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Clark Quinn’s Learnings about Learning

Moving forward

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I’ve argued before that there’s a pretty clear path forward for organizations.  The necessity to become agile means that the old ‘command and control’ approach won’t cut it any longer. What’s required is tapping into the ability of people to work together.  The new structure is focused on teams (stayed tuned for my review of Amy Edmondson’s Teaming) that  are given the tasks to solve problems, trouble shoot, design new products and services, and generally continue to adapt.  In short, to learn. And I want to talk about the L&D role here, at least the potential one.

Certain elements are required.  The teams need  a number of things to be effective.  They have  to be given  meaningful tasks, to have the freedom to pursue them, to have the ability to experiment (and fail) as necessary, and to be accountable.  To collaborate successfully to accomplish their goals, they need certain features internally:  they need to have diverse representation, be open to new ideas, have time for reflection, and it has to  be safe to contribute.

This takes a new approach from the organization. It takes leadership to make such a culture, and the culture itself has to make it possible for these to occur and to get people to be motivated to contribute.  Two  elements really contribute: contribution,  and transparency.  People need to know what each other is doing, and be willing to chip in and assist.  This happens both within teams and beyond.

So what is L&D’s role?  First, to model the desired behavior. L&D should be practicing what it preaches in experimenting and continually improving. There should be teams assigned to tasks, and the practitioners should be acting as members of their communities.   They should be evangelizing, piloting, and sharing their successes with this approach, while continually learning more.

Then, L&D should be working with others as teams to meet their client needs.  They should be working to innovate around the solutions.  They should be promoting and executing on pilots that get fleshed out.  And they should be gradually raising awareness about the processes and the culture.

Done well, this movement reduces turnover, increases engagement, and produces better outcomes.  It’s not trivial; there are nuances and challenges that will have to be addresses.  On the other hand, evidence is converging that this  is the future of business. So are you preparing for it, or waiting to be blind-sided?  If you’re looking for guidance in getting going, I’m easy to find.

 

Two separate systems?

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I frequently say that L&D needs to move from just ensuring optimal execution to also supporting continual innovation.  Can these co-exist, or are they fundamentally different?  I really don’t know, but it’s worth pondering.

Kotter (the change management guru), has begun to advocate for a dual-operating system approach, where companies jointly support an operational hierarchy and an innovation network that are coupled.  I haven’t read his book on the topic, but it seems to be a bit  extrinsic, a way of bolting on innovation instead of making it intrinsic to the operation.

On the other hand, there is quite a bit of expression for more flexible systems, a more podular approach. Teaming or small nodes are increasingly appearing as not just for innovation, but ongoing operation. However, it’s not clear how the various different areas are coordinated, so how marketing across pods maintains coherent.

CoherentOrgExpandedThis is what led to our Coherent Organization model.  The notion is that the teams are coming in from, and reporting back up through, their communities. And their communities are communicating both within, and outside of, the organization.

It’s not clear to me whether the team  approach can scale to a global organization, or whether you need the hybrid model.  I can see that the hybrid model would appeal to existing business folks who would be concerned about optimization in execution.  I can see that the new model would at least require fundamental changes in mechanisms, and perhaps a willingness to tradeoff absolute perfection in execution to maintain continuing innovation and customer-responsiveness.

While  intuitively the more biologically inspired approach sounds like the longer-term solution, it’s non-trivial in terms of creating cultures that are appropriately conducive.  I think that organizational operations may be at an inflection point, and there does seem to be data that supports more radical flexibility.   I think a performance ecosystem coupled with a learning organization environment is likely going to be the way to move.  How you get there is part of the revolution that’s needed. Start small, scale out, etc. And I hope L&D can help lead the way.

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.

Showing my age, er, experience

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I’ve been reading What the Dormouse Said (How the Sixties Counterculture Shaped the Personal Computer Industry), and it’s bringing back some memories.  Ok, so most of this stuff is older than I am, but there are a few connections, so it’s reminiscing time.  I’ve said some of this before, I believe, so feel free to wander on.  This is me just thinking aloud.

I was taking some computer science classes because I’d found out that biology was rote memorization and cut-throat medical (which I did  not want to do; I was hoping for marine bio), and a buddy was doing it.  Given that I was at UCSD at the time, I naturally learned  UCSD Pascal (as well as Fortran, which I fortunately forgot almost immediately, and Mixal likewise). I enjoyed algorithms, however, and could solve problems. I also was enchanted with AI (despite my first prof).  And I was  tutoring for some extra pocket money, math and science (even classes I hadn’t taken yet!).

