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Clark Quinn’s Learnings about Learning

Radical Coherency

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Tied to my last post about insufficient approaches, I was thinking again about the Coherent Organization  . Coherency is powerful, but it could be a limiting metaphor.   So I want to explore it a bit further.

First, coherency is powerful.   Lasers, for example, are just light, the same as comes from your lightbulbs. Except that the wavelengths are aligned and focused. When they‘re at the same frequency, in the same direction, suddenly you can cut steel!

However, an easy interpretation is that you get this right, and it‘s then sufficient. But that‘s no longer sufficient in organizations. As things change, you need coherency and agility. How do you get both?

I‘m suggesting that coherency has to be on many dimensions.   So you have coherency with the organization‘s purpose, but people are coherent with each other, and with the customers, and with best principles.   And that latter is important, as best practices won‘t transfer unless they‘re abstracted and recontextualized.

So what I‘m arguing for is a more radical coherency, a coherency that‘s in synchrony in an ecosystem perspective. Where people are communicating and collaborating in ways that apply best principles in an way that integrates them into an aligned whole that‘s greater than the sum of the parts.

This is a learning organization, but one that‘s integrating many disparate elements. That, I think, is a desirable and achievable goal, but it’s more than one program. It’s a campaign that needs an initial focus, and a plan to successfully integrate it into practice first, and then to scale it to both shift practice and culture. It’s non-trivial, but I think it’s more than worthwhile: it’s necessary. What do you think?

Simple Insufficiency

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As things get more complex, organizations are looking to get more agile. And they‘re looking at a wide variety of approaches in different areas. It can be agile, digital transformation, design thinking, and more. And, by and large, these are all good things. And all of them are quite simply insufficient. Why do I suggest this insufficiency? Because the solution is complex.

Organizations are complex organisms. If you try to address them with simple solutions, you will perturb them, but the results will not be as expected. Whether you believe the 70% failure rate of org change initiatives, the fact is that many or most organizational change initiatives don‘t achieve the desired outcomes. As we explore this more, we understand that it requires a ‘ground war, not an air war‘ as Sutton & Rao put it in Scaling Up Excellence. And I‘ll posit that there‘s more.

This isn‘t unknown; regardless of label, the folks who are responsible for such initiatives typically argue that that it‘s a process. Yet orgs still look for the simple packaged program that will turn things around. And while it‘s understandable, it‘s decreasingly likely to work. It takes a system approach.

And what I haven‘t seen, and I‘m willing to hear of one, is a comprehensive program that addresses the full suite of skills and culture together that constitute a coherent organization. And that‘s a non-trivial compendium of elements. There are the cultural elements, and skills, and tools, and more. PKM, WoL (SyW), 70:20:10, teaming, collaborating and communicating, etc, are all elements, but they need to be tied together.

My point, I guess, is that there needs to be an entry point, but also a plan to develop the full suite of skills and move the culture. And, like most meta-learning, it needs to be done around something. So you need a concrete focus to start, some problem you‘re working on that you‘ll do in the new way, and practice the processes and develop the competencies and culture as you go. For the org, it should be a necessary new extension to the organization‘s competencies. For L&D, it should be first applied for some L&D project.

In both cases it needs a plan and support for acquisition. And include a realistic time frame for starting, and then spreading. It‘s not simple, but it‘s necessary. Anything else, I fear, is truly insufficient.

Two good books on learning

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In addition to the existing good books out there (Julie Dirksen’s Design for How People Learn, Patti Schank’s new series, e.g. her book on Practice & Feedback, &  Brown, et al’s Make it Stick), I was pointed to two others. One I’d heard about but hadn’t gotten to yet, the other was new to me. And now that I’ve finished them, both are worth recommending and adding to your reading list.

Benedict Carey’s  How We Learn  is an accessible overview of the science of learning. As a journalist (not a scientist), he documents his own unlikely journey as a learner, and how that matches up with what’s known. His idiosyncratic study habits, he discovers, are actually not that far off from what really does work for learning (as opposed to passing tests, and that’s an important distinction).  He includes practical implications and maintains a motivating style to help others to put the practice advice to work. His point, it’s what you do as much as how.

