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Clark Quinn’s Learnings about Learning

Mental models are the agents of learning

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I was talking with my friend and colleague Harold Jarche about how he’s expanding his valuable Personal Knowledge Mastery to teams and at the organizational level. Walking through his diagram, what is critical, what is being exchanged, are mental models. And I thought this an interesting insight.

I’ve talked before about mental models, and they’re important for learning. What they do is provide a causal basis for understanding what happened, and predicting what  will happen.  And that’s important. From such models, we can therefore evaluate different options, and choose the one that has the best outcome.  They may not help in new areas, but they give us a basis for new and unique combinations of circumstances.

So, individually, we make decisions based upon models. In fact, our brains build models to explain the world. (Important for instruction to provide good ones.)  And so when we experiment and reflect we may try to capture these models. Senge, in The Learning Organization talked about mental models as one of his 5 disciplines.

When we’re brought in on a team as a complementary set of knowledge and skills to solve a problem, we’re coming in with our models. In the ‘coherent organization‘ model, these have been developed through our community of practice, and are brought to bear on challenges we’re addressing. Results are shared back, particularly new insights. Similarly, our communities should be tracking others for models to appropriate and adapt.

Thus, the mental tools we use in this new age of information and innovation are conceptual causal models. We need tools to capture, represent, and share these models. And most importantly, I reckon, we need to understand the nature of these models to facilitate us taking the best advantage of them.

Our models may be exchanged, but they’re not transactional. Like a smile, we can give the away and still have them. But we can, and should, continue to acquire and develop them. Models are our value to our field and organizations.

Plagiarism and ethics

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I recently wrote on the ethics of L&D, and I note that I  didn’t  address one issue. Yet, it’s very clear that it’s still a problem. In short, I’m talking about plagiarism and attribution.  And it needs to be said.

In that article, I  did say:

That means we practice ethically, and responsibly. We want to collectively determine what that means, and act accordingly. And we must call out bad practices or beliefs.

So let me talk about one bad practice: taking or using other people’s stuff without attribution.  Most of the speakers I know can cite instances when they’ve seen their ideas (diagrams, quotes, etc) put up by others without pointing back to them.  There’s a distinction between citing something many people are talking about (innovation, microlearning, what have you) with your own interpretation, and literally taking someone’s ideas and selling them as your own.

One of our colleagues recently let me know his tools had been used by folks to earn money without any reimbursement to him (or even attribution).  Others have had their diagrams purloined and used in presentations.  One colleague found pretty much his entire presentation used by someone else!  I myself have seen my writing appear elsewhere without a link back to me, and I’m not the only one.

Many folks bother to put copyright signs on their images, but I’ve stopped because it’s too easy to edit out if you’re halfway proficient with a decent graphics package.  And you can do all sorts of things to try to protect your decks, writing, etc, but ultimately it’s very hard to protect, let alone discover that it’s happening. Who knows how many copies of someone’s images have ended up in a business presentation inside a firm!  People have asked, from time to time, and I have pretty much always agreed (and I’m grateful when they do ask). Others, I’m sure, are doing it anyway.

This isn’t the same as asking someone to work for free, which is also pretty rude. There are folks who will work for ‘exposure’, because they’re building a brand, but it’s somewhat unfair. The worst are those who charge for things, like attendance or membership, or organizations who make money, yet expect free presentations!  “Oh, you could get some business from this.”  The operative word is ‘could’.  Yet they  are!

Attribution isn’t ‘name dropping‘. It’s showing you are paying attention, and know the giants whose shoulders you stand on.  Taking other people’s work and claiming it as your own, particularly if you profit by it, is theft. Pure and simple.  It happens, but we need to call it out.  Calling it out can even be valuable; I once complained and ended up with a good connection (and an apology).

