I’ve been going through several different forms of expert documentation. As a consequence, I’ve been experiencing a lot of the problems with that! Experts have trouble articulating their thinking. This requires some extra work on the part of those who work with them, whether instructional designers, technical writers, editors, whoever. There are some reliable problems with experts and explanations that are worth reviewing.

The start of the problem is that the way we acquire expertise is to take our conscious thinking and automatize it, basically. We want to make our thinking so automatic that we no longer have to consciously think about it. So, we basically compile it away. Which creates a problem. For one, what we process into memory may not bear a close resemblance to what we have heard and applied. That is, the semantic language we use to guide our practice and internalize may not be what we store as we automate it.

It’s also the case that we lose access to that compiled away expertise. There’s evidence of this, for one from the results of research by the Cognitive Technology group at the University of Southern California showing experts can’t access about 70% of what they do! Another piece of evidence is the widespread failure of so-called ‘expert systems’ in the 80s, resulting in the AI winter. Whether the locus of the problem is in what actually gets stored, or access to it, the result is that  what we were told to do, and say we do, may not actually be close to what we actually do.

Another problem is that experts also lose touch with what they grappled with as novices. What they take for granted isn’t even on the radar of novices. So it’s difficult to get them to provide good support for acquiring skills or understanding. Their attempts at explanations for reference of instruction fail.

All told, this leads to systematic gaps in content. I’ve been seeing this manifest in explanations that may say what to do, but not why or how. There may be a lack of examples, and the thinking behind the examples I  do see isn’t there.  There’s also a lack of visual support. They’re not including diagrams when it’s conceptual relationships that need understanding. They’re also not including images when context is needed. They shouldn’t necessarily be blamed, because they don’t need the support and can’t even imagine that others do!

It’s clear that experts should not be the ones doing the explanations. They’re experts, and they have valuable input, but there needs to be a process to avoid these problems. We need tech writers, IDs, and others to work with experts to get this right. Too often we see experts being tasked with doing the explanations, and we live with the consequences.

What to do? One step is to let experts know that their expertise is in their domain, but the expertise in extracting that expertise and presenting it lies in others. To do so convincingly, you’ll need the science about why. For another, know techniques to unearth that underlying thinking. Also allow time in your schedule for this to happen. Don’t think the SME can just give you information; you’ll have to process what you get to rearrange it into something useful. You may also need some sticks and carrots.

As I wrestle with the outputs of experts, here’s my plea. There are wonderful ways experts and explanations can work out, but don’t take it for granted. Don’t give experts the job of communicating to anyone but other experts, or to experts on working with experts to get explanations. Fair enough?

I was asked to go to read an article and weigh in. First, please don’t do this if you don’t know me. However, that’s not the topic here, instead, I want to comment on the article. Realize that if you ask me to read an article, you’re opening yourself up to my opinion, good  or bad. This one’s interesting, because it’s both. Then the question is how do you deal with good and bad advice all in one.

This article is about microlearning. If you’ve been paying attention (and there’s no reason you should be), I’ve gone off on the term before. I think it’s used loosely, and that’s a problem because there are separate meanings, which require separate designs, and not distinguishing them means it’s not clear you know what you’re talking about. (If someone uses the term, I’m liable to ask which they mean! You might do the same.).

This article starts out saying that 3-5 minute videos are  not  microlearning. I have to agree with that. However, the author then goes on to document 15 points that are important about microlearning. I’ll give credit for the admission that there’s no claim that this a necessary and complete set. Then, unfortunately, I also have to remove credit for providing no data to  support the claims!  Thus, we have to evaluate each on it’s own merits.  Sorry, but I kinda prefer some sort of evidence, rather than a ‘self-evident’ fallback.

For instance, there’s a claim for brevity. I’ve liked the admonition (e.g. by JD Dillon) that microlearning should be no longer, and no shorter, than necessary. However, there’s also a claim here that it should be “3 – 10 minutes of attention span”. Why? What determines this? Human attention is complex, and we can disappear into novels, or films, or games, for hours. Yes, “Time for learning is a critical derailer”, but…it’s a factor of how important, complex, and costly if wrong the topic is. There’s no one magic guideline.

