Sarah Prevette closed the Learning Solutions conference with a rapid fire overview of Design Thinking and a passionate case for making the success skills of the future to be entrepreneurship. Starting with her experiences, she laid out success factors, and suggested that these skills were learnable and should be the curriculum.
In a thoughtful presentation, Sarah Lewis kicked off the second day of the Learning Solutions conference. She used compelling stories of innovations to paint a picture of mastery, failure, and grit, the elements of success.
Baratunde Thurston opened this year’s Learning Solutions conference with a funny and interesting keynote talking about storytelling. Hard to capture humor in a mindmap, but his takehome was a valuable concept.
I was on a phone conversation and was asked whether I compared my clients against others in the business to help them figure out where they’re at. E.g. do I offer my partners the chance to benchmark. And after a bit of thought, I said that no, I didn’t, and explained why. Moreover, they found my answer intriguing, so I thought I’d share it with you.
So, as I’ve said before, I don’t like best practices. In fact, as I’ve written before, I think we shouldn’t benchmark ourselves against others. That’s a bad practice. Why? Because then we’re comparing ourselves against a relative measure. And I think we should be comparing ourselves to a principled metric about where we could and should be.
In fact, in the Revolution book, I created such a benchmark. Using my performance ecosystem model, I took the six fundamental elements and elaborated them. The first core element I documented is a learning culture. That’s accompanied by the approach to formal learning, looking at your instructional design and delivery. Then you move to performance focus, how you’re supporting performance in the world. We move on to social, how you’re facilitating informal learning and innovation. The next step is how you measure what you’re doing. Finally, there’s your infrastructure, how you’re creating the ecosystem environment. For each here I have a principle and an approach.
What I’ve done in the benchmarking instrument is take each of these and extend them. So, for each element broke it into two components, as there are nuances. And, for each, I proposed four levels of maturity:
Thus, rather than benchmarking yourself against others, you have a principled approach with which to measure yourself. This instrument is fully elaborated in the Revolutionize L&D book, and goes into detail on each of the twelve rows.
And that was my response to the query. As a person, on principle you’re not supposed to compare yourself to others, but to your own progress. How to set your benchmarks? Against formal criteria. The same is true for organizations. I’ve tried to make a scrutable framework, the Revolution Field Guide, so to speak. So, please, look to best principles, not practices, and evaluate yourself similarly.
I’ve been complaining, as part of the myths tour, that everyone wants the magic bullet. But, as I was commenting to someone, there are huge tech opportunities we’re missing. How can I have it both ways? Well, I’m talking about two different techs (or, rather, many). The fact is, we’re chasing the wrong technologies.
The problem with the technologies we’re chasing is that we’re chasing them from the wrong beginning. I see people chasing microlearning, adaptive learning, video, sims, and more as the answer. And of course that’s wrong. There can’t be one all-singing all-dancing solution, because the nature of learning is remarkably diverse. Sometimes we need reminders, sometimes deep practice, some times individualization makes sense, and other times it’s not ideal.
The part that’s really wrong here is that they’re doing this on top of bad design! And, as I believe I’ve mentioned, gilded bad design is still bad design. Moreover, if people actually spent the time and money first on investing just in improving their learning design, they’d get a far better return on investment than chasing the latest shiny object. AND, later investments in most anything would be better poised to actually be worthwhile.
That would seem to suggest that there’s not a sensible tech to chase. After, of course, authoring tools and creating elearning. And that’s not true. Investment in, say, sims makes sense if you’re using it to implement good design (e.g. deep practice). As part of a good learning design strategy. But there’s something deeper I’m talking about. And I’ve talked about it before.
What I’m talking about are content systems. They may seem far down the pike, but let me (again) make the case about why they make sense now, and for the future. The thing is, being systematic about content has both short-term and long-term benefits. And you can use the short-term ones to justify the long-term ones (or vice-versa).
In the short term, thinking about content from a systems perspective offers you rigor. While that may seem off-putting, it’s actually a benefit. If you design your content model around good learning design, you are moving towards the first step, above, about good design. And, if you write good descriptions within those elements, you really provide a foundation that makes it difficult to do bad design.
My point is that we’re ignoring meaningful moves to chase chimera. There are real value steps to make, including formalizing design processes and tools about good design. And there are ways to throw your money away on the latest fad. It’s your choice, but I hope I’ve made a case for one interpretation. So, what’s yours?
In a conversation today, I mentioned that previously I’ve thought that perhaps the best next ‘man in the moon’ project would be to put an entire K12 curriculum up online. And, I’ve also thought that the only way to really fix things is to train trainers of teachers to learn to facilitate learning around meaningful activity. And, of course, both are needed. What am I thinking?
So, there are huge gaps in the ways in which folks have access to learning. For example, I worked on a project that was trying to develop some K12 curricula online, to provide support for learners in HS that might not have sufficiently capable learners. The project had started with advanced learners, but recognized that wasn’t the only gap. And this is in California! So I have argued for a massive project, but using advanced curricula and pedagogy.
And, at the other end, as I spoke at a conference looking to talk about improving education in India. There, they have a much bigger need for good teachers than they can reach with their education schools. I was arguing for a viral teacher prep. The idea being not just to train teachers, but train the trainers of those teachers. Then the training could go viral, as just teaching teachers wouldn’t go fast enough.
And both are right, and not enough. In the conversation, I resurrected both points and am now reflecting how they interact. The simple fact is that we need a better curriculum and a better pedagogy. As Roger Schank rightly points out, things like the quadratic equation are nuts to keep in a K12 curricula. The fact is that our curricula came from before the Industrial Age and is barely adequate there. Yet we’re in an Information Age. And our pedagogy is aligned to tests, not to learning nor doing. We should be equipping kids with actionable knowledge to make meaningful decisions in their lives, not with arbitrary and abstract knowledge that isn’t likely to transfer.
