animal with googly eyes to put students more at ease. Maybe it could make soothing cooing noises like a tribble, or like a cat purring. Conversely, it could be made to scuttle ominously around the desk and to appear like a spider with venomous-looking fangs, making gentle hissing noises, to remind students of the much lamented presence of in-person invigilators. Indeed, maybe it could be made to look like a caricature of a professor. More advanced models could emit bad smells to replicate invigilator farts or secret smoking habits. It could be made small and mobile, so that students could take it with them if they needed a bathroom break, during which it might play soothing muzak to put the student at ease, while recording everything they do. It would have to be tough, waterproof, and sterilizable, in order to cope with the odd frustrated student throwing or dunking it.

Perhaps it could offer stern spoken warnings if anomalies or abuses are found, and maybe connect itself to a human invigilator (I hear that they are cheaper in developing nations) who could control it and watch more closely. Perhaps it could be equipped with non-lethal weaponry to punish inappropriate behaviour if the warnings fail, and/or register students on an offenders database.  It could be built to self-destruct if tampered with.

Though this is clearly something every university, school, and college would want, and the long-term savings would be immense, such technologies don’t come cheap. Quite apart from the hardware and software development costs, there would be a need for oodles of bandwidth and storage of the masses of data the robot would generate.

I have a solution to that, too: commercial sponsorship.

We could partner with, say, Amazon, who would be keen to mine useful information about the students’ surroundings and needs identified using the robot’s many sensors. A worn curtain? Stubborn stains? A shirt revealing personal interests? Send them to Amazon! Maybe Alexa could provide the voice for interactions and offer shopping advice when students stop to sharpen their pencils (need a better pencil? We have that in stock and can deliver it today!). And, of course, AWS would provide much of the infrastructure needed to support it, at fair educational prices. I expect early adopters would be described as ‘partners’ and offered slightly better (though still profitable) deals.

And there might be other things that could be done with the content. Perhaps the written answers could be analyzed to identify potential Amazon staffers. Maybe students expressing extremist views could be reported to the appropriate government agency, or at least added to a watch-list for the institution’s own use.

Naysayers might worry about hackers breaking into it or subverting its transmissions, or the data being sent to a country with laughable privacy laws, or the robot breaking down at a critical moment, or errors in handwriting recognition, but I’m sure that could be dealt with, the same as we deal with every other privacy, security, and reliability issue in IT in education. No problem. No sir. We have lawyers.

The details still need to be ironed out here and there, but the opportunities are endless. What could possibly go wrong? I think we should take this seriously. Seriously.

At last, a serious use for AI: Brickit

https://brickit.app/

Brickit is what AI was made for. You take a picture of your pile of LEGO with your phone or tablet, then the app figures out what pieces you have, and suggests models you could build with it, including assembly plans. The coolest detail, perhaps, is that, having done so, it highlights the bricks you will need in the photo you took of your pile, so you can find them more easily. I’ve not downloaded it yet, so I’m not sure how well it works, but I love the concept.

The fan-made app is iOS only for now, but an Android version is coming in the fall. It’s free, but I’m guessing it may make money in future from in-app purchases giving access to more designs, options to purchase missing bricks, or something along those lines.

It would be cooler if it connected Lego enthusiasts so that they could share their MOCs (my own constructions) with others. I’m guessing it might use the LXFML format, which LEGO® itself uses to export designs from its (unsupported, discontinued, but still available) LEGO DIgital Designer app, so this ought to be easy enough. It would be even cooler if it supported a swap and share feature, so users could connect via the app to get hold of or share missing bricks. The fact that it should in principle be able to catalogue all your pieces would make this fairly straightforward to do. There are lots of existing sites and databases that share MOCs, such as https://moc.bricklink.com/pages/moc/index.page, or the commercial marketplace https://rebrickable.com/mocs/#hottest; there are brick databases like https://rebrickable.com/downloads/ that allow you to identify and order the bricks you need;  there are even swap sites like http://swapfig.com/ (minifigures only); and, of course, there are many apps for designing MOCs or downloading others. However, this app seems to be the…er…missing piece that could make them much more useful. 

