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Clark Quinn’s Learnings about Learning

Ethics, science process, and disagreements

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Recently, one of our LDA members provided a link to this argument, as a follow-on to a book club discussion. The book we covered in this meeting was David McRaney’s How Minds Change (a worthwhile read), and the article raises an issue that links science process, discussions, and society. Hence, it’s extremely interesting. This argument covers science process in a particular instance of theorizing, so it’s worth thinking about.

To start, the article talks about two theories of foundational disagreement. He cites the prominent theory of Jonathan Haidt’s about five (or six, or?) foundational moral values. I was sold on that theory after reading Haidt’s The Righteous Mind (as recommended by my late mother!). So, to see an alternative was of interest. And, I have to say, that the evidence makes sense. The article does a good job of laying out the competing approaches, which is interesting in and of itself. There are questions of methodology, allowing for some divergence. In short, why do we agree and disagree?

Of equal, if not more interest, however, is the discussion of the process of the debate. That is, as Naomi Oreskses points out in Why Trust Science, science is about consensus. And Haidt’s been a good popularizer of his model. When a challenger to the entrenched theory arises, we end up with a battle of ideas, ala Thomas Kuhn’s The Structure of Scientific Revolutions. The better explanation takes a while to establish the data, and the acolytes of the previous approach mount a defensive action, but ultimately we accept the new story. If, and this is important, it continues to demonstrate superior explanatory power.

The article lays out, in interesting prose, the nature of the arguments, but also the nature of the process of argumentation. There’s presentation, and attacks, and in short a riveting story of science in action. Yet, it’s happening (largely) as science, with the arguments based on data, not on personal attacks.

It also raises issues of the overall ethics of the science process. This is relatively personal, as it’s a debate I’m having with a family member. In short, is the scientific process to be trusted, or is it too biased and we need an alternative? This gets into Science Denial, as Gale Sinatra & Barbara Hofer’s book discusses, That’s what I’m faced with, sadly.

As with some other things, I suggest that while science isn’t perfect, it’s better than any other approach. Sure, there continue to be misuses, as have the products that science provides us. I’ve got bias, as I’ve had science training and have been an active participant as well as translator. So take what I say with the proverbial boulder of salt. However, if you want to quibble, you may have to travel by horse to talk to me in person; you’re reading this via the benefits of the science process, and you wouldn’t want to be hypocritical, right?

Presentation Process

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What is my process for creating a presentation? I realize that it’s at least two parts, that interact. First, there’s a story needing telling. What is it? What is the goal, and what story will get us there? Then, there’s the question of communicating the story. What are the important points to communicate? Together, it prompts some thinking on how to put it all together. It’s also to establish that creating a presentation is much different than just fronting up to an interview or conversation; there’s work!  So, this is a reflection on the presentation process.

So, first, there needs to be a story. It typically comes from something I want to talk about. There are a number of things that I talk about, being old and having ideas (strong opinions loosely held ;). That includes learning science, meaningful engagement, design processes, the performance ecosystem, and more (most of which I’ve written books on). Lately, that includes AI, though no book. Still, there’s an audience, and I confer with the organizing body about what’s the right topic.

From there, there’re two parts. One is figuring out what story to tell. In one sense, it’s reaching back to what I’ve said on it before, but…it’s also influenced by new thoughts. The world isn’t static, and so too my presentations generate afresh. I do start anew, unless I’m deliberately asked to redo a previous topic (and even then, I revisit). The main question is where do I want to end, and how do I want to get there.

The second part are the steps along the way. This means, basically, what points do I want to make, and how to make those points? I resort to two typical mechanisms, I confess. I’ll use images (read: photos) to communicate ideas (rather than bullet points). This is deliberate, as I believe (and I welcome research pro/con) that the lack of implicit connection opens up the attendees thoughts prior to me making the link. Then there are diagrams to communicate the underlying models. These, I’ve been led to know requires me to build them. Most are complex enough that it helps to start with a core and then elaborate.

Sometimes I provide some examples, usually in my discussion, but occasionally with screen captures. That’s a good thing, and I should do it more! Of course, there’s a small problem that sometimes I’m talking about things that are proprietary. Or, that are far enough ahead that they haven’t yet seen the light of day.

It’s important to recognize that this doesn’t happen linearly. I move forward, and backward, multiple times as I create a presentation. I move chunks around, change representations, etc. Really, presentations are a chance to reflect, and have even led to me changing my thinking (and so, my diagrams!). Research on design actually shows people cycling between bottom-up and top-down behaviors, and I resemble that remark! BTW, I could use AI in parts of this, except for the fact that the AI runs on stolen IP, amongst other problems, so not yet.

