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

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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)

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 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

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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

My keynote slides from Confluence 2021 – STEAM engines: on building and testing the machines in our students’ minds

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STEAM Engines

These are my slides for my keynote talk at the IEEE 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence-2021), hosted by Amity University, India, 28th January 2021. Technically it was 27th January here in Vancouver when I started, but 28th January when I finished. I hate timezones.

The talk winds up being about how to be a (mainly online) teacher in science, technology, engineering, and mathematics (STEM) – not how to teach, as such – but it gets to the point circuitously through discussing some aspects of the nature of technology, using a subset of my coparticipation model. In (very brief) the idea behind that is that ‘technology’ means organizing stuff to do stuff (any stuff), and we are not just users but participants in that organization, either playing our roles correctly (hard technologies) or organizing stuff ourselves (soft technologies). Almost always, thanks to the fact that almost all technologies are assemblies of and with other technologies, it is a mix of the two. In the technologies of learning there are many coparticipants, all playing roles, soft or hard or both. The designated teacher is only one of these, of varying significance.

The talk dwelt on the technological nature of teaching itself, and on the technological nature of the results of teaching. Teaching (as a distributed process) can usefully be seen as a process of building technologies in learners’ minds, some hard (training), some soft (teaching). These technologies can, like all technologies, be assembled together or with others, so our minds are both enacted and extended through technologies with one another and with the constructed world around us.

In STEM subjects there is a tendency to focus a lot more on building hard technologies than on soft technologies, because there tends to be a lot of hard stuff to learn before you can do anything much at all. There are many other subjects like this, including one of the biggest, language learning. The same is actually true in softer disciplines but students tend to come equipped with a lot of the basic hard stuff – especially language, debating skills, etc – already, so a really big part of the machine already exists. However, as much as it is in the liberal arts (the ‘A’ in STEAM), it is actually the soft technologies – what we do with those hard machines in our minds, the soft technologies we assemble with them – that actually matters, personally, in the workplace, and in our social lives. Also, from a motivational perspective it is normally a really bad idea to force people to learn a lot of hard stuff without them actually having a personal need or desire to do so. Training people in the hard stuff without using it in a soft, personally/socially relevant and meaningful context is a recipe for failure, though the fact that hard skills and knowledge can be accurately measured means that assessments of it tend to create an illusion of success. ‘Success’, though, just means that the hard machine works as intended, not that it actually does anything useful.

Avoiding this chicken and egg problem – the need for hard skills before you can do anything, but the uselessness of them in isolation – is not difficult. In fact, it is how we learn to speak, and many other things. It means letting go of the notion that teachers control everything, embracing the distributed nature of teaching, and designing ways of learning that support autonomy, achievable challenge, and relatedness. To do this means making learning (not just its products) visible, creating a culture and tools for sharing, and designing in support processes to help learners overcome obstacles. Basically, from a designated teacher’s perspective, it’s about letting go and staying close. It’s much the same as how we bring up our kids, as it happens.

It was an odd session, a lecture with no direct interaction. In itself, this would not be a great learning experience for anyone. However – and this is one of my big points – it is the assembly that matters, not the individual components, and I was not the one doing that assembly. Seen as a component of learning, attended without coercion or extrinsic goals, my little lecture is something that can be assembled to make something quite useful.

Slides for Confluence 2012Download

How distance changes everything: slides from my keynote at the University of Ottawa

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These are the slides from my keynote at the University of Ottawa’s “Scaffolding a Transformative Transition to Distance and Online Learning” symposium today. In the presentation I discussed why distance learning really is different from in-person learning, focusing primarily on the fact that they are the motivational inverse of one another. In-person teaching methods evolved in response to the particular constraints and boundaries imposed by physics, and consist of many inventions – pedagogical and otherwise – that are counter-technologies designed to cope with the consequences of teaching in a classroom, a lot of which are not altogether wise. Many of those constraints do not exist online, and yet we continue to do very similar things, especially those that control and dictate what students should do, as well as when, and how they should do it. This makes no sense, and is actually antagonistic to the natural flow of online learning. I provided a few simple ideas and prompts for thinking about how to go more with the flow.

The presentation was only 20 minutes of a lively and inspiring hour-long session, which was fantastic fun and provided me with many interesting questions and a chance to expand further on the ideas.

uottawa2020HowDistanceChangesEverything

Technology, technique, and teaching

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These are the slides from my recent talk with students studying the philosophy of education at Pace University.

This is a mashup of various talks I have given in recent years, with a little new stuff drawn from my in-progress book. It starts with a discussion of the nature of technology, and the distinction between hard and soft technologies that sees relative hardness as the amount of pre-orchestration in a technology (be it a machine or a legal system or whatever). I observe that pedagogical methods (‘pedagogies’ for short) are soft technologies to those who are applying them, if not to those on the receiving end. It is implied (though I forgot to explicitly mention) that hard technologies are always more structurally significant than soft ones: they frame what is possible.

All technologies are assemblies, and (in education), the pedagogies applied by learners are always the most important parts of those assemblies. However, in traditional in-person classrooms, learners are (by default) highly controlled due to the nature of physics – the need to get a bunch of people together in one place at one time, scarcity of resources,  the limits of human voice and hearing, etc – and the consequent power relationships and organizational constraints that occur.  The classroom thus becomes the environment that frames the entire experience, which is very different from what are inaccurately described as online learning environments (which are just parts of a learner’s environment).

Because of physical constraints, the traditional classroom context is inherently very bad for intrinsic motivation. It leads to learners who don’t necessarily want to be there, having to do things they don’t necessarily want to do, often being either bored or confused. By far the most common solution to that problem is to apply externally regulated extrinsic motivation, such as grades, punishments for non-attendance, rules of classroom behaviour, and so on. This just makes matters much worse, and makes the reward (or the avoidance of punishment) the purpose of learning. Intelligent responses to this situation include cheating, short-term memorization strategies, satisficing, and agreeing with the teacher. It’s really bad for learning. Such issues are not at all surprising: all technologies create as well as solve problems, so we need to create counter technologies to deal with them. Thus, what we normally recognize as good pedagogy is, for the most part, a set of solutions to the problems created by the constraints of in-person teaching, to bring back the love of learning that is destroyed by the basic set-up. A lot of good teaching is therefore to do with supporting at least better, more internally regulated forms of extrinsic motivation.