This is a book to give to learners to help them understand themselves as learners. The colloquial style and personal anecdotes make the messages comprehensible and relevant. The book includes a full suite of advice about how to learn best.  While it may be hard to convince learners to read a book on learning, this may well be the most valuable investment they can make.

On the other hand, Anders Ericsson’s Peak is very much the translated (co-authored by Robert Pool, a journalist) science book. It’s full of revelations, but laid out with scientific experiments to complement a very thorough set of case studies. What it does beautifully is unmask the myth of ‘native talent’ and unpack the details that lead to expertise. And those details, specifically are about  deliberate practice.  

Most importantly, in my mind, is the summary that points out that our focus should not be so much on expert performance but instead on helping so many achieve meaningful levels of ability that they’ve been turned off to by bad stories.  Too often people will say “I can’t do math” and instead such abilities can be developed wonderfully. This book, while relevant to individuals, has much more insight to provide for learning designers.  It separates out  why you want models like  activity-based learning.  And why what we do too often in classrooms and online aren’t helpful.

I’d put these near the top of my recommended reading lists.

So I was, at least partly, wrong

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A number of years ago, I wrote that pre-testing learners was user abusive (with a caveat). My argument was sensible, qualified, but apparently wrong. Now that I’ve more of the story, it’s time to rectify my mistake. Of course, there are still remaining questions ;).

My claim was that while pre-testing might have some small benefit, forcing users to test on things they don’t know isn’t nice.  Moreover, I attributed that benefit to activating relevant material, and suggested that there were more humane ways to do it. However, if the pre-test could show that learners  did know it, and so be able to skip it, it’d be worthwhile.

However, research has now shown more benefits to pre-testing. That is, causing learners to search for information they don’t have somehow makes the memory traces more susceptible to successful learning subsequently.  Without a full neurological explanation, it appears that the activation goes deeper than just associative awakening. It also appears to be for more than just memory, but actual performance.

This, then, argues that pre-testing  is a good thing. Now, I haven’t been able to find a comparison where this pre-testing was compared to a compelling story or question that didn’t require an actual response. Still, I’m willing to believe that the actual requirement for search in a test is more powerful than mere related stories.

And this also makes the case stronger, in my mind, for problem-based learning. That is, if you’re faced with a problem you don’t know the answer to (and it’s a comprehensive question representing the overall learning goal), both the need to look for the answer and (ideally) a compelling story in which it’s important make a good case for the learning to be more effective.

Which doesn’t mean I don’t still feel it’s abusive, but it’s in a good cause.  And it still could be that the learner doesn’t actually have to take a ‘test’, but instead in some less formal way is asked to retrieve the answer.  And it might not.

Regardless, I feel obligated to change my opinion when data contravenes, even in part, a story I previously believed. And it doesn’t even hurt much ;).  Here’s to good design!

Mundanities

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This post is late, as my life has been a little less reflective, and a little more filled with some mundane issues.  There’re some changes here around the Quinnstitute, and they take bandwidth.  For a small update on these mundanities with some lessons:

standing deskFirst, I moved office from the side of the house back to the front. My son had occupied it, but he’s settled into an apartment for college, and I prefer the view out to the street (to keep an eye on the neighborhood). Of course, this entailed some changes:

My ergonomic chair stopped working, and it took several days to a) find out someone who’d repair it, b) get it there, wait for it to get fixed, and get it back.  It’s worth it (a lot less than replacing) and ergonomics is important.

Speaking of which, I also now could get a standup desk, or in my case one of those convertible desks that lets you raise and lower your workspace. I’ve been wanting one since the research has come out on the problems with sitting.  We’d previously constructed a custom desktop (with legs from Ikea!), for the odd shaped room, so it was desirable to just put it on top. So far, so good. Strongly recommended.

Also bought a used bookshelf (rather than move the one from the old office).  Real wood, real heavy.  Used those ‘forearm forklift’ straps to get it in. They work!  And, this being earthquake country, had to strap it to the wall. Still to come: filling with books.

