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Clark Quinn’s Learnings about Learning

Archives for May 2018

Services

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From time to time, it’s worth a reminder that Quinnovation (the firm behind the blog) is available to help you.  Here are the services you can look to from me, in case you want to accelerate your success.

And a wee bit of self-promotion: if expertise comes from years of practice, how about 3+ decades of investigating the breadth and depth of learning & performance, and exploration of technology support?  Why not get assistance from where the thinking originates, not the several-steps away diluted version?

Consulting:

Learning Design: are your design processes yielding the outcomes they should and need to? I have worked with many organizations to generate or tighten learning design processes to reflect learning science (not myths). I recognize that most organizations can’t completely revamp their approaches, so I look to the small changes with the biggest impact. A white paper talks about this.

Performance Ecosystem Strategy: are you leading your organization forward in learning (read: innovation) or are you still taking orders for courses?  Based on the book, I’ve helped a number of organizations understand the full spectrum of possibilities, evaluate their situation, and prioritize short-, medium-, and long-term steps.  Another white paper talks about this.

Games & Mobile: I’ve helped a number of organizations get their minds, strategies, and design processes around mobile and/or games, based upon  those  books.

Workshops

Want to get your team up to speed on learning science, strategy, games, mobile, or more?  I have workshops on each that are interactive, engaging, and effective. Preferably, they’re coupled with followup to extend the learning (applying the learning science), and that can be done in a variety of ways.

Presentations

A number of organizations around the world have booked me to speak to their audiences. They have been about the subjects of my books, or the future of learning technology in general. And have indicated they were quite satisfied with the result ;). If you want a credible, engaging presenter around intelligence augmentation, I’m a candidate.

Writing

In addition to books, I write white papers, blog posts, and articles for others. I could do the same for you.

Coaching

If you’re a learning leader that would like assistance over time addressing your organization’s needs, it would certainly be worth a conversation. I haven’t done this formally, but it seems like a natural extension.

And, of course, there are combinations of these services as well. You can find out more at the official Quinnovation site. Next week we return you to your regularly scheduled blog at this same channel.

Nuances Matter

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I’ve argued before that the differences between well-designed and well-produced learning, and just well-produced learning, are subtle. And, in general, nuances matter. So, in my recent book, the section on misconceptions spent a lot of time unpacking some terms. The goal there was ensuring that the nuances were understood. And a recent event triggered even more reflection on this.

Learnnovators, a company I’ve done a couple of things with (the Deeper eLearning series, and the Workplace of the Future project), interviewed me once quite a while ago. I was impressed then with the depth of their background research and thoughtful questions. And they recently asked to interview me on the book. Of course, I agreed. And again they impressed me with the depths of their questions, and I realized in this case there was something specific going on.

In their questions, they were unpacking what common concerns would be about some of the topics.  The questions dug in to ways in which people might think that the recommendations are contrary to personal experience, and more.  There were very specifically looking for ways in which folks might think to reject the findings.  And that’s important. I believe I had addressed most of them in the book, but it was worth revisiting them.

And that’s the thing that I think is important about this for our practice. We can’t just do the surface treatment. If we just say: “ok we need some content, and then let’s write a knowledge test on it”, we’ve let down our stakeholders.  If we don’t know the cognitive properties of the media we use, don’t sweat the details about feedback on assessment, don’t align the practice to the needed performance, etc., we’re not doing our job!

And I don’t mean you have to get a Ph.D. in learning science, but you really do need to know what you’re doing. Or, at least, have good checklists and quick reference guides to ensure you’re on track. Ideally, you review your processes and tools for alignment to what’s known. And the tools themselves could have support. (Ok, to a limit, I’ve seen this done to the extent of handcuffs on design.)

Nuances matter,  if you care about the outcomes (and if you don’t, why bother? ;).  I’ve been working on both a checklist and on very specific changes that apply to various places in design processes that represent the major ways folks go wrong. These problems are relatively small, and easy to fix, and are designed to yield big improvements. But unless you know what they are, you’re unlikely to have the impact you intend.

Context is key

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Workflow learning is one of the new buzzphrases. The notion is that you deliver learning to the point of need, instead of taking people away from the workflow. And I’m a fan. But it’s not as easy as it sounds!  Context is a critical issue in making this work, and that’s non-trivial.

When we create learning experiences, typically we do (or should) create an artificial context for learners to practice in. And this makes sense when the performance has high consequences.  However, if people are in the workflow, there is a context already. Could we leverage that context for learning instead of creating one?  When would this make sense?

I’d suggest that there are two times workflow learning makes sense. For one, if the performers aren’t novices, this becomes an opportunity to provide learning at the point of need to elaborate and extend learning. Say, refining knowledge on sales, marketing, or product when touching one of them.  For another, it would make sense if the consequences aren’t high and the ease of repair is easy. So, sending on a workpiece that will get checked anyways.

