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Clark Quinn’s Learnings about Learning

Misusing affordances?

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Affordances is a complex term. Originally coined by Gibson, and popularized by Norman, it’s been largely used in terms of designing interfaces. Yet, it’s easy to misinterpret. I may have been guilty myself! In the past, I used it as a way to characterize technologies. Which isn’t really the intent, as it’s about sensory perception and action. So maybe I should explain what I mean, so you don’t think I’m misusing affordances.

To be clear, in interface design, it’s about the affordances you can perceive. If something looks like it can slide (e.g. a scrollbar), it lets you know you might be able to move the target of a related window in a field. Similarly a button affords pushing. One of the complaints about touch screens is that as people work to overload more functions on gestures. There might be affordances you can’t perceive: does a two-fingered swipe do anything differently than a single-finger swipe?

In my case, I’m talking more about what a technology supports. In my analysis of virtual worlds and mobile devices, I was looking to see what their core capabilities are, and so what we might naturally do with them. Similarly with media, what are their core natures?

So, for instance, an LMS’s core affordance is managing courses. Video captures dynamic context.  You might be able to do course management with a spreadsheet and some elbow grease, or you can mimic video with a series of static shots (think: Ken Burns) and narration, but the purpose-designed tool is likely going to be better. There are tradeoffs. You can graft on capabilities to a core, still an LMS won’t naturally serve as a resource repository or social media platform.

It’s an analytical tool, in my mind. You should end up asking: what’s the DNA? For example, you can match the time affordance of different mobile devices to the task. You can determine whether you need a virtual world or VR based upon whether you truly need visual or sensory immersion, action, and social (versus the tradeoffs of cost and cognitive overhead).

With an affordance perspective, you can make inferences about technologies. For instance, LXPs are really (sometimes smart) portals. AI (artificial intelligence)’s best application is IA (intelligence augmentation). AR’s natural niche, like mobile, is performance support. This isn’t to say that each can’t be repurposed in useful ways. AR has the potential to annotate the world. LXPs can be learning guides for those beyond novice stage. AI can serve in particular ways like auto-content parsing (more an automation than an augmentation). Etc.

My intent is that this way of thinking helps us short-circuit that age-old problem that we use new technologies first in ways that mimic old technologies (the old cliche of tv starting out by broadcasting radio shows). It’s a way to generate your own hype curve for technologies: over-enthusiasm leading to overuse, disappointment, and rebirth leveraging the core affordances. Maybe there’s a better word, and I’ve been misusing affordances, but I think the concept is useful. I welcome your thoughts.

Prompted by prep for the advanced seminar on instructional tech for the upcoming Learning & Development Conference.

Projects That Didn’t Fly

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I’ve had the pleasure of leading the design of a number of projects that have had some impact. These include a mobile app a company could point to. Also a game that helped real kids. Even a context-sensitive performance support system that was worth a patent. Then, of course, are the projects that didn’t, for whatever reason, see the light of day.  So here are some reflections on a few projects that didn’t fly.

Back in the mid-90s, I was part of a government-sponsored initiative in online learning, and we were looking for a meaningful project. We made a connection to two folks with a small company that taught about communicating to the press. They could’ve come out with a book, but they wanted to do something more interesting. We collaborated on an online course on speaking to the media. I partnered with an experienced digital producer, and backstopped with a university-based media team. We had a comic skit writer, and cartoonists, to augment our resources. The result was technically sophisticated, educationally sound, and engaging both visually and in prose. It never flew, however, as we didn’t partner it with a viable business model. Which was reflective of the times.

Then, at the end of the 90’s, I was asked to lead a team developing an adaptive learning system. The charge was to help learners understand themselves as learners. I had a stellar team: software engineer, AI expert, psychometrician, learning science guru, visual designer, and an interface designer. The model was to do an initial profile, then present you with learning elements (concepts, examples, practice, etc) and update your model based on your performance. There was even a machine learning component to improve the models as we went along. We actually got a first draft up and running (10 elements in the student model), before ego and greed undermined and killed it. The lessons learned, of course, have continued to inform me, including, for instance, my calls for content systems.

