Learnlets
Clark Quinn's Learnings about Learning
(The Official Quinnovation blog)

27 April 2017

Innovation Thoughts

Clark @ 8:10 am

So I presented on innovation to the local ATD chapter a few weeks ago, and they did an interesting and nice thing: they got the attendees to document their takeaways. And I promised to write a blog post about it, and I’ve finally received the list of thoughts, so here are my reflections.  As an aside, I’ve written separate articles on L&D innovation recently for both CLO magazine and the Litmos blog so you can check those out, too.

I started talking about why innovation was needed, and then what it was.  They recalled that I pointed out that by definition an innovation is not only a new idea, but one that is implemented and leads to better results.  I made the point that when you’re innovating, designing, researching, trouble-shooting, etc, you don’t know the answer when you start, so they’re learning situations, though informal, not formal.  And they heard me note that agility and adaptation are premised on informal learning of this sort, and that the opportunity is for L&D to take up the mantle to meed the increasing need.

There was interest but some lack of clarity around meta-learning. I emphasize that learning to learn may be your best investment, but given that you’re devolving responsibility you shouldn’t assume that individuals are automatically possessed of optimal learning skills. The focus then becomes developing learning to learn skills, which of needs is done across some other topic. And, of course, it requires the right culture.

There were some terms they heard that they weren’t necessarily clear on, so per the request, here are the terms (from them) and my definition:

  • Innovation by Design: here I mean deliberately creating an environment where innovation can flourish. You can’t plan for innovation, it’s ephemeral, but you can certainly create a felicitous environment.
  • Adjacent Possible: this is a term Steven Johnson used in his book Where Good Ideas Come From, and my take is that it means that lateral inspiration (e.g. ideas from nearby: related fields or technologies) is where innovation happens, but it takes exposure to those ideas.
  • Positive Deviance: the idea (which I heard of from Jane Bozarth) is that the best way to find good ideas is to find people who are excelling and figure out what they’re doing differently.
  • Hierarchy and Equality: I’m not quite sure what they were referring to hear (I think more along the lines of Husband’s Wirearchy versus hierarchy) but the point is to reduce the levels and start tapping into the contributions possible from all.
  • Assigned roles and vulnerability: I’m even less certain what’s being referred to here (I can’t be responsible for everything people take away ;), but I could interpret this to mean that it’s hard to be safe to contribute if you’re in a hierarchy and are commenting on someone above  you.  Which again is an issue of safety (which is why I advocate that leaders ‘work out loud’, and it’s a core element of Edmondson’s Teaming; see below).

I used the Learning Organization Dimensions diagram (Garvin, Edmondson & Gino)  to illustrate the components of successful innovation environment, and these were reflected in their comments. A number mentioned  psychological safety in particular as well as  the other elements of the learning environment. They also picked up on the importance of leadership.

Some other notes that they picked up on included:

  • best principles instead of best practices
  • change is facilitated when the affected individual choose to change
  • brainstorming needs individual work before collective work
  • that trust is required to devolve responsibility
  • the importance of coping with ambiguity

One that was provided that I know I didn’t say because I don’t believe it, but is interesting as a comment:

“Belonging trumps diversity, and security trumps grit”

This is an interesting belief, and I think that’s likely the case if it’s not safe to experiment and make mistakes.

They recalled some of the books I mentioned, so here’s the list:

  • The Invisible Computer by Don Norman
  • The Design of Everyday Things by Don Norman
  • My Revolutionize Learning and Development (of course ;)
  • XLR8 by John Kotter (with the ‘dual operating system‘ hypothesis)
  • Teaming to Innovate by Amy Edmondson (I reviewed it)
  • Working Out Loud by John Stepper
  • Scaling Up Excellence by Robert I. Sutton and Huggy Rao (blogged)
  • Organize for Complexity by Niels Pflaeging (though they heard this as a concept, not a title)

It was a great evening, and really rewarding to see that many of the messages stuck. So, what are your thought around innovation?

