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Clark Quinn’s Learnings about Learning

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Ambiguity Denial Syndrome?

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I was talking with a colleague at an event one of the past weeks, and I noted down the concept of ambiguity denial syndrome. And I’m retrospectively making up what we were talking about, but it’s an interesting idea to me.

FractalSo one of the ways I start out a talk (including later today for a government agency) is to talk about chaos. I use a fractal, and talk about the properties a fractal has.  You know, that it’s a mathematical formulation that paints an image from which patterns emerge, yet at any point you really don’t know where it’s going to go next.

I use this to explain how our old beliefs in an ability to plan, prepare, and execute were somewhat misguided.  What we did was explain away the few times it didn’t work. But as things move faster, the fact that things are not quite as certain as we’d believe means we have to become more agile, because we can less tolerate the mistakes.

The  point I’m making, that the world increasingly requires an ability to deal with ambiguity and unique situations. And our learning designs, and organization designs, and our cultures, need to recognize this. And yet, in so many ways, they don’t.

At the individual level, we’re not equipping folks with the right tools. We should be providing them with models to use to interpret and adapt to situations (explain and predict). Our learning designs should have them dealing with a wide variety and degrees of certainty in  situations.  And we should be testing and refining them, recognizing that learners aren’t as predictable as concrete or steel.  Instead we see one-shot development of information  dumps and knowledge tests, which aren’t going to help organizations.

At the interpersonal level, we should be facilitating people to engage productively, facilitating the development of viable processes for working and learning together. We know that the room is smarter than the smartest person in the room (if we manage the process right), and that  we’ll get the best results when we empower people and support their success. We need them working out loud, communicating and collaborating, to get the best. Instead, we still see top-down hierarchies and solo work.

In short, we see people denying the increasing complexity that the world is showing us.  Implicitly or explicitly, it’s clear that many folks believe that they can, and must, control things, instead of looking to adapt on the fly.  We have new organizational models for this, and yet we’re not even seeing the exploration yet.  I acknowledge that change is hard, and navigating it successfully is a challenge. But we have lots of guidance here too.

Too many processes I see reflect industrial age thinking, and we’re in an information age. We have greater capacity amongst our people, and greater challenges to address, with less tolerance for mistakes.  We need to address, even embrace ambiguity, if we are to thrive. Because we can, and we should.  It’s the only sensible way to move forward in this increasingly complex world. So, are you ready?

Learning in Context

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In a recent guest post, I wrote about the importance of context in learning. And for a featured session at the upcoming FocusOn Learning event, I’ll be talking about performance support in context.  But there was a recent question about how you’d do it in a particular environment, and that got me thinking about the the necessary requirements.

As context (ahem), there are already context-sensitive systems. I helped lead the design of one where a complex device was instrumented and consequently there were many indicators about the current status of the device. This trend is increasing.  And there are tools to build context-sensitive helps systems around enterprise software, whether purchased or home-grown. And there are also context-sensitive systems that track your location on mobile and allow you to use that to trigger a variety of actions.

Now, to be clear, these are already in use for performance support, but how do we take advantage of them for learning. Moreover, can we go beyond ‘location’ specific learning?  I think we can, if we rethink.

So first, we  obviously  can use those same systems to deliver specific learning. We can have a rich model of learning around a system, so a detailed competency map, and then with a rich profile of the learner we can know what they know and don’t, and  then when they’re at a point where there’s a gap between their knowledge and the desired, we can trigger some additional information. It’s in context, at a ‘teachable moment’, so it doesn’t necessarily have to be assessed.

This would be on top of performance support, typically, as they’re still learning so we don’t want to risk a mistake. Or we could have a little chance to try it out and get it wrong that  doesn’t actually get executed, and then give them feedback and the right answer to perform.  We’d have to be clear, however, about why learning is needed in  addition to the right answer: is this something that  really needs to be learned?