The advice continues in this frame: there’re calls for simplicity, minimalism, etc. Most of these are good principles,  when appropriately constrained. However, arbitrary calls for “one concept at a time is the golden rule”  isn’t necessarily right, and isn’t based on anything other than “our brains need time for processing”. Yes, that’s what automation is about, but to build chunks for short term memory, we have to activate things in juxtaposition. Is that one concept? It’s too vague.

However, it could be tolerated if some of the advice didn’t fall prey to fallacious reasoning. So, for instance, the call for gamification leans into “Millennials and Gen Z workforce” claims. This is a myth. Gamification itself is already dubious, and using a bad basis as an assumed foundation exacerbates the problem.  There are other problems as well. For one, automatically assuming social is useful is a mistake. Tying competition into the need to compete is a facile suggestion. Using terms like ‘horde’ and ‘herd’ actually feels demeaning to the value of community. A bald statement like “Numbers speak louder than words!” similarly seems to suggest that marketing trumps matter. I don’t agree.

Overall, this article is a mixed bag. So then the question arises, how do you rate it? What do you do? Obviously, I had to take it apart. The desire for a comment isn’t sufficient to address a complex suite of decent principles mixed up with bad advice and justified (if at all) on false premises. I have to say that this isn’t worth your time. There’s better advice to be had, including on microlearning. In general, I’ll suggest that if there’s good and bad advice all in one, it’s overall bad. Caveat emptor!

It occurs to me that maybe not everyone has the same view of timekeeping that I do. So I thought I’d make the case for good timekeeping. To me, it’s about  coping with change.

To me, it starts with respect. This includes respect for the audience, the speaker, topic, and context. There are times when timekeeping  should be lax, I believe. My first take is that the defining circumstances are when there are no people involved who aren’t already part of it, there’s not a fixed time agenda, speaker times aren’t set, and the outcome is more important than punctuality.. In other words, rare for a public event.

When an event is public, there tend to be some other constraints. Most importantly, there’s liable to be a schedule. People need to know when to arrive. If it’s a more than one event in the schedule, extending beyond 1 hour, and the audience is diverse (e.g. not just one company), time becomes increasingly important.

Why should we care? Back to the starting point, people might be coming in to see someone in particular. If the schedule isn’t adhered to, folks who were counting on a particular time could be disappointed. If there’s a start and end time, and block, speakers further down the agenda could be impacted if someone runs long. Neither is fair.

I have experienced folks who seem to be unaware of time. In my personal experience as a speaker, I was on a joint presentation where the leadoff presenter seemed to forget that there was anyone else on the agenda! He was gracious once it was pointed out, but it was uncomfortable for me to have to break in and remind him. I’ve also seen people unaware that they were running long, and others unable to amend their presentation on the fly when they  did become aware. I’ve literally seen someone have to stop where they are instead of finding a way to wrap up!

Having had early experience  being  a moderator, after being the victim of such sessions, I made a commitment (in line with the event organizer’s intent) to be rigorous. I’ve now no qualms about, after giving fair warning, stopping someone who hasn’t maintained control. To the contrary of a possible position, I think it’s rude  not to! It’s the speaker’s fault, no one else’s. They aren’t being professional and respectful of the audience and the other speakers. That also includes getting out of the way in a timely manner if it’s a scheduled room, leaving time for the next person to set up.

So my guidelines for timekeeping:

1. Designate someone as the timekeeper; there can’t be a question over who’s job it is.
2. Warn speakers ahead of time about the rigor and practices. No excuses!
3. Have a practice for signaling if things are getting close, e.g. signs for # of mins left, colored lights, messages in chat (tho’ some people seem unable to process them; beware), what have you.
4. As a presenter, if you don’t have a good basis for assessing your likely length (e.g. I’m about a slide a minute, though with some quick builds it can be faster), practice and check your timing! Realize that live it’s likely to go a bit longer than your practice. Trim if necessary.
5. Also, have enough awareness of your material that you can adapt on the fly. Sometimes other things happen (once the power went out in a presentation), and you have to adjust.
6. Be firm; interrupt and stop speakers when it’s time.

That’s off the top of my head; I’m sure there are more comprehensive and thorough lists. My point is to be aware, and prepared. As a speaker, I appreciate it. As an audience member as well. That’s my case for good timekeeping.  What do you think?

I’ve long maintained that our organizational practices are too often misaligned with how our brains really work. I’ve attributed that to a legacy from previous eras. Yet, I realize that there may be another legacy, a cognitive one. Here I’ll suggest we need to move beyond Industrial Age thinking.

