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Clark Quinn’s Learnings about Learning

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!

Good and bad advice all in one!

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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!

Generic Thinking Skills?

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Recently, a colleague asked a few of us about our views on critical thinking skills. This is actually a contentious topic. There are broad claims of the need for them, increasingly, even showing up in job advertisements. On the other hand, researchers and others have weighed in against them, saying that expertise is the only lever. I tend to lump critical thinking skills in with the broader issue of generic thinking skills, so what are the issues?

Upfront, I’ll admit that I like the concept of generic thinking skills. Say, for instance,  learning-to-learn skills. That is, domain independent skills that lead to better approaches. It seems to make sense that, in the absence of specific knowledge, some general approaches are more useful than others. For instance, faced with a new domain, I’d be inclined to expect that systematic experimentation and observation would be better than random trial and error.

On the other hand, prominent psychologists like John Sweller and Paul Kirschner have said that domain-specific skills are the only way to bet. There is significant evidence that expertise matters in successful approaches to problem-solving, and others. While we have some innate skills for domains that are biologically primary, learning in other domains requires expertise.

Is there, then, any evidence for generic skills? Based on Micki Chi’s work on the value of self-explanation, Kate Bielaczyc and others have found that instruction on systematically explaining steps in examples help, across domains. In my own Ph.D. thesis, I trained folks on analogical reasoning skills, and found improvement (for component skills that weren’t a) already ceilinged or b) were perceived to be immutable, across different problem types.

How, then, do we reconcile these conflicting viewpoints? My (self- :) explanation is that it’s a matter of degree, a continuum rather than a dichotomy. The more domain knowledge you possess, the more likely you are to find a good answer. However, what if you’re in a new domain where you don’t have relevant expertise to hand? In that case, I’ll suggest that there are benefits to some approaches over others, and training those general skills is justifiable. That is, general skills are weaker than domain specific skills, but general skills are better than nothing!

We know that there are practices that improve outcomes. For instance, I’ve written about how to, and not to, do brainstorming. Similarly, I believe Harold Jarche’s Seek-Sense-Share model works across domains. Systematic creativity is  not an oxymoron!  That’s the story I’m holding on to about generic thinking skills. What are your thoughts on the topic?

Accreditation?

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As occasionally happens, I was asked a question on LinkedIn. In this case, it was about my thoughts on accreditation. Also, as occasionally happens, I thought that I’d share my thoughts in this forum, and look for feedback to improve my thinking. So here’re some thoughts on accreditation. I welcome yours!

First, let’s be clear, I am not an expert on accreditation. I haven’t accredited anything, for one ;). I  did look into it, at one point many years ago. I’ve also served on independent board of directors or advisory boards for several entities. In the former case, we have a legal responsibility to provide guidance. In the latter case, we provide the best guidance, but of course the organization isn’t obliged to comply. The former, in particular, serves as a quality check, or a form of accreditation.

When I looked into accreditation for educational institutions, the requirement isn’t about the actual curriculum, but instead that there is a library and that there are processes for review and revision of course offerings. That is, it was about the support for learning and quality processes, not the actual offering. This creates a process support that should ensure quality, yet also the ability to apply this to institutions with a wide variety of offerings.

Institutions can also seek accreditation by organizations in particular areas of curriculum. Offerings in computer science, business, and others for instance, receive review and then can receive approval by bodies that represent the particular field. This depends on the quality of the organization doing the accreditation and their processes, of course.

There’s also accreditation on the quality of the educational process. You can also be reviewed and accredited on the basis of your pedagogy, for instance your online teaching approach. It depends, of course, on what they stipulate as quality, but that’s always going to be the case.

There are, of course, dubious accreditations. It’s not unknown for an organization or collection thereof to establish their own accrediting body that basically rubber stamps the organization(s). Caveat emptor.

In general, I think that having a scrutable external validation is a good check on quality. Whether that makes sense is probably an issue of scope. A small offering of a particular course might benefit from an independent advisory board, which provides some oversight. The larger the organization and the scope of activity, the greater the need for some external validity check.

From the other side, I think a certificate or credentials help the learner signify what they’ve accomplished.  However, without accreditation or at least a scrutable process, how do you know the skill/knowledge is appropriate and accurate? I think accreditation has the potential to be a ‘reality check’ on any offering.

