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Clark Quinn’s Learnings about Learning

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The Optimistic View

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In yesterday’s post, I quipped about how L&D wasn’t doing well, and I want to clarify my perspective. Because, you see, I’m an optimist!  I really only complain to create improvement. And so let me wax philosophical about why we can and should be hopeful about L&D.

Thumb upOrganizations need to learn. And they learn as a function of individuals learning, alone and together. This learning can be ineffective or effective; there are skills involved. And yet, assuming these skills isn’t a safe bet.

And this is a big picture: it’s as much about informal learning as it is formal. Or, so should it be. We need to be ensuring our formal learning is working, but learning is a continuum that extends beyond the course. Evidence shows that we learn through social interaction and through our own work experience.  If we only do formal, we’re missing a large part (estimates are around 80%).

And here’s the thing: who in the organization  besides L&D  should  be doing this?  Who else understands how we learn? Ideally, of course, we  do know this, but let’s make that a working assumption (if you want help here, that’s what I do ;).

So, if we take what we know about learning, individual and social, and apply that, what happens? If we share policies and practices around tools that facilitate learning (curation, creation, collaboration), what is the organizational benefit?  We can be facilitating and spreading best principles to optimize and accelerate organizational knowledge.

I want to suggest that facilitating the learning of the organization, particularly in this era of increasing change and competition, is  the key to organizational success.  Thus, L&D has the opportunity to be not peripheral but central!  And that, to me, is not only desirable, but right.

Hence, I’m optimistic.  And I’ll keep pushing us to achieve the potential that’s on the table. Ready to join me?

Tools and Design

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I’ve often complained about how the tools we have make it easy to do bad design. They make it easy to put PPTs and PDFs on the screen and add a quiz. And not that that’s not so, but I decided to look at it from the other direction, and I found that instructive. So here’re some thoughts on tools.

Authoring tools, in general, are oriented on a ‘page’ metaphor; they’re designed to provide a sequence of pages. The pages can contain a variety of media: text, audio, video.  And then there are special pages, the ones where you can interact.  And, of course, these interactions are the critical point for learning. It’s when you have to act, to  do, that you retrieve and apply knowledge, that learning really happens.

What’s critical is  what you do.  If it’s just answering knowledge questions, it’s not so good.  If it’s just ‘click to see more’, it’s pretty bad.  The critical element is being faced with a decision about an action to take, then apply the knowledge to discriminate between the alternatives, and make a decision.  The learner has to commit!  Now, if I’m complaining about the tools making it easy to do bad things, what would be good things?

That was my thinking: what would be ideal for tools to support? I reasoned that the interactions should represent things we do in the real world.  Which, of course, are things like fill in forms, write documents, fill out spreadsheets, film things, make things.  And these are all done through typical interactions like drag, drop, click, and more.

Which made me realize that the tools aren’t the problem!  Well, mostly; click to see more is still problematic.  Deciding between courses of action can be done as just a better multiple choice question, or via any common form of interaction: drag/drop, reorder, image click, etc. Of course, branching scenarios are good too, for so-called soft skills (which are increasingly the important things), but tools are supporting those as well.  The challenge  isn’t inherent in the tool design.  The challenge is in our thinking!

As someone recently commented to me, the problem isn’t the tools, it’s the mindset.  If you’re thinking about information dump and knowledge test, you can do that. If you’re thinking about putting people into place to made decisions like they’ll need to make, you can do that. And, of course, provide supporting materials to be able to make those decisions.

I reckon the tool vendors are still focused on content and a quiz, but the support is there to do learning designs that will really have an impact.  We may have to be a bit creative, but the capability is on tap. It’s up to (all of) us to create design processes that focus on the important aspects.  As I’ve said before, if you get the design right, there are  lots of ways to implement it!

Stay Curious

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One of my ongoing recommendations to people grew out of a toss-off line, playing off an advertisement. Someone asked about a strategy for continuing to learn (if memory serves), and I quipped “stay curious, my friends”.  However, as I ponder it, I think more and more that such an approach is key.

I was thinking of this trend the other day as “intellectual restlessness”. What I’m talking about is being intrigued by things you don’t understand that have persisted or recently crossed your awareness, and pursuing it.  It’s not just saying “how interesting”, but recognizing connections, and pondering how it could change what you do. Even to the point of actually changing!

It also would include pointing interesting things to other people who would benefit.  This doesn’t always have to happen, but in the spirit of cooperation (in the Jarche sense), we could and should contribute, curate, when we can.  And, ideally, leaving trails of your explorations that others can benefit from. Writings, diagrams, videos, what have you, helps yourself as well as others.