Also, I practice delivery. I do not shoot for an exact presentation, because those inevitably feel canned. I confess, I like TedTalks and good keynotes, but if they’re word-perfect, I do note that as well, negatively. What I want to ensure is that the message stays consistent, even though my presentation varies each time. That’s ok, and makes for a more natural presentation (and also allows for audience participation, so questions can be addressed). That’s not necessarily true for keynotes, which I do, but there’s evidence I do tell engaging and meaningful stories for those as well! So, some thoughts on presentation process. What did I miss? How does this reflect your process?

Going it alone?

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I’ve been an independent consultant for the past quarter century. Which would make you think I go it alone. And, occasionally, I do. I’ve written my books myself. I’ve gone into consulting engagements on my own. I typically do my presentations on my own. Yet, not really. And, it’s not what I recommend. So, why do we think we should be going it alone?

It’s easy to think I’ve done much of my activities alone (and sometimes it feels like it). Yet, when you think about it, it’s really not. I’ve had publishers and editors on books. Also, of course, I’m building on the work of others (that I cite; academic holdover). Plus, I’ve had folks provide feedback that has improved my thinking. That includes on presentations and articles, which tend to have editors as well, or at least program managers. And, then there’s the feedback by the victims, er, audience. And even consulting I’m engaging with the clients and having them help me understand the context and review drafts of reports before acceptance. It’s essentially not alone!

Folks thinking about social, like Mark Britz (co-author of Social By Design) or Harold Jarche (of Personal Knowledge Mastery fame) remind us that we’re better together. That can be what is determined either authoritatively or collectively, and then there’s what’s offered freely. Still, the benefits of working together, of social cognitive processing, are that we are highly likely to get better when we get others’ feedback.

Which is why I feel bad when I hear about the lone L&D practitioner in an org, or someone doing a course by themselves. Ok, for the former, ideally they’re interacting with the SMEs at least, but hopefully they’re also getting support in connecting with peers at events like conferences or chapter meetings. For the latter, there at least could be checkpoints where your work gets review and feedback. Heck Watts Humphrey noted this in software engineering, we should likewise worry about it. We need feedback to improve, and that comes from others. (In a tighter loop than quarterly.)

I also worry as people more and more seem to be choosing, or being ‘voluntold’, to use tools that minimize interaction. It’s already been seen that this diminishes personal development. It’s also concerning for building culture, and more. In short, we don’t want people not collaborating; that’s where new ideas come from! While some of the tools may help the less-than-average person be better in the moment, they’re not developing over time. That’s not a long-term path to success.

You simply shouldn’t be going it alone. You should continually be learning, and that means getting feedback. And, of course, learning to learn from that feedback in all forms, and reading or watching something can be that. It can be challenging (says the introvert) to work with others, but when it happens the outcome is almost always better. At least if you follow good process! So please, find someone to be your partner in your endeavors. I’m trying, I hope you will too!

Why aren’t things changing?

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Change is everywhere. We’ve heard again and again that the pace of change is increasing. Certainly we’re seeing more chaos in our society. Yet, some things don’t seem to change, L&D in particular. Why is that? Why aren’t things changing?

For one, I’m not alone in advocating for more. Jay Cross was promoting Informal Learning, and Marc Rosenberg was talking Beyond eLearning before I came out with Revolutionize L&D. Since then, (and, of course, before) there’s been a growing number of people talking about how we need to stop being ‘order takers’ and start being strategic about our energy and use of technology. Guy Wallace and the whole Performance Consulting industry is one facet. We see books from the likes of Lori Niles-Hoffman, Jess Almlie, and even Keith Keating, amongst others. We hear from folks as wise as Jane Bozarth, David Kelly, Connie Malamed, Ruth Clark, Will Thalheimer, Julie Dirksen, Don Taylor, Michael Allen, Nigel Paine, Megan Torrance, Matt Richter, and more (the list goes on). The message is pretty clear: only do courses when they make sense, and then do them well.

So why do we continue to see companies producing and consuming ‘info dump’ elearning? Training that’s just bullet points? Why do we have tools that continue to make it easy to put up content and add a knowledge test, let alone having ‘click to see more’? Why are there ice-breakers and ‘team-building’ activities that have no meaningful relation to the topic? Where’s the spaced learning? How come we continue to have learning experiences that are engaging, but not effective?

