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.

For reasons that aren’t quite clear (even to me), I was thinking about where, on a continuum, do L&D elements fit? Where does performance support go? Formal learning? Informal learning? I began to think that it depends on what focus you’re thinking of. So here’re some nascent thoughts on emphasis and effort.

To start with, I generally think of formal learning as the starting point. For instance, in thinking about performance & development (as an alternative to learning & development), I put training first. Similarly, in my strategy work, I likewise suggest the first step is to put learning science more central in training. Here, the order is:

• Formal Learning
• Performance Support
• Informal Learning

I’m looking as much as where we typically start. This may well be because training is always the line of first-response (throw training at it!). Also perhaps because it’s familiar (it looks like school).

However, in another cut at it, I started with performance support. Here, I was thinking more about the utility to achieve goals rather than the way L&D allocates resources. That is, from a performer’s perspective, if the answer can be in the world, it should. I can use a tool to achieve my goal rather than have to take a course. Still, taking a pre-digested course is easier than having to work together to collaborate and solve it. Of course, if someone else has the answer, just asking and getting it is easier than working to create an unknown answer. (So, do I need to separate out communication from collaboration? Hmm…) Thus, the list here might be:

• Performance Support
• Formal Learning
• Informal Learning

However, if I look at it from the effort required from L&D, a new order emerges. Here, formal learning is hardest. That is, if you’re doing it right. To successfully get a persistent change in the way someone does something is harder than even facilitating informal learning, and performance support is easiest. Not saying that any are trivial, mind you, designing good job aids isn’t easy, it’s just not as hard as designing a whole course. Then the list comes out like this:

• Performance Support
• Informal Learning
• Formal Learning

I guess there isn’t one answer. To do this successfully, however, requires an understanding how to do all of the above, and then apply as priorities demands. If you’ve expert performers, you’ll do something different than if you have high turnover. If you’re doing something complex, your design strategies may differ from something important. However, you do need to know the tradeoffs in emphasis and effort to make the right calls. Am I missing something important here?

In a recent post, I talked about how expertise is compiled away, and the impact on designing learning and documentation. Someone, of course, asked how do you then work with SMEs to get the necessary information. Connie Malamed, one of our recognized research translators, has recently written about getting tacit knowledge, but I also want to address the more usual process.  I thought I’d written about it somewhere, but I can’t find it. So here are some thoughts on working with SMEs.

First, I’ve heard from several folks experienced in this that any one SME may not have both necessary elements. One element is to have a good model to guide the performance. The second elements is the ability to articulate that model. Their solution is to work with SMEs in groups. Guy Wallace (Eppic), Roger Schank (Socratic Arts), and Norman Bier (CMU) have all mentioned to me that they’ve found utility in getting SMEs together as a group and having that knowledge negotiation unpack the necessary learnings. They’re all folks worth listening to. You have to manage the process right, of course, but if you can do it, it’s useful.

I suggest that you also want several different types of SMEs. You want not only the top performers, and theoretical experts, you also want just-past novices (also attributable to Guy) and supervisors. Theorists can give you models, while top performers can talk about the practical implications. Novices can let you know what they found hard to understand, and supervisors provide insight into what performers typically do wrong. All are helpful information for different parts of the learning.

Another trick I use is to focus on decisions. I argue that making better decisions will be more important to organizations than the ability to recite knowledge. SMEs do have access to all the knowledge they learned, and it’s easy for them to focus on that. That’s where you get ‘info dump’ courses and bullet point-laden slides. By using decisions as a focus, you cut through the knowledge. “What decisions will they make differently/better as a result of this knowledge?” is a helpful question.

You can use questionnaires as well. Asking specifically about the elements: models, misconceptions, consequences, can be a good preliminary step before you actually talk to them. Or have a template for content for them to fill out. Any guidance and structure helps keep them focused.

Another preparatory step is to create a draft proposal of the information. You’ll likely be getting a dump of PDFs and PPTs). Process that material, and make your first, best, guess. It’s easier to critique than generate, so if you’re willing to be wrong (and why not), you can have them shoot holes in what you did. You’ll have focused on decisions (right?), and they’ll fix it, but you’ll have biased them for action.

Of course, you want to ensure you test for confirmation. You should circulate what you have learned, and get validation. You’ll need to have clear objectives that operationalize your learnings. You then should prototype and test what you’ve developed and see if it actually changes the behavior in useful ways. Ensure that your focus actually leads to the necessary change.

There are other elements you want from SMEs, such as their personal interest. However, it’s critical that you get them to focus on behavior change. It’s not easy, but it’s part of the job. Working with SMEs, correctly, is key to designing learning experiences that address real needs. These are my thoughts, what are yours?