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Clark Quinn’s Learnings about Learning

The roots of LXD

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Instructional design, as is well documented, has it roots in meeting the needs for training in WWII. User experience (UX) came from the Human Computer Interaction (HCI) revolution towards User Centered Design. With a vibrant cross-fertilization of ideas, it’s natural that evolutions in one can influence the other (or not).  It’s worth thinking about the trajectories and the intersections that are the roots of LXD, Learning eXperience Design.

I came from a background of computer science and education. In the job for doing the computer support for the office doing the tutoring I had also engaged in, I saw the possibilities of the intersection. Eager to continue my education, I avidly explored learning and instruction, technology (particularly AI), and design. And the relationships, as well.

Starting with HCI (aka Usability), the lab I was in for grad school was leading the charge. The book User-Centered System Design  was being pulled together as a collection of articles from the visitors who came and gave seminars, and an emergent view was coming. The approaches pulled from a variety of disciplines such as architecture and theater, and focused on elements including participatory design, situated design, and iterative design. All items that now are incorporated in design thinking.

At that time, instructional design was going through some transitions. Charles Reigeluth was pulling together theories in the infamous ‘green book’  Instructional Design Theories and Models.  David Merrill was switching from Component Display Theory to ID2.  And there was a transition from behavioral to cognitive ID.

This was a dynamic time, though there wasn’t as much cross-talk as would’ve made sense. Frankly, I did a lot of my presentations at EdTech conferences on implications from HCI for ID approaches. HCI was going broad in exploring a variety of fields to tap in popular media (a lot was sparked by the excitement around  Pinball Construction Set), and not necessarily finding anything unique in instructional design. And EdTech was playing with trying to map ID approaches to technology environments that were in rapid flux.

These days, LXD has emerged. As an outgrowth of the HCI field, UX emerged with a separate society being created. The principles of UX, as cited above, became of interest to the learning design community. Explorations of efforts from related fields – agile, design thinking, etc, – made the notion of going beyond instructional design appealing.

Thus, thinking about the roots of LXD, it has a place, and is a useful label. It moves thinking away from ‘instruction’ (which I fear makes it all to easy to focus on content presentation). And it brings in the emotional side. Further, I think it also enables thinking about the extended experience, not just ‘the course’.  So I’m still a fan of Learning Experience Design (and now think of myself as an LXD strategist, considering platforms and policies to enable desirable outcomes).

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As a side note, Customer Experience is a similarly new phenomena, that apparently arose on it’s own. And it’s been growing, from a start in post-purchase experience, through Net Promoter Scores and Customer Relationship Management. And it’s a good thing, now including everything from the initial contact to post-purchase satisfaction and everything in between. Further, people are recognizing that a good Employee Experience is a valuable contributor to the ability to deliver Customer Experience. I’m all for that.

Sub-symbolic and Situated

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At the time that the connectionist folks were working on neural nets, another similar approach was genetic algorithms. Both were working in a different way than the previous formal approaches to AI. The distinction between the two became known as symbolic vs sub-symbolic. And it’s useful to review why, particularly in the current climate of increasing interest in AI and cognitive science. An interesting outcome is that the sub-symbolic work exposed the contextualized nature of our reasoning. So there’s a link between sub-symbolic and situated cognition.

The prevailing model, starting with the cognitive revolution which arguably began in 1956 (an auspicious year ;) was a formal logical one. Whether in ‘production’ rules of IF THEN, or other formal mechanisms, the notion was to operate on semantic objects like numbers and concepts. This reflected, at the time, the belief that we’re formal logical thinkers.

As cognitive research continued, there was a growing recognition that our behaviors didn’t match particularly well with formal logic (c.f. Kahnemann & Tversky’s work, summed up in  Thinking Fast and Slow). Several cognitive scientists separately came up with structures that more aptly described some of the properties we saw: Roger Schank called them scripts (he was focused on episodic thinking, not semantic), Marvin Minksy called them frames, and Dave Rumelhart called them schemas (after Bartlett).

