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Clark Quinn’s Learnings about Learning

And Listen

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Listening is a vital skill.  It’s something that made my mother very popular, because she listened, remembered, and asked about whatever you said the next time you saw her. She cared, and it showed. I wish I was as good a listener!  But it’s critical to really listen (or as some have it, not just listen, but hear).

It’s part of a skillset necessary to innovate. Innovation can be about problem-solving, and design thinking has it that it’s really about problem-finding.  That is, you want to understand the real problem first.  And to really understand the problem, the initial divergence, is to listen. It is listening to people, but also signals in general, what the data tells you.

And so, listening is an important part of communicating and collaborating.  We need to hear what’s being said (and maybe even what’s  not being said), to truly hear. And we likely will need to ask, as well.  This is good, because it shows we’re paying attention.  Talking is speaking  and listening.

And what precipitated this discussion is that in my new column for Learning Solutions (Quinnsights ;), I asked for any questions, and there was one that will be the topic of my next article for them. And I thought that was a good principle.

So, here’s the question:

Is there anything in particular you’d like me to post about here?

 As it is, I post about what I’m thinking about or working on (usually somewhat anonymously).   However, I could benefit to hear what you’re thinking about.  And post on it if I can.  Of course, you should be posting on what you’re thinking about too (#ShowYourWork #WorkOutLoud), but hey, why not cross-communicate?  As it is, I appreciate the comments I get, but this is just a way to feed my brain.

So, this is me listening.  Anyone want to catch my ear?

Let’s talk

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“Conversations are the stem cells of learning.” – Jay Cross

I recently read something that intrigued me. I couldn’t find it again, so I’ll paraphrase the message.  As context, the author was talking about how someone with a different world view was opining about the views of the author. And his simple message was “if you want to know what I, or an X, thinks, ask me or an X. Don’t ask the anti-X.”  And I think that’s important.  We need to talk together to figure things out. We have to get out of our comfort zone.

It’s all too evident that we seem to be getting  more divisive. And it’s too easy these days to only see stuff that you agree with.  You can choose to only follow channels that are simpatico with your beliefs, and even supposedly unbiased platforms actually filter what you see to keep you happy. Yet, the real way to advance, to learn, is to see opposing sides and work to find a viable resolution.

Innovation depends on creative tension, and we need to continue to innovate.  So we need to continue to engage.  Indeed, my colleague Harold Jarche points to the book  Collaborating with the Enemy  and argues that’s a  good thing.  The point is that when things are really tough, we have to go beyond our boundaries.  And life is getting more complex.

So I keep connections with a few people who don’t think like me, and I try to understand the things that they say. I don’t want to listen just to those who think like me, I recognize that I need to understand their viewpoints if we’re going to make progress.  Of course, I can’t guarantee reciprocity, but I can recognize that’s not my problem.

And I read what academic research has to say. I prefer peer-review to opinion, although I keep an open mind as to the problems with academic research as well. I have published enough, and reviewed many submissions, so I recognize the challenges.  Yet it’s better than the alternative ;).

This is, however, the way we have to be as professionals. We have to understand other viewpoints.  It matters to our world, but even in the small little worlds we inhabit professionally.  We need to talk.  And face to face. It matters, it turns out.  Which may not be a surprise.  Still, getting together with colleagues, attending events, and talking, even disagreeing (civilly) are all necessary.

So please, talk.  Engage.  Let’s figure stuff out and make things better. Please.

 

2018 Trajectories

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Given my reflections on the past year, it’s worth thinking about the implications.  What trajectories can we expect if the trends are extended?  These are  not predictions (as has been said, “never predict anything, particularly the future”).  Instead, these are musings, and perhaps wishes for what could (even  should) occur.

I mentioned an interest in AR and VR.  I think these are definitely on the upswing. VR may be on a rebound from some early hype (certainly ‘virtual worlds’), but AR is still in the offing.  And the tools are becoming more usable and affordable, which typically presages uptake.

