meta-learning Archives - Page 16 of 57

How do you drive yourself?

12 December 2019 by Clark 1 Comment

How do I drive myself? I was asked that in a coaching session. The question is asking how I keep learning. There are multiple answers, which I’ve probably talked about before, but I’ll reflect here. I think it’s important to regularly ask: “how do you drive yourself?”

As it’s the end of the year, my conversant was looking at professional development. It’s the time to ask for next year’s opportunities, and the individual was breaking out of our usual conversation to talk about this topic. And so he asked me what I did.

And my first response, which I’ve practiced consciously at least since grad school, is that I accept challenges. That is, I take on tasks that stretch me. (It might be that ‘sucker’ tattoo on my forehead, but note that my philanthropic bandwidth is pretty stretched. ;). This is professionally and personally.

That is, I look to find challenges that I think are within my reach, but not already my grasp. Or, to put it another way, in my Zone of Proximal Development. Accepting assignments or engagements where, with effort, I can succeed, but it’s not guaranteed.

Which means, of course, that there’s risk as well. Occasionally, I do screw up. Which I really really hate to do. Which is a driver for me to push out of my comfort zone and succeed. Or, at least, learn the lesson.

There’s more, of course. One thing I did started with my first Palm Pilot (the Palm III, the accompanying case is still my toiletry bag!). I had to justify to myself the expense, so I made sure that I really used it to success. This was part of the driver of the thinking that showed up in Designing mLearning, how to complement cognition. IA instead of AI, so to speak.

I also live the mantra “stay curious, my friends”. I’m still all too easily distracted by a new idea, but I don’t think that’s a bad thing. Well, as long as it’s balanced with executing against the challenges.

That’s how I drive myself. So, how do you drive yourself?

Passion and Learning

26 November 2019 by Clark Leave a Comment

My better half recently got a sample of special butter. A gift from a co-worker (an interesting story), and led me to reflect on the link between passion and learning.

M’lady’s co-worker is a fan of good butter. I was able to view a picture of her refrigerator, and the assortment of butters rivals what you might see in a fine grocery! We did a tasting between the ordinary butter we ordinarily purchase and this special butter. The difference was noticeable. I was reminded of the fine butter they serve when in Europe. Or at really fine restaurant.

This may seem odd, but think about it a bit. What do you care enough about to really understand? At various times I’ve been known to wax poetic about beer, cooking, waves, and more. And, of course, cognition, learning, engagement, and design. I managed to get educated about (American) football and cricket (yes, cricket) from inspired roommates. The list goes on.

And what’s fun is learning from these folks just why they find it so interesting. Which is related to the task of finding the intrinsic interest for designing learning. Talk to the experts! They’ve spent hours becoming experts, what motivated them? If you can find that, you’ve got a handle on it.

And I’m sure you’ve learned something from someone who was passionate about it. That’s usually a good indication that they’re also knowledgable, but there are caveats on that. People can get passionate about myths, too. There are reasons to be cautious. In general, however, you’re liable to be lucky.

Passionate people not only make fields comprehensible, they tend to drive fields forward. If you’re here, I’m expecting you’re passionate about performance & development. Maybe even up for a revolution! Let’s connect passion and learning to make it better.

Play to Learn

17 October 2019 by Clark Leave a Comment

Thinking more about Friston’s Free Energy Principle and the implications for learning design prompted me to think about play. What drives us to learn, and then how do we learn? And play is the first answer, but does it extend? Can we play to learn beyond the natural?

The premise behind the Free Energy principle is that organisms (at every level) learn to minimize the distance between our predictions and what actually occurs. And that’s useful, because we use our predictions to make decisions. And it’s useful if our decisions get better and better over time. To do that, we build models of the world.

Now, it’d be possible for us to just sit in a warm dark room, so our predictions are right, but we have drives and needs. Food, shelter, sex, are drives that can at least occasionally require effort. The postulate is that we’ll be driven to learn when the consequences of not learning are higher than the effort of learning.

At this level, animals play to learn things like how to hunt and interact. Parents can help as well. At a higher level than survival, however, can play still work? Can we learn things like finance, mathematics, and other human-made conceptions this way? It’d be nice to make a safe place to ‘play’, to experiment.

