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Clark Quinn’s Learnings about Learning

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Content systems not content packages

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In a conversation last week (ok, an engagement), the topic of content systems came up. Now this is something I’ve argued for before, in several ways. For one, separate content from how it’s delivered. And, pull content together by rules, not hardwired. And it’s also about the right level of granularity. It’s time to revisit the message, because I thought it was too early, but I think the time is fast coming when we can look at this.

This is in opposition to the notion of pre-packaged content. MOOCs showed that folks want to drill in to what they need. Yet we still pull everything together and launch it as a final total solution. We are moving to smaller chunks (all for the better; even if it is burdened with a misleading label). But there’s more.

The first point is about content models. That we should start designing our content into smaller chunks. My heuristic is the smallest thing you’d give one person or another. My more general principle is that resolves to breaking content down by it’s learning role: a concept model is different than an example is different than a practice.

This approach emerged from an initiative on an adaptive learning system I led. It now has played out as a mechanism to support several initiatives delivering content appropriately. For one, it was supporting different business products from the same content repository. For another it was about delivering the right thing at the right time.

Which leads to the second point, about being able to pick and deliver the right thing  for the context.  This includes adaptive systems for learning, but also context-based performance support. With a model of the learner, the context, and the content, you can write rules that put these together to optimally identify the right thing to push.

You can go further. Think of two different representatives from the same company visiting a client. A sales person and a field engineer are going to want different things in the same location. So you can add a model of ‘role’ (though that can also be tied to the learner model).

There’s more, of course. To do this well requires content strategy, engineering, and management. Someone put it this way: strategy is what you want to deliver, engineering is how, and management is overseeing the content lifecycle.

Ultimately, it’s about moving from hardwired content to flexible delivery. And that’s possible and desirable. Moreover, it’s the future. As we see the movement from LMS to LXP, we realize that it’s about delivering just what’s needed when useful. Recognizing that LXPs are portals, not about creating experiences, we see the need for federated search.

There’s more: semantics means we can identify what things are (and are not), so we can respond to queries. With chatbot interfaces, we can make it easier to automate the search and offering to deliver the right thing to the right person at the right time.

The future is here; we see it in web interfaces all over the place. Why aren’t we seeing it yet in learning? There are strong cognitive reasons (performance support, workflow learning, self-directed and self-regulated learning).  And the technology is no longer the limitation. So let’s get on it. It’s time to think content systems, not content packages.

 

How do you drive yourself?

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How do I drive myself? I was asked that in a coaching session. The question is asking how I keep learning. There are multiple answers, which I’ve probably talked about before, but I’ll reflect here. I think it’s important to regularly ask: “how do you drive yourself?”

As it’s the end of the year, my conversant was looking at professional development. It’s the time to ask for next year’s opportunities, and the individual was breaking out of our usual conversation to talk about this topic. And so he asked me what  I  did.

And my first response, which I’ve practiced consciously at least since grad school, is that I accept challenges. That is, I take on tasks that stretch me. (It might be that ‘sucker’ tattoo on my forehead, but note that my philanthropic bandwidth is pretty stretched. ;). This is professionally  and personally.

That is, I look to find challenges that I think are within my reach, but not already my grasp. Or, to put it another way, in my Zone of Proximal Development. Accepting assignments or engagements where, with effort, I can succeed,  but it’s not guaranteed.

Which means, of course, that there’s risk as well. Occasionally, I do screw up. Which I  really really hate to do. Which is a driver for me to push out of my comfort zone and succeed. Or, at least, learn the lesson.

There’s more, of course. One thing I did started with my first Palm Pilot (the Palm III, the accompanying case is still my toiletry bag!).  I had to justify to myself the expense, so I made sure that I really used it to success. This was part of the driver of the thinking that showed up in Designing mLearning,  how to complement cognition. IA instead of AI, so to speak.

I also live the mantra “stay curious, my friends”. I’m still all too easily distracted by a new idea, but I don’t think that’s a bad thing. Well, as long as it’s balanced with executing against the challenges.

That’s how I drive myself. So, how do you drive yourself?

Unpacking some nuances

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In my book  Engaging Learning, I had a suite of elements  for both effective education practice and engaging experiences. Of course, the point was that they perfectly aligned. However, I’m unpacking a couple of them, and I thought it’d be helpful to ensure that I am clear about them, and so that you are too. So I’m unpacking some nuances in two different groups of elements.