Then, around the mid-2000s, I was given the task to devise a content model for a publisher.  They wanted to develop once and populate a variety of business products. Drawing on previous experience, I developed a robust model, which started from individual elements and supplemented and aggregated them in a systematic way. This also ended sadly. In this case, the software side never reached fruition.

There are lots of reasons good intentions can go awry.  In my case, it wasn’t going to be on a lack in the learning design ;). What I’ve learned, however, is that learning design isn’t the only element that matters. There’s vision, and execution, and partners, and more. All are ways in which things can go wrong. Yet, that doesn’t mean we shouldn’t try. It just means that we should, to the extent of our abilities, also try to ensure the success of the other comments. It’s worth exploring projects that didn’t fly so as to see how future ones might.

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.

Templates as content model extensions

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I’ve been touting content models for, well, years now. Interestingly, I’m currently doing some more concrete work on them, from the bottom-up. Instead of looking at top-down implementation of governance and structure, the focus is on guidance for creating resources at scale. Yet the two are related, and I think it’s worth looking at templates as content model extensions.

The notion of content models is that instead of creating full courses, we build content in chunks, and pull them together by rule. Or even more appropriately, deliver the appropriate chunk to the right person at the right time. It’s been happening for web design for years, but for some reason the notion of content management systems lags in L&D. Yes, there are entailments – governance, strategy, engineering – but the alternative is that lingering legacy content that’s out of date but no one can deal with.

That’s the top level focus. Underpinning this, of course, is getting the content right, and that means having some good definitions around the content. I’d done that many moons ago, and in a current engagement it’s reemerging. The situation is that there are a number of people all writing content around this particular initiative, and it’s uncoordinated (sound familiar?). The realization that clients are struggling is enough of a driver to look for a solution.

Without a content management system, as yet, it still makes sense to systematize the resources around a map of the space, ensuring they align to what we know about how people learn and perform. That latter is important, because many times they just need the answer now, not a full course.

What we’ve ended up doing is creating meta-content that tell how to develop content that meets particular needs. With entailments, such as assembling a representative team to determine what’s needed and the labels to use. It also involves drafting and testing these content guides, prior to broader use.

It’s the tactical step of a strategic goal to provide support for people to successfully meet their needs. And, to be clear, to reduce the reliance on the support staff. Leveraging the cognitive and learning sciences, we’re building templates as content model extensions. This is before there’s even the technology support available to be more proactive, but planning for the possible future is part of the strategy.

I’ll be presenting a session on this at the DevLearn conference in October. If you’re interested and going to be there, I welcome seeing you.

Activities ‘beyond the course’

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wrapped presentSo, somehow I got on Myra Roldan‘s #MyraMonday question list. She asks a question every Monday, dobbing in some likely (or, in my case, gullible) victims to respond. And I do (unusually ;), because occasionally it’s good to challenge your mind. This past week, the question was particularly interesting: about what you’d do if there weren’t elearning. Of course, there were the usual answers, but several were very interesting. Here’re some of the ideas and underlying thinking about activities ‘beyond the course’.

So, I had heard about someone who was exploring ‘escape rooms’ for learning. (Spoiler: it was Myra, hence the question. ;) I was reminded, however, and added in some other ideas:

surprise box, host a murder, scavenger hunt, choose your own adventure…

My thinking is that there are lots of ways of invoking intent and action, and providing feedback. The box could contain some content and instructions, e.g. “film yourself doing…” or “do X and write it up”. Or some actual device to act upon (think of the old science kits they sent with correspondence courses). Host a murder mystery party would be some Live Action Role-Playing (LARP) activity that includes instructions, roles, and reflection guidance (a group ‘surprise box’). Scavenger hunt could have you looking for resources for new arrivals to learn their way around, or to do safety checks, or… Choose your own adventure book is basically a text-based branching scenario.

Kevin Thorn (last week’s You Oughta Know guest of the LDAccelerator) suggested comics Not surprising, since that was the topic we had him on for, and also the focus of his thesis. Comics are underused, I believe, and yet have valuable properties. Another viable way to develop learning, particularly if you can tie them to challenges.

