Mick Ebeling, of Not Impossible Labs, opened the TechKnowledge conference with an inspiring keynote. He told engaging stories about achieving the impossible because it just took commitment. He evangelized contributing, and getting contributions by emphasizing the brand benefits of doing good.
11 January 2017
In the recent Chief Learning Officer magazine, I wrote an article on the basics of the cognitive science of learning. Given the evidence that “L&D isn’t doing near what it could and should, and what it is doing it is doing badly, other than that it’s fine” (as I say), at least one of the potential barriers is that L&D isn’t truly aware of what science says about their profession.
And I truly believe that if you’re a professional, you should be aware of the fundamental scientific basis of your profession. Pilots need to know aeronautics, physicians need to know physiology, etc. And therefore, I reckon L&D needs to know the cognitive background. But there’s more.
Knowing a suitable level of cognitive science is one thing, using that to assess your practices is another. Too often, we have what we call ‘inert knowledge’: we know it, but we don’t apply it. That’s not helpful. What has to happen is that processes need to be evaluated, improvements identified, interventions prioritized, enablement enacted, and progress reviewed. It’s just part of being a professional!
There are other sorts of audits possible (I know folks who do performance audits, and knowledge audits, etc), but I’m increasingly thinking that the one that matters is the one that aligns with how our brains work. Not at the neural level (there’s little of impact there), but at the cognitive level. Note that cognitive science includes social, conative and affective components (e.g. the culture and motivation), and neural, for that matter ;).
This isn’t an academic exercise. The increasing competition enabled by technology already suggests that optimal execution is only the cost of entry, and continual innovation will be the only sustainable differentiator. Both are cognitive functions, and the best outcomes will only be achieved when organizations are acting in accordance with how we think, work, and learn. This is about equipping your organization to kick some proverbial tail.
I’m drafting an initial such instrument, with associated recommendations. I welcome your thoughts, and any interest in engaging around this.
10 January 2017
Imagine my surprise that I missed the demise of Seymour Papert this past year (yet another loss). I’ve looked back to see what I was doing on 31 July and how I missed it, and we were preparing for a week in the wilderness. So it’s certainly likely I wasn’t deeply involved in the news. This is a shame, because I’ve been a fan of Papert’s work for quite literally decades. So here’s a belated tribute.
My first job out of college was designing and programming educational computer games. I’d been exposed to some innovative thinking through my undergraduate thesis advisors, Hugh Mehan and Jim Levin. Having read Papert’s Mindstorms, I gave it to my parents to help them understand why I did what I did (unsuccessfully ;). The book is subtitled “Children, Computers, and Powerful Ideas”, and argued that learning computing was a vehicle for learning to think.
Papert had studied with Jean Piaget, and proceeded to be a leader of the constructivism movement applying the notion of exploratory learning environments. I subsequently learned about Piaget (and post-Piaget, and Vygotsky) in my graduate studies, so I can see how the notion of developmental readiness and opportunities to create understanding through exploration could lead to the work Papert did.
Logo, the computer language for learning, was developed by Papert along with Wallace Feurzieg. It’s simple commands controlling a ‘turtle’ and gradually getting richer play challenges was the start to computer understanding for learners for decades, and has influenced computer language learning in many ways. Apple’s Playgrounds uses similar small steps to control a creature to start teaching Swift.
He was invited to co-lead the MIT AI lab with Marvin Minsky. He worked with Minsky on Perceptrons, which were an early exploration of the connectionist networks now so prevalent in artificial intelligence. There remains a controversy over whether and how the book influenced research in the area of symbolic and sub-symbolic intelligence approaches.
Papert was instrumental in much of the thinking that has shaped what we do in learning technology. I’m grateful for his contributions.
28 December 2016
This is the last Learnlet for 2016, and so it’s time for some reflections on what has been an ‘interesting’ year. I’ll admit it’s been rough, what with losing so many people known through popular media. I guess you get to an age where more and more people who’ve you’ve grown up with in one way or another begin to pass on. And of course serious changes nationally and internationally. But there are some learnings as well.
So, I did a fair bit of speaking in 2016, keynoting conferences in New York and Beijing, as well as more private events live and online. I spoke about mobile learning, deeper learning design, innovation, as well as the L&D revolution. And, of course, I attended the usual suite of industry conferences, notably the eLearning Guild events and Online Educa. I also was engaged in a number of consulting engagements, working with folks to deepen their understanding (and mine), to achieve meaningful outcomes.
One learning is the value of travel outside the US. I actually lived outside the US for 7 years (in Australia), and the perspective of seeing how others live, and looking at the rest of the world (and back at the US) from other perspectives is a valuable grounding. The view I had of China before my recent trips was quite different than the reality. I can say the same from previous experience with India. It’s too easy to be insular. Instead, it’s helpful to be curious.
And that’s an industry comment too. I continue to talk (e.g. my workshop in Berlin) and write about deeper learning design. And I continue to evangelize about it (c.f. the Serious eLearning Manifesto with my colleagues, and the recent Future of Work project). And yet, the industry seems to continue on in ignorance. The tools still reflect more of a focus on content instead of experience, for instance. Things get better, but surprisingly slowly. How long until we start treating learning design with the appropriate respect? We need to get out of our comfort zone!