I want to go a wee bit further, though; can we build it around what the learner is doing?  How could we know?  Besides increasingly complex sensor logic, we can use  when they are.  What’s on their calendar?  If it’s tagged appropriately, we can know at least what they’re  supposed to be doing.  And we can develop not only specific system skills, but more general business skills: negotiation, running meetings, problem-solving/trouble-shooting, design, and more.

The point is that our learners are in contexts all the time.  Rather than take them away to learn, can we develop learning that wraps around what they’re doing? Increasingly we can, and in richer and richer ways. We can tap into the situational motivation to accomplish the task in the moment, and the existing parameters, to make ordinary tasks into learning opportunities. And that more ubiquitous, continuous development is more naturally matched to how we learn.

Working wiser?

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Noodling:   I’ve been thinking about Working Smarter, a topic I took up over four years ago.  And while I still think there’s too little talk about it, I wondered also about pushing it further.  I also talked in the past about an interest in wisdom, and what that would mean for learning.  So what happens when they come together?

Working smarter, of course, means recognizing how we  really think, work, and learn, and aligning our processes and tools accordingly. That includes recognizing that we  do use external representations, and ensuring that the ones we want in the world are there, and we also support people being able to create their own. It means tapping into the power of people, and creating ways for them to get together and support one another through both communication and collaboration.  And, of course, it means using Serious learning design.

But what, then, does working  ‘wiser’ mean?  I like Sternberg’s model of wisdom, as it’s actionable (other models are not quite specific enough).  It talks about taking into account several levels of  caring about others, several time scales, several levels of action, and all influenced by an awareness of values.  So how do we work that into practices and tools?

Well, pragmatically, we can provide rubrics for evaluation of ideas that include considerations of others inside and outside your circles of your acquaintances, and in short- and long-term timeframes, and the impacts on existing states of affairs, ultimately focusing on the common good. So we can have job aids that provide guidance,  or bake it into our templates.  These, too, can be shown in collaboration tools, so the outputs will reflect these values.  But there’s another approach.

But, at core, it’s really about what you value, and that becomes about culture.  What values does the organization care about?  Do employees know about the organization’s ultimate goal and role?  Is it about short-term shareholder return, or some contribution to society?  I’m reminded about the old statements about whether you’re about selling candles or providing light.  And do employees know how what they do fits in?

It’s pretty clear that the values implicit in  steps to make workplaces more effective are really about making workplaces more humane, that is: respecting our inherent nature.  And movements like this, that provide real meaning, ongoing support, freedom of approach, and time for reflection, are to me about working not just smarter but also wiser.

We can work smarter with tools and practices, but I think we can work better, wiser, with an enlightened approach to who we are working with and how we work to deliver real value to not only customers but to society.  And, moreover, I think that doing so would yield better organizational  outcomes.

Ok, so have I gone off the edge of the hazy cosmic jive?  I am a native Californian, after all, but I’m thinking that this makes real business sense.  I think we can do this, and that the outputs will be better too, in all respects.  No one says it’d be easy, but my suspicion is it’d be worthwhile.

2015 Reflections

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It’s the end of the year, and given that I’m an advocate for the benefits of reflection, I suppose I better practice what I preach. So what am I thinking I learned as a consequence of this past year?  Several things come to mind (and I reserve the right for more things to percolate out, but those will be my 2016 posts, right? :):

