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Clark Quinn’s Learnings about Learning

Archives for May 2014

Vale Don Kirkpatrick

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Last week, Don Kirkpatrick passed away.  Known for his four ‘levels‘ of measuring learning, he’s been hailed and excoriated.  And it’s instructive to see why on both sides.

He derived his model as an approach to determine the impact of an intervention on organizational performance.  He felt that you worked backward from the change you needed, to determine whether the workplace performance was changing, as then to see if that could be attributed to the training, and ultimately to the learner.  He numbered his steps so that step 1 was seeing what learners thought, 2 was that learners  could demonstrate a change, 3 was that the change was showing up in the workplace post intervention, and 4 was it impacting business measures.

This actually made a lot of sense. Rather than measuring the cost of hour of seat time or some other measure of efficiency, or, worse, not measuring at all, here was a plan that was designed to focus on meaningful change that the business needed.  It was obvious, and yet also obviously needed.    So his success in bringing awareness to the topic of business impact is to be lauded.

There were two major  problems, however.  For one, having numbered it the way that it was, people seemed that they could take a partial attempt.  Research shows that the number of people would only do step 1 or 2, and these are useless without ultimately including 4.  He even later wondered if he should have numbered the approach in the reverse.  The numbers have been documented (from a presentation with results from the ASTD Benchmarking Forum) as dropping in implementation from 94% doing level 1, 34% doing level 2, 13% doing level 3, and 3% doing level 4.  That’s  not  the idea!

The second problem was that whether or not he intended it (and there are reasons to believe he didn’t), it become associated only with training interventions.  Performance support interventions or social network outcomes  could similarly be measured (at least on levels 3 and 4), yet the language was all about training, which made it easy for folks to wrongly conclude that training was your only tool.  And we still see folks using courses as the only tool in their repertoire, which just isn’t aligned with how we think, work, and learn (hence the revolution).

Kirkpatrick rode this tool for the rest of his career,  created a family business in it, and he wasn’t shy about suggesting that you buy a book to learn about it.  I certainly can’t fault him for it either, as he did have a sensible model and it could be put into effective use.  There are worse ways to earn a living.

Others have played upon his model.  The Phillips have made a similar career with their fifth level, ROI, measuring the cost of impacting level 4 against the value of the impact.  Which isn’t a bad move to make  after you focus on making an impact.  Similarly, a client opined that there was also level 0, are the learners even showing up for the training!

In assessing the impact, part of me is mindful that tools can be used for good or ill.  Powerpoint doesn’t kill people, people do, as the saying goes.  Still, Kirkpatrick could’ve renumbered the steps, or been more outspoken about the problems with just step 1.

So, I laud his insight, and bemoan the ultimate lack of impact.  However, I reckon it’s better to argue about it than be ignorant.  Rest in peace.

Setting Story

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I’ve been thinking about the deep challenge of motivating uninterested learners.  To me, at least part of that is making the learning of intrinsic interest.  And one of those elements is practice, and this is arguably the most important element to making learning work.  So how to do we make practice intrinsically interesting?

One of the challenging but important components of designing meaningful practice is choosing a context in which that practice is situated.  It’s really about finding a story line that makes the action meaningful to both the learner and the learning. It’s creative (and consequently fun), but it’s also not intrinsically obvious (which I’ve learned after trying to teach it in both game design and advanced ID workshops). There are heuristics to be followed (there’s no guaranteed formula except brainstorm, winnow, trial, and refine), however, that can be useful.

While Subject Matter Experts (SMEs) can be the bane of your existence while setting learning goals (they have conscious access to no more than 30% of what they do, so they tend to end up reciting what they know, which they do have access to),  they can be very useful when creating stories. There’s a reason why they’ve spent the requisite time to  become experts in the field, and that’s an aspect we can tap into. Find out  why it’s of interest to them.  In one instance, when asking experts about computer auditing, a colleague found that auditors found it like playing detective, tracking back to find the error.  It’s that sort of insight upon which a good game or practice exercise can hinge.

One of the tricks to work with SMEs is to talk about decisions.  I argue that what is most likely to make a difference to organizations is that people make better decisions, and I also believe that using the language of decisions helps SMEs focus on what they  do, not what they know.  Between your performance gap analysis of the situation, and expert insight into what decisions are key, you’re likely to find the key performances you want learners to practice.