Because pedagogies are soft technologies, skill is needed to use them well. Harder pedagogies, such as Direct Instruction, that are more prescriptive of method tend (on average) to work better than softer pedagogies such as problem-based learning, because most teachers tend towards being pretty average: that’s implicit in the term, after all. Lack of skill can be compensated for through the application of a standard set of methods that only need to be done correctly in order to work. Because such methods can also work for good teachers as well as the merely average or bad, their average effectiveness is, of course, high. Softer pedagogical methods such as active learning, problem-based learning, inquiry-based learning, and so on rely heavily on passionate, dedicated, skilled, time-rich teachers and so, on average, tend to be less successful. However, when done well, they outstrip more prescriptive methods by a large margin, and lead to richer, more expansive outcomes that go far beyond those specified in a syllabus or test. Softer technologies, by definition, allow for greater creativity, flexibility, adaptability, and so on than harder technologies but are therefore difficult to implement. There is no such thing as a purely hard or purely soft technology, though, and all exist on a spectrum,. Because all pedagogies are relatively soft technologies, even those that are quite prescriptive, almost any pedagogical method can work if it is done well: clunky, ugly, weak pedagogies used by a fantastic teacher can lead to great, persistent, enthusiastic learning. As Hattie observes, almost everything works – at least, that’s true of most things that are reported on in educational research studies :-). But (and this is the central message of my book, the consequences of which are profound) it ain’t what you do, it’s the way that you do it, that’s what gets results.

Problems can occur, though, when we use the same methods that work in person in a different context for which they were not designed. Online learning is by far the most dominant mode of learning (for those with an Internet connection – some big social, political, economic, and equity issues here) on the planet. Google, YouTube, Wikipedia, Reddit, StackExchange, Quora, etc, etc, etc, not to mention email, social networking sites, and so on, are central to how most of us in the online world learn anything nowadays. The weird thing about online education (in the institutional sense) is that online learning is far less obviously dominant, and tends to be viewed in a far less favourable light when offered as an option. Given the choice, and without other constraints, most students would rather learn in-person than online. At least in part, this is due to the fact that those of us working in formal online education continue to apply pedagogies and organizational methods that solved problems in in-person classrooms, especially with regard to teacher control: the rewards and punishments of grades, fixed length courses, strictly controlled pathways, and so on are solutions to problems that do not exist or that exist in very different forms for online learners, whose learning environment is never entirely controlled by a teacher.

The final section of the presentation is concerned with what – in very broad terms – native distance pedagogies might look like. Distance pedagogies need to acknowledge the inherently greater freedoms of distance learners and the inherently distributed nature of distance learning. Truly learner-centric teaching does not seek to control, but to support, and to acknowledge the massively distributed nature of the activity, in which everyone (including emergent collective and networked forms arising from their interactions) is part of the gestalt teacher, and each learner is – from their perspective – the most important part of all of that. To emphasize that none of this is exactly new (apart from the massive scale of connection, which does matter a lot), I include a slide of Leonardo’s to-do list that describes much the same kinds of activity as those that are needed of modern learners and teachers.

For those seeking more detail, I list a few of what Terry Anderson and I described as ‘Connectivist-generation’ pedagogical models. These are far more applicable to native online learning than earlier pedagogical generations that were invented for an in-person context. In my book I am now describing this new, digitally native generation as ‘complexivist’ pedagogies, which I think is a more accurate and less confusing name. It also acknowledges that many theories and models in the family (such as John Seely Brown’s distributed cognitive apprenticeship) predate Connectivism itself. The term comes from Davis’s and Sumara’s 2006 book, ‘Complexity and Education‘, which is a great read that deserves more attention than it received when it was published.

Slides: Technology, technique and teaching

Small talk, big implications

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fingerprint (public domain) An article from Quartz with some good links to studies showing the very many benefits of interacting with others, even at a very superficial level. I particularly like the report of a study showing the (quite strong) cognitive benefits of small talk.

It’s all solid stuff that supports much of what I and many others have written about the value of belongingness and social interaction in learning but, like much research in fields such as psychology, education, sociology, and so on, it makes some seemingly innocuous but fundamentally wrong assertions of fact. For instance:

“Those who were instructed to strike up a conversation with someone new on public transport or with their cab driver reported a more positive commute experience than those instructed to sit in silence.”

What, all of them? That seems either unbelievably improbable, or the result of a flawed methodology, or a sign of way too small a sample size. The paper itself is inaccessibly paywalled so I don’t know for sure, but I suspect this is actually just a sloppy description of the findings. It is not the result of bad reporting in the Quartz article, though: it is precisely what the abstract of the paper itself actually claims. The researchers make several similar claims like “Those who were instructed to strike up a hypothetical conversation with a stranger said they expected a negative experience as opposed to just sitting alone.” Again – all of them? If that were true, no one would ever talk to strangers (which anyone that has ever stood in a line-up in Canada knows to be not just false but Trumpishly false), so this is either a very atypical group or a very misleading statement about group members’ behaviours. The findings are likely, on average, correct for the groups studied, but that’s not the way it is written.

The article is filled with similarly dubious quotes from distinguished researchers and, worse, pronouncements about what we should do as a result. Often the error is subtly couched in (accurate but misleadingly ambiguous) phrasing like “The group that engaged in friendly small talk performed better in the tests.” I don’t think it is odd to carelessly read that as ‘all of the individuals in the group performed better than all of those in the other groups’, rather than that, ‘on average, the collective group entity performed better than another collective group entity’, which is what was actually meant (and that is far less interesting). From there it is an easy – but dangerously wrong – step to claim that ‘if you engage in small talk then you will experience cognitive gains.’ It’s natural to want to extrapolate a general law from averaged behaviours, and in some domains (where experimental anomalies can be compellingly explained) it makes sense, but it’s wrong in most cases, especially when applied to complex systems like, say, anything involving the behaviour of people.

It’s a problem because, like most in my profession, I regularly use such findings to guide my own teaching. On average, results are likely (but far from certain) to be better than if I did not use them, but definitely not for everyone, and certainly not every time.  Students do tend to benefit from engagement with other students, sure. It’s a fair heuristic, but there are exceptions, at least sometimes. And the exceptions aren’t just a statistical anomaly. These are real people we are talking about, not average people. When I do teaching well – nothing like enough of the time –  I try to make it possible for those that aren’t average to do their own thing without penalty. I try to be aware of differences and cater for them. I try to enable those that wish it to personalize their own learning. I do this because I’ve never in my entire life knowingly met an average person.

Unfortunately, our educational systems really don’t help me in my mission because they are pretty much geared to cater for someone that probably doesn’t exist. That said, the good news is that there is a general trend towards personalized learning that figures largely in most institutional plans. The bad news is that (as Alfie Kohn brilliantly observes) what is normally meant by ‘personalized’ in such plans is not its traditional definition at all, but instead ‘learning that is customized (normally by machines) for students in order that they should more effectively meet our requirements.’  In case we might have forgotten, personalization is something done by people, not to people. 

Further reading: Todd Rose’s ‘End of Average‘ is a great primer on how to avoid the average-to-the-particular trap and many other errors, including why learning styles, personality types, and a lot of other things many people believe to be true are utterly ungrounded, along with some really interesting discussion of how to improve our educational systems (amongst other things). I was gripped from start to finish and keep referring back to it a year or two on.

