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Clark Quinn’s Learnings about Learning

The Role of a Storyboard?

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In a recent conversation, the issue of storyboards came up. It was in the larger context of representations for content development. In particular, for communicating with stakeholders (read: clients ;). The concern was how do you communicate the content versus representing the experience. So, the open question is what is the role of a storyboard?

So, there are (at least) several elements that are important in creating a learning experience. One is, of course, the domain. What are the performance objectives? Moreover, how are you providing content support. Specifically, what models are you using, and what examples. Then, of course, there’s the practice. Ideally, practice aligns with the performance objectives, and the models and examples support the practice.

There’s also the experience flow. How are you hooking learners up front? We are concerned with balancing the quantity of content with the actual practice, keeping engagement throughout.

In both cases, we need to communicate these to the clients. Too often, of course, clients raise concerns about making sure ‘the content’ is covered. In many cases, they really don’t understand that less is better, and have a large amount they’ve heard from subject matter experts (SMEs). Not knowing, of course, that SMEs have access to what they know about the domain, but less about what they do! Thus, they’re looking to make sure the content’s covered.

There will also be concern about the experience. This, likely not ideally, comes after assurance about the content. I personally have experienced situations where stakeholders say ‘ok’ to a storyboard, but then balk at the resulting experience. Some (surprisingly high) proportion of folks can’t infer an experience from a storyboard.  This has been echoed by others.

The question is what is the role of a storyboard. In game design, there is a (dynamic) design document that captures everything as it develops. Is this the right representation to communicate the experience? It  communicates to developers, but is it good for clients? I argue that we want more iterative representations, for instance getting sign-off on what we’ve heard from the analysis and documenting what will be the focus of the design. We also want to separate out the domain from the experience.

Overall, I advocate representing the experience, for instance in (mocked up) screenshots with narration to represent a sample interaction. That can accompany the storyboard, but when folks have to sign-off on an experience, and they can’t get it from the usual representations, you’ll need an augment. I wonder whether we should fight against presenting the content that’ll be covered.

We should show the objectives, models, and examples, but fight against content ‘coverage’. Cathy Moore does a good job in her ‘Action Mapping’ to argue that what the minimum is to achieve success on appropriate performance tasks is a good goal. I agree, as does learning science. The role of a storyboard is to capture development for developers. It may not be the right communication tool for stakeholders. I welcome your thoughts.

Grounded in practice

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Many years ago, I was accused of not knowing the realities of learning design. It’s true that I’ve been in many ways a theorist, following what research tells us, and having been an academic. I also have designed solutions, designed design processes, and advised orgs. Still, it’s nice to be grounded in practice, and I’ve had the opportunity of late.

So, as you read this, I’m in India (hopefully ;), working with Upside Learning. I joined them around 6 months ago to serve as their Chief Learning Strategist (on top of my work as Quinnovation, as co-director of the Learning Development Accelerator, and as advisor to Elevator9). They have a willingness to pay serious attention to learning science, which as you might imagine, I found attractive!

It’s been a lot of marketing: writing position papers and such. The good news is it’s also been about practice. For one, I’ve been running workshops for their team (such as the Missing LXD workshop with the LDA coming up in Asia-friendly times this summer). We’ve also created some demos (coming soon to a sales preso near you ;). I’ve also learned a bit about their clients and usual expectations.

It’s the latter that’s inspiring. How do we bake learning science into a practical process that clients can comprehend? We’re working on it. So far, it seems like it’s a mix of awareness, policy, and tools. That is, the design team must understand the principles in practice, there need to be policy adjustments to support the necessary steps, and the tools should support the practice. I’m hoping we have a chance to put some serious work into these in my visit.

Still, it’s already been eye-opening to see the realities organizations face in their L&D roles. It only inspires me more to fight for the changes in L&D that can address this. We have lots to offer orgs, but only if we move out of our comfort zone and start making changes. Here’s to the revolution L&D needs to have!