Old Infoworld magazinesI was reminiscing that more than 30 years ago, on top of my job designing educational computer games, I was already curious. I still have copies of the magazines containing reviews I did (one hardware, one software), as well as a journal article based upon undergraduate research I was fortunate to participate in.

And that persistence in curiosity has led to a trail of artefacts. You may have come across the books, book chapters, articles, presentations, etc. And, of course, this blog for the past decade and more. (May it continue!) However, I’m not here to tout my wares, but instead to point to the benefit of being curious.

As things change faster, a continuing interest is what provides an ongoing ability to adapt. All the news about the ongoing changes in jobs and work isn’t likely to lessen.  Staying curious benefits you, your colleagues and friends, and I reckon society in general.  You want to look at many sources of information, track tangential fields, and be open to new ideas.

This isn’t just your choice, of course, ideally your organization is supportive. These lateral inputs are a component of innovation, as is time to allow for serendipity and incubation. Orgs that want to be able to be agile will need this capabilities as well. I suppose organizations need to stay curious as well!

 

Habits of Work #wolweek

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It’s Working Out Loud (WOL) Week, and that’s always a valuable time for reflection. It so happens that the past few weeks I’ve been working with an organization, and they were ripe for WOL. The problem was what, specifically, should they do to make this work? They had barriers.  My (off the cuff) recommendations were around creating some habits of work.

For context, they’re a very distributed organization, and have been for decades. They’ve a number of locations spread around over a space of hundreds of miles.  As a consequence,  they’re well-practiced at a variety of distance communication modalities. They have well-equipped video conferencing rooms, social media tool, and of course email.  And yet, their communication is very formal. They’re busy of course, so  while they recognize the benefits of sharing better, it’s hard for them to implement.

There would be  rewards, of course. They have distributed teams supporting the same sorts of actions.  Various job roles do similar work with a variety of stakeholders, and would benefit by sharing best practices, creating communities of practice around those job roles. However, for a variety of reasons, including  ineffective use of the available tools, time pressures, and general lack of awareness and practice, the practices  not in play.

As part of my ‘critical friend‘ role, I made some suggestions, including working out loud. They asked for specific steps they might take. So what’d I recommend? Several things:

  • Narrating their work: they need to find a way to represent their progress on each project, and include a ‘rationale’ that captures the thinking  behind their decisions.
  • Creating communities: they should establish a group (with  whatever tool) for each role, and do some community management around it to generate dialog and learnings.
  • Walking the walk: if the leadership (not of the overall organization, just  the leaders of this learning unit, at least to start ;) practices  the working (and failing) out loud, it would be motivation to others.  It runs better when everyone sees it’s safe to make mistakes as long as you share lessons learned).

This was off the cuff, and I might  have suggested more, but this is a reconstituted list that I think captures some  major necessary areas. It’s about practices that build the culture. These will need support, but they are the core ideas that can drive a move to a more open, sharing workplace. One that leads to continual improvement and innovation.

 

Artificial Intelligence or Intelligence Augmentation

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In one of my networks, a recent conversation has been on Artificial Intelligence (AI) vs Intelligence Augmentation (IA). I’m a fan of both, but my focus is more on the IA side. It triggered some thoughts that I penned to them and thought I’d share here [notes to clarify inserted with square brackets like this]:

As context, I‘m an AI ‘groupie’, and was a grad student at UCSD when Rumelhart and McClelland were coming up with PDP (parallel distributed processing, aka connectionist or neural networks). I personally was a wee bit enamored of genetic algorithms, another form of machine learning (but a bit easier to extract semantics, or maybe just simpler for me to understand ;).

Ed Hutchins was talking about distributed cognition at the same time, and that remains a piece of my thinking about augmenting ourselves. We don‘t do it all in our heads, so what can be in the world and what has to be in the head?  [the IA bit, in the context of Doug Engelbart]

And yes, we were following fuzzy logic too (our school was definitely on the left-coast of AI ;).  Symbolic logic was considered passe‘! Maybe that‘s why Zadeh [progenitor of fuzzy logic] wasn‘t more prominent here (making formal logic probabilistic may have seemed like patching a bad core premise)?  And I managed (by hook and crook, courtesy of Don Norman ;) to attend an elite AI convocation held at an MIT retreat with folks like McCarthy, Dennett, Minsky, Feigenbaum, and other lights of both schools.  (I think Newell were there, but I can‘t state for certain.)  It was groupie heaven!