Reviews suggest that it doesn’t always succeed in finding a model and might not always identify all the pieces. Also, I don’t think there’s a phone camera in the world with fine enough resolution to capture my son’s remarkably large LEGO collection. Even spreading the bricks out to take pictures would require more floor-space than any of us have in our homes. But what a great idea!

Originally posted at: https://landing.athabascau.ca/bookmarks/view/9558928/at-last-a-serious-use-for-ai-brickit

A few thoughts on learning management systems, and on integrated learning environments and their implementation

Why do we build digital learning systems to mimic classrooms?

It is understandable that, when we teach in person, we have to occupy and make different uses of the same or similar environments like classrooms, labs, workshops, lecture theatres, and offices. There are huge financial, physical, and organizational constraints on making the environment fit the task, so it would be madness to build a whole new classroom every time we wished to run a different class.

Online, we could build anything we like

But why do we do the same when we teach online? There are countless tools available and, if none are suitable, it is not too hard to build them or modify them to suit our needs. Once they are built, moving between them just takes a tap of a screen or the click of a mouse. Heck, you can even occupy several of them at once if you have a decent monitor or more than one device.

So why don’t we do this?

Here are a few of the more obvious reasons that using the perfect app for the context of study rarely happens:

  • Teachers’ lack of knowledge of the options (it takes time and effort to discover what’s available).
  • Teachers’ lack of skill in using them (most interesting tools have a learning curve, and that gets steeper in inverse proportion to the softness and diversity of the toolset, so most teachers don’t even know how to make the most of what they already have).
  • Lack of time and/or money for development (a real-life application is what it contains, not just the shell that contains it, and it is not always as easy to take existing stuff and put it in a new tool as it might be in a physical space).
  • Costs and difficulties in management (each tool adds costs in managing faults, configuration, accounting for use, performance, and security).
  • Cognitive load involved for learners in adapting to the metaphors, signposts, and methods needed to use the tool itself.

All of these are a direct consequence of the very diversity that would make us want to use different apps in the first place. This is a classic Faustian bargain in which the technology does what we want, and in the process creates new problems to solve.  Every virtual system invents at least some of the dynamics of how people and things interact with it and within it. In effect, every app has its own physics. That makes them harder to find out about, harder to learn, harder to develop, costlier to manage, and more difficult to navigate than the static, fixed facilities found in particular physical locations. They are all different, there are few if any universals, and any universal today may become a conditional tomorrow. Gravity doesn’t necessarily work the same way in virtual systems.

image of a pile of containersAnd so we get learning management systems

The learning management system (LMS) kind of deals with all of these problems: poorly, harmfully, boringly, and painfully, but it does deal with them. Currently, most of the teaching at Athabasca University is through the open source Moodle LMS, lightly modified by us because our needs are not quite like others (self-pacing and all that). But Moodle is not special: in terms of what it does and how it does it, it is not significantly different from any other mainstream LMS – Blackboard, Brightspace, Canvas, Sakai, whatever.

Almost every LMS essentially automates the functions, though not exactly the form, of traditional classrooms. In other parts of the world people prefer to use the term ‘managed learning environment’ (MLE) for such things, and it is the most dominant representative of a larger category of systems usually described as virtual learning environments (VLEs) that also includes things like MOOs (multi-user dungeons, object oriented), immersive learning environments, and simpler web-based teaching systems that replicate aspects of classrooms such as Google Classroom or Microsoft’s gnarly bundle of hastily repurposed rubbish for teaching that I’m not sure even has a name yet. Notice the spatial metaphors in many of these names.

