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Clark Quinn’s Learnings about Learning

The necessary program?

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Things are moving faster, and careers are supposed to be changing more frequently. What does that mean for university degrees (or other employment preparation)?  Yes, university degrees aren’t necessarily  just employment preparation, but I’m thinking about a degree program that provides a useful preparation for the coming world. And I think it’s got several key components.

For one, anything we do, in working together and in meeting client needs,  must be aligned with how our brains work. Industrial design, interface design, learning design, marketing; increasingly everything  about  our products and services must be producing  experiences.  And, if the prognostications are to be believed, experiences that  transform  us.  Increasingly, organizations will need to work in such ways, and those that understand this will be core. Similarly, increasingly products and services will likewise need to adapt. At core, everything we do revolves around thinking,  and our brains aren’t changing. Understanding cognition is a sustainable value.

A second such areas is understanding information technology. Increasingly, the capability of computational systems to serve as configurable information processing machines is fundamental to society. It’s the perfect cognitive complement, doing well what our brains don’t, and vice-versa.  And while the technology continues to evolve, some core understandings don’t. Computational thinking is focused on breaking down problems into tractable steps, and that’s part of it. And understanding how AI works (e.g. machine learning, symbolic logic, neural nets, etc), and even quantum computing, are tools to solve problems. People need to understand computational technology to complement our cognitive capability, and it’s stable enough to again be a sustainable edge.

Then, the question is, what complements these to provide a solid foundation. I have two answers: one is design (e.g. design thinking), and the other is business. And I had trouble reconciling this until recently.  So, in some sense, design is an  outcome of our cognitive processes, and an application. (To design is human!)  But being systematic about it is a useful integration of the other two. For those who haven’t had experience in business, however, an overall understanding of business is key.  This suggests that a full bachelor’s program would include design  and business, while a master’s program could focus more specifically on the design (assuming some business experience).

Could these be minors on some other area people might want? It might be good to supplement this with specific interests whether bio, art, or what have you.  You do want to support people’s passions. But I’ll suggest that these elements  should be part of all folks preparation for life going forward.  So, what do you think?

 

Chief Cognitive Officer?

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Businesses are composed of core functions, and they optimize them to succeed. In areas like finance, operations, and information technology, they prioritize investments, and look for continual improvement. But, with the shift in the competitive landscape, there‘s a gap that’s being missed. And I‘m wondering if a focus on cognitive science needs to be foregrounded.

In the old days, most people were cogs in the machine. They weren‘t counted on to be thinking, but instead a few were thinking for the many. And those who could do so were selected on that basis. But that world is gone.

Increasingly, anything that can be automated should be automated.   The differentiators for organizations are no longer on the execution of the obvious, but instead the new advantage is the ability to outthink the competition. Innovation is the new watchword.   People are becoming the competitive advantage.

However, most organizations aren‘t working in alignment with this new reality. Despite mantras like ‘human capital management’ or ‘talent development’, too many practices are in play that are contrary to what‘s known about getting the best from people. Outdated views like putting information into the head, squelching discussion, and avoiding mistakes are rife. And the solutions we apply are simplistic.

Ok, so neuroscientist John Medina  says our understanding of the brain is ‘childlike‘.   Regardless, we have considerable empirical evidence and conceptual frameworks that give us excellent advice about things like distributed, situated, and social cognition. We know about our mistakes in reasoning, and approaches to avoid making mistakes. Yet we‘re not seeing these in practice!

What I‘m suggesting is a new focus.   A new area of expertise to complement technology, business nous, financial smarts, and more.   That area is cognitive expertise. Here I’m talking about someone with organizational responsibility, and authority, to work on aligning practices and processes with what‘s known about how we think, work, and learn. A colleague suggested that L&D might make more sense in operations than in HR, but this goes further. And, I suggest, is the natural culmination of that thought.

So I‘m calling for a Chief Cognitive Officer. Someone who‘s responsibility ranges from aligning tools (read: UI/UX) with how we work, through designing continual learning experiences, to leveraging collective intelligence to support innovation and informal learning.   Doing these effectively are all linked to an understanding of how our brains operate, and having it distributed isn‘t working.  The other problem is that not having it coordinated means it‘s idiosyncratic at best.