 

Curriculimb

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Ok, so I’m going to go out on a limb here, and talk a wee bit about what I’ve been learning about designing curricula. I care about doing it right (and probably haven’t always). It’s not the average course that’s the issue, but big ones, or multiple courses addressing skill gaps. It’s been challenging to find a systematic approach, which is why I’m teetering on a curriculimb.

So, the issue is how to develop a curriculum. I know in higher ed (I was there once) it tends to be a process of figuring out what content they need, and distributing across courses. It’s probably more art than science, where you move stuff around until it feels like you’ve got the right sized amount of content for each subject and it covers the ‘right stuff’. How people meet the criteria can vary. In a more research institution, I could design my HCI course my way. In more teaching-focused institutions, people may actually be given course syllabi to teach to!

My problem is when I have an uncertain amount of content, say for a large domain, and I want to develop specific capabilities. On principle, we should work backwards from the final performance. Which might include some very rich types of capabilities, so we might have a lot of concepts and practice involved. We’d need to create a large map. We might even break it up into conceptual stages (e.g. with programming: learning conditionals and then loops), and addressing them separately.

You probably also need to provide some practice to deal with misconceptions. That is, where are folks likely to get off track and maybe discouraged? Then you want to create practice for that. The things you’d rather they learned before it matters.

When I looked for good principles around this, it seemed like most of what I found basically said it’s iterative, there are no overarching principles (except work backwards and iterate ;). Which was less than satisfying, and some evidence-based practice would be nice.

Now, one of the things I was pondering in the dark of the night was how AI could help. I’ve been hearing how it can parse content and create maps. However, I also realized that to do so, it needs well-structured content. Kind of a circular argument. I think we need people to define it then AI can align it.

Again, right now it seems more like an art than a science. And I get that; it’s a lot like designing in engagement: create a first best guess and then test. Still, there are some solid results in engagement that give us some grounds for the first pass. I feel less like that at the next level up. So, I’m out on a curriculimb, and welcome help getting down!

Attention is underrated

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Attention is a complex phenomena. Thinking that we can simply address is probably naive. Worse, there is at least one pervasive myth about it. Trivial attention is probably overrated, but meaningful attention is underrated.

Attention, I’ll suggest, is how we pay conscious awareness to our thinking. We pay attention to the sensory stream that’s available, and as working memory is has limits, our attention chooses what ends up being in working memory (which is where we see conscious thought). This is the picture I paint in Learning Science for Instructional Designers,  my recent book on how we learn. That’s how I learned it in grad school, and little seemed to change that.

As an aside, I suggest that the basic human information processing loop is something that is critical to understand. This is true for learning designers, but I would suggest there’s broader applicability. Knowing how information flows:

  • from sensory store to working memory via attention
  • from working memory to long term memory via elaboration
  • back to working memory via retrieval
  • and to decision from working memory

as a simplified story, shows how humans work in many ways. It gets more complex in important ways, but this is a key basis. On top of it comes aspects of how we think, and learn, but this is the core.  It benefits anyone dealing with people, basically: UI, marketing, etc. In short, most everyone.

Recent pictures of the information processing loop suggest, however, that attention has a bigger purview. They have it influencing most of the above. Which may be more accurate, in that if you need to attend to what’s in working memory, and manage the process of attending to information while evaluating what decision to make. You must maintain conscious focus on what you want to learn.

The myth, which still persists, is that our attention span has dropped to 8 seconds. Which folks tout as less than that of a goldfish. (How do we know what the attention span of a goldfish is?) The origin of this myth came from StatBrain misinterpreting a study, and was amplified since it was published by Microsoft Canada.  Marketing, mind you, not their research group! A myth I busted in a previous book!

There is apparently some evidence that our attention span has dropped (to 4o-something seconds, not eight), but we can still disappear into movies, novels, and games for hours. I reckon it’s about how engaging it is. Which, not completely surprisingly, is the topic of my most recent book, Make It Meaningful.

