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Clark Quinn’s Learnings about Learning

Defining Microlearning?

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Last week on the #chat2lrn twitter chat, the topic was microlearning. It was apparently prompted by this post by Tom Spiglanin which does a pretty good job of defining it, but some conceptual confusion showed up in the chat that makes it clear there’s some work to be done.  I reckon there may be a role for the label and even the concept, but I wanted to take a stab at what it is and isn’t, at least on principle.

So the big point to me is the word ‘learning’.  A number of people opined about accessing a how-to video, and let’s be clear: learning doesn’t have to come from that.   You could follow the steps and get the job done and yet need to access it again if you ever needed it. Just like I can look up the specs on the resolution of my computer screen, use that information, but have to look it up again next time.  So it could be just performance support, and that’s a  good thing, but it’s not learning.  It suits the notion of micro content, but again, it’s about getting the job done, not developing new skills.

Another interpretation was little bits of components of learning (examples, practice) delivered over time. That is learning, but it’s not microlearning. It’s distributed learning, but the overall learning experience is macro (and much more effective than the massed, event, model).  Again, a good thing, but not (to me) microlearning.  This is what Will Thalheimer calls subscription learning.

So, then, if these aren’t microlearning, what is?  To me, microlearning has to be a small but complete learning experience, and this is non-trivial.  To be a full learning experience, this requires a model, examples, and practice.  This could work with very small learnings (I use an example of media roles in my mobile design workshops).  I think there’s a better model, however.

To explain, let me digress. When we create formal learning, we typically take learners away from their workplace (physically or virtually), and then create contextualized practice. That is, we may present concepts and examples (pre- via blended, ideally, or less effectively in the learning event), and  then we create practice scenarios. This is hard work. Another alternative is more efficient.

Here, we layer the learning on top of the work learners are already doing.  Now, why isn’t this performance support? Because we’re not just helping them get the job done, we’re explicitly turning this into a learning event by not only scaffolding the performance, but layering on a minimal amount of conceptual material that links what they’re doing to a model. We (should) do this in examples and feedback on practice, now we can do it around real work. We can because (via mobile or instrumented systems) we know where they are and what they’re doing, and we can build content to do this.  It’s always been a promise of performance support systems that they could do learning on top of helping the outcome, but it’s as yet seldom seen.

And the focus on minimalism is good, too.  We overwrite and overproduce, adding in lots that’s not essential.  C.f. Carroll’s Nurnberg Funnel or Moore’s Action Mapping.  And even for non-mobile, minimalism makes sense (as I tout under the banner of the Least Assistance Principle).  That is, it’s really not rude to ask people (or yourself as a designer) “what’s the least I can do for you?”  Because that’s what people generally really prefer: give me the answer and let me get back to work!

Microlearning as a phrase has probably become current (he says, cynically) because elearning providers are touting it to sell the ability of their tools to now deliver to mobile.   But it can also be a watch word to emphasize thinking about performance support, learning ‘in context’, and minimalism.  So I think we may want to continue to use it, but I suggest it’s worthwhile to be very clear what we mean by it. It’s not courses on a phone (mobile elearning), and it’s not spaced out learning, it’s small but useful full learning experiences that can fit by size of objective or context ‘in the moment’.  At least, that’s my take; what’s yours?

Starting from the end

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Week before last, Will Thalheimer and I had another one of our ‘debates’, this time on the Kirkpatrick model (read the comments, too!).  We followed up last week with a live debate.  And in the course of it I said something that I want to reiterate and extend.

The reason I like the Kirkpatrick model is it emphasizes  one thing that I see the industry failing to do.  Properly applied (see below), it  starts with the measurable change  you need  to see in the organization, and you work backwards from there. You go back to the behavior change you need in the workplace to address that measure, and from there to the changes in training and/or resources to create that behavior change.  The important point is starting with a business metric.  No ‘we need a course on this’, but instead: “what business goal are we trying to impact”.