Similarly, it was the time of emergence of ‘situated cognition‘ too (a contentious debate with proponents like Greeno and even Bill Clancy while old school symbolics like Anderson and Simon argued to the contrary).  Which reminds me of Harnad‘s Symbol Grounding problem, a much meatier objection to real AI than Dreyfuss’ or the Chinese room concerns, in my opinion.

I do believe we ultimately  will achieve machine consciousness, but it‘s much further out than we think. We‘ll have to understand our own consciousness first, and that‘s going to be tough, MRI and other such research not withstanding. And it may mean simulating our cognitive architecture on a sensor equipped processor that must learn through experimentation and feedback as we do. e.g. taking a few years just to learn to speak! (“What would it take to build a baby” was a developmental psych assignment I foolishly attempted ;)

In the meantime, I agree with Roger Schank (I think he was at the retreat too) that most of what we‘re seeing, e.g. Watson, is just fast search, or pattern-learning. It‘s not really intelligent, even if it‘s doing it like we do (the pattern learning). It‘s useful, but it‘s not intelligent.

And, philosophically, I agree with those who have stated that we must own the responsibility to choose what we take on and what we outsource. I‘m all for self-driving vehicles, because the alternative is pretty bad (tho‘ could we do better in driver training or licensing, like in Germany?).  And I do want my doctor augmented by powerful rote operations that surpass our own abilities, and also by checklists and policies and procedures, anything that increases the likelihood of a good diagnosis and prescription.  But I want my human doctor in the loop.  We still haven‘t achieved the integration of separate pattern-matching, and exception handling, that our own cognitive processor provides.

The change is here

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For a number of years now (at least six), I’ve been beating the drum about the need for organizations to be prepared to address change. I’ve argued that things are happening faster, and that organizations are going to have to become more agile.  Now we’re seeing the evidence that the change has arrived.

a change purseTwo recent reports highlight the awareness. Gallup released a report  on The State of the American Workplace recently that talks about the lack of engagement at work.  Deloitte also released a report,  Rewriting the rules in the digital age,  that documents trends shifting the office environment.  With different perspectives, they both overlap in discussing the importance of culture.  It’s about creating an environment where people are empowered and enabled to contribute.

The Gallup  report concludes with new behaviors for leaders and managers.  The first point for leaders is to use data and focus on culture. This, to me, involves leveraging technology and creating an environment. L&D could be leading using performance data captured through the  ExperienceAPI, and facilitating the culture shift in courses and developing coaching. Their prescription for managers is  to move to be coaches (and again, L&D should be both developing the skills and facilitating the processes).  And employees need to take ownership of their own development, which means L&D should focus on both meta-learning and ensuring resources (curation  and creation) as well.

The second report is the more interesting one for me, because it’s about the trends and the ways to adapt.  The top two trends are the Organization of the future (c.f. The Workplace of the Future  :) and Careers and learning.  The former is about redesigning organizations to become agile.  The latter is about a redefinition of learning.  They are a wee bit old-school, however, as while they do discuss innovation throughout, it isn’t a core focus and their definition of learning doesn’t include informal learning.  It’s still a top-down model.  But again, clear opportunities for L&D.

The key leverage points, to me, are learning and technology.  And here I mean more self-directed and collaborative learning conducted not formally, but facilitated. Social learning really can’t be top-down!  Important technologies are for communicating and collaborating, as well as tools to search and find resources.

And while the focus is on HR, including recruitment and leadership, I reckon that L&D should have a key place here, as indicated. The world’s changing, and L&D needs to adapt.  It’s time to innovate L&D to support organizational innovation. Are you ready?

Tackling the tough stuff

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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.

A Cognitive Audit?

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image of brainIn the recent Chief Learning Officer magazine, I wrote an article on the basics of the cognitive science of learning. Given the evidence that “L&D isn’t doing near what it could and should, and what it is doing it is doing badly, other than that it’s fine” (as I say), at least one of the potential barriers is that L&D isn’t truly aware of what science says about their profession.

And I truly believe that if you’re a professional, you should be aware of the fundamental scientific basis of your profession. Pilots need to know aeronautics, physicians need to know physiology, etc. And therefore, I reckon L&D needs to know the cognitive background. But there’s more.

Knowing a suitable level of cognitive science is one thing, using that to assess your practices is another. Too often, we have what we call ‘inert knowledge’: we know it, but we don’t apply it. That’s not helpful. What has to happen is that processes need to be evaluated, improvements identified, interventions prioritized, enablement enacted, and progress reviewed. It’s just part of being a professional!

There are other sorts of audits possible (I know folks who do performance audits, and knowledge audits, etc), but I’m increasingly thinking that the one that matters is the one that aligns with how our brains work. Not at the neural level (there’s little of impact there), but at the cognitive level. Note that cognitive science includes social, conative and affective components (e.g. the culture and motivation), and neural, for that matter ;).