Address of the bookmark: https://qz.com/1134958/small-talks-positive-benefits-outweigh-your-fear-of-being-awkward/

Originally posted at: https://landing.athabascau.ca/bookmarks/view/2849927/small-talk-big-implications

This was actually accepted for an IEEE conference and then published

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I invite you to draw your own conclusions about this paywalled paper and the amount of quality control and editorial input that goes into IEEE publications nowadays. Here’s the abstract, which is one of the more coherent passages in the paper:

Abstract—The momentum contemplate evaluates the relationship among online social recreations and the e-learning utilization by look at the impact of social, subjective and teaching nearness on e-learning use between female understudies by method for playing on the web social diversions. This study utilizes an exploratory research plan, comfort test procedure. The outcomes propose that all scales are basically related with E- learning use. It is found that E-learning uses is emphatically tremendous and has a direct related with social nearness. The relationship between E-learning use and psychological nearness has a decidedly strong enormous connection; in like manner, the relationship between E-learning use and teaching nearness has an emphatically strong colossal connection. The disclosures inferred that the characteristic of online social amusements; both intellectual and teaching nearness impact E-learning utilization.

There’s not enough research about female understudies. I’m glad that someone is filling that gap. It’s well worth what otherwise appear to be the subscription fees IEEE is charging (US$33 in case you were wondering) . 

Address of the bookmark: http://ieeexplore.ieee.org/document/8052647/

Originally posted at: https://landing.athabascau.ca/bookmarks/view/2760723/this-was-actually-accepted-for-an-ieee-conference-and-then-published

Cocktails and educational research

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A lot of progress has been made in medicine in recent years through the application of cocktails of drugs. Those used to combat AIDS are perhaps the most well-known, but there are many other applications of the technique to everything from lung cancer to Hodgkin’s lymphoma. The logic is simple. Different drugs attack different vulnerabilities in the pathogens etc they seek to kill. Though evolution means that some bacteria, viruses or cancers are likely to be adapted to escape one attack, the more different attacks you make, the less likely it will be that any will survive.

Simulated learningUnfortunately, combinatorial complexity means this is not a simply a question of throwing a bunch of the best drugs of each type together and gaining their benefits additively. I have recently been reading John H. Miller’s ‘A crude look at the whole: the science of complex systems in business, life and society‘ which is, so far, excellent, and that addresses this and many other problems in complexity science. Miller uses the nice analogy of fashion to help explain the problem: if you simply choose the most fashionable belt, the trendiest shoes, the latest greatest shirt, the snappiest hat, etc, the chances of walking out with the most fashionable outfit by combining them together are virtually zero. In fact, there’s a very strong chance that you will wind up looking pretty awful. It is not easily susceptible to reductive science because the variables all affect one another deeply. If your shirt doesn’t go with your shoes, it doesn’t matter how good either are separately. The same is true of drugs. You can’t simply pick those that are best on their own without understanding how they all work together. Not only may they not additively combine, they may often have highly negative effects, or may prevent one another being effective, or may behave differently in a different sequence, or in different relative concentrations. To make matters worse, side effects multiply as well as therapeutic benefits so, at the very least, you want to aim for the smallest number of compounds in the cocktail that you can get away with. Even were the effects of combining drugs positive, it would be premature to believe that it is the best possible solution unless you have actually tried them all. And therein lies the rub, because there are really a great many ways to combine them.

Miller and colleagues have been using the ideas behind simulated annealing to create faster, better ways to discover working cocktails of drugs. They started with 19 drugs which, a small bit of math shows, could be combined in 2 to the power of 19 different ways – about half a million possible combinations (not counting sequencing or relative strength issues). As only 20 such combinations could be tested each week, the chances of finding an effective, let alone the best combination, were slim within any reasonable timeframe. Simplifying a bit, rather than attempting to cover the entire range of possibilities, their approach finds a local optimum within one locale by picking a point and iterating variations from there until the best combination is found for that patch of the fitness landscape. It then checks another locale and repeats the process, and iterates until they have covered a large enough portion of the fitness landscape to be confident of having found at least a good solution: they have at least several peaks to compare. This also lets them follow up on hunches and to use educated guesses to speed up the search. It seems pretty effective, at least when compared with alternatives that attempt a theory-driven intentional design (too many non-independent variables), and is certainly vastly superior to methodically trying every alternative, inasmuch as it is actually possible to do this within acceptable timescales.

The central trick is to deliberately go downhill on the fitness landscape, rather than following an uphill route of continuous improvement all the time, which may simply get you to the top of an anthill rather than the peak of Everest in the fitness landscape. Miller very effectively shows that this is the fundamental error committed by followers of the Six-Sigma approach to management, an iterative method of process improvement originally invented to reduce errors in the manufacturing process: it may work well in a manufacturing context with a small number of variables to play with in a fixed and well-known landscape, but it is much worse than useless when applied in a creative industry like, say, education, because the chances that we are climbing a mountain and not an anthill are slim to negligible. In fact, the same is true even in manufacturing: if you are just making something inherently weak as good as it can be, it is still weak. There are lessons here for those that work hard to make our educational systems work better. For instance, attempts to make examination processes more reliable are doomed to fail because it’s exams that are the problem, not the processes used to run them. As I finish this while listening to a talk on learning analytics, I see dozens of such examples: most of the analytics tools described are designed to make the various parts of the educational machine work ‘ better’, ie. (for the most part) to help ensure that students’ behaviour complies with teachers’ intent. Of course, the only reason such compliance was ever needed was for efficient use of teaching resources, not because it is good for learning. Anthills.

This way of thinking seems to me to have potentially interesting applications in educational research. We who work in the area are faced with an irreducibly large number of recombinable and mutually affective variables that make any ethical attempt to do experimental research on effectiveness (however we choose to measure that – so many anthills here) impossible. It doesn’t stop a lot of people doing it, and telling us about p-values that prove their point in more or less scupulous studies, but they are – not to put too fine a point on it – almost always completely pointless.  At best, they might be telling us something useful about a single, non-replicable anthill, from which we might draw a lesson or two for our own context. But even a single omitted word in a lecture, a small change in inflection, let alone an impossibly vast range of design, contextual, historical and human factors, can have a substantial effect on learning outcomes and effectiveness for any given individual at any given time. We are always dealing with a lot more than 2 to the power of 19 possible mutually interacting combinations in real educational contexts. For even the simplest of research designs in a realistic educational context, the number of possible combinations of relevant variables is more likely closer to 2 to the power of 100 (in base 10 that’s  1,267,650,600,228,229,401,496,703,205,376). To make matters worse, the effects we are looking for may sometimes not be apparent for decades (having recombined and interacted with countless others along the way) and, for anything beyond trivial reductive experiments that would tell us nothing really useful, could seldom be done at a rate of more than a handful per semester, let alone 20 per week. This is a very good reason to do a lot more qualitative research, seeking meanings, connections, values and stories rather than trying to prove our approaches using experimental results. Education is more comparable to psychology than medicine and suffers the same central problem, that the general does not transfer to the specific, as well as a whole bunch of related problems that Smedslund recently coherently summarized. The article is paywalled, but Smedlund’s abstract states his main points succinctly:

“The current empirical paradigm for psychological research is criticized because it ignores the irreversibility of psychological processes, the infinite number of influential factors, the pseudo-empirical nature of many hypotheses, and the methodological implications of social interactivity. An additional point is that the differences and correlations usually found are much too small to be useful in psychological practice and in daily life. Together, these criticisms imply that an objective, accumulative, empirical and theoretical science of psychology is an impossible project.”