Please, please, ask for permission, call out folks who you see  are plagiarizing, and generally act in proper ways. I’m sure  you are, but overall some awareness raising still needs to happen.  Heck, I know we see amazing instances in people’s resumes and speeches of it, but it’s still not right.  The people in L&D I’ve found to be generally warm and helpful (not surprisingly). A few bad apples isn’t surprising, but we can do better. All I can do is ask you to do the right thing yourself, and call out bad behavior when you do see it.  Thanks!

 

No all-singing all-dancing solution

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I was pinged on LinkedIn by someone who used the entrée of hearing me speak in next week’s Learning Solutions conference to begin discussing LMS capabilities. (Hint: they provide one.)  And I thought I’d elaborate on my response, as the discussion prompted some reflections.  In short, what are the arguments for and against having a single platform to deliver an ecosystem solution?

In Revolutionize Learning & Development, I argue for a Performance & Development ccosystem. The idea is more than courses, it’s performance support, social, informal, etc. It’s about having a performer-centric support environment that has tools and information to hand to both help you perform in the moment  and develop you over time. The goal is to support working alone and together to meet both the anticipated, and unanticipated, needs of the organization.

On principle, I tend to view an ‘all singing all dancing’ solution as likely to fail on some part of that. It’s implausible that a system would have all the capabilities needed.  First, there are  many functionalities: access to formal learning, supporting access to known or found resources, sharing, collaborating, and more.  It’s unlikely that all those can be done well in one platform. Let alone, doing them in ways that matches any one organization’s ways of working.

I’m not saying the LMS is a bad tool for what it does. (Note: I am not in the LMS benchmark business; there are other people that do that and it’s a full time job.) However, can an LMS be a full solution? Even if there is some capability in all the areas, what’s the likelihood that it’s best-of-breed in all? Ok, in some small orgs where you can’t have an IT group capable of integrating the necessary tools, you might settle for working around the limitations. That’s understandable. But it’s different than choosing to trust one system. It’s just having the people act as the glue instead of the system.

It’s always about tradeoffs, and so integrating best-of-breed capabilities around what’s already in place would make more sense to me.  For instance, how *could* one system integrate enterprise-wide federated search as a stand-alone platform? It’s about integrating a suite of capabilities to create a performer-centric environment. That’s pretty much beyond a solo platform, intrinsically. Am I missing something?

And Listen

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Listening is a vital skill.  It’s something that made my mother very popular, because she listened, remembered, and asked about whatever you said the next time you saw her. She cared, and it showed. I wish I was as good a listener!  But it’s critical to really listen (or as some have it, not just listen, but hear).

It’s part of a skillset necessary to innovate. Innovation can be about problem-solving, and design thinking has it that it’s really about problem-finding.  That is, you want to understand the real problem first.  And to really understand the problem, the initial divergence, is to listen. It is listening to people, but also signals in general, what the data tells you.

And so, listening is an important part of communicating and collaborating.  We need to hear what’s being said (and maybe even what’s  not being said), to truly hear. And we likely will need to ask, as well.  This is good, because it shows we’re paying attention.  Talking is speaking  and listening.

And what precipitated this discussion is that in my new column for Learning Solutions (Quinnsights ;), I asked for any questions, and there was one that will be the topic of my next article for them. And I thought that was a good principle.

So, here’s the question:

Is there anything in particular you’d like me to post about here?

 As it is, I post about what I’m thinking about or working on (usually somewhat anonymously).   However, I could benefit to hear what you’re thinking about.  And post on it if I can.  Of course, you should be posting on what you’re thinking about too (#ShowYourWork #WorkOutLoud), but hey, why not cross-communicate?  As it is, I appreciate the comments I get, but this is just a way to feed my brain.

So, this is me listening.  Anyone want to catch my ear?

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.

 

Addressing Changes

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Yesterday, I listed some of the major changes that L&D needs to acknowledge. What we need now is to look at the top steps that need to be taken.  As serious practitioners in a potentially valuable field, we need to adapt to the changing environment as much as we need to assist our charges to do so. So what’s involved?