What Rumelhart subsequently saw was that the properties he was trying to capture were very hard to represent in formal logic. He went on, with his colleague Jay McLelland and their collaborators) to develop what they called Parallel Distributed Processing (PDP). These are now known as neural nets (NNs) and are the basis for much of machine learning.

I was in the lab at the time Dave and Jay were working on neural nets, but detoured down a different path. Following work on analogical reasoning (my Ph.D. thesis topic), I became aware of the work Holland, Holyoak, Nisbett, & Thagard were doing with induction. Their framework was genetic algorithms (GAs). Both GAs and NNs use input strings and output strings to work, but internally they represent things differently.

After so much work on symbolic reasoning, here were mechanisms operating beneath the symbolic level. Yet they were attempting to create symbolic behavior. NNs obviously, more closely resemble our cognitive architecture (though GAs are still used in some areas like program generation). So, our conscious thinking  is symbolic, but our actual cognition is happening below our conscious thinking. Hence things like illusions, fallacies, myths, and more.

What emerged from this realization is that our cognition isn’t just sub-symbolic, but  situated.  That is, what is conscious is a combination of what comes in from our senses, and what we know. In fact, with the limited attention we have,  much of what we think we’re perceiving, we’re actually generating!

This it accounts for why we’re bad at doing things by rote; we’re liable to confound steps and contexts. This ends up being important because it means we have to work harder for any learning interventions to work effectively  across  contexts. The relationship between sub-symbolic and situated is, at least to me, and interesting story of the development of cognitive science.

Yet, it still means that our learning  works most effectively at the conscious level of symbols, because that can accelerate learning over having to deal with everything through practice and feedback.  (And explains why programs talking about neural really aren’t working there.) We still need those, but conscious models can provide a framework to become self-improving over time. So don’t forget to provide the models, and sufficient practice, and feedback.

Lucky on Foundations

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I was thinking about my next directions, and it led to me to think a bit about my foundations. And I realized I’ve been very lucky (and I’m grateful). I’ve had good parents, mentors, colleagues, and friends. But I’ve also had some fortunate timings, and it’s worth reflecting how I’ve been lucky on foundations upon which to build. (A personal reflection, not necessarily worth your time ;)

It started with college, really. I’d always been a typical lad, but with an extra serving of geek (I didn’t fit in with any clique so hung with a few similarly chaotic-good chaps :).  I started college interested in marine bio, but there was no formal link between undergrad study and Scripps. The bio program was all cut-throat med, and while I  could cut it, it was all rote memorization and deadly boring. So…

I took some comp sci classes, and was tutoring for extra money on the side.  Lucky chance: I got a job doing the computer support for the office that coordinated the tutoring. That sparked my awareness of the connections between computers and learning. Of course, back then, at my school, there was no such program. Luck 2: my school had a program where you could design your  own major. I found a couple of professors doing a project on using email for classroom discussion (circa ’78; we had the DARPAnet, otherwise there  was no email; more luck). They agreed to sponsor my project.

After graduating, I looked all over the country for an org that wanted someone interested in computers and learning. More luck, I finally came across Jim Schuyler, and as he was starting DesignWare, I got a job! And, importantly, it was designing and programming on the earliest personal computers. And I realized that there was real potential for learning in games! But I also realized that we didn’t know enough how to design them. And then I read about ‘cognitive engineering’ (applying what we know about cognition to the design of systems).

I was accepted into the cog program with Don Norman, who’d written the article. And this was another major stroke of luck. While Don’s students were researching how to build systems for how people think, my twist was about how people learn. I got to study behavioral, cognitive, social, even machine learning!  Also, Don’s lab partner Dave Rumelhart was conducting his research with Jay McClleland on what became neural nets. You can’t help but get exposed to related research through lab meetings, seminars, and more, even if you’re not active in the particular work. And Ed Hutchins was doing his work on distributed cognition.  This was a fundamental shift in perspective from formal to situated cognition.