You could simply substitute ‘education’ for ‘psychology’ in this, and it would read the same. But it gets worse, because education is as much about technology and design as it is about states of mind and behaviour, so it is orders of magnitude more complex than psychology. The potential for invention of new ways of teaching and new states of learning is essentially infinite. Reductive science thus has a very limited role in educational research, at least as it has hitherto been done.

But what if we took the lessons of simulated annealing to heart? I recently bookmarked an approach to more reliable research suggested by the Christensen Institute that might provide a relevant methodology. The idea behind this is (again, simplifying a bit) to do the experimental stuff, then to sweep the normal results to one side and concentrate on the outliers, performing iterations of conjectures and experiments on an ever more diverse and precise range of samples until a richer, fuller picture results. Although it would be painstaking and longwinded, it is a good idea. But one cycle of this is a bit like a single iteration of Miller’s simulated annealing approach, a means to reach the top of one peak in the fitness landscape, that may still be a low-lying peak. However if, having done that, we jumbled up the variables again and repeated it starting in a different place, we might stand a chance of climbing some higher anthills and, perhaps, over time we might even hit a mountain and begin to have something that looks like a true science of education, in which we might make some reasonable predictions that do not rely on vague generalizations. It would either take a terribly long time (which itself might preclude it because, by the time we had finished researching, the discipline will have moved somewhere else) or would hit some notable ethical boundaries (you can’t deliberately mis-teach someone), but it seems more plausible than most existing techniques, if a reductive science of education is what we seek.

To be frank, I am not convinced it is worth the trouble. It seems to me that education is far closer as a discipline to art and design than it is to psychology, let alone to physics. Sure, there is a lot of important and useful stuff to be learned about how we learn: no doubt about that at all, and a simulated annealing approach might speed up that kind of research. Painters need to know what paints do too. But from there to prescribing how we should therefore teach spans a big chasm that reductive science cannot, in principle or practice, cross. This doesn’t mean that we cannot know anything: it just means it’s a different kind of knowledge than reductive science can provide. We are dealing with emergent phenomena in complex systems that are ontologically and epistemologically different from the parts of which they consist. So, yes, knowledge of the parts is valuable, but we can no more predict how best to teach or learn from those parts than we can predict the shape and function of the heart from knowledge of cellular organelles in its constituent cells. But knowledge of the cocktails that result – that might be useful.

 

 

Researching things that don't exist

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As the end of my sabbatical is approaching fast, I am still tinkering with a research methodology based on tinkering (or the synonymous bricolage, to make it sound more academic). Tinkering is an approach to design that involves making things out of what we find around us, rather than as an engineered, designed process. This is relatively seldom seen as valid approach to design (though there are strong arguments to be made for it), let alone to research, though it underpins much invention and discovery. Tinkering is, by definition, a step into the unknown, and research is generally concerned with knowing the unknown (or at least clarifying, confirming or denying the partly- or tentatively-known). This is not a direct path, however.

Research can take many forms but, typically and I think essentially, the sort that we do in academia is a process of discovery, rather than one of invention. This is there in the name – ‘recherche’ (the origin of the term) means to go about seeking, which implies there is something to be found. The word ‘discovery’ suggests that there is something that exists that can be discovered, whereas inventions, by definition, do not exist, so they are never exactly discovered as such.

While we can seldom substitute ‘invention’ for ‘discovery’, the borders are blurry. Did Maxwell discover his equations or did he invent them? What he discovered was something about the order of the universe, that his (invented) equations express, but the equations formed an essential and inextricable part of that discovery. R&D labs get around the problem by simply using two terms so that you know they are using both. The distinction is similarly blurry in art: an artwork is normally not, at least in a traditional sense, research because, for most art, it is a form of invention rather than discovery. But sculptors often talk of discovering a form in stone or wood. And, even for the most mundane of paintings or drawings, artists are in a dialogue with their media and with what they have created, each stroke building on and being influenced by those that came before. A relative of mine recently ran an exhibition of works based on the forms suggested by blots of ink and water, which illustrates this in sharper relief than most, and I do rather like these paintings from Bradley Messer that follow the forms of wood grain. Such artists discover as much as they create and, like Maxwell’s equations, their art is an expression of their discovery, not the discovery itself, though the art is equally a means of making that discovery. Discovery is even more obvious in ‘found’ art such as that of some of the Dadaists, though the ‘art’ part of it is arguably still the invention, not the discovered object itself. Duchamp Fountaine And, as Dombois observes  there are some very important ways research and art can connect: research can inform art and be about art, and art can be about research, can support research and can arise from it. Dombois also believes art can be a means of performing research. Komar and Melamid’s ‘most-wanted paintings’ project is a good example of art not only being informed by research itself being a form of research. Their paintings resulted from research into what ‘the people’ wanted in their paintings. The paintings themselves challenge what collective taste means, and the value of it, changing how we know and make use of such information. And the artwork itself is the research, of which the paintings are just a part. 

Inventions (including art works) use discoveries and, from our inventions, we can make discoveries (including discoveries about our inventions). Invention makes it possible to make novel discovery, but the research is that discovery, not the inventions that lead to it. Research perceived as invention means discovering not what is there but what is not there, which is a little bizarre. More accurately, perhaps, it is seeking to discover what is latently there. It is about discovering possible futures. But even this is a bit strange, inasmuch as latent possibilities are, in many cases, infinite. I don’t think it counts as discovery if you are picking a few pieces from a limitless range of possibilities. It is creation that depends entirely on what you put into it, not on something that can be discovered in that infinity. But, perhaps, the discovery of patterns and regularities in that infinite potential palette is the research. This is because those infinite possibilities are maybe not as infinite as they seem. They are at the very least constrained by what came before, as well as by a wide range of structural constraints that we impose, or have imposed upon us. What is nice about tinkering is that, because it is concerned with using things around us, the forms we work on already have such patterns and constraints. 

Tinkering is concerned with exploring the adjacent possible. It is about looking at the things around you (which, in Internet space, means practically everywhere) and finding ways to put them together in new ways to do new things. These new things can then, themselves, create new adjacent possibles, and so it goes on. Beyond invention, tinkering is a tool for making new discoveries. It is a way of having a conversation with objects in which the tinker manipulates the objects and the objects in turn suggest ways of putting them together. It can inspire new ways of thinking. We discover what our creations reveal. Writing (such as this) is a classic example of this process. The process of writing is not one of recording thoughts so much as it is one of making new ones. We scaffold our thoughts with the words we write, pulling ourselves up by our own bootstraps as we do so in order to build further thoughts and connections.

