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Clark Quinn’s Learnings about Learning

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.

LXD Strategy

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In the continuing process of resolving what I want to do when I grow up (rest assured, not happening), I’ve been toying with a concept. And I’ve come up with the phrase: Learning Experience Design (LXD)  Strategist. Which of course, begs the question of just what LXD strategy  is. So here’s my thinking.

To me, LXD is about the successful integration of learning science and engagement. Yes, cognitive science studies both learning and engagement, but in my experience the two aren’t integrated specifically well. You either get something flashy but empty, or something worthwhile but dreary dull. I remember a particular company that produced rigorous learning that you’d rather tear your eyes out than actually consume. And, similarly, seeing an award winning product that was flashy, but underneath was just drill and kill. For something that shouldn’t be.

Learning experiences should emotionally hook you (e.g. ensuring you know that you need it, and that you don’t know it). Then it should take the necessary steps such as sufficient spaced meaningful practice resourced with appropriate models and examples and specifically feedback. Ultimately, it should transform the learner. Learners go from not having a clue to having a basic ability to do  and how to continue to develop.

What is LXD  strategy?  Here I’m thinking about helping orgs restructure their design processes, and their org structure, to support delivering learning experience designs. This includes ensuring up front that this really does deserve learning instead of some other intervention, such as performance support. Then it includes how you work with SMEs, how you discern key decisions, wrap practice into contexts, etc. It’s also about using the tools – media and technology – to create a well-integrated experience. Note that the integration can include classrooms, ambient content and interactivity, and more. It’s about getting the design right, then implementing.

LXD strategy is about ensuring that resources and practices are aligned to create experiences that meet real org needs under pragmatic constraints. That’s what I’ve been doing in much of my work, and where my interests lead me as well. And it’s still a part of the performance ecosystem. Understanding that relationship is critical, when you start thinking about moving individuals from novices, through practitioners, to expertise. And the numbers of areas that will need this are going to increase.

LXD is, in my mind, the way we should be thinking about ID is now as LXD. And we need to not only think about what it is, and how to do it, but also how we organize to get it done. That, I think, is an important and worthwhile endeavor. So, what’s  your thinking?

Level of polish?

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A debate broke out amongst some colleagues the other day about the desirable level of polish in our elearning. One colleague was adamant that we were undermining our position by using low quality production. There was a lot of agreement. I had a slightly different view. Even after finding out he was talking more about external-facing content than internal, I still have some differences. After weighing in, I thought it required a longer response, and of course it has to go here.

So, the main complaint was that so much elearning looks dated and incomplete. And I agree!  And others chimed in that this doesn’t have to be, while all agreed that it doesn’t need to approach game quality in effect. Then, in my mind, the question switches to “what is good enough?” And I think we do need an answer to that. And, it turns out, to also answer “and what does it take?”

What is good enough?

So, my first concern is the quality of the design. My mantra on design states that it has to be right first. Then you can implement it. If it isn’t right from the get-go, it doesn’t matter  how you implement it. And the conversation took some time to sort this out. But let’s assume that the design’s right. Then, how much production values do you need?

The original complaint was that we’re looking slack by comparison. When you look at what’s being done in other, related, fields, our production values look last decade, if not last century!  And I couldn’t agree more. But does that matter?  And that’s where we start getting into nuances. My bottom line question is: “what’s the business case?”

So, I suggest that the investment in production values is based upon how important the ‘experience’ is. If it’s internal, and it’s a critical skill, the production values should be only enough to ensure that learners can identify the situation and perform appropriately (or get feedback).  It needs a minimum level of professionalism, and that’s it.  If you’re selling it to high-end customers and want to charge a premium price, you’ll need much more, of course.

The issue was that we’re losing credibility if we don’t approach a minimal level of competency. There were many arguments about the locus: fear of going out of bounds, managers oppression, low level tools, lack of skills, and more. And these all have validity. We should stipulate a minimal level. Perhaps the serious eLearning  Design Manifesto? :) We can do better.

What does it take?

This was the other issue. It was pointed out that design teams in other disciplines work in layers: from concept to realization. Jesse James Garrett has a lovely diagram that represents this for information architecture. And others pointed out that there are multiple skills involved, from dialog writing, through media production and interface design (they’re conceptually separate), and the quality of the programming and more. The more you need polish, the more you need to invest in the appropriate skill sets.  This again is a matter of marshaling the appropriate resources against the business case.

I think one of the issues is that we overuse courses when other solutions are more effective and efficient. Thus, we don’t have and properly allocate the resources to do the job right when it does positively absolutely has to be in the head. Thus, we do have a lot of boring, information dump courses. And we could be doing more with engaging practice, and less content presentation. That’s a design issue to begin, and then a presentation one.

Ultimately, I agree that bad elearning undermines our credibility. I do think, however, that we don’t need  unnecessary polish. Gilded bad design is still bad design. But then we should align our investment with the professional reception we need. And if we have trouble doing that, we need to rethink our approaches. The right level of investment for the context is the right response; we need the right live of polish. But the assessment the context is complex. We shouldn’t treat is simplistically, but instead systemically. If we get that right, we have a chance to impress folks with our astute sense of doing the right thing with the right resources. Less than that is a path to irrelevancy, and doing more is a path to redundancy. Where do  you want to go?

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.

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!

Graham Roberts #Realities360 Keynote Mindmap

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Graham Roberts kicked off the 2nd day of the Realities 360 conference talking about the Future of Immersive Storytelling. He told about their experiences and lessons building an ongoing suite of experiences. From the first efforts through to the most recent it was insightful. The examples were vibrant inspirations.

Cognition external

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reading outsideI was thinking a bit about distributed cognition, and recognized that there as a potentially important way to tease that apart. And I’ll talk it out first here, and maybe a diagram will emerge. Or not. The point is to think about how external tools can augment our thinking. Or, really, a way that at least partly, we have cognition external.

The evidence says that our thinking isn’t completely in our head. And I’ve suggested that that makes a good case for performance support. But I realize it goes further in ways I’ve thought about it elsewhere. So I want to pull those together.

The alternative to performance support, a sort of cognitive scaffolding, is to think about representation. Here we’re not necessarily supporting any particular performance, but instead supporting developing thinking. I shared Jane Hart’s diagram yesterday, and I know that it’s a revision of a prior one. And that’s important!

The diagram is capturing her framework, and such externalizations are a way to share; they’re a social as well as artifactual sharing. It’s part of a ‘show your work‘ approach to continuing to think. Of course, it doesn’t have to be social, it can be personal.

So both of these forms of distributed cognition are externalizing our thinking in ways that our minds have trouble comprehending. We can play around with relationships by spatially representing them. We can augment our cognitive gaps both formally through performance support, and informally by supporting externalizing our thinking.  Spreadsheets are another tool to externalize our thinking. So, too, for that matter, is text.

So we can augment our performance, and scaffold our thinking. Both can be social or solitary, but they both qualify as forms of distributed cognition (beyond social). And, importantly, both then should be consciously considered in thinking about revolutionizing L&D. We should be designing for cognition external.  The tools should be there, and the facilitation, to use either when appropriate. So, think distributed, as well as situated, and social. It’s how our brains work, we ought to use that as a guide. You think?