The lab ran a Unix system, so I was getting steeped in computing systems to complement my personal computer work, along with the cognition focus. I subsequently did a post-doc at LRDC, getting deeper steeped into cognitive learning, and then joined a school of Computer Science, getting further background in computation. I was on the internet before there was a web (and foolishly was rather complacent about it)! And it’s enabled me to keep an eye on new developments like mobile and content and more, and understand their core affordance.

I also got steeped in design, having a chance to look at graphic, industrial, software, architecture, and other approaches (more luck). I combined that with a study of the academic literature, of course. These three foundations have been the basis of my work: applying cognitive and learning sciences to the design of technology to create learning and performance systems.

There’s much more to the story, of course. Serendipity continued in jobs and people to guide me, I’m happy to say.  Mentors being shy, you can’t really thank folks enough, so if I’ve been lucky in foundations, it’s my job to pass it on. I hope that this blog helps in  some way!

Little Whinging

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Every once in a while, I have had enough of some things, and want to point them out.  I do so not just to complain, but to talk about good principles that have implications beyond just the particular situation. So, here I go with a little whinging.

Services

Of late, when I call in for assistance, the phone system automatically asks me to verify some information. It can be an account number, or just to confirm some data like my house number. This is all good up until the point when I get connected to a live person, and they then ask me for that same data. Many times, as it’s escalated (“yes, it’s plugged in” and “yes, I’ve already tried rebooting it”), I get passed on to another person. And get asked for the same data  again.

When pushed, “it’s our systems”. And that’s not good enough. What’s the lesson?  You need your systems synched together. The employees need a performance ecosystem that’s integrated, if you’re going to be able to deliver a good customer experience. Reminded of the fact that Dominos is spending more money fighting to not have to be accessible than the estimate to actually make their system accessible!?!

This plays out in another way. So I’m having internet troubles. It’s intermittent (admittedly, that make it hard to diagnose), and it’s not disconnecting, it’s just slowing  way down, and then going back to blazing fast.But it’s creating hiccups for my conference calls and webinars. I’m paying a pretty penny for this.

So, they do some remote stuff to the modem and say call back if it’s not better. And it’s not. So they send a tech. Who says it’s in the network, not the local connections and other techs will work on it, and I don’t have to be present, and they work 24/7 and it should be fixed in a couple of days. And then, I get a call which I return and am told it’ll be fixed by late this morning. And then it’s not. So I call again, and first, the person doesn’t seem to have access to the previous notes (which I’d made a point of), and asks me a bunch of questions. Which I’ve already answered previously in the same call. Then, they arrange to send a tech out! Isn’t that the definition of insanity, trying the same thing and expecting a different outcome?

The problem here is the lack of coordination between the different elements. The latest phone person said that they had the notes from the previous tech, and that this one has different skills, but the previous person had told a different story. It’s  that that concerns me; the lack of consistency shatters my already-fragile confidence in them.  They should have a good linked record (the ecosystem again), but be able to address obvious mismatches elegantly.

Products

two different glass bottomsOk, so this one’s less obvious, but it’s relevant. Here’s my claim: I want products that aren’t just dishwasher-safe, I want them dishwasher-smart!  What am I talking about?  Look at these two glasses. It may be hard to see, but the one on the left has a three-lobed groove in the bottom. While there’s sufficient surface to stand steadily, it also drains. The one on the right, however, has a concavity in the bottom. So, when it goes in the dishwasher (or the dish drainer for that matter), water pools and it doesn’t dry efficiently. WHY?

Look, you should be designing products so the affordances (yeah, I said the ‘a’ word ;) work  for consumers. I like my backup battery (thanks Nick and SealWorks) because it has a built-in cable!  You don’t have to carry a separate one. This goes for learning experiences as well; make the desired behaviors obvious. Leave the challenges to the deliberate ones discriminating appropriate decisions from misconceived ones. And authoring tools should make it easy to do good pedagogy and difficult to do info dump and knowledge test! Ahem.