  1. The Revolution  is real: the evidence mounts that there is a need for change in L&D, and when those steps are taken, good things happen. The latest  Towards Maturity report shows that the steps taken by their top-performing organizations are very much about aligning with business,  focusing on performance, and more.  Similarly, Chief Learning Officer‘s Learning Elite Survey similarly point out to making links across the organization and measuring outcomes.  The data supports the principled observation.
  2. The barriers are real: there is continuing resistance to the most obvious changes. 70:20:10, for instance, continues to get challenged on nonsensical issues like the exactness of the numbers!?!?  The fact that a Learning Management System is not a strategy still doesn’t seem to have penetrated.  And so we’re similarly seeing that other business units are taking on the needs for performance support, social media, and ongoing learning. Which is bad news for L&D, I reckon.
  3. Learning design is  rocket science: (or should be). The perpetration of so much bad elearning continues to be demonstrated at exhibition halls around the globe.  It’s demonstrably true that tarted up information presentation and knowledge test isn’t going to lead to meaningful behavior change, but we still are thrusting people into positions without background and giving them tools that are oriented at content presentation.  Somehow we need to do better. Still pushing the Serious eLearning Manifesto.
  4. Mobile is well on it’s way: we’re seeing mobile becoming mainstream, and this is a good thing. While we still hear the drum beating to put courses on a phone, we’re also seeing that call being ignored. We’re instead seeing real needs being met, and new opportunities being explored.  There’s still a ways to go, but here’s to a continuing awareness of good mobile design.
  5. Gamification is still being confounded: people aren’t really making clear conceptual differences around games. We’re still seeing linear scenarios confounded with branching, we’re seeing gamification confounded with serious games, and more.  Some of these are because the concepts are complex, and some because of vested interests.
  6. Games  seem to be reemerging: while the interest in games became mainstream circa 2010 or so, there hasn’t been a real sea change in their use.  However, it’s quietly feeling like folks are beginning to get their minds around Immersive Learning Simulations, aka Serious Games.   There’s still ways to go in really understanding the critical design elements, but the tools are getting better and making them more accessible in at least some formats.
  7. Design is becoming a ‘thing’: all the hype around Design Thinking is leading to a greater concern about design, and this is a good thing. Unfortunately there will probably be some hype and clarity to be discerned, but at least the overall awareness raising is a good step.
  8. Learning to learn seems to have emerged: years ago the late great Jay Cross and I and some colleagues put together the Meta-Learning Lab, and it was way too early (like so much I touch :p). However, his passing has raised the term again, and there’s much more resonance. I don’t think it’s necessarily a  thing yet, but it’s far greater resonance than we had at the time.
  9. Systems are coming: I’ve been arguing for the underpinnings, e.g. content systems.  And I’m (finally) beginning to see more interest in that, and other components are advancing as well: data  (e.g. the great work Ellen Wagner and team have  been doing on Predictive Analytics), algorithms (all the new adaptive learning systems), etc. I’m keen to think what tags are necessary to support the ability to leverage open educational resources as part of such systems.
  10. Greater inputs into learning: we’ve seen learning folks get interested in behavior change, habits, and more.  I’m thinking we’re going to go further. Areas I’m interested in include myth and ritual, powerful shapers of culture and behavior. And we’re drawing on greater inputs into the processes as well (see 7, above).  I hope this continues, as part of learning to learn is to look to related areas and models.

Obviously, these are things I care about.  I’m fortunate to be able to work in a field that I enjoy and believe has real potential to contribute.  And just fair warning, I’m working on a few areas  in several ways.  You’ll see more about learning design and the future of work sometime in the near future. And rather than generally agitate, I’m putting together two specific programs – one on (e)learning quality and one on L&D strategy – that are intended to be comprehensive approaches.  Stay tuned.

That’s my short list, I’m sure more will emerge.  In the meantime, I hope you had a great 2015, and that your 2016 is your best year yet.

CERTainly room for improvement

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As mentioned before, I’ve become a member of my local Community Emergency Response Team (CERT), as in the case of disaster, the official first-responders (police, fire, and paramedics) will be overwhelmed.  And it’s a good group, with a lot of excellent  efforts in processes and tools as well as drills.  Still, of course, there’s  room for improvement.  I encountered one such at our last meeting, and I think it’s an interesting case study.

So one of the things you’re supposed to do in conducting search and rescue is to go from building to building assessing damage and looking for people to help.  And one of the useful things to do is to mark the status of the search and the outcomes, so no one wastes effort on an already explored building. While the marking is  covered in training and there’re support tools to help you remember,  ideally it’d be memorable, so that you  can regenerate the information and  don’t have to look it up.