For one, I talk about contextualized, anchored, and relevant.  These three are related, but each plays a different role, and it’s important to be concerned about each separately.

Contextualized isn’t difficult. Research (e.g. Jonassen’s work) has shown that we perform better working from problems that are concrete rather than abstract. (Which is why those abstract problems kids are assigned in schools are so  wrong!). We work better with concrete problems with facilitation to support abstraction and transfer. Otherwise we get ‘inert knowledge’, knowledge that we can pass a test on, but will never even activate in a relevant problem situation.

Anchored, here, means that it’s a real use of the knowledge. Instead of using problems about fractions of a crayon, for instance, it might be about serving food (pies, pizza). Similarly, engineering equations about curves could be used for a roller coaster instead of a abstract pattern. The activity using the knowledge should be the way it’d be used in the world.

That’s related to, but different from, being relevant. Not relevant to the learning, but to the learner.  That is, the problem they’re solving is one that the learner cares about. So, for maritime enthusiasts, we might use geometry to figure out sail angles to the wind. While for gamers, we might use it to calculate graphics.

The second dichotomy is about active versus exploratory. They’re related, but each has an independent component.

For exploratory, I’m talking about learners having choices. That is, there are alternate choices of action. They can choose one or the other. The alternatives to the right answer, by the way, isn’t obvious or silly, but instead represents reliable ways learners get it wrong.

For active, I mean they must commit. It’s not enough to roll over the options and see the feedback, they have to choose one, and then see if it was right or not. And give consequences of their choices before feedback!

Unpacking some nuances helps, I hope, to ensure you address each separately, and consequently appropriately.  Here’s to nuanced design!

Passion and Learning

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My better half recently got a sample of special butter. A gift from a co-worker (an interesting story), and led me to reflect on the link between passion and learning.

M’lady’s co-worker is a fan of good butter. I was able to view a picture of her refrigerator, and the assortment of butters rivals what you might see in a fine grocery!  We did a tasting between the ordinary butter we ordinarily purchase and this special butter. The difference was noticeable. I was reminded of the fine butter they serve when in Europe. Or at really fine restaurant.

This may seem odd, but think about it a bit. What do you care enough about to really understand? At various times I’ve been known to wax poetic about beer, cooking, waves, and more. And, of course, cognition, learning, engagement, and design. I managed to get educated about (American) football and cricket (yes, cricket) from inspired roommates. The list goes on.

And what’s fun is learning from these folks just  why  they find it so interesting. Which is related to the task of finding the intrinsic interest for designing learning. Talk to the experts! They’ve spent hours becoming experts, what motivated them? If you can find that, you’ve got a handle on it.

And I’m sure you’ve learned something from someone who was passionate about it. That’s usually a good indication that they’re also knowledgable, but there are caveats on that.  People can get passionate about myths, too. There  are  reasons to be cautious. In general, however, you’re liable to be lucky.

Passionate people not only make fields comprehensible, they tend to drive fields forward. If you’re here, I’m expecting you’re passionate about performance & development. Maybe even up for a revolution! Let’s connect passion and learning to make it better.

 

Play to Learn

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Tic Tac Toe gameThinking more about Friston’s Free Energy Principle and the implications for learning design prompted me to think about play. What drives us to learn, and then how  do we learn?  And play is the first answer, but does it extend? Can we play to learn beyond the natural?

The premise behind the Free Energy principle is that organisms (at every level) learn to minimize the distance between our predictions and what actually occurs. And that’s useful, because we use our predictions to make decisions. And it’s useful if our decisions get better and better over time. To do that, we build models of the world.

Now, it’d be possible for us to just sit in a warm dark room, so our predictions are right, but we have drives and needs. Food, shelter, sex, are drives that can at least occasionally require effort. The postulate is that we’ll be driven to learn when the consequences of not learning are higher than the effort of learning.

At this level, animals play to learn things like how to hunt and interact. Parents can help as well.  At a higher level than survival, however, can play still work? Can we learn things like finance, mathematics, and other human-made conceptions this way? It’d be nice to make a safe place to ‘play’, to experiment.

Raph Koster, in his  A Theory Of Fun,  tells us that computer games are fun just because they require learning. You need to explore, and learn new tricks to beat the next level.  And computer games can be about surviving in made-up worlds.

