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Clark Quinn’s Learnings about Learning

The case for PKM

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Seek > Sense > ShareApparently, an acquaintance challenged my colleague Harold Jarche’s Personal Knowledge Mastery (PKM)  model.  He seemed to consider the possibility that it’s a fad. Well, I do argue people should be cautious about claims. So, I’ve talked about PKM before, but I want to elaborate. Here’s my take on the case for PKM.

As context, I think meta-learning, learning to learn, is an important suite of skills to master. As things change faster, with more uncertainty and ambiguity, the ability to continually learn will be a critical differentiator. And you can’t take these skills for granted; they’re not necessarily optimal, and our education systems generally aren’t doing a good job of developing them. (Most of school practices are antithetical to self learning!)

Information is key.  To learn, you need access to it, and the chance to apply. Learning on one’s own is about recognizing a knowledge gap, looking for relevant information, applying what you find to see if it works, and once it does, to consolidate the learning.

Looking at how you deal with information – how you acquire it, how you process it, and how you share your learnings – is an opportunity to reflect. Think of it as double-loop learning, applying your learning to your own learning. We’re often no so meta-reflective, yet that ends up being a critical component to improving.

Having a framework to scaffold this reflection is a great support for improving. Then the question becomes what is the right or best support?  There are lots of people who talk about bits and pieces, but what Harold’s done is synthesize them into a coherent whole (not a ‘mashup’). PKM integrates different frameworks, and creates a practical approach.  It is simple, yet unpacks elegantly.

So what’s the evidence that it’s good?  That’s hard to test.  The acquaintance was right that just university uptake wasn’t a solid basis (I found a renowned MBA program recently that was still touting MBTI!).  The hard part would be to create a systematic test. Ideally, you’d find an organization that implements it, and documents the increase in learning. However, learning in that sense is hard to measure, because it’s personal. You might look for an increase in aggregate measures (more ideas, faster trouble-shooting), but this is personal  and is dependent on outside factors like the  culture for learning.

When you don’t have such data, you have to look for some triangulating evidence. The fact that multiple university scholars are promoting it isn’t a bad thing. To the contrary, uptake at individual institutions without a corporate marketing program is actually quite the accolade!  The fact that the workshop attendees tout it personally valuable it also a benefit. While we know that individual attendee’s reports on the outcomes of a workshop don’t highly correlate with actual impact, that’s not true for people with more expertise. And the continued reflection of value is positive.

Finally, a point I made at the end of my aforementioned previous reflection is relevant. I said: “I realize mine is done on sort of a first-principles basis from a cognitive perspective, while his is richer, being grounded in others‘ frameworks.”  Plus, he’s been improving it over the years, practicing what he preaches. My point, however, is that it’s nicely aligned with what you’d come at from a cognitive perspective. Without empirical data, theoretical justification combined with scholarly recognition and personal affirmations are a pretty good foundation.

There’re meta-lessons here as well: how to evaluate programs, and the value of meta-learning. These are worth considering. Note that Harold doesn’t need my support, and he didn’t ask me to do this. As usual, my posts are triggered by what crosses my (admittedly febrile) imagination. This just seemed worth reflecting on. So, reflections on your part?

Experimentation specifics

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I’m obviously a fan of innovation, and experimentation is a big component of innovation. However, I fear I haven’t really talked about the specifics.  The details matter, because there are smart, and silly, ways to experiment. I thought I’d take a stab at laying out the specifics of experimentation.

First, you have to know what question you’re trying to answer. Should we use a comic or a video for this example?  Should we use the content management system or our portal tool to host our learning and performance support resources?  What’s the best mechanism for spacing out learning?

An important accompanying question is “how will we know what the answer is?”  What data will discriminate?  You need to be looking for a way to tell, we know, we can’t know, or we need to revise and do again.

Another way to think about this is: “what will we do differently if we find this?” and “what will we do differently if it turns out differently?” The point is to know not just what you’ll know, but  what it means.

You want to avoid random experimentation. There  are the ‘lets try it out’ pilots that are exploratory, but you still want to know what question your answering. Is it “what does it take to do VR” or “let’s try using our social media platform to ‘show our work'”.

Then you need to design the experiment. What’s the scope? How will you run it? How will you collect data? Who are your subjects?  How will you control for problems?

One of the claims has regularly been “don’t collect any data you don’t know what you’ll do with”.  These days, you can run exploratory data analysis, but still, accumulating unused data may be a mistake.

The after-experiment steps are also important. Major questions include: “what did we learn”, “do we trust the results”, and “what will we do as a result”. Then you can followup with the actions you determined up front that would be predicated on the outcomes you discover.

