Learnlets
Clark Quinn's Learnings about Learning
(The Official Quinnovation blog)

17 December 2014

Why L&D?

Clark @ 8:33 am

One of the concerns I hear is whether L&D still has a role.  The litany is that they’re so far out of touch with their organization, and science, that it’s probably  better to let them die an unnatural death than to try to save them. The prevailing attitude of this extreme view is that the Enterprise Social Network is the natural successor to the LMS, and it’s going to come from operations or IT rather than L&D.  And, given that I’m on record suggesting that we revolutionize L&D rather than ignoring it, it makes sense to justify why.  And while I’ve had other arguments, a really good argument comes from my thesis advisor, Don Norman.

Don’s on a new mission, something he calls DesignX, which is scaling up design processes to deal with “complex socio-technological systems”.   And he recently wrote an article about why DesignX that put out a good case why L&D as well.  Before I get there, however, I want to point out two other facets of his argument.

The first is that often design has to go beyond science. That is, while you use science when you can, when you can’t you use theory inferences, intuition, and more to fill in the gaps, which you hope you’ll find out later (based upon later science, or your own data) was the right choice.  I’ve often had to do this in my designs, where, for instance, I think research hasn’t gone quite far enough in understanding engagement.  I’m not in a research position as of now, so I can’t do the research myself, but I continue to look at what can be useful.  And this is true of moving L&D forward. While we have some good directions and examples, we’re still ahead of documented research.  He points out that system science and service thinking are science based, but suggests design needs to come in beyond those approaches.   To the extent L&D can, it should draw from science, but also theory and keep moving forward regardless.

His other important point is, to me, that he is talking about systems.  He points out that design as a craft works well on simple areas, but where he wants to scale design is to the level of systemic solutions.  A noble goal, and here too I think this is an approach L&D needs to consider as well.  We have to go beyond point solutions – training, job aids, etc – to performance ecosystems, and this won’t come without a different mindset.

Perhaps the most interesting one, the one that triggered this post, however, was a point on why designers are needed.  His point is that others have focuses on efficiency and effectiveness, but he argued that designers have empathy for the users as well.  And I think this is really important.  As I used to say the budding software engineers I was teaching interface design to: “don’t trust your intuition, you don’t think like normal people”.  And similarly, the reason I want L&D in the equation is that they (should) be the ones who really understand how we think, work, and learn, and consequently they should be the ones facilitating performance and development. It takes an empathy with users to facilitate them through change, to help them deal with fears and anxieties dealing with new systems, to understand what a good learning culture is and help foster it.

Who else would you want to be guiding an organization in achieving effectiveness in a humane way?   So Don’s provided, to me, a good point on why we might still want L&D (well, P&D really ;) in the organization. Well, as long as they also addressing the bigger picture and not just pushing info dump and knowledge test.  Does this make sense to you?

#itashare #revolutionizelnd

16 December 2014

Challenges in engaging learning

Clark @ 8:05 am

I’ve been working on moving a team to deeper learning design.  The goal is to practice what I preach, and make sure that the learning design is competency-aligned, activity-based, and model-driven.  Yet, doing it in a pragmatic way.

And this hasn’t been without it’s challenges.  I presented to the team my vision, we worked out a process, and started coaching the team during development.  In retrospect, this wasn’t proactive enough.  There were a few other hiccups.

We’re currently engaged in a much tighter cycle of development and revision, and now feel we’re getting close to the level of effectiveness and engagement we need.  Whether a) it’s really better, and b) whether we can replicate it yet scale it as well is an open question.

At core are a few elements. For one, a rabid focus on what learners are doing is key.  What do they need to be able to do, and what contexts do they need to do it in?

The competency-alignment focus is on the key tasks that they have to do in the workplace, and making sure we’re preparing them across pre-class, in-class, and post-class activities to develop that ability.  A key focus is having them make the decision in the learning experience that they’ll have to make afterward.

I’m also pushing very hard on making sure that there are models behind the decisions.  I’m trying hard to avoid arbitrary categorizations, and find the principles that drove those categorizations.

Note that all this is not easy.  Getting the models is hard when the resources provided don’t include that information.  Avoiding presenting just knowledge and definitions is hard work.  The tools we use make certain interactions easy, and other ones not so easy.  We have to map meaningful decisions into what the tools support.  We end up making  tradeoffs, as do we all.  It’s good, but not as good as it could be.  We’ll get better, but we do want to run in a practical fashion as well.