Learning or Performance Strategy

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Of late, I’m working in a couple of engagements where the issue of learning and performance strategy have come up. It has prompted some thoughts both on my part and the part of my clients. I think it’s worth laying out some of the issues and thinking, and of course I welcome your thoughts. So here are some reflections on whether to use learning or performance strategy as an organizing concept.

In one case, an organization decreed that they needed a learning strategy. Taken with my backwards design diagram  from the learning science book, I was tasked with determining what that means. In this case, the audience can’t be mandated with classes or tutorials. So really, the only options are to support performance in the moment and develop them over time. Thus we focus on job aids and examples. I think of it as a ‘performance strategy’, not a learning one.

In the other case, an organization is executing on a shift from a training philosophy to a performance focus. Which of course I laud, but the powers-that-be expect it to yield less training without much other change. Here I’m pushing for performance support, and the thinking is largely welcome. However, it’s a mindset shift for a group that previous was developing training.

I general, I support thinking that goes beyond the course, and for the optimal execution side of a full ecosystem, you want to look at outcomes and let that drive you. It includes performance consulting, so you’re applying the  right solution to performance gaps, not the convenient one (read: ‘courses’ ;). Thus, I think it makes more sense to talk performance strategy than learning one.

Even then, the question becomes what does such a strategy really entail, whether learning  or performance. Really, it’s about having a plan in place to systematically prioritize needs and address them in effective ways. It’s not  just design processes that reflect evidence-informed principles, though it includes that. It’s also, however, ways to identify and track problems, attach organizational costs and solution costs, and choose where to invest resources. It includes front-end analysis, but also ongoing-monitoring.

It also involves other elements. For one, the technology to hand; what solutions are in use and ensuring a process of ongoing reviews. This includes both formal learning tools including the LMS and LXP, but also informal learning tools such as social media platforms and collaborative documents. Another issue is management: lifecycle monitoring, ownership, and costs.

There’s a lot that goes into it, but being strategic about your approach keeps you from just being tactical and missing the forest for the trees. A lot of L&D is reactive, and I am suggesting that L&D needs to be come proactive. This includes going from courses to performance, as a first step. The next step is to facilitating informal learning and driving innovation in the organization. Associated elements include meaningful measurement  and truly understanding how we learn for a firm basis upon which to ground both formal  and informal learning. Those are my thoughts a learning or performance strategy, what am I missing?

What makes a good book?

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I was in contact with a person about a potential book, and she followed up with an interesting question: what’s the vision I have for publishing? She was looking for what I thought was a good book. Of course, I hadn’t really articulated it! I responded, but thought I should share my thinking with you as well. In particular, to get your thoughts!  So, what makes a good book? (I’m talking non-fiction here, of course.)

My first response was that I like books that take a sensible approach to a subject. That is, they start where the learner is and get them realizing this is an important topic. Then the book walks them through the thinking with models and examples. Ultimately, a book should leave them equipped to do new things. In a sense, it’s the author leading the reader through a narrative that leaves them with a different and valuable view of the world.

I think these books can take different forms. Some shake up your world view with new perspectives, so for example Don Norman‘s Design of Everyday Things or Todd  Rose‘s The End of Average. Another types are  ones that provide deep coverage of an important topic, such as Patti Shank‘s  Write Better Multiple-Choice Questions.  A third type are ones that lead you through a process, such as Cathy Moore‘s Map It. These are rough characterizations, that may not be mutually exclusive, but each can be done to fit the description above.

To me the necessary elements are that it’s readable, authoritative, and worthwhile. That is, first there’s a narrative flow that makes it easy to process. For instance, Annie Murphy Paul’s The Extended Mind takes a journalistic approach to important phenomena.  Also, a book needs an evidence-base, grounding in documented experience and/or science. It can re-spin topics (I’m thinking here about Lisa Feldman Barrett’s  How Emotions Are Made), but must have a viable reinterpretation. Finally, it has to be something that’s worth covering. That may differ by reader, but it has to be applicable to  a field. You should leave with a new perspective and potentially new capabilities.

That’s what came off the top of my head. What am I missing in what makes a good book?