There are positive signs. My engagements with Learnnovators has demonstrated that at least some folks care about quality. And I had several client engagements specifically focused on better learning design. There just need to be more efforts in this area. It’s not hard to tweak processes to generate outcomes that not only look like good elearning, but actually have a high likelihood of an impact.
I’ve done a lot of reading this year (most recently The Fifth Discipline, which puts lots of what I’ve learned about organizations into a context). It amazes me that with robust science at the organizational level as well as the learning science level, we still see so much action in organizations (and society) contrary to what’s demonstrably known. There are positive signs here too, but still too few. It’s challenging, as it involves crossing discipline and business boundaries, yet the benefits are promising.
And I think the hype about technology improvements are premature. Wearables continue, of course. And VR has reached the stage where it’s easy to experiment. Yet in each case, we’re still in the stage before standards emerge that will make a real market. AR and content strategy are still nascent, but there’s much potential. Fortunately, analytics is seeing a boon from the standardization around xAPI. We need to stick to the core learning affordances of new technology to truly grasp the potential.
Looking forward, I see much opportunity, as implied by the gaps indicated above. There’s real opportunity for improvement in the use of technology to facilitate outcomes. We can do personal and organizational learning better. We can leverage technology in ways that are closer aligned with how our brains work. As a precursor, we’ll need a broader understanding of cognition, but that’s doable. I’m happy to help ;).
And let me just add a very heartfelt thanks to those of you who I’ve interacted with, this year and in the past. Whether reading the blog, making comments, engaging on social media, attending sessions or workshops, and of course via engagements, I’m very grateful. I hope to connect with you in the future, in any of the above ways or any other. I continue to learn through and with you, and that’s a gift. Again, thank you.
Goodbye 2016, and here’s to making positive changes in the new year. May it be your best yet.
27 December 2016
I was thinking about the ways in which organizations can support performance. That is, we can and should be aligning with how we think, work, and learn. So we can provide tools to support us in the moment, we can provide tools to help us work together, and we can develop people all slowly over time. In short, I was thinking about cognitive alignment, and I was going to write about it, but it turns out I already have! However, I also realized that there was an opportunity to extend that to cultural alignment, and I think that’s important as well.
So, one of the things we can do to optimize outcomes is to give people performance support. In particular, we can provide tools to address gaps that emerge from our cognitive architecture. We can also provide policies about things we’re supposed to do. And that’s all good. However, some of that might not be necessary under the right circumstances.
I was thinking about the specific case of acting in ways that are consonant with the values of the organization. For instance, in a well-known upscale department store chain, the staff have the leeway to spend on the order of $1K to address any emerging customer problem. I reckon the store figures that’s the future worth of a happy customer. And that’s acting in alignment with the culture of the organization.
The point I want to make is that by having an explicit culture in the organization, you might not have to provide performance support. If the desired approach is understood, it can be generated from understanding the organization’s value. If you know what’s expected, you can perform in alignment without needing external clues and cues.
There are clear benefits from a learning organization in terms of innovation and employee engagement, but what about the other side? I suggest that the right culture can also benefit the ‘optimal execution’ side. In short, there’s little reason to do aught but begin a move to a more enlightened culture. At least, that’s what seems to me to be the case. How about you?
15 December 2016
In the course of my research, I came across the project shown here, as represented by the accompanying video. In the video, they show (and tout) the value of their approach to developing pattern recognition around mathematics. Further, they argue that it’s superior to the typical rule presentation and practice. And I can buy that, but with many caveats that I want to explore.
So it’s clear that we learn by abstracting patterns across our experiences. We can provide models that guide, but ultimately it’s the practice that works. An extreme example is chicken-sexing (mentioned in the transcript); determining the gender of new-born chicks. Here, no one can articulate the rationale, it’s merely done by attempts and correct/incorrect feedback! And the clear implication is that by having learners do repetitive tasks of looking for patterns, they get better at it.
And, yes, they do. But the open question is what is the learning benefit of that. Let’s be clear, there are plenty of times we want that to happen. As I learned during my graduate studies, pilots are largely trained to react before their brains kick in: the speed at which things happen are faster than conscious processing. When speed and accuracy is important, nay critical, we want patterned responses. And it does work for component skills to more complex ones in well-defined domains. But…
When we need transfer, and things are complex, and we aren’t needing knee-jerk responses, this doesn’t work. I would like to train myself to recognize patterns of behavior and ways to deal with them effectively, for instance (e.g. in difficult presentation situations, or negotiations). On the other hand, in many instances I want to preclude any immediate responses and look for clues, ponder, explore, and more.
The important question is when we want rote performance and when we don’t . Rote ability to do math component skills I’m willing to accept. But I fear a major problem with math instruction in schools is about doing math, not about thinking like a mathematician (to quote Seymour Paper). And I don’t want students to be learning the quadratic equation (one of Roger Schank’s most vivid examples) instead of how math can be used a problem-solving tool. The nuances are subtle, to be sure, but again I’m tired of us treating learning like color-by-numbers instead of the rocket science it should be.