The construction of all technologies works the same way, though it is often hidden behind walls of abstraction and deliberate design. If, rather than design-then-build, we simply tinker, then the abstraction falls away. The paths we go down are unknown and unknowable in advance, because the process of construction leads to new ideas, new concepts, new possibilities that only become visible as we build. Technologies are (all) tools to think with at least as much as they are tools to perform the tasks we build them for, and tinkering is perhaps the purest way of building them. And this is what makes tinkering a process of discovery. The focus is not on what we build, but on what we discover as a direct result of doing so – both process and product. Tinkering is a scaffold for discovery, not discovery itself. This begins to feel like something that could underpin a methodology.

With this in mind, here is an evolving set of considerations and guidelines for tinkering-based research that have occurred to me as I go along.

Exploring the possible

To be able to explore the adjacent possible, it is first necessary to explore the possible. In fact, it is necessary to be immersed in the possible. At a simple level, this because the bigger your pile of junk, the more chances there are of finding interesting pieces and interesting combinations. But there are other sub-aspects of this that matter as much: the nature of the pile of junk, the skills to assemble the junk, and immersion in the problem space. 

1) The pile of junk

Tinkering has to start with something – some tools, some pieces, some methods, some principles, some patterns. It is important that these are as diverse as possible, on the whole. If you just have a pile of engine parts then the chances are you are going to make another engine although, with a tinker-space containing sufficiently diverse patterns, you might make something else. There is a store near me that sells clocks, lights and other household objects made from bits of old electrical equipment and machinery, and it is wonderful. Similarly, some of the finest blues musicians can make infinite complexity out of just three chords and a (loosely) pentatonic scale. But having diverse objects, methods, patterns and principles certainly makes it easier than just having a subset of it all.

It is important that the majority of the junk is relatively complex and self-contained in itself – that it does something on its own, that it is already an assembly of something. Doing bricolage with nothing but raw materials is virtually impossible – they are too soft (in a technology sense). You have to start with something, otherwise the adjacent possible is way too far away and what is close is way too boring. The chances are that, unless you have a brilliant novel idea (which is a whole other territory and very rare) you will wind up making something that already exists and has probably been done better. This is still scrabbling around in the realms of the possible. The whole point is to start with something and assemble it with something else to make it better, in order to do something that has never been done before. That’s what makes it possible to discover new things. Of course, the complexity does not need to be in physical objects: you might have well-assembled theories, models, patterns, belief systems, aesthetic sensibilities and so on that could be and probably will be part of the assembly. And, since we are not just talking about physical objects but methods, principles, patterns etc, this means you need to immerse yourself in the process – to do it, read about it, talk about it, try it. 

2) The tools of assembly

It is not enough to have a great tinker-space full of bits and pieces. You need tools to assemble them. Not just physical tools, but conceptual tools, skills, abilities, etc. You can buy, make, beg, borrow or steal the tools, but skills to use them take time to develop. Of course, one of the time-honoured and useful ways to do that is to tinker, so this works pretty well. Again, this is about immersion. You cannot gain skills unless you apply them, reflect on it, apply them again, in a never-ending cycle.

There is a flip side to this though. If you get to be too skillful then you start to ignore things that you have discovered to be irrelevant, and irrelevant things aren’t always as irrelevant as they seem. They are only irrelevant to the path you have chosen to tread. Treading multiple paths is essential so, once you become too much of an expert, it is probably time to learn new skills. It is hard to know when you are too much of an expert. Often, the clue is that someone with no idea about the area suggests something and you laughingly tell them it cannot be done. Of course it can. This is technology. It’s about invention. You are just too smart to know it.

Being driven by your tools (including skills) is essential and a vital part of the methodology – it’s how the adjacent possible reveals itself. But it’s a balance. Sometimes you go past an adjacent possible on your way and then leave it so far behind that you forget it is there at all. It sometimes takes a beginner to see things that experts believe are not there. It can be done in all sorts of ways. For example, I know someone who, because he does not want to be trapped by his own expertise, constantly retunes his guitar to new tunings, partly to make discoveries through serendipity, partly to be a constant amateur. But, of course, a lot of his existing knowledge is reusable in the new context. You do not (and cannot) leave expertise behind when learning new things – you always bring your existing baggage. This is good – it’s more junk to play with. The trick is to have a ton of it and to keep on adding to it.

3) The problem space

While simply playing with pieces can get you to some interesting places, once you start to see the possibilities, tinkering soon becomes a problem-solving process and, as you follow a lead, the problem becomes more and more defined, almost always adding new problems with each one solved. Being immersed in a problem space is crucial, which tends to make tinkering a personal activity, not one that lends itself well to formally constructed groups. Scratching your own itch is a pretty good way to get started on the tinkering process because, having scratched one itch, it always leads to more or, at least, you notice other itches as you do so.

If you are scratching someone else’s itch then it can be too constraining. You are just solving a known problem, which seldom gets you far beyond the possible and, if it does, your obligations to the other person make it harder for you to follow the seam of gold that you have just discovered along the way that is really the point of it. It’s the unknown problems, the ones that only emerge as we cross the border of the adjacent possible, that matter here. Again, though, this is a balance. A little constraint can help to sustain a focus and doing something that is not your own idea can spark serendipitous ideas that turn out to be good.

Just because it is not really a team process doesn’t mean that other people are not important to it. Talking with others, exchanging ideas, gaining inspiration, receiving critique, seeing the world through different eyes – all this is very good. And it can also be great to work closely with a small number of others, particularly in pairs – XP relies on this for its success. A small number of people do not need to be bogged down with process, schedules, targets, and other things that get in the way of effective tinkering, can inspire one another, spot more solutions, and sustain motivation when the going gets rough. 

The Structural Space

One of the points of bricolage is that it is structured from the bottom up, not the top down. Just because it is bottom-up structure does not mean it is not structure. This is a classic of example of shaping our tools and our tools shaping us (as McLuhan put it), or shaping our dwellings while our dwellings shape our lives (as Churchill put it a couple of decades earlier). Tinkering starts with forms that influence what we do with them, and what we do with them influences what we do next – our creations and discoveries become the raw material for further creations and discoveries. Though rejecting deliberate structured design processes, I have toyed with and tried things like prototyping, mock-ups and sketches of designs, but I have come to the opinion that they get in the way – they abstract the design too much. What matters in bricolage is picking up pieces and putting them together. Anything beyond vague ideas and principles is too top-down. You are no longer talking with the space but with a map of the space, which is not the same thing at all.

Efficiency

One of the big problems with tinkering is that it tends to lead to highly inefficient design, from an engineering perspective. Part of the reason for that is that path dependencies set in early on. A bad decision early can seriously constrain what you do later. One has only to look at our higher education systems, the result of massively distributed large scale tinkering over nearly a thousand years, to see the dangers here. The vast majority of what we continue to do today is mediaeval in origin and, in a lot of cases, has survived unscathed, albeit assembled with a few other things along the way.

Building from existing pieces can limit the damage – at least you don’t have to pull everything apart if it turns out that it is not a fruitful path. It is also very helpful to start with something like Lego, that is designed to be fitted together this way. Most of my work during my sabbatical has involved programming using the Elgg framework, which is very elegantly designed so that, as long as you follow the guidelines, it naturally forms into at least a decent outline structure. On the other hand, as I have found to my cost, it is easy to put enough work into something that it makes it very discouraging when you to have to start again. As the example of educational systems shows, some blocks are so foundational and deeply linked with everything else, that they affect everything that follows and simply cannot be removed without breaking everything.

