design Archives : Page 2 of 58 : Learnlets

The roots of LXD

21 August 2019 by Clark Leave a Comment

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?

17 July 2019 by Clark Leave a Comment

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

16 July 2019 by Clark Leave a Comment

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

11 July 2019 by Clark 1 Comment

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

9 July 2019 by Clark Leave a Comment

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

26 June 2019 by Clark Leave a Comment

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

12 June 2019 by Clark Leave a Comment

I 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?

Labels for what we do

4 June 2019 by Clark 5 Comments

Of late there’s been a resurrection of a long term problem. While it’s true for our field as a whole, it’s also true for the specific job of those who design formal learning. I opined about the problem of labels for what we do half a year ago, but it has raised its head again. And this time, some things have been said that I don’t fully agree with. So, it’s time again to weigh in again.

So, first, Will Thalheimer wrote a post in which he claims to have the ultimate answer (in his usual understated way ;). He goes through the usual candidates of labels for what we do – instructional designer, learning designer, learner experience designer – and finds flaws.

And I agree with him on learning designer and instructional designer. We can’t actually design learning, we can only create environments where learning can happen. It’s a probabilistic game. So learning designer is out.

Instructional designer, then, would make sense, but…it’s got too much baggage. If we had a vision of instruction that included the emotional elements – the affective and conative components – I could buy it. And purists will say they do (at least, ones influenced by Keller). But I will suggest that the typical vision is of a behavioristic approach. That is, with a rigorous focus on content and assessment, and less pragmatic approaches to spacing and flexibility.

He doesn’t like learning engineer for the same reason as learning designer: you can’t ‘engineer’ learning. I don’t quite agree. One problem is that right now there are two interpretations of learning engineer. My original take on that phrase was that it’s about applying learning science to real problems. Just as a civic engineer applies physics…and I liked that. Though, yes, you can lead learners to learning, but you can’t make them think.

However, Herb Simon’s original take (now instantiated in the IEEE’s initiative on learning engineering) focused more on the integration of learning science with digital engineering. And I agree that’s important, but I’m not sure one person needs to be able to do it all. Is the person who engineers the underlying content engine the same one as the person who designs the experiences that are manifest out of that system? I think the larger picture increasingly relies on teams. So I’m taking that out of contention for now.

Will’s answer: learning architect. Now, in my less-than-definitive post last year, I equated learning experience designer and learning architect, roughly. However, Will disparages the latter and heaps accolades on the former. My concern is that architects design a solution, but then it gets not only built by others, but gets interior designed by others, and… It’s too ‘hands off’! And as I pointed out, I’ve called myself that recently, but in that role I may have been more an architect ;).

His argument against learning experience designer doesn’t sit well with me. Ignoring the aspersions cast against those who he attributes the label to, his underlying argument is that just designing experiences isn’t enough. He admits we can’t ensure learning, but suggests that this is a weak response. And here’s where I disagree. I think the inclusion of experience does exactly what I want to focus on: the emotional trajectory and the motivational commitment. Not to the exclusion of the learning sciences, of course. AND, I’d suggest, also recognizing that the experience is not an event, but an extended set of activities. Specifically, it will be across technologies as needed.

The problem, as Jane Bozarth raised in a column, is more than just this, however. What research into the role shows is that there are just too many jobs being lumped under the label (whatever it is). Do you develop too? Do you administer the LMS? The list goes on.

I think we need to perhaps have multiple job titles. We can be an instructional designer, or a learning experience designer, or an instructional technologist. Or even a learning engineer (once that’s clear ;). But we need to keep focused, and as Jane advised, not get too silly (wizard?). It’s hard enough as it is to describe what we do without worrying about labels for it. I think I’ll stick with learning experience designer for now. (Not least because I’m running a workshop on learning experience design at DevLearn this fall. ;) That’s my take, what’s yours?

New reality

22 May 2019 by Clark Leave a Comment

I’ve been looking into ‘realities’ (AR/VR/MR) for the upcoming Realities 360 conference (yes, I’ll be speaking). And I found an interesting model that’s new to me, and of course prompts some thoughts. For one, there’s a new reality that I hadn’t heard of! So, of course, I thought I’d share.

The issue is how do AR (augmented reality) and VR (virtual reality) relate, and what is MR (mixed reality). The model I found (by Milgram, my diagram slightly relabels) puts MR in the middle between reality and virtual reality. And I like how it makes a continuum here.

So this is the first I have heard of ‘augmented virtuality’ (AV). AR is the real world with some virtual scaffolding. AV has more of the virtual world with a little real world scaffolding. A virtual cooking school in a real kitchen is an example. The virtual world guides the experience, instead of the real world.

The core idea to me is about story. If we’re doing this with a goal, what is the experience driver? What is pushing the goal? We could have a real task that we’re layering AR on top of to support success (more performance support than learning). In VR, we totally have to have a goal in the simulated world. AV strikes me as something that has a virtual world created story that uses virtual images and real locations. Kind of like The Void experience.