 

26 April 2017

Human Learning is Not About to Change Forever

Clark @ 8:09 am

In my inbox was an announcement about a new white paper with the intriguing title Human Learning is About to Change Forever. So naturally I gave up my personal details to download a copy.  There are nine claims in the paper, from the obvious to the ridiculous. So I thought I’d have some fun.

First, let’s get clear.  Our learning runs on our brain, our wetware. And that’s not changing in any fundamental way in the near future. As a famous article once had it: phenotypic plasticity triumphs over genotypic plasticity (in short, our human advantage has gained  via our ability to adapt individually and learn from each other, not through species evolution).   The latter takes a long time!

And as a starting premise, the “about to” bit implies these things are around the corner, so that’s going to be a bit of my critique. But nowhere near all of it.  So here’s a digest of the nine claims and my comments:

  1. Enhanced reality tools will transform the learning environment. Well, these tools will certainly augment the learning environment (pun intended :). There’s evidence that VR leads to better learning outcomes, and I have high hopes for AR, too. Though is that a really fundamental transition? We’ve had VR and virtual worlds for over a decade at least.  And is VR a evolutionary or revolutionary change from simulations? Then they go on to talk about performance support. Is that transforming learning? I’m on record saying contextualized learning (e.g. AR) is the real opportunity to do something interesting, and I’ll buy it, but we’re a long way away. I’m all for AR and VR, but saying that it puts learning in the hands of the students is a design issue, not a technology issue.
  2. People will learn collaboratively, no matter where they are.  Um, yes, and…?  They’re already doing this, and we’ve been social learners for as long as we’ve existed. The possibilities in virtual worlds to collaboratively create in 3D I still think is potentially cool, but even as the technology limitations come down, the cognitive limitations remain. I’m big on social learning, but mediating it through technology strikes me as just a natural step, not transformation.
  3. AI will banish intellectual tedium. Everything is awesome. Now we’re getting a wee bit hypish. The fact that software can parse text and create questions is pretty impressive. And questions about semantic knowledge isn’t going to transform education. Whether the questions are developed by hand, or by machine, questions are not intrinsically interesting. And AI is not yet to the level (nor will it be soon) where it can take content and create compelling activities that will drive learners to apply knowledge and make it meaningful.
  4. We will maximize our mental potential with wearables and neural implants. Ok, now we’re getting confused and a wee bit silly. Wearables are cool, and in cases where they can sense things about you and the world means they can start doing some very interesting AR. But transformative? This still seems like a push.  And neural implants?  I don’t like surgery, and messing with my nervous system when you still don’t really understand it? No thanks. There’s a lot more to it than managing to control firing to control limbs. The issue is semantics: if we’re not getting meaning, it’s not really fundamental. And given that our conscious representations are scattered across our cortex in rich patterns, this just isn’t happening soon (nor do I want that much connection; I don’t trust them not to ‘muck about’).
  5. Learning will be radically personalized.  Don’t you just love the use of superlatives? This is in the realm of plausible, but as I mentioned before, it’s not worth it until we’re doing it on top of good design.  Again, putting together wearables (read: context sensing) and personalization will lead to the ability to do transformative AR, but we’ll need a new design approach, more advanced sensors, and a lot more backend architecture and semantic work than we’re yet ready to apply.
  6. Grades and brand-name schools won’t matter for employment.  Sure, that MIT degree is worthless! Ok, so there’s some movement this way.  That will actually be a nice state of affairs. It’d be good if we started focusing on competencies, and build new brand names around real enablement. I’m not optimistic about the prospects, however. Look at how hard it is to change K12 education (the gap between what’s known and what’s practiced hasn’t significantly diminished in the past decades). Market forces may change it, but the brand names will adapt too, once it becomes an economic necessity.
  7. Supplements will improve our mental performance.  Drink this and you’ll fly! Yeah, or crash. There are ways I want to play with my brain chemistry, and ways I don’t. As an adult!  I really don’t want us playing with children, risking potential long-term damage, until we have a solid basis.  We’ve had chemicals support performance for a while (see military use), but we’re still in the infancy, and here I’m not sure our experiments with neurochemicals can surpass what evolution has given us, at least not without some pretty solid understanding.  This seems like long-term research, not near-term plausibility.
  8. Gene editing will give us better brains.  It’s alive!  Yes, Frankenstein’s monster comes to mind here. I do believe it’s possible that we’ll be able to outdo evolution eventually, but I reckon there’s still not everything known about the human genome or the human brain. This similarly strikes me as a valuable long term research area, but in the short term there are so many interesting gene interactions we don’t yet understand, I’d hate to risk the possible side-effects.
  9. We won’t have to learn: we’ll upload and download knowledge. Yeah, it’ll be great!  See my comments above on neural implants: this isn’t yet ready for primetime.  More importantly, this is supremely dangerous. Do I trust what you say you’re making available for download?  Certainly not the case now with many things, including advertisements. Think about downloading to your computer: not just spam ads, but viruses and malware.  No thank you!  Not that I think it’s close, but I’m not convinced we can ‘upgrade our operating system’ anyway. Given the way that our knowledge is distributed, the notion of changing it with anything less than practice seems implausible.

