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

26 January 2017

Silo APIs?

Clark @ 8:06 AM

I was in a conversation with my colleague Charles Jennings about organizational innovation, and one of the topics that arose was that of barriers to successful organizational function. In particular, we were talking about how the division of responsibility between organizational development (OD), leadership development, and learning & development is a problem. And I think the problem is bigger. Separating out functions into silos makes sense in a deterministic world, but that doesn’t characterize our current environment.

Now, separation of functions can be useful. Certainly in software engineering, having application program interfaces (APIs) have led to the ability to connect powerful capabilities.  A program can call a function and get data returned via an API, and the software doesn’t have to care how the function’s carried out.

In the org equivalent we could have a business unit request a course, for example, and L&D responds with said course. In fact, that’s not atypical.  Yet it’s problematic in human terms. The business unit may not have done the due diligence, the performance analysis, that ensures a course is the right solution.

Ok, we could change it: the business unit could indicate the performance problem and L&D could respond. However, again there’s a problem. Without understanding how things are done, L&D’s solution won’t be contextually accurate.  Any intervention won’t reflect how things are done unless interactions occur.

And that’s the point. Any meaningful work – problem-solving, trouble-shooting, improvement, innovation, research, design etc – any learning, is complex. And, done right, they inherently require engagement and interaction.  Moreover, we also know that the best solutions come from creative friction, people interacting.  Communication and collaboration is key!

Engagement between silos works best when you mix members from each.  Or, to put it another way, breaking down the silos is the only way to get the best outputs for the important work, the work that will advance the organization whether removing errors, creating new products or processes, etc.

People are complex (the human brain is arguably the most complex thing in the known universe).  Solutions that tap into that complexity, instead of trying to avoid it, are bound to yield the best insights. We’ve now got a lot of insight into processes that facilitate getting the best outcomes. It’s time to engage with it, to the benefit of the organization.

25 January 2017

Culture or Cultures?

Clark @ 8:02 AM

A twitter pointer led me to an HBR article arguing that We’re Thinking about Organizational Culture all Wrong.  In it, the author argues that it’s fallacious to think that there’s just one organizational culture, , and that all people buy into it.  I agree, and yet where the author leads us is, I think, misleading, or at least not as helpful as it could be.

The argument includes two major thrusts. The first is that the cultural values may be interpreted differently.  What you mean by ‘free’ and what I mean may differ.  Take, for instance, the difference between ‘free beer’ and ‘free speech’ (a classic example).  And this certainly can be the case. The second is that people may comply with the culture even if they don’t agree with it. There are multiple reasons, such as job security, that could support this.

The result, according to the article, is that corporate ‘culture’ isn’t a set of shared values, it’s a “web of power relationships”.  That’s quite a leap, but the point is apt: these relationships can facilitate,  or hinder, individual goals.  However, one statement near the end rings wrong for me:

“Reliance on culture as a way to create unity can mislead those in positions of power into thinking that the core values expressed by the organization are actually uncritically accepted by employees.”.

I agree, but I think it’s simplistic. No one in power should be naive enough to believe that anyone uncritically accepts any values. Instead, the view should be to recognize what core values facilitate the most effective outcomes for the organization, and then follow some well-tested rules about change:

  • sell the vision
  • make it a choice
  • support
  • know how to address the expected problems
  • be prepared to address the unexpected
  • evangelize
  • reward
  • test and tweak
  • persist

It may make sense to start small and spread virally rather than make it an overall change initiative.  Still, I think it’s a worthwhile goal.

There is a clear value proposition about having a culture that supports innovation, and identifiable components.  Abandoning the effort because culture is complex seems a missed opportunity.  The benefits are big. Cultures are developed and do change. Doing so systematically, and systemically, seem to me to be the path to competitive success.  What am I missing?

19 January 2017

Errors and misconceptions

Clark @ 8:08 AM

explosionWhen I was a grad student, my advisor looked a lot at error. HIs particular focus was to prevent it through good interface design.  He characterized them as of two types: slips and mistakes.  Slips are when you have the right intent, but from elements of our architecture end up making the wrong move.  Mistakes are when your intentions are wrong. From the learning perspective, it’s the latter we want to address.

So, I’ve argued that there’s some randomness in our architecture. We’re bad at doing rote things, even when we know what to do.  Athletes, for instance, practice for hours a day, day after day and their competitions are typically decided by who makes the fewest errors.  In fact, we’ve identified and created support tools to minimize these errors, like checklists.  We’re supposed to design systems so they minimize the opportunity for slips. These don’t always work; I was embarassed in my most recent presentation to find a couple of typos. They clearly had come in via auto-correct, but I’d missed them subsequently!  There may well be one in this post, too, some form of slip or another.

