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Clark Quinn’s Learnings about Learning

Grappling with Groups

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I’m a fan of the power of social learning. When people get together (and the process is managed right), the outcomes of a negotiated understanding can be powerful.  However, in designing learning, working in groups  has some real negative perceptions  and  realities. The open question is: what to do?

The problems are well-known. As my kids complained, on group projects some team members will reliably slack, letting the most driven student do the work.  Even with a commitment, there can be differences in working style: getting started early versus preferring to do it under pressure.

Some things have been tried. When I assigned group projects, I told my students I expected them to do equal work, and would grade accordingly. If it didn’t end up being the case, they were to each write up a report on what each team member did, including themselves. Others require this, regardless, and that sounds like  a smart way to make concrete a requirement for contribution.

One  thing to be addressed  is invigilation. Is the work being tracked  in any way?  If they’re working in a collaborative environment that tracks contributions via versioning or some other way, then there’s a trail of work that can be scrutinized. Extra work, to be sure, but it’d serve as a tie-breaker if there was some question about contribution.

Another  issue is support  for working in groups. When I first assigned group work, it became clear that they didn’t know how (?!?!).  So I wrote up a little guide to doing group work, and those problems subsided.  Working together is a skill that shouldn’t be taken for granted. There should be some explicit statement of expectations if you can’t determine whether there’s reliable prior experience. (Certainly, it seems that the teachers weren’t providing guidance or oversight, in the case of my kids.)

As an aside: make sure the students know  why you’re asking them to work in groups. I’ve learned that learners will be much more willing to undertake what you assign if you explain the rationale that justifies your choice!

Then there’s them question  of  just  when group work makes sense. Given that the value-added benefit is the negotiated understanding, it would make sense to do that when the material is complex, and there’s a risk of an individual taking a unique, incomplete, and or imperfect understanding. At times when you want to assess an individual’s ability to deliver, you wouldn’t want a group project!

There’s also the  issue of the  nature of the task.  Are you just having them come to a shared understanding in representing their thinking (e.g. a response to a question) or actually produce a work product of some sort (a video, presentation, report, etc).  If you can get what you need with less effort, you shouldn’t assign a more complex project.

Which brings up the issue of the scope of the work. I would expect that the more imposing the total amount of work is, the more it would invoke those with time or effort concerns to be lulled to the lazy side.  Keeping the scope small might contribute to a greater willingness to participate.

Breaking up the deliverables is one way to manage student effort. If you have interim deliverables, it helps manage the process  and the time.  Certainly, early in a curriculum, you could provide this scaffolding (and make it explicit), and then gradually hand off responsibility for the learners to internalize  the self-management. (Meta-learning!)

Breaking it up can also manage to address the contribution. If individual submissions are required before group ones, you can at least have the learners having had to contribute  thought before sharing and creating a greater understanding.

Finally, there’s the issue of group work in an independent schedule. In a cohort model (scheduled timetable) it’s easy, but otherwise, how do you do it?  If there’s ‘critical mass’, you can have learners arrange to meet with anyone available. If there’re more, you could even have them indicate working style preferences: quick, early, what media channels. Otherwise, it’s more challenging (or a non-issue, just don’t do it).

There are lots of issues and potential solutions for addressing group work.  I can’t say I’ve found an easy solution, despite having wrestled with it. I think it’s important, so I’m curious what you’ve tried and found out!

Some new elearning companies ;)

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As I continue to track what’s happening, I get the opportunity to review a wide number of products and services. While tracking them all would be a full-time job, occasionally some offer new ideas.  Here’s a collection of those that have piqued my interest of late:

Sisters eLearning: these folks are taking a kinder, gentler  approach to their products and marketing their services.  Their signature offering is  a suite of templates for your elearning featuring cooperative play.  Their approach in their custom development is quiet and classy. This  is reflected in the way they  promote themselves at conferences: they all wear mauve  polos  and sing beautiful  a capella.  Instead of giveaways, they  quietly provide free home-baked mini-muffins for all.

Yalms: these folks are offering  the ‘post-LMS’. It’s not an LMS, and  instead offers course management, hosting, and tracking.  It addresses compliance, and checks a whole suite of boxes such as media portals, social, and many non-LMS things including xAPI. Don’t confuse them with an LMS; they’re beyond that!

MicroBrain: this company has developed a system that makes it easy to take  your existing courses and chunk  them  up into little bits. Then it pushes them out on a  schedule.  It’s a serendipity model, where there’s a chance it just might be the right bit at the right time, which is certainly better than your existing elearning. Most  importantly, it’s mobile!