Then I got a job doing the computer support for the office that did the tutoring (literally carrying decks of cards in Algol to run through the computer center). And a light went off; computers for learning!  There was no major then at my school, but there was a program to design my own major, and I found a couple of professors willing to serve as my advisors  (thank you, Hugh Mehan and Jim Levin). They even let me work on a project with them (email for classroom discussion, circa 1978; we had ARPANET, the predecessor to the internet).  It eventually even got published as a journal article.

I called all over the country, trying to find someone who needed a person interested in computer learning.  I even interviewed at Xerox PARC with John Seely Brown, courtesy of Tom Malone (I didn’t get the job; they wanted something I’d done but I didn’t know their term for it!).  After a small job doing some statistical work for a research project, I managed to get a job designing and programming educational computer games for DesignWare (you can still play some of  the products here, the magic of  the internet).  We went from Basic to Forth (for speed and small size), though I later moved away from coding with the demise of HyperCard ;).

And the main connection to the cool stuff, besides the interview at PARC, was visiting the West Coast Computer Faire.  It was cool in and of itself, but there I met David Suess, who along with Bill Bowman was starting Spinnaker, a company to do home educational software.  DesignWare had been doing games to go along with publisher offerings, and I was pushing  the home market.  After a conversation, I introduced David to my boss Jim Schuyler (Sky) and off we went. As a reward, I got to do FaceMaker. Eventually, DesignWare started doing it’s own titles, and I also did Spellicopter and Creature Creator before I realized I wanted to go back to grad school.

Along the way I also read Byte magazine and tracked efforts like SmallTalk and folks like Alan Kay.  I’ve subsequently had the pleasure to meet him, as well as  Doug Engelbart and Ted Nelson, so I’ve somewhat closed the loop on those heady days.  There’s much more between then and now, but that’s enough for one post. And most of my counterculture experiences were behind me by that time, so I didn’t really get a chance to see those connections, but it was an exciting time, and a great exposure to the possibilities.

Moving forward

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A few weeks ago, I posted about laying out activities in a  space dividing the execution side from the innovation side, and in the head from in the world.  None of you took the bait about talking what it meant (I’m  so disappointed), but it continued to ponder it myself. And at least one idea came to mind.

LearningSpaceImplicationsSo what I’m thinking is that the point is to  not be using our heads to be doing simple execution. Machines (read: robots or computation agents) are very good at doing what they’re told. Reliably, and repeatably.  They may need oversight, but in many ways we’re seeing this play out.

What we should be doing is trying to automate execution. We aren’t good at doing rote things, and having us do them is silly.  Ideally you automate them, or outsource them in some way.  Let’s save our minds for doing important work.

Of course, many times the situations we’re increasingly seeing are not matters of simply executing. As things get more ambiguous, more novel, more  chaotic, we’re really discovering we need to have people handle those situations in innovative ways. So they’re really being moved over regardless.

And, of course, we want that innovation to be fueled by data, information in the world being made available to support making these decisions. Big analytics, or even little analytics are good basis, as are models and support tools to facilitate the processes.  And, of course, this doesn’t have to be all in one head, but drawing upon teams, communities, and networks to get solution.

The real point is to let machines do what they can do well, and leave to us what we do well. And, what we  want to be responsible for.  As I see it, the role of technology is to augment us, not replace us.  It’s up to us to make the choices, but we have the opportunity to work in ways that align with how our brains really think, work, and learn.  I reckon that choice is a no-brainer ;).

Learning in context

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In preparation for the upcoming FocusOn Learning Conference, where I’ll be running a workshop  about cognitive science for L&D, not just for learning but also for mobile and performance support, I was thinking about how  context can be leveraged to provide more optimal learning  and performance.  Naturally, I had to diagram it, so let me talk through it, and you let me know what you think.

ApartLearningWhat we tend to do, as a default, is to take people away from work, provide the learning resources away from the context, then create a context to practice in. There are coaching resources, but not necessarily the performance resources.  (And I’m not even mentioning the typical lack of sufficient practice.) And this makes sense  when the consequences of making a mistake on the task are irreversible and costly.  E.g. medicine, transportation.  But that’s not as often as we think. And there’s an alternative.