More importantly are what are termed ‘mistakes’.  Here we’re starting with the wrong intent.  This, from a learning perspective, is what I term a ‘misconception’.  Here, we’re supposed to be using a particular mental model to guide our performance, but we might not have the right model, or no model and we import a wrong one. Again, we should design to minimize these as well, but we also want to make sure we’ve got the right mental models to begin with.  This happens if we haven’t been given the right one, or activated an irrelevant one inappropriately.

Ideally, we want to identify the most likely ways we go wrong, and then make sure we understand what those are and why. From there, then make them available as options in practice. Then we can remediate them at the moment.  Of course, we’re also supposed to have the right model, and highlight how the model guides performance in examples, and then again through the feedback on both correct  and incorrect performance.

Designing practice that supports making the right decisions in context, and also the opportunity to make the wrong choice and get useful feedback, is a key learning design skill. Quiz questions that just test knowledge aren’t likely to lead to meaningful difference in performance.  Practice where the alternative to the right answer are silly or obvious (unless you really know the model) is a waste of your resources and your learner’s time.  Make practice meaningful. Not doing so is an error, too!

18 January 2017

Cognitive Business

Clark @ 8:07 AM

One of my mantras is that organizations need to align better with how we think, work, and learn.  However, my focus has been specifically on what L&D can be doing (as that’s the folk I mostly talk to). But it occurs to me that it really goes farther.  There are applications of cognitive science (including neuroscience, cognitive psychology, sociology, philosophy, anthropology, etc) to more areas of business than just L&D.  And it’s worth being explicit about this.

I was recently reading how marketing has leveraged understanding of behavior change at a deep level. We need to incorporate this in our learning design, but it should go beyond training and learning and be involved in helping people understand why their work is important and how they contribute.

And similarly, the notion that our thinking is both situated (e.g. reconstructed in the moment, not formally abstract) and distributed (across representations, not all in the head) has broader implications. It’s not just about performance support, but should influence policies and tools as well.

And the fact that innovation is social, and an outcome of slow percolation, influences more than facilitating communication and collaboration. It should influence corporate culture and expectations and time frames.

The list goes on: research says that organizational change works better starting small and scaling rather than a monolithic effort.  We know that design processes are better when they’re cyclical rather than waterfalls. We’ve discovered that our inability to perform rote tasks flawlessly argues for changes in work processes and expectation. And we’ve found out that treating people fairly leads to better outcomes including retention, loyalty, and more.  Ultimately, we’ll want to be making smart cyborg choices about what to have people do and what  technology should do.

In short, we can be working smarter in many ways.  It’s hard to change from the old hierarchical models, but we’re continually learning that other approaches work better.  Heck, we may even be able to start working wiser!  Here’s hoping.

12 January 2017

Rahaf Harfoush #ATDTK Keynote Mindmap

Clark @ 9:07 AM

Rahaf Haroush opened the second day of the 2017 ATD TechKnowledge conference. She made clear some important points about the potential for technology.  For instance she made the case for context-sensitive performance support, social network analysis, and a learning culture. An interesting point was that existing business practices were developed in times of data scarcity. She closed by advocating experimentation, evolution, and alignment with values.  A very nice support for the revolution ;).

Harfoush Keynote Mindmap

11 January 2017

Mick Ebeling #ATDTK Keynote Mindmap

Clark @ 9:20 AM

Mick Ebeling, of Not Impossible Labs, opened the TechKnowledge conference with an inspiring keynote. He told engaging stories about achieving the impossible because it just took commitment. He evangelized contributing, and getting contributions by emphasizing the brand benefits of doing good.

Ebeling Keynote Mindmap

A Cognitive Audit?

Clark @ 8:01 AM

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.

10 January 2017

Vale Seymour Papert

Clark @ 8:03 AM

Imagine my surprise that I missed the demise of Seymour Papert this past year (yet another loss). I’ve looked back to see what I was doing on 31 July and how I missed it, and we were preparing for a week in the wilderness.  So it’s certainly likely I wasn’t deeply involved in the news.  This is a shame, because I’ve been a fan of Papert’s work for quite literally decades.  So here’s a belated tribute.

My first job out of college was designing and programming educational computer games.  I’d been exposed to some innovative thinking through my undergraduate thesis advisors, Hugh Mehan and Jim Levin.  Having read Papert’s Mindstorms, I gave it to my parents to help them understand why I did what I did (unsuccessfully ;).  The book is subtitled “Children, Computers, and Powerful Ideas”, and argued that learning computing was a vehicle for learning to think.