At the same time, fed up with  all  the companies that provide internet and cable television, we decided to change. (We changed mobile providers back in January.)  As I noted previously, companies use policies to their advantage. One of the approaches is that they sell you a two year package, but then there’s no notification that the time’s up and the rate jumps up. And you can’t find just a low rate provider (I don’t even mind if it’s higher than the bonus deal). Everyone uses this practice. Sigh.

As I said, I can’t find anyone better, but just decided to change. That involved conversations, and research, and installation time, and turning off the old systems.  At least we’re getting a) a lower rate, b) nicer DVR, and c) faster internet.  For the time being. While the new provider promised to ping me before the plan runs out, the old provider says they can’t. See what I mean?  Regardless, I’ve got a trigger before it expires to sign up anew. Or change again.  That’s the lesson on this one.

And of course there are some conversations about some upcoming presentations. I was away last week presenting, and have one coming up next month (ATD China Summit, if you’re near Shanghai say hello) and several in November at AECT  in Jacksonville.  You’ve seen some of the AI reflections, more likely to come on the new topics.

And there’s been some background work. Reading a couple of books, and working on two projects. Stay tuned for a couple of new things early next year.

The lesson, of course, is trying to find time to reflect while you’re executing on mundanities is more challenging, but still a valuable investment.  I fight to make time, I hope you do too!

Organizational terms

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Listening to a talk last week led me to ponder the different terms for what it is I lobby for.  The goal is to make organizations accomplish their goals, and to continue to be able to do so.  In the course of my inquiry, I explored and uncovered several different ‘organizational’ terms.  I thought I should lay them out here for my (and your) thoughts.

For one, it seemed to be about organizational  effectiveness. That is, the goal is to make organizations not just efficient, but capable of optimal levels of performance.  When you look at the Wikipedia definition, you find that they’re about “achieving the outcomes the organization intends to produce”.  They do this through alignment, increasing tradeoffs, and facilitating capacity building.  The definition also discusses improvements in decision making, learning, group work, and tapping into the strictures of self-organizing and adaptive systems, all of which sound right.

Interesting, most of the discussion seems to focus on not-for-profit organizations. While I agree on their importance, and have done considerable work with such organizations, I guess I’d like to see a broader focus. Also, and this is purely my subjective opinion, the newer thoughts seem grafted on, and the core still seems to be about producing good numbers. Any time you use the phrase ‘human capital’, I am leery.

Organizational engineering is a phrase that popped to mind (similar to learning engineering). Here, Wikipedia defines it as an offshoot of org development, with a focus on information processing. And, coming from cognitive psychology, that sounds good, with a caveat.  The reality is, we’re flawed as ideal thinkers. And in the definition it also talks about ‘styles’, which are a problem all on their own. Overall, this appears to be more a proprietary suite of approaches under a label. While it uses nice sounding terms, the reality (again, my inferences here) is that it may be designed for an audience that doesn’t exist.

The final candidate is organizational development. Here the definition touts “implementing effective change”. The field is defined as interdisciplinary and drawing on psych, sociology, and more.  In addition to systems thinking and and decision-making, there’s an emphasis on organizational learning and on coaching, so it appears more human-focused. The core values also talk about human beings being valued for themselves, not as resources, and looking at the complex picture.  Overall this approach resonates with me more, not just philosophically, but pragmatically.

As I look at what’s emerging from the scientific study of people and organizations, as summed up in a variety of books I’ve touted here, there are some very clear  lessons. For, one, people respond when you treat the as meaningful parts of a worthwhile endeavor. When you value people’s input and trust them to apply their talents to the goals, things get done. Caring enough to develop them in ways that are supportive, not punitive, and not just your goals but theirs’ too, retains their interest and commitment. And when you provide them with an environment to succeed and improve, you get the best organizational outcomes.

There’s more about how to get started.  Small steps, such as working in a small group (*cough* L&D? *cough* ;), and developing the practices and the infrastructure, then spreading, has been shown to be better than a top-down initiative. Experimenting and reviewing the outcomes, and continually tweaking likewise.  Ensuring that it’s coaching, not ‘managing’ (managers are the primary reason people leave companies).  Etc.