On reason, of course, is the industry. The vendors do tend to focus on producing ‘content’; even their touted uses of AI are to make content faster and cheaper (but the missing leg of the engineering stool is ‘better’, why is that?). Let’s track completions, rather than impact, eh? We don’t have to talk across silos that way, so it’s easier. And, it’s less work to make content tools than to engineer learning experiences. But…isn’t that our real mission?

Another reason, of course, is stakeholder awareness. There are expectations that we should build courses quickly, and that information leads to behavior change. Both are wrong, of course. But why do these beliefs persist? Aren’t we, and shouldn’t we be, extinguishing them? It’s more than a quarter century into the 21st one, you’d think folks would know better. Particularly when it affects their ability to succeed!

Our own awareness may be a barrier too. That is, there are lots of folks who come into ID without preparation. As Cammy Bean noted, folks are starting as the Accidental Instructional Designer. It’s also hard to buck the hierarchy when you’re new. And it’s rewarding to get high scores on our courses by attendees. Particularly if we don’t know better. Still, it’s painful to bear.

Now, I do believe, and see, gradual change. It is getting better. Yet, while I’m not known for patience, it’s still taking way too long! We have the opportunity to be making our orgs so much better. We could be extending learning, developing learning-to-learn skills, fostering innovation, and meeting real needs, instead of dumping information worthlessly. We’re wasting money! So, yes, I’m frustrated.  Are you? Why aren’t things changing? What am I missing?

Reflections on reflection

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Reflection of fall trees in a lakeHaving come up a couple of times now, it’s probably time to think ‘out loud’ about reflection. Yes, very meta and all, but I’ve come to think that there are two or more different types of reflection. That, of course, would be confusing and thus problematic. So, here are some reflections on reflection.

So, first, I typically talk about reflection as the complement to action: learning is action and reflection. Then, I extend that to say that instruction should be designed action and guided reflection. When I say ‘reflection’ in that sense, it’s everything but the practice: models, examples, and feedback. Which is a simplification, but still not completely wrong.

However, we probably need a tighter definition. Because, for instance, we can ask people to reflect on the content, e.g. an elaborative or generative activity. We can ask them to reflect on their actions in practice. For that matter, we can ask them to reflect on the overall experience. Sure, each is valuable, but they’re different.

So, when we ask people to reflect on models and examples, we’re asking them to make connections between the content and their previous knowledge. That’s whether personal experience or prior learning (or both, of course, intermixed). Which is a necessary part of building their understanding, their schema. We want to enrich those connections, to increase the likelihood of activation in relevant settings.

Then, we ask them to reflect on their performance in retrieval. We can give them feedback, but as they progress that should fade. Indeed, ‘after action’ reviews are just this sort of activity. Learners need to internalize the self-monitoring, to become self-improving learners. So here we could ask them what they did right or wrong before we give them feedback. This, too, is valuable.

We can also ask them to reflect on the overall experience: what worked, what didn’t, what they’d like that they want more of or didn’t see, and what they’d like less of. Here we’re asking them to reflect on the pedagogy: what improvements can we provide? This is valuable more for us, of course, to refine the learning experience, but it’s still useful.

The problem is, we often use the term of ‘reflection’ for all of these. Reflection is great, but we need to deal with the specifics of each. Yes, we can term the elaborative one a ‘generative’ activity, so arguably that’s taken care of. But the other two are still both confounded. We could use the term ‘review’ for the case of overall experience evaluation, if we wanted to remove confusion. However, my main point is not to solve, at least not yet, but instead to point out the issue (this blog is for preliminary thoughts, at least ;). Those are my reflections, what are yours?

Fantasizing about transfer

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One of the perennial issues that hangs over designing learning experiences is that of fantasy. That is, we set examples and practice in contexts. Most of the time, to be fair, those contexts can and should be real. But there are times when fantasy may be beneficial (as I’ve heard from game researcher and learning design Professor Karl Kapp). The question is, when? I had a thought when out for my usual walk (not unusually), so let me expand a bit on some ideas, fantasizing about transfer.

First, it’s clear that context matters. My rationale is that the settings seen across examples and practice – the context ‘space‘ as it were – determines where you’re going to recognize the opportunities for application. Our brains abstract the underlying structure from multiple opportunities (e.g. recognizing what doesn’t change across instances). That structure likely will apply in more situations than we can create in the learning experience, so it’s good. But how far we transfer is determined by how varying the situations are that we see.

