Learnlets

Secondary

Clark Quinn’s Learnings about Learning

On blogging

by 5 Comments

A recent chain of events led to a realization, and then a recognition, and some cogitation. What am I talking about? Well, it comes down to some reflections on blogging. So here’re some thoughts.

It started when my ISP wanted to do his quinquennial (yeah, I had to look it up) OS upgrade on the servers. Ultimately, it led me to review my site, which included my blogroll. Quelle horreur, it was almost completely out of date! Some people I’ve lost touch with, most who aren’t blogging any more or even in our field! In updating it, however, I found that there are many fewer people who seemed to be blogging. Which is interesting, though there are stalwarts in my upgraded blogroll.

There are lots of places people are putting up their prose thoughts. You can sign up for newsletters (I get a few), and many posts appear on LinkedIn. There are also article sites like Learning Solutions magazine and eLearnMag, amongst others. I have avoided having a newsletter;  I don’t like the idea of collecting folks’ email addresses and using it as a communication tool. (Completely contrary to the advice I receive about marketing.) I also don’t want to post just on LinkedIn, though it’s an increasing way people interact. Instead, I will keep posting here, trying to maintain at least one post a week.

There are myriad reasons I want to continue to blog. First, it’s for me. With a commitment of one post a week, it causes me to search for things to think, and then write, about. Not that there’s a dearth (to the contrary!), but there are ups and downs, and it’s good to have a driver. Blogging has caused me to do more than skim, and actually synthesize things (it’s led me to have thoughts on just about everything!). It’s also a place to lob my other way of thinking, diagramming. The practice of writing, of course, is probably good for my books, with a caveat.

The blog allows me to be more personal, doing things like using too many italics, and use more idiosyncratic references and grammar. Of course, it’s not always perfectly reread, so sometimes I have to go edit it after it’s posted! Which isn’t good for books. It also keeps me terse (a problem I’ve had since high school, my AP English teacher was sure I wouldn’t pass the test for that reason, but it actually was a benefit). Maybe too… Which may be good for books; at least mine are mostly pretty short and to the point ;). It’s also allowed me to share interim ideas and get feedback.

So, I find blogging to be valuable. I’ll happily follow the folks that I can that way. (I use Feedblitz as an email aggregator as I prefer email rather than a dedicated reader.) Or happy to come across their posts wherever, and even some newsletters. I appreciate folks who share their thinking in many ways, though I don’t really listen to podcasts nor watch vids, as I can read faster, and I don’t have a commute. Besides, having watched people I care about get taken down the rabbit hole watching vids (my take: doesn’t give you time to pause and ponder), I think I’ll prefer prose.

So those are some thoughts on blogging. I welcome seeing your comments here, on LinkedIn, or any other way you care to share.

Reality Checks

by 2 Comments

Of late, there seem to be a rising number of claims: for X or Y, or against Z. This, by the way, happens outside L&D as well, so feel free to extrapolate. Here, however, I want to talk about the necessity of, and some practices for, reality checks.

The problem is that people have vested interests in particular views. Many of the claims that are pushed generate revenue for them, directly or indirectly. They may want you to buy their product, avail yourself of their services, or more. And I get it; I too need to keep the wolves from the door. However, there are ways legit and less so to do it.

So one of the first reality checks is: what does who stand to gain? What’s their angle? Just as when I criticize something and you should rightly query why I’m raising the issue, similarly you should be asking the same of the claim. What’s their angle?

I’m pretty clear that I want our industry to be solid, and yes I want to be someone you might bring in to assist you in avoiding the pitfalls and hew to the best outcomes for your org. Similarly, the folks I’m critiquing might have an angle. They may have a tool, for instance, that they want you to use. Find out what their personal benefit is!

This looking at both sides is a second reality check. I recently heard a colleague claim that when he looks at something new, he immediately looks for contradicting evidence. That’s pretty smart, given that our cognitive architecture has a confirmation bias. That is, we’re inclined to look for information that supports our beliefs, and discount any other. I reckon it’s worth keeping an open mind.

This is a way you can go deeper. What do others say? Are their trusted folks who are supporting the view, or are they leery? What’s their expertise? Some folks will allude to some relevant expertise only for it to be shown that it’s tangential. Similarly, what’s the data say? Is there data? How valid is it? Is it relevant to you?

Ultimately, I want you to  stay curious! I reckon that we all can learn more, and should. Learning more doesn’t mean just accumulating information, it means being willing to be wrong, admitting it, and improving. You need to be running your own reality checks on what you, and others, believe. Here’s to a steady increase in the reality of our field!