Then, Alan Natachu weighed in with even more creative ideas:

Lots of infographics and cryptograms
Book ciphers
Red / Blue filters (look through a colored lens to reveal a hidden message)
Tune into a custom radio frequency that repeats a message
Text messages to a secret contact (a.k.a. Phone a friend)

Again, we’re looking at ways to get people to process content (and apply it). What I like is how he started tapping into alternate technologies. It’s easy to stay in our comfort zone, as I was doing. It’s useful to take some time to reflect and deliberately explore alternatives. Different questions (like Myra’s) can prompt some out of the box thinking, as can deliberate prompts to consider other things. That is, systematic creativity isn’t an oxymoron ;).

There is a followup on this: why aren’t we doing these things  already?  We should be looking at other mechanisms. Yes, there are some learnings, and some resource requirements. However, once they’re part of our repertoire, they become just another tool in our quiver.

We can, and should, be looking at activities ‘beyond the course’. There’re the benefits of novelty, but also different affordances. Better yet, we could theme them to align with particular courses. There is a real opportunity to make our learning stick better, and that is the real bottom line. So let’s get creative  and achieve better outcomes.

 

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?

Aligning and enabling transformation

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In what was my last Quinnsights column for Learning Solutions, I wrote about how the transformation wasn’t (or shouldn’t) be digital. In many ways we aren’t aligned with what’s best for our thinking. Thus,  digitizing existing approaches doesn’t make sense. Instead, we should be fixing our organizational alignment first,  then  digitizing. The opportunity is in aligning  and enabling transformation.

First, we should be looking at  all  the levels of organizational alignment. At the individual level we can be doing things like implementing federated search, to support individual learning. This should be coupled with providing development of writing good search strings and evaluating search outcomes. This also means curating a suite of resources aligned with learning directions and future opportunities. The point being that we should be supporting evidence-based methods for individual development, then supporting digitally. For instance, supporting learning-to-learn skills. Taking them for granted is a mistake! It’s also about ongoing support for development, e.g. coaching. Good practices help, and tools that document approaches and outcomes can assist.

At the group level, there are again ways in which we can be fostering effectiveness. This includes having good collaboration tools, and assisting people in using them well. It can also be about policies that make ‘show your work’ safe. Then you can augment with ‘show your work’ tool. Again, having the right practices and policies makes the digital transformation investment more valuable. You could pick the wrong tools if you’re instituting the old ways instead of doing the process work first.

This holds true at the organizational level as well, of course. The policies and practices cross the organization. Thus, what works for teams comes from an organizational focus on learning. Then, the digital investments are focused on the most optimal outcomes. The alternative, digitizing unaligned practices, can only hinder improvement to be a successful organization.

There are a lot of myths about what works. This includes learning myths, but also bad HR practices. Many stem from maintaining approaches that are carryovers from industrial age business. Instead, we should be leveraging our knowledge of thinking to be strategic. L&D can be critically contributing to organizational success! Or not. There’s a big opportunity to shift practices in a positive direction, with upsides for outcomes. However, it takes the understanding and the will. What will you do?

This is related to the talk I’ll be giving as the opening keynote for the ATD Japan Summit in December (though I’m filming it for virtual delivery). I get my thinking done here first ;).  

Tech Thoughts

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I’m late with a post this week, owing to several factors, all relating to technology. I hadn’t quite pulled together a complete lesson, but by writing down these tech thoughts, I got there. (A further argument for the benefits of reflection.)

It started with upgrading our phones. We needed to (I tend to hand mine down, but really we both needed an upgrade this time). Of course there are hiccups, particularly since m’lady’s was so old that it couldn’t do the amazing thing mine had done. What happened with mine was that you just put the old phone and the new phone together and the new one just sucks the data off the old one and then the old one asks if you want to wipe it clean!  That’s some serious user experience. Something we should look more to in our LXD, so that we’re doing proper backwards design and we have the right combination of tools and learning to make performance relatively effortless.

Then another thing was quite interesting. An individual linked to me on the basis of a citation in a book. I didn’t know the book, so he sent me a picture of the page. He also asked if I could read Dutch. Sadly, no. However, I had recently upgraded my OS, and when I opened the picture, I noticed I could click on the text. Of. The. Picture!  I could select all the text (my OS was doing OCR on the picture live!), and then I could paste into Google Translate (another amazing feat) and it recognized it as Dutch and translated it into English. Whoa!