Little boxes made of ticky tacky

The people who originally designed LMSs back in the 90s (I did so myself) based their designs on the functions and entities found in a traditional university because that was their context, and that was where they had to fit. Metaphorically, an LMS or MLE is a big university building with rather uniform classrooms, with perhaps a yard where you can camp out with a few other systems (plugins, LTI hooks, etc) that conform to its requirements and that are allowed in to classrooms when invited, and a few doors and gateways (mainly hyperlinks) linking it circuitously or in jury-rigged fashion to other similarly weakly connected buildings (e.g. places to register, places to seek support, places to talk to an advisor, places to complain, places to find books, and so on). It doesn’t have metaphorical corridors, halls, common rooms, canteens, yards, libraries or any of the other things that normally make up a physical university. You rarely get to even be aware of other classrooms beyond those you are in. Some people (me in a past life) might give classrooms cute names like ‘the learning cafe’ but it’s still just another classroom. You teleport from one classroom to the next because what happens in corridors (really a big lot of incredibly important pedagogically useful stuff, as it happens) is not perceived by the designers as a useful classroom function to be automated or perhaps, more charitably, they just couldn’t figure out how to automate that.

Reified roles

It’s a very controlled environment where everyone has a programmatically enforced role (mostly reflecting traditional educational roles), that may vary according to the room, but that are far less fluid than those in physical spaces. There are strong hierarchies, and limited opportunities for moving between them. Some of those hierarchies are new: the system administrator, for instance, has way more power than anyone in a physical university to determine how learning happens, like an architect with the power to move walls, change the decor, add extensions, and so on, at will. The programmers of the system are almost god-like in their command of its physics. But the ways that they give teachers (or learning designers, or administrators) control, as designers, directors, and regulators of the classroom, are perhaps the most pernicious. In a classroom a teacher may lead (and, by default, usually does). In an LMS, a teacher (or someone playing that role) must lead. The teacher sees things that students cannot, and controls things that the students may not. A teacher configures the space, and determines with some precision how it will be used. With a lot of effort and risk, it can be made to behave differently, but it almost never is.

Functions are everything

An LMS is typically built along functional lines, and those functions are mostly based on loose, superficial observations of what teachers and students seem to do in physical classrooms. The metaphorical classrooms are weird, because they are structured by teaching (seldom learning) function rather than along pedagogical lines: for instance, if you want to talk with someone, you normally need to go to a separate enclosed area inside the classroom or leave a note on the teacher’s desk. Same if you want to take a test, or share your work with others. Another function, another space. Some have many little rooms for different things. Lectures are either literally that (video recordings) or (more usefully, from a learning perspective), text and images to be read on screen, based on the assumption that the only function of lectures is information transmission (it is so very, very much not – that’s its least useful and least effective role). There’s seldom a chance to put even put up your hand to question something. Notices can usually only be pinned on the wall by teachers. Classroom timetables are embodied in software because of course you need a rigid and unforgiving timetable in a medium that sells itself on enabling learning anywhere, any time. Some, including Moodle, will allow you to break up the content differently, but it’s still another timetable; just a timetable without dates. It’s still the teacher who sets the order, pacing and content.

Robot overlords

It’s a high-tech classroom. There are often robots there that are programmed to make you behave in ways determined by those higher in the hierarchy (sometimes teachers, sometimes administrators, sometimes the programmers of the software). For instance, they might act as gatekeepers that prevent you from moving on to the next section before completing the current one, or they might prevent you submitting work before or after a specified date. They might mark your work. There are surveillance cameras everywhere, recording your every move, often only accessible to those with more powerful roles (though sometimes a robot or two might give you a filtered view of it).

Beginnings and ends

You can’t usually go back and visit when your course is over because someone decided it would be a good idea to set opening and closing enrolment dates and assumed that, when they were done, the learning was done (which of course it never is – it keeps on evolving long after explicit teaching and testing occurred). Again, it’s because physical classes are scheduled and terms come to an end because they must be, not because it makes pedagogical sense. And, like almost everything, you can override this default, but hardly anyone ever does, because it brings back those Faustian bargains, especially in manageability.