We can wrap the learning around the context.  Our individual is  in the world, and performing the  task. There can  be coaching (particularly at the start, and then gradually removed as the individual  moves to acceptable competence). There are also performance resources – job aids, checklists, etc – in the environment. There also  can be learning resources, so the individual can continue to self-develop, particularly in the increasingly likely situation that the task has some ambiguity or novelty in it. Of course, that only works if we have a learner  capable of self learning (hint hint).

The problems with always taking people away from their jobs are multiple:

  • it is costly to interrupt their performance
  • it can be costly to create the artificial context
  • the learning has a lower likelihood to make it back to the workplace

Our brains don’t learn in an event model, they learn in little bits over time. It’s more natural,  more  effective, to dribble the learning out at the moment of need, the learnable moment.  We have the capability, now, to  be more aware of the learner, to deliver support in the moment, and develop learners over time. The way their brains actually learn.  And we should be doing this.  It’s more effective as well as more efficient.  It requires moving out of our comfort zone; we know the classroom, we know training.  However, we now also know that the effectiveness of classroom training can be very limited.

We have the ability to start making learning effective as well as efficient. Shouldn’t we do so?

Deeper Learning Reading List

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So, for my last post, I had the Revolution Reading List, and it occurred to me that I’ve been reading a bit about deeper learning design, too, so I thought I’d offer some pointers here too.

The starting point would be Julie Dirksen’s Design For How People Learn (already in it’s 2nd edition). It’s a very good interpretation of learning research applied to design, and very readable.

A new book that’s very good is Make It Stick, by Peter Brown, Henry Roediger III, and Mark McDaniel, the former being a writer who’s worked with two scientists to take learning research into 10 principles.

And let me mention two Ruth Clark books. One with Dick Mayer from UCSB, e-Learning and the Science of Instruction, that focuses on the use of media.  A second with Frank Nguyen and the wise John Sweller, Efficiency in Learning, focuses on cognitive load (which has many implications, including some overlap with the first).

Patti Schank has come out with a concise compilation of research called The Science of Learning that’s available to ATD members. Short and focused with her usual rigor.  If you’re not an ATD member, you can read her  blog posts that contributed (click ‘View All’).

Dorian Peters book on Interface Design for Learning also has some good learning principles as well as interface design guidance.  It’s not the same for learning as for doing.

Of course, a classic is a compilation of research by a blue-ribbon team lead by John Bransford,  How People Learn, (online or downloadable).  Voluminous, but pretty much state of the art.

Another classic is  the Cognitive Apprenticeship  model of Allen Collins & John Seely Brown. A holistic model abstracted across some seminal work, and quite readable.

The Science of Learning Center has an academic integration of research to instruction theory by Ken Koedinger, et al,  The Knowledge-Learning-Instruction Framework, that’s freely available as a PDF.

I’d be remiss if I don’t point out the Serious eLearning Manifesto, which has 22 research principles underneath the 8 values that differentiate serious elearning from typical versions.  If you buy in, please sign on!

And, of course, I can point you to my own series for Learnnovators on Deeper ID.

So there you go with some good material to get you going. We need to do better at elearning, treating it with the importance it deserves.  These don’t necessarily tell you how to redevelop your learning design processes, but you know who can help you with that.  What’s on your list?

Revolution Reading List

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I’m still a book guy (whether print or ebook), and have been reading a number of tomes of late. And, more and more, we’re seeing books that talk about the revolution itself or relevant components.  Here’re a few that have come to my awareness of late, and I have perused in some depth, relative to my own  Revolutionize Learning & Development:

Jane Hart’s  Modern Workplace Learning is an excellent complement to my book, with detailed descriptions of a rich suite of practices that foster a learning workplace.

Another ITA colleague, Harold Jarche, has his Perpetual Beta book series, which is a curated collection of his posts about the changing nature of work that make the case for the revolution and and covering personal knowledge mastery skills that are a necessary accompaniment.

And Charles Jennings, along with Tulser colleagues Jos Arets and Vivian Heijnen, have 70:20:10: Towards 100% Performance  which is a (very) detailed  set of processes to address performance needs from go to whoa but working backwards from the  ongoing support, not forward from the course.