I think the excitement about AI will continue, but I reckon we’re already seeing a bit of a backlash. I think that’s fair enough. And I’m seeing more talk about Intelligence Augmentation, and I think that’s a perspective we continue to need. Informed, of course, by a true understanding of how we think, work, and learn.  We need to design to work  with us.  Effectively.

Fortunately, I think there are signs we might see more rationality in L&D overall. Certainly we’re seeing lots of people talking about the need for improvement. I see more interest in evaluation, which is also a good step. In fact, I believe it’s a good  first step!

I hope it goes further, of course. The cognitive perspective suggests everything from training & performance support, through facilitating communication and collaboration, to culture.  There are many facets that can be fine-tuned to optimize outcomes.Similarly, I hope to see a continuing improvement in learning engineering. That’s part of the reason for the Manifesto and the Quinnov 8.  How it emerges, however, is less important than that it  does.  Our learners, and our organizations, deserve nothing less.

Thus, the integration of cognitive science into the design of performance and innovation solutions will continue to be my theme.  When you’re ready to take steps in this direction, I’m happy to help. Let me know; that’s what I do!

Reflections on 2017

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The end of the calendar year, although arbitrary, becomes a time for reflection.  I looked back at my calendar to see what I’d done this past year, and it was an interesting review.  Places I’ve been and things I’ve done point to some common themes.  Such are the  nature of reflections.

One of the things I did was speak at a number of events. My messages have been pretty consistent along two core themes: doing learning better, and going beyond the course.  These were both presented at TK17 that started the year, and were reiterated, one or the other, through other ATD and Guild events.

With one exception. For my final ATD event of the year, I spoke on Artificial Intelligence (AI). It was in China, and they’re going big into AI. It’s been a recurrent interest of mine since I was an undergraduate. I’ve been fortunate to experience some seminal moments in the field, and even dabble.  The interest in AI does not seem to be abating.

Another persistent area of interest has been Augmented Reality (AR) and Virtual Reality (VR). I attended an event focused on Realities, and I continue to believe in the learning potential of these approaches. Contextual learning, whether building fake or leveraging real, is a necessary adjunct to our learning.  One AR post of mine even won an award!

My work continues to be both organizational learning, but also higher education. Interestingly, I spoke to an academic audience about the realities of workplace learning!  I also had a strategic engagement with a higher education institution on improving elearning.

I also worked on a couple of projects. One I mentioned last week, a course on better ID.  I’m still proud of the eLearning Manifesto (as you can see in the sidebar ;).  And I continue to want to help people do better using technology to facilitate learning.  I think the Quinnov 8 are a  good way.

All in all, I still believe that pursuing better and broader learning and performance is a worthwhile endeavor. Technology is a lovely complement to our thinking, but we have to do it with an understanding of how our brains work.  My last project from the year is along these lines, but it’s not yet ready to be announced. Stay tuned!

The Quinnov 8: An online course

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Ok, so I told you the story of the video course I was creating on what I call the Quinnov 8, and now I’ll point to it.  It’s available through Udemy, and I’ve tried to keep the price low.  With their usual discounts, it should be darn near free ;).  Certainly no more than a few cups of coffee.

It’s about an hour of video of me talking, with a  few diagrams and text placeholders.  I’ve included quizzes for each of the content sections. Also, I have assignments to go away and apply the principles to your own work.  Finally, I created a page or several for each section showing some ideas, models, and more.

I do  not recommend going through it in one run. I can’t control it, but as I mention in the course, you want to space it out. We know that that leads to better outcomes. Instead, I recommend spacing it out a section a week or so perhaps, and doing the work and coming back to reactivate before moving on.