Dull caricatures of physical spaces

Basically, the LMS is an automated set of metaphorical classrooms that hardens many of the undesirable by-products of educational systems in software in brain-dead ways that have little to do with how best to teach, and that stretch the spatial metaphors that inform it beyond breaking point. Each bit of automation and each navigational decision hardens pedagogical choices. For all the cozy metaphors, programmers invent rather than replicate physics, in the process warping reality in ways that do no good and much harm. Classrooms solved problems of physics for in-person teaching and form part of a much larger structure that has evolved to teach reasonably well (including corridors, common rooms, canteens, and libraries, as it happens). Their more visible functions are only a part of that and, arguably, not the main part. There is much pedagogy embedded in the ways that physical universities, whether by accident or design, have evolved over centuries to support learning in every quadrangle and nook of a coffee shop. LMSs just focus on a limited subset of teaching roles, and empower the teacher in ways that caricature their already excessive dominance in the classroom (which only occurred because it had to, thanks to physics and the constraints it imposed).

LMSs are crap, but they contain recognizable semblances of their physical counterparts and just enough configurability and flexibility to more or less work as teaching tools, a bit, for everyone, almost no matter what their level of digital proficiency might be. They more or less solve the Faustian bargains listed earlier, but they do so by stifling what we wanted and should have been able to do in the first place with online tools, in the process creating new and quite horrific problems, as well as demolishing most of what makes physical universities work in the first place. It never has been true that virtual learning environments are learning environments – they are only ever parts of them – and there are places to escape from them, such as the Landing, other virtual systems, or even just plain old email, but then all those Faustian bargains come back to haunt us again. There has to be a better way.

Beyond the LMS

Cognisant of the issues, Athabasca University is now some way down the path to developing its own distinctive solutions to these problems, in a multi-year multi-million-dollar initiative known as (following the spatial metaphor) the Integrated Learning Environment (ILE). The ILE is not an application. It is an umbrella term for a lot of different, usually independent systems working together as one. Though some of the most interesting opportunities are still only loosely imagined, perhaps because they cause problems that are fiendishly hard to solve (e.g. how can we integrate systems that we build ourselves without creating risks for the rest of the ILE, and what happens when they need to be maintained?) a lot of progress is being made on the non-teaching foundations on which the rest depends (student admin systems, support tools, procedures, etc), as well as on the most visible and perhaps the biggest of its parts, BrightSpace, a proprietary commercial LMS that is meant to replace Moodle, for no obvious pedagogical or technical reasons (it’s no better). It might make economic sense. I don’t know, but I do know that open source software typically costs a fair bit to own, albeit because of the things that make it a much better idea (freedom, flexibility, ownership, etc). There is probably a fair bit of time and money being spent with Desire2Learn (makers of Brightspace) on the things that we spent a fair bit of time and money on many years ago to make Moodle a bit less classroom-like. The choice no doubt has something to do with how reliably and easily it can be made to work with some of the other proprietary commercial systems that someone has decided will make up the ILE. It bothers me greatly that we are not trying hard to choose open source solutions, for reasons that will become clearer in the rest of this post. However, (pedagogically speaking) all the mainstream LMSs are much of a muchness, making the same mistakes as one another in very similar ways, so it probably won’t wreck too much of what we already do within Moodle. But, on its own, it won’t move us much further forward and we could do it better. That’s what the ILE is supposed to do – to make the LMS just a part of a much larger teaching environment, intimately connected with the rest of what the university does for or with students, and extensible with new and better ways of learning, teaching, and assessing learning.