On the flip side, owing to the unusually heavy rain (for California), first our internet went out, and then the power. Fortunately both were working by the next morning. However, after that my backup drives kept dismounting and I couldn’t execute a backup reliably. I thought it might be the laptop, and I did a couple of increasingly difficult remedial measures. Nope. Had the drives been damaged by the power outage? Power outages aren’t quite new around here (we’re a bit up a hillside, and squirrels regularly blow the transformer), yet it hadn’t happened before.

Then I was on a Zoom call, and I started having hiccups in the microphone and camera. Even typing. What? When I switched to the laptop camera, it was all good.  All the things, drives, microphone, external monitor, are connected by a external hub. The hub had gone wonky! Instead of having to replace drives, I picked up a new hub last nite, and all’s good now. Phew!

I guess my take home tech thoughts is that we’re making true a promise I’ve mentioned when talking mobile: we really do have magic. (Asimov famously said any truly advanced technology is indistinguishable from magic.) We can do magical things like talk at distance, have demons do tasks on our behalf (picture text transcribing and translation), etc. On the other hand, when it doesn’t work, it can be hard to identify the problem!  Overall, it’s a win. Well, when it’s designed right! Which involves testing and tuning. As Dion Hinchcliffe put it: “Seamless #cx is now table stakes.” So let’s get designing, testing, and tuning, and make magical experiences.

More lessons from bad design

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I probably seem like a crank, given the way I take things apart. Yet, I maintain there’s a reason beyond “get off my lawn!” I point out flaws not to complain, but instead to point to how to do it better. (At least, that’s my story and I’m sticking to it. ;) Here’s another example, providing more lessons from bad design.

In this case, I’ll be attending a conference and the providers have developed an application to support attendees. In general, I look forward to these applications. They provide ways to see who’s attending, and peruse sessions to set your calendar. There are also ways to connect to people. However, two major flaws undermine this particular instance.

The first issue is speed. This application is  slow! I timed it; 4 seconds to open the list of speakers or attendees. Similarly, I clicked on a letter to jump through the list of attendees. The amount of time it takes varied from 4 to 8 seconds. Jumping to the program took 6 seconds.

While that may seem short, compare that to most response times in apps. You essentially can’t time them, they’re so fast. More than a second is an era in mobile responsiveness. I suspect that this app is written as a ‘wrapped’ website, not a dedicated app. Which works sometimes, but not when the database is too big to be responsive. Or it could just be bad coding. Regardless, this is  basically unusable. So test the responsiveness before it’s distributed to make sure it’s acceptable. (And then reengineer it when it isn’t.)

That alone would be sufficient to discount this app, but there’s a second problem. Presumably for revenue reasons, there are ads that scroll across the top. Which might make sense to keep the costs of the app down, but there’s a fundamental problem with our visual architecture.

Motion in the periphery of our vision is distracting. That was evolutionarily adaptive, allowing us to detect threats from places that we weren’t focusing on. Yet, when it’s not a threat, and we  are trying to focus on something, it interferes. We learned about this in the days of web pages with animated gifs: you couldn’t process what you were there to consume!

In this app, the scrolling of the ads makes it more difficult to read the schedule, attendee lists, and other information. Thus, the whole purpose of the application is undermined. You could have static ads that are randomly attached to the pages you click on. The audience is likely to go to several pages, so all the ads will get seen. Having them move, however, to ensure that you see them all undermines the whole purpose of the app.

Oddly enough, there are other usability problems here. On the schedule, there’s a quick jump to times on a particular day. Though it stops at 2PM!?!? (The conference extends beyond that; my session’s at 4PM.) You’d think you could swipe to see later times on that ‘jump’ menu, but that doesn’t work. I can’t go farther, because the usability makes it too painful; we may miss more lessons from bad design.

Our cognitive architecture is powerful, but has limitations. Designing to work in alignment with our brains is a clear win; and this holds true for designing for learning as well as performance support. Heck, I’ve written a whole book  about how our minds work, just to support our ability to design better learning! Conflicting with our mental mechanisms is just bad design. My goal is that with more lessons in bad design, we can learn to do better. Here’s to good design!