Overall, this is reads like more a sci-fi fan’s dreams than a realistic assessment of what we should be preparing for.  No, human learning isn’t going to change forever.  The ways we learn, e.g. the tools we learn with are changing, and we’re rediscovering how we really learn.

There are better guides available to what’s coming in the near term that we should prepare for.  Again, we need to focus on good learning design, and leveraging technology in ways that align with how our brains work, not trying to meld the two.  So, there’re my opinions, I welcome yours.

19 April 2017

Top 10 Tools for @C4LPT 2017

Clark @ 8:06 am

Jane Hart is running her annual Top 100 Tools for Learning poll (you can vote too), and here’s my contribution for this year.  These are my personal learning tools, and are ordered according to Harold Jarche’s Seek-Sense-Share models, as ways to find answers, to process them, and to share for feedback:

  1. Google Search is my go-to tool when I come across something I haven’t heard of. I typically will choose the Wikipedia link if there is one, but also will typically open several other links and peruse across them to generate a broader perspective.
  2. I use GoodReader on my iPad to read PDFs and mark up journal submissions.  It’s handy for reading when I travel.
  3. Twitter is one of several ways I keep track of what people are thinking about and looking at. I need to trim my list again, as it’s gotten pretty long, but I keep reminding myself it’s drinking from the firehose, not full consumption!  Of course, I share things there too.
  4. LinkedIn is another tool I use to see what’s happening (and occasionally engage in). I have a group for the Revolution, which largely is me posting things but I do try to stir up conversations.  I also see and occasionally comment on posting by others.
  5. Skype let’s me stay in touch with my ITA colleagues, hence it’s definitely a learning tool. I also use it occasionally to have conversations with folks.
  6. Slack is another tool I use with some groups to stay in touch. People share there, which makes it useful.
  7. OmniGraffle is my diagramming tool, and diagramming is a way I play with representing my understandings. I will put down some concepts in shapes, connect them, and tweak until I think I’ve captured what I believe. I also use it to mindmap keynotes.
  8. Word is a tool I use to play with words as another way to explore my thinking. I use outlines heavily and I haven’t found a better way to switch between outlines and prose. This is where things like articles, chapters, and books come from. At least until I find a better tool (haven’t really got my mind around Scrivener’s organization, though I’ve tried).
  9. WordPress is my blogging tool (what I’m using here), and serves both as a thinking tool (if I write it out, it forces me to process it), but it’s also a share tool (obviously).
  10. Keynote is my presentation tool. It’s where I’ll noodle out ways to share my thinking. My presentations may get rendered to Powerpoint eventually out of necessity, but it’s my creation and preferred presentation tool.

Those are my tools, now what are yours?  Use the link to let Jane know, her collection and analysis of the tools is always interesting.

18 April 2017

What you learn not as important as how you learn!

Clark @ 8:09 am

I’m going a bit out on a limb here, with a somewhat heretical statement: what you learn is more important than how you learn!  (You could say pedagogy supersedes curricula, but that’s just being pedantic. ;) And I’m pushing the boundaries of the concept a bit, but I think it’s worth floating as an idea. It’s meta-learning, of course, learning how to learn!   The important point is to focus on what’s being developed.  And I mean this at two levels.