At core it’s about aligning product and service design with how we think, work, and learn. It should be in the products we purchase, and in the products we use.  Heck, I can help if you want assistance in figuring this out, and baking it into your workflows. (I used to teach interface design, having had a Ph.D. advisor who is a guru thereof.). Do read Don Norman’s  The Design of Everyday Things  if you’re curious about any of this. It’s one of those rare books that will truly change the way you look at the world. For the better.

Design, whether instructional or industrial or interface or anything else that touches people needs to  understand those people. Please ensure you do, and then use your powers for good.

Direct Instruction and Learning Experience Design

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After my previous article on direct instruction versus guided discovery, some discussion mentioned Engelmann’s Direct Instruction (DI). And, something again pointed me to the most comprehensive survey of educational effects. So, I tracked both of these down, and found some interesting results that both supported, and confounded, my learning. Ultimately, of course, it expanded my understanding, which is always my desire. So it’s time to think a bit deeper about Direct Instruction and Learning Experience Design.

Engelmann’s Direct Instruction is very scripted. It is rigorous in its goals, and has a high amount of responses from learners.  Empirically, DI has great success, with some complaints about lack of teacher flexibility. It strikes me as very good for developing core skills like reading and maths.  I was worried about the intersection of many responses a minute and more complex tasks, though it appears that’s an issue that has been addressed. I couldn’t find the paper that makes that case, however.

Another direction, however, proved fruitful.  John Hattie, an educational researcher, collected and conducted reviews of 800+ meta-analyses to look at what worked (and didn’t) in education.  It’s a monumental work, collected in his book Visible Learning. I’d heard of it before, but hadn’t tracked it down. It was time.

And it’s impressive in breadth  and depth.  This is arguably the single most important work in education. And it opened my eyes in several ways.  To illustrate, let me collect for you the top (>.4)  impacts found, which have some really interesting implications:

  • Reciprocal teaching (.74)
  • Providing feedback (.72)
  • Teaching student self-verbalization (.67)
  • Meta-cognition strategies (.67)
  • Direction instruction (.59)
  • Mastery learning (.57)
  • Goals-challenging (.56)
  • Frequent/effects of testing (.46)
  • Behavioral organizers (.41)

Reciprocal teaching and meta-cognition strategies coming out highly, a great outcome. And of course I am not surprised to see the importance of feedback. I have to say that I  was surprised to see direct instruction and mastery learning coming out so high.  So what’s going on?  It’s related to what I mentioned in the afore-mentioned article, about just what the definition of DI is.

So, Hattie says: …”what the critics mean by direct instruction is didactic teacher-led talking from the front…” And, indeed, that’s my fear of using the label. He goes on to point out the major steps of DI (in my words):

  1. Have clear learning objectives: what should the learner be able to  do?
  2. Clear success criteria (which to me is part of 1)
  3. Engagement: an emotional ‘hook’
  4. A clear pedagogy: info (models & examples), modeling, checking for understanding
  5. Guided practice
  6. Closure of the learning experience
  7. Reactivation: spaced and varied practice

And, of course, this is pretty much everything I argue for as being key to successful learning experience design. And, as I suspected, DI is not what the label would lead you to believe (which I  do think is a problem).  As I mentioned in a subsequent post, I’ve synthesized my approach across many elements, integrating the emotional elements along with effective education practice (see the alignment).  There’s so much more here, but it’s a very interesting result. Direct Instruction and Learning Experience Design have a really nice alignment.

And a perfect opportunity to remind you that I’ll be offering a Learning Experience Design workshop at DevLearn, which will include the results of my continuing investigation (over decades) to create an approach that’s doable and works. Hope to see you there!

Drink your own champagne?