The design for the marking is pretty clear: you first make a diagonal slash when you start investigating a building, and then you make a crossing slash  when you’ve made your assessment. And  specific information is to be recorded in each quarter of the resulting X: left, right, top, and bottom.  (Note that the US standard set by FEMA doesn’t correspond to the international standard from the  International Search & Rescue Advisory Group, interestingly).

However, when we brought it up in a recent meeting (and they’re very good about revisiting things that quickly fade from memory), it was obvious that most people couldn’t recall what goes where. And when I heard what the standard was, I realized it didn’t have a memorable structure.  So, here are the four things to record:

  • the group who goes in
  • when the group completes
  • what hazards may exist
  • and how many people and what condition they’re in*

So how would  you  map these to the quadrants?  And in one sense it doesn’t matter  if there’s a sensible rationale behind them. One sign that there’s not?  You can’t remember what goes where.

Our  local team leader was able to recall that the order is: left – group, top – completion, right – hazards, and bottom – people.  However, this seems to me to be less than  memorable, so let me explain.

To me, wherever you put the in, left or top, the coming out ought to be opposite. And given our natural flow, group going in makes sense to the left, and coming out ought to go on the right.  In – out.  Then, it’s relatively arbitrary where hazards and people go.  I’d make a case that top-of-mind should be the hazards found to warn others, but that the people are the bottom line (see what I did there?).  I could easily make a case for the reverse, but either would be a mnemonic to support remembering.  Instead, as far as I can tell, it’s completely arbitrary. Now, if it’s not arbitrary and there is a rationale,  it’d help to share  that!

The point being, to help people remember things that are in some sense arbitrary, make a story that makes it memorable. Sure, I can look it up, assuming that the lookup book they handed out stays in the pocket in my special backpack.  (And I’m likely to remember now, because of all this additional processing, but that’s  not what happens in the training.)  However,  making it regenerable from some structure gives you a much better chance of having it to hand. Either a model or a story is better than arbitrary, and one’s possible with a rewrite, but as it is, there’s neither.

So there’s a lesson in design to be had, I reckon, and I hope you’ll put it to use.

* (black or dead, red or needing immediate treatment for life-threatening issues, yellow or needing non-urgent treatment, and green or ok)

A Competent Competency Process

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In the process of looking at ways to improve the design of courses, the starting point is good objectives. And as a consequence, I‘ve been enthused about the notion of competencies, as a way to put the focus on what people do, not what they know. So how do we do this, systematically, reliably, and repeatably?

Let‘s be clear, there are times we need knowledge level objectives. In medicine or any other field where responses need to be quick and accurate, we need a very constrained vocabulary. SO drilling in the exact meanings of words is valuable, as an example. Though ideally, that‘s coupled with using that language to set context or make decisions. So “we know it‘s the right medial collateral ligament, prep for the surgery” could serve as a context, or we could have a choice to operate on the left or right atrial ventricle as a decision point. As Van Merriënboer‘s 4 Component Instructional Design talks about, we need to separate out the knowledge from the complex problems we apply it to. Still, I suggest that what‘s likely to make a difference to individuals and organizations is the ability to make better decisions, not recite rote knowledge.

So how do we get competencies when we want them? The problem, as I‘ve talked about before, is that SMEs don‘t have access to 70% of what they actually do, it‘s compiled away. We then need good processes, so I‘ve talked to a couple of educational institutions doing competencies, to see what could be learned. And it‘s clear that while there‘s no turnkey approach, what‘s emerging is a process with some specific elements.

One thing is that if you‘re trying to cover a whole college level course, you‘ve got to break it up. Break down the top level into a handful of competencies. Then you continue to take each of those apart, and perhaps another level, ‘til you have a reasonable scope. This is heuristic, of course, but with a focus on ‘do‘, you have a good likelihood to get here.