Working together

Tinkering is quite hard to do in teams, apart from as sounding boards for reflection on a process already in motion. It is instructive to visit LegoLand to see how it can work, though. In the play spaces of LegoLand one sees kids (and more than a few adults) working alone on building things, but they are doing so in a very social space. They talk about what they are doing, see what others are doing and, sometimes, put their bits of assemblies together, making bigger and more complex artefacts. We can see similar processes at work in GitHub, a site where programmers, often working alone, post projects that others can fork and, through pull-requests, return in modified form to their originators or others, with or without knowing them or ineracting with them in any other way. It’s a wonderful evolutionary tinker-space. If programs are reasonably modular, people can work on different pieces independently, that can then be assembled and reassembled by others. Inspiration, support, patterns of thinking and problem solving, as well as code, flow through the system. The tinkering of others becomes a part of your own tinker-space.  It’s a learning space – a space where people learn but also a space that learns. The fundamental social forms for tinkering are not traditional, purpose-driven, structured and scheduled teams (groups), but networks and, more predominantly, sets of people connected by nothing but shared interest and a shared space in which to tinker.

Planning

As well as resulting in inefficient systems, tinkering is not easy to plan. At the start, one never knows much more than the broad goal (that may change or may not even be there at all) and the next steps. You can build very big systems by tinkering (back to education again but let’s go large on this and think of the whole of gaia) but it is very hard to do so with a fixed purpose in mind and harder still to do so to a schedule. At best, you might be able to roughly identify the kind of task and look to historical data to help get some statistical approximation of how long it might take for something useful to emerge.

A corollary of the difficulty of planning (indeed, that it is counter-productive to do so) is that it is very easy to be thrown off track. Other things, especially those that involve other people that rely on you, can very quickly divert the endeavour. At the very least, time has to be set aside to tinker and, come hell or high water, that time should be used. Tinkering often involves following tenuous threads and keeping many balls in the air at once (mixing metaphors is a good form of tinkering) so distractions are anethema to the effective tinkerer. That said, coming up for a breath of air can remind you of other items in the tinker-chest that may inspire or provoke new ways of assembling things. It is a balance.

Evolution, not design

Naive creationists have in the past suggested that the improbability of finding something as complex as even a watch, let alone the massively more complex mechanisms of the simplest of organisms, means that there must be an intelligent designer. This is sillier than silly. Evolution works by a ratchet, each adaptation providing the basis for the next, with some neat possibilities emerging from combinatorial complexity as well. Given enough time and a suitable mechanism, exponentially increasingly complex systems are not just possible put overwhelmingly probable. In fact, it would be vastly more difficult to explain their absence than their existence. But they are not the result of a plan. Likewise for tinkering with technologies. If you take two complex things and put them together, there is a better than fair chance that you will wind up with something more complex that probably does more than you imagined or intended when you stuck them together.  And, though maybe there is a little less chance of disaster than the random-ish recombinations of natural evolution, the potential for the unexpected increases with the complexity. Most unexpected things are not beneficial – the bugs in every large piece of software attest to that, as do most of my attempts at physical tinkering over the course of my lifetime. However, now and then, some can lead to more actual possibles. The adjacent possible is what might happen next but, in many cases, changes simply come with baggage. Gould calls these exaptations – they are not adaptations as such, but a side-effect or consequence of adaptation. Gould uses the example of the Spandrels of St Marco to illustrated this point, showing how the structure of the cathedral of St Marco, with its dome sitting on rounded arches, unintentionally but usefully created spaces where they met that proved to be the perfect place to put images of saints – in fact, they seem made for them. But they are not – the spaces are just a by-product of the design that were coopted by the creators of the cathedral to a useful purpose. A lot of systems work that way. It is the nature of their assembly to create both constraints and affordances, path dependencies and patterns early on deeply defining later growth and change. Effective tinkering involves using such spandrels, and that means having to think about what you have built. Thinking deeply.

The Reflection Space

Just tinkering can be fun but, to make it a useful research process, it should involve more than just invention. It should also involve discovery. It is essential, therefore, that the process is seen as one of reflective dialogue with the creations we make. Reflection is not just part of an iterative cycle – it is embedded deeply and inextricably throughout the process. Only if we are able to constructively think about what we are doing as well as what we have done can this generate ideas, models, principles and foundations for further development. It is part of the dialogue with the objects (physical, conceptual, etc) that we produce and, perhaps even more importantly, it is the real research output of the tinkering process. Reflection is the point at which we discover rather than just invent. In part it is to think about the meaning and consequence, in part to discover the inevitable exaptions, in part to spot the next adjacent possible. This is not a simple collaboration. Much of the time we argue with the objects we create – they want to be one way but we want them to be another and, from that tension, we co-create something new.  

We need to build stories and rich pictures as much as we need to build technologies. Indeed, it doesn’t really matter that much if we fail to produce any useful artefact through tinkering, as long as the stories have value.  From those stories spin ideas, inspirations, and repeatable patterns. Stories allow us to critique what we have done and learn from it, to see it in a broader context and, perhaps, to discover different contexts where the ideas might apply. And, of course, these stories should be shared, whether with a few friends or the world, creating further feedback loops as well as spreading around what we have discovered.

Stories don’t have to be in words. Pictures are equally and often more useful and, often most useful of all, the interactions with our creations can tell a story too. This is obviously the case in things like games, Arduino projects or interactive site development but is just as true of making things like furniture, accessories and most of the things that can be made or enhanced with Sugru.

Here are two brief stories that I hope begin to reveal a little of what I mean.

A short illustrative story

Early in my sabbatical I wrote one Elgg plugin that, as it emerged, I was very pleased with, because it scratched an itch that I have had for a long time. It allowed anyone to tag anything, and for duplicate tags used by different people to be displayed as a tag cloud instead of the normal list of tags that comes with a post. This was an assembly of many ideas, and was a conversation with the Elgg framework, which provided a lot of the structure and form of what I wanted to achieve. In doing it, I was learning how to program in Elgg but, in shaping Elgg, I was also teaching it about the theories that I had developed over many years. If it had worked, it would have given me a chance to test those theories, and the results would probably have led to some refinements, but that was really a secondary phase of the research process and not the one that I was focusing on.