So, please, avoid the myths, and learn the core. Attention is underrated, as is the whole human information processing loop. Learn it, and benefit.

Misleading Malarkey

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Recently, I saw a claim that was, well, a tad extreme. Worse, I think it was wrong, and possibly harmful. Thus, I feel it’s right to address it, to avoid misleading malarkey.

So, here’s the claim that riled me up:

Short-form edutainment is the most effective teaching method for both children and adults. TikTok and YouTube shorts will ultimately replace high schools and universities. Employment sector will phase out LMS systems and replaced with AI-powered compliance tools. If you are considering instructional design as a career, you may want to become a YouTuber or TikToker instead.

If you’ve tuned in at all, you’ll know that I’m a fan of engagement, properly construed.  Heck, it’s the topic of my most recent book! So, talking about the value of engagement in learning is all to the good. However…

…this claim goes over the top. Most notably, there’s the claim that edutainment is the most effective teaching method. If only! That puts me off, because teaching should yield a learning outcome, and just watching video shorts won’t do that (under most circumstances). Not surprisingly, I asked for research.

The author pointed to a study where mice genetically low on dopamine learned better when given dopamine. Yes, but the study had the mice do more than just watch videos, they performed tasks! I tried to go deeper, saying that engagement may be desirable, but it’s not sufficient. Without practice, watching entertaining and informative material (e.g. edutainment) isn’t a path to learning outcomes.

The conversation was derailed by my comment that edutainment had gotten a bad name from games. In the 80s, in an industry I was in, this was the case! I was accused of having a ‘gamification’ mindset! (Ahem.)  I tried steering the conversation back to the point it’s not about gamification, it’s about engagement combined with practice.

Interestingly, there was an almost parallel conversation about how engagement wasn’t the same as learning (which I pointed to in the exchange). The general take is that engagement is desirable but insufficient. Yes! Yet here we see the claim that engagement is all we need!

I believe in engagement for learning. I just don’t believe that by itself it will lead to learning. Learning science supports both the value of engagement, and the necessity of practice and feedback. That’s all. But claims like the above are misleading malarkey. It may be we’re talking an outrageous marketing claim (infamy is better than not being known at all?), but when it misleads, it’s a problem. Am I missing something?

Missing LXD Workshop

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We interrupt your regularly scheduled reading for this commercial announcement:

What is Learning Experience Design (LXD)? Further, why should you care? Finally, (and arguably most important) what does it mean you should do differently? Those, to me, are important questions. My short answer is that LXD is the elegant integration of learning science and engagement. Which, to me, implies some important nuances on top of what’s traditionally done in instructional design (ID). How to address it? There’s actually quite a lot in LXD, but it’s also a lot of overlap with traditional ID practices and processes. I reckon the easiest (and best) way to address it is to talk about the delta. That is, what’s different between the two. So, in my role for Upside Learning, I developed a missing LXD workshop. We ran it internally to good outcomes, and now, you can take it!

I believe that the difference starts with objectives; you can’t make a meaningful experience if you don’t have learners acquiring relevant new skills (not just an information dump). From there, there are nuances on designing individual practice activities, and then aggregated into practices (that is, putting practices together). Moving on, we look at the content elements of models and examples, and then the emotional aspects of learning. The workshop closes by looking at a design process that accommodates these. Recognizing that folks don’t want to throw out their whole process to start anew, it works from a generic model.

In the workshop, I cover each of those topics in a week; so it’s a six week experience. In between, I ask attendees to do some interim processing to both cement their understanding and to change their practices. Each week we’ll cover underlying concepts, see examples of what we’re talking about, actively process the information, and do a major application task.

To make this available more broadly, Upside’s partnered with the Learning Development Accelerator (LDA) to deliver it. Full disclosure: I’m co-director of the LDA, and Chief Learning Strategist for Upside Learning (in addition to my ongoing role for Quinnovation). (So, it’s all about me! :) Seriously, I think this puts together the tools I believe are necessary to lift our industry.

