meta-learning Archives - Page 26 of 56

Diagram!

8 February 2017 by Clark Leave a Comment

One of the things that I feel is a really useful tool in my ongoing learning, in my ‘making sense of the world’ is diagramming. I find diagrams to be a really powerful way to understand not just elements, but relationships. And yet it doesn’t feel like diagramming gets enough respect. So I want to make a case for the diagram.

Language is good. Our brains have evolved to use it. But it has trouble communicating complex relationships. For an example, once I wrote this:

They found that while subjects would rate the analogies, from best to worst, as literally similar, true analogy, mere appearance, and false analogy, their recall for stories, from best to worst, was literally similar, mere appearance, true analogy, and false analogy.

Try discerning the important difference! My PhD advisor kindly pointed out that actually parsing this was hard, and recommended a diagram instead. Here’s a rendition of what resulted:

In this case it’s much easier to see how the two differed. (If you want to find out what’s important in the diagram, I’m happy to talk about analogical reasoning for as long as you can stand it! ;)

The point I’m making is that there are times when diagrams are very useful for communicating. And, if you’ve followed this blog for a fair amount of time, you’ve seen I use diagrams a lot. I use them to think ‘out loud’, and I think it’s important. As Larkin & Simon argued in their Cognitive Science article, Why a Diagram is (Sometimes) Worth Ten Thousand Words, diagrams let us map conceptual relationships to spatial ones. And so if I want to understand the conceptual relationships, I start laying out spatially, and adjust until they make sense to me.

And my concern is that we aren’t using this powerful visual tool enough. Sketchnotes are really nice ways to capture presentations, and depending on the skill of the noter, they may communicate it all, or help recall if you’ve seen it. Similarly, my mindmaps of keynotes capture the flow of the discussion and the relationships (at least as I parsed it), but may only make sense if you heard the talk.

But representing things with diagrams is not only a personal thinking tool, it can be a powerful way to communicate concepts, and that’s an important component of a good learning experience design, providing a conceptual model to guide performance.

So I’m surprised we don’t talk about diagrams more. It may seem hard (certainly trying to create an infographic is harder than it seems, from my experience ;), but there’s some systematicity to it. There are principles, and types of diagrams, and more to explore. And tools that make it easier (though even Powerpoint or Keynote can be used to make diagrams). Diagrams aren’t the only visuals that help (c.f. graphs and tables), but they’re an important tool in your thinking toolbox. I encourage you, as part of your meta-learning toolkit, to play around and get your mind around diagrams. Your thinking, and your learning design, can be better as a consequence.

Other writings

1 February 2017 by Clark Leave a Comment

It occurs to me to mention some of the other places you can find my writings besides here (and how they differ ;). My blog posts are pretty regular (my aim is 2/week), but tend to have ideas that are embryonic or a bit ‘evangelical’. First, I’ve written four books; you can check them out and get sample chapters at their respective sites:

Engaging Learning: Designing e-Learning Simulation Games

Designing mLearning: Tapping Into the Mobile Revolution for Organizational Performance

The Mobile Academy: mLearning For Higher Education

Revolutionize Learning & Development: Performance and Information Strategy for the Information Age

They’re designed to be the definitive word on the topic, at least at the moment.

I’ve also written or co-written a number of chapters in a variety of books. The books include The Really Useful eLearning Instruction Manual, Creating a Learning Culture, Michael Allen’s eLearning Annual 2009, and a bunch of academic handbooks (Mobile Learning, Experiential Learning, Wiley Learning Technology ;). These tend to be longer than an article, with a pretty thorough coverage of whatever topic is on tap.

Then there are articles in a variety of magazines. These tend to be aggregated thoughts that are longer than a blog post, but not as through as a chapter. In particular, they are things I think need to be heard (or read). So, my writing has shown up in:

eLearnMag

Learning Solutions

CLO

The topics vary. (For the eLearnMag ones, you’ll have to search for my name owing to their interface, and they tend to be more like editorials.)

And then there are blog posts for others that are a bit longer than my usual blog post, and close to an article in focus:

The Deeper eLearning series for Learnnovators

A monthly article for Litmos.

These, too, are more like articles in that they’re focused, and deeper than my usual blog post. For the latter I cover a lot of different topics, so you’re likely to find something relevant there in many different areas.

I’m proud of it all, but for a quick update on a topic, you might be best seeing if there’s a Litmos post on it first. That’s likely to be relatively short and focused if there is one. And, of course, if it’s a topic you’re interested in advancing in and I can help, do let me know.

Cognitive Business

18 January 2017 by Clark Leave a Comment

One of my mantras is that organizations need to align better with how we think, work, and learn. However, my focus has been specifically on what L&D can be doing (as that’s the folk I mostly talk to). But it occurs to me that it really goes farther. There are applications of cognitive science (including neuroscience, cognitive psychology, sociology, philosophy, anthropology, etc) to more areas of business than just L&D. And it’s worth being explicit about this.

I was recently reading how marketing has leveraged understanding of behavior change at a deep level. We need to incorporate this in our learning design, but it should go beyond training and learning and be involved in helping people understand why their work is important and how they contribute.

And similarly, the notion that our thinking is both situated (e.g. reconstructed in the moment, not formally abstract) and distributed (across representations, not all in the head) has broader implications. It’s not just about performance support, but should influence policies and tools as well.

And the fact that innovation is social, and an outcome of slow percolation, influences more than facilitating communication and collaboration. It should influence corporate culture and expectations and time frames.

The list goes on: research says that organizational change works better starting small and scaling rather than a monolithic effort. We know that design processes are better when they’re cyclical rather than waterfalls. We’ve discovered that our inability to perform rote tasks flawlessly argues for changes in work processes and expectation. And we’ve found out that treating people fairly leads to better outcomes including retention, loyalty, and more. Ultimately, we’ll want to be making smart cyborg choices about what to have people do and what technology should do.

In short, we can be working smarter in many ways. It’s hard to change from the old hierarchical models, but we’re continually learning that other approaches work better. Heck, we may even be able to start working wiser! Here’s hoping.

A Cognitive Audit?

11 January 2017 by Clark 2 Comments

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.

Vale Seymour Papert

10 January 2017 by Clark 1 Comment

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.

2016 Reflections

28 December 2016 by Clark Leave a Comment

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.

Cultural Alignment

27 December 2016 by Clark 1 Comment

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?

(When) Is pattern-matching enough?

15 December 2016 by Clark Leave a Comment

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?

Learning Strategy Issues

7 December 2016 by Clark Leave a Comment

Online Educa logo 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?

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