Experimentation is a necessary component of growth. You have to have a mindset that you learn from the experiment, regardless of outcome. You should have a budget for experimentation  and expect a degree of failure. It’s ok to lose, if you don’t lose the lesson!  And share your learnings so others don’t have to make the same experiment.  So experiment, just like I did here; is this helpful?  If not, what would it need to be useful?

Citations

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Following on my thoughts on writing yesterday, this was a topic that didn’t fit (the post got too long ;).  So here we go..  Colleagues have written that citations are important. If you’re making a claim, you should be able to back it up. On the other hand, if you’re citing what you think is ‘received wisdom’, do you need to bother?  Pondering…

Now, citations can interfere with the flow, I believe. If not the reading, they can interfere with the flow of my writing! (And, I’ve been accused of ‘name dropping‘, where instead I believe it’s important to both acknowledge prior work and show that you know what’s been done.) Still, it’s important to know what to cite, and when.

I admit that I don’t always cite the claims I make. Because, I take it as a given.  I may say something like “we know” or otherwise presume that what I’m saying is accepted premise. One problem, of course, is that I don’t know what others know (and don’t). And, of course, that this isn’t an official article source, this is my blog ;). Still, when I’m talking about something new to me (like thoughts from books), I will cite the locus.

Articles are different. When I write those, I try to provide sources. In both cases I generally don’t go to the extent of journal article links, because I’m not expect that folks have easy access to them, and so prefer to cite more commonly available resources, like books that have ‘digested’ the research.

And when I write ‘take down’ articles, I don’t cite the offender. It’s to make the point, not shame anyone. If you’re really curious, I’m sure you can track it down.

And, realize I don’t have easy access to journals either. Not affiliated with an institution, I don’t have access to the original articles behind a pay wall. I tend to depend on people who summarize including books and articles that summarize. Still, I’ve a grounding for over a decade in the original materials and am able to make inferences. And of course occasionally I’ll be wrong. Sometimes, I’ll even admit it ;).

The issue really is when do you need to make a citation. And I reckon it’s when you’re stating something that folks might disagree with. And I can’t always anticipate it. So I’ll try to consistently point to the basis for any claims I think might be arguable, or state that it’s my (NSH :) opinion.  And you can always ask!  Fair enough?

Editing, process, topics, and other reflections

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My lass let me know there was a typo in my recent post on Transformation.  I’m thrilled that she’s reading them (!), but she triggered many thoughts about my writing approach. I thought I’d share how I deal with blogging, articles, and writing in general, as a ‘show your work‘ effort. And, in a sense, solicit your thoughts on approach, editing, and topics (amongst other things).

Process

It starts with my commitment to two blog posts a week. And I’m pretty sure I average that, since while I occasionally only get one, I also occasionally get three (say, during a week at a conference with mindmaps).  That means, however, that sometimes I’m brimming with ideas and have them queued up a week or two in advance, and sometimes I’m writing them at the last minute (*cough* this one *cough*).  When I know I’ll be on the road on a particular week, I definitely try to have them in the hopper in advance.

Regardless, I tend to write each in one fell swoop. Something sparks a thought, and I rush to get it down. Sometimes I’ll have an idea elsewhere, and jot myself a one line reminder, and need to generate the full prose. But my writing’s often like that: once I’m going, I have to let that full idea gestate. Even when writing a full book (as I’ve done a time or two ;), I outline it in a go, and then write sections in a burst.

Now, I write in several channels: my blog, my committed articles, and of course books. And, not surprisingly, I write them differently.  The blog comes out ‘as is’. I do reread it after it’s first done, typically, but as my lass discovered, it can have flaws. I reread my Trends article after posting, for instance, and noticed a couple of flaws. (I’ve fixed them, of course, similarly when folks comment in one way or another about something I’ve left confusing or wrong.)

My articles are different. I write them typically in one go, but I always hang on to them for at least a day, and reread with fresh eyes. I think that’s obligatory for such efforts. In one case, I have an editor who reads them with a careful eye, and always sends back a revised version. I don’t get to  see the revisions (which is frustrating), but the articles are always improved. Editing is valuable!

For books, as I mentioned, I outline it, then write sections. And, depending on the book, the experience changes. With  Engaging Learning, it had been percolating for so long it kind of flew out of my fingers onto the page.  For  Designing mLearning, it was different; I outlined, and wrote, and as I got further in I found myself rearranging the structure and going back to add things.  The Revolutionize L&D book was closer to the Designing mLearning book, with two changes. I didn’t reorganize as much, but I kept going back and adding stuff. It was hard to finish!

