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Clark Quinn’s Learnings about Learning

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’?

Myths in one week…

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Next week, I’ll be presenting on myths at ATD’s International Conference (Tues, 1PM).  Moreover, there’ll be a book signing at 4PM!  I hope to see you there, and, for more reasons than you might first imagine.

For one, ATD’s supposed to be supplying me with special bookmarks.  Always nice to have a book mark specific to the book, I reckon. I haven’t seen them yet, but if they’re leveraging the cool design work of Fran Fernandez they used for the book, they’ll be great.  But wait, there’s more…

Pick of debunker ribbonI’ve also arranged for some special ‘Debunk’ badge ribbons.  These limited edition collectors items (*ahem* :) are available to those who can show me their copy of the book (digital or print).  It’s to proudly wear on your badge showing you’re fighting on the side of learning science.  (As to the pic: the ribbon was not  supposed to be ‘cantelope’. Fortunately, the company is making it right so these ones have gone back. They will be orange, and the print and design will be the same. Fingers crossed they arrive in time!)

There are other ways to find out more.  You can of course buy the book; either through Amazon (Kindle too) or via ATD (PDF too). (Rumor has it that using the code ‘SPRINGBOOKS18’ at ATD will get you 10% off!)

Of course, there’s the ATD webinar for members on May 24th at 11AM PT (2PM ET). There’ll be one for the Debunker Club on June 6th at 10AM PT (1PM ET), details forthcoming.    Other webinars are in the works, so stay tuned.

And there’ll be interviews. Also forthcoming.  Yes, I’m trying to get the word out, but it’s for a good cause: better learning!

So, if you’re going to ICE, please do say hello (and safe travels).  I know San Diego (and love it): undergrad and grad school at UCSD, and brother still lives there, so I visit a lot. My recommendations: fish tacos (Rubios is a safe bet), carnitas (e.g. Old Town Mexican Cafe), and carne asada burritos (but only at a taqueria, not at a restaurant). There are some great local brews; Stone, Pizza Port, and Ballast Point all make a good drop.  Also, margaritas  if you can get them made properly, not with a mixer. Hope to see you there!

SMEs for Design

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In thinking through my design checklist, I was pondering how information comes from SMEs, and the role it plays in learning design. And it occurred to me visually, so of course I diagrammed it.

The problem with getting design guidance from SMEs is that they literally can’t tell us what they do!  The way our brains work, our expertise gets compiled away. While they  can tell us what they know (and they do!), it’s hard to get what really needs to be understood.  So we need a process.

Mapping SME Qs to ID elementsMy key is to focus on the  decisions that learners will be able to make that they can’t make now. I reckon what’s going to help organizations is not what people know, but how they can apply that to problems to make better choices.  And we need SMEs who can articulate that. Which isn’t all SMEs!

That  also means that we need models. Information that helps guide learners’ performance while they compile away their expertise. Conceptual  models  are the key here; causal relationships that can explain what  did  happen or predict what  will happen, so we can choose the outcomes we want. And again, not all SMEs may be able to do  this part.

There’s also other useful information SMEs can give us. For one, they can tell us where learners go wrong. Typically, those errors aren’t random, but come from bringing in the wrong model.  It would make sense if you’re not fully on top of the learning.  And, again we may need more than one SME, as sometimes the theoretical expert (the one who can give us models and/or decisions) isn’t as in tune with what happens in the field, and we may need the supervisor of those performers.

Then, of course, there are the war stories. We need examples of wins (and losses).  Ideally, compelling ones (or we may have to exaggerate). They should  be (or end up) in the form of stories, to facilitate processing (our brains are wired to parse stories).  Of course, after we’re done they should refer to the models, and show the underlying thinking, but that may be our role (and if that’s hard, maybe we either have the wrong story or the wrong model).

Finally, there’s one other way experts can assist us. They’ve found this topic interesting enough to spend the years necessary to  be the experts.  Find out why they find it so fascinating!  Then of course, bake that in.

And it makes sense to gather the information from experts in this order. However, for learning, this information plays roles in different places.  To flip it around, our:

  • introductions need to manifest that intrinsic interest (what will the learners be able to do  that they care about?)
  • concepts need to be presenting those models
  • examples need to capture those stories
  • practice need to embed the decisions and
  • practice needs to provide opportunities to exhibit those misconceptions  before they matter
  • closing may also reference the intrinsic experience in closing the emotional experience

That’s the way I look at it.  Does this make sense to you? What am I missing?