There are more elements to weave in: layering on some general biz skills is embryonic.  Our use of examples needs to get more systematic.  As does our alignment of learning goal to practice activity.  And we’re struggling to have a slightly less didactic and earnest tone; I haven’t worked hard enough on pushing a bit of humor in, tho’ we are ramping up some exaggeration.  There’s only so much you can focus on at one time.

We’ll be running some student tests next week before presenting to the founder.  Feeling mildly confident that we’ve gotten a decent take on quality learning design with suitable production value, but there is the barrier that the nuances of learning design are subtle. Fingers crossed.

I still believe that, with practice, this becomes habit and easier.  We’ll see.

25 November 2014

Transformative Experiences

Clark @ 8:05 am

I’ve had the pleasure last week of keynoting Charles Sturt University’s annual Education conference.  They’re in the process of rethinking what their learning experience should be, and I talked about the changes we’re trying to make at the Wadhwani Foundation.

I was reminded of previous conversations about learning experience design and the transformative experience.   And I have argued in the past that what would make an optimal value proposition (yes, I used that phrase) in a learning market would be to offer a transformative learning experience.  Note that this is not just about the formal learning experience, but has two additional components.

Now, it does start with a killer learning experience.  That is, activity-based, competency-driven, model-guided, with lean and compelling content.  Learners need role-plays and simulations to be immersed in practice, and scaffolded with reflection to develop their flexible ability to apply these abilities going forward.  But wait, there’s more!

As a complement, there needs to be a focus on developing the learner as well as their skills. That is, layering on the 21st Century skills: the ability to communicate, lead, problem-solve, analyze, learn, and more.  These need to be included and developed across the learning experience.  So learners not only get the skills they need to succeed now, but to adapt as things change.

The third element is to be a partner in their success.  That is, don’t give them a chance to sink or swim on the basis of the content, but to look for ways in which learners might be struggling with other issues, and work hard to ensure they succeed.

I reckon that anyone capable of developing and delivering on this model provides a model that others can only emulate, not improve upon.  We’re working on the first two initially at the Foundation, and hopefully we’ll get to the latter soon.  But I reckon it’d be great if this were the model all were aspiring to.  Here’s hoping!

 

 

18 November 2014

L&D and working out loud #wolweek

Clark @ 6:15 am

This week is Working Out Loud week, and I can’t but come out in support of a principle that I think is going to be key to organizational success. And, I think, L&D has a key role to play.

The benefits from working out loud are many. Personally, documenting what you’re doing serves as a reminder to yourself and awareness for others. The real power comes, however, from taking that next level: documenting not just what you’re doing, but why. This helps you in reflecting on your own work, and being clear in your thinking. Moreover, sharing your thinking gives you a second benefit in getting others’ input which can really improve the outcome.

In addition, it gives others a couple of benefits. They get to know what you’re up to, so it’s easier to align, but if your thinking is any good, it gives them the chance to learn from how you think.

So what is the role of L&D here? I’ll suggest there are two major roles: facilitating the skills and enabling the culture.

First, don’t assume folks know what working out loud means. And even if they do, they may not be good at it in terms of knowing how to indicate the underlying thinking. And they likely will want feedback and encouragement. First, L&D needs to model it, practicing what they preach. They need to make sure the tools are easily available and awareness is shared. Execs need to be shown the benefit and encouraged to model the behavior too. And L&D will have to trumpet the benefits, accomplishments, and encourage the behavior.

None of this is really likely to succeed if you don’t have a supportive culture. In a Miranda organization, no one is going to share. Instead, you need the elements of a learning organization: the environment has to value diversity, be open to new ideas, provide time for reflection, and most of all be safe. And L&D has to understand the benefits and continue to promote them, identify problems, and work to resolve them.

Note that this is not something you manage or control. The attitude here has to be one of nourishing aka (seed, feed, and weed). You may track it, and you want to be looking for things to support or behaviors to improve, but the goal is to develop a vibrant community of sharing, not squelching anything that violates the hierarchy.

Working out loud benefits the individual and the organization in a healthy environment. Getting the environment right, and facilitating the practice, are valuable contributions, and ones that L&D can, and should, contribute to.

#itashare

5 November 2014

#DevLearn 14 Reflections

Clark @ 9:57 am

This past week I was at the always great DevLearn conference, the biggest and arguably best yet.  There were some hiccups in my attendance, as several blocks of time were taken up with various commitments both work and personal, so for instance I didn’t really get a chance to peruse the expo at all.  Yet I attended keynotes and sessions, as well as presenting, and hobnobbed with folks both familiar and new.