Before any other human being got to use the system, the research process was shaping and refining the ideas. With each stage of development I was making discoveries. A big one was the per-post tag cloud. My initial idea had simply been to allow people to tag one another’s posts. This would have been very useful in two main ways. Firstly, it would give people the chance to meaningfully bookmark things they had found interesting. Rather than the typical approach of putting bookmarks into organized hierarchies, tags could be used to apply faceted categorizations, allowing posts to cross hierarchical boundaries easily and enabling faceted classification of the things people found interesting. Secondly, the tags would be available to others, allowing social construction of an ontology-like thing, better search, a more organized site. Tags are already very useful things but, in Elgg, they are applied by post authors and there are not enough of them for strong patterns to develop on their own in any but quite large systems. One of the first things I realized was that this meant the same tag might be used for the same post more than once.  It was hard to miss in fact, because what I saw when I ran the program was multiple tags for each post – the system I had assembled was shouting at me. Having built a tag cloud system in the 1990s before I even knew the word ‘tag’ let alone ‘tag cloud’ I was primed to spot the opportunity for a tag cloud, which is a neat way to give shape and meaning to a social space. Individually, tags categorize into binary categories. Collectively, they become fuzzy and scalar – an individual post can be more of one tag than another, not because some individual has decided so, but because a crowd has decided so. This is more than a folksonomy. It is a kind of collaborative recommender system, a means to help people recognize not just whether something is good or bad but in what ways it is good or bad. Already, I was thinking of my PhD work which involved fuzzy tags I called ‘qualities’ (e.g. ‘good for beginners’, ‘comprehensive’, ‘detailed’, etc) that allowed users of my CoFIND system not just to categorize but to rate posts, on multiple pedagogical dimensions. Higher tag weight is an implicity proxy for saying that, in the context of what is described by this tag, the post has been recommended. As I write this (writing is great tinkering – this is the power of reflection) I realize that I could explicitly separate such tags from Elgg’s native tags, which might be a neat way to overcome the limitations of the system I wrote about 15 years ago, that was a good idea but very unusable. Anyway…

It worked like a dream, exactly as I had planned, up to the point that I tried to allow people to see the things they had tagged, which was pretty central to the idea and without which the whole thing was pretty pointless: it is highly improbably that individuals would see great value in tagging things unless they could use those tags to find and organize stuff on the site. As it turns out, the Elgg developers never thought tags might be used this way, so the owner of a tag is not recorded in the system. The person that tags a post is just assumed to be the owner of the post. I’m not a great Elgg developer (which is why I did not realise this till it was too late) but I do know the one cardinal rule – you never, ever, ever mess with the core code or the data model. There was nothing I could do except start again, almost completely from scratch. That was a lot of work – weeks of effort. It was not entirely wasted – I learned a lot in the process and that was the central purpose of it all. But it was very discouraging. Since then, as I have become more immersed in Elgg, my skills have improved. I think I can now see roughly how this could be made to work. The reason I know this is because I have been tinkering with other things and, in the process, found a lightweight way of using relationships to link individuals and objects that, in the ways that matter, can behave much like tags. Now that I have the germ of an idea about how to make this pedagogically powerful, hopefully I will have time to do that. 

Another illustrative story

One of my little sabbatical projects (that actually it turned out to be about the biggest, and it’s not over yet) was to build an OpenBadge plugin. This was actually prompted by and written for someone else. I would not thought of it as a good itch to scratch because I happen to know something about badges and something about learning and, from what I have seen, badges (as implemented so far) are at best of mixed value in learning. In the vast majority of instances that I have seen them used, they can be at the very best as demotivating as they are motivating. Much of the time it is worse than that: they turn into extrinsic proxies that divert motivation away from learning almost entirely. They embed power structures and create divisions. From a learning perspective, they are a pretty bad idea. On the plus side, they are a very neat way to do credentials which is great if that is what you are aiming for, opening up the potential for much more interesting separation of teaching and accreditation, diverse learning paths, and distributed learning, so I don’t hate them. In fact, I quite like them. But their pedagogical risks mean that I don’t love them enough to have even considered writing a plugin that implements them.

Despite reservations, I said I would do it. It didn’t seem like a big task because I reckoned I could just lightly modify one of a couple of existing (non-open) badge plugins that had already been written for Elgg.  I also happened to have some parts lying round – my pedagogical principles, the Elgg framework, the Mozilla OpenBadge standard documentation, various code snippets for implementing OpenBadges – that I could throw together. Putting these pieces together made me realize early on that social badging could be a good idea that might help overcome several of my objections to their usual implementations. Because of the nature of Elgg, the obvious way to build such a plugin would be such that anyone could make a badge, and anyone could award one, making use of Elgg’s native fine-grained bottom-up permissions. This meant that the usual power relationships implied in badging would not be such a problem. This was an interesting start.

Because Elgg has no roles in its design (apart from a single admin role for the site builder and manager), and so no explicit teaching roles, this could have been potentially tricky from a trust perspective – although its network features would mean you could trust awards by people you know, how would you trust an award from someone you don’t know and who is not playing a traditional teacher role in a power hierarchy? Even with the native Elgg option to ‘recommend’ a badge (so more people could assert its validity) this could become chaotic. But my principles told me that teacher control is a bad thing so I was not about to add a teacher role.

After tossing this idea around for a few minutes, I came up with the idea of inheritable badges – in other words, a badge could be configured so that you could only award a badge if you had received it yourself. In an instant, this began to look very plausible. If you could trace the badge to someone you trust (eg. a teacher or a friend or someone you know is trustworthy), which is exactly what Elgg would make possible by default, then you could trust anyone else who had awarded the badge to at least have the competence that the badge signifies, and so be more likely to be able to accurately recognize it in someone else. This was neat – it meant that accreditation could be distributed across a network of strangers (as in a MOOC) without the usual difficulties of the blind accrediting the blind that tend to afflict peer assessment methods in such contexts. Better still, this is a great way to signify and gain social capital, and to build deeper and richer bonds in a community of strangers. It is, I think, among the first scalable approaches to accreditation in a connectivist context, though I have not looked too deeply into the literature, so stand to be corrected.

Later, as I tinkered and became immersed in the problem, thinking how it would be used, I added a further option to let a badge creator specify a prerequisite award (any arbitrarily chosen badge) that must be held before a badge could be awarded. As well as allowing more flexibility than simple inheritance, this meant that you could introduce roles by the back door if you wished, by allowing someone to award a ‘teacher’ badge or similar, and only allowing people holding that badge to make awards of other badges.  I then realized this was a generalized case of the same thing as the inheritance feature, so got rid of the inheritance feature and just added the option to make a prerequisite of the current badge itself. It is worthy of note that this was quite difficult to do – had I planned it from the start, it would have been trivial, but I had to unpick what I had done as well as build it afresh.

Social badging, peer assessment, scalable viral accreditation, social capital, motivation  – this was looking cool. Furthermore, tinkering with an existing framework suggested other cool things. By default, it was a lot easier to build this if people could award badges to themselves. The logical next step would have been to prevent them from doing this but, as I saw it working, I realised self-badging was a very good idea! It bothered me for a moment that it might be a bit confusing, at least, not to mention appearing narcissistic if people started awarding themselves badges. However, Elgg posts can be private, so people giving themselves badges would not have to show them to others. But they could, and that could be useful. They could make a learning contract with someone else or a group of people, and allow them to observe, thus not only improving motivation and honesty, but also building bonding social capital. So, people could set goals for themselves and award themselves badges when they accomplished them, and do so in a safe social context that they would be in control of. It might be useful in many self-directed learning contexts. 