With my books, I’ve always had an editor. The ones from the publisher varied in quality (good experiences generally), but I also have m’lady serve as my first (and best) editor. And I’ve learned to truly value an editor. The benefit of a second eye without the assumptions and blinders the writer brings is great!

Topics

The ideas come differently as well. My blog tends to get whatever I’m thinking about (like this). My articles tend to be a deeper dive into whatever I think (or we agree, with my editor) is important. I keep a list of potential topics for each, and take whatever feels ‘right’ for the month.

Books, of course, are a bigger story. For one, you need a publisher’s agreement (unless you self-publish). My first book was based upon my research for years on games and engagement. The mLearning books were publisher requests, and yet I had to believe I could do a proper job. Revolutionize emerged from my work with people and orgs and looking at the industry as a whole, and was something I think needed to be said. My latest, on myths, was also requested, but also something I felt comfortable doing (and needed to be done).

(Interestingly, on the requested books, I first checked to see if someone else might write it instead, but when the obvious candidates declined, I was happy to step up. I got their voices in anyway. ;)

The hard part, sometimes, is coming up with topics. The commitment to two posts a week is a great catalyst for thinking, but sometimes I feel bereft. I welcome suggestions for topics for any of the above as well. Someone asked what my next book would be, and I asked them what they thought it should be.  However, I’m not ready to write a memoir yet; I’m not done!  Thoughts solicited on any or all of the above.

Developing learning to learn skills

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I’m an advocate of meta-learning, that is: learning to learn. Not just because it’s personally empowering, but because it can and should be  organizationally empowering. The problem is, little is talked about how to develop it. And I have to say that what I  do see, seems inadequate. So I thought I’d rant, for a post, on what is involved in developing learning to learn skills.

First, of course, you have to identify what they  are!  What are learning to learn skills?  Harold Jarche’s PKM is a good start, talking about seek > sense > share. Obviously, there’s more to it than just that, so it’s about seeking actively but also setting up systems to continually feed you new, potentially tangential thoughts. And how to evaluate what you get. Then, it’s about being able to process the inputs in ways that help you understand, or do, something new. What does it  mean, in practice?  Finally, of course, it’s about sharing, in two ways. For one, contributing to others’ questions and work. Then it’s also sharing your own thoughts and work.

That’s (largely) working alone, but there are also specifics about how you work and play well with others. Do you know how to best manage the process of solving a problem together?  How can you ask questions, and answer them, in ways that people will recognize and participate?   People need models and frameworks that guide performance.

Of course, just knowing this isn’t enough.  There are some necessary additional steps. The first is evangelizing and sharing the best principles for working together. So, people have to know about the principles, and be encouraged to use them.  And even be rewarded, whether just with praise or actual promotion of their successes. There should also be models, examples. So L&D should be practicing what they preach, and working and learning ‘out loud’.  Show, and narrate, your own work!  And, this is still not enough.

Most importantly, you have to  develop the skills. Actively. So, content about them, and examples are good. But learning is, at core, about mentored practice.  And it can’t be in the abstract, it’s about doing it with real tasks. You can set up such opportunities in your formal learning (and should), but you should also be coaching around real work.

At least, you should be facilitating proper approaches in public forums, like social media.  You can quietly coach individuals about good practices if they’re off target.  You can point out, as a meta-discussion, when people are learning effectively.  Annotate the thinking behind what learners can and should be doing.

The worst thing is to leave it to chance, or assume your learners are effective self-learners. The evidence is that they’re not. Sadly, our education system doesn’t do a good job of this. Nor do our organizations. But we could. This is about more effective innovation, really. Learning manifests as new ways of doing things. Innovation is about better ways of doing things. If we evaluate our learnings and apply the ones that are improvements, we’re innovating. Both for specific needs and as a ongoing background process.  And if indeed innovation is the only sustainable differentiator, this is the best investment you can make for the organization.

And, if you’re truly contributing to the central success factor in the organization, you’re becoming essential to the organization. As you should be. So seize the opportunity, and make meta-learning a priority. Develop learning to learn skills consciously, and conscientiously.  It’s an innovative, and valuable, thing to do :).

 

And the myths go on

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Yet another silly post I stumbled upon.  And last week at a conf someone said they liked my take-downs. If you disagree, let me know, but otherwise here’s yet another bunch of marketing hype.  Hopefully no one uses this for any real decisions!

