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

26 April 2017

Human Learning is Not About to Change Forever

Clark @ 8:09 am

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 isn’t going to transform education. Whether the questions are developed by hand, or by machine, questions are not intrinsically interesting. 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 control firing to control limbs. The issue is 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.

25 April 2017

Workplace of the Future video

Clark @ 8:07 am

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!

18 April 2017

What you learn not as important as how you learn!

Clark @ 8:09 am

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.

28 March 2017

Adaptive or just good design?

Clark @ 8:09 am

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?

15 March 2017

Technology or preparation?

Clark @ 8:10 am

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?

14 March 2017

Microdesign

Clark @ 8:01 am

There’s been a lot of talk about microlearning of late – definitions, calls for clarity, value propositions, etc – and I have to say that I’m afraid some of it (not what I’ve linked to) is a wee bit facile. Or, at least, conceptually unclear.  And I think that’s a problem. This came up again in a recent conversation, and I had a further thought (which of course I have to blog about ;).  It’s about how to do microdesign, that is, how to design micro learning. And it’s not trivial.

VirusSo one of the common views of micro learning is that it’s just in time. That is, if you need to know how to do something, you look it up.  And that’s just fine (as I’ve recently ranted). But it’s not learning. (In short: it’ll help you in the moment, but unless  you design it to support learning, it’s performance support instead).  You can call it Just In Time support, or microsupport,  but properly, it’s not micro learning.

The other notion is a learning that’s distributed over time. And that’s good.  But this takes a bit more thought. Think about it. If we want to systematically develop somebody over time, it’s not just a steady stream of ‘stuff’.  Ideally, it’s designed to optimally get there, minimizing the time taken on the part of the learner, and yet yield reliable improvements.  And this is complex.

In principle, it should be a steady development, that reactivates and extends learners capabilities in systematic ways. So, you still need your design steps, but you have to think about granularity, forgetting, reactivation, and development in a more fine-grained way.  What’s the minimum launch?  Can you do ought but make sure there’s an initial intro, concept, example, and a first practice?  Then, how much do we need to reactivate versus how much do we have to expand the capability in each iteration? How much is enough?  As Will Thalheimer says in his spaced learning report, the amount and duration of spacing depends on the complexity of the task and the frequency with which it’s performed.

When do you provide more practice, versus another example, versus a different model?  What’s the appropriate gap in complexity?  We’ll likely have to make our best guesses and tune, but we have to think consciously about it.  Just chunking up an existing course into smaller bits isn’t taking the decay of memory over time and the gradual expansion of capability. We have to design an experience!

Microlearning is the right thing to do, given our cognitive architecture. Only so much ‘strengthening’ of the links can happen in any one day, so to develop a full new capability will take time. And that means small bits over time makes sense. But choosing the right bits, the right frequency, the right duration, and the right ramp up in complexity, is non-trivial.  So let’s laud the movement, but not delude ourselves either that performance support or a stream of content is learning. Learning, that is systematically changing the reliable behavior of the most complex thing in the known universe, is inherently complex. We should take it seriously, and we can.

7 March 2017

Learning Design Insights

Clark @ 8:06 am

I attended a recent Meetup of the Bay Area Learning Design & Technology, and it led to some insights. As context, this is a group that meets in the evening maybe once or every other month or so.  It’s composed of students or new graduates as well as experienced-practitioners. The topic was Themes from a Hat (topics are polled and then separate discussions are held). I was tapped to host the Learning Design conversation (there were three others: LMS, Measurement, and Social Learning), and that meant that a subset of the group sat in on the discussion. We had four separate discussions for each group, so everyone had a chance to discuss every topic (except us topic hosts ;).

I’d chosen to start with 3 or four questions to prompt discussion:

  • What is good learning design?
  • Are you doing good learning design?
  • What are the barriers to good learning design?
  • What can we do to improve learning design?

In each case, we never got beyond the first question!  However, in the course of the discussions, we ended up talking quite a bit about the others.  I confess that I’m a just a wee bit opinionated and a stickler for conceptual clarity, so I probably spoke too much about important distinctions.  Yet there were also some valuable insights from the group.

First, it was a great group: enthusiastic, with a wide range of experience and backgrounds.  Folks had come into the field from different areas, everything from neuroscience to rabbinical practice!  And there were new students still in a Master’s program, job seekers, and those who were active in work.  Everyone contributed.  While it meant missing #lrnchat, it was worthwhile to have a different experience.  And everyone was kind enough to understood when I had to have my knee up as rehab (thanks!).

The responses to the first question were very interesting: what is good learning design?  While most everyone talked about features of the experience, we also were talking both the outcome and the process.  There even emerged a discussion about what learning was.  I offered  the traditional (behaviorist) description: a change in behavior in the same context, e.g. responding in a different (and presumably better) way.  I also mentioned my usual: learning is action and reflection; instruction is designed action and guided reflection.