picture of lego bricksLego bricks make poor metaphors

When we were first imagining the ILE, though the approach was admirably participative, engaging much of the university community, I was very worried by the things we were encouraged to focus on. It was all about the functionality, the usability, the design, the tools, the pedagogies, the business systems that supported them. Those things matter, for sure, and should be not be ignored, but they should and will change and grow all the time: in fact, part of the point of building this thing is to do just that. Using the city metaphor, pretty much all that we (collectively) considered were the spaces (the rooms, mainly), and the stuff that goes on inside them, much like LMS designers thought of universities as just collections of classrooms in which teaching functions were performed. Space and stuff are, not uncoincidentally, exactly what Stewart Brand identified long ago as inevitably being the fastest-changing, most volatile parts of any town or city (after site, structure, skin, and services). I’ve written a fair bit on the universality of this principle across all systems. It’s a solid structural principle that applies as much to ecosystems and educational systems as to cities. As Brand observes himself, drawing from O’Neill et al (1986), the larger, slower-changing elements of any system affect the smaller, faster-changing more than vice versa. This is for much the same reasons that path dependencies set in. It’s about the prior providing the context for what follows. Flexible things have to fit into the gaps left by less flexible, older, pre-existing things. In physical spaces, of course these tend to be bigger and/or slower, but the same is true in virtual spaces, where size seldom matters that much, but hardness (inflexibility, brittleness) really does. Though lip service was paid to the word ‘integrated’ in our discussions,  I had the strong feeling that the kind of integration we had in mind was that of a Lego set. In fact, I think we were aiming to find a ‘Lego Athabasca University’ set, with assembly instructions and a picture on the box. The vendors who came to talk with us made much of how effectively they could do that, rather than how effectively they could make it possible for others to do that.

Metaphors matter. Lego bricks have to fit together tightly, in pre-specified ways, especially if you are following a plan. If you want to move them around, you have to dismantle a bit of the structure to fit them in. It’s difficult to integrate things that are not bricks, or that are made by different toy companies to work in different ways. At best you get what Brand calls ‘magazine architecture’, or ‘no road’ architecture, beautiful, fit for purpose, intricate and solid, but slow to learn. Lego is not a terrible way to build, compared with buying everything pre-assembled, but it could be improved.

Signals and boundaries

Drawing inspiration from John Holland’s brilliant last work, Signals & Boundaries, I tried to make the case that, instead, we should be focusing on the boundaries (the interfaces between the buildings and the rest of the city), and the signals that pass between them (the people, the messages, etc, the forms they take and how they move around). In Brand’s terms, I wanted us to be thinking about skin and services, and perhaps even structure, though site – Athabasca University – was a given. Though a few people nodded in agreement, I think it mainly fell on deaf ears. We wanted oven-ready solutions, not the infrastructure to enable those solutions. Though the city metaphor works well, because we are talking about human constructions, others would result in similar ways of thinking: cells in bodies, organisms in ecosystems, brains, termite mounds, and so on. All are organized by boundaries (at many levels of hierarchy) and the signals that pass between them.

The Lego set metaphor – whether deliberately or not – seems to have prevailed for now. A lot of old buildings are being slated for demolition and a lot of new virtual buildings are now being erected as part of this development, many of them chosen not because of problems with existing buildings but so that they can more easily connect together and live in the same cloud. This will very likely work, for now, but it is not cheap and it is not flexible, especially given the fact that most of it is not open so, like a rental property, we are not allowed to fix things, add utilities, change the walls, etc, and we are wholly dependent on the landlords being nice to us and each other (knowing that some – ahem, Microsoft – have a long history of abusing their tenants). Those buildings will age. We will find them cramped. Some will age faster than others, and will have to be modified to keep up, perhaps at high cost. Companies renting them might go out of business or change their terms so we might have to demolish the buildings and rent/make new ones. We will be annoyed at how they do things, usually without asking us. We will hate the landlords who dictate what we can do and how we can do it, and who will keep upping the rent while not doing what we ask. We will want more, and the only way to get it will be to build extensions, buy new brick sets, if it is not enough to pay someone to remodel the interiors (and it won’t be). Of course, because most of the big structural elements will not be open source, we will not be able to do that ourselves.