One problem is that there‘s too little of cognitive awareness anywhere in the organization.  Where does it belong?  The people closest are (or should be) the L&D (P&D) people.  If not, what’s their role going to be?  Someone needs to own this.

Digital transformation is needed, but to do so without understanding the other half of the equation is sort of like using AI on top of bad data; you still get bad outcomes.  It’s time to do better. It’s a radical reorg, but is it a necessary change?  Obviously, I think it is. What do you think?

Skeptical Optimist or Hopeful Cynic? A Science Mindset

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Is there any difference? At core, my new book  is  about learning science. And, as I’ve lamented before, the lack of understanding of cognitive science is a barrier to better L&D. However, it takes a certain mindset to put this into practice in practical ways.

Should you overall be optimistic or cynical?  Applying cognitive science (see what I did there?), I would err on the side of optimism.  Research suggests a positive attitude is overall better.  Thus, I guess I’m arguing for the former ;). The alternative, a cynic still looking for good, is less optimal.

However, optimism tempered with  a healthy skepticism! There are those who’d take advantage of naivete, as has reliably been exposed.  A vigilant evaluation of what’s presented is healthy for dialog and moving the industry forward!

You need to be prepared for the variety of ways people can mislead you (and even themselves).  Without a decent understanding of scientific validity, you might be swayed by statistical sleight-of-hand.  Worst case, you listen to those who carry the standard of rigor in evaluation.  I don’t necessarily mean the scientists, because they don’t always present it in comprehensible ways (writing in their native academese).  Instead, there are those who serve as translators of research to practice.

People like Will Thalheimer, Patti Shank, Julie Dirksen, Guy Wallace, Mirjam Neelen, and more (including yours truly), have boiled down learning science into practical approaches. Whether it’s overviews, processes, or even acronyms, their guidance is soundly based.  We may not always agree, but you’re far better off betting on them than on those with a vested interest.

On your own, of course, you should be conducting several validity checks.  Who’s telling you, and what’s in it for them?  Is their message supported by external validation? Are there alternate views? Does it pass the ‘sniff’ test (that is, does it make a plausible causal story)?  Of course, “if it sounds too good to be true, it probably is”.

In addition to empirical grounds, one should also evaluate the theoretical basis.  Did the work emerge from empirical data, or was it made from someone’s musings, and untested?  Is there a reason to accept the underlying frameworks?

Overall, I suggest that practitioners in learning first need to be grounded in understanding  how we learn. Then, I reckon we need to be rigorous in evaluating new approaches.  There will be wheat amongst the chaff, but the relative ratios are the issue. Make sure you’re finding nuggets, not tailings (I like my metaphors mixed).

 

Busting Myths!

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Myths book coverAs I have hinted, I’ve been working on a project that is related to what learning science has to do with learning design.  And I can finally announce the project!  I’ve been writing a book on debunking learning myths & superstitions, and unpacking some misconceptions. I’m happy to say that it’s finally available for pre-order (ATD members here, Amazon here). It’s myth-smashing time!

The focus here is on workplace learning, as the title suggests. There already has been a book oriented toward the education market, but this one is particularly focused on myths that impact learning & development. The title is Millennials, Goldfish & Other Training Misconceptions:  Debunking Learning Myths and Superstitions.   There are 3 major categories of things addressed:

  • Myths: beliefs that are the source of effort and investment that have been proven to be false.  It’s surprising how many there are, but they persist. I have addressed 16 of them.  I talk about the appeal, the possibilities and problems, how research could answer the question, and what the research says.
  • Superstitions: these are practices that aren’t really advocated, but continue to be observed in practice. And they’re not necessarily the subject of specific research, but instead we can make principled arguments against them. I have documented five of these, with the approaches, the plausible case, and why it’s not accurate.
  • Misconceptions: these are topics that are hotly debated, with typically smart people on both sides, but yet contention remains.  After identifying what both sides are arguing, what I try to point out is what is worth taking away. Or when it’s useful.

In each case,  I identify what you  should be doing.  The point is not to just point out the flaws, but have us using good approaches.  And have a wee bit of fun ;).