Of course, we  could just do performance support, and not worry about the learning, but we can do that  and support learning as well. So, having an additional bit of learning content at the right time, whether alone or in conjunction with performance support, is a ‘good thing’.  The difficulties come when we get down to specifics.

Specifically,  how do we match the right content with the task? There are several ways. For one, it can just be pull. Here the individual asks for some additional help and/or learning. This isn’t completely trivial either, because you have to have a search mechanism that makes it easy for the performer to get the right stuff. This means federated search, vocabulary control, and more. Nothing you shouldn’t already be worrying about for pull learning anyways, but for the record.

Second, you could do push. Here it gets more dicey.  One way is to have content tied to specific instances. This can be hand done as some tools have made possible. That is, you instrument content with help where you find, or think, it could be needed. The other way is to be smart  about  the context.

And this is where it gets complicated. For such workflow learning to work, you really want to leverage the context, so you need to be able to  identify  the context.  How do you know what they’re doing? Then you need to map that context to content. You could use some signal (c.f. xAPI) that tells you when someone touches something. Then you could write rules that map that touch to the associated content. It might even by description, not hardwired, so the system’s flexible. For instance, it might change the content depending on how many times and how recently this person has done this task.  This is all just good learning engineering, but the details matter.

Making workflow learning work is a move towards a more powerful performance ecosystem and workforce, but it requires some backend effort.  Not surprising, but worth being clear on.

Cognitive and Learning Sciences

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You’ll see a lot of vendors/sessions/webinars touting neuroscience or brain-based. And you really shouldn’t believe it.  Yes, our brains are composed of neurons, and we do care about what we know about brains.  BUT, these aren’t the right terms!  Ironically, we have to be smarter than that.  Why?

Library levelsNo argument that neuroscience is advancing, rapidly. With powerful tools like MRI, we can understand lots more about what the brain does. And as we do, our understanding overall advances. But  for our purposes, neural is the wrong level.

Yes, learning is really about strengthening neural links. However, we don’t address individual neurons. Instead our thinking is really patterns of activation  across neurons. So, we activate patterns. And, typically, if we’re addressing higher-level thinking than motor reactions (think: decisions about actions), we’re activating complex combinations of patterns.

To do so, we’re working at the symbolic level. Images representing concepts, diagrams, and  language. And this is the  cognitive level! It’s the level above neural.  And above that, the social.  And while it’s about the brain, saying it’s based on the brain is a muddy concept. Do you mean neural, or cognitive, or…? Clarity matters.

Cognitive science as a field was defined to be an integrative approach to everything about our thinking: consciousness, language, emotion, and more.  Departments of cognitive science tend to include psychologists, linguists, sociologists, anthropologists, philosophers, and, yes, neuroscientists.  For instruction, and other aspects like performance support and informal learning, however, cognitive (or social) is the right level.

And, to be clear, learning sciences are a subset of the cognitive sciences. So you really should have a working understanding of the basics of learning science if you’re designing courses. And of the bigger picture of cognitive science to do the new L&D.

Conceptual clarity  about  our field, is important  to our field. We need to know what we’re doing, and resist hype that is misleading if not flat-out wrong. It’s nice to think we’re doing cool stuff, but not if we don’t have the basics down. Invest in solid learning and performance design first. Then we can get fancy.

Mental models are the agents of learning

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I was talking with my friend and colleague Harold Jarche about how he’s expanding his valuable Personal Knowledge Mastery to teams and at the organizational level. Walking through his diagram, what is critical, what is being exchanged, are mental models. And I thought this an interesting insight.

I’ve talked before about mental models, and they’re important for learning. What they do is provide a causal basis for understanding what happened, and predicting what  will happen.  And that’s important. From such models, we can therefore evaluate different options, and choose the one that has the best outcome.  They may not help in new areas, but they give us a basis for new and unique combinations of circumstances.

So, individually, we make decisions based upon models. In fact, our brains build models to explain the world. (Important for instruction to provide good ones.)  And so when we experiment and reflect we may try to capture these models. Senge, in The Learning Organization talked about mental models as one of his 5 disciplines.

When we’re brought in on a team as a complementary set of knowledge and skills to solve a problem, we’re coming in with our models. In the ‘coherent organization‘ model, these have been developed through our community of practice, and are brought to bear on challenges we’re addressing. Results are shared back, particularly new insights. Similarly, our communities should be tracking others for models to appropriate and adapt.

Thus, the mental tools we use in this new age of information and innovation are conceptual causal models. We need tools to capture, represent, and share these models. And most importantly, I reckon, we need to understand the nature of these models to facilitate us taking the best advantage of them.

Our models may be exchanged, but they’re not transactional. Like a smile, we can give the away and still have them. But we can, and should, continue to acquire and develop them. Models are our value to our field and organizations.

Sign of a change?