Papert had studied with Jean Piaget, and proceeded to be a leader of the constructivism movement applying the notion of exploratory learning environments. I subsequently learned about Piaget (and post-Piaget, and Vygotsky) in my graduate studies, so I can see how the notion of developmental readiness and opportunities to create understanding through exploration could lead to the work Papert did.

Logo, the computer language for learning, was developed by Papert along with Wallace Feurzieg. It’s simple commands controlling a ‘turtle’ and gradually getting richer play challenges was the start to computer understanding for learners for decades, and has influenced computer language learning in many ways. Apple’s  Playgrounds uses similar small steps to control a creature to start teaching Swift.

He was invited to co-lead the MIT AI lab with Marvin Minsky.  He worked with Minsky on Perceptrons, which were an early exploration of the connectionist networks now so prevalent in artificial intelligence. There remains a controversy over whether and how the book influenced research in the area of symbolic and sub-symbolic intelligence approaches.

Papert was instrumental in much of the thinking that has shaped what we do in learning technology.  I’m grateful for his contributions.

5 January 2017

Mobile Lesson

Clark @ 8:04 AM

Designing mLearning bookI’m preparing my keynote for a mobile conference, and it’s caused an interesting reflection.  My mlearning books came out in 2011, and subsequently I’ve written on the revolution.  And I’ve been speaking on both of late, but in some ways the persistent interest in mobile intrigues me.

While my services are pushing the better design of and the bigger picture of elearning, mobile isn’t going away. My trip to China to keynote this past year was on mlearning (and one the year before), and now again I’m talking on the topic.  What does this mean?

As I wrote before, China is much bigger into mobile than we are. It’s likely because we had more ubiquity of internet access and computers, but they’re also a highly mobile populace.  And it makes sense that they’re showing a continuing interest. In fact, they specifically asked for a presentation that was advanced, not my usual introduction.

I’m also going to be presenting on more advanced thinking to the audience coming up, because the entire focus of the event is mlearning and I infer that they’re already up on the basics.  The focus in my books was to get people thinking differently about mobile (because it’s not about courses on a phone), but certainly that was understood in China. I think it’s also understood by most of the developers. I’m less certain about the elearning field (corporate and education), at least not yet.

In many ways, mobile was a catalyst for the revolution.  I think of mlearning as much more than courses, and my models focused on performance support and social more than formal learning. That is really one of the two-fold focuses on the revolution (the “L&D isn’t doing near what it could and should”; to complement the “and what it is doing, it is doing badly” :).  In that way, these devices can be a wedge in the door for a broader focus.

Yet mobile is just a platform for enabling the type of experiences, the types of cognitive support, as any other platform  from conversation to artificial intelligence.  It is an important one, however, with the unique properties of doing things whenever & wherever you are and doing things because of when and where you are.

So I get that mlearning is of interest because of the ubiquity, but the thinking that goes into mobile really goes beyond mobile.  It’s about aligning with us, supporting our needs to communicate and collaborate.  That’s still a need, a useful message, and an opportunity.  Are you mobilizing?


3 January 2017

Socio-cultural engineering?

Clark @ 8:06 AM

Creating a Learning Culture bookLet’s not start off the new year being trepidatious, shall we?  Ok, social engineering and cultural engineering have bad connotations in a number of ways.  Yet, if I can talk about learning engineering, the desirable properties of cultures for learning, and moves in that direction, aren’t we really talking about socio-cultural engineering?  Can sense be made?

To start with, let me posit that there’s fairly good convergence on the elements that contribute to an effective ‘learning culture’: there needs to be purpose, explicit description and development of skills, tapping into diversity, making it safe to share, responsibility, and more.  The point is that we know what makes environments where the best ideas are generated, developed, and put into practice.

The second thing we know, with less certainty but growing awareness, is how to get there.  It’s a ground game: being clear, working hard, walking the walk.  It’s not easy, as the stories of when the committed leaders moves (or is moved) on and subsequent regression bear out.  Yet it can be, and has been, done.

So, if  we’re choosing cultural values, and working towards them, both in ways that reflect what science tells us about doing our best, aren’t we really doing such engineering? Yes, social engineering also refers to another means for breaking security systems. And cultural engineering has various legacy implications including ‘culture’ (read: theatre, music, etc) and even misguided political movements in the past.  Maybe we need a better term, but I think the concept of moving in a positive environmental direction is something to be considered systematically.

The open question is, does this make sense at a societal level as well?  Ok, not going there.  But regardless, I reckon that there’s a strong link between learning and the organizational culture. Organizational Development, I guess, is the field that does this, though they seem to not focus on actual skills as much as facilitation. That’s not a bad start, but perhaps there’s an opportunity to break down silos here. Getting these elements aligned.  Which, of course, is an organizational change.  Pondering, and I welcome your thoughts.

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