I was talking with my better half, who’s now working at a nursery. Over time, she has related stories of folks coming to ask for assistance. And the variety is both interesting and instructive. There’s a vast difference of how people can be working with you.

So, for one, she likes to tell stories of people who come in saying “you know, I want something ‘green'”.  Or, worse, “I want a big tree that doesn’t require any watering at all”. (Er, doesn’t exist.)  The one she told me today was this lady who came in wanting “you know, it’s white and grows like <hand gesture showing curving over like a willow>”.  So m’lady showed her a plant fitting the description. But “no, it’s not got white flowers”.  It ended up being a milkweed, which isn’t white and stands straight up!

What prompted this reflection was the situation she cited of this other customer. He comes in with a video of the particular section he wants to work on this time, with measurements, and a brief idea of what he’s thinking. Now this is a customer that’s easy to help; you can see the amount of shade, know the size, and have an idea of what the goal is.

I related this (of course ;), to L&D. What you’d like is the person who comes and says “I have this problem: performance should be <desired measurement> but instead it’s only <current measurement>. What steps can we take to see if you can help?”  Of course, that’s rare.  Instead you get “I need a course on X.”  At least, until you start changing the game.

JD Dillon tweeted “…But in real life they can’t just say NO to the people who run the organization. ‘Yes, and …’ is a better way to get people to start thinking differently.” And that’s apt. If you’ve always said “yes”, it’s really not acceptable to suddenly start saying “no”.  Saying “Yes and…” is a nice way to respond. Something like “Sure, so what’s the problem you’re hoping this course will solve?”

And, of course, you should be this person too. “Let me tell you why I’d like to buy a VR headset,” and go on to explain how this critical performance piece is spatial and visceral and you want to experiment to address it. Or whatever. Come at it from their perspective, and you have a better chance, I reckon.

You won’t always get the nice customers, but if you take time and work them through the necessary steps at first, maybe you can change them to be working with you. That’s better than working for them, or fighting with them, no?

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?

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).

—

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.

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?