One element that appeared in all four groups was ‘engaging’.  Exactly that word. (Only once did I feel compelled to mention that Engaging Learning was the title of my first book! ;)  There were other terms that encompassed it, including ‘experience’, ‘stimulating’, and ‘motivating’.  I was pleased to see the recognition of the value! To define it, discussion several times ranged across things like challenging practice and making it meaningful to learners.

Another element that reoccurred was ‘memorable’. It seemed what was meant was ‘retention’ (over time until needed) rather than the learning experience was worth recalling. This did bring up a discussion of what led to retention and a discussion of spaced learning.  That is, the fact that our brains can only strengthen associations so much in one day before sleep is needed. Slow learning.  Reactivation.

That same discussion came up with another repeated term: micro learning.  There appeared to be little differentiation between different interpretations of that term, so I made distinctions (as one does ;).  People too often use the term micro learning to mean looking something up just when needed (such as a video about how to do something).  And that’s valuable.  Yet it can  lead to successful performance in the moment without any learning (e.g. forgotten shortly thereafter). Which is fine, but it’s not learning! Microlearning might be some very small thing that can be learned right in the moment, but I reckon those are rare. What I really think micro learning could and should be is for spaced learning.  I think that to do that successfully is a non-trivial exercise, by the way.

We covered other topics about design, too.  In at least one group we talked about SME limitations and how to work with them. We also talked about the benefits of collaboration, and knowing your audience. And engaging the audience, making the learning meaningful to them and the organization. Minimalism came up in several different ways as well, not wasting the learner’s time.

One question had arisen in discussion with colleagues, and I took the opportunity in a couple groups to ask about their design practices. The question was how frequent was the process of giving a course demand to a designer and having them work alone from go to whoa.  It varied, but it seemed like there was some of that, there was also a fair bit of both collaboration at least at certain points, and some iterative testing. This was heartening to hear!  Doing  performance consulting and meaningful measurement, however, did appear somewhat challenging.

Overall, there’s an opportunity for some deeper science behind elearning, yet I was very heartened by the enthusiasm and that the design processes weren’t as ‘solitary waterfall’ as I feared. So, who’s up for a deeper learning science workshop?  ;)

 

22 February 2017

Another model for support

Clark @ 8:08 am

I was thinking about today’s post, wherein I was talking about a couple of packages that  might help organizations move forward. I was reflecting back on some previous posts about engagement models, and was reminded of a more recent one. And I realized this has played out in a couple of ways. And these approaches did provide away to  develop the organization’s abilities to develop better learning.  So this is another model for support for developing at least the learning side of the equation.

consulting talesIn a couple of instances , I’ve worked with organizations on a specific project, but in a particular way.  For each, my role was to lead the design. In one case, it was for a series of elearning modules. My role was to develop the initial template that the rest of the content fit.  Note that this isn’t a template for tarting it up, but instead a template about what the necessary elements and details around them were to ensure that the elements (e.g. intro, concept, practice, etc) both fit together and reflected the best learning science. In a more recent instance, it was on a specific focus, but there were several modules that used a similar structure.

What happens, importantly, is that by working collaboratively, we learn together.  Each of these organizations was in the business of developing content, but they were looking to raise their game. So, for instance, through leading the Workplace of the Future project but sharing the thinking behind it, by working out loud in that sense, it’s possible to develop a shared understanding.  And in the latter case, though they’d read the Deeper eLearning series, they got a lot more out of working it through with me.  (And, I’ll suggest, more than also reading the subsequent blog posts I wrote about the project.)

In each case, we created an overall template for the learning, and then detailed what the elements for the template were, and the critical components. When we applied it, usually with me doing it first, and then handing off. It’s really a Cognitive Apprenticeship approach.

So, it’s a slightly more involved approach, with a much more variable scope, but in conjunction with other approaches I’ve mentioned like critiquing content or design processes, it’s one way to get a jump on deeper learning science.  Just trying to think of models that can support improvement, and that’s what I’m trying to push.

 

21 February 2017

Support for moving forward

Clark @ 8:08 am

I have to admit I’ve been a bit surprised to see that movements towards improving elearning and learning strategy  haven’t had more impact. On the learning design side, e.g. the Serious eLearning Manifesto and our Future of Work project, it still seems there’s a focus on content presentation.  And similarly with learning strategy, so despite the Revolution, it doesn’t appear that there’s any big move in L&D to take a bigger perspective.  And my question is: “why not?”

So I’ve been trying to think what might be the barriers to move forward.  What could keep folks from at least taking initial steps?  Maybe folks are making moves, but I haven’t seen much indication.  So naturally I wondered what sort of support could be needed to move forward.