The point I’m getting to is that the best learning should be play; low stakes exploration, tapping into the elements of engagement to make the experience compelling. You want a story about what your goal is, and a setting that makes that goal reasonable, and more.

To put it another way, learning  should be play. Not trivial, but ‘hard fun’.  If we’re not making it safe, and providing guided discovery to internalize the relationships they need, to build the models that will make better decisions, we’re not making learning as naturally aligned as it can be. So please, let your people play to learn. Design learning experiences, not just ‘instruction’.

 

Endorsements, rigor, & scrutability

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I was recently asked to endorse two totally separate things. And it made me reflect on just what my principles for such an action might be. So here’s an gentrified version of my first thoughts on my principles for endorsements:

First, my reputation is based on rigor in thought, and integrity in action. Thus, anyone I‘d endorse both has to be scrutable both in quality of design and in effectiveness in execution.

So, to establish those, I need to do several things.

For one, I have to investigate the product. Not just the top-level concept, but the lower-level details. And this means not only exploring, but devising and performing certain tests.

And that also means investigating the talent behind the design. Who‘s responsible for things like the science behind it and the ultimate design.

In addition, I expect to see rigor in implementation. What‘s the development process? What platform and what approach to development is being used? How is quality maintained? Maintainability? Reliability? I‘d want to talk to the appropriate person.

And I‘d want to know about customer service. What‘s the customer experience? What‘s the commitment?

There‘ve been a couple of orgs that I worked with over a number of years, and I got to know these things about them (and I largely played the learning science role ;), so I could recommend them (tho‘ they didn‘t ask for public endorsements) and help sell them in engagements. And I was honest about the limitations as well.

I have a reputation to maintain, and that means I won‘t endorse ‘average‘. I will endorse, but it‘s got to be scrutable at all levels and exceptional in some way so that I feel I‘m showing something unique and exceptional but will also play out favorably over time. If I recommend it, I need people to be glad if they took my advice. And then there’s got to be some recompense for my contribution to success.

One thing I hadn‘t thought of on the call was a possibility of limited or levels of endorsement. E.g. “This product offers a seemingly unique solution that is valuable in concept”, but not saying “I can happily recommend this approach”. Though the value of that is questionable, I reckon.

Am I overreaching in what I expect for endorsements, or does this make sense?

Tools for LXD?

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I’ve been thinking on LXD for a while now, not least because I’ve an upcoming workshop at DevLearn in Lost Wages in October. And one of the things I’ve been thinking about are the tools we use for LXD. I’ve created diagrams (such as the Education Engagement Alignment), and quips, but here I’m thinking something else. We know that job aids are helpful; things like checklists, and decision trees, and lookup tables. And I’ve created some aids for the Udemy course on deeper elearning I developed. But here I want to know what  you are using as tools for LXD? How do you use external resources to keep your design on track?

The simple rationale, of course, is that there are things our brains are good at, and things they’re not. We are pattern-matchers and meaning-makers, naturally making up explanations for things that happen. We’re also creative, finding solutions under constraints. Our cognitive architecture is designed to do this; to help us adapt to the first-level world we evolved in.

However, our brains aren’t particularly good at the second-level world we have created. Complex ideas require external representation. We’re bad at remembering rote and arbitrary steps and details. We’re also bad at complex calculations.  This makes the case for tools that help scaffold these gaps in our cognition.

And, in particular, for design. Design tends to involve complex responses, in this case in terms of an experience design. That maps out over content, time, and tools. Consequently, there are opportunities to go awry. Therefore, tools are a plausible adjunct.

You might be using templates for good design. Here, you’d have a draft storyboard, for instance, that insures you’re including a meaningful introduction, causal conceptual model, examples, etc. Or you might have a checklist that details the elements you should be including. You could have a model course that you use as a reference.

My question, to you, is what tools are you using to increase the likelihood of a quality design, and how are they working for you?  I’d like to know what you’ve found helpful as tools for LXD, as I look to create the best support I can. Please share!

Clear about the concept

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I went to hear a talk the other day. It was about competency-based education (CBE) for organizations. Ostensibly. And, while I’m now affiliated with IBSTPI, it’s not like I’m a competency expert. And maybe I expect too much, but I really hope for people to be clear about the concept. Alas, that’s not what I found.