This reminded me of the Augmented Reality Games (ARGs) that were talked about quite a bit back in the day. They can be driven by media, so they’re not necessarily limited to locations. A colleague had built an engine that would allow experiences driven by communications technologies: text messages, email, phone calls, and these days we could add in tweets and posts on social media and apps. These, on principle, are great platforms for learning experiences, as they’re driven by the tools you’d actually use to perform. (When I asked my colleagues why they think they’ve ‘disappeared’, the reason was largely cost; that’s avoidable I believe.)

I like this continuum, as it puts ARGs and VR and AR in a conceptually clear framework. And, as I argue for extensively, good models give us principled bases for decisions and design. Here we’ve got a way to think about the relationship between story and technology that will let us figure out what makes the best approach for our goals. This new reality (and the others) will be part of my presentation next month. We’ll see how it manifests by then ;).

Learning Lessons

16 May 2019 by Clark 1 Comment

So, I just finished teaching a mobile learning course online for a university. My goal was not to ‘teach’ mobile so much as develop a mobile mindset. You have to think differently than what the phrase ‘mobile learning’ might lead you to think. And, not surprisingly, some things went well, and some thing didn’t. I thought I’d share the learning lessons, both for my own reflection, and for others.

As a fan of Nilson’s Specifications Grading, I created a plan for how the assessment would go. I want lots of practice, less content. And I do believe in checking knowledge up front, then having social learning, and a work product. Thus, each week had a repeated structure of each element. It was competency based, so you either did it or not. No aggregation of points, but instead: you get this grade if you do: this many assignments correct, and write a substantive comment in a discussion board and comment on someone else’s this many times, and complete this level on this many knowledge checks. And I staggered the deadlines through the week, so there’d be reactivation. I’ve recommended this scheme on principle, and think it worked out good in practice, and I’d do it again.

In many ways it ‘teacher proofs’ the class. For one, the students are giving each other feedback in the discussion question. The choice of discussion question and assignment both were designed to elicit the necessary thinking, which makes the marking of the assignment relatively easy. And the knowledge checks set a baseline background. Designing them all as scenario challenges was critical as well.

And I was really glad I mixed things up. In early weeks, I had them look at apps or evaluated ones that they liked. For the social week, I had them collaborate in pairs. In the contextual week, they submitted a video of themselves. They had to submit an information architecture for the design week. And for the development week, they tested it. Thus, each assignment was tied to mobile.

It was undermined by a couple of things. First, the LMS interfered. I wrote careful feedback for each wrong answer for each question on the knowledge checks. And, it turns out, the students weren’t seeing it! (And they didn’t let me know ’til the 2nd half of the abbreviated semester!) There’s a flag I wasn’t setting, but it wasn’t the default! (Which was a point I then emphasized in the design week: start with good defaults!)

And, I missed making the discussions ‘gradeable’ until late because of another flag. That’s at least partly on me. Which meant again they weren’t getting feedback, and that’s not good. And, of course, it wasn’t obvious ’til I remedied it. Also, my grading scheme doesn’t fit into the default grading schema of the LMS anyways, so it wasn’t automatically doable anyways. Next time, I would investigate that and see if I could make it more obvious. And learn about the LMS earlier. (Ok, so I had some LMS anxiety and put it off…)

With 8 weeks, I broke it up like this:

Overview: mobile is not courses on a phone. The Four C’s.
Formal Learning: augmenting learning.
Performance Support: mobile’s natural niche
Social: connecting to people ‘on the go’
Contextual: the unique mobile opportunity
Design: if you get the design right…
Development: practicalities and testing.
Strategy: platform and policy.

And I think this was the right structure. It naturally reactivated prior concepts, and developed the thinking before elaborating.

For the content, I had a small set of readings. Because of a late start, I only found out that I couldn’t use my own mLearning book when the bookstore told me it was out of print (!). That required scrambling and getting approval to use some other writings I’d done. And the late start precluded me from organizing other writings. No worries, minimal was good. And I wrote a script that covered the material, and filmed myself giving a lecture for each week. Then I also provided the transcript.

The university itself was pretty good. They capped the attendance at 20. This worked really well. (Anything else would’ve been a deal breaker after a disaster many years ago when an institution promised to keep it under 32 and then gave me 64 students.) And there was good support, at least during the week, and some support was available even over the weekend.

Overall, despite some hiccups and some stress, I think it worked out (particularly under the constraints). Of course, I’ll have to see what the students say. One other thing I’d do that I didn’t do a good job of generally (I did with a few students) was explain the pedagogy. I’ve learned this in the past, and I should’ve done so, but in the rush to wrestle with the systems, it slipped through the cracks.

Those are my learning lessons. I welcome your feedback and lessons!