To be clear, since the advance notice timeframe puts this in summer, we’re offering it in Asia time-frames first (tho’ anyone is welcome!):

Australian Eastern Standard Time: July 7, 14, 21, 28, August 4 and 11 from 12h00 to 14h00 each day
Singapore Time: July 7, 14, 21, 28, August 4 and 11 from 10h00 to 12h00 each day
India Standard Time: July 7, 14, 21, 28, August 4 and 11 from 07h30 to 09h30 each day
New York Time: July 6, 13, 20, 27, August 3 and 10 from 22h00 to 24h00 each day

We’re offering it for US$100 to LDA members, and US$350 to non-members (for only $40 more, you get the full LDA offerings as well).

We’re planning to offer the missing LXD workshop again at a later date at East Coast/Europe friendly times (probably at a steeper price, we’ll have worked the bugs out ;). You can find out more at the LDA site. It’s got learning science and engagement bundled up into a coherent whole, for those who’ve already been doing ID and want to lift their game. I hope you’ll find it worth your while.

We now return you to your regularly scheduled reading until next week at the usual time.

The details matter

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For many reasons, I end up reading relevant books to our field. A recent one underway is Wiggins & McTighe’s Understanding by Design. In it, I found a quote that really resonated. It highlights to me one of the biggest barriers I think we face, that the details matter. Not everyone will see this, however.

So, the quote is from Bransford, Brown, & Cocking’s masterwork, How People Learn, funded by the National Academies of Sciences. It chronicles what was known at the time about the subject of learning, aggregating learning science research. I don’t know if it’s true outside the US, but within you can get a free PDF copy!

Wiggins & McTighe’s book is a primary argument for working backwards. They’re not concerned with the pedagogy, but the planning. Of course, it also matters what your learning goals are. Thus, they also discuss what understanding means. That’s where this quote comes from:

Many approaches to instruction look equivalent when the only measure of learning is memory. …Instructional differences become more apparent when evaluated from the perspective of how well the learning transfers to new problems and settings.

This resonates because it highlights something I think we struggle with. To folks who don’t know any better, as I’ve argued before, well-produced, versus well-designed and well-produced, is hard to distinguish. As a respondent noted, we don’t always even test memory! Yet our goals should be (retention and) transfer.

I think the field has fallen into a superstition that information dump and knowledge test is learning! Which is mistaken, but if you don’t know any better, it’s hard to tell. Reckon we have to continue to focus on outcomes, measuring if  learning transfers to new problems and settings. When we do, we’ll have evidence to help make the case for learning that works. Then we can have the resources to pay attention, reflecting that the details matter. ‘Til then, we’ll continue to fight to do it right.

Comic openings

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Humor is important, we have to address this seriously!

Speaking of irreverent, I’ve argued, in the past, that we don’t use comics enough in learning. In general, I mean serious comics, graphic novel formats, to tell dramatic stories. However, we can use comics for humor, as well. In particular, to accomplish our motivation goals on opening. So here I ponder comic openings.

To start, as I mention in talking about making learning meaningful, I believe you need to open up people emotionally. Even before you open up cognitively! We know that activating relevant knowledge is important cognitively, but I suggest that it won’t stick as well unless you’ve piqued their awareness. I’m arguing that we need a visceral awareness that this is relevant.

It doesn’t take much, but I suggest it is worth doing. I like to use the consequences of having, or not, this ability is what’s key. There are things you can do that you couldn’t without it. Making that clear is, to me, a WIIFM (What’s In It For Me).

I believe you can do this dramatically or humorously. That is, you could make a dramatic story of saving someone because of the information you have, or the bad outcomes from not having it. Then there’s the alternative.