So, it started out reasonably well, talking about how competencies are valuable. There were a number of points, and many made sense, although some were redundant. Maybe I missed some nuance? I try to be open-minded. It’s about creating clear definitions of performance, and aligning those with assessments. Thus, you’re working on very clear descriptions of what people should be doing.

It got  interesting when the speaker decided to link CBE to Universal Design for Learning (UDL).  And it’s a good program.  UDL talks about using multiple representations to increase the likelihood for different learners to be able to comprehend and respond. This, in the talk, was mapped to three different segments: engaging the learners in multiple ways, communicating concepts in multiple ways, and allowing assessment in multiple ways. And this is good. For learning. Does it make sense for CBE?

To start, the argument was, you should make the rationale for the learning in multiple ways. While in general CBE inherently embodies meaningfulness in the nature of clear and needed skills, I don’t have a problem with this. I argue you should hook learners in emotionally  and cognitively, and those can be separate activities. There was a brief mention of something like ‘learning styles’, but while now wary, I was ready to let it go.

However, the talk went on to make a case for multiple representations of content. And here the slide  explicitly  said ‘learning styles’ and used VARK. And don’t get me wrong, multiple representations and media are good,  but not for learning styles! The current status is that there’s essentially no valid instrument to measure learning styles, and no evidence that even if you did, that it makes a difference. None. So, of course, I raised the issue. And we agreed that maybe not for learning styles, but multiple representations weren’t bad.

The final point was that there could be multiple forms of assessment. At this point, I wasn’t going to interrupt again, but at the end of the session raised the point that the critical element of CBE is aligning the assessment with the performance! You can’t have them do an interpretative dance about identifying fire hazards, for instance, you have to have them identify fire hazards! So, here the audience ultimately agreed that variability was acceptable  as long as it measured the actual performance. Again, I don’t think the speaker was clear about the concept.

There were two major flaws in this talk. One was casually mashing up a couple of essentially incommensurate ideas. CBE and UDL aren’t natural partners. There can be overlapping concepts, but… The second, of course, is using a popular but fundamentally flawed myth about learning. If you’re going to claim authority, don’t depend on broken concepts.

To put it another way, I think it’s fair to expect speakers to be clear about the concept. (Either that, or maybe the lesson is that Clark shouldn’t be allowed to listen to normal speakers. ;)  Please, please, know what you’re talking about before you talk about it. Is that too much to ask?

Craft and commercial?

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Occasionally I try to look at the broader swings we see (in a variety of things). In learning technology, there’s been a gross pendulum swing, and maybe smaller ones. I think we’ve swung between craft and commercial approaches to design, and I’m hoping we’re on a return swing.

When we first started playing with learning technology, every approach was pretty much hand-crafted. We didn’t have specific tools for learning outcomes, and we had to apply generic tools like computer systems and the like. Early approaches like Plato were custom crafted, and the individual applications on top of that. And a small industry was built upon this basis to build solutions at scale, but the market never emerged. The whole solution was too costly, despite the power.

The PC revolution initially meant individuals or small teams built solutions. There did emerge authoring systems (e.g. Pilot) and even a meta-language for developing human-computer learning interactions. However, the usage was small. People handbuilt things like games (e.g. Robot Odyssey and SnooperTroops), though a few companies arose to do this systematically.

As technology changed, so to did the platforms. Video discs and Computer- and then Web-Based Training emerged. Companies emerged to do them at scale, but things were changing rather fast. Flash came about as a web-based lingua franca, where programs could run in most browsers with a plug in.  And, specifically for learning, Authorware became a powerful tool.

Still as things changed quickly, most solutions were driven by a real need, and hand-crafting was the norm.  But, of course, this changed.

With the horrors of 9/11, travel went from an increasingly affordable luxury to undesirable. The demand came for ‘elearning’, reducing costs from travel and overhead. With it came tools that made it easy to take content, add a quiz, and pop it up on a screen. A shift came from quality to quantity.

And this has continued in many guises. The difference, I  hope, is that the pendulum is swinging back.  The signs I see are an increase in interest in learning science. Several contributions may come from the Guild’s DemoFest, Julie Dirksen’s Design for How People Learn, Will Thalheimer’s Debunker Club, and the Serious eLearning Manifesto. We’re learning more about good design, and more people are picking up on it. We’re talking learning experience design, integrating learning science with engagement.