One of the things I‘ve heard across various entities trying to get meaningful objectives is working with more than one SME. If you can get several, you have a better chance of triangulating on the right outcomes and objectives. They may well disagree about the knowledge, but if you manage the process right (emphasize ‘do‘, lather, rinse, repeat), you should be able to get them to converge. It may take some education, and you may have to let them get the

Not just any SMEs will do. Two things are really valuable: on the ground experience to know what needs to be done (and doesn‘t), and the ability to identify and articulate the models that guide the performance. Some instructors, for instance, can teach to a text but really aren‘t truly masters of the content nor are experienced practitioners. Multiple helps, but the better the SME, the better the outcome.

I believe you want to ensure that you‘re getting both the right things, and all the things. I‘ve recommended to a client about triangulating not just with SMEs, but with practitioners (or, rather, the managers of the roles the learners will be engaged in), and any other reliable stakeholders. The point is to get input from the practice as well as the theory, identifying the models that support proper behavior, and the misconceptions that underpin where they go wrong.

Once you have a clear idea of the things people need to be able to do, you can then identify the language for the competencies. I‘m not a fan of Bloom‘s (unwieldy, hard to reliably apply), but I am a fan of Mager-style definitions (action, context, metric).

After this is done, you can identify the knowledge needed, and perhaps created objectives for that, but to me the focus is on the ‘do‘, the competencies. This is very much aligned with an activity-based learning model, whereby you immediately design the activities that align with the competencies before you decide the content.

So, this is what I‘m inferring. There would be good tools and templates you could design to go with this, identifying competencies, misconceptions, and at the same time also getting stories and motivations. (An exercise left for the reader. ;) The overall goal, however, of getting meaningful objectives is key to getting good learning design. Any nuances I‘m missing?

Supporting our Brains

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One of the ways I’ve been thinking about the role mobile can play in design is thinking about how our brains work, and don’t.  It came out of both mobile and the recent cognitive science for learning workshop I gave at the recent DevLearn.  This applies more broadly to performance support in general, so I though I’d share where my thinking is going.

To begin with, our cognitive architecture is demonstrably awesome; just look at your surroundings and recognize your clothing, housing, technology, and more are the product of human ingenuity.  We have formidable capabilities to predict, plan, and work together to accomplish significant goals.  On the flip side, there’s no one all-singing, all-dancing architecture out there (yet) and every such approach also has weak points. Technology, for instance, is bad at pattern-matching and meaning-making, two things we’re really pretty good at.  On the flip side, we have some flaws too. So what I’ve done here is to outline the flaws, and how we’ve created tools to get around those limitations.  And to me, these are principles for design:

table of cognitive limitations and support toolsSo, for instance, our senses capture incoming signals in a sensory store.  Which has interesting properties that it has almost an unlimited capacity, but for only a very short time. And there is no way all of it can get into our working memory, so what happens is that what we attend to is what we have access to.  So we can’t recall what we perceive accurately.  However, technology (camera, microphone, sensors) can recall it all perfectly. So making capture capabilities available is a powerful support.

Similar, our attention is limited, and so if we’re focused in one place, we may forget or miss something else.  However, we can program reminders or notifications that help us recall important events that we don’t want to miss, or draw our attention where needed.

The limits on working memory (you may have heard of the famous 7 ±2, which really is <5) mean we can’t hold too much in our brains at once, such as interim results of complex calculations.  However, we can have calculators that can do such processing for us. We also have limited ability to carry information around for the same reasons, but we can create external representations (such as notes or  scribbles) that can hold those thoughts for us.  Spreadsheets, outlines, and diagramming tools allow us to take our interim thoughts and record them for further processing.

We also have trouble remembering things accurately. Our long term memory tends to remember meaning, not particular details. However, technology can remember arbitrary and abstract information completely. What we need are ways to look up that information, or search for it. Portals and lookup tables trump trying to put that information into our heads.