What’s In It For Them?

by Leave a Comment

One of the things I talk about in my most recent book,  Make It Meaningful, is the importance of communicating the WIIFM (What’s In It For Me). I do think it’s important, but in recent work I’ve found an interesting alternative. I’m not sure I completely have my mind around how to address it, so as I’m wont to do, here’s some ‘thinking out loud’ about What’s In It For Them (WIIFT).

To start, WIIFM is about connecting learners to a visceral understanding of the reason for the learning experience. There should be a clear value proposition, to them.  It can be either having to do with either the consequences of having the resulting skill, or not. The point is that they ‘get’ that they need this (then there’s more). I believe that learners will invest in learning if they understand why.

However, in this instance, we have audiences who may or may not be interested. This is a suite of offerings, different for different potential clients. What we want here is for them to quickly determine  whether there’s WIIFM. We don’t think everyone will be appropriate for every thing we’re providing. Importantly, we don’t want them to waste time on ones that aren’t relevant. So we very quickly want to establish what’s in it  for the appropriate audience.

There are a number of ways to send signals. For one, the filename and the title of the resource can (and should) be clear what this particular thing about. Then, there should be a brief description of why this particular thing exists. Then, there can be a brief introduction saying what is going on. Obviously, all should align, so that folks can get in with the minimal effort to get there.

This, to me, suggests that the intro either explicitly making it clear  who we think  is   the audience, or provides an initial statement of what the outcomes are so that individuals can self-select. I’m not sure yet which I think is better, or even whether it’s useful to do both. There’s a tradeoff, of course; brevity is useful, and so is clarity. I suppose we can always make our best guess in the instance. For sure we’ll test it.

So, I’ve been led to wonder how to communicate What’s In It For Them so that they know whether they’re ‘them’ or not! There are also probably converging influences. I reckon marketing has this issue, as does documentation? What have seen/done/found out? I welcome your input.

 

The ‘late adopter’ strategy

by 2 Comments

I was asked about the latest techno-hype, bionic reading. At the same time, there’s a discussion happening about learning affordances of the metaverse. I realize my strategy is the same, which I learned many years ago (wish I could remember from whom!). The short version is, wait until the dust settles. Why? Let’s evaluate the late adopter strategy.

So, for anything new, there all-too-frequently seems to be a lot of flash. In my experience, a lot more than substance! That is, many things rise, and most fall. When things calm down after the initial exuberance, most simply disappear. There are myriad factors: acquisition and shut down by competitors, other elements fail despite a good premise, or even unexpected factors outside of control (e.g. a pandemic!). Of course, the usual suspect is that there’s no real there there!

I remember the hype over Second Life, and recognizing that the core elements were 3D and social. Yet, what we saw were slide presentations in a virtual world. Which was nonsensical. I’ve suggested before that you can infer the properties of new technologies, in many cases, by considering their cognitive affordances. I’ll await the meta-verse manifestation, but it seems to me to be the same, just more immersion. Still, lots of technical and cognitive overhead to make it worthwhile.

Similarly with bionic reading. There’s now  lots  of anecdotal suggestions that it’s better. That’s not the same, however, as a true experimental study. Individual experiences don’t always correlate with actual impact. There’re myriad reasons for this too, e.g. self-fulfilling prophecy, perception vs reality, etc. Still, I really want to have some more convergent evidence. Here it’s harder to do the affordances. Yes, it might support people who have difficulty reading, but might it interfere with others? How will we know?

On the basis of the above, however, I suggest waiting until something’s been around, and then if it persists, start investigating what the affordances might be. Many things have come and gone, and I’m glad I didn’t bite. I might then be late to a platform, but that’s OK. I still tend to get opportunities to innovate around ideas of application  after they’re established, because, well, that’s what I do ;). Affordances help, as does lateral thinking and having on tap  lots of mental models to spark ideas.

We’re too easily enchanted with the latest shiny object. No argument it’s worth experimenting with them, but don’t swallow the hype until you’ve either had your own data, or someone else’s. I reckon rushing in has a greater opportunity for loss than gain. Let those with needs, resources, and opportunity take the first cuts. There’s no need to bleed prematurely, there’ll be plenty of opportunities to need to tune and test again even once principles emerge. So that’s my take on the value of a ‘late adopter’ strategy. What’s yours?

The cognitive basis of LXD

by Leave a Comment

Image of the brainWhen ATD asked me to write the learning science book, I’d already had the intention of writing a Learning Experience Design (LXD) book. I’d even begun, and the first section on learning was underway, so essentially I was partly done! I’d also realized that it was going to be monumental undertaking. This is because LXD, to me, encompasses three things, all based on cognitive science. To properly address it, I would have to be talking a master’s course, not just a book!. So here I’d like to make the case why I think that there’s a cognitive basis of LXD.