You also want to find out all the ways learners go wrong.  Here you may well hear instructors and/or SMEs say “no matter what we do, they always…”. And that’s the things you want to know, because novices don’t tend to make random errors.  Yes, there’s some, owing to our cognitive architecture (it’s adaptive), which is why it’s bad to expect people to do rote things, but it’s a small fraction of mistakes.  Instead, learners make patterned mistakes based upon mistakes in their conceptualizations of the performance, aka misconceptions.  And  you want to trap those because you’ll have a chance to remediate them in the learning context. And they make the challenge more appropriately tuned.

You also need the consequences of both the right choice and the misconceptions. Even if it’s just a multiple choice question, you should show what the real world consequence is before providing the feedback about why it’s wrong. It’s also the key element in scenarios, and building models for serious games.

Then the  trick is to ask SMEs about all the different settings in which these decisions embed. Such decisions tend to travel in packs, which is why scenarios are better practice than simple multiple choice, just as scenario-based multiple choice trumps knowledge test.  Regardless, you want to contextualize those decisions, and knowing the different settings that  can be used gives you a greater palette to choose from.

Finally, you’ll want to decide how close you want the context to be to the real context.  For certain high-stakes and well-defined tasks, like flying planes or surgery, you’ll want them quite close to the real situation.  In other situations, where there’s more broad applicability and less intrinsic interest (perhaps accounting or project management), you may want a more fantastic setting that facilitates broader transfer.

Exaggeration is a key element. Knowing what to exaggerate and when is not yet a science, but the rule of thumb is leave the core decisions to be based upon the important variables, but the context can be raised to increase the importance.  For example, accounting might not be riveting but your job depends on it.  Raising the importance of the accounting decision in the learning experience will mimic the importance, so you might be accounting for a mob boss who’ll terminate your existence if you don’t terminate the discrepancy in his accounts!  Sometimes exaggeration can serve a pedagogical purpose as well, such as highlighting certain decisions that are rare in real life but really important when they occur. In one instance, we had asthma show up with a 50% frequency instead of the usual ~15%, as the respiratory complications that could occur required specific approaches to address.

Ultimately, you want to choose a setting in which to embed the decisions. Just making it abstract decreases the impact of the learning, and making it about knowledge, not decisions, will render it almost useless, except for those rare bits of knowledge that have to absolutely be in the head.  You want to be making decisions using models, not recalling specific facts. Facts are better off put in the world for reference, except where time is too critical. And that’s more rare than you’d expect.

This may seem like a lot of work, but it’s not that hard, with practice.  And the above is for critical decisions. In many cases, a good designer should be able to look at some content and infer what the  decisions involved  should be.  It’s a different design approach then transforming knowledge into tests, but it’s critical for learning.  Start working on your practice items first, aligned with meaningful objects, and the rest will flow. That’s my claim, what say you?

‘Sharing’ culture

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I was in a recent conversation about a company facing strong growth and worried about the impact on culture.  Companies with a positive culture, a valuable offering, and a good business model are liable to face growth issues, and maintaining or starting a good culture becomes a critical issue to maintaining the organization’s success.

This company had a positive culture, in that people were diverse, friendly, upbeat, and committed to contributing. These are all positive elements that had led to the early success. Growth, both through hiring and acquisitions, was leading to concerns about the ability for those factors to continue.

One of the things that wasn’t obvious from the initial portrayal of the company was whether folks there were capturing and sharing what they were doing, how they were working, what challenges they were facing, and what results they were seeing. In a small company, this happens naturally through conversation, but face to face communication isn’t scalable.

One obvious possibility is to implement or more systematically leverage an enterprise social network (ESN; essentially    using social media in the org).  Working out loud, as it’s known, has many benefits.  As people share their work, others can comment and improve it.  People can ask for help and get collaboration on those new problems and innovation needs that are increasingly arising.  Mistakes can be made and the lessons learned can be shared so no others have to make the same mistakes.