Raph Koster, in his A Theory Of Fun, tells us that computer games are fun just because they require learning. You need to explore, and learn new tricks to beat the next level. And computer games can be about surviving in made-up worlds.

The point I’m getting to is that the best learning should be play; low stakes exploration, tapping into the elements of engagement to make the experience compelling. You want a story about what your goal is, and a setting that makes that goal reasonable, and more.

To put it another way, learning should be play. Not trivial, but ‘hard fun’. If we’re not making it safe, and providing guided discovery to internalize the relationships they need, to build the models that will make better decisions, we’re not making learning as naturally aligned as it can be. So please, let your people play to learn. Design learning experiences, not just ‘instruction’.

Clear about the concept

19 September 2019 by Clark Leave a Comment

I went to hear a talk the other day. It was about competency-based education (CBE) for organizations. Ostensibly. And, while I’m now affiliated with IBSTPI, it’s not like I’m a competency expert. And maybe I expect too much, but I really hope for people to be clear about the concept. Alas, that’s not what I found.

So, it started out reasonably well, talking about how competencies are valuable. There were a number of points, and many made sense, although some were redundant. Maybe I missed some nuance? I try to be open-minded. It’s about creating clear definitions of performance, and aligning those with assessments. Thus, you’re working on very clear descriptions of what people should be doing.

It got interesting when the speaker decided to link CBE to Universal Design for Learning (UDL). And it’s a good program. UDL talks about using multiple representations to increase the likelihood for different learners to be able to comprehend and respond. This, in the talk, was mapped to three different segments: engaging the learners in multiple ways, communicating concepts in multiple ways, and allowing assessment in multiple ways. And this is good. For learning. Does it make sense for CBE?

To start, the argument was, you should make the rationale for the learning in multiple ways. While in general CBE inherently embodies meaningfulness in the nature of clear and needed skills, I don’t have a problem with this. I argue you should hook learners in emotionally and cognitively, and those can be separate activities. There was a brief mention of something like ‘learning styles’, but while now wary, I was ready to let it go.

However, the talk went on to make a case for multiple representations of content. And here the slide explicitly said ‘learning styles’ and used VARK. And don’t get me wrong, multiple representations and media are good, but not for learning styles! The current status is that there’s essentially no valid instrument to measure learning styles, and no evidence that even if you did, that it makes a difference. None. So, of course, I raised the issue. And we agreed that maybe not for learning styles, but multiple representations weren’t bad.

The final point was that there could be multiple forms of assessment. At this point, I wasn’t going to interrupt again, but at the end of the session raised the point that the critical element of CBE is aligning the assessment with the performance! You can’t have them do an interpretative dance about identifying fire hazards, for instance, you have to have them identify fire hazards! So, here the audience ultimately agreed that variability was acceptable as long as it measured the actual performance. Again, I don’t think the speaker was clear about the concept.

There were two major flaws in this talk. One was casually mashing up a couple of essentially incommensurate ideas. CBE and UDL aren’t natural partners. There can be overlapping concepts, but… The second, of course, is using a popular but fundamentally flawed myth about learning. If you’re going to claim authority, don’t depend on broken concepts.

To put it another way, I think it’s fair to expect speakers to be clear about the concept. (Either that, or maybe the lesson is that Clark shouldn’t be allowed to listen to normal speakers. ;) Please, please, know what you’re talking about before you talk about it. Is that too much to ask?

Sub-symbolic and Situated

13 August 2019 by Clark Leave a Comment

At the time that the connectionist folks were working on neural nets, another similar approach was genetic algorithms. Both were working in a different way than the previous formal approaches to AI. The distinction between the two became known as symbolic vs sub-symbolic. And it’s useful to review why, particularly in the current climate of increasing interest in AI and cognitive science. An interesting outcome is that the sub-symbolic work exposed the contextualized nature of our reasoning. So there’s a link between sub-symbolic and situated cognition.

The prevailing model, starting with the cognitive revolution which arguably began in 1956 (an auspicious year ;) was a formal logical one. Whether in ‘production’ rules of IF THEN, or other formal mechanisms, the notion was to operate on semantic objects like numbers and concepts. This reflected, at the time, the belief that we’re formal logical thinkers.