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I was talking with a vendor of a robust suite of tools. In the course of it, in my usual teasing way, I asked a question. And, while I wasn’t surprised at the answer, I was ‘concerned’. And so should you be. So I’m going to suggest you start asking of your vendors “Do you drink your own champagne?”

So, this was a manufacturer of an LMS (and some other, related, platforms). And they market their advanced capabilities. And, really, I have no problem with their tools; they seem pretty enlightened.  So I asked whether they used their own tools.

And there are reasons to do so. For one, to have credibility, for sure. And, to truly know your own product. But the really important reason is to be able to understand the use experience and tune accordingly. Customer research is an important tool here as well, but it’s not the only one. You really need to use something to truly know what works and what doesn’t.

It’s a form of experimentation. I test myself by trying to apply my principles in my endeavors, and then try to take on new situations to try out my beliefs more broadly.  And so should you, at the individual and organizational level. Using your own tool is a form of this.  It is, essentially, testing your theory with research!

And I think most folks with services and such are likely to practice what they preach.  And that could be for some bad things as well as good (thinking: learning styles vendors).  But I was surprised when the answer was only “somewhat”.  That’s not really good enough.

So, I’m going to suggest that this should be a question you ask of every vendor. I’m also going to suggest every vendor ensure that they do use their own tool. Internally, for their own work. Whether it’s authoring tools, a course management system, a portal, a web meeting tool, what have you. If you don’t drink your own champagne, you’re not only undermining trust, you’re losing a valuable source of information. Now, pass the bubbly, would you?

Theory or Research?

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There’s a lot of call for evidence-based methods (as mentioned yesterday): L&D, learning design, and more. And this is a good thing. But…do you want to be basing your steps on a particular empirical study, or the framework within which that study emerged? Let me make the case for one approach. My answer to theory or research is theory. Here’s why.

Most research experiments are done in the context of a theoretical framework. For instance, the work on worked examples comes from John Sweller’s Cognitive Load theory. Ann Brown & Ann-Marie Palincsar’s experiments on reading were framed within Reciprocal Teaching, etc. Theory generates experiments which refine theory.

The individual experiments illuminate aspects of the broader perspective. Researchers tend to run experiments driven by a theory. The theory leads to a hypothesis, and then that hypothesis is testable. There  are some exploratory studies done, but typically a theoretical explanation is generated to explain the results. That explanation is then subject to further testing.

Some theories are even meta-theories! Collins & Brown’s Cognitive Apprenticeship  (a favorite) is based upon integrating several different theories, including the Reciprocal Teaching, Alan Schoenfeld’s work on examples in math, and the work of Scardemalia & Bereiter on scaffolding writing. And, of course, most theories have to account for others’ results from other frameworks if they’re empirically sound.

The approach I discuss in things like my Learning Experience Design workshops is a synthesis of theories as well. It’s an eclectic mix including the above mentioned, Cognitive Flexibility, Elaboration, ARCS, and more. If I were in a research setting, I’d be conducting experiments on engagement (pushing beyond ARCS) to test my own theories of what makes experiences as engaging and effective. Which, not coincidentally, was the research I was doing when I  was  an academic (and led to  Engaging Learning). (As well as integration of systems for a ubiquitous coaching environment, which generates many related topics.)

While individual results, such as the benefits of relearning, are valuable and easy to point to, it’s the extended body of work on topics that provides for longevity and applicability. Any one study may or may not be directly applicable to your work, but the theoretical implications give you a basis to make decisions even in situations that don’t directly map. There’s the possibility to extend to far, but it’s better than having no guidance at all.

Having theories to hand that complement each other is a principled way to design individual solutions  and design processes. Similarly for strategic work as well (Revolutionize L&D) is a similar integration of diverse elements to make a coherent whole. Knowing, and mastering, the valid and useful theories is a good basis for making organizational learning decisions. And avoiding myths!  Being able to apply them, of course, is also critical ;).

So, while they’re complementary, in the choice between theory or research I’ll point to one having more utility. Here’s to theories and those who develop and advance them!