This was triggered by seeing two separate announcements of new learning opportunities.  Both are focused on current skills, so both are focusing on advanced curricula, things that are modern. While the pedagogy of one isn’t obvious (though claimed to be very practical), the other clearly touts the ways in which the learning happens. And it’s good.

So the pedagogy is very hands on. In fact, it’s an activity-based curricula (in my terms), in that you progress by completing assignments very closely tied to what you’ll do on the job. There are content resources available (e.g. expert videos) and instructor feedback, all set in a story.  And this is better than a content-based curricula, so this pedagogy is really very apt for preparing people to do jobs.  In fact, they are currently applying it across three different roles that they have determined are necessary.

But if you listen to the longer version (video) of my activity-based learning curricula story, you’ll see I carry the pedagogy forward. I talk about handing over responsibility to the learner, gradually, to take responsibility for the activities, content, product, and reflection.  This is important for learners to start becoming self-improving learners.  The point is to develop their ability to do meta-learning.

To do so, by the way, requires that you make your pedagogy visible for the choices that you made, and why.  Learners, to adopt their own pedagogy, need to see a pedagogy. If you narrate your pedagogy, that is document your alternatives and rationales of choices, they can actually understand more about the learning process itself.

And this, to me, is the essence of the claim. If you start a learning process about something, and then hand off responsibility for the learning, while making clear the choices that led there, learners become self-learners. The courses that are designed in the above two cases will, of necessity, change. And graduates from those courses might be out of date before long, unless they’ve learned how to stay current. Unless they’ve learned meta-learning. That can be added in, and it may be implicit, but I’ll suggest that learning to learn is a more valuable long-term outcome than the immediate employability.

So that’s my claim: in the long term, the learner (and society) will be better off if the learner can learn to self-improve.  It’s not an immediate claim or benefit, but it can be wrapped around something that is of immediate benefit.  It’s the ‘secret sauce’ that organizations could be adding in, whether internally or in their offerings. What surprises me is how seldom I see this approach taken, or even discussed.

14 February 2017

Meta-Learning Tools?

Clark @ 8:06 am

I wrote an article for Jane Hart’s Modern Workplace Learning magazine, triggered by my thought that in her tools survey, I didn’t see a lot about a certain set of reflection (c.f. last weeks posts on diagramming) and experimentation tools: meta-learning tools. In particular, for the latter, I wondered about what there was to track your own learnings.  And Jane commented to me that she knew of one, and I was reminded of more.

Now, I don’t know much about any of these, but she mentioned PebblePad, and I noted that I’ve talked with Degreed before, and saw that HT2 has a tool called Red Panda. And I think this could become an interesting area.  Coupled with tools that support learning streams, personal learning could be boosted.

So tools like Axonify, Anders Pink, and EdCast all have varying models about making knowledge available and streaming bits and pieces over time. They’re pull as well, but for one definition of microlearning (that of streaming small bits over time to develop, e.g. slow learning), they could be a valuable part of personal development.

If we then track our learnings (and not just what’s through the tool, but other things we do such as attending events, interviewing people, etc), we can maintain ourselves on a path to efficacy.  That is, if we’ve registered goals, and broken it up into steps, and track our progress (and reward ourselves), we have a higher likelihood of continuing our improvement.

What I haven’t seen, as yet, and think could be an important part of this, is layering on  additional support for learning itself, meta-learning. For each type of learning activity, there could be support for doing that well, including setting and reviewing learning paths.

There’s more pressure for individuals to take responsibility for their own learning (as well as for enlightened organizations that want to support learning). So we need to be getting systematic about not only support for the content, but also for the process. This provides the opportunity is to accelerate the process. And our success.

9 February 2017

Diagramming

Clark @ 8:05 am

So yesterday I talked about the value of diagrams, but I thought I’d add a bit about the process of actually creating diagrams. Naturally, I created a diagram about it.

Diagram of diagram designI created this diagram for a session I ran on diagramming a number of years ago.  In that session I talked about our cognitive architecture, why we need models, how diagrams work, properties and design issues, and more. At the end, I proposed a potential process for it.  It captures an ideal picture of how diagramming could work.