OffDevPeeps: these folks a full suite of technology development services  including mobile, AR, VR, micro, macro, long, short, and anything else you want, all done at a competitive  cost. If you  are focused on the ‘fast’ and ‘cheap’ side of the trilogy, these are the folks to talk to. Coming soon to an inbox  near you!

DanceDanceLearn: provides a completely unique offering. They have developed an authoring tool that makes it easy for you to animate dancers moving in precise formations that spell out content. They also have a synchronized swimming version.  Your content can be even more engaging!

There, I hope you’ll find these of interest, and consider checking them out.

Any relation between the companies portrayed and real entities is purely coincidental.  #couldntstopmyself #allinfun

Designing Microlearning

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Yesterday, I clarified what I meant about microlearning. Earlier, I wrote about designing microlearning, but what I was really talking about was the design of spaced learning. So how should you design the type of microlearning I really feel is valuable?

To set the stage, here’re we’re talking about layering learning on performance in a context. However, it’s more than just performance support. Performance support would be providing a set of steps (in whatever ways: series of static photos, video, etc) or supporting those steps (checklist, lookup table, etc).  And again, this is a good thing, but microlearning, I contend, is more.

To make it learning, what you really need is to support developing an ability to understand the rationale behind the steps, to support adapting the steps in different situations. Yes, you can do this in performance support as well, but here we’re talking about  models.  

What (causal) models give us is a way to explain what has happened, and predict what will happen.  When we make these available around performing a task, we unpack the rationale. We want to provide an understanding behind the rote steps, to support adaptation of the process in difference situations. We also provide a basis for regenerating missing steps.

Now, we can also be providing examples, e.g. how the model plays out in different contexts. If what the learner is doing now can change under certain circumstances, elaborating how the model guides  performing differently in different context provides the ability to transfer that understanding.

The design process, then, would be to identify the model guiding the performance (e..g.  why  we do things in this order, and it might be an interplay between structural constraints (we have to remove this screw first because…) and causal ones (this is the chemical that catalyzes the process).  We need to identify and determine how to represent.

Once we’ve identified the task, and the associated models, we  then need to make these available through the context. And here’s why I’m excited about augmented reality, it’s an obvious way to make the model visible. Quite simply, it can be layered  on top of the task itself!   Imagine that the workings behind what you’re doing are available if you want. That you can explore more as you wish, or not, and simply accept the magic ;).

The actual task  is the practice, but I’m suggesting providing a model explaining  why it’s done this way is the minimum, and providing examples for a representative sample of other appropriate contexts provides support when it’s a richer performance.  Delivered, to be clear, in the context itself. Still, this is what I think  really constitutes microlearning.  So what say you?

Clarifying Microlearning

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I was honored to learn that a respected professor of educational technology liked my definition of micro-learning, such that he presented it as a recent conference.  He asked if I still agreed with it, and I looked back at what I’d written more recently. What I found was that I’d suggested some alternate interpretations, so I thought it worthwhile to be absolutely clear about it.

So, the definition he cited was:

Microlearning is a small, but complete, learning experience, layered on top of the task learners are engaged in, designed to help learners learn how to perform the task.

And I agree with this, with a caveat. In the article, I’d said that it could  also be a small complete learning experience, period. My clarification on this is that those are unlikely, and the definition he cited was the most likely, and likely most valuable.

So, I’ve subsequently said  (and elaborated on the necessary steps):

What I really think microlearning could and should be is for spaced learning.

Here I’m succumbing to the hype, and trying to put a positive spin on microlearning. Spaced learning is a good thing, it’s just not microlearning. And microlearning really isn’t helping them perform the task in  the moment (which is a good thing too), but instead leveraging that moment to also extend their understanding.

No, I like the original definition, where we layer learning on top of a task, leveraging the context and requiring the minimal content to take a task and make it a learning opportunity. That, too, is a good thing. At least I think so. What do you think?

To show or not to show (and when)

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At  an event the other evening, showing various career technology tools, someone  said something that I thought was just wrong. I asked afterwards, and then  explained why I thought it was wrong. The response was “well, there can be different ways to go about it”. And frankly, there really can’t.  Think for yourself about why I might say so, and then let me show you why.

The trigger was a  design program talking about their design courses. And the representative was saying that once a learner had created a project, it was shown to everybody. Which sounds good, since ‘sharing is caring’, or at least it’s a good example of working out loud. And, in general, this is a good idea. But I think it’s not in learning.

In brainstorming (e.g. informal learning), we know that sharing  before others have had their  chance to think, it can color their output. This limits the exploration of the total possible space of opportunities that would come from a diverse team. Hearing another response likely will limit  that  spaces that might get explored. Instead, the goal is to diverge before converging.