One of the offshoot benefits of such sharing is that it takes the positive cultural attributes already being shown and makes them visible (if implicitly) as well.  It’s not guaranteed, but with an awareness of the behaviors and manifestations of culture through the network, a systematic process could lead to that positive culture scaling  and yield those additional benefits that accompany working out loud.

It takes all the elements of a learning culture and organizational change, of course. You need to continue to welcome diversity, be open to new ideas, and have it safe to contribute.  You also need to develop a vision, message it, have the leadership model it, facilitate it, anticipate problems and be prepared to address them, and ultimately reward the desired outcomes.  But this is doable.

The benefits of a positive culture are becoming known, and the value of social networks are also emerging. Linking them together is not only necessary, but the benefits are more than the sum of the parts.

#itashare

Getting contextual

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For the current ADL webinar series on mobile, I gave a presentation on contextualizing mobile in the larger picture of L&D (a natural extension of my most recent books).  And a question came up about whether I thought wearables constituted mobile.  Naturally my answer was yes, but I realized there’s a larger issue, one that gets meta as well as mobile.

So, I’ve argued that we should be looking at models for guiding our behavior.  That we should be creating them by abstracting from successful practices, we should be conceptualizing them, or adopting them from other areas.  A good model, with rich conceptual relationships, provides a basis for explaining what has happened, and predicting what will happen, giving us a basis for making decisions.  Which means they need to be as context-independent as possible.

WorkOppsSo, for instance, when I developed the mobile models I use, e.g. the 4C‘s and the applications of learning (see figure), I deliberately tried to create an understanding that would transcend the rapid changes that are characterizing mobile, and make them appropriately recontextualizable.

In the case of mobile, one of the unique opportunities is contextualization.  That means using information about where you are, when you are, which way you’re looking, temperature or barometric pressure, or even your own state: blood pressure, blood sugar, galvanic skin response, or whatever else skin sensors can detect.

To put that into context (see what I did there): with desktop learning, augmenting formal could be emails that provide new examples or practice that spread out over time. With a smartphone  you can do the same, but you could also have a localized information so that because of where you were you might get information related to a learning goal. With a wearable, you might get some information because of what you’re looking at (e.g. a translation or a connection to something else you know), or due to your state (too anxious, stop and wait ’til you calm down).

Similarly for performance support: with a smartphone you could take what comes through the camera and add it onto what shows on the screen; with glasses you could lay it on the visual field.  With a watch or a ring, you might have an audio narration.  And we’ve already seen how the accelerometers in fit bracelets can track your activity and put it in context for you.

Social can not only connect you to who you need to know, regardless of device or channel, but also signal you that someone’s near, detecting their face or voice, and clue you in that you’ve met this person before.  Or find someone that you should meet because you’re nearby.

All of the above are using contextual information to augment the other tasks you’re doing.  The point is that you map the technology to the need, and infer the possibilities.  Models are a better basis for elearning, too so that you teach transferable understandings (made concrete in practice) rather than specifics that can get outdated.  This is one of the elements we placed in the Serious eLearning Manifesto, of course.  They’re also useful for coaching & mentoring as well, as for problem-solving, innovating, and more.

Models are powerful tools for thinking, and good ones will support the broadest possible uses.  And that’s why I collect them, think in terms of them, create them, and most importantly, use them in my work.   I encourage you to ensure that you’re using models appropriately to guide you to new opportunities, solutions, and success.

Peeling the onion

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I’ve been talking a bit recently about deepening formal design, specifically to achieve learning that’s flexible, persistent, and develops the learner’s abilities to become self-sustaining in work and life.  That is, not just for a course, but for a curriculum.  And it’s more than just what we talked about in the Serious eLearning Manifesto, though of course it starts there.    So, to begin with, it needs to start with meaningful objectives, provide related practice, and be trialed and developed, but there’s more, there are layers of development that wrap around the core.

One element I want to suggest is important is also in the Manifesto, but I want to push a bit deeper here.  I worked to put in that the elements behind, say, a procedure or a task, that you apply to problems, are models or concepts.  That is, a connected body of conceptual relationships that tie together your beliefs about why it should be done this way.  For example, if you’ve a procedure or process you want people to follow, there is (or should be) a  rationale  behind it.