Direct Instruction or Guided Discovery

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Recently, colleague Jos Arets of the 70:20:10 institute wrote a post promoting evidence-based work. And I’m a big fan, both of his work and the post. In the post, however, he wrote one thing that bugs me. And I realize I’m flying in the face of many august folks on whether to promote direct instruction or guided discovery. So let me explain myself ;).

It starts with a famous article by noted educational researchers Paul Kirschner, John Sweller, and Richard Clark. In it, they argue against “constructivist, discovery, problem-based, experiential, and inquiry-based teaching”. That’s a pretty comprehensive list. Yet these are respected authors; I’ve seen Richard Clark talk, have talked with John Sweller personally, and have interacted with Paul Kirschner online. They’re smart and good folks committed to excellent work. So how can I quibble?

First, it comes from their characterization of the opposition as ‘minimally guided’.Way back in 1985, Wallace Feurzig was talking about ‘guided discovery’, not pure exploration. To me, that’s a bit of a ‘straw man’ argument. Not minimally guided, but appropriately guided, would seem to me to be the appropriate approach.

Further, work by David Jonassen for one, and a meta-analysis conducted by Stroebel & Van Barneveld for another, suggested different outcomes. The general outcome is problem-based (as one instance being argued against) doesn’t yield  quite as good performance on a subsequent test, but is retained longer  and transfers better. And those, I suggest, are the goals we  should care about.  Similarly, research supports attempting to solve problems even if you can’t before you learn.

And I worry about the phrase “direct instruction”. That easy to interpret as ‘information dump and knowledge test’; it sounds like the old ‘error-free learning’! I’m definitely  not accusing those esteemed researchers of implying that, but I am afraid that under informed instructors could take that implication. It’s all too easy to see too much of that in classrooms. Teacher strategies tend to ignore results like spaced, varied, and deliberate practice. Similarly, the support for students to learn effective study skills is woeful.

Is there a reconciliation? I suggest there is. Professors Kirschner, Sweller, & Clark would, I suggest, expect sufficient practice to a criteria, and that the practice should match the desired performance. I suspect they want learners solving meaningful problems in context, which to me  is problem-based learning. And their direct instruction would be targeted feedback, along with models and examples. Which is what I strongly suggest. The more transfer you need, however, the broader contexts you need. Similarly, the more flexible application required would suggest the gradual removal of scaffolding.

So I really think that guided exploration, and meaningful direct instruction, will converge in what eventuates in practice. Look,  insufficiently guided practice isn’t effective, and I suspect that they wouldn’t suggest that bullet points are effective instruction. I just want to ensure that we focus on the important elements, e.g. what we highlighted in the Serious eLearning Manifesto. There  is a reason to think that direct instruction or guided discovery isn’t the dichotomy proposed, I’ll suggest. FWIW.

Dimensions of difficulty

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As one of the things I talk about, I was exploring the dimensions of difficulty for performance that guide the solutions we should offer.  What determines when we should use performance support, automate approaches, we need formal training, or a blend, or…?  It’s important to have criteria so that we can make a sensible determination. So, I started trying to map it out. And, not surprisingly, it’s not complete, but I thought I’d share some of the thinking.

So one of the dimensions is clearly complexity.  How difficult is this task to comprehend? How does it vary? Connecting and operating a simple device isn’t very complex. Addressing complex product complaints can be much more complex. Certainly we need more support if it’s more complex. That could be trying to put information into the world if possible. It also would suggest more training if it  has to be in the head.

A second dimension is frequency of use. If it’s something you’ll likely do frequently, getting you up to speed is more important than maintaining your capability. On the other hand, if it only happens infrequently, it’s hard to try to keep it in the head, and you’re more likely to want to try to keep it in the world.