And so, too, in learning. I’ve argued for assignment submission systems that only allow you to see the other submissions  once you’ve submitted your own. Until you’ve struggled yourself with the challenge, you won’t  get the most out of seeing how others have solved the situation.

If you immediately share the first submission, it may affect those who aren’t that far along yet.  Some may even end up holding off to see what others do! This undermines the integrity of the assignment. One explanation that was given was to provide guidance to others, but that, to me, is the role of the assignment specification.

There is, however, real value in seeing the other submissions once you’ve completed yours. Seeing other approaches helps broaden the understanding. Better yet is to have discussion  on them, as when  critiquing others (constructively) you internalize the monitoring. This discussion  also  provides the opportunity to experiment with working out loud that eventually develops good working habits.

(I’ve similarly argued, by the way, that ‘rollover’ questions  -where the answer is shown once you move your pointer over the question- don’t lead to any meaningful learning. If you haven’t made the mental effort to  commit to a response, it won’t stick as well.)

So I believe that, if you’re developing people’s ability to  do, you have a responsibility to do so in the most advantageous way. That includes making effort to use the best approach to sharing assignments. I was surprised (and dismayed) to see someone arguing to the contrary! I implore you to do the details on the approaches you work, for your learners’, and the learning’s, sake.

Human Learning is Not About to Change Forever

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In my inbox was an announcement about a new white paper with the intriguing title  Human Learning is About to Change Forever.  So naturally I gave up my personal details to download a copy.  There are nine claims in the paper, from the obvious to the ridiculous. So I thought I’d have some fun.

First, let’s get clear.  Our learning runs on our brain, our wetware. And that’s not changing in any fundamental way in the near future. As a famous article once had it: phenotypic plasticity triumphs over genotypic plasticity (in short, our human advantage has gained    via  our ability to adapt individually and learn from each other, not through  species evolution).   The latter takes a long time!

And as a starting premise, the “about to” bit implies these things are around the corner, so that’s going to be a bit of my critique. But nowhere near  all of it.  So here’s a digest of the  nine claims and my comments:

  1. Enhanced reality tools will transform the learning environment.  Well, these tools will  certainly augment the learning environment  (pun intended :). There’s evidence that VR leads to better learning outcomes, and I have high hopes for AR, too. Though is that a really fundamental transition? We’ve had VR and virtual worlds for over a decade at least.  And is VR a evolutionary or revolutionary change from simulations? Then they go on to talk about performance support. Is that transforming learning? I’m on record saying contextualized learning (e.g. AR) is the real opportunity to do something interesting, and I’ll buy it, but we’re a long way away. I’m all for AR and VR, but saying that it puts learning in the hands of the students is a design issue, not a technology issue.
  2. People will learn collaboratively, no matter where they are.  Um, yes, and…?  They’re already doing this, and we’ve been social learners for as long as we’ve existed. The possibilities in virtual worlds to collaboratively create in 3D I still think is potentially cool, but even as the technology limitations come down, the cognitive limitations remain. I’m big on social learning, but mediating it through technology strikes me as just a natural step, not transformation.
  3. AI will banish intellectual tedium. Everything is  awesome.  Now we’re getting a wee bit hypish. The fact that software can parse text and create questions is pretty impressive. And questions about semantic knowledge aren’t going to transform education. Whether the questions are developed by hand, or by machine, they aren’t likely on their own to lead to new abilities to do. And AI is not yet to the level (nor will it be soon) where it can take content and create compelling activities that will drive learners to apply knowledge and make it meaningful.
  4. We will maximize our mental potential with wearables and neural implants. Ok, now we’re getting confused and a wee bit silly. Wearables are cool, and in cases where they can sense things about you and the world means they can start doing some very interesting AR. But transformative? This still seems like a push.  And neural implants?  I don’t like surgery, and messing with my nervous system when you still don’t really understand it? No thanks.  There’s a lot more to it than managing to adjust firing to control limbs. The issue is again about the semantics: if we’re not getting meaning, it’s not really fundamental. And given that our conscious representations are scattered across our cortex in rich patterns, this just isn’t happening soon (nor do I want that much connection; I don’t trust them not to ‘muck about’).
  5. Learning will be radically personalized.  Don’t you just love the use of superlatives?  This is in the realm of plausible, but as I mentioned before, it’s not worth it until we’re doing it on  top of good design.  Again, putting together wearables (read: context sensing) and personalization will lead to the ability to do transformative AR, but we’ll need a new design approach, more advanced sensors, and a lot more backend architecture and semantic work than we’re yet ready to apply.
  6. Grades and brand-name schools won‘t matter for employment.  Sure, that MIT degree is worthless! Ok, so there’s some movement this way.  That will actually be a nice state of affairs. It’d be good  if we started focusing on competencies, and build new brand names around real enablement. I’m not optimistic about the prospects, however. Look at how hard it is to change K12 education (the gap  between what’s known and what’s practiced hasn’t significantly diminished in the past decades). Market forces may change it, but the brand names will adapt too, once it becomes an economic necessity.
  7. Supplements will improve our mental performance.  Drink this and you’ll fly! Yeah, or crash.  There are ways I want to play with my brain chemistry, and ways I don’t. As an adult!  I really don’t want us playing with children, risking potential long-term damage, until we have a solid basis.  We’ve had chemicals support performance for a while (see military use), but we’re still in the infancy, and here I’m not sure our experiments with neurochemicals can surpass what evolution has given us, at least not without some pretty solid understanding.  This seems like long-term research, not near-term plausibility.
  8. Gene editing will give us better brains.  It’s  alive!  Yes, Frankenstein’s monster comes to mind here. I do believe it’s possible that we’ll be able to outdo evolution eventually, but I reckon there’s still not everything known about the human genome  or the human brain. This similarly strikes me as a valuable long term research area, but in the short term there are so many interesting gene interactions we don’t yet understand, I’d hate to risk the possible side-effects.
  9. We won‘t have to learn: we‘ll upload and download knowledge. Yeah, it’ll be  great!  See my comments above on neural implants: this isn’t yet ready for primetime.  More importantly, this is supremely dangerous. Do I trust what you say you’re making available for download?  Certainly not the case now with many things, including advertisements. Think about downloading to your computer: not just spam ads, but viruses and malware.  No thank you!  Not that I think it’s close, but I’m not convinced we can ‘upgrade our operating system’ anyway. Given the way that our knowledge is distributed, the notion of changing it with anything less than practice seems implausible.