All this shouldn’t be a surprise, but it’s not trivial to do but takes persistence.  And, it flies in the face of much of management and HR practices.  I don’t really care what we label it, I just want to find a way to talk about things that makes it easy for people to know what I’m talking about.  There are goals to achieve, so my main question is how do we get there?  Anyone want to get started?

Transparency

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I believe that transparency is a good thing. It builds trust, as it makes it hard to hide things.  And trust is important. So, in the spirit of transparency, it occurred to me to share a little bit about me and this blog. Here I lay out who I am, why I write it, and what I write about.

You can find out more via the ‘about Clark Quinn’ link in the right column, but in brief, I saw the connection between computing and learning as an undergraduate, and it’s been my career ever since. It’s not just my vocation, but it’s my avocation: I  enjoy exploring cognition and technology. And while I’ve done the science and track it, what I revel in (and have demonstrable capability for), is applying cognitive and learning science to create new approaches and fine-tune existing ones.  Learning engineering, if you will.

And, for a variety of reasons, I do this as a consultant. I make my living providing strategic guidance for clients.  I speak at events, and write books, but my main income is from consulting. Which means you  should hire me.  I assist organizations to improve their processes and products, both tactically and strategically. My clients have been happy, and find it’s good value. What you get are unique ideas that are practical and yet effective. Ideas you aren’t likely to have come up with, but are valuable. I really do Quinnovate! Check out the Quinnovation site for more.    Of course, I do have to live in the real world, and so I need to find ways to do this that are mutually beneficial.

Yet generating business isn’t why I write this blog.  I started writing this blog as an experiment and originally tried to write 5 days a week (but was happy if that ended up being 2-3 times a week).  My commitment now is 2 per week (which rarely yields 1 or 3).  And I haven’t monetized it: there’s no advertising, and while I occasionally talk about where I’m speaking or the like, I haven’t used this as a way to sell things. Hopefully that can continue.

So, the reason I write is to think ‘out loud’.  It’s largely for me: it makes me think. I’m just always curious!  I’ve previously recounted the story about how I was on a panel answering questions from the audience, and one of my fellow panelists commented that I had an answer for everything. And the reason is in the ongoing attempt to populate the blog, I’ve looked at lots of things. As my client engagements have been in many different areas, I also have wide-ranging experience to draw upon.  And I just naturally reflect, but getting concrete: diagramming and/or writing, provides additional benefits.

Thus, the process of continually writing (for over 10 years now) means I’m looking at lots of things, reflecting on them, and sharing my thoughts. I also make a point to look at related fields, and look for connections. I also look at what’s happening with technology. In general, I look with a critical eye, as I was trained as a scientist.  I think that’s valuable as well, because there still is a lot of nonsense trotted out, and there’s always some new buzzword that’s being loosely tossed about. Blogging’s given me cause to continue to tune my thinking, and at least some folks have commented that they’ve found it useful.

Mostly I write about things related to technology, learning, and individual and organizational implications. It includes diversions to innovation, design, wisdom, performance support, and the like, because they’ve implications for practice. In many ways I see approaches that aren’t well aligned with how we think, work, and learn, and that strikes me as both a shame, and an opportunity to improve. And that’s what I enjoy, finding ways to improve what we do.

So that’s it: I blog to facilitate my understanding, because cognitive science and technology  is my passion. It isn’t a direct business move.  I do need to make a living, and prefer to do it in the area of my passion, and fortunately have been successful so far.  (Which isn’t to say you shouldn’t find a reason to use me, there are never enough opportunities to assist in improvement, and I’m not a sales person ;).  And yes, this life is a learning experience all in itself!  I hope this is clear, but in the interests of transparency I welcome your inquiries and comments. Stay curious, my friends.