Now, if want near transfer, we need to use situations close to the ones learners will experience. But what about far transfer, where we want recognition in all appropriate (and no inappropriate) situations? The argument has been, and I’m a proponent, that the lack of explicit mooring to the real world actually facilitates that transfer. We still need a situation, and the relevant structure has to be maintained, but can we change the trappings? So, for instance, a company set a business simulation in a Wild West saloon, You were still managing the books, but what you were accounting for (ahem) were things that were simplified and exaggerated. Which took the focus away from the mundane details and let you focus on the business principles.

And that’s a good rationale, but what occurred to me is another potential argument. I was thinking about fear, in the case of difficult issues. For instance, bullying, sexual harassment, and the like. Things that are scary to experience, particularly if you’re vulnerable. Would setting it in a fantastic world make it easier to listen to the messages? You’re dealing with aliens, or demons, not real people? I can’t say for certain (is there research on this? I’m not aware of any, but that doesn’t mean it hasn’t occurred!), but it seems plausible.

Note that while facilitation is always recommended, in this case you’d definitely need some. Essentially, you can’t assume people will make the appropriate inferences and transfer back to the real world. In general, practice by itself isn’t a full learning experience. (Even if you put models and examples and feedback right in the game world, you still shouldn’t assume the extension.) You want to help folks recognize the relationships between the learning experience and real performance. That’s a given, pretty much, but it’s especially true when talking fantasy.

Look, we can be fantasizing about transfer, but let’s engineer it. We can use fantasy settings, I’ll suggest, at least in certain situations, but never assume that transfer will occur. Or we really will be fantasizing!

Defying fracticality

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Ok, so I’m playing fast and loose; ‘fracticality’ isn’t a word. Yet, the world is fractal, in the sense that everything unpacks. We also have to make decisions about what to do, without having time, nor inclination, to go to depths that aren’t relevant. How do we strike that balance? How do we go about defying fracticality? Some reflections…

I’m naturally curious, and track what research tells us. And, research continues, and unpacks new depths. For instance, we:

  • know models are important, but then they need to be causal, and connected, and conceptual…
  • want ‘desirable difficulty’, but then it’s more than challenge, it’s also context varying, and spacing, and feedback fading, and…
  • need examples, but they have to explicitly include models, be interesting and relevant, have outcomes…
  • want to elaborate, but then learn that certain activities are generative and others aren’t effective…

The list goes on, and each of these expands! How do we cope?

For one, it occurs to me that we need at least a minimum viable level. We can adopt that notion of ‘minimum viable product’, and recognize that learning should, at least, have:

  • a clear objective
  • a rallying introduction
  • an appropriate model
  • several relevant examples
  • a suite of meaningful practice extended over time
  • a satisfying closing
  • some measurement beyond ‘enjoyment’ towards impact

If we have the basics down, we can budget and justify what we’re doing. And, likely, this can all be done within the existing constraints. We have to acknowledge the world we work in as well as the one we’re building, after all.

We elaborate from there. If we show improvement, and we should, we then lobby to do more that’ll yield even bigger impacts. We can and should space out the learning. We can consider where it’s complex and maybe start with a simple model, and then expand, with more examples. What is the minimum set of contexts to support transfer? We can consider expertise, and adjust our starting and pedagogy appropriately. We can also expand beyond courses and look for when performance support makes more sense, or a combination. Then there’s community and informal learning. And strategy, politics, …

Associated with this is expanding our own understanding. We need basics, and then we need to keep understanding more. We can’t stop at just meeting the basic needs, for a variety of reasons. These include what the competition is doing, but also our own professionalism. We shouldn’t allow ourselves to be complacent, but keep improving our own understanding and then our practice as well.

The world is fractal, and everything people do continues to unpack. The only path to defying fracticality is pick an initial level that’s minimally viable, achieve it, and then start expanding upon it. You’ll get pushback, but you’ll also find that as you get more capable, things get automated and you have more bandwidth. Tools get more capable as well. It’s an ongoing process, but it’s one worth indulging in. If this isn’t the field for you, find somewhere where you are interested in continuing to explore. Stay curious, my friends.

(The nuances of learning are part of our LDA Learning Science Conference. The stuff that’s not learning, but around it as part of our, that is L&D’s, ability to succeed is what we are covering in our L&D As Ecosystem conference. FYI)

My technology for performance

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I’ve talked in the past about my tools for learning, as Jane Hart’s survey prompts. Yet, Christy Tucker asks about software stacks (for consulting), and I realize there’s a different answer when I’m talking about doing versus learning. Yes, as Harold Jarche says, “work is learning and learning is the work”, but there are times I’m using tools to keep myself on track rather than to render my ongoing thinking. I augment my abilities with tech, and some is just about executive function rather than learning. So what is my technology for performance?

