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Clark Quinn’s Learnings about Learning

Archives for March 2022

My Personal Knowledge Management Approach

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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.

Emphasis and Effort

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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?

Working with SMEs

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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?

Experts and Explanations

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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

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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!