The keynotes were arguably even better than before, and a high bar had already been set.

Neil deGrasse Tyson was eloquent and passionate about the need for science and the lack of match between school and life.    I had a quibble about his statement that doing math teaches problem-solving, as it takes the right type of problems (and Common Core is a step in the right direction) and it takes explicit scaffolding.  Still, his message was powerful and well-communicated. He also made an unexpected connection between Women’s Liberation and the decline of school quality that I hadn’t considered.

Beau Lotto also spoke, linking how our past experience alters our perception to necessary changes in learning.  While I was familiar with the beginning point of perception (a fundamental part of cognitive science, my doctoral field), he took it in very interesting and useful direction in an engaging and inspiring way.  His take-home message: teach not how to see but how to look, was succinct and apt.

Finally, Belinda Parmar took on the challenge of women in technology, and documented how small changes can make a big difference. Given the madness of #gamergate, the discussion was a useful reminder of inequity in many fields and for many.  She left lots of time to have a meaningful discussion about the issues, a nice touch.

Owing to the commitments both personal and speaking, I didn’t get to see many sessions. I had the usual situation of  good ones, and a not-so-good one (though I admit my criteria is kind of high).  I like that the Guild balances known speakers and topics with taking some chances on both.  I also note that most of the known speakers are those folks I respect that continue to think ahead and bring new perspectives, even if in a track representing their work.  As a consequence, the overall quality is always very high.

And the associated events continue to improve.  The DemoFest was almost too big this year, so many examples that it’s hard to start looking at them as you want to be fair and see all but it’s just too monumental. Of course, the Guild had a guide that grouped them, so you could drill down into the ones you wanted to see.  The expo reception was a success as well, and the various snack breaks suited the opportunity to mingle.  I kept missing the ice cream, but perhaps that’s for the best.

I was pleased to have the biggest turnout yet for a workshop, and take the interest in elearning strategy as an indicator that the revolution is taking hold.  The attendees were faced with the breadth of things to consider across advanced ID, performance support, eCommunity, backend integration, decoupled delivery, and then were led through the process of identifying elements and steps in the strategy.  The informal feedback was that, while daunted by the scope, they were excited by the potential and recognizing the need to begin.  The fact that the Guild is holding the Learning Ecosystem conference and their release of a new and quite good white paper by Marc Rosenberg and Steve Foreman are further evidence that awareness is growing.   Marc and Steve carve up the world a little differently than I do, but we say similar things about what’s important.

I am also pleased that Mobile interest continues to grow, as evidenced by the large audience at our mobile panel, where I was joined by other mLearnCon advisory board members Robert Gadd, Sarah Gilbert, and Chad Udell.  They provide nicely differing viewpoints, with Sarah representing the irreverent designer, Robert the pragmatic systems perspective, and Chad the advanced technology view, to complement my more conceptual approach.  We largely agree, but represent different ways of communicating and thinking about the topic. (Sarah and I will be joined by Nick Floro for ATD’s mLearnNow event in New Orleans next week).

I also talked about trying to change the pedagogy of elearning in the Wadhwani Foundation, the approach we’re taking and the challenges we face.  The goal I’m involved in is job skilling, and consequently there’s a real need and a real opportunity.  What I’m fighting for is to make meaningful practice as a way to achieve real outcomes.  We have some positive steps and some missteps, but I think we have the chance to have a real impact. It’s a work in progress, and fingers crossed.

So what did I learn?  The good news is that the audience is getting smarter, wanting more depth in their approaches and breadth in what they address. The bad news appears to be that the view of ‘information dump & knowledge test = learning’ is still all too prevalent. We’re making progress, but too slowly (ok, so perhaps patience isn’t my strong suit ;).  If you haven’t, please do check out the Serious eLearning Manifesto to get some guidance about what I’m talking about (with my colleagues Michael Allen, Julie Dirksen, and Will Thalheimer).  And now there’s an app for that!

If you want to get your mind around the forefront of learning technology, at least in the organizational space, DevLearn is the place to be.

 

29 October 2014

Neil deGrasse Tyson #DevLearn Keynote Mindmap

Clark @ 9:54 am

Neil deGrasse Tyson opened this year’s DevLearn conference. A clear crowd favorite, folks lined up to get in (despite the huge room). In a engaging, funny, and poignant talk, he made a great case for science and learning.