First, look at the three elements of LXD: learning, experience, and design. For experience, you can think: engagement and/or emotion. That is, ensuring that there’re explicit feelings associated, not just occurrences. Each one of those three things, then, has a cognitive underpinning.

As I’ve discussed previously, learning science was an outgrowth of cognitive science. The inter-disciplinary approach to cognition that inspired the formation of cognitive science subsequently led to learning science. Design, too, was a subject of study. I happened to be a grad student at the time that user-centered approaches, subsequently UX, were being explored. This, too, is cognitive; first because design approaches have to reflect aligning with how users brains work. Then, also, because design processes have to accommodate how designers brains work, and don’t!

Then we come to the experience side. It turns out that understanding ‘experience’ is a cognitive exercise as well. Why are we driven by curiosity? How come we remember emotionally-charged events better? What creates positive affect? It’s an interdisciplinary approach as well, integrating research on emotion and events and more. It’s the topic of my just-released book (which includes design as well, to serve as the complement to my learning science book).

I continue to explore all three, from a professional responsibility and personal interest. I admit I nerd out about these things, and am always eager to find out more and discuss it. And  I’ve do have  a bias. My Ph.D. is in Cog Psych, so I do look at world with that filter. But I also see that the perspective provides some useful leverage. My current ideal is to make experiences that are transformative, in that they change people in ways that they want, or need, to change. That’s the goal.

I will continue to maintain that knowing the underpinning architecture, and then the manifestations in the three areas, are important. I believe that knowing the cognitive basis of LXD is an advantage in being able to execute against the requirements in optimal ways. So, am I missing anything?

 

Confidence and Correctness

by Leave a Comment

Not surprisingly, I am prompted regularly to ponder new things. (Too often, in the wee hours of the morning…) In this case, I realize I haven’t given a lot of thought to the role of confidence  (PDF). It’s a big thing in the system my co-author on an AI and ID paper, Markus Bernhardt, represents, so I realized it’s time to think  about it some more. Here are some thoughts on confidence and correctness.

Confidence by correctnessThe idea is that it matters whether you get it right, or not, and whether you’re confident, or not. That is, they interact (creating the familiar four quadrant model). You can be wrong and unconfident (lo/no), wrong and confident (hi/no),  right and unconfident (lo/yay), and right and confident (hi/yay). Those are arguably importantly different. In particular for what they imply about what sort of intervention makes sense.

I was pondering what this suggests for interventions. I turned it 90 degrees to the left, to put low/no to the left, or beginning spot, and hi/yay to the right, and the other two in-between.  Simplified, my view is that if you’re wrong and not confident, you don’t know it. If you’re wrong and believe you know it, you’re at a potential teachable moment. When you’re right, but not confident, you’re ready for more practice. If you’re right and confident, it may be time to move on.

Which suggests, looking back at my previous exploration of worked examples, that the very first thing to do is to provide worked examples if they’re new. At some point, you give them practice. If they get it right but aren’t confident, you give more practice at roughly the same level. If they’re wrong but confident, you give them feedback (and arguably ramp them backwards). Eventually they’re getting it right  and confident, and at that point you move on (except for some spaced and varied reactivation).

Assessing confidence is an extra step, but there seems to be a valid reason to incorporate it in your learning design. The benefits of being able to more accurately target your interventions, at least in an adaptive system, suggest that the effort is worth it. That’s my initial thinking on confidence and correctness. What’s yours?

My Personal Knowledge Management Approach

by Leave a Comment

Last week, in our Learning Development Accelerator You Oughta Know session, we had Harold Jarche as a guest. Harold’s known for many things, but in particular his approach to continual learning. Amongst the things he shared was a collection of others’ approaches. I checked and I hadn’t made a contribution! So with no further ado, here’s my personal knowledge management approach.

First, Harold’s Personal Knowledge Management (PKM) model has three components: seek, sense, and share. Seeking is about information coming in, that is, what you’re looking for and the feeds you track. It can be in any conceivable channel, and one of the important things is that it’s  your seeking. Then, you make sense of what comes in, finding ways to comprehend and make use of it. The final step is to share back out the sense you’ve made. It’s a notion of contributing back. Importantly, it’s not that necessarily anybody consumes what you share, but the fact that you’ve prepared it for others is part of the benefit you receive.

Seek

Most seeking is two-fold, and mine’s no exception. First of all there’s the ‘as needed’ searches for specific information. Here I typically use DuckDuckGo as my search engine, and often end up at Wikipedia. With much experience, I trust it.  If there are multiple hits and not a definitive one, I’ll scan the sources as well as the title, and likely open several. Then I review them until I’m happy.