You can also have humorous aspects of having the info. For instance, saving the day not because the hero knows it, but the sidekick happens to, instead. However, my favorite approach is to humorously point out the consequences of not having the knowledge. (OK, admitting my predilection for sarcasm. ;) This can be done with just a simple comic! I’ve done so in content we developed for a client, and am doing so again in a demo we’re creating.

It might also set the tone for the learning. It can help learners relax, trust that the environment’s safe, reducing anxiety.

I suggest this is easy to create, easy to develop, quick, engaging for the learners, and effective. Now, I don’t have specific research on that (I’d love to: anyone got pointers or want to do the study?). However, I think it’s a plausible inference from what we do know from learning science.

I’ll also acknowledge that there are times this won’t be the best approach! Certain topics probably aren’t good candidates, similarly certain audiences might similarly not match. However, I do think it’s more broadly applicable than we think. Even for modules within an overall topic. If they’re inexpensive and high impact, use them liberally. So I’ll suggest we use comic openings liberally!

Irreverence

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I’m not flamboyant, nor funny. I’m occasionally irreverent, and those who know me personally can probably regale you with my love for puns and wordplay. Of course, I think irreverence is undervalued, and am inclined to think it may be the only truly effective tool for addressing myths, superstitions, and misconceptions. So I took some time on my walk today and thought about it a bit. Here’re the results with some extensions.

X Styles (e.g. learning, leadership, etc)

Why don’t you just use astrology? It’s cheaper, and has about as much validity!

Dale’s Cone

People will believe about 10% of what you write, 50% of what has a cute alliterative phrase, 70% of what you tout in a video, and 90% of what’s in a well-produced infographic. (Aside: I think this is the secret to marketing.)

Images processed 60K times faster than text

That has to be right, because images are processed visually, while text is processed…hey, wait a minute!

Attention span of a goldfish

Because we no longer…’scuse me, I’ve a notification on my phone…

Generations

This new generation has no respect for tradition, said the ancient Greek philosophers.

I could go on, but that would spoil the fun. I’d like to hear yours! Cheers to irreverence!

Tradeoffs in aesthetics

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For the LDA debate this month, Ruth Clark talked to Matt Richter and I about aesthetics in learning. Ruth, you should know, is the co-author of eLearning and the Science of Instruction, amongst other books, a must-have which leverages Rich Mayer’s work on multimedia learning. Thus, she’s knowledgeable about what the research says. What emerged in the conversation was a problem about tradeoffs in aesthetics, that’s worth exploring.

So, for one thing, we know that gratuitous media interferes with learning. From John Sweller’s work on cognitive load theory, we know that processing the unnecessary data reduces cognitive resources available to support learning. There’s usually enough load just with the learning materials. Unless the material materially supports learning, it should be avoided.

On the other hand, we also know that we should contextualize learning. The late John Branford’s work with the Cognitive Technology Group while at Vanderbilt, for instance, demonstrated this. As the late David Jonassen also demonstrated with his problem-based learning, we retain and transfer better with concrete problems. Thus, creating a concrete setting for applying the knowledge is of benefit to learning.

What this sets up, of course, is a tradeoff. That is, we want to use aesthetics to help communicate the context, but we want to keep them minimal. How do we do this? Even text (which is a medium), can be extraneous. There really is only one true response. We have to create our first best guess, and then we test. The testing doesn’t have to be to the level of scientific rigor, mind you. Even if it just passes the scrutiny of fellow team members, it can be the right choice, though ideally we run it by learners.

What we have to fight is those who want to tart it up. There will be folks who want more aesthetics. We have to push back against that, particularly if we think it interferes with learning. We need to ensure that what’re producing doesn’t violate what’s known. It’s not always easy, and in situations we may not always win, but we have to be willing to give it a go.

There are tradeoffs in aesthetics, so we have to know what matters. Ultimately, it’s about the learning outcomes. Thus, focusing on the minimum contextualization, and the maximum learning, is likely to get us to a good first draft. Then, let’s see if we can’t check. Right?