Overall, this is reads like more a sci-fi fan’s dreams than a realistic assessment of what we should be preparing for.  No, human learning isn’t going to change forever.  The ways we learn, e.g. the tools we learn with are changing, and we’re rediscovering how we really learn.

There are better guides available to what’s coming in the near term that we should prepare for.  Again, we need to focus on good learning design, and leveraging technology in ways that align with how our brains work, not trying to meld the two.  So, there’re my opinions, I welcome yours.

Workplace of the Future video

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Someone asked for a video on the  Workplace of the Future  project, so I created one. Thought I’d share it with you, too.  Just a walkthrough with some narration, talking about some of the design decisions.

One learning for me (that I’m sure you knew): a script really helps!  It took multiple tries, for a variety of reasons.  I’m not a practiced video creator, so gentle, please!

What you learn not as important as how you learn!

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I’m going a bit out on a limb here, with a somewhat heretical statement: what you learn is more important than how you learn!  (You could say pedagogy supersedes curricula, but that’s just being pedantic. ;)  And I’m pushing the boundaries of the concept a bit, but I think it’s worth floating as an idea. It’s meta-learning, of course, learning how to learn!   The important point is to focus on what’s being developed.  And I mean this at two levels.

This was triggered by seeing two separate announcements of new learning opportunities.  Both are focused on current skills, so both are focusing on advanced curricula, things that are modern. While the pedagogy of one isn’t obvious (though claimed to be very practical), the other clearly touts the ways in which the learning happens. And it’s good.

So the pedagogy is very hands on. In fact, it’s an activity-based curricula (in my terms), in that you progress by completing assignments very closely tied to what you’ll do on the job. There are content resources available (e.g. expert videos) and instructor feedback, all set in a story.  And this is better than a content-based curricula, so this pedagogy is really very apt for preparing people to do jobs.  In fact, they are currently applying it across three different roles that they have determined  are necessary.

But if you listen to the longer version  (video) of my activity-based learning curricula story, you’ll see I carry the pedagogy forward. I talk about handing over responsibility to the learner, gradually, to take responsibility for the activities, content, product, and reflection.  This is important for learners to start becoming self-improving learners.  The point is to develop their ability to do  meta-learning.

To do so, by the way, requires that you make your pedagogy visible  for the choices that you made, and why.  Learners, to adopt their own pedagogy, need to see  a pedagogy. If you narrate your pedagogy, that is document your alternatives and rationales of choices, they can actually understand more about the learning process itself.

And this, to me, is the essence of the claim. If you start a learning process about  something, and then hand off responsibility for the learning, while making clear the choices that led there, learners become self-learners. The courses that are designed in the above two cases  will, of necessity, change. And graduates from those courses might be out of date before long,  unless they’ve learned  how to stay current. Unless they’ve learned meta-learning.  That can be added in, and it may be implicit, but I’ll suggest that learning to learn is a more valuable long-term outcome than the immediate employability.