As cognitive research continued, there was a growing recognition that our behaviors didn’t match particularly well with formal logic (c.f. Kahnemann & Tversky’s work, summed up in Thinking Fast and Slow). Several cognitive scientists separately came up with structures that more aptly described some of the properties we saw: Roger Schank called them scripts (he was focused on episodic thinking, not semantic), Marvin Minksy called them frames, and Dave Rumelhart called them schemas (after Bartlett).

What Rumelhart subsequently saw was that the properties he was trying to capture were very hard to represent in formal logic. He went on, with his colleague Jay McLelland and their collaborators) to develop what they called Parallel Distributed Processing (PDP). These are now known as neural nets (NNs) and are the basis for much of machine learning.

I was in the lab at the time Dave and Jay were working on neural nets, but detoured down a different path. Following work on analogical reasoning (my Ph.D. thesis topic), I became aware of the work Holland, Holyoak, Nisbett, & Thagard were doing with induction. Their framework was genetic algorithms (GAs). Both GAs and NNs use input strings and output strings to work, but internally they represent things differently.

After so much work on symbolic reasoning, here were mechanisms operating beneath the symbolic level. Yet they were attempting to create symbolic behavior. NNs obviously, more closely resemble our cognitive architecture (though GAs are still used in some areas like program generation). So, our conscious thinking is symbolic, but our actual cognition is happening below our conscious thinking. Hence things like illusions, fallacies, myths, and more.

What emerged from this realization is that our cognition isn’t just sub-symbolic, but situated. That is, what is conscious is a combination of what comes in from our senses, and what we know. In fact, with the limited attention we have, much of what we think we’re perceiving, we’re actually generating!

This it accounts for why we’re bad at doing things by rote; we’re liable to confound steps and contexts. This ends up being important because it means we have to work harder for any learning interventions to work effectively across contexts. The relationship between sub-symbolic and situated is, at least to me, and interesting story of the development of cognitive science.

Yet, it still means that our learning works most effectively at the conscious level of symbols, because that can accelerate learning over having to deal with everything through practice and feedback. (And explains why programs talking about neural really aren’t working there.) We still need those, but conscious models can provide a framework to become self-improving over time. So don’t forget to provide the models, and sufficient practice, and feedback.

Lucky on Foundations

9 August 2019 by Clark Leave a Comment

I was thinking about my next directions, and it led to me to think a bit about my foundations. And I realized I’ve been very lucky (and I’m grateful). I’ve had good parents, mentors, colleagues, and friends. But I’ve also had some fortunate timings, and it’s worth reflecting how I’ve been lucky on foundations upon which to build. (A personal reflection, not necessarily worth your time ;)

It started with college, really. I’d always been a typical lad, but with an extra serving of geek (I didn’t fit in with any clique so hung with a few similarly chaotic-good chaps :). I started college interested in marine bio, but there was no formal link between undergrad study and Scripps. The bio program was all cut-throat med, and while I could cut it, it was all rote memorization and deadly boring. So…

I took some comp sci classes, and was tutoring for extra money on the side. Lucky chance: I got a job doing the computer support for the office that coordinated the tutoring. That sparked my awareness of the connections between computers and learning. Of course, back then, at my school, there was no such program. Luck 2: my school had a program where you could design your own major. I found a couple of professors doing a project on using email for classroom discussion (circa ’78; we had the DARPAnet, otherwise there was no email; more luck). They agreed to sponsor my project.

After graduating, I looked all over the country for an org that wanted someone interested in computers and learning. More luck, I finally came across Jim Schuyler, and as he was starting DesignWare, I got a job! And, importantly, it was designing and programming on the earliest personal computers. And I realized that there was real potential for learning in games! But I also realized that we didn’t know enough how to design them. And then I read about ‘cognitive engineering’ (applying what we know about cognition to the design of systems).