Personally, I use Apple’s Reminders to render ‘ToDos’. As I’ve said, if I’ve promised you something and it doesn’t get into my digital world, we never had the conversation. That also is true for Apple’s Calendar. I used to use them separately, but now I’m coordinating between them. I used to block out time to get things done, but I’ve now set up a cal.com to book time, and it looks at my calendar. So, I now use timing on reminders to get things done that are urgent, and save the calendar for time-specific things.

Financially, I use Quickbooks to send/receive invoices. I should switch, but haven’t yet. The problem is that you’re kind of locked in unless you change on your calendar boundary. I also use my bank’s app or website to dod things like send/receive payments. Occasionally I use PayPal, too.

I’d mention Notes, as a way to mull things, but that’s really learning. Though I do grab and store recipes there (and share with fam). Not that I make ’em all, but it’s where I can keep the ones I find online. Likewise I take notes on biz meetings with Notability, but again that’s not really performance, it falls more into the ‘learning category’. I do have some templates that I’ve created in Word, e.g. proposals, reports, and schedule of fees. Probably should migrate to a platform that’s more open. Libre? Open Office? There I go, standing up again…

Now, that’s for me, myself, and I, but I also do things with Elevator 9 and the Learning Development Accelerator (LDA). For both, I’m using Slack to talk to people. I have separate channels for both, but am glad they’re both using the one platform. Email too, of course. Teams was part of a a previous engagement, and am frankly glad to step away. I also use Zoom, a lot. Again, happy to not use Teams or Google Meet for that purpose.

Collaborative docs are different. They’re writing, but for others, so they begin to cross the chasm (really, it’s a continuum, but…). So, I’ve used Google Docs. I really haven’t collaborated using 360, because I don’t have that type of license. I also have used Apple’s Pages with the folks who do run Macs.

Usually, my browser’s for learning, but I also use it to get things done. That’s one of the reasons I recently made the switch from Safari to Vivaldi. For one, it’s a ‘Chrome’-equivalent browser, but doesn’t have the ownership probs that Brave suffers from. It also doesn’t have the ‘tracking’ problems Google introduces (it’s why my search engine is DuckDuckGo, too).  It’s problematic, in that it’s (too) customizable, for power users, but the defaults aren’t bad. Still learning about it, but I’ve mostly got it under control (e.g. I think I’ve a solution to the microphone issue that was bedeviling an LDA vid attempt). Though I’m reasonably tech savvy…

One other tool of note is Notion. I wouldn’t necessarily choose it myself, but it works. You can imagine I’m not keen on the strong pushes it (and everything else) are making towards AI, but it’s in use for LDA project and knowledge management, and it’s working. We’re a) probably not making full use, and b) could’ve used something simpler, but…we have someone familiar with it coaching us, so it’s all good.

So that’s my technology for performance. It’s not sophisticated, but it’s manageable, and affordable. Thoughts? Yours?

It’s no longer AI!

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There’s been a persistent belief in artificial intelligence (AI) practitioners that kind of explains the ongoing development. The mantra is “if we can understand it, it’s no longer AI”. This is relevant to what we’re seeing today, in important ways. So let’s unpack that.

The point of AI is that we’re trying to model intelligent behavior, usually human intelligence. It doesn’t have to be human, of course, we can be trying to model intelligent behavior that’s not human!  However, at least for much of AI, we’re trying to understand our own thinking. After all, the human brain is arguably the most complex thing in the known universe!  For instance, we really still don’t understand consciousness; why does it even exist? And, what is it really? We’re getting closer, but we’re not really there.

I’ve mentioned before that machine learning came about as a reaction to the failure of symbolic approaches to capture some of the nuances of human behavior. For instance, recognizing images in noisy environments is something we were better at than systems. Symbolic systems were ‘brittle’, in that they did what they did, but failed as soon as you tried to generalize. The neural net approach, where we trained systems, more closely mimicked how we ourselves perform. And, really, the general trend has been that as soon as we understand it, it’s no longer AI, it’s just good programming. Which is fine. We’ve advanced our understanding, and now we can build it.