We also have a tendency to skip steps. We have some randomness in our architecture (a benefit: if we sometimes do it differently, and occasionally that’s better, we have a learning opportunity), but this means that we don’t execute perfectly.  However, we can use process supports like checklists.  Atul Gawande wrote a fabulous book on the topic that I can recommend.

Other phenomena include that previous experience can bias us in particular directions, but we can put in place supports to provide lateral prompts. We can also prematurely evaluate a solution rather than checking to verify it’s the best. Data can be used to help us be aware.  And we can trust our intuition too much and we can wear down, so we don’t always make the best decisions.  Templates, for example are a tool that can help us focus on the important elements.

This is just the result of several iterations, and I think more is needed (e.g. about data to prevent premature convergence), but to me it’s an interesting alternate approach to consider where and how we might support people, particularly in situations that are new and as yet untested.  So what do you think?

Agile?

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Last Friday’s #GuildChat was on Agile Development.  The topic is interesting to me, because like with Design Thinking, it seems like well-known practices with a new branding. So as I did then, I’ll lay out what I see and hope others will enlighten me.

As context, during grad school I was in a research group focused on user-centered system design, which included design, processes, and more. I subsequently taught interface design (aka Human Computer Interaction or HCI) for a number of years (while continuing to research learning technology), and made a practice of advocating the best practices from HCI to the ed tech community.  What was current at the time were iterative, situated, collaborative, and participatory design processes, so I was pretty  familiar with the principles and a fan. That is, really understand the context, design and test frequently, working in teams with your customers.

Fast forward a couple of decades, and the Agile Manifesto puts a stake in the ground for software engineering. And we see a focus on releasable code, but again with principles of iteration and testing, team work, and tight customer involvement.  Michael Allen was enthused enough to use it as a spark that led to the Serious eLearning Manifesto.

That inspiration has clearly (and finally) now moved to learning design. Whether it’s Allen’s  SAM  or Ger Driesen’s  Agile Learning Manifesto, we’re seeing a call for rethinking the old waterfall model of design.  And this is a good thing (only decades late ;).  Certainly we know that working together is better than working alone (if you manage the process right ;), so the collaboration part is a win.

And we certainly need change.  The existing approaches we too often see involve a designer being given some documents, access to a SME (if lucky), and told to create a course on X.  Sure, there’re tools and templates, but they are focused on making particular interactions easier, not on ensuring better learning design. And the person works alone and does the design and development in one pass. There are likely to be review checkpoints, but there’s little testing.  There are variations on this, including perhaps an initial collaboration meeting, some SME review, or a storyboard before development commences, but too often it’s largely an independent one way flow, and  this isn’t good.

The underlying issue  is that waterfall models, where you specify the requirements in advance and then design, develop, and implement just don’t work. The problem is that the human brain is pretty much the most complex thing in existence, and when we determine a priori what will work, we don’t take into account the fact that like Heisenberg what we implement will change the system. Iterative development and testing allows the specs to change after initial experience.  Several issues arise with this, however.

For one, there’s a question about what is the right size and scope of a deliverable.  Learning experiences, while typically overwritten, do have some stricture that keeps them from having intermediately useful results. I was curious about what made sense, though to me it seemed that you could develop your final practice first as a deliverable, and then fill in with the required earlier practice, and content resources, and this seemed similar to what was offered up during the chat to my question.

The other one is scoping and budgeting the process. I often ask, when talking about game design, how to know when to stop iterating. The usual (and wrong answer) is when you run out of time or money. The  right answer would be when you’ve hit your metrics, the ones you should set before you begin that determine the parameters of a solution (and they can be consciously reconsidered as part of the process).  The typical answer, particularly for those concerned with controlling costs, is something like a heuristic choice of 3 iterations.  Drawing on some other work in software process, I’d recommend creating estimates, but then reviewing them after. In the software case, people got much better at estimates, and that could be a valuable extension.  But it shouldn’t be any more difficult to estimate, certainly with some experience, than existing methods.