And, of course, even if we did have such a curriculum online, we’d need teachers who could facilitate learning in this way. And that’s a barrier not just in India. The point being that most of the world is suffering with bad curricula and pedagogy. How do we make this change.
And I don’t have an answer. I think we should put both online, and support on the ground. We need that content, available through mobile to reach beyond the developed world, and we need the facilitators. They can be online, as I think about it, but they need to understand the context on the ground if they’re not there. They are context-specific necessities. And this is a massive problem.
Principle says: start small and scale. There are institutions doing at least parts of this, but scaling is a barrier. And again, I have no immediate solution other than a national (or international) initiative. We don’t want just one without the other. I don’t want teachers facilitating the old failed curricula, and I don’t want current pedagogies working on the new curricula. (And I shudder at the thought of a pre-college test in the old style trying to assess this new model!) I welcome your thoughts!
So last week I was the strategy track coach for the Training 19 conference. An experiment! That meant that I picked the sessions from a list of those who put their session proposals up for ‘strategy’, and could choose to open and/or close the track. I chose both. And there were thoughts on strategy from the sessions and the attendees that are worth sharing.
I chose the sessions mainly on two criteria: coverage of the topics, and sessions that sounded like they’d give real value. I was lucky, the latter happened! While I didn’t get the complete coverage I wanted, I did get a good spread of topics. So I think the track worked. As to the coaching, there wasn’t much of that, but I’ve sent in suggestions for whoever does it next year.
I knew two of the presenters, and some were new. My goal, again, was real coverage. And they lived up to it. Friend and colleague Michael Allen practiced what he preached while talking about good learning design, as he does. He was followed by Karen Polhemus & Stephanie Gosteli who told a compelling tale of how they were managing a huge initiative by combining training with change management. Next was JD Dillon, another friend, talked about his experiences building learning ecosystems that deemphasized courses based upon data and his inferences. Alwyn Klein made an enthusiastic and compelling case for doing performance consulting before you start. Haley Harris & Beth Wisch went deep about data in talking about how they met the needs for content by curating. Joe Totherow talked games as a powerful learning tool. Finally, Alex Kinnebrew pushed for finding stakeholder voices as a complement to data in making strategy.
I bookended these talks. I opened by making the case for doing optimal execution right, meaning doing proper learning design and performance support. Then I talked about driving for continual innovation with social and informal. I closed by laying out the performance ecosystem diagram (ok, so I replaced ‘elearning’ in the diagram with ‘training’, and that’s probably something I keep), and placed the coming talks on it, so that attendees would know where the talks fit. I mostly got it right ;). However, the feedback suggested that for those who complained, it’s because I took too long to get to the overview. Useful feedback.
I finished with a 3 hour strategy session where I walked people through each element of the ecosystem (as I cut it), giving them examples, providing self-assessment, and items to add to their strategy for that element. I closed by suggesting that it was up to them to sequence, based upon their particular context. Apparently, people really liked this opportunity. One challenge was the short amount of time; this is usually run as a full day workshop.
It’s clear that folks are moving to thinking ‘outside of the box’, and I’m thrilled. There were good audiences for the talks in a conference focused on doing training! It’s definitely time for thoughts on strategy. Perhaps, as has happened before, I was ahead of the time for the revolution. Here’s to a growing trend!
Danielle Feinberg of Pixar shared her story of using computer science to create the visual art and storytelling for Pixar movies. She illustrated the process of being creative under constraints by being ‘scrappy and clever’. She also illustrated the process with representations of intermediate stages and the stunning results from the movies. Inspiring, as hard to capture in a mindmap.
David Eagleman gave a humorous and insightful keynote at the Training 19 conference. He helped us see how the unconscious relates to conscious behavior, and how to break out and tap into creativity. Here’s my mindmap:
Recently, I came across an article about a new explanation for behavior, including intelligence. This ‘free energy principle’ claims that entities (including us) “try to minimize the difference between their model of the world and their sense and associated perception”. To put it in other words, we try to avoid surprise. And we can either act to put the world back in alignment with our perceptions, or we have to learn, to create better predictions.
Now, this fits in very nicely with the goal I’d been trying to talk about yesterday, generating surprise. Surprise does seem to be a key to learning! It sounds worth exploring.
The theory is quite deep. So deep, people line up to ask questions of the guy, Karl Friston, behind it! Not just average people, but top scientists need his help. Because this theory promises to yield answers to AI, mental illness, and more! Yet, at core, the idea is simply that entities (all the way down, wrapped in Markov blankets, at organ and cell level as well) look to minimize the differences between the world and their understanding. The difference that drives the choice of response (learning or acting) is ‘surprise’.
This correlates nicely with the point I was making about trying to trigger transformative perceptions to drive learning. This suggests that we should be looking to create these disturbances in complacency. The valence of these surprises may need to be balanced to the learning goal (transformative experience or transformative learning), but if we can generate an appropriate lack of expectation and outcome, we open the door to learning. People will want to refine their models, to adapt.
Going further, to also make it desirable to learn, the learner action that triggers the mismatch likely should be set in a task that learners viscerally get is important to them. The suggestion, then, is create a situation where learners want to succeed, but their initial knowledge shows that they can’t. Then they’re ready to learn. And we (generally) know the rest.
It’s nice when an interest in AI coincides with an interest in learning. I’m excited about the potential of trying to build this systematically into design processes. I welcome your thoughts!