This book is very much intended as a tool. It’s to pull out when you have a question, and very specifically when someone wants to push you to do something that’s contrary.  It’s a reference tool that you should have on your shelf for when these questions arise.

While the book won’t be available ’til late April, I can now let you know that it’s already available for pre-order.  In conjunction with ATD, the publisher, we’re finalizing all the aspects.  If you’re not an ATD member, you can also get it here.

I’ll be talking on the topic of myths, covering a limited subset, for Training Mag’s Network in a webinar on April 11 at 9 PT, noon ET.  See you there?

And I’ll be addressing the larger issue of being professional about learning science, including myths, for ATD in a webinar on May 24 at 11AM PT, 2PM ET.

Here’s to busting myths!

 

John Medina #ATDTK Keynote Mindmap

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John Medina of Brain Rules  fame opened the second day of ATD’s TechKnowledge conference. In a rapid-paced and amusing presentation, he went through how we understand others, and can get better.  This was, he hypothesized, the core of talent development: understanding others and helping them improve.

Keynote mindmap

Learning to Learn

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As a response to my post where I offered to ‘listen’, I’ve had several comments giving me topics, and so I thought I should respond.  One asked about meta-learning (learning to learn), in the particular situation of courses with a variety of expertise levels, and getting into issues of learner responsibility.  The author pointed to a presentation on learning to learn, that had a nice framework, and I thought I should elaborate.

The framework mentioned talked about three stages of expertise: apprentice, journeyman (using the traditional term, is there a move to ‘journey person’ or…?), and mastery.  Within these, you watch as an apprentice, practice as a journeyman, and share as a master.  Which isn’t a bad approximation of the whole ‘cognitive apprenticeship‘ approach.

The article misses some nuances, of course (and the author acknowledged this). For instance, in practice, the role of deliberate practice is important, it’s not  just repetition, but the ‘right’ repetition. And my commenter brought up the role of epistemological stance, that the learners need to  own their own learning.

The starting point from the comment, however, was the fact that the audiences being seen varied in background knowledge; some were relative novices, others were experienced.  To me, that calls for a ‘leveling’ approach.  Here, you have preparatory material that you can test out of, otherwise you go through it. This helps ensure that the audience starts the learning experience with a baseline of at least language. You  don’t want to be presenting content in that valuable face-to-face time!

The details involved in making learning experiences work are many. It’s about what to teach, how, how to address audience diversity, and more. It’s about meta-learning for ourselves and our learners.    That’s why I advocate learning about how we learn, the cognitive science that (should) drive how we do what we do. So, who wants to learn?

Developing Decisions

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I mentioned in yesterday’s post that one thing I do in getting objectives is  focus on decisions. And, simple ones will get automated; we can train AI to handle these. What will make the difference between ordinary and extraordinary organizations is the ability to make decisions in this new VUCA environment (volatile, uncertain, complex, and ambiguous). And it made me wonder how you develop the ability to make better choices.

AI can be trained in a couple of ways to answer questions and make these decisions. We can use machine learning to train a system on a historical database (watching out for bias).  We can use semantic analysis to read documents and make a system that can answer questions about them. But such systems are very limited; they can’t handle questions at the periphery of the knowledge well, and fall apart at related areas. Which people are better at,  if their expertise has been developed.

Now, developing this expertise isn’t straightforward.  If there were simple decision trees, we could automate them as above. Instead, what works best is expert models that have been abstracted across dialog and practice. This needs to be augmented with an awareness of adjacent fields. So, for instance, for instructional design, we should have an awareness of interface design, graphic design, media production, etc. So how do we develop this?

We certainly need to develop the expert models we know play a role. But this gets circular with the above unless we find a way to break out of the predictable. I suggested one approach to this with my ‘shades of grey’ post, having groups work together to make categorization choices: is this or is this not legal.  This was, however, more focused on compliance and there’s a much wider situation.

We first need to identify the situations, the relevant models, and the scope of likely variation. We can’t provide specific data (or we’d train the system on it), so we need to anticipate a spread.  And we could just train that, but I want to go further.