This one talks about ‘generation Z’, and implications for L&D. Ok, so we’re off on the wrong foot from the get-go.  These are listed as 1995-2014. (Er, um, as Jessica Kriegel pointed out last week, isn’t the whole point of the millennial label that they’re ‘2000’? )  However, there’s no evidence to point to reliable generational differences. What differences there are can be attributed to age, and it’s still a form of age discrimination, how about treating people by how they individually behave?

So there’s a list of differentiators, sourced from elsewhere. You go to the elsewhere, and it’s preferences, and anecdotal. Neither one are good bases for making broad claims. There are several cites in the list, as well. From marketing sites. So the author clearly doesn’t understand good data.  What are they talking about? Here’s a subset:

  • Digital multitaskers: well, we know that’s inefficient, but haven’t we seen that taken up by device, not age group? It’s certainly true for millennials as well, and seems to be true for everyone who’s gotten on to mobile devices.
  • Secretly social: (wth?) they share, but with control. As do most astute folks beyond high school.
  • Diverse: er, yes, so’s the whole US. And, more and more, the world. How is this definitional? And do you think they really don’t still have biases?
  • Quick Information Processors/Communicators:  dealing with chunks, quickly but not necessarily accurately. Isn’t that, er, just kind of human?

The recommendations list is similarly silly:

  • Update job descriptions: make sure they’re up-to-date.  Really?  This isn’t just good practice?
  • Expunge bias: ditto
  • Go where the talent is: use appropriate social media. C’mon, already; any other statements of the obvious?
  • Benefits: emphasize the WIIFM. Can you imagine?

The overarching theme here is ‘do good things’.  Why isn’t this appropriate for  every job search?  And the same thing continues when recommendations for your courses:

  • Digital and Visual Content: Use media? Really?  Who’d have thought of it?
  • Reassess your Library and Curricula: you don’t need diversity, but you do need soft skills. Here I think there is bad advice, instead of the generally ‘best principles argued for the wrong reasons’.  Just because you hear more messages of tolerance (yay!), doesn’t mean you know how to be inclusive, and are aware of unconscious bias. (That’s why it’s  unconscious!)

And the same overall pattern of good advice pretending to be specific to a generation holds true for the final list.   (I’m paraphrasing the advice here):

  • Embrace diversity
  • Provide social connection tools
  • Give them the ability to contribute
  • Include them
  • Don’t try to ‘own’ their time

Tell me if you think any of these should be not true for other folks than these new folks?  I think this approach is a bad idea, overall. You’re providing decent advice (er,  mostly), but doing so through a myth-perpetuating framing. That’s still myth-perpetuating!

Ok, so this was from a company that’s trying to flog their services. It still seems like it’s written by a person more focused on marketing than matter. And I think we need to unpack these, and push back. Generation Z is just as discriminatory as millennials,  gender, and other differences that are attempts to avoid dealing with people as individuals.  If we don’t kick up our heels, we won’t get better efforts. And we should.

Intellectricity

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Many years ago, I met a guy who worked for Apple. They were allowed to have their own job titles, and his was “Intellectrician”. I thought that was a very nice turn of phrase. And, as I just ordered new business cards, I put  Intellectricity as the tagline instead of “Learning & Performance Strategy” or other permutations with Technology and such in the title.  Why?

The goal, of course, is to have a phrase that folks will read it and go “what’s that about?”, as some of my colleagues helped me remember. If you can spark a conversation, you have a chance to do a little evangelism/education. (And maybe some business interest?)  Also, I think it actually captures what I believe and like to do pretty well.

You’ve likely heard or read me harp many times on how companies aren’t well aligned with how we think, work, and learn. The cognitive violations are many, from how we design our learning, to design our workplaces, policies, tool use, and culture. If we redesigned what we’re doing, creating strategies to get better practices in place, we’d be unleashing the organizational intellect!  Hence, ‘intellectricity’.

And this is pretty much what I’m on about, in several ways:

  • knowing what formal learning  really  looks like, and designing our design processes accordingly
  • recognizing what facilitates informal learning in the short term (the ‘solve this’ type of problem-solving’)
  • facilitating long-term informal learning by practices and tools suites
  • fostering a culture where innovation thrives

This is a partial list that goes fractal really quickly with practices and principles around each area. The point is that these elements are key to organizational ‘thrival’.  Overall, they’re about optimizing the intellectual activity of the organization, learning quickly to be agile.

We’ll see if this tactic works to generate conversations and then new thinking. As Jay Cross used to say “conversations are the stem cells of learning”.  Practicing what we preach.  So here’s to Intellectricity: more conversations and more learning.

Competencies and Innovation?

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This may seem like an odd pairing, so bear with me.  I believe that we want to find ways to support organizations moving in the direction of innovation and learning cultures. Of course, I