Note: the solution can just be a tool, it doesn’t have to always be learning.  For example, if what people need to access accurately are the specific product features of one of a multitude of solutions that are in rapid flux (financial packages, electronic hardware, …), trying to get it in the head accurately isn’t a good goal. Having people able to access the information ‘in the head’ is an exercise in futility, and you’re better off putting the information ‘in the world’.  (Which is why I want to change from Learning & Development to Performance & Development, it’s not about learning, it’s about doing!)

The problems with Kirkpatrick are several.  For one, even he admitted he numbered it wrong.  The starting point is numbered ‘four’, which misleads people.  So we get the phenomena that people do stage 1, sometimes stage 2, rarely do they get to stage 3, and stage 4 is almost non-existent, according to ATD research.  And stage 1, as Will rightly points out, is essentially worthless, because the correlation between what learners think of the learning and the actual impact is essentially zero!  Finally, too often Kirkpatrick is wrongly considered as only to evaluate training (even the language on the site, as the link above will show you, talks only about training). It  should be about the impact of an intervention  whatever the means (see above).  And the impact is what the Kirkpatrick model properly is about, as I opined in the blog debate.

So, in the live debate, I said I’d be happy for any other model that focused on working backwards. And was reminded that, well, I proposed just that a while ago!  The blog post is the short version, but I also wrote this rather longer and more rigorous  paper  (PDF), and  I’m inclined think it’s one of my more important  contributions to design (to date ;). It’s a fairly thorough look at the design process  and where we go wrong (owing to our cognitive architecture), and a proposal for an alternative approach based upon sound principles.   I welcome your thoughts!

Labeling 70:20:10

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In the Debunker Club, a couple of folks went off on the 70:20:10 model, and it prompted some thoughts.  I thought I’d share them.

If you’re not familiar with 70:20:10, it’s a framework for thinking about workplace learning that suggests we need to recognize that the opportunity  is about much more than courses. If you ask people how they learned the things they know to do in the workplace, the  responses suggest that somewhere around 10% came from formal learning, 20% from informal coaching and such, and about 70% from trial and error.  Note the emphasis on the fact that these numbers aren’t exact, it’s just an indication (though considerable evidence suggests that the contribution  of formal learning is somewhere between 5 and 20%, with evidence from a variety of sources).

Now, some people complain that the numbers can’t be right, no one gets perfect 10 measurements. To be fair, they’ve been fighting against the perversion of Dale’s Cone, where someone added numbers on that were bogus but have permeated learning for decades and can’t seem to be exterminated. It’s like zombies!  So I suspect they’re overly sensitive to whole  numbers.

And I like the model!  I’ve used it to frame some of my work, using it as a framework to think about what  else we can do to support performance. Coaching and mentoring, facilitating social interaction, providing challenge goals, supporting reflection, etc.  And again to justify accelerated organizational outcomes.

The retort I hear is that “it’s not about the numbers”, and I agree.  It’s just  a  tool to help shake people out of the thought that a course is the only solution to all needs.  And, outside the learning community, people  get it.  I have heard that, over presentations to hundreds of audiences of executives and managers, they all recognize that the contributions to their success came largely from sources other than courses.

However, if it’s not about the numbers, maybe calling it the 70:20:10 model may be a problem.  I really like Jane Hart’s diagram about Modern Workplace Learning as another way to look at it, though I really want to go beyond learning  too.  Performance support may achieve outcomes in ways that don’t require or deliver any learning,  and that’s okay. There’re times when it’s better to have knowledge in the head than in the world.

So, I like the 70:20:10 framework, but recognize that the label may be a barrier. I’m just looking for any tools I can use to help people start thinking ‘outside the course’.  I welcome suggestions!

Measurement?

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Sorry for the lack of posts this week; Monday was shot while I migrated my old machine to a new one (yay)!  Tuesday was shot with catching up. Wed was shot with lost internet, and  trying to migrate the lad to my old machine.  So today I realize I haven’t posted all week (though you got extra from me last week ;)!  So here’s one reflection on the conference last week.

First, if you haven’t seen it, you should check out the debate I had with the good Dr. Will Thalheimer over at his blog about the Kirkpatrick model.  He’s upset with it as it’s not permeated by learning, and I argue that it’s role is impact, not learning design (see my diagram at the end).  Great comments, too!  We’ll be doing a hangout on it on Friday the 3rd of April.

