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

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

A Cognitive Audit?

Clark @ 8:01 am

image of brainIn the recent Chief Learning Officer magazine, I wrote an article on the basics of the cognitive science of learning. Given the evidence that “L&D isn’t doing near what it could and should, and what it is doing it is doing badly, other than that it’s fine” (as I say), at least one of the potential barriers is that L&D isn’t truly aware of what science says about their profession.

And I truly believe that if you’re a professional, you should be aware of the fundamental scientific basis of your profession. Pilots need to know aeronautics, physicians need to know physiology, etc. And therefore, I reckon L&D needs to know the cognitive background. But there’s more.

Knowing a suitable level of cognitive science is one thing, using that to assess your practices is another. Too often, we have what we call ‘inert knowledge’: we know it, but we don’t apply it. That’s not helpful. What has to happen is that processes need to be evaluated, improvements identified, interventions prioritized, enablement enacted, and progress reviewed. It’s just part of being a professional!

There are other sorts of audits possible (I know folks who do performance audits, and knowledge audits, etc), but I’m increasingly thinking that the one that matters is the one that aligns with how our brains work. Not at the neural level (there’s little of impact there), but at the cognitive level. Note that cognitive science includes social, conative and affective components (e.g. the culture and motivation), and neural, for that matter ;).

This isn’t an academic exercise. The increasing competition enabled by technology already suggests that optimal execution is only the cost of entry, and continual innovation will be the only sustainable differentiator. Both are cognitive functions, and the best outcomes will only be achieved when organizations are acting in accordance with how we think, work, and learn. This is about equipping your organization to kick some proverbial tail.

I’m drafting an initial such instrument, with associated recommendations. I welcome your thoughts, and any interest in engaging around this.

10 January 2017

Vale Seymour Papert

Clark @ 8:03 am

Imagine my surprise that I missed the demise of Seymour Papert this past year (yet another loss). I’ve looked back to see what I was doing on 31 July and how I missed it, and we were preparing for a week in the wilderness.  So it’s certainly likely I wasn’t deeply involved in the news.  This is a shame, because I’ve been a fan of Papert’s work for quite literally decades.  So here’s a belated tribute.

My first job out of college was designing and programming educational computer games.  I’d been exposed to some innovative thinking through my undergraduate thesis advisors, Hugh Mehan and Jim Levin.  Having read Papert’s Mindstorms, I gave it to my parents to help them understand why I did what I did (unsuccessfully ;).  The book is subtitled “Children, Computers, and Powerful Ideas”, and argued that learning computing was a vehicle for learning to think.

Papert had studied with Jean Piaget, and proceeded to be a leader of the constructivism movement applying the notion of exploratory learning environments. I subsequently learned about Piaget (and post-Piaget, and Vygotsky) in my graduate studies, so I can see how the notion of developmental readiness and opportunities to create understanding through exploration could lead to the work Papert did.

Logo, the computer language for learning, was developed by Papert along with Wallace Feurzieg. It’s simple commands controlling a ‘turtle’ and gradually getting richer play challenges was the start to computer understanding for learners for decades, and has influenced computer language learning in many ways. Apple’s  Playgrounds uses similar small steps to control a creature to start teaching Swift.

He was invited to co-lead the MIT AI lab with Marvin Minsky.  He worked with Minsky on Perceptrons, which were an early exploration of the connectionist networks now so prevalent in artificial intelligence. There remains a controversy over whether and how the book influenced research in the area of symbolic and sub-symbolic intelligence approaches.

Papert was instrumental in much of the thinking that has shaped what we do in learning technology.  I’m grateful for his contributions.

28 December 2016

2016 Reflections

Clark @ 8:07 am

2016 out, 2017 inThis is the last Learnlet for 2016, and so it’s time for some reflections on what has been an ‘interesting’ year.  I’ll admit it’s been rough, what with losing so many people known through popular media. I guess you get to an age where more and more people who’ve you’ve grown up with in one way or another begin to pass on. And of course serious changes nationally and internationally.  But there are some learnings as well.

So, I did a fair bit of speaking in 2016, keynoting conferences in New York and Beijing, as well as more private events live and online. I spoke about mobile learning, deeper learning design, innovation, as well as the L&D revolution.  And, of course, I attended the usual suite of industry conferences, notably the eLearning Guild events and Online Educa.  I also was engaged in a number of consulting engagements, working with folks to deepen their understanding (and mine), to achieve meaningful outcomes.