Look, it’s great to find more effective methods, but let’s also be smart about the effective use of them. In my mind, that’s part of learning engineering. And I’m by no means accusing the approach that started this discussion of getting it wrong, this is my own editorial soapbox ;). There’s much we can and should be doing, and new tools are welcome. But let’s also think about when they make sense. So, does this make sense?
7 December 2016
The other thing that I was involved in at Online Educa in Berlin was a session on The Flexible Worker. Three of us presented, each addressing one particular topic. One presentation was on collaborating to produce an elearning course on sleeping better, with the presenter’s firm demonstrating expertise in elearning, while the other firm had the subject matter expertise on sleep health. A second presentation was on providing tools to trainers to devolve content development locally, addressing a problem with centrally-developed content. My presentation was on the gaps between what L&D does and how our brains work, and the implications. And, per our design, issues emerged.
The format was interesting: our presentations were roughly 10 minutes each. And we were using a tool (sli.do) to collect and rank questions. Then we had the audience work at their tables (in the round) to come up with their answers to the top questions, which we then collected and the panelists riffed on the outcomes. We got through three questions as a group, and I thought the outcomes were quite interesting. In short, as a rapporteur at the closing business session, I suggested that the topic ended up being about flexible work, not flexible working.
The top question that emerged had to do with how to support effective search (after I expounded on problem with the notion that it all had to be in the head). The sourced answers included crowd-sourcing the tags for finding objects, using a controlled vocabulary, and auto-analyzing the content to determine tags. I suggest a hybrid solution, in general. The interesting thing here was the audience picking up on the need to go beyond courses and start looking at resources.
The next question was how to move from a training to a performance culture. And it was another exciting development to hear them thinking this way. The solutions offered included coaching, supporting the importance of self-learning (meta-learning, yay!), and working both top-down and bottom-up. I also suggested that measuring was a likely catalyst that could begin to draw attention to outcomes (just as I reckon competencies are the lever in higher-ed).
The third question was about ensuring quality in a localized learning environment (e.g. user-generated content). The concern was that the knowledge of learning design wouldn’t necessarily be widespread. Suggestions included making the content editable for collaborative improvements, or using rankings, and scaffolding of improvement through the community. Here too, a focus on learning itself could assist.
What’s encouraging to me is that each of these questions was really about moving to a transformative viewpoint. The audience was clearly thinking ‘beyond the course’. They were focusing on supporting performers in learning, and resources, and leveraging the community, all activities consonant with the revolution.
An interesting aside came in the closing session. Several folks were mentioning a need for change, and an audience member asked “why?” He was a consultant, and his clients already seemed to be moving forward. I suggested he was seeing the best, and that many folks were not there (mentioning the Towards Maturity data as well as the problems I identified in the beginning of the Revolution book. And it’s a problem that too many people don’t yet see the missed opportunities and don’t feel the pain (and are frankly not looking).
So, there are opportunities to start taking small steps in the direction of taking on a bigger perspective and making the role of L&D more strategic. It first takes an awareness of the problems (my old line: “L&D isn’t doing near what it could and should, and what it is doing it is doing badly, other than that things are fine” :), and then a strategy to move forward. The strategy depends on where the organization is to begin with, but there are systematic principles to guide progress. That’s what I do, after all! It’s nice to see awareness growing. So, are you ready to start taking some positive steps?
17 November 2016
Tony DeRose opened the second day of DevLearn with a geeky (and intriguing) presentation on the links between math and story in making animation. With clips and anecdotes he showed how it works, and inspired about how they’re connecting this to STEM.
16 November 2016
Magician Penn Jillette opened the DevLearn conference with a fascinating presentation on storytelling, telling his story and unpacking magic for us.
8 November 2016
Demoing is a form of working out loud, right? So I recently was involved in a project with Learnnovators where we designed some demo elearning (on the workplace of the future), and documented the thinking behind it. (The posts, published by Learning Solutions, are aggregated here.) And now there’s be a chance to see it! So, a couple of things to note.
First, this is Work Out Loud Week, and you should be seeing considerable attention to working out loud (aka Show Your Work). On principle, this is a good practice (and part of the Workplace of the Future, to be recursive). I strongly recommend you have an eye out for events and posts that emerge. There’s an official site for Work Out Loud week: Wolweek.com, and a twitter account: @Wolweek, and the hashtag #wolweek, so lots of ways to see what’s up. There are many benefits that accrue, not least because you need to create a culture where this practice can live long and prosper. Once it does, you see more awareness of activity, improved outcomes, and more.
Second, if you’ll be at DevLearn next week, I’ll be demoing the resulting course at the DemoFest (table 84). Come by and share your thoughts and/or find out what the goal was, the tradeoffs faced, and the resulting decisions made. Of course, I encourage you to attend my workshop on elearning strategy and mythbusting session as well. I’ll also be haunting the xAPI camp on the Tuesday. Hope to see you there!