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28 October 2014

Cognitive prostheses

Clark @ 8:05 am

While our cognitive architecture has incredible capabilities (how else could we come up with advances such as Mystery Science Theater 3000?), it also has limitations. The same adaptive capabilities that let us cope with information overload in both familiar and new ways also lead to some systematic flaws. And it led me to think about the ways in which we support these limitations, as they have implications for designing solutions for our organizations.

The first limit is at the sensory level. Our mind actually processes pretty much all the visual and auditory sensory data that arrives, but it disappears pretty quickly (within milliseconds) except for what we attend to. Basically, your brain fills in the rest (which leaves open the opportunity to make mistakes). What do we do? We’ve created tools that allow us to capture things accurately: cameras and microphones with audio recording. This allows us to capture the context exactly, not as our memory reconstructs it.

A second limitation is our ‘working’ memory. We can’t hold too much in mind at one time. We ‘chunk’ information together as we learn it, and can then hold more total information at one time. Also, the format of working memory largely is ‘verbal’. Consequently, using tools like diagramming, outlines, or mindmaps add structure to our knowledge and support our ability to work on it.

Another limitation to our working memory is that it doesn’t support complex calculations, with many intermediate steps. Consequently we need ways to deal with this. External representations (as above), such as recording intermediate steps, works, but we can also build tools that offload that process, such as calculators. Wizards, or interactive dialog tools, are another form of a calculator.

Processing information in short term memory can lead to it being retained in long term memory. Here the storage is almost unlimited in time and scope, but it is hard to get in there, and isn’t remembered exactly, but instead by meaning. Consequently, models are a better learning strategy than rote learning. But external sources like the ability to look up or search for information is far better than trying to get it in the head.

Similarly, external support for when we do have to do things by rote is a good idea. So, support for process is useful and the reason why checklists have been a ubiquitous and useful way to get more accurate execution.

In execution, we have a few flaws too. We’re heavily biased to solve new problems in the ways we’ve solved previous problems (even if that’s not the best approach. We’re also likely to use tools in familiar ways and miss new ways to use tools to solve problems. There are ways to prompt lateral thinking at appropriate times, and we can both make access to such support available, and even trigger same if we’ve contextual clues.

We’re also biased to prematurely converge on an answer (intuition) rather than seek to challenge our findings. Access to data and support for capturing and invoking alternative ways of thinking are more likely to prevent such mistakes.

Overall, our use of more formal logical thinking fatigues quickly. Scaffolding help like the above decreases the likelihood of a mistake and increases the likelihood of an optimal outcome.

When you look at performance gaps, you should look to such approaches first, and look to putting information in the head last. This more closely aligns our support efforts with how our brains really think, work, and learn. This isn’t a complete list, I’m sure, but it’s a useful beginning.

24 October 2014

#DevLearn Schedule

Clark @ 8:30 am

As usual, I will be at DevLearn (in Las Vegas) this next week, and welcome meeting up with you there.  There is a lot going on.  Here’re the things I’m involved in:

  • On Tuesday, I’m running an all day workshop on eLearning Strategy. (Hint: it’s really a Revolutionize L&D workshop ;).  I’m pleasantly surprised at how many folks will be there!
  • On Wednesday at 1:15 (right after lunch), I’ll be speaking on the design approach I’m leading at the Wadhwani Foundation, where we’re trying to integrate learning science with pragmatic execution.  It’s at least partly a Serious eLearning Manifesto session.
  • On Wednesday at 2:45, I’ll be part of a panel on mlearning with my fellow mLearnCon advisory board members Robert Gadd, Sarah Gilbert, and Chad Udell, chaired by conference program director David Kelly.

Of course, there’s much more. A few things I’m looking forward to:

  • The keynotes:
    •  Neil DeGrasse Tyson, a fave for his witty support of science
    • Beau Lotto talking about perception
    • Belinda Parmar talking about women in tech (a burning issue right now)
  • DemoFest, all the great examples people are bringing
  • and, of course, the networking opportunities

DevLearn is probably my favorite conference of the year: learning focused, technologically advanced, well organized, and with the right people.  If you can’t make it this year, you might want to put it on your calendar for another!

7 October 2014

Service Thinking and the Revolution?

Clark @ 8:36 am

A colleague I greatly respect, who has a track record of high impact in important positions, has been a proponent of service science.  And I confess that it hadn’t really penetrated.  Yet last week I heard about it in a way that resonated much more strongly and got me thinking, so let me share where it’s leading my thinking, and see what you say.