Jane Bozarth’s Show Your Work is a valuable (and beautifully designed) book that talks about the why and how of showing your work (an important component of the Revolution), peppered with examples.

Nigel Paine has penned  The Learning Challenge, a book that takes a similar  stance as my own Revolution book, but with some changes in  emphasis.  A slightly different way to look at the changes.

Bill Bruck has published his own tome,  Speed to Proficiency, which similarly  covers some of the problems and recommendations as the Revolution book.

We should not forget some classics, e.g. Jay Cross’s game-changing  book on  Informal Learning, which really altered  the way we think about workplace learning.

A classic on the social side, Tony Bingham & Marcia Conner’s The New Social Learning is in it’s second edition.

Of course, Marc Rosenberg’s early  Beyond eLearning was a landmark in going beyond the course to a performance ecosystem.

BTW, I’ve requested read  Amy Edmondson’s Teaming, so that may   joined the list.

Which reminds me, I’ve previously talked about 3 books on team structures, and 2 on changing culture (here and here), also relevant.

I don’t agree with all that appears in all the books, but they all help illuminate  the ways we need to be thinking. And  if you want  help implementing, you know who to contact.  So, what’s on your wall?

 

Work Experiment

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At a  point some days ago, I got the idea to map out different activities by their role as executing versus innovating, and whether it’s in the head or in the world. And I’ve been playing with it since.  I’m mapping some  ways of getting work done, at least the mental aspects, across those dimensions.

LearningSpace

I’m not sure I’ve got things in the right places.  I’m not even sure what it really means. I’ve some ideas, but I think I’m going to try something new, and ask you what  you think it means.  So, what’s interesting and/or important here?

Top 10 Tools for Learning 2016

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It’s that time again: Jane Hart is running her 2016 (and 10th!) Top 100 Tools for Learning poll. It’s a valuable service, and points out some interesting things and it’s interesting to see the changes over time.  It’s also a way to see what others are using and maybe find some new ideas.  She’s now asking that you categorize them as Education, Training & Performance Support, and/or Personal Learning & Productivity.  All  of mine fall in the latter category, because my performance support tools are productivity tools!  So here’re my votes, FWIW:

Google Search is, of course, still my top tool. I’m looking up things several if not many times a day. It’s often a gateway to Wikipedia, which I heavily rely on, but a number of times I find other sources that are equally valuable, such as research or practice sites that have some quality inputs.

Books are still a major way I learn. Yes, I check out books from the library and read them.  I also acquire and read them on my iPad, such as Jane’s great  Modern Workplace Learning.  In my queue is Jane Bozarth’s  Show Your Work.  

Twitter is a  go-to. I am pointed to many serendipitously interesting things, and of course I point to things as well. The learning chats I participate in are another way twitter helps.  Tweetdeck is my twitter tool; columns are a must.

Skype  is a tool I use for communicating with folks to get things done, but also to have conversations (e.g. with my ITA colleagues), whether chat or voice.

Facebook  is also a way I stay in touch with friends and colleagues (those colleagues that I also consider friends; Facebook is more a personal learning tool than  a business tool for me).

LinkedIn  is a way to stay in touch with people, and in particular the  L&D Revolution group is where I want to keep the dialog alive about the opportunity. The articles in LinkedIn  are occasionally of interest too, and it’s always an education to see who wants to link ;).

WordPress is my blogging tool (where you’re at right now), and it’s a way I think ‘out loud’ and the feedback I get is a wonderful way to learn.  Things that eventually appear in presentations and writing typically appear here first, and some of the work I do for others manifests here (typically anonymized).

Word  is my go-to writing tool, and while I use Pages at times too (e.g. if I’m traveling with my iPad), Word is my industrial strength tool.  Writing forces me to get concrete about my thinking.

Omnigraffle  is as always  my diagramming tool, and it’s definitely a way I express and refine my thinking.  Obviously, you’ll see my diagrams here, but also in presentations and articles/chapters/books. And, of course, my mindmaps.

Keynote  is my presentation creating tool. I sometimes  have to export to PowerPoint, but Keynote is where I work natively.  It helps me turn my ideas from diagrams and/or writing into a story to tell with visual support.

So those are my ‘learning’ tools, for now. Some are ‘content’, some are social media, some are personal representational tools, but reading and talking with others and representing my own thinking are  major learning activities for me.