Perhaps it seems too overwhelming?  In the manifesto we did say we don’t expect people taking it all on at once, but we know folks sometimes have trouble breaking it down. Similarly, there’re a lot of components to the full performance ecosystem.  One possibility is that folks don’t know where to start.  I wrote sometime shortly after the manifesto’s release that the best place to start was with practice. And I’ve similarly argued that perhaps the best revolution catalyst is measurement. But maybe that’s too general?

So I wondered if perhaps some specific support would assist.  And so I’ve put together a package for each that’s an initial assessment to identify what’s working, what’s not, and from which to give some initial recommendations.  And I’ve tried to price them so that they’re not too dear, too hard to get approval for, but provide maximum value for minimal investment. Both are based upon the structure of previous successful engagements. (The learning strategy one is a little more because it’s a wee bit more complex. ;)  Both are also based upon frameworks I’ve developed for each:

elearning design is based upon deeper elearning and the leverage points in the design process

elearning strategy is based upon the performance ecosystem model and the implications for developing and delivering solutions.

In each I’m spending time beforehand reviewing materials, and then just two days on site to have some very targeted interviews and meetings.  The process involves talking to representative stakeholders and then working with a core team to work through the possibilities and prioritize them. It also includes an overview of the frameworks for each as a basis for a shared understanding.

The goal is to use an intensive investigation to identify what’s the current status, and the specific leverage points for immediate improvement and longer-term shifts. The output is a recommendation document that documents what’s working and where there are opportunities for improvement and what the likely benefits and costs are.

This isn’t available directly from the Quinnovation site: I’m starting here to talk to those who’ve been tracking the arguments. Maybe that’s the wrong starting point, but I’ve got to start somewhere. I welcome feedback on what else you might expect or want or what would help.

If you’d like to check out the two packages and start moving forward, have a look here and feel free to followup through the contact link.  You’ve got to have the 3 Rs: responsibility, resources, and resolve.  If I can help, glad to hear it.  If not, but there’s something else, let me know.  But I really do want to help move this industry forward, and I’ll continue to try to find ways to make that happen.  I invite you to join me!

16 February 2017

Tackling the tough stuff

Clark @ 8:07 am

tacklingI was reflecting a wee bit on my books (and writings in general), and realized that there’s somewhat of a gap when I talk about games, and mobile, and more.  And it’s not unconscious, but instead principled, even if it arises somewhat implicitly. So I thought I’d talk briefly about why I tend to focus on the design, and not the practical implementations. Briefly, I think the places we fall short are not in executing, but in conceptualizing. And so I focus on tackling what I think is the tough stuff. I think we need to address the things that are more complex. My claim is that if we understand them, we have a better chance of achieving our goals and delivering the necessary outcomes.

I have stated before that I think we can implement most anything we can conceive, the problem is that our conceptions are limited.  So, I talk about design based on knowing how we think, work, and learn. I think we need these foundations if we’re truly going to realign what we do to actually work.  Frankly, I think we’re working under some misapprehensions (read: myths) that are limiting our ability to succeed.

When I talk about thinking, the myth is that it’s all in our head and logically principled.  It turns out that, instead, our thinking is very biased by circumstance and pre-existing beliefs, and we avoid effortful work.  We trust our instincts in far more circumstances than we should!  Similarly, we distribute our thinking across the world: our tools and representations assist us, and yet we don’t focus enough effort on ensuing that those are effectively designed.  There’s a real possibility for a valuable shift here.

My focus in working is to recognize that it’s not as individual as our business processes would assume. The ‘individual innovator’ myth is busted, and the empirical results are that we get better outputs when we work together. Certainly for innovation and creative work. Yet we isolate our work, assigning individual resources.  Similarly, people work best when given meaningful goals, but instead we micromanage too often. Again, there are big opportunities to improve our outcomes by reviewing our approaches.

And on learning, I’ve railed time and again about what’s not working, and been joined by colleagues in opposition.  We learn through designed action and guided reflection, not information dump and knowledge test.  Yet that’s not what we see. And again I suggest only small changes are needed to have a substantial impact.

So, in my books, I don’t talk about so much about how to build a game, or the ways to implement mobile learning, or social learning tools.  These will change. What you want to get your mind around is about our minds. Then you can design solutions that can be implemented in any  number of ways.  I may not be successful at communicating the solutions, but in general when I speak, run workshops, or yes write, people seem to convey that I’ve had some effect on helping them get a handle on these new approaches.  In addition, figuring out how to apply them is why I’m here.

What I’ve been able to do, successfully across years and organizations, is help align processes, products, services, and more with how our brains work.  And then work within the available resources to create solutions that reflect those insights in innovative and yet practical solutions.  It takes time to develop the type of thinking I want organizations to adopt, but it’s doable, and I’ve worked with a number of organizations to do just that. Taking the time to address the tough stuff is a bit of an effort. I think it’s an investment in success.  It’s doable, so the only real open question is whether you’re ready to make a shift in thinking, that leads to a shift in doing, that leads to a better impact for your organization.  And only you can answer that.

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