I was accepted into the cog program with Don Norman, who’d written the article. And this was another major stroke of luck. While Don’s students were researching how to build systems for how people think, my twist was about how people learn. I got to study behavioral, cognitive, social, even machine learning! Also, Don’s lab partner Dave Rumelhart was conducting his research with Jay McClleland on what became neural nets. You can’t help but get exposed to related research through lab meetings, seminars, and more, even if you’re not active in the particular work. And Ed Hutchins was doing his work on distributed cognition. This was a fundamental shift in perspective from formal to situated cognition.

The lab ran a Unix system, so I was getting steeped in computing systems to complement my personal computer work, along with the cognition focus. I subsequently did a post-doc at LRDC, getting deeper steeped into cognitive learning, and then joined a school of Computer Science, getting further background in computation. I was on the internet before there was a web (and foolishly was rather complacent about it)! And it’s enabled me to keep an eye on new developments like mobile and content and more, and understand their core affordance.

I also got steeped in design, having a chance to look at graphic, industrial, software, architecture, and other approaches (more luck). I combined that with a study of the academic literature, of course. These three foundations have been the basis of my work: applying cognitive and learning sciences to the design of technology to create learning and performance systems.

There’s much more to the story, of course. Serendipity continued in jobs and people to guide me, I’m happy to say. Mentors being shy, you can’t really thank folks enough, so if I’ve been lucky in foundations, it’s my job to pass it on. I hope that this blog helps in some way!

Theory or Research?

17 July 2019 by Clark Leave a Comment

There’s a lot of call for evidence-based methods (as mentioned yesterday): L&D, learning design, and more. And this is a good thing. But…do you want to be basing your steps on a particular empirical study, or the framework within which that study emerged? Let me make the case for one approach. My answer to theory or research is theory. Here’s why.

Most research experiments are done in the context of a theoretical framework. For instance, the work on worked examples comes from John Sweller’s Cognitive Load theory. Ann Brown & Ann-Marie Palincsar’s experiments on reading were framed within Reciprocal Teaching, etc. Theory generates experiments which refine theory.

The individual experiments illuminate aspects of the broader perspective. Researchers tend to run experiments driven by a theory. The theory leads to a hypothesis, and then that hypothesis is testable. There are some exploratory studies done, but typically a theoretical explanation is generated to explain the results. That explanation is then subject to further testing.

Some theories are even meta-theories! Collins & Brown’s Cognitive Apprenticeship (a favorite) is based upon integrating several different theories, including the Reciprocal Teaching, Alan Schoenfeld’s work on examples in math, and the work of Scardemalia & Bereiter on scaffolding writing. And, of course, most theories have to account for others’ results from other frameworks if they’re empirically sound.

The approach I discuss in things like my Learning Experience Design workshops is a synthesis of theories as well. It’s an eclectic mix including the above mentioned, Cognitive Flexibility, Elaboration, ARCS, and more. If I were in a research setting, I’d be conducting experiments on engagement (pushing beyond ARCS) to test my own theories of what makes experiences as engaging and effective. Which, not coincidentally, was the research I was doing when I was an academic (and led to Engaging Learning). (As well as integration of systems for a ubiquitous coaching environment, which generates many related topics.)

While individual results, such as the benefits of relearning, are valuable and easy to point to, it’s the extended body of work on topics that provides for longevity and applicability. Any one study may or may not be directly applicable to your work, but the theoretical implications give you a basis to make decisions even in situations that don’t directly map. There’s the possibility to extend to far, but it’s better than having no guidance at all.

Having theories to hand that complement each other is a principled way to design individual solutions and design processes. Similarly for strategic work as well (Revolutionize L&D) is a similar integration of diverse elements to make a coherent whole. Knowing, and mastering, the valid and useful theories is a good basis for making organizational learning decisions. And avoiding myths! Being able to apply them, of course, is also critical ;).

So, while they’re complementary, in the choice between theory or research I’ll point to one having more utility. Here’s to theories and those who develop and advance them!

Direct Instruction or Guided Discovery

16 July 2019 by Clark Leave a Comment

Recently, colleague Jos Arets of the 70:20:10 institute wrote a post promoting evidence-based work. And I’m a big fan, both of his work and the post. In the post, however, he wrote one thing that bugs me. And I realize I’m flying in the face of many august folks on whether to promote direct instruction or guided discovery. So let me explain myself ;).

