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Clark Quinn’s Learnings about Learning

Small thoughts about Smalltalk

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A couple of weeks ago, The Computer History Museum hosted a panel session on Smalltalk, which I watched via video. Alan Kay (who’s vision for the Dynabook drove Smalltalk) came in via recorded video. Dan Ingall (the technical guru) joined by live video link. Adele Goldberg (who documented and tested it), showed up live. John Markoff, well known Silicon Valley documenter, hosted. All to talk about Smalltalk. It prompted some small thoughts about Smalltalk.

I was a regular Byte magazine reader, back in the day. I had created my own major in Computer-Based Education, and was designing and programming educational computer games. I’d done academic research as part of the degree requirements, so I was aware of the work at Xerox PARC. (In fact, I flunked a job interview there because I didn’t know what ‘protocol analysis’ meant, though it turns out that’s what I’d been doing!) So, when the Byte issue in Aug 1981 on Smalltalk came out (I checked the date), I was enchanted.

Smalltalk is an object-oriented language that is dynamic, in that you can edit and immediately run it again; it’s not compiled. It was also reflective, in that make itself visible and operate on itself, like Lisp. In Smalltalk, you model your world in objects and they communicate by messages. It has windows, icons, and interactions comes from the mouse as much or more than by the keyboard. You can edit the objects while running and they change. While it wasn’t available to me, I was a fan of the concept.  (Machines running Smalltalk were what Steve Jobs saw on his PARC visit that led to the Lisa and then the Macintosh.)

It’s ironic that between then and when I ended up teaching in a school of computer science, I somehow lost that focus.  I’d gone to grad school to get a grounding in cognitive science in just such a place. After a post-doc looking at learner models, I ended up teaching interface design (and researching educational technology). Along the way I got involved in other issues, though I did get involved in HyperCard, which in many ways was Smalltalk Lite(tm ;).

In the talk, besides the enlightenment of the thinking behind it, there was also the practical aspects. While relatively lean, the language did take up memory and as a dynamic machine wasn’t blindingly fast. There apparently were also decisions about pricing and markets that were classic Xerox. Thus, while it was and is a fabulous modeling environment (still in use in a variety of markets), it didn’t take over the world.  When Steve Jobs built the NeXT computer, he took on the object-oriented model of Smalltalk, but used C as the core language for a variety of pragmatic reasons.

In the session, they talked about the vision of Seymour Papert and Logo, and how they wanted more. Alan Kay walked around with a cardboard model of what a Dynabook would look like, and people begged to buy one. Doug Englebart’s work also was an inspiration. It was a glorious flashback to the days when we dreamt bigger than our tech would support. These days, it seems, we’ve reversed that. I’ve heard that computing isn’t living up to the potential we have for digital technology to be an optimal augment for cognition, and I agree. We can do better, and should. So these are some small thoughts about Smalltalk. And get off my lawn!

Top 10 Learning Tools for 2022

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I continue to be a fan of Jane Hart‘s work, and her annual survey of Top 10 Learning Tools is only one of the reasons (another being her co-conspirator, along with Harold Jarche & Charles Jennings in the Internet Time Alliance). As a consequence, it’s time for my annual list, this time the Top 10 Learning Tools for 2022.

To be fair, my list could be reproduced from last year’s. However, a couple of tools have become more prevalent, and one’s slipped back, so…  I’ll rearrange my list for this year, given that I’m not writing a book right now, with an expectation that it may swing back.

Text

Writing is a primary way for me to think through things, and that’s not changing. I could say email, but that’s not where I put my most cogent thoughts.

1. WordPress. My blogging tool, is a major part of my learning process. In meeting my commitment of at least a post a week, I’m motivating myself to continue to explore new topics.

2. Google Docs. In collaborating with folks on a suite of things we’re working on, we’re learning together. We’re using a few other tools as well, one in particular, but it doesn’t allow simultaneous editing. Sorry, allowing people to work at the same time is the future.