The premise comes from business. We transitioned from a largely agricultural economy to a manufacturing economy, of goods and services. Factories got economic advantage from scale. We also essentially treated people as parts of the machine. Taylorism, aka scientific management, looked at how much a person could produce if they were working as efficiently as possible, without damage. So few were educated, and we didn’t have sufficiently sophisticated mechanisms. Times change, and we’re now in an information age. Yet, a number of our approaches are still based upon industrial approaches. We’re living on a legacy.

Now I’m taking this is to our models of mind. The cognitive approach is certainly more recent than the Industrial Age, but it carries its own legacies. We regularly take technology as metaphors for mind. Before the digital computer, for instance, telephone switching was briefly used as a model. The advent of the digital computer, a general purpose information system, is a natural next step. We’re information processing machines, so aren’t we like computers?

It turns out, we’re not. There’s considerable evidence that we are not formal, logical, reasoning machines. In fact, we do well what it’s hard to get computers to do, and vice-versa. We struggle to remember large quantities of data, or abstract and arbitrary information, and to remember it verbatim. Yet we also are good at pattern-matching and meaning-making (sometimes  too good; *cough* conspiracy theories *cough*). Computers are the opposite. They can remember large quantities of information accurately, but struggle to do meaning-making.

My concern is that we’re still carrying a legacy of formal reasoning. That is, the notion that we can do it all in our heads, alone, continues though it’s been proven inaccurate. We make inferences and take actions based upon this assumption, perhaps not even consciously!

How else to explain, for instance, the continuing prevalence of information presentation under the guise of training? I suggest there’s a lingering belief that if we present information to people, they’ll logically change their behavior to accommodate. Information dump and  knowledge test are a natural consequence of this perspective. Yet, this doesn’t lead to learning!

When we look at how we really perform, we recognize that we’re contextually-influenced, and tied to previous experience. If we want to do things differently, we have to  practice doing it differently. We can provide information (specifically mental models, examples, and feedback) to facilitate both initial acquisition and continual improvement, but we can’t just present information.

If we want to truly apply learning science to the design of instruction, we have to understand our brains. In reality, not outdated metaphors. That’s the opportunity, and truly the necessity. We need to move beyond Industrial Age thinking, and incorporate post-cognitive perspectives. To the extent we do, we stand to benefit.

I’ve been in a variety of higher education roles in several ways: as a victim, er, student; as a grad student; post-doc; tenured/promoted faculty member; textbook publishing consulting;  strategic elearning consulting… Further, in general, I’m a supporter. I do have quibbles, and one is the persistence of learning myths. Trust me, I wrote a whole book on what the research says about them! In addition to having talked about org learning myths, let me explore some elements of higher-education myths.

I saw an article in the top education news source in the country, The Chronicle of Higher Education. I get their daily newsletter, just to keep my finger on the pulse. However, this article was touting issues for Gen Z students. Yet, research says that the ‘generations’ framework isn’t valid. There’s no reliable data that generations is a viable discrimination. In fact, it literally  is discrimination (in terms of using arbitrary distinctions to label people.

This is only part of the broader problem. A colleague regularly chides his alma mater for continuing to believe in learning styles. This, too, is a myth! While learners do differ, there’s no evidence we should adapt learning to learning styles, let alone can we reliably identify them. It’s appealing, but wrong. Not that it isn’t also prevalent amongst K12 teachers as well.

Which is related to another problem, business school curricula. I was surprised, and dismayed, to find that a prominent business school has personality instruments as part of it’s curricula! This includes MBTI, which is discredited both theoretically and empirically. Other such instruments, also with flaws, continue to be indicated. I’m sure there may be some financial motivation as well. (E.g. like Apple & Microsoft did offering huge discounts to schools, to get new users used to their experience.)

We should not tolerate learning myths in university. Aren’t these bastions of science? Ok, that’s another myth, that universities aren’t riven with politics, but that’s not the focus here. Still, universities should be better at rejecting myths, just as they should also be better about using the best pedagogies. Which they also aren’t doing, by and large ;).

There are more myths about universities, and issues like what their role in society  should be. That’s not what I’m talking about here, though. For all their other issues, they should  not be perpetuators of higher-education myths. (Here’s hoping they’re also not guilty of the ‘attention span of a goldfish’ myth!)