It starts with a famous article by noted educational researchers Paul Kirschner, John Sweller, and Richard Clark. In it, they argue against “constructivist, discovery, problem-based, experiential, and inquiry-based teaching”. That’s a pretty comprehensive list. Yet these are respected authors; I’ve seen Richard Clark talk, have talked with John Sweller personally, and have interacted with Paul Kirschner online. They’re smart and good folks committed to excellent work. So how can I quibble?

First, it comes from their characterization of the opposition as ‘minimally guided’.Way back in 1985, Wallace Feurzig was talking about ‘guided discovery’, not pure exploration. To me, that’s a bit of a ‘straw man’ argument. Not minimally guided, but appropriately guided, would seem to me to be the appropriate approach.

Further, work by David Jonassen for one, and a meta-analysis conducted by Stroebel & Van Barneveld for another, suggested different outcomes. The general outcome is problem-based (as one instance being argued against) doesn’t yield quite as good performance on a subsequent test, but is retained longer and transfers better. And those, I suggest, are the goals we should care about. Similarly, research supports attempting to solve problems even if you can’t before you learn.

And I worry about the phrase “direct instruction”. That easy to interpret as ‘information dump and knowledge test’; it sounds like the old ‘error-free learning’! I’m definitely not accusing those esteemed researchers of implying that, but I am afraid that under informed instructors could take that implication. It’s all too easy to see too much of that in classrooms. Teacher strategies tend to ignore results like spaced, varied, and deliberate practice. Similarly, the support for students to learn effective study skills is woeful.

Is there a reconciliation? I suggest there is. Professors Kirschner, Sweller, & Clark would, I suggest, expect sufficient practice to a criteria, and that the practice should match the desired performance. I suspect they want learners solving meaningful problems in context, which to me is problem-based learning. And their direct instruction would be targeted feedback, along with models and examples. Which is what I strongly suggest. The more transfer you need, however, the broader contexts you need. Similarly, the more flexible application required would suggest the gradual removal of scaffolding.

So I really think that guided exploration, and meaningful direct instruction, will converge in what eventuates in practice. Look, insufficiently guided practice isn’t effective, and I suspect that they wouldn’t suggest that bullet points are effective instruction. I just want to ensure that we focus on the important elements, e.g. what we highlighted in the Serious eLearning Manifesto. There is a reason to think that direct instruction or guided discovery isn’t the dichotomy proposed, I’ll suggest. FWIW.

Reconciling Cognitions and Contexts

3 July 2019 by Clark Leave a Comment

In my past two posts, I first looked at cognitions (situated, distributed, social) by contexts (think, work, and learn), and then the reverse. And, having filled out the matrixes anew, they weren’t quite the same. And that, I think, is the benefit of the exercise, a chance to think anew. So what emerged? Here’s the result of reconciling cognitions and contexts.

So, taking each cell back in the original pass of cognitions by contexts, what results? I took the Think row to, indeed, be Harold Jarche’s Seek > Sense > Share model (ok, my interpretation). We have in Situated, the feeds you’ve set up to see, and then the particular searches you need in the current context. Then, of course, you experiment and represent as ways to externalize thinking for Distributed. Finally, you share Socially.

For Work, not practices but principles (and the associated practices therefrom) as well as facilitation to support Situated Work. Performance support is, indeed, the Distributed support for Work. And Socially, you need to collaborate on specific tasks and cooperate in general.

Finally, for Learning, for a Situated world you need (spread) contextualized practice to support appropriate abstraction of the principles. You want models and examples to support performance in the practice, as Distributed resources. And, finally, for Social Learning, you need to communicate (e.g. discussions) and collaborate (group projects).

What’s changed is that I added search and feeds, and moved experiment, in the Think row. I went to principles from practices to support performance in ambiguity, left performance support untouched, and stayed with collaborations and cooperation instead of just shared representations (they’re part of collaborate). And, finally, I made practice about contexts, went from blended learning to support materials for learning, and interpreted social assignments as communicating and collaborating.

The question is, what does this mean? Does it give us any traction? I’m thinking it does, as it shifts the focus in what we’re doing to support folks. So I think it was interesting and valuable (to my thinking, at least ;) to consider reconciling cognitions and contexts.

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