The other interesting thing that happened is on the first day I was cornered three times for deep conversations on measurement. This is a good thing, mostly, but one in particular was worth a review.  The discussion for this last centered on whether measurement was needed for most initiatives, and I argued yes, but with a caveat.

There was an implicit thought that for many things that measurement wasn’t needed.  In particular, for informal learning when we’ve got folks successfully developed as effective self-learners and a good culture, we don’t need to measure. And I agree, though we might want to track (via something like the xAPI) to see what things are effective or not.

However, I did still think that any formal interventions, whether courses, performance support, or even specific social initiatives should be measured. First, how are you going to tune it to get it right? Second, don’t you want to attach the outcome to the intervention? I mean, if you’re doing performance consulting, there should be a gap you’re trying to address or why are you bothering?  If there is a gap, you have a natural metric.

I am pleased to see the interest in measurement, and I hope we can start getting some conceptual clarity, some good case studies, and really help make our learning initiatives into strategic contributions to the organization.  Right?

Michael Furdyk #LSCon Keynote Mindmap

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Michael Furdyk gave an inspiring talk this morning about his trajectory through technology and then five ideas that he thought were important elements in the success of the initiatives he had undertaken. He gave lots of examples and closed with interesting questions about how we might engage learners through badges, mobile, and co-creation.

Tom Wujec #LSCon Keynote Mindmap

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Tom Wujec gave a discursive and well illustrated talk about how changes in technology were changing industry, ultimately homing in on creativity.  Despite a misstep mentioning Kolb’s invalid learning styles instrument, it was entertaining and intriguing.

 

Tech Limits?

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A couple of times last year, firms with some exciting learning tools approached me to talk about the market.  And in both cases, I had to advise them that there were some barriers they’d have to address. That was brought home to me in another conversation, and it makes me worry about the state of our industry.

So the first tool is based upon a really sound pedagogy that is consonant with my  activity-based learning approach.  The basis is giving learners assignments very much like the assignments they’ll need to accomplish in the workplace, and then resourcing them to succeed.  They wanted to make it easy for others to create these better learning designs (as part of a campaign for better learning). The only problem was, you had to learn the design approach as well as the tool. Their interface wasn’t ready for prime time, but the real barrier was getting people to be able to use a new tool. I indicated some of the barriers, and they’re reconsidering (while continuing to develop content against this model as a service).

The second tool supports virtual role plays in a powerful way, having smart agents that react in authentic  ways. And they, too, wanted to provide an authoring tool to create them.  And again my realistic assessment of the market was that people would have trouble understanding the tool.  They decided to continue to develop the experiences as a service.

Now, these are somewhat esoteric designs, though the former  should be  the basis of our learning experiences, and the latter would be a powerful addition to support a very common and important type of interaction.  The more surprising, and disappointing, issue came up with a conversation earlier this year with a proponent of a more familiar tool.

Without being specific (I’ve not received permission to disclose the details in all of the above), this person indicated that  when training  a popular and fairly straightforward tool, that the biggest barrier wasn’t the underlying software model. I was expecting that too much of training was based upon rote assignments without an underlying model, and that is the case, but instead there was a more fundamental barrier: too many potential users just didn’t have sufficient computer skills!  And I’m not talking about programming code, but instead fundamental understandings of files and ‘styles‘ and other core computing elements just were not present in sufficient quantities in these would-be authors. Seriously!

Now I’ve complained before that we’re not taking learning design seriously, but obviously we’re compounded by a lack of fundamental computer skills.  Folks, this is  elearning, not chalk learning, not chalk talk, not  edoing, etc.  If you struggle to add new apps on your computer, or find files, you’re not ready to be an elearning developer.

I admit that I struggle to see how folks can assume that without knowledge of design, nor knowledge of technology, that they can still be elearning designers and developers. These tools are scaffolding  to allow your designs to be developed. They don’t  do  design, nor will they magically cover up for lacks of tech literacy.