So that’s my claim: in the long term, the learner (and society) will be better off if the learner can learn to self-improve.  It’s not an immediate claim or benefit, but it can be wrapped around something that  is of immediate benefit.  It’s the ‘secret sauce’ that organizations could be adding in, whether internally or in their offerings. What surprises me is how seldom I see this approach taken, or even discussed.

Adaptive or just good design?

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A few posts ago, I wrote  about how we might be rushing too fast into cognitive computing. Not that there’s anything wrong with augmenting us, but I was wondering if we’d be better off focusing on developing our non-cognitive systems first. And, of course, it occurred to me after a conversation that there might be another example of this ‘tech fix before smart fix’ problem: adaptive learning over good  design. Is this a tech solution to the wrong problem?

So, I’m a fan of adaptive learning. Heck, I led a team that developed an adaptive learning system back circa 2000 (ahead of the times, as always ;). And I know  that there are good things we can do with adaptive learning.  Some are still relatively impractical (e.g. intelligent tutoring systems), some make sense (e.g. adapting on learner performance),  and, of course, some are pretty silly (e.g. learning styles).  Still, done well, adaptive learning could and should be a benefit.  (And serious games are  adaptive!)

But I would posit that before you charge off for adaptive learning, you make sure you’re doing  good learning design first.  Adaptation on top of good learning design is likely to add some extra benefits, but adaptation on old learning design just doesn’t make sense.  And, I’ll suggest, good learning design is cheaper, and likely to have a bigger impact.

So, for instance, adapting a large knowledge-based course will still leave it as a problematic solution in search of a problem.  Creating a better course focusing on critical  skills with meaningful practice is going to have a bigger impact on the bottom line than adapting the course for  learner progress. It’s not a bad thing, but it’s a secondary concern, I reckon.

Games are a special case of adapting on the basis of learner performance. When well done, they’re the next best thing to live practice.  Yet, at core, they too need good design.

There are a lot of adaptive solutions emerging, with a strong push to optimize your outcomes.  Some of them have a pretty good basis, too. But your goal is to achieve business impact, and what will hit that first, and best, is starting with good design.  Adaptive isn’t a panacea, it’s a fillip to  design, not a replacement.    Master the rules of good design, then come back and add in those extra elements, whether adaptivity and/or game mechanics.   Please?

Technology or preparation?

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In listening to a recent presentation on the trends affecting the workplace and HR, there was mention  about how organizations were using more cognitive technology, AI, etc. and this was changing jobs. There were two additional notes.  First, these efforts aren’t (largely) leading to job losses, as these folks were being reskilled. Second, HR wasn’t involved in 65% of this.  That’s a concern. But one of the things I wondered was whether all the new, smart technology really would help as much as was intended  or needed.

So here’s some context (I may have heard this in conjunction with an early experiment in using mobile devices to support drug trials).  Pharmaceutical companies are continually trying new drugs. One claim is  that if people would follow their medicine regimens, many of these new drugs wouldn’t be necessary.  That is, the drugs are often times trying to require fewer doses with simpler instructions to make up for inappropriate use.

Likewise,  the origin of performance support.  The question is where does the locus of responsibility belong. Interface design people were upset about performance support systems, arguing (correctly) that performance support was being used to make up for bad system design in the first place.  In fact, Don Norman’s book  The Invisible Computer was about how interface design wasn’t being brought in early enough.  The point being that properly designed interfaces would incorporate support for our cognitive limitations inherently, not externally.

So,  many of the things we’re doing are  driven by bad implementation. And that’s what I started wondering: are we using smart technology to enhance an optimized workforce, or to make up for a lack of adequate preparation?  We could be putting in technology to make up for what we’ve been unsuccessful at doing through training and elearning (because we’re not doing that well).

To put it another way, would we get better returns applying what’s known about how we think, work, and learn than bringing in technology? Would adequate preparation be a more effective approach than throwing technology at the problem, at least in some of the cases?   There are strong reasons to use technology to do things we struggle at doing well, and in particular to augment us.  But perhaps a better investment, at least in some cases, would be to  appropriately distribute tasks between the things our brains do well and what technology does better.

Let me be clear; there are technologies that will do things more reliably than humans, and do things humans would prefer not to. I’m all for the latter, at least ;). And we should optimize both technology and people.    I’m a fan of technology to augment us  in ways we want to be augmented.   So my point is more to consider are we doing enough to prepare people and support them working together.  Your thoughts?