I’ve argued both that Learning Experience Design (LXD) is an improvement on Instructional Design (ID), and that LXD is the  elegant integration of learning science with engagement. However, that doesn’t really unpack what are the critical ID/LXD differences. I think it’s worth looking at those important distinctions both in principle and practice. Here, I’m talking about the extensions to what’s already probably in place.

Principle

In principle, I think it’s the engagement part that separates the two. True, proper ID shouldn’t ignore it. However, there’s been too little attention. For instance, only one ID theorist, John Keller, has really looked at those elements. Overall, it’s too easy to focus purely on the cognitive. (Worse, of course, is a focus purely on knowledge, which really  isn’t good ID).

I suggest that this manifests in two ways. First, you need an initial emotional ‘hook’ to gain the learner’s commitment to the learning experience. Even before we open them up cognitively (though, of course, they’re linked)! Then, we need to manage emotions through out the experience. We want to do thinks like keep challenge balanced, anxiety low enough not to interfere, build confidence, etc.

We have tools we can use, like story, exaggeration, humor, and more to assist us in these endeavors. At core, however, what we’re focusing on is making it a true ‘experience’, not just an instructional event. Ideally, we’d like to be transformational, leaving learners equipped with new skills and the awareness thereof.

Practice

What does this mean in practice? A number of things. For one, it takes creativity to consider ways in which to address emotions. There are research results and guidance, but you’ll still want to exercise some exploration. Which also means you have to be iterative, with testing. I understand that this is immediately scary, thinking about costs. However, when you stop trying to use courses for everything, you’ll have more resources to do courses right. For that matter, you’ll actually be achieving outcomes, which is a justification for the effort.

Our design process needs to start gathering different information. We need to get performance objectives; what people actually need to do, not just what they need to know. You really can’t develop people if you’re not having them perform and getting feedback. You also need to understand  why this is needed, why it’s important, and why it’s interesting. It is, at least to the subject matter experts who’ve invested the time to  be experts in this…

Your process also needs to have those creative breaks. These are far better if they’re collaborative, at least at the times when you’re ideating. While ideally you have a team working together on an ongoing basis, in many cases that may be problematic. I suggest getting together at least at the ideating stage, and then after testing to review findings.

You’ll also want to be testing against criteria. At the analysis stage, you should design criteria that will determine when you’re ‘done’. When you run out of time and money is  not the right answer! Test usability first, then effectiveness, and then engagement. Yes, you want to quantify engagement. It doesn’t have to be ‘adrenaline in the blood’ or even galvanic skin response, subjective evaluations by your learners is just fine. If you are running out of time and money before you’re achieving your metrics, you can adjust them, but now you’re doing it on consciously, not implicitly.

I’m sure there more that I’m missing, but these strike me as some critical ID/LXD differences. There are differences in principle, which yield differences in practice. What are your thoughts?

On my walk yesterday, I was reflecting on our You Oughta Know with Christy Tucker  (a great session, as usual), who talked about scenarios. It got me pondering, in particular, about different interpretations of ‘gamification‘. As I dictated a note to myself as I walked (probably looking like one of those folks who holds phone calls on their perambulations), I found myself discussing the differences between two approaches. So here’re some thoughts on gamification or the alternative.

To start, let’s say we have a learning goal. For instance, how to deal with customers. A typical approach would be, after an initial course, to stream out questions about different aspects of the principles. For this, you might give points after correctly answering n. Once you answer n, you get X points (10, 100, 1000, whatever). 2n gets you 2X points or maybe 3X. These points may entitle you to prizes: swag, time off, office party. Pretty typical gamification stuff.

Then, consider an alternative: they do successively more challenging scenarios. That is, initially it’s an easy customer with a straightforward problem. Then, it’s a mix of more difficult customers with simple problems and easy customers with more difficult problems. Finally, you’re dealing with difficult customers  and difficult problems. Along the way, you give badges for successive levels of customer difficulty, and similarly for handling increasing levels of difficulty of problems.

Which of these is easier to implement? Will one or the other lead to better handling of customers? Which will lead to long-term engagement of your employees? Of course, these are extremes. You can have the questions in the ‘prize’ situation get steadily more challenging. They can even be written as ‘mini-scenarios’. You can mix in scenarios with knowledge questions.