What the ILE really should be

The ILE is, I think, poorly named, because it should not be an environment at all. Following the building metaphor, the ILE is (or should be) more like the system that connects a lot of buildings, bringing them together into a coherent, safe, livable community. It’s infrastructure and services; it is the roads, the traffic signals, the doors, the sidewalks, the water pipes, the waste pipes, the electricity, the network cables; it is the services – fire, police, schools, traffic control, etc; it is all the many rules, standards, norms and regulations that make them work together to help make an environment in which people can live, work, play, and grow. It’s part of the environment – the part that makes it work – but it is not the environment itself. The environment itself is Athabasca University, not just the tools, processes, and systems that support its functions. That includes, most importantly, the people who are part of the university, or who are visitors to it, who are not just users of the environment or dwellers in its walls, but who are or should be the most significant and visible parts of it, just as trees are part of the environment of forests, not users of the forest. Those people live in physical as well as other virtual environments (social media, Word documents, websites, etc) that the ILE can connect together too, to make them a part of it, so the spatial metaphor gets weird at this point. The ILE makes environmental boundaries fuzzy, permeable, and shifting. It’s not an ILE, it’s an ILI – an integrated learning infrastructure.

If we focused on the connections and interfaces, and on how information and processes need to pass across them, and if we thought hard about the nature of those signals, then we could build a system that is resilient, that adapts, that lasts, that grows, that evolves, with parts that we can seamless replace or improve because the interfaces – the building facades, the mains pipes, the junction boxes, etc – will mostly stay the same, evolving slowly as they should. This is about strategy, not planning,  a way of thinking about systems rather than a sequence of things to do.

Some of the key people involved in the process realize this. They are talking about standards, protocols, and projects to build interfaces between systems, and imagining future needs, though they are inevitably distracted by the process of renting Lego bricks, so I am not sure how much they will be able to stay focused on that. I hope they prevail over those who think they are building a set of classrooms and tightly connected admin offices out of self-contained interlocking bricks because our future depends on getting it right. We are aiming to grow. It just takes one critical piece in the Lego building to fail to support that, and the rest falls apart like a… well, like a pile of bricks.

References

Brand, S. (1997). How buildings learn. Phoenix Illustrated. https://www.penguinrandomhouse.ca/books/320919/how-buildings-learn-by-stewart-brand/9780140139969

Holland, J. H. (2012). Signals and Boundaries: Building Blocks for Complex Adaptive Systems. MIT Press.  https://mitpress.mit.edu/books/signals-and-boundaries

O’Neill, R.V., DeAngelis, D.L, Waide, J. B., & Allen, T. F. H. (1986). A Hierarchical Concept of Ecosystems. Princeton University Press. http://www.gbv.de/dms/bs/toc/025157787.pdf

Postman, N. (1998). Five things we need to know about technological change. Denver, Colorado, 28.  https://student.cs.uwaterloo.ca/~cs492/papers/neil-postman–five-things.html

Mediaeval Teaching in the Digital Age (slides from my keynote at Oxford Brookes University, May 26, 2021)

 front slide, mediaeval teaching

These are the slides from my keynote today at the Oxford Brookes “Theorizing the Virtual” School of Education Research Conference. As theorizing the virtual is pretty much my thing, I was keen to be a part of this! It was an ungodly hour of the day for me (2am kickoff) but it was worth staying up for. It was a great bunch of attendees who really got into the spirit of the thing and kept me wide awake. I wish I could hang around for the rest of it but, on the bright side, at least I’m up at the right time to see the Super Flower Blood Moon (though it’s looking cloudy, darn it).  In this talk I dwelt on a few of the notable differences between online and in-person teaching. This is the abstract…