 

Patty McCord Litmos Keynote Mindmap

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Patty McCord, famous for the Netflix Culture Deck, spoke on culture. She talked about sharing the stage with sports coaching legends, and how they were personal but focused. Her stories of the early days of Netflix and how they made tough but fair decisions were peppered with important lessons.

Keynote mindmap

Mark Kelly C3 Keynote Mindmap

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Astronaut Mark Kelly gave a warm, funny, and inspiring talk.  He used stories from his youth, learning to fly, becoming an astronaut, and being husband to Gabby Gifford to emphasize key success factors.

(I confess that owing to his style of elocution, punctuating stories with very pithy comments, I may have missed a point or two at the beginning until I picked up on it.)

 

AI Reflections

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Last night I attended a session on “Our Relationship with AI” sponsored by the Computer History Museum and the Partnership on AI. In a panel format, noted journalist John Markoff moderated Apple‘s Tom Gruber, AAAI President Subbarao Kambhampati, and IBM Distinguished Research Scientist Francesca Rossi. The overarching theme was:  how are technologists, engineers, and organizations designing AI tools that enable people and devices to understand and work with each other?

It was an interesting session, with the conversation ranging from what AI is, to what it could and should be used for, and how to develop it in appropriate ways. Addresses were concerns about AI’s capability, roles, and potential misuses.  Here I’m presenting just a couple of thoughts triggered, as I’ve previously riffed on IA (Intelligence Augmentation) and Ethics.

One of the questions that arose was whether AI is engineering or science. The answer, of course, is both. There’s ongoing research on how to get AI to do meaningful things, which is the science part. Here we might see AI that can learn to play video games.  Applying what’s currently known to solve problems is the engineering part, like making chatbots that can answer customer service questions.

On a related note was what  can AI do.  Put very simply, the proposal was that AI could do what you can make a judgment on in a second. So, whether what you see is a face, or whether a claim is likely to be fraudulent.  If you can provide a good (large) training set that says ‘here’s the input, and this is what the output should be’, you can train a system to do it.  Or, in a well-defined domain, you can say ‘here are the logical rules for how to proceed’, and build that system.

The ability to do these tasks, was another point, is what leads to fear. “Wow, they can be better than me at this task, how soon will they be better than me on many tasks?”  The important point made is that these systems can’t generalize beyond their data or rules.  They can’t say: ‘oh I played this video driving game so now I can drive a car’.

Which means that the goal of artificial  general intelligence, that is, a system that can learn and reason about the real world, is still an unknown distance away.  It would either have to have a full set of  knowledge about the world,  or you’d have to have both the capacity and the experience that a human learns from (starting as a baby).  Neither approach has demonstrated any approach of being close.

A side issue was that of the datasets.  It turns out that datasets can have or learn implicit biases. A case study was mentioned how Asian faces triggered ‘blinking’ warnings, owing to the typical eye shape. And this was from an Asian company!  Similarly, word recognition ended up biasing woman towards associations with kitchens and homes, compared to men.  This raises a big issue when it comes to making decisions: could loan-offerings, fraud-detection, or other applications of machine learning inherit bias from datasets?  And if so, how do we address it?

Similarly, one issue was that of trust. When do we trust an AI algorithm?  One suggestions was that it would come through experience (repeatedly seeing benevolent decisions or support).  Which wouldn’t be that unusual. We might also employ techniques that work with humans: authority of the providers, credentials, testimonials, etc. One of my concerns then was could that be misleading: we trust one algorithm, and then transfer that trust (inappropriately) to another?  That wouldn’t be  unknown in human behavior either.  Do we need a whole new set of behaviors around NPCs? (Non Player Characters, a reference to game agents that are programmed, not people.)

One analogy that was raised was to the industrial age. We started replacing people with machines. Did that mean a whole bunch of people were suddenly out of work?  Or did that mean new jobs emerged to be filled?  Or, since we’re now doing human-type tasks, will there be less tasks overall? And if so, what do we do about it?  It clearly should be a conscious decision.

It’s clear that there are business benefits to AI. The real question, and this isn’t unique to AI but happens with all technologies, is how we decide to incorporate the opportunities into our systems. So, what do you think are the issues?