Ok, so I may be a bit jaded about new brandings on what should already be good practice, but I think anything that helps us focus on developing in ways that lead to quality outcomes is a good thing.  I encourage you to work more collaboratively, develop and test more iteratively, and work on discrete chunks. Your stakeholders should  be glad you did.

 

Designing Learning Like Professionals

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I’m increasingly realizing that the ways we design and develop content are part of the reason why we’re not getting the respect we deserve.  Our brains are arguably the most complex things in the known universe, yet we don’t treat our discipline as the science it is.  We need to start combining experience design with learning engineering to really start delivering solutions.

To truly design learning, we need to understand learning science.  And this does  not mean paying attention to so-called ‘brain science’. There is legitimate brain science (c.f. Medina, Willingham), and then there’s a lot of smoke.

For instance, there’re sound cognitive reasons why information dump and knowledge test won’t lead to learning.  Information that’s not applied doesn’t stick, and application that’s not sufficient doesn’t stick. And it won’t transfer well if you don’t have appropriate contexts across examples and practice.  The list goes on.

What it takes is understanding our brains: the different components, the processes, how learning proceeds, and what interferes.  And we need to look at the right levels; lots of neuroscience is  not relevant at the higher level where our thinking happens.  And much about that is still under debate (just google ‘consciousness‘ :).

What we do have are robust theories about learning that pretty comprehensively integrate the empirical data.  More importantly, we have lots of ‘take home’ lessons about what does, and doesn’t work.  But just following a template isn’t sufficient.  There are gaps where have to use our best inferences based upon models to fill in.

The point I’m trying to make is that we have to stop treating designing learning as something anyone can do.  The notion that we can have tools that make it so anyone can design learning has to be squelched. We need to go back to taking pride in our work, and designing learning that matches how our brains work. Otherwise, we are guilty of malpractice. So please,  please, start designing in coherence with what we know about how people learn.

If you’re interested in learning more, I’ll be running a learning science for design workshop at DevLearn, and would love to see you there.

Symbiosis

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One of the themes I‘ve been strumming in presentations is one where we complement what we do well with tools that do well the things we don‘t. A colleague reminded me that JCR Licklider  wrote of this decades ago (and I‘ve similarly followed the premise from the writings  of Vannevar Bush, Doug Engelbart, and Don Norman, among others).

We‘re already seeing this.     Chess has changed from people playing people, thru people playing computers and computers playing computers, to computer-human pairs playing other computer-human pairs. The best competitors aren‘t the best chess players or the best programs, but the best pairs, that is the player and computer that best know how to work together.

The implications are to stop trying to put everything in the head, and start designing systems that complement us in ways that assure that the combination is the optimized solution to the problem being confronted. Working backwards [], we should decide what portion should be handled by the computer, and what by the person (or team), and then design the resources and then training the humans to use the resources in context to achieve the goals.

Of course, this is only in the case of known problems, the ‘optimal execution‘ phase of organizational learning. We similarly want to have the right complements to support the ‘continual innovation‘ phase as well. What that means is that we have to be providing tools for people to communicate, collaborate, create representations, access and analyze data, and more. We need to support ways for people to draw upon and contribute to their communities of practice from their work teams. We need to facilitate the formation of work teams, and make sure that this process of interaction is provided with just the right amount of friction.

Just like a tire, interaction requires friction. Too little and you go skidding out of control. Too much, and you impede progress. People need to interact constructively to get the best outcomes. Much is known about productive interaction, though little enough seems to make it‘s way into practice.

Our design approaches need to cover the complete ecosystem, everything from courses and resources to tools and playgrounds. And it starts by looking at distributed cognition, recognizing that thinking isn‘t done just in the head, but in the world, across people and tools. Let‘s get out and start playing instead of staying in old trenches.