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Clark Quinn’s Learnings about Learning

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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.

‘Form’ing learning

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Last week I ran a workshop for an online university that is working to improve it’s learning design.  Substantially.  They’re ramping up their staff abilities, and we’d talked about how I could help.  They have ‘content’, but wanted to improve the learning design around this.  While there are a number of steps to take (including how you work with SMEs, the details you attend to  in  your content, etc), their internal vocabulary talks about ‘knowledge checks’ and the goal was to do those better as they migrate existing courses to a new platform with a suite of assessment types.

So, first of all, my focus was on formative evaluation.  If we take activity-based learning seriously, we need to ensure that there are meaningful tasks set that can provide feedback.  They are fans of Make It Stick (mentioned in my Deeper eLearning reading list), so it was easy to help them recognize that good activities require learners retrieve the information  in context, so each formative evaluation should be a situation requiring a decision.

Ok, so not every formative evaluation should be such a situation. But for things that need to be known by rote, I recommend tarted-up ‘drill and kill’. And it became clear, they’re fine at developing standard knowledge checks, it’s the more important ones that needed work.

I started out reviewing the principles, not least because they had a larger audience they wanted to appreciate the background being applied.  Then we moved on to more hands-on work.  First we worked through the different types of assessment types (moving from true/false to more complex assessments like ‘submit and compare’).  We then proceeded to review a first pass to understand the overall course requirements and likely important milestone assessments. We concluded by working through some examples of tough challenges (they’d submitted) and workshopping how to revise them.

There was more behind this, including my understanding more of their context and task, but overall it appeared to develop their understanding of how  to take  formative evaluation and turn it into an opportunity to truly develop learners in ways that will benefit them after the learning experience.

Of course, focusing on decisions was a key component, and we visited and revisited the issues of working with SMEs. This included getting contexts, and how exaggeration is your friend.  The result is that they’re much better equipped to develop ‘knowledge checks’ that go far beyond knowledge, and actually develop skills that are critical to success after graduation.

This is the type of thinking that organizations from K12 through higher ed and workplace learning (whether corporate, not-for-profit, or government) need to adopt if they’re going to move to learning experiences that actually develop meaningful new abilities.  It’s also about good objectives and more, but what the learner actually does, how they are required to use the knowledge, is critical to the outcome. So, are you ready to make learning that works?

Ambiguity Denial Syndrome?

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I was talking with a colleague at an event one of the past weeks, and I noted down the concept of ambiguity denial syndrome. And I’m retrospectively making up what we were talking about, but it’s an interesting idea to me.

FractalSo one of the ways I start out a talk (including later today for a government agency) is to talk about chaos. I use a fractal, and talk about the properties a fractal has.  You know, that it’s a mathematical formulation that paints an image from which patterns emerge, yet at any point you really don’t know where it’s going to go next.

I use this to explain how our old beliefs in an ability to plan, prepare, and execute were somewhat misguided.  What we did was explain away the few times it didn’t work. But as things move faster, the fact that things are not quite as certain as we’d believe means we have to become more agile, because we can less tolerate the mistakes.

The  point I’m making, that the world increasingly requires an ability to deal with ambiguity and unique situations. And our learning designs, and organization designs, and our cultures, need to recognize this. And yet, in so many ways, they don’t.

At the individual level, we’re not equipping folks with the right tools. We should be providing them with models to use to interpret and adapt to situations (explain and predict). Our learning designs should have them dealing with a wide variety and degrees of certainty in  situations.  And we should be testing and refining them, recognizing that learners aren’t as predictable as concrete or steel.  Instead we see one-shot development of information  dumps and knowledge tests, which aren’t going to help organizations.

At the interpersonal level, we should be facilitating people to engage productively, facilitating the development of viable processes for working and learning together. We know that the room is smarter than the smartest person in the room (if we manage the process right), and that  we’ll get the best results when we empower people and support their success. We need them working out loud, communicating and collaborating, to get the best. Instead, we still see top-down hierarchies and solo work.

In short, we see people denying the increasing complexity that the world is showing us.  Implicitly or explicitly, it’s clear that many folks believe that they can, and must, control things, instead of looking to adapt on the fly.  We have new organizational models for this, and yet we’re not even seeing the exploration yet.  I acknowledge that change is hard, and navigating it successfully is a challenge. But we have lots of guidance here too.

Too many processes I see reflect industrial age thinking, and we’re in an information age. We have greater capacity amongst our people, and greater challenges to address, with less tolerance for mistakes.  We need to address, even embrace ambiguity, if we are to thrive. Because we can, and we should.  It’s the only sensible way to move forward in this increasingly complex world. So, are you ready?

Meta-Learning Manifestations

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I recently mentioned that one of my reflections on the past year was that learning to learn, aka meta-learning, is emerging.  And this has come about in several ways recently, and I think it’s a relatively ‘meta’ thing to do ;) to look at the principles across these areas.

So, yesterday I was talking with a colleague about libraries. And one of the things that I noted was that in talking about the future of libraries, I hadn’t discussed a particular role they could and should play.  The reflection was that even in the future role, librarians are more than just the conduits to the information (or people or equipment), but also demonstrating  how they served that role. That is, don’t just show me the results of the search, show me how you thought about the search, and why you chose the search tool you used, and how you created your query, and…

And he assured me that indeed librarians were being taught this. Moreover,  at San Francisco Public Libraries they actually had dual monitors where the staff member could look, but the patron could  also view the activity, and the staff member could work ‘out loud‘.

And this is important.  Because until our schools start doing a better job of this, we’re not going to  be able to assume that our employees and citizens are actually good at learning.  You can only teach meta-learning on top of real goals, and we (should) have those in schools, so it’s the ideal place and arguably the best contribution schools can provide in this rapidly changing environment.

And it’s not like the investments in learning technology are addressing this either.  As I mentioned when I talked about AI for learning, we’re not really seeing the extra layer that will address that (though it’s doable).  As it is, we’re creating adaptive systems that replicate the existing curricula, which would be ok  if our curricula were defensible (hint: it isn’t). Advanced pedagogy can be great, but it is wasted on the existing curricula.

So, there’re are opportunities for learning to learn (which have real benefits) to be enabled across organizational work, library work, schools, and systems.  And we’re really not seeing anywhere near the uptake that would benefit our efforts.

However, we  are seeing more discussion. And I’m imploring you to start thinking about it, talking about it, and beginning to  do it! It’s doable, and  arguably the best investment we could and should be making.  Are you ready?