We need to get a grasp on technology  affordances. We don’t need to that the latest technology exists, whether AI, AR, or VR.  Instead, we have to understand what they mean  in the context of our brains.  What key capabilities are brought?  Can VR go beyond entertainment to help us learn better? How can AI partner with us?  If we can make practical use of AR, what would we do with it?

In conjunction, we need to  understand the realities about us.  We need to take ownership and have a suitable background in how people  really think, work, and learn. Further, we need to recognize that they’re all tied together, not separate things. So, for instance, we learn as we work, we think as we learn, etc.

For example, we need to understand situated and distributed cognition. That is, we need to grasp that we’re not formal logical thinkers, but instead very context dependent, and that our thinking is across our tools. As a consequence, we need to design solutions that recognize our individual situations, and leverage technology as an augment. So we want to design human/computer system solutions to problems, not just human or system solutions.

We also need to understand cultural elements. We work better when we are given meaningful work, freedom to pursue those goals, and get the necessary support to succeed. This is  not micromanagement, but instead, is leadership and coaching. We also need an environment where it’s safe, expected even, to experiment and even to make mistakes.

We also need to understand that we work better (read: produce better results), when we work together in particular ways. Where we understand that we should allow individual thought first, but then pool those ideas. And we need to show our work and the underlying thinking. Moreover, again, it has to be safe to do so!

And, these are all tied together into a systemic approach!  It can’t be piecemeal, because working together and out loud can’t be divorced from the technology used to enable these capabilities. And giving people meaningful work and not letting them work together, or vice-versa, just won’t achieve the necessary critical mass.

Finally, we also need to do this in alignment with the business. And, lets be clear, in ways that can be measured!  We need to be understanding what are the critical performance needs of the organization, and demonstrate that we’re impacting them in the ways above.

This can be done, and it will be the hallmark of successful organization. We’re already seeing a wide variety of converging evidence that these changes lead to success. The question is, are you going to lead your organization forward into the future, or keep your head down and do what you’ve always done?

Acknowledging Changes

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There are a serious number of changes that are affecting organizations.  We’re seeing changes in the information flow, in technology, and in what we know about ourselves. Importantly, these are things that L&D needs to acknowledge and respond to.  What are these changes?

It’s old news that things are happening faster. We’re being overwhelmed with information, and that rate is accelerating. On the other hand, our tools to manage the information flow are also advancing.

Which is the second topic. We’re getting more powerful technology. We can create systems that do tasks that used to be limited to humans. They can also partner with us, providing information based upon who we are, what we’re doing, and what else is going on.

And there are increasing demands for accountability (and transparency). Your actions should be justified. What are you doing, why, and what effect is it having? If you can’t answer these questions, you’re going to be looking for a job.

Most importantly, we’ve learned quite a bit about ourselves that is contrary to many pre-existing beliefs. Specifically ones that influence organizational approaches.  Our myths about how we think, work, and learn are holding us back from achieving optimal outcomes.

For one, there’s a persistent belief that our thinking is in our heads.  Yet research shows that our thinking is distributed across our tools. We use external representations to capture at least part of our thinking, and access information that we can’t keep in our heads effectively.  Yet we seem to depend on courses to put it in the head instead of tools to put it in the world.

Our thinking is also distributed across others. “You’re no longer what you know, but who you know” is a new mantra. So is “the room is smarter than the smartest person in the room” (with the caveat: if you manage the process right ;). Informal and social learning is the work.  Yet we still act as if we believe that people should solve problems independently.

And we also act as if how we learn is by information dump.  Add a quiz, so we know they can recognize the right answer if they see it, and they’ve learned!  Er, no. Science tells us that this is perhaps the worst thing we could do to facilitate learning.

In short, our practices are out of date. We’re using patch-it (or ignore-it) solutions to systemic issues.  We address simple things as if they’re not all connected. It’s time to get on top of what’s known, and then act accordingly.  Are you ready to join the 21st century?

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?

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?