What I want to suggest, however, is that  not doing the latter, the scenarios, is going to keep any initiative from having the biggest impact. They’re competency-based, providing explicit levels of capability. They’re also a chance to practice when it doesn’t matter, before it does.

This shouldn’t stand alone. Of course there should be coaching, and increasing responsibility, and more. It’s not about just the formal learning. Extending the learning experience should include both formal and informal mechanisms. The point I want to make, however, is that having learners perform in practice they way they’ll need to perform when it matters, is the best preparation. Yes, you need knowledge (the stuff that, increasingly, AI can handle), but then you need meaningful practice.

Of course, if it’s something you do frequently after the learning experience, coaching may be enough. However, if aspects of it are rare but important, scenarios are the important reactivation practice that will keep skills tuned. So, that’s my take on gamification or alternates. How would you fine tune my response?

As I’ve noted before, someone on LinkedIn asked a question, and it’s prompting a reply. In this case, the question was in response to my previous post on superstitions (for new L&D practitioners). He asked “How did we even get here?” I’ve talked before about the sorry state of our industry, but haven’t really shared my thinking on why this is the case. My short response was that it’s complex. Here’s the longer response, trying to answer why L&D isn’t better.

First, I think we’re suffering from some mistaken beliefs. In particular, that presenting information will lead to behavior change. As I’ve noted before, I think this is a legacy of our beliefs that we’re formal logical reasoners. That is, if we were such beings (we’re not), this would likely be true. We’d respond to information by changing how we act. Instead, of course, we don’t change our behavior without practice, reinforcement, etc.

Another contributor, I suggest, is that a belief that if we can perform, we can teach. We can, therefore, take the best performer, and turn them into a trainer. Which is mistaken for a couple of reasons. For one, expertise is compiled away, and isn’t accessible. Estimates suggest around 70% of what experts do, they literally can’t tell us. It’s also a mistake to think that just anyone can teach. There’re specific skills that need to go into it.

Of course, we’re not aware of our flaws. We don’t measure, by and large. Even when we do, we too often measure the wrong things.  So, we see the bad practice of just looking at what learners think of the experience. Which has little correlation with the actual impact. We seldom look to see if the learning has actually changed any behavior, let alone whether it’s now at an acceptable level.

I do think we also still see the effects of 9/11. When we didn’t want to travel, we went to elearning. Rapid eLearning tools emerged to make it fast to take the PPTs and PDFs from the previous courses and put them onscreen with an added quiz. This has led to expectations that courses can be churned out quickly. Indeed, except that these ‘courses’ won’t have any impact!

One other factor is that our stakeholders also don’t know nor care. They know they need to invest in learning, so they do. It’s a cost center, not a driver of business success. No one is (yet) calling us on the carpet to justify our success. That’s changing, however. I just would like for us to be proactive, not reactive. Moreover, there’s a bigger opportunity on tap, not only to help the organization execute on the things that it needs to do, but also to facilitate the new knowledge the org will need.

In short, we don’t seem know what learning is, and we’re blind to the fact that our approaches aren’t useful. These, of course, are all premises I’ve addressed in my call to Revolutionize L&D. I still think there’s a meaningful role for L&D to play, but we have to lift our game. That’s my explanation of why L&D isn’t better, what’s yours?

It’s become obvious (even to me) that there are a host of teachers moving to L&D. There are also a number of initiatives to support them. Naturally, I wondered what I could do to assist. With my reputation as a cynic apparently well-secured, I’m choosing to call out some bad behaviors. So here are some superstitions for new practitioners to watch out for!

As background, these aren’t the myths that I discuss in my book on the topic. That would be too obvious. Instead, I’m drawing on the superstitions from the same tome, that is things that people practice without necessarily being aware, let alone espousing them. No, these manifest through behaviors and expectations rather than explicit exhortation.