I’d want to use such a process to choose situations, and then design group work, for the reasons I identified here. (Resourced with models and examples, of course.)  We want to get learners working together to address complex problems. We want them to use their various understandings to illuminate the underlying models.  If you can get productive discussion (and this needs to be designed in and facilitated), the learners’ thinking will be enriched. (And they may have folks to call on when the situations  do  arise ;).

Collaboration in learning is second best to collaboration in problem-solving. We should do the latter when we can, but we should do the former anyway. For better learning, and for those times when there isn’t the luxury of working with others.

I reckon this would lead to better decision-making ability. What do you think?

Let’s talk

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“Conversations are the stem cells of learning.” – Jay Cross

I recently read something that intrigued me. I couldn’t find it again, so I’ll paraphrase the message.  As context, the author was talking about how someone with a different world view was opining about the views of the author. And his simple message was “if you want to know what I, or an X, thinks, ask me or an X. Don’t ask the anti-X.”  And I think that’s important.  We need to talk together to figure things out. We have to get out of our comfort zone.

It’s all too evident that we seem to be getting  more divisive. And it’s too easy these days to only see stuff that you agree with.  You can choose to only follow channels that are simpatico with your beliefs, and even supposedly unbiased platforms actually filter what you see to keep you happy. Yet, the real way to advance, to learn, is to see opposing sides and work to find a viable resolution.

Innovation depends on creative tension, and we need to continue to innovate.  So we need to continue to engage.  Indeed, my colleague Harold Jarche points to the book  Collaborating with the Enemy  and argues that’s a  good thing.  The point is that when things are really tough, we have to go beyond our boundaries.  And life is getting more complex.

So I keep connections with a few people who don’t think like me, and I try to understand the things that they say. I don’t want to listen just to those who think like me, I recognize that I need to understand their viewpoints if we’re going to make progress.  Of course, I can’t guarantee reciprocity, but I can recognize that’s not my problem.

And I read what academic research has to say. I prefer peer-review to opinion, although I keep an open mind as to the problems with academic research as well. I have published enough, and reviewed many submissions, so I recognize the challenges.  Yet it’s better than the alternative ;).

This is, however, the way we have to be as professionals. We have to understand other viewpoints.  It matters to our world, but even in the small little worlds we inhabit professionally.  We need to talk.  And face to face. It matters, it turns out.  Which may not be a surprise.  Still, getting together with colleagues, attending events, and talking, even disagreeing (civilly) are all necessary.

So please, talk.  Engage.  Let’s figure stuff out and make things better. Please.

 

Reflections on 2017

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The end of the calendar year, although arbitrary, becomes a time for reflection.  I looked back at my calendar to see what I’d done this past year, and it was an interesting review.  Places I’ve been and things I’ve done point to some common themes.  Such are the  nature of reflections.

One of the things I did was speak at a number of events. My messages have been pretty consistent along two core themes: doing learning better, and going beyond the course.  These were both presented at TK17 that started the year, and were reiterated, one or the other, through other ATD and Guild events.

With one exception. For my final ATD event of the year, I spoke on Artificial Intelligence (AI). It was in China, and they’re going big into AI. It’s been a recurrent interest of mine since I was an undergraduate. I’ve been fortunate to experience some seminal moments in the field, and even dabble.  The interest in AI does not seem to be abating.

Another persistent area of interest has been Augmented Reality (AR) and Virtual Reality (VR). I attended an event focused on Realities, and I continue to believe in the learning potential of these approaches. Contextual learning, whether building fake or leveraging real, is a necessary adjunct to our learning.  One AR post of mine even won an award!

My work continues to be both organizational learning, but also higher education. Interestingly, I spoke to an academic audience about the realities of workplace learning!  I also had a strategic engagement with a higher education institution on improving elearning.

I also worked on a couple of projects. One I mentioned last week, a course on better ID.  I’m still proud of the eLearning Manifesto (as you can see in the sidebar ;).  And I continue to want to help people do better using technology to facilitate learning.  I think the Quinnov 8 are a  good way.

All in all, I still believe that pursuing better and broader learning and performance is a worthwhile endeavor. Technology is a lovely complement to our thinking, but we have to do it with an understanding of how our brains work.  My last project from the year is along these lines, but it’s not yet ready to be announced. Stay tuned!