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!)

I’m deeply steeped in the cognitive sciences, owing to a Ph.D. in cognitive psych. Fortuitively, this was at the time my advisor was creating the cognitive science program (and more). So I’ve a bias. Yet I also have a fair bit of empirical evidence that taking a cognitive perspective accomplishes things that are hard to do in other ways. So let me make the case that the cognitive perspective is more than just a useful one, but arguably a necessary one.

I‘ll start by reflecting back on something I wrote before, about virtual world affordances. At the time, platforms like Second Life were touting the advantages of an immersive navigable world. Of course, the promises were all-encompassing: everything would move to virtual worlds. In retrospect, it didn‘t eventuate. Why? I argue it’s because the cognitive overhead of virtual worlds means that there has to be a sustained value proposition, and that came from when you truly need 3D immersion and social.  

Similarly, when I wrote my books on games and mobile, I focused on the cognitive impacts. The first reason was because technology was changing so fast that anything hardware-specific would be out of date before the book was published. The second is because our brains don‘t change that fast, so what works will work regardless of the technology .  

Note that our understanding of cognition has changed. We‘re now in a ‘post-cognitive‘ era, where the notion that all our formal, logical thinking is done in our heads is wrong. Research is showing that we‘re far more ‘situated‘ than we think, and distributed as well. That includes distributed across external representations and other people! It’s very contextual, and it’s not all in our heads!

So these days, when I look at things, I try to look with a cognitive (ok, post-cognitive) perspective. I look to see how things align, or not, with how our brains work. When I evaluate learning technologies, for instance, I look to see how well they do things like provide meaningful practice: active and contextualized. You can also see when particular technologies (e.g. VR/AR/AI) will be valuable, and not. Similarly, when I look at workplace change proposals, I look at how well they reflect our mechanisms for adapting to change.  

I‘ll argue that these perspectives are valuable. You can quickly see why most training doesn‘t work, cut through hype from vendors, create explanations about why myths are mythtaken, etc. You can save money, be more effective, etc when you align with how our brains work. I‘ve talked before about how there are gaps. This is the flip side, how to avoid those gaps, and do better.   In short, you‘re better able to assist your organization in being more effective (and efficient).  

That‘s why I‘m pleased that I am able to put these basics into the learning science book, and workshops. It‘s possible to get better at this sort of perspective. It‘s also possible to get it on tap as needed. However, it does take both the cognitive understanding and the experience in applying it. So, how‘s your cognitive perspective?

On a side note, I want to encourage you to consider my workshop at DevLearn on Make It Meaningful, a full day exploring how we make learning experiences deeply engaging (adding to effectiveness). This is also the topic of my online workshop through the Learning Development Accelerator. This is, to me, the most important topic to  complement  learning science. (Available as a book and workshop. ;) In both cases, I’m trying  to help us  stop making boring courses that people want to avoid, and suggest that this  can be done for most any topic. It also leads to more effective learning outcomes! Hope to see you at one! (Of course, if your organization would like your own private version, let me know!)