• Giving people information will lead them to change. While we know this isn’t true, it still seems to be prevalent. I’ve argued before about why I think this exists, but what matters is what it leads to. That is, information dump and knowledge-test courses. What instead we need is not just a rationale, but also practice and then ongoing support for the change.
• If it looks like school, it’s learning. We’ve all been to school, and thus we all know what learning looks like, right? Except many school practices are only useful for passing tests, not for actually solving real problems and meeting real goals. (Only two things wrong: the curriculum and the pedagogy, otherwise school’s fine.) It, however, creates barriers when you’re trying to create learning that actually works. Have people look at the things they learned outside of school (sports, hobbies, crafts, etc) for clues.
• People’s opinion is a useful metric for success. Too often, we just ask ‘did you like it’. Or, perhaps, a ‘do you think it was valuable’. While the latter is better than the former, it’s still not good enough. The correlation between people’s evaluation of the learning and the actual impact is essentially zero. At least for novices. You need more rigorous criteria, and then test to achieve.
• A request for a course is a sufficient rationale to make one. A frequent occurrence is a business unit asking for a course. There’s a performance problem (or just the perception of one), and therefore a course is the answer. The only problem is that there can be many reasons for a performance problem that have nothing to do with knowledge or skill gaps. You should determine what the performance gap is (to the level you’ll know when it’s fixed), and the cause.  Only when the cause is a skill gap does a course really make sense.
• A course is always the best answer. See above; there are  lots  of reasons why performance may not be up to scratch: lack of resources, wrong incentives, bad messaging, the list goes on. As Harless famously said, “Inside every fat course there‘s a thin job aid crying to get out.” Many times we can put knowledge in the world, which makes sense because it’s actually  hard to get information and skills reliably in the head.
• You can develop meaningful learning in a couple of weeks. The rise of rapid elearning tools and a lack of understanding of learning has led to the situation where someone will be handed a stack of PPTs and PDFs and a rapid authoring tool and expected to turn out a course. Which goes back to the first two problems. While it might take that long to get just a first version, you’re not done. Because…
• You don’t need to test and tune. There’s this naive expectation in the industry that if you build it, it is good. Yet the variability of people, the uncertainty of the approach, and more, suggest that courses should be trialed, evaluated, and revised until actually achieving the necessary change. Beware the ‘build and release’ approach to learning design, and err on the side of iterative and agile.

This isn’t a definitive list, but hopefully it’ll help address some of the worst practices in the industry. If you’re wary of these superstitions for new practitioners, you’ll likely have a more successful career. Fingers crossed and good luck!

There’s some overlap here with messages to CXOs  1 and 2, but a different target here.  

Our brains like to categorize things; in fact, we can’t really  not do it. This has many benefits: we can better predict outcomes when we can categorize the situation, we can respond in appropriate ways via shared conceptualizations, and so on. It also has some downsides: stereotyping, for one. I reckon there’re tradeoffs, of course. But we also have to worry about when we over-use categorization, we can risk making the wrong bucket lists.

Our desire for simplification and categorization is manifest. The continued interest in reading one’s horoscope, for instance. And the continued success of personality typings, despite the evidence of their lack of utility. Other than the Big 5 or HEXACO, the rest are problematic at best. I’m just reading Annie Murphy Paul’s  The Cult of Personality Testing  (the predecessor to her  The  Extended Mind) and hearing abuses like Rorschach tests being used in child custody decisions is really horrific. Similarly, to hear that people are being denied employment based on their color (not race, but their ‘color’ on a particular test, blue or orange) isn’t new and continues (as does the other, sadly).  Most of these tests don’t stand up to scientific scrutiny!

This is the explanation of learning styles, too, another myth that won’t die. Generations similarly. We like to have simplification. Further, there are times it’s useful. For example, recording your blood type can prevent potentially life-threatening complications. Having a basis to adapt learning, such as people’s performance (success or failure), also. Even more so if additional factors are added, such as confidence. Yet, we can overdo it. We might over-categorize, and miss important nuances.

Todd Rose’s  The End of Average made an excellent case for not trying to conform people into one bucket. In it, he points out that when we assign a single grade for complex performance, we miss important nuances. For instance, if you get it wrong,  why did you get it wrong? It matters in terms of the feedback we might give you. If you had one misconception instead of another, you should get different feedback than if you had the other.

How do we reconcile this? There’re benefits to simplifications, and risks. We have to be careful to simplify as much as we can,  and no simpler. Which isn’t an easy task to undertake. The best recommendation I can make is to be mindful of the risks when you do simplify. Maybe start more broadly, and then winnow down? Explicitly consider the risks and costs as well as the benefits and savings. We’re using learner personas in a project. Many times, these personas can differ on important dimensions, and characterize the audience space in ways that a simple ‘the learner’ can’t capture.

Overall, we want to make sure we’re only using simplifications and categorizations in ways that are both helpful  and scrutable. When we do so, we can avoid the wrong bucket lists. That should be our goal, after all.