What we encode in the weights of networks is essentially what’s known by the system. We don’t really know what we know, and we create models to explain how things work. We can get more complex with non-linear models (which our brains struggle with), but non-linearity itself is an understanding we’ve created to explain how the world works. Yet, what we actually capture in our networks isn’t available for scrutiny. Same with neural nets. Most networks were small, relatively speaking (massive, but small compared to our brain). They were also essentially limited. Trained on X, or Y, so check recognition or loan approval, but our brains can handle much more.

We’re seeing now that while the algorithms have made some modest improvements, we’ve vastly scaled the net size to achieve large language models (LLMs). However, we’re still running limited foci. So, for instance, language (training on the text of the internet), or images, or video, or music. Yes, there are systems that are integrating, but as far as I know (which might be wrong), they’re really grafted and not integrated completely. Most folks I know go to different tools for different things.

The result is that, particularly in text, we now have the general experience that we can ask questions of LLMs, and get meaningful replies. And, make no mistake, this is useful. For general language tasks, such systems are great. They’re doing pretty great jobs of summarizing prose. This passes the so-called “Turing Test”. (Alan Turing posited that if you had a system and interacted with it so that you couldn’t discriminate between system and human, it passed the test.) An article now suggests that a reasonable assessment would say we’ve achieved artificial general intelligence (AGI).

Apparently, I’m not a reasonable person. They’ve addressed my complaint, they posit. The article (in the parts I could read before the paywall) say that such systems do, indeed, have a world model  I argue that such systems don”t really know what a ‘dog’ is, having never seen one, petted one, raised one, walked one. Read prose describing all, yes. Experienced it? No. To me, there’s a difference (and I’m channeling Stevan Harnad’s Symbol Grounding Problem).

I also believe that there’s an AI architecture that’s more likely to achieve AI. (Particularly if we give it sensors to experience the world; and imagine what progress if they got the same hype as LLMs!) LLMs are trained on the vast corpora of text (illegitimately, I’ll suggest), and so understand an average that’s greater than any one person. (They’re also trained on other data, like video or images, for other media.) But they’re not as good as an expert in the field. They can do impressive things in areas you aren’t an expert in, but an expert in those areas can point out flaws. Yes, they can solve unsolved problems in, for instance, math, and pass tests, but those are knowledge tests, not ‘do’.

Ultimately, I don’t have to agree with the consensus, and maybe I’m just a grumpy old man (and get off my lawn!). I would like the progress we’ve made and what we can do with LLMs, if it weren’t for the hype that’s distorting business and mindspace. AI is more than LLMs, despite the way folks are speaking, and the valuations aren’t real. Also, we now know what they’re doing, and how, so I can say “it’s no longer AI” and mean it ;).

Timing matters

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There are many phenomena associated with time. I know it was such a topic of my late friend Jay Cross that he named his vehicle for agency Internet Time. A few phenomena have arisen in my thinking, and it’s time to just review some of the reasons why timing matters.

So, for one, things seem to come in waves. It’ll be too quiet, and then too busy; too frenetic a rhythm. I might prefer a smoother rate, but…it’s not something we get to control. (At least, individually; societally we have chosen speed over quality, what with requirements for short-term returns.) Now, I do my best to cope, using the downtimes to work on background tasks. So that, of course, when it’s crunch time I feel like I can still function.

That was also true when I’ve struggled. For one, when I had back pain (before I got surgery), I had good days and bad. My solution was to get things done on the good days, so I was okay on the bad ones. That again is a coping strategy.

Overall, things are going faster than we’d like. We are dealing with increasing change, and increasing information. Things like Harold Jarche’s Personal Knowledge Mastery (PKM) are tools we increasingly need to use, to be able to adapt.

Timing matters in other ways, too. Some times the time has to be right for an idea to stick. I regularly tell the story about how I mentioned the concept of ‘explorable interfaces’ (from Jean-Marc Robert) to my PhD supervisor, to no avail. Which surprised me, because our lab was looking at usability! However, I brought the concept up a few years later, and suddenly it was a great idea! Same idea, but right time.

It’s true for learning as well. It’s not something we can rush! Yet we try to have an event and then move on, not recognizing learning takes time. We need repeated reactivation and feedback for learning to occur, yet that’s seldom the design of our organizational solutions. Yet, technology gives us the ability to address this, if only we have the will.

And, we may need to make sure it’s the right time. We need to understand why something’s relevant to us before we’re ready for the message. That’s something I think we don’t focus on enough, hence my most recent tome.

We live in interesting times, as the saying has it. There’s little reason to believe things will slow down, even if they should. So, we need to be able to deal with it. Personally, and for our learners. Timing matters, as does how we deal with it. What are your strategies?