So, let’s get realistic.  Learn about learning design, and get comfortable with tech, or please,  please, don’t do elearning.  And I promise not to do music, architecture, finance, and everything else I’m not qualified to. Fair enough?

 

Giving it away, or worse

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The other day, I was wondering about the possibilities of removing mandatory courses.  Ok, maybe not mandated compliance, but any others.  And then a colleague took it further, and I like it.  So what are we talking about?

I was thinking that, if you give people a meaningful mission (ala Dan Pink’s Drive), the learner (assuming reasonable self-learning skills, a separate topic), they would take responsibility for the learning they needed.  We could have courses around, or maybe await their desires and point them to outside resources, etc, unless it’s specifically internal.  That is, we become much more pull (from the user) than push (from us).

However, my colleague Mark Britz took it further.  He argued that instead of not making them go, instead we’d charge them what it cost to provide the learning!  That is, if folks wanted training or webinars or…, they’d pay for the privilege.  As he put it, if requests for elearning, being cautious about signing up, etc happened: “I couldn’t be happier!”

His point is that it would drive people to more workflow learning, more social and shared learning, etc.  And that’s a good thing.   I might couple that with some way to make sure they knew how to work, play, and learn well together, but it’s the different view that’s a needed jumpstart.

It’s a refreshing twist on the ‘if we build it it is good’ sort of mentality, and really helps focus the L&D unit on doing things that will significantly improve outcomes for others.  If you can make a meaningful  impact, people will have to pay for your assistance.  You want change?  You’ll pay but it’ll be worth it.

If we’re going to kick off a revolution, we need to rethink what we’re about and how we’re doing it.  Mark’s upended view is a necessary kick in the status quo to get us to think anew about what we’re doing and why.

I recommend you read his original post.

Making Sense of Research

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A couple of weeks ago, I was riffing on sensors:  how mobile devices are getting equipped with all sorts of new sensors and the potential for more and what they might bring.  As part of that discussion was a brief mention of sensor nets, how aggregating all this data could be of interest too. And low and behold, a massive example was revealed last week.

The context was the ‘spring forward’ event Apple held where they announced their new products.  The most anticipated one was the Apple Watch (which was part of the driving behind my post on wearables), the new iConnected device for your wrist.  The second major announcement was their new Macbook, a phenomenally thin new laptop with some amazing specs on weight and screen display, as well as some challenging tradeoffs.

One announcement that was less noticed was the announcement of a new research endeavor, but I wonder if it isn’t the most game-changing element  of them all.  The announcement was ResearchKit, and it’s about sensor nets.

So, smartphones have lots of sensors.  And the watch will have more.  They can already track a number of parameters about you automatically, such as your walking.  There can be more, with apps that can ask about your eating, weight, or other health measurements.  As I pointed out, aggregating data from sensors could do things like identify traffic jams (Google Maps already does this), or collect data like restaurant ratings.

What Apple has done is to focus specifically on health data from their HealthKit, and partner with research hospitals. What they’re saying to scientists is  “we’ll give you anonymized health data, you put it to good use”. A number of research centers are on board, and already collecting data about asthma and more.  The possibility is to use analytics that combine the power of large numbers with a bunch of other descriptive data to be able to investigate things at scale.  In general, research like this is hard since it’s hard to get large numbers of subjects, but large numbers of subjects is a much better basis for study (for example, the China-Cornell-Oxford Project that was able to look at a vast breadth of diet to make innovative insights into nutrition and health).

And this could be just the beginning: collecting data en masse (while successfully addressing privacy concerns) can be a source of great insight if it’d done right.  Having devices that are with you and capable of capturing a variety of information gives the opportunity to mine that data for expected, and unexpected, outcomes.

A new iDevice is always cool, and while it’s not the first smart watch (nor was the iPhone the first smartphone, the iPad not the first tablet, nor the iPod the first music play), Apple has a way of making the experience compelling.  Like with the iPad, I haven’t yet seen the personal value proposition, so I’m on the fence.  But the ability to collect data in a massive way that could support ground-breaking insights and innovations in medicine? That has the potential for affecting millions of people around the world.  Now  that is impact.