So, you need to know the elements of the model you want to relate, identify the relationships, and any dimensions that characterize differences between the elements. Then you have to choose how you’re going to represent them: shape, color, weight, font, and more.

With your elements, you can then place them, connect them, and add the visual coding.  Then, of course, you tune.

This is an ideal process, but in reality it’s much more flexible, at least when I’m creating a model as a way of understanding.  I typically iterate, creating placeholder elements, and moving them around until I think I have the right ones.  Then I go about connecting them to make sure I have the relationships right. Then I work on adding dimensions, and colors, and aligning them, and grouping them, and… Except that I might add some elements, then group, or connect, then add more, and…  it’s a very iterative process.  It’s a creative process that involves lots of experimentation, revision, and more.

Sometimes, I even use a diagram and then realize it’s not working and revise it. So, for instance, I blogged about a representation of social process.  I got some feedback that it wasn’t very clear.  So, I made a second stab at it, and I think it worked better. Certainly, I continue to use it without complaints.

And I’m the first to admit that my diagrams may not look as good as the ones that professional graphic designers could create, but they’re good enough (and OmniGraffle does a good job of making it easy for me to make them up to a standard I think is at least acceptable and useful; it’s probably overkill but I’ve stuck with it for years now).  And that’s the point.  If they help you think better, it’s good enough. If it helps you communicate effectively with someone else, even better.  Diagrams are cognitive tools, offloading conceptual complexity to graphic relationships and visual processing.  And with the complex problems we increasingly face, I reckon the more tools the better.

 

8 February 2017

Diagram!

Clark @ 8:05 am

One of the things that I feel is a really useful tool in my ongoing learning, in my ‘making sense of the world’ is diagramming.  I find diagrams to be a really powerful way to understand not just elements, but relationships.  And yet it doesn’t feel like diagramming gets enough respect.  So I want to make a case for the diagram.

Language is good. Our brains have evolved to use it. But it has trouble communicating complex relationships.  For an example, once I wrote this:

They found that while subjects would rate the analogies, from best to worst, as literally similar, true analogy, mere appearance, and false analogy, their recall for stories, from best to worst, was literally similar, mere appearance, true analogy, and false analogy.

Try discerning the important difference!  My PhD advisor kindly pointed out that actually parsing this was hard, and recommended a diagram instead. Here’s a rendition of what resulted:

structure task outcomes diagram

In this case it’s much easier to see how the two differed.  (If you want to find out what’s important in the diagram, I’m happy to talk about analogical reasoning for as long as you can stand it! ;)

The point I’m making is that there are times when diagrams are very useful for communicating.  And, if you’ve followed this blog for a fair amount of time, you’ve seen I use diagrams a lot. I use them to think ‘out loud’, and I think it’s important.  As Larkin & Simon argued in their Cognitive Science article, Why a Diagram is (Sometimes) Worth Ten Thousand Words, diagrams let us map conceptual relationships to spatial ones. And so if I want to understand the conceptual relationships, I start laying out spatially, and adjust until they make sense to me.

And my concern is that we aren’t using this powerful visual tool enough.  Sketchnotes are really nice ways to capture presentations, and depending on the skill of the noter, they may communicate it all, or help recall if you’ve seen it. Similarly, my mindmaps of keynotes capture the flow of the discussion and the relationships (at least as I parsed it), but may only make sense if you heard the talk.

But representing things with diagrams is not only a personal thinking tool, it can be a powerful way to communicate concepts, and that’s an important component of a good learning experience design, providing a conceptual model to guide performance.

So I’m surprised we don’t talk about diagrams more. It may seem hard (certainly trying to create an infographic is harder than it seems, from my experience ;), but there’s some systematicity to it. There are principles, and types of diagrams, and more to explore.  And tools that make it easier (though even Powerpoint or Keynote can be used to make diagrams).  Diagrams aren’t the only visuals that help (c.f. graphs and tables), but they’re an important tool in your thinking toolbox.  I encourage you, as part of your meta-learning toolkit, to play around and get your mind around diagrams. Your thinking, and your learning design, can be better as a consequence.