Visuals

Tapping into our spatial processing capabilities ends up being important for me, both to personally understand things as well as communicate.

3. OmniGraffle. While I seem to have not used it as often, it’s still a major way I experiment with syntheses of ideas. Dear, and with much more capability than I need, but I haven’t found a reasonable alternative. Diagramming, mapping conceptual relationships to spatial, is a powerful way for me to make sense of things.

4. OmniOutliner. I don’t use all the features, and it’s dear, but again, haven’t found another outliner with the one key addition I need, columns. Spreadsheets don’t support outlining, as far as I’ve seen. And this is visual in that the representation of the structure is critical for me.

5. Keynote. Creating presentations is another way to think about how to share. The need to link elements together into a bigger picture is an important element of learning, to me.

Social

Interacting with others is a big part of learning, for me (despite my introversion).

6. Twitter. Following folks on twitter, even occasionally interacting with them, is a way to keep track of what’s happening, and what’s interesting.

7. LinkedIn. The posts I see on LinkedIn are often of interest, and occasionally people point me to things that are worthy of my attention (in one way or another!).

8. Discord. This one is new to me, and it’s still early on in my experience, but I’m finding it an interesting way to interact with colleagues.

9. Zoom. Like everyone else, I’m on a fair amount of Zoom calls (still my preferred environment for videoconferencing), fortunately not enough to experience fatigue yet ;).

Search

Search is a great part of my learning, looking up anything I hear about and don’t know.

10. Duck Duck Go. Duck has become my preferred search engine, because it’s claim to not track is comforting to me. I don’t use their browser, but I find their hits to be pretty spot-on.

So there, that’s my Top 10 Learning Tools for 2022. I encourage you to find a way to add yours to Jane’s list. It’s always interesting to see what emerges from the aggregate responses.

L&D Language is Limiting?

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In our most recent LDA You Oughta Know event, our guest touched on the language we use. It struck a chord for me, thinking about how we refer to things. It led me to wonder whether, in fact, we’re hampering ourselves. So here are some thoughts around the question of whether L&D language is limiting us?

So, Serena Gonsalves-Fersch heads talent for SoftwareONE, a global company. For her dissertation, she interviewed a number of folks about what L&D is doing. While her comments were extremely worthwhile, it was more a toss-off comment about using terms like “talent management” that got me to leave a note to riff on this topic.

So let’s start with those overarching terms. Human Capital, Human Resources, and Talent strike me as ways to dissociate from thinking of people, and instead think of using assets. You might invest in them, but are you investing in your people, or in the capability of your organization? The latter may sound sensible, but it leaves open the question “at what expense”. Do you care if they burn out from the way you use them?  Shouldn’t we talk about our people, employees, or those we’re responsible to and for? Perhaps I’m overly sensitive to the issues, but too often I see the approach being impersonal and if not inhumane.

Similarly, what about the phrase Corporate University? I am fond of the case study Mark Britz presented in Revolutionize L&D, where he said that for his organization, he recognized that what was needed was a community to share, given that they were disconnected but experts. What folks really mean by a corp uni is a training academy, but a uni isn’t a good model. Instead of deep theory with little practical application, it’s almost the reverse. Learning should be continual and ubiquitous, not sent off to separate environs. Even when you do specific formal interventions, they should be seen as integral, not isolated in an ivory tower.

Similarly, I’ve sounded off on the problems of Training & Development, or Learning & Development. Training and learning are means to an end. What we want are people performing optimally, and continually developing. It might even be Performance & Innovation (if you take the revolution seriously ;).

It’s clear language does determine the ways we think about things (is it a mishap or a catastrophe?). When we use language that characterizes activity in certain ways, we implicitly put constraints on it. This is true for every formulation, of course, but perhaps it behooves us to think consciously about the language we use. Do we know if L&D language is limiting us? Let’s make sure that we’re not prematurely handicapping ourselves by our framing.

On blogging

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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

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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?

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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

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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

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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

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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

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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.