The content is organized around what I’m terming the Quinnov 8, the eight elements I think are core to making the step to better elearning design.  While the ideal is to push to a robust iterative and prototyping model, I’m focusing mostly on the small steps that will give you the greatest leverage. The elements are:

  1. Performance consulting: what to do  before you decide to course
  2. Objectives: making the  right decisions about what to focus on
  3. SMEs: working with them for objectives and more
  4. Practice: making practice meaningful
  5. Models: the conceptual frameworks that guide performance
  6. Examples: the link between concepts and application.
  7. Engagement: wrapping the front and back to create  experiences
  8. Process: the extra steps to make this work

I’m trying to go  deep, that is to unpack the levels of cognitive depth to explain how the Quinnov 8 elements work.  I’ve identified the challenges I’ve faced, and I may well update it over time, but it’s at a stage I think I can at least give you the chance to explore.  I welcome your feedback, but I reckon this is one way you can further your understanding on a significant budget.

Pernicious problems

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I’m using a standard for organizational learning quality in the process of another task.  Why or for whom doesn’t matter. What  does matter is that there are two problems in their standard that indicate we still haven’t overcome some pernicious problems.  And we need to!

So, for the first one, this is in their standard for developing learning solutions:

Uses blended models that appeal to a variety of learning styles.

Do you see the problem here?  Learning styles are debunked! There’s no meaningful and valid instrument to measure them, and no evidence that adapting to them is of use.  Appealing to them is a waste of time and effort. Design for the learning instead!  Yet here we’re seeing someone conveying legitimacy by communicating this message.

The second one is also problematic, in their standard for evaluation:

Reports typical L&D metrics such as Kirkpatrick levels, experimental models, pre- and post-tests and utility analyses.

This one’s a little harder to see. If you think about it, however, you should see that pre- and post-test measures aren’t  good measures.  What you’re measuring here is a delta, and the problem is, you would  expect a delta. It doesn’t really tell you anything. You shouldn’t have even bothered if the performance isn’t up to scratch! What you want to do is confirm that you’re achieving a higher level of performance set objectively. Are they now able to perform? Or how many are?  Doing the pre-post is like doing normative reference (e.g. grading on a curve) when you should be doing criteria-referenced performance.

And this is from an organization that’s purports to communicate L&D quality! These are both from their base level of operation, which means it’s acceptable. This is evidence that our problems aren’t just in practice, they’re pernicious; they’re present in the mindset of even the supposed experts. Is it any wonder the industry is having trouble?  And I haven’t rigorously reviewed the standard, I was merely using it (I wonder what I’d find if I did?).

Maybe I’m being too harsh. Maybe the wording doesn’t imply what I think it does.  But I’ll suggest that we need a bit more rigor, a bit more attention to science in what we do. What have I missed?

 

 

Expertise

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Expertise is an elusive thing. It comes from years of experience in a field.  However, it turns out that it doesn’t just accumulate. You need very specific practice and/or useful feedback to develop it.  And the more expertise one has, the better you are able to apply it to situations. Which has implications for what you do and when and how you do it.

Expertise is valuable. The properties of expertise include that it’s compiled away to be essentially automatic. Which implies it’s not accessible for conscious introspection. (Which is why experts quite literally cannot tell you what they do!)  On the other hand, their responses to situations in their area of expertise are likely to be as good as you can get.  They apply mental models they’ve developed to solve problems.

If you want to develop expertise as an individual, you need to understand how to practice.  Deliberate practice, as Ericsson details, is the key.  You need to practice at the limits of your ability, and consciously learn from the outcomes.  It’s not just doing the job, it’s pushing the boundaries, and actively reflecting.

If you want to develop expertise as an organization  internally, the situation is very much the same.  You need resources to develop people, and stretch assignments with feedback and coaching to optimally develop the expertise.

Of course, you can bring in expertise from outside, as well.  The question then becomes one of when and who.  You can contract out work, which makes sense when the activity isn’t part of your core ability.  Outsourcing to technology or external expertise is fine for things that are in areas that are well developed.