And  you should help learners discover and see the relationships between the model and the steps, through examples and the feedback they get on practice.  If they can internalize the understanding behind steps, they are better prepared for the inevitable changes to the tools they use, the materials they work on, or the process changes what will come from innovation.  Training them on X, when X will ultimately shift to Y, isn’t as helpful unless you help them understand the principles that led to performance on X and will transfer to Y.

Another element is that the output of the activities should create scrutable deliverables  and  also annotate the thoughts behind the result.  These provide evidence of the thinking both implicit and explicit, a basis for mentors/instructors to understand what’s good, and what still may need to be addressed, tin the learner’s thinking.  There’s also the creation of a portfolio of work which belongs to the learner and can represent what they are capable of.

Of course, the choices of activities for the learner initially, and the design of them to make them engaging, by being meaningful to the learner in important ways, is another layer of sophistication in the design.  It can’t just be that you give the traditional boring problems, but instead the challenges need to be contextualized. More than that (which is already in the Manifesto), you want to use exaggeration and story to really make the challenges compelling.  Learning  should   be hard fun.

Another layer is that of 21st Century skills (for examples, the SCANS competencies).  These can’t be taught separately, they really need to manifest across whatever domain learnings you are doing. So you need learners to not just learn concepts, but apply those concepts to specific problems. And, in the requirements of the problem, you build in opportunities to problem-solve, communicate, collaborate, e.g. all the foundational and workplace skills. They need to reappear again and again and be assessed (and developed) separately.

Ultimately, you want the learner to be taking on responsibility themselves.  Later assignments should include the learner being given parameters and choosing appropriate deliverables and formats for communication.  And this requires and additional layer, a layer of annotation on the learning design. The learners need to be seeing  why the learning was so designed, so that they can internalize the principles of good design and so become self-improving learners. You, for example, in reading this far, have chosen to do this as part of your own learning, and hopefully it’s a worthwhile investment.  That’s the point; you want learners to continue to seek out challenges, and resources to succeed, as part of their ongoing self-development, and that comes by having seen learning design and been handed the keys at some point on the journey, with support that’s gradually faded.

The nuances of this are not trivial, but I want to suggest that they  are doable.  It’s a subtle interweaving, to be sure, but once you’ve got your mind around it (with scaffolded practice :), my claim is that it can be done, reliably and repeatedly.   And it should.  To do less is to miss some of the necessary elements for successful support of  an individual to become the capable and continually self-improving learner that we need.

I touched on most of this when I was talking about Activity-Based Learning, but it’s worthwhile to revisit it (at least for me :).

Facilitating Innovation

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One of the things that emerged at the recent A(S)TD conference was that a particular gap might exist. While there are resources about learning design, performance support design, social networking, and more, there’s less guidance about facilitating innovation.  Which led me to think a wee bit about what might be involved.  Here’s a first take.

So, first, what are the elements of innovation?  Well, whether you  listen to Stephen Berlin Johnson on the story of innovation, or Keith Sawyer on ways to foster innovation, you’ll see that innovation isn’t individual.  In previous work, I looked at models of innovation, and found that either you mutated an existing design, or meld two designs together.  Regardless, it comes from working and playing well together.

The research suggests that you  need to make sure you are addressing the right problem, diverge on possible solutions via diverse teams under good process, create interim representations, test, refine, repeat.  The point being that the right folks need to work together over time.

The barriers are several.  For one, you need to get the cultural elements right: welcoming diversity, openness to new ideas, safe to contribute, and time for reflection.  Without being able to get the complementary inputs, and getting everyone to contribute, the likelihood of the best outcome is diminished.

You also shouldn’t take for granted that everyone knows how to work and play well together.  Someone may not be able to ask for help in effective ways, or perhaps more likely, others may offer input in ways that minimize the likelihood that they’ll be considered.  People may not use the right tools for the job, either not being aware of the full range (I see this all the time), or just have different ways of working. And folks may not know how to conduct brainstorming and problem-solving processes effectively  (I see this as well).

So, the facilitation role has many opportunities to increase the quality of the outcome.  Helping establish culture, first of all, is really important.  A second role would be to understand and promote the match of tools to need. This requires, by the way, staying on top of the available tools.  Being concrete about learning and problem-solving processes, and  educating them and looking for situations that need facilitation, is another role  Both starting up front and educating folks before these skills are needed are good, and then monitoring for opportunities to tune those skills are valuable.  Finally, developing process facilitation skills,  serving in that role or developing the skills, or both, are critical.