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I’ve been touting my recent book on debunking learning myths. Not because it’ll make me independently wealthy (if  only!), but because it’s a continuation of my campaign for more learning science in our profession. We need this change! And I wonder if something that happened is a sign of progress.

At the ATD International Conference, as my publishers, they naturally had the book in the bookstore. They told me they’d slightly over-ordered, but lo and behold,  it sold out.  What’s more, they ordered more,  and they sold out too!

Now, there’re lots of things to unpack here.  First, it’s a great design. While I did the writing and the ideas for the comics, they chose the cover and title (both over my idea, and they were right ;) and the size, paper, cover material, etc.  In short, they did the design. And people have commented on the cover graphic  and the tactile feel of the book.

And they priced it right, too. It’s under $20. Which makes it an easy purchase. (Several different folks brought multiple; I signed  six for one person!)

What’s more, it wasn’t even  my idea!  The ‘power behind the throne’ (as I call him) asked for me to address this topic. Sure, I’ve railed about the myths, but I wouldn’t have thought of actually writing a book to address it.

But, honestly, those are additional factors. I don’t want it to do well for any of those reasons. I’m hoping, seriously hoping, that the success is because finally there’s a growing awareness that our profession needs to become more, er, professional.

There’re a lot of little moves that are signaling more effort to lift our game. I and others have taken steps.   Of course, I am available to do more. For instance, I have a learning design process audit I offer that’s reasonably priced and will identify wastes  and opportunities for the smallest change in your  approach for the maximum impact. But however you do it, please do extinguish the myths and practice the known.  Please!

Quip: learning & instruction

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I spoke at the ATD International Conference last week on myths. I said a number of things (and a number were said about it, too :). However, one comment seems to be getting more traction than others. Moreover, it’s something I say regularly. So I thought I should add it to my collection of Quinn Quips.

The statement is simple:

Learning is action and reflection; instruction is  designed action and  guided  reflection.

What do I mean here? In life, things happen. We make choices, and there are consequences. When we observe them, and reflect, we begin to notice patterns. Some of this  can happen unconsciously, but if we want to improve fastest, reflecting helps. This can involve just thinking, or writing, or diagramming, or other ways of representing the contingencies and emerging models.

However, when we want to guide learning, e.g. instruct, one of the tasks we can undertake is creating a problem, and asking the learner to solve it. If we provide resources, and support the thinking afterwards, we increase the likelihood of learning outcomes.

A critical feature of this statement is that the choices of action that we design, and the choices of resources to support reflection (content  and representation tools), are critical. And, of course, we might need a series of activities (or application opportunities) to support learning.

An interesting option that emerges here is the opportunity for contextual learning. When an individual is engaged in a task relevant for learning, we can take advantage of it. With resources and reflection facilitation, a performance requirement becomes a learning opportunity!

It’s important that we understand the difference, but recognize (and reflect) the core.

Barack Obama #ATD2018 Keynote Mindmap

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The official opening event to kickoff ATD’s International Conference, was our 44th President, Barack Obama. Prompted by questions from Tony Bingham, he eloquently addressed education, values, and more. Thoughtful, witty, and ultimately wise, an inspiring session.

Hard Fun Projects

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As a basic premise of my book on designing engaging learning, I maintain that learning can, and  should, be ‘hard fun’. When you look at learning and engagement, you find this perfect alignment of elements. And, it occurred to me, that’s also true for good project work.  And here I don’t just mean coursework assignments (though that too fits), but organizational innovation should also be hard fun!

As I’ve stated before in various places, when you’re designing new solutions, problem-solving, trouble-shooting, doing research, etc, you don’t know the answer when you begin.  Therefore you’re learning when you do so!  It’s not formal learning, it’s informal, but it’s still learning.  So what works in learning should make sense for innovation too.

And in learning, the alignment I found between elements of effective education and engaging learning make sense.  Both require (amongst others):

  • clear goals
  • appropriate challenge
  • meaningfulness of the problem to the context
  • meaningfulness to the learners
  • experimentation
  • feedback

And those also define a meaningful project for solving in the workplace.

That is, first  you need to have a clear goal. The size and scope of the task should be within the reach, but not the grasp, of the team. The project has to have a clear benefit to the organization.  And the team should be appropriately constituted with skills and committed to the project. The methods required for the innovation will be experimentation and feedback.  Of course, you also need diversity on the team, safety to experiment, accountability for the results.  (Which is helpful for formal learning too!)

We can, and should, be setting up our projects to meet these criteria. We get better outcomes, research tells us. That not only includes the product of the work, but team engagement as well. This is also a possible start to creating a culture of experimentation and continual learning. Which also has long-term upsides.

This came to me because I was asked in an interview what were the most fun projects I’d done. I realized that working with folks together to address problems, like when I led a team to develop an adaptive learning system, fit the bill.  And that’s work I love, whether having a group together to collectively work out better design processes or performance and development strategy.  Folks who’ve worked with me similarly have found it valuable. So who’s up for some ‘hard fun’?