And a third obvious dimension is importance. If the consequences aren’t too onerous if there are mistakes, you can be more slack. On the other hand, say if lives are on the line, the consequences of failure raise the game. You’d like to automate it if you could (machines don’t fatigue), but of course the situation has to be well defined. Otherwise, you’re going to want lot of training.

And it’s the interactions that matter. For instance, flight errors are hopefully rare (the systems are robust), typically involve complex situations (the interactions between the systems mean engines affect flight controls), and have big consequences!  That’s why there is a huge effort in pilot preparation.

It’s hard to map this out. For one, is it just low/high, or does it differentiate in a more granular sense: e.g. low/medium/high?  And for three dimensions it’s hard to represent in a visually compelling way. Do you use two (or three) two dimensional tables?

Yet you’d like to capture some of the implications: example above for flight errors explains much investment. Low consequences suggest low investment obviously. Complexity and infrequency suggest more spacing of practice.

It may be that there’s no  one answer. Each situation will require an assessment of the mental task. However, some principles will overarch, e.g. put it in the world when you can. Avoiding taxing our mental resources is good. Using our brains for complex pattern matching and decision making is likely better than remembering arbitrary and rote steps. And, of course, think of the brain and the world as partners, Intelligence Augmentation, is better than just focusing on one or another. Still, we need to be aware of, and assessing, the dimensions of difficulty as part of our solution.  Am I missing some? Are you aware of any good guides?

Engaging Learning and the Serious eLearning Manifesto

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Way back in ’05, my book on games for learning was published. At its core was an alignment between what made an effective education practice and what makes engaging experiences. There were nine elements that characterized why learning should be ‘hard fun’.  More recently, we released the Serious eLearning Manifesto. Here we had eight values that differentiated between ordinary elearning and  serious elearning. So, the open question is how do these two lists match up? What is the alignment between Engaging Learning and the Serious eLearning manifesto?

The elements of the Serious eLearning Manifesto (SeM) are pretty straightforward. They’re listed as:

  • performance focused
  • meaningful to learners
  • engagement driven
  • authentic contexts
  • realistic decisions
  • real-world consequences
  • spaced practice
  • individualized challenges

The alignment (EEA: Effectiveness-Engagement Alignment) I found in Engaging Learning was based upon research I did on designing games for learning. I found elements that were repeated across proposals for effective education practice, and ones that were stipulated for engaging experiences. And I found a perfect overlap. Looking for a resolution between the two lists of elements looks something like:

  • clear goals
  • balanced challenge
  • context for the action
  • meaningful to domain
  • meaningful to learner
  • choice
  • active
  • consequences
  • novelty

And, with a little wordsmithing, I think we find a pretty good overlap!  Obviously, not perfect, because they have different goals, but the important elements of a compelling learning experience emerge.

I could fiddle and suggest that clear goals are aligned to a performance focus, but instead that’s coming from making their learning be meaningful to the domain. I suggest that what really matters to organizations will be the ability to  do, not know.  So, really, the goals are implicit in the SeM; you shouldn’t be designing learning  unless you have some learning goals!

Then, the balanced challenge is similar to the individualized challenge from the SeM. And context maps directly as well. As do consequences. And meaningfulness to learners. All these directly correspond.

Going a little further, I suggest that having choice (or appearance thereof) is important for realistic decisions. There should be alternatives that represent misconceptions about how to act. And, I suggest that the active focus is part of being engaging. Though, so too could novelty be. I’m not looking at multiple mappings but they would make sense as several things would combine to make a performance focus, as well as realistic decisions.

Other than that, on the EEA side the notion of novelty is more for engaging experiences than necessarily specific to serious elearning.  On the SeM side, spaced practice is unique to learning. The notion of a game implies the ability for successful practice, so it’s implicit.

My short take, through this exercise, is to feel confident in both recommendations. We’re talking learning experience design here, and having the learning combine engagement as well is a nice outcome. I note that I’ll be running a Learning Experience Design workshop at DevLearn in October in Las Vegas, where’ll we’ll put these ideas to work. Hope to see you there!