1 February 2017

Other writings

Clark @ 8:04 am

It occurs to me to mention some of the other places you can find my writings besides here (and how they differ ;).  My blog posts are pretty regular (my aim is 2/week), but tend to have ideas that are embryonic or a bit ‘evangelical’. First, I’ve written four books; you can check them out and get sample chapters at their respective sites:

Engaging Learning: Designing e-Learning Simulation Games

Designing mLearning: Tapping Into the Mobile Revolution for Organizational Performance

The Mobile Academy: mLearning For Higher Education

Revolutionize Learning &  Development: Performance and Information Strategy for the Information Age

They’re designed to be the definitive word on the topic, at least at the moment.

I’ve also written or co-written a number of chapters in a variety of books.  The books include The Really Useful eLearning Instruction ManualCreating a Learning Culture, Michael Allen’s eLearning Annual 2009,  and a bunch of academic handbooks (Mobile Learning, Experiential Learning, Wiley Learning Technology ;).  These tend to be longer than an article, with a pretty thorough coverage of whatever topic is on tap.

Then there are articles in a variety of magazines.  These tend to be aggregated thoughts that are longer than a blog post, but not as through as a chapter. In particular, they are things I think need to be heard (or read).  So, my writing has shown up in:

eLearnMag

Learning Solutions

CLO

The topics vary. (For the eLearnMag ones, you’ll have to search for my name owing to their interface, and they tend to be more like editorials.)

And then there are blog posts for others that are a bit longer than my usual blog post, and close to an article in focus:

The Deeper eLearning series for Learnnovators

A monthly article for Litmos.

These, too, are more like articles in that they’re focused, and deeper than my usual blog post.  For the latter I cover a lot of different topics, so you’re likely to find something relevant there in many different areas.

I’m proud of it all, but for a quick update on a topic, you might be best seeing if there’s a Litmos post on it first.  That’s likely to be relatively short and focused if there is one. And, of course, if it’s a topic you’re interested in advancing in and I can help, do let me know.

18 January 2017

Cognitive Business

Clark @ 8:07 am

One of my mantras is that organizations need to align better with how we think, work, and learn.  However, my focus has been specifically on what L&D can be doing (as that’s the folk I mostly talk to). But it occurs to me that it really goes farther.  There are applications of cognitive science (including neuroscience, cognitive psychology, sociology, philosophy, anthropology, etc) to more areas of business than just L&D.  And it’s worth being explicit about this.

I was recently reading how marketing has leveraged understanding of behavior change at a deep level. We need to incorporate this in our learning design, but it should go beyond training and learning and be involved in helping people understand why their work is important and how they contribute.

And similarly, the notion that our thinking is both situated (e.g. reconstructed in the moment, not formally abstract) and distributed (across representations, not all in the head) has broader implications. It’s not just about performance support, but should influence policies and tools as well.

And the fact that innovation is social, and an outcome of slow percolation, influences more than facilitating communication and collaboration. It should influence corporate culture and expectations and time frames.

The list goes on: research says that organizational change works better starting small and scaling rather than a monolithic effort.  We know that design processes are better when they’re cyclical rather than waterfalls. We’ve discovered that our inability to perform rote tasks flawlessly argues for changes in work processes and expectation. And we’ve found out that treating people fairly leads to better outcomes including retention, loyalty, and more.  Ultimately, we’ll want to be making smart cyborg choices about what to have people do and what  technology should do.

In short, we can be working smarter in many ways.  It’s hard to change from the old hierarchical models, but we’re continually learning that other approaches work better.  Heck, we may even be able to start working wiser!  Here’s hoping.

11 January 2017

Mick Ebeling #ATDTK Keynote Mindmap

Clark @ 9:20 am

Mick Ebeling, of Not Impossible Labs, opened the TechKnowledge conference with an inspiring keynote. He told engaging stories about achieving the impossible because it just took commitment. He evangelized contributing, and getting contributions by emphasizing the brand benefits of doing good.

Ebeling Keynote Mindmap

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