Michio Kaku opened the second day of DevLearn with a keynote on the future of the mind. He portrayed extrapolations of current research to some speculative ideas of what our future could mean. He talked about research from physics (?!?) on MRI, AI, and more to provide new capabilities.
I had the pleasure of being the opening keynote at the People Matters L&D conference in Mumbai this past week, with a theme of ‘disruption’. In it, I talked about some particular myths and their relation to our understanding of our own brains. Following my presentation, I sat through some other presentations. And heard at least one other myth being used to flog solutions. So, fewer myths, please.
My presentation focused on the evidence that we’re still operating under the assumption that we’re logical reasoners (which I pointed out, isn’t apt). I mentioned annual reviews, bullet points presos, unilateral decisions, and more. I also cited evidence that L&D isn’t doing well, so it is a worry. Pointing to post-cognitive frameworks like predictive coding, situated & distributed cognition, and more, I argued that we need to update our practices. I closed by urging two major disruptions: measurement, and implementing a learning culture in L&D before taking it out to the broader org.
In a subsequent presentation, however, the presenter (from a sponsoring org) was touting how leadership needed to accommodate millennials. I’m sorry, but there’s considerable evidence that ‘generation differences’ are a myth. The boundaries are arbitrary, there’re no significant differences in workplace values, and every effect is attributable to age and experience, not generation. (Wish I could find a link to the ‘eulogy for millennials myth’ two academics wrote.)
Another talk presented a lot of data, but ultimately seemed to be about supporting user preferences. Sorry, but user preferences, particularly for novices, aren’t a good guide. There was also a pitch for an ‘all-singing, all-dancing’ solution. Which could be appealing, if you’re willing to live with the tradeoffs. For instance, locking into whatever features your provider is willing to develop, and living without best-0f-breed for all components.
Yes, it’s marketing hype. However, marketing hype should be based on reality, not myths. I can get promising a bit more than you can deliver, and focusing on features you’re strong on. I can’t see telling people things that aren’t true. My first step in dealing with the post-cognitive brain is to know the cognitive and learning sciences, so you’ll know what’s plausible and what’s not. Not to PhD depth, but to have a working knowledge. That’s the jumping off point to much that’s the necessary disruption, revolution, that L&D needs to have. And fewer myths, please!
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.
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.
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!
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.
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.
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.
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 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.
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.
So, 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.
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?
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 ;).