Otherwise, you can bring in consultants. The latter is particularly useful when you are moving in a new direction or want to deepen your understandings. A good consultant will work with you to not only help address the situation, but internally develop your own understanding. The key is working collaboratively and transparently. Yes, I’m a vested interest, but I believe these things are true on principle and should be in practice.

Expertise is core to situations you know you need expertise in, but also in those that are new. When you need innovation, you need expertise in the complementary areas that you are applying to address the situation.  You don’t want to develop learning  except  in the problem.  At least, that’s  my expert opinion. Which, of course, is on tap if needed ;).

Innovations

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Sparked by a colleague, I’m reading The Digital Transformation Playbook, by David Rogers. In the chapter on innovation, he talks about two types of experimentation: convergent and divergent. And I was reminded that I think of two types of innovations as well.  So what are they?

Experimentation

He talks about how experimentation is the key to innovation (in fact, the chapter title is Innovate by Rapid Experimentation). His point is that you need to be continually experimenting, rapidly.  And throughout the organization, not just in separate labs. Also, it’s ok to fail, as long as the lesson’s learned.  And then he distinguishes between two types of experimentation.

The first is convergent. Not surprisingly, this is when you’re trying to eliminate options and make a decision.  This is your classic A/B testing, for example. Here you might try out two or three different solutions, to see which one works best. You create the options, and have measures you’ll use to determine the answer.  You might ask: should we use a realistic video or a cartoon animation? A situation where there isn’t a principled answer, and you need to make a decision.

Divergent experimentation is, instead, exploratory. Here you give folks some ideas, or a prototype, and see what happens. You don’t know what you’ll get, but you’re eager to learn.  What would a scenario look like here?

Innovation

These roughly correspond to the two types of innovation I think of. One is the ‘we need to solve this’ type. I think of this as short-term innovation. Here we are problem-solving or trouble-shooting.  You bring together a team of relevant capabilities and otherwise as diverse as possible. You facilitate the process. And you’re likely to try convergent experimentation.

At the other end is the serendipitous, long-term innovation that happens because you create an environment where ideas can gestate.  You’ve got access to the adjacent possible, and the opportunities to explore and share. It’s safe to experiment and fail.  People are  supposed to take time to reflect! This is more closely aligned to divergent experimentation.

Note that this is  all learning, as you don’t know the answers when you start!  The success of organizational learning, however, is a product of both. You need to solve the problems you know you have, and allow for ideas to generate solutions to problems you didn’t know you had.  Or, more optimistically, to search through idea spaces for opportunities you didn’t know to look for.

Rogers is right that continual experimentation is key.  It has to become baked into how you do what you do.  Individually, and organizationally.  And you can’t really get it unless you start practicing it yourself.  You need to continually challenge yourself, and try things both to fix the problems, and to explore things that are somewhat tangential. Your own innovations will be key to your ability to foster them elsewhere.

Too many orgs are only focused on the short-term.  And while that may solve shareholder return expectations, it’s not a receipt for longer-term organizational survival.  You need both types of innovations. So, the question is whether you can assist your org in making a shift to the serendipitous environment.  Are you optimizing your innovation?

Video Lessons

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So, I’ve been creating a ‘deeper elearning’ course for one of the video course providers. And I’m not mentioning where it is (yet), since it’s still under development.  But to do this, I had to do some serious learning about creating video.  And there were some realizations in this, of course.

One of the decisions to be made was how to include graphics.. My mentor/colleague/friend showed me (by video chat) his elegant setup.  He has green screens, and lights, and has a full studio in a separate room as well. Of course, he’s been doing video for decades.  I’ve hardly done much besides taking a multimedia course at least 20 years ago. And narrating the occasional Keynote deck.

In the meantime I asked around, and colleagues were pretty unanimous on ScreenFlow being  the tool to use.  So I got a copy. And, indeed, I was able to film myself.  Moreover, I quickly found out I could include diagrams and text right on the screen! That eliminated the need for a green screen.