Innovation isn’t an event, it’s a process, and it’s something that I want P&D (Learning & Development 2.0 :) to be supporting. The organization needs it, and who better?

#itashare

Beyond Talent Development

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On Tuesday, with a big presentation, the American Society for Training & Development announced a rebranding. The new name is the Association for Talent Development, going from ASTD to ATD. And while this is a necessary move, I think it wasn’t the best change they could’ve made.

ASTD needed the change, for two reasons. For one, ASTD has membership in, and runs events, around the world. There may be other orgs (e.g. CSTD for Canada), but the 800 lb gorilla is ASTD. Second, training, while still a large proportion of what ASTD does (rightly or wrongly), is increasingly being joined by other approaches such as coaching and mentoring.

The first reason resonates, but I have a problem with the second. To put it another way, I believe that the change to Association makes sense, but Talent Development doesn’t.  As I stated in Revolutionize Learning & Development, I believe that the necessary direction for organizations is to couple optimal performance with continual innovation. What’s required from Learning & Development, then, is to support all manners of performance and develop continual innovation.

What’s involved is not only to support training when it needs to be ‘in the head’, but using performance support when we can. And we need to develop and facilitate organizational innovation. The latter means not only developing individual (and group) ability to interact constructively, but to facilitate useful interactions of all sorts.

And here’s the rub. I see Talent Development as developing people through training, mentoring, and coaching, but I see the potential role for the folks in what now is termed L&D to be not only the development of people’s ability, but their ability to perform, even if it isn’t developing the person. That is, using performance support when it makes sense should be part of the unit’s responsibility, even when it doesn’t develop the person. Similarly, I see facilitating constructive interaction (curating resources, removing barriers to interaction and supporting tool use, etc), whether it develops people or not, as a vital role.

That’s the reason I chose to suggest, in the book, that the unit should be renamed Performance & Development; supporting both optimal execution and continual innovation in all relevant ways. The opportunity is to be the strategic organizational resource to ensure that all the intellectual resources of the organization are contributing.

And that is the reason I have a problem with Talent Development. To me, Talent Development is focused only on developing people instead of facilitating overall organization performance. And I think that’s falling short of the opportunity, and the need. Don’t get me wrong, I laud that ASTD made a change, and I think Talent Development is a good thing. Yet I think that our role can and should be more.  I  wish they’d thought a little broader, and covered all of the potential contributions.  So, maybe, Association of Performance & Development or APD. Regardless, it’s a dynamic organization that offers a lot. I just wonder who’s going to fill the gaps.

Learning Quotient?

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I had the good fortune to be invited to the Future of Work event that was held here in Silicon Valley two  weeks ago, and there were four breakouts, one of which was on learning and knowledge management.  You can guess which one I was on (though tempted by the leadership and culture one; there was overlap).

Within that breakout the activity was to pick four topics and further break out.  The issue was meeting workplace needs given the changing nature of work, and I suggested that perhaps the biggest need was to focus on skills that held true across domain, so called meta-cognitive skills, and learning to learn (a total surprise, right?).  That was one that people were interested in, so that’s what we discussed.

We broke down learning into some component elements.  We talked about how your beliefs about learning (your epistemological stance) mattered, as well as your intention to learn, and how effective you were at learning alone and with others.  It also matters how well you use tools and external representations, as well as your persistence.

What emerged was that learning skills shouldn’t be taken for granted.  And consequently, one of the attendees suggested that perhaps along with IQ and EQ, we should be looking at people’s LQ or learning quotient.  I just saw an advertisement that said EQ (they called it EI Emotional Intelligence, probably to avoid trademark infringement) was better than IQ because you can improve your EQ scores. However, the evidence suggests you can improve your LQ scores too.

A decade ago now, Jay Cross and I were pushing the meta-learning lab, and I still think Jay was right claiming that meta-learning might be your best investment.  So, are you aware of how you learn? Have you improved how you learn?  Can you help others learn more effectively?  I believe the answer is yes, and we not only can, but  should.