One learning is the value of travel outside the US.  I actually lived outside the US for 7 years (in Australia), and the perspective of seeing how others live, and looking at the rest of the world (and back at the US) from other perspectives is a valuable grounding.  The view I had of China before my recent trips was quite different than the reality. I can say the same from previous experience with India.  It’s too easy to be insular.  Instead, it’s helpful to be curious.

And that’s an industry comment too.  I continue to talk (e.g. my workshop in Berlin) and write about deeper learning design.  And I continue to evangelize about it (c.f. the Serious eLearning Manifesto with my colleagues, and the recent Future of Work project). And yet, the industry seems to continue on in ignorance.  The tools still reflect more of a focus on content instead of experience, for instance. Things get better, but surprisingly slowly. How long until we start treating learning design with the appropriate respect? We need to get out of our comfort zone!

There are positive signs. My engagements with Learnnovators has demonstrated that at least some folks care about quality. And I had several client engagements specifically focused on better learning design.  There just need to be more efforts in this area. It’s not hard to tweak processes to generate outcomes that not only look like good elearning, but actually have a high likelihood of an impact.

I’ve done a lot of reading this year (most recently The Fifth Discipline, which puts lots of what I’ve learned about organizations into a context).  It amazes me that with robust science at the organizational level as well as the learning science level, we still see so much action in organizations (and society) contrary to what’s demonstrably known. There are positive signs here too, but still too few.  It’s challenging, as it involves crossing discipline and business boundaries, yet the benefits are promising.

And I think the hype about technology improvements are premature. Wearables continue, of course. And VR has reached the stage where it’s easy to experiment.  Yet in each case, we’re still in the stage before standards emerge that will make a real market.  AR and content strategy are still nascent, but there’s much potential.  Fortunately, analytics is seeing a boon from the standardization around xAPI. We need to stick to the core learning affordances of new technology to truly grasp the potential.

Looking forward, I see much opportunity, as implied by the gaps indicated above. There’s real opportunity for improvement in the use of technology to facilitate outcomes. We can do personal and organizational learning better.  We can leverage technology in ways that are closer aligned with how our brains work. As a precursor, we’ll need a broader understanding of cognition, but that’s doable.  I’m happy to help ;).

And let me just add a very heartfelt thanks to those of you who I’ve interacted with, this year and in the past. Whether reading the blog, making comments, engaging on social media, attending sessions or workshops, and of course via engagements, I’m very grateful. I hope to connect with you in the future, in any of the above ways or any other. I continue to learn through and with you, and that’s a gift. Again, thank you.

Goodbye 2016, and here’s to making positive changes in the new year.  May it be your best yet.

 

27 December 2016

Cultural Alignment

Clark @ 8:09 am

I was thinking about the ways in which organizations can support performance. That is, we can and should be aligning with how we think, work, and learn. So we can provide tools to support us in the moment, we can provide tools to help us work together, and we can develop people all slowly over time.  In short, I was thinking about cognitive alignment, and I was going to write about it, but it turns out I already have!  However, I also realized that there was an opportunity to extend that to cultural alignment, and I think that’s important as well.

So, one of the things we can do to optimize outcomes is to give people performance support.  In particular, we can provide tools to address gaps that emerge from our cognitive architecture.  We can also provide policies about things we’re supposed to do.  And that’s all good.  However, some of that might not be necessary under the right circumstances.

I was thinking about the specific case of acting in ways that are consonant with the values of the organization. For instance, in a well-known upscale department store chain, the staff have the leeway to spend on the order of $1K to address any emerging customer problem.  I reckon the store figures that’s the future worth of a happy customer. And that’s acting in alignment with the culture of the organization.

The point I want to make is that by having an explicit culture in the organization, you might not have to provide performance support. If the desired approach is understood, it can be generated from understanding the organization’s value.  If you know what’s expected, you can perform in alignment without needing external clues and cues.

There are clear benefits from a learning organization in terms of innovation and employee engagement, but what about the other side? I suggest that the right culture can also benefit the ‘optimal execution’ side.  In short, there’s little reason to do aught but begin a move to a more enlightened culture.  At least, that’s what seems to me to be the case. How about you?

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