One time I heard something exciting, a concept called interface ‘explorability‘ when I was doing a summer internship at NASA while a grad student.  When I brought it back to the lab, my advisor didn’t really resonate.  Then, some time later (a year or two)  he was discussing a concept and I mentioned that it sounded a lot like that ‘explorability’, and he suddenly wanted to know more. The point being that there is a time when you’re ready to hear a message. And that’s me with service science.

The concept is considering a mutual value generation process between provider and customer, and engineering it across the necessary system components and modular integrations to yield a successful solution.  As organizations need to be more customer-centric, this perspective yields processes to do that in a very manageable, measurable way.  And that’s the perspective I’d been missing when I’d previously heard about it, but Hastings & Saperstein presented it last week at the Future of Talent event in the form of Service Thinking, which brought the concept home.

I wondered how it compared to Design Thinking, another concept sweeping instructional design and related fields, and it appears to be synergistic but perhaps a superset. While nothing precludes Design Thinking from producing the type of outcome Service Thinking is advocating, I’m inferring that Service Thinking is a bit more systematic and higher level.

The interesting idea for me was to think of bringing Service Thinking to the role of L&D in the organization. If we’re looking systematically at how we can bring value to the customer, in this case the organization, systematically, we have a chance to look at the bigger picture, the Performance & Development view instead of the training view.  If we take the perspective of an integrated approach to meeting organizational execution and innovation needs, we may naturally develop the performance ecosystem.

We need to take a more comprehensive approach, where we integrate technology capabilities, resources, and people into an integrated whole. I’m looking at service thinking, as perhaps an integration of the rigor of systems thinking with the creative customer focus of design thinking, as at least another way to get us there.  Thoughts?

24 September 2014

Better Learning in the Real World

Clark @ 8:25 am

I tout the value of learning science and good design.  And yet, I also recognize that to do it to the full extent is beyond most people’s abilities.  In my own work, I’m not resourced to do it the way I would and should do it. So how can we strike a balance?  I believe that we need to use smart heuristics instead of the full process.

I have been talking to a few different people recently who basically are resourced to do it the right way.  They talk about getting the right SMEs (e.g. with sufficient depth to develop models), using a cognitive task analysis process to get the objectives, align the processing activities to the type of learning objective, developing appropriate materials and rich simulations, testing the learning and using  feedback to refine the product, all before final release.  That’s great, and I laud them.  Unfortunately, the cost to get a team capable of doing this, and the time schedule to do it right, doesn’t fit in the situation I’m usually in (nor most of  you).  To be fair, if it really matters (e.g. lives depend on it or you’re going to sell it), you really do need to do this (as medical, aviation, military training usually do).

But what if you’ve a team that’s not composed of PhDs in the learning sciences, your development resources are tied to the usual tools, your budgets far more stringent, and schedules are likewise constrained? Do you have to abandon hope?  My claim is no.

Law of diminishing returns curveI believe that a smart, heuristic approach is plausible.  Using the typical ‘law of diminishing returns’ curve (and the shape of this curve is open to debate), I  suggest that it’s plausible that there is a sweet spot of design processes that gives you an high amount of value for a pragmatic investment of time and resources.  Conceptually, I believe you can get good outcomes with some steps that tap into the core of learning science without following the letter.  Learning is a probabilistic game, overall, so we’re taking a small tradeoff in probability to meet real world constraints.

What are these steps? Instead of doing a full cognitive task analysis, we’ll do our best guess of meaningful activities before getting feedback from the SME.  We’ll switch the emphasis from knowledge test to mini- and branching-scenarios for practice tasks, or we’ll have them take information resources and use them to generate work products (charts, tables, analyses) as processing.  We’ll try to anticipate the models,  and ask for misconceptions & stories to build in.  And we’ll align pre-, in-, and post-class activities in a pragmatic way.  Finally, we’ll do a learning equivalent of heuristic evaluation, not do a full scientifically valid test, but we’ll run it by the SMEs and fix their (legitimate) complaints, then run it with some students and fix the observed flaws.

In short, what we’re doing here are  approximations to the full process that includes some smart guesses instead of full validation.  There’s not the expectation that the outcome will be as good as we’d like, but it’s going to be a lot better than throwing quizzes on content. And we can do it with a smart team that aren’t learning scientists but are informed, in a longer but still reasonable schedule.

I believe we can create transformative learning under real world constraints.  At least, I’ll claim this approach is far more justifiable than the too oft-seen approach of info dump and knowledge test. What say you?

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