The second part is the feeds. I have a number of blogs I’m subscribed to. There are also the people I follow on Twitter. On LinkedIn, a while ago I actively removed all my follows on my connections, and only retained ones for folks I trust. As I add new people, I similarly make a selection of those I know to trust, and ones who look interesting from a role, domain, location, or other diversity factor.  An important element is to be active in selecting feeds, and even review your selections from time to time.

Sense

Sometimes, I’m looking for a specific answer, and it gets put into my work. Other times, it’s about processing something I’ve come across. It may lead me to diagramming, or writing up something, frequently both (as here). Diagramming is about trying to come to grip with conceptual relationships by mapping them to spatial ones. Writing is about creating a narrative around it.

Another thing I do is apply knowledge, that is put it into action. This can be in a design, or in writing something up. This is different than just writing, for me. That is, I’m not just explaining it, I’m using it in a solution.

Share

To share, I do things like blog, do presentations and workshop, and write books. I also write articles, and sometimes just RT. Harold mentioned, during the session, that sharing should be more than just passing it on, but also adding value. However, I do sometimes just like or share things, thinking spreading it to a different audience is value. If you’re not too prolific in your output, I reckon that the selected shares add value. Of course, in general if I pass things on I do try to make a note, such as when sharing someone else’s blog that I thought particularly valuable.

So that’s my process. It’s evolving, of course. We talked about how our approaches have changed; we’ve both dropped the quantity of posts, for instance. We’re also continually updating our tools, too. I’ve previously noted how comments that used to appear on my blog now appear on LinkedIn.

To be fair, it’s also worth noting that this approach scales. So workgroups and communities can do a similar approach to continually processing. Harold’s done it in orgs, and it factors nicely into social learning as well. One attendee immediately thought about how it could be used in training sessions!

So that’s a rough cut at my PKM process. I invite you to reflect on yours, and share it with Harold as well!

I discuss PKM in both my Revolutionize L&D book, and my Learning Science book.

Experts and Explanations

by 1 Comment

blueprint pencil rulerI’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?

Examples before practice

by 4 Comments

I’ve been wrong before, and I’ll be wrong again, and that’s ok <warning: link is NSFW>. It’s like with science: if you change your mind, you weren’t lying before, you’ve learned more now. So I’ve been wrong about the emphasis between practice and examples. What I’ve learned is that, in general, practice isn’t the only area of importance, and the benefits of examples before practice.

So, as part of the Learning Development Accelerator‘s YOK (You Oughta Know) series, I got the chance to interview John Sweller. I’ve known John, I’m very honored to say, from my days at UNSW. I was aware of his reputation as a cog sci luminary, but he also turned out to be a really  nice person. He’s the originator of Cognitive Load Theory (CLT), and he was kind enough to agree to talk about it.

As background, he’s tapped into David Geary’s biologically primary and biologically secondary learning. The core idea is that some things we’ve evolved to learn, like speaking. Then there are things we’ve developed intellectually, like reading and writing, that aren’t natural. Instruction is to assist us to acquire the latter.  The latter typically has high ‘element interactivity’, whereby there are complex interrelationships to master. That is, it’s complex.

CLT posits that we have limited cognitive capacity, and overwhelming that capacity interferes with learning. The model talks about two types of load. The first is intrinsic load, that implied by the learning task. The second is extrinsic load, coming from additional factors in the particular situation. The premise is that learning complex things (biologically secondary) has such a high intrinsic load that we really need to focus on managing load so we can gradually acquire the entailed relationships.

There are a number of implications of CLT, but one is about the value of worked examples. An important element is showing the thinking  behind the steps. A second empirical result is that worked examples  are better than practice! At least, initially, for novices. Yet this upends one of my recommendations, which is generally that the most important thing we can do to improve our learning is focus on better practice. I still believe that, but now with the caveat after worked examples.  

Now, he didn’t tell us when that happens, e.g. when you switch from worked examples to practice. However, like the answer to how much spacing needed for the spaced practice effect, I suspect the answer is ‘it depends’. There’s the ‘expertise reversal’ effect that says as you gain experience, the value of worked examples falls and the value of practice raises. That point, I’d suggest, is dependent on the prior knowledge of the learners, the complexity of the material, the scope, and more.

I’m now recommending, particularly for new material, that improving the learning outcomes includes meaningful practice  after  quality worked examples. That’s my new, better, understanding. Make sense?

As an aside, I talked about CLT in my most recent book, on learning science, with a diagram. In it, I only included intrinsic and extrinsic, as those two seemed critical, yet the classic theory also includes  germane intrinsic load. One of the audience members asked him about that, and John opined that he probably needn’t have included germane. Vindication!

Good and bad advice all in one!

by 2 Comments

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!