Pedagogical methods (ways of teaching) are solutions to problems of helping people to learn, in a context filled with economic, physical, temporal, legal, moral, social, political, technological, and organizational constraints. In mediaeval times books were rare and unaffordable, and experts’ time was precious and limited, so lectures were a pragmatic solution, but they in turn created more problems. Counter-technologies such as classes, classrooms, behavioural rules and norms, courses, terms, curricula, timetables and assignment deadlines were were devised to solve those problems, then methods of teaching (pedagogies) were in turn invented to solve problems these counter-technologies caused, notably including:
· people who might not want (or be able) to be there at that time,
· people who were bored and
· people who were confused.
Better pedagogies supported learner needs for autonomy and competence, or helped learners find relevance to their own goals, values, and interests. They exploited physical closeness for support, role-modelling, inspiration, belongingness and so on. However, increasingly many relied on extrinsic motivators, like classroom discipline, grades and credentials to coerce students to learn. Extrinsic motivation achieves compliance, but it makes the reward or avoidance of the punishment the goal, persistently and often permanently crowding out intrinsic motivation. Intelligent students respond with instrumental approaches, satisficing, or cheating. Learning seldom persists; love of the subject is subdued; learners learn to learn in ineffective ways. More layers of counter-technologies are needed to limit the damage, and so it goes on.
Online, the constraints are very different, and its native forms are the motivational inverse of in-person learning. An online teacher cannot control every moment of a learner’s time, and learners can use the freedoms they gain to take the time they need, when they need it, to learn and to reflect, without the constraints of scheduled classroom hours and deadlines. However, more effort is usually needed to support their needs for relatedness. Unfortunately, many online teachers try (or are required) to re-establish the control they had in the classroom through grading or the promise of credentials, recreating the mediaeval problems that would otherwise not exist, using tools like learning management systems that were designed (poorly) to replicate in-person teaching functions. These are solutions to the problems caused by counter-technologies, not to problems of learning.
There are better ways, and that’s what this session is about.

front slide, mediaeval teaching

Educational technology: what it is and how it works | AI & Society

https://rdcu.be/ch1tl

This is a link to my latest paper in the journal AI & Society. You can read it in a web browser from there, but it is not directly downloadable. A preprint of the submitted version (some small differences and uncorrected errors here and there, notably in citations) can be downloaded from https://auspace.athabascau.ca/handle/2149/3653. The published version should be downloadable for free by Researchgate members.

This is a long paper (about 10,000 words), that summarizes some of the central elements of the theoretical model of learning, teaching and technology developed in my recently submitted book (still awaiting review) and that gives a few examples of its application. For instance, it explains:

  • why, on average researchers find no significant difference between learning with and without tech.
  • why learning styles theories are a) inherently unprovable, b) not important even if they were, and c) a really bad idea in any case.
  • why bad teaching sometimes works (and, conversely, why good teaching sometimes fails)
  • why replication studies cannot be done for most educational interventions (and, for the small subset that are susceptible to reductive study, all you can prove is that your technology works as intended, not whether it does anything useful).

Abstract

This theoretical paper elucidates the nature of educational technology and, in the process, sheds light on a number of phenomena in educational systems, from the no-significant-difference phenomenon to the singular lack of replication in studies of educational technologies.  Its central thesis is that we are not just users of technologies but coparticipants in them. Our participant roles may range from pressing power switches to designing digital learning systems to performing calculations in our heads. Some technologies may demand our participation only in order to enact fixed, predesigned orchestrations correctly. Other technologies leave gaps that we can or must fill with novel orchestrations, that we may perform more or less well. Most are a mix of the two, and the mix varies according to context, participant, and use. This participative orchestration is highly distributed: in educational systems, coparticipants include the learner, the teacher, and many others, from textbook authors to LMS programmers, as well as the tools and methods they use and create.  From this perspective,  all learners and teachers are educational technologists. The technologies of education are seen to be deeply, fundamentally, and irreducibly human, complex, situated and social in their constitution, their form, and their purpose, and as ungeneralizable in their effects as the choice of paintbrush is to the production of great art.

Originally posted at: https://landing.athabascau.ca/bookmarks/view/8692242/my-latest-paper-educational-technology-what-it-is-and-how-it-works