My video imageI had a couple of lights, and without them my screen reflected on my glasses.  However, that’s not really fixable, since I didn’t get the anti-glare coating when I had them made.  Doh!  Next time, for sure. I positioned a couple of lights off to each side, and they reduced (though not eliminated) the glare.

We were moving my office back to the front of the house (long story), so we moved a bookcase behind me, with my library.  It looks good, but…you don’t see much of it anyway.  I filmed standing up (on my new stand/sit desk converter), and I block most of the background anyway (except for the Albert Einstein poster that sits on the wall).

Having read up,  I knew to have a written script, which, without a prompter, I just positioned to the top of the screen under the camera.  Of course I changed it a bit, and adlibbed a bit, but mostly stuck to what I’d written. It’s not quite as spontaneous (and goofy) as I am in person, but it ensures consistent quality. And I filled in diagrams a few times, and added some text a few times, to help keep pace.

Frankly, it’s not great, but I had a deadline.  It’s too much of me talking, without animation. But this is done by me, alone, under a tight deadline. And that’s my error, too, since I have video anxiety almost as bad as my phone anxiety, and dragged my heels until things were too late.  Dang emotions getting in the way again! (Even when you know this.)

I also created some quizzes, in mini-scenario fashion pretty much. That is, there’s a fair bit of dialog that you either are asked and/or choose to respond with. Because it’s only a multiple choice option, I was somewhat constrained.  I subsequently was prodded for some assignments, and found I could do what I’d talked about.  I used the assignment tool to create questions that asked learners to go out and do things and then provide them with some guidance to self-evaluate.

One thing I learned is that I don’t have a good mental model of how the software works. I ‘get’ the tracks, but there’s another aspect I don’t understand. So, it turns out though I’d filmed myself at 720p, and exported at 720p, it still had an unnecessary border. Fortunately, in stumbling around I found a ‘crop’ setting that forced it to 1280 x 720 (720p), but I don’t understand  why that was necessary!?!?

I still want to add some examples (as documents) before I feel it’s fully ready to go. And I now sympathize much more with those who struggle to do good learning design under real-world constraints.  It’s also certainly been an example of my accepting assignments that are within my reach, but not within my grasp; my learning style ;).   More later, but thought I’d share my struggles and learning. I welcome your feedback.

Higher Ed & Job Skills?

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I sat in on a twitter chat yesterday, #DLNChat, that is a higher ed tech focused group (run by EdSurge). The topic was the link between higher ed and job skills, and I was a wee bit cynical. While I think there are great possibilities, the current state of the art leaves a lot to be desired.

So, I currently don’t think higher ed does a good job of preparation for success in business. Higher ed focuses too much on knowledge, and uses assignments that don’t resemble the job activities.  Frankly, there aren’t too many essays in most jobs!

Worse, I don’t think higher ed does a good job of developing meta-cognitive and meta-learning skills. There is little attempt to bridge assignments  across courses, so your presentations in psychology 101 and sociology 202 and business 303 aren’t steadily tracked and developed. Similarly with research projects, or strategy, or… And there’re precious little (read: none) typically found where you actually make decisions like you would need to.

And, sadly, the use of technology isn’t well stipulated either. You might use a presentation tool, a writing tool, or a spreadsheet, maybe even collaboratively, but it’s not typically tied to external resources and data.

Yes, I know there are exceptions, and it may be changing somewhat, but it still appears to be the case. Research, write a paper, take a test.

Yet the role of developing higher skills is possible and valuable.  We could be providing more meaningful assignments, integrating meta-learning layers, and developing both meaningful skills and meta-skills.

This doesn’t have to be done at the expense of the types of things professors believe are important, but just with a useful twist in the way the knowledge is applied. It might lead to a revision of the curriculum, at least somewhat, but I reckon it’d likely be for the better ;).

Our education system, both K12 and higher-ed, isn’t doing near what it could, and should. As Roger Schank says, only two things wrong: what we teach, and how we teach it.  We can do better. Will we?