If you look at other industries like automobiles, we went from craft to commercial (c.f. assembly line manufacturing). While we’re unlikely to go back to fully crafted, owing to safety regulations, we’re seeing more options for establishing individual representation. And in furniture and clothing we’re seeing more craft.

The quality is important, and if we swing back to craft now, maybe when we swing back we’ll be commercial reflecting learning quality, not expediency. In some sense it doesn’t matter between craft and commercial, as long as it’s good. And hopefully that becomes a defining characteristic of our industry. Fingers crossed!

LXD Roles

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In my thinking about LXD strategy, I also was thinking about what roles are necessary. While you can do handoffs, what are the core skills you need to make this happen? And it’s not that you need all these people, but you need these roles. Someone may be a polymath (though I’m somewhat resistant that one person can do them all sufficiently well), but it’s really not fair to expect someone to be a one-person shop. So what  are the core LXD roles?

At core are two component skills.

Designer: this person looks at the performance need, and validates that this is a role for a learning experience, and then designs it, including any tools that don’t exist. Interacting with SMEs is a component; digesting down into the critical decisions, and then embedding practice in contexts. It’s creative, and while drawing on the strengths of the team, it’s the source of engagement strategy as well as learning outcomes. Also determining data collection and interpretation around design success. So it’s ID, and more.

Technologist: ensures that the developed media are integrated and delivered. This includes being able to use AR and VR when necessary, and string together the media and embed appropriately. This isn’t necessarily a programmer, but instead is technically capable. Such as putting in xAPI statements.

Then, there’s the media.  All of them!  So we’re talking a variety of roles here.

Writing: this is writing both to read  and to hear.  Dialog is different than prose, for one.  And prose for reading is different than, say, academese! It’s about taking the prose and boiling it down. I may have previously recounted how on an early project, my dev partner hired a script writer. That person took my elegantly crafted prose and hacked it up by 30-40%.  All to the good. I now can do the same on pretty much anyone’s (including my own). We often think we can write, but there’s as much skill in writing well as there is any other media production. Like, say,…

Graphics: this is about both images and and  graphics. Again, different, but I’d expect someone to be able to generate a diagram or infographic, but also source and masterfully integrate any images. It includes knowledge about fonts and colors and space and how they generate thoughts and feelings. It’s look and feel, and more.

Video: here we’re not talking about script writing (see above) nor acting, but instead filming and directing. Creating dynamic visuals. That is, knowing how to produce video so that what comes out looks professional and meets the need. Lighting, editing, and more.  It’s not just the filming. There are two different skills here (look at the credits in movies!), but I think we can roll them up. And it can be inserting images, animations, etc too. Video, here, includes compiling what ends up on screen. This may overlap with…

Audio: this is similar to video, but works  with it. It’s about synching the audio, but also music, and sound effects, and combining them in a way that ends up working with the video to produce an output. It can be standalone as well, say with a podcast. It’s about microphones, clips, and more.

Two additional roles (that might be combined).

Resource coordinator: this person is a bit like a project manager, but is responsible for finding images, actors, and any other resources as well as permissions. This person is resourceful and savvy at negotiating bureaucracies as well as intellectual property rights.

Project manager: in my best projects, there has been a project manager who’s developed and maintained the project schedule. This person knows when to prompt about upcoming deadlines, and chase malingerers.

And, of course, oversight.

Leader: someone who knows a bit about all of it, and can review and provide feedback. The development of the individuals should come from the assignments and the feedback. There’s also a role for collecting data about performance as a basis for strategy setting and tracking. And, of course, creating a culture of safety coupled with accountability, and experimentation.

 To be clear, not all of these are necessarily dedicated to the one project, as the categorization suggests. Project managers often are responsible for several (read: too many) projects, and media production may be a central resource. The point is that you need the required expertise, not to be winging it. And I know far too many folks are expected to do so. Yet I worry that the resulting output may not actually be effective.

And this is in a context, assuming there’s performance consulting up front, and a coaching and development program (and resources) in place at the backend. Ongoing development is part of the design, but at some point that gets handed over to the community of practice and manager/leader ongoing activities. Determining that point is part of the design. So too ensuring that there’s a sufficiency of self-learning resources and their use is modeled.

These are the LXD roles I think are necessary. I’m sure I’m forgetting something, so I welcome you weighing in.