This isn’t an academic exercise. The increasing competition enabled by technology already suggests  that optimal execution is only the cost of entry, and continual innovation will be the only sustainable differentiator. Both are cognitive functions, and the best outcomes will only be achieved when organizations are acting in accordance with how we think, work, and learn. This is about equipping your organization to kick some proverbial tail.

I’m drafting an initial such instrument, with associated recommendations. I welcome your thoughts, and any interest in engaging around this.

Organizational Effectiveness for L&D?

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Last week included an interesting series of events and conversations.  There was a formal event on innovation in learning technology (that was  only partly  so), and a presentation by a colleague. I also had a couple of conversations, one with said colleague following his more formal event, and another with another colleague before the initial event. And from  that latter conversation came an interesting revelation.  The concept  was Organizational Effectiveness, and the question is the relevance to L&D.

Now my colleague in the conversation that preceded the innovation event is wise, with a broad experience across HR.  And I was mentioning that it was hard to see a real sense of urgency in L&D around the problems I can’t help but notice. So, for instance,  I see much elearning that doesn’t reflect serious design.  And similarly, I see too many L&D organizations not looking beyond the course as their responsibility.

My colleague’s  perspective was interesting. He opined that by and large, he saw the need for formal learning shrinking, and that more and more HR was focusing on providing self-learning resources instead of course.  While this doesn’t explain L&D complacency, it certainly would explain the lack of interest in investment in improvement. And while it should drive an interest in a broader performance ecosystem, that was seen as the responsibility from other areas.

In particularly, he talked about OD and OE.  Now, I’ve heard of Organizational Development, but have always seen it to be about change management (rightly or wrongly).  However, OE was new to me. He explained it was Organizational Effectiveness, and that intrigued me.  Effectiveness would certainly include the typical L&D role of learning, but also performance support.  It’s the ‘optimal execution’ side of my call for organizational success. If we remapped OD  to be about continual innovation, and OE to be about optimal execution, we could have a value footing.

Interestingly, I see that the areas covered as components (not the first four, but these:  Decision Making,  Change & Learning,  Group Effectiveness, &  Self-Organizing & Adaptive Systems) seem to be intrinsic to the work I think organizations need (and have been arguing for L&D to take on). The point is, it doesn’t have to be L&D (and given the lack of awareness and urgency, maybe it can’t be). Maybe I need to look more closely at OE (and hope that it’s not just another rebranding).

The focus seems to be on not-for-profits, and achieving outcomes, not redefining them, so perhaps it’s too limited, but somehow I’d like to get a framing that starts generating action.  It’s time to start looking at working smarter and organizational meta-learning, because it appears to me that the need and opportunity are huge.  What are the leverage points?  I welcome any pointers, feedback, ideas, etc.!

 

Collaborating when it matters

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A dear friend and colleague just wrote about his recent (and urgent) chemo and surgery.  I won’t bore you with the details (the odds are you don’t know him), but one thing stuck with me that I do want to share.

As context, he discovered he had a rare and aggressive cancer, and this  ventured into the unknown, with a sense of urgency.   He fortunately had access to arguably the world’s best resources on this, but the ‘rare’ bit means that there wasn’t a lot of data:

“The treatment options were unclear because they didn‘t have enough real data  to know what was most likely to work..I didn‘t know that the lack of data was so profound that intuition and personal experience, not data, would play a central role in the decisions.”

Collaboration was critical.  There were two different domains in play, and they had to work and play well together. An oncologist and a specialist in the location were required to determine a course of action:

“If you‘re ever in a situation like this, having world-class experts is so critical! I could see the mental wheels  turning, the quick parlay  back and forth between the experts, leading to the  suggestion…”

And, interestingly, his voice was an important one:

“Amazing how much the decision seemed to also rest with me, not just with the experts.”

They knew they didn’t know, and they wanted to understand his preferences.  He had a voice, instead of being told what to do. If you don’t know, look for preferences.

This is what decision-making looks like when it matters and it’s new: open collaboration. This also reminds me of Jane Bozarth’s story about her husband’s situation, where again expertise and preparation matter.  The details are not trivial, they’re  critical.

And these situations are increasing. Whether life-threatening or not, and even with the power of data, we’re going to be facing increasingly challenging decisions.  We need to learn when and how to collaborate.  One person following a script (which should be automated) is increasingly less likely to be the answer. An individual equipped with models, and resources including others, is going to be the minimal necessary solution.