These were not ideas that simply flowed in my head from start to finish: it was a direct result of dialogue with what I was creating that this came about, and it could only have done so because I already had ideas and principles about things like portfolios, learning contracts and social learning floating around in my toolkit, ready to be assembled. I did include the admin option to turn off self-awarding at a system level in case anyone disagreed with me, and because I could imagine contexts where it might get out of hand. I even (a little reluctantly) made it possible to limit badge awarding to admins only, so that there could be a ‘root’ badge or two that would provide the source of all accreditation and awarding. Even then, it could still be a far more social approach to accreditation than most, making expertise not just something that is awarded with an extrinsic badge, but also something that gives real power to its holder to play an important role in a learning community.

This is not exactly what my sponsors asked for: they wanted automation, so that an administrator could set some criteria and the system would automatically award badges when those criteria had been met.  Although I reckon my social solution meets the demand for scalability that lay at the heart of that request, I realized that, with some effort, I could assemble all of this with a karma point plugin that I happened to have in my virtual toolshed in order to enable automated badge awarding for things like posting blogs, etc. Because there was no obvious object for which such an award could be given as it could relate to any arbitrary range of activities, I made the object providing evidence to be the user’s own profile. Again, this was just assembling what was there – it was an adjacent possible, so I took it. I could, if I had not been lazy, have generated a page displaying all of the evidence, but I did not (though I still might – it is an adjacent possible that might be worth exploring). And so, of course, now it is possible to award a badge to a user, rather than for a specific post which, though not normally a good idea from a motivation perspective, could have a range of uses, especially when assembled with the tabbed profile we built earlier (what I refer to in academic writings as a ‘context switcher’ and that can be used as a highly flexible portfolio system).

These are just a sample of many conversations I had with the tools and objects that were available to me. I influenced them, they influenced me. There were plenty of others – exaptions like my discovery that the design I had opted for, which made awards and badges separate objects, meant that I had a way of making awards persistent and not allowing badge owners to sneakily change them afterwards, for example, thus enhancing trust in the system. Or that the Elgg permissions model made it very simple to reliably assert ownership, which is very important if you are going to distribute accreditation over multiple sites and systems. Or that the fact that it turned out to be an incredibly complex task to make it all work in an Elgg Group context was a blessing because I therefore looked for alternatives, and found that the pre-requisite functionality does the job at least as well, and much more elegantly. Or that the Elgg views system made it possible to fairly easily create OpenBadge assertions for use on other sites. The list goes on. 

It was not all wonderful though. Sometimes the conversation got weird. My plan to start with an existing badge plugin quickly bit the dust. It turns out that the badge plugins that were available were both of the kind I hate – they awarded badges to individuals, not for specific competences. To add injury to injury, they could be awarded only by the administrator, either automatically through accrued points or manually. This was exactly the kind of power structure that I wanted to get away from. From an architectural perspective, making these flawed plugins work the way I wished would have been much harder than writing the plugin from scratch. However, in the spirit of tinkering, I didn’t start completely from scratch. I looked around for a plugin that would do some of the difficult stuff for me. After playing with a few, I opted standard Elgg Files plugin, because that ought to have made light work of storing and organizing the badge images. In retrospect, maybe not the best plan, but it was a starting point. After a while I realized I had deleted or not used 90% of the original plugin, which was more effort than it was worth. I also got stuck in a path dependency again, when I wanted to add multiple prerequisites (ie you could specify more than one badge as a prerequisite) : by that time, my ingenious single-prerequisite model was so firmly embedded that it would have taken more than a solid week to change it. I did not have the energy, or the time.  And, relatedly, my limited Elgg skills and lack of forward planning meant that I did not always divide the code into neatly reusable chunks. This still continues to cause me trouble as I try to make the OpenBadge feature work. Reflecting on such issues is useful – I now know that multiple inheritence makes sense for this kind of system, which would not have occurred to me if I hadn’t built a system with a single-prerequisite data model. And I have a better idea about what kind of modularity works best in an Elgg system.

Surfing the adjacent possible

Like all stories worthy of the name, my examples are highly selective and probably contain elements of fiction in some of the details of the process. Distance in time and space changes memories so I cannot promise that everything happened in the order and manner presented here – it  was certainly a lot more complicated, messy and detailed than I have described it to be. I think this fictionlizing is crucial, though. Objective reporting is exactly not what is needed in a bricolage process. It is the sense-making that matters, not religious adherence to standards of objectivity. What matters are the things we notice, the things we reflect on and things we consider to be important. Those are the discoveries. 

This is a brief and condensed set of ten of the main principles that I think matter in effective tinkering for research:

  1. do not design – just build
  2. start with pieces that are fully formed
  3. surround yourself with both quantity and diversity in tools, materials, methods, and perspectives
  4. dabble hard – gain skills, but be suspicious of expertise
  5. look for exaptations and surf the adjacent possible
  6. avoid schedules and goals, but make time and space for tinkering, and include time for daydreaming
  7. do not fear dismantling and starting afresh
  8. beware of teams, but cultivate networks: seek people, not processes
  9. talk with your creations and listen to what they have to say
  10. reflect, and tell stories about your reflections, especially to others

As I read these ideas it strikes me that this is the very antithesis of how research, at least in fields I work in, is normally done and that it would be extremely hard to get a grant for this. With a deliberate lack of process control, no clear budgets, no clear goals, this is not what grant awarders would normally relish. Whatever. It is still worth doing.

Tinkering as a research methodology offers a lot – it is a generative process of discovery that builds ideas and connections as much as it builds objects that are interesting or useful. It is far from being a random process but it is unpredictable. That is why it is interesting. I think that some aspects of it resemble systematic literature review: the discovery and selection of appropriate pieces to assemble, in particular, is something that can be systematized to some extent and, just as in a literature review, once you start with a few pieces, other pieces fall naturally into place. It is very closely related to design-based research and action research, with their formal cycles and iterative processes, although the iteration cycle in tinkering is far finer grained, it is not as rigid in its requirements, and it deliberately avoids the kind of abstractions that such methodologies thrive on. It might be a subspecies though. It definitely resembles and can benefit from soft systems methodologies, because it is the antithesis of hard systems design. Rich pictures have a useful role to play, in particular, though not at the early stages they are used in soft systems methods. And, unlike soft systems, the system isn’t the goal.

Finally, tinkering is not a solution to everything. It is a means of generating knowledge. On the whole, if the products are worthwhile, then they should probably feed into a better engineered system. Note, however, that this is not prototyping. Though products of tinkering may sometimes play the role of a prototype at a later stage in a product cycle, the point of the process is not to produce a working model of something yet to come. That would imply that we know what we are looking for and, to a large extent, how we will go about achieving it. The point is to make discoveries. 

This is not finished yet. It might just turn out to be a lazy way to do research or, perhaps, just another name for something that is already well pinned down. It certainly lacks rigour but, since the purpose is generative, I am not too concerned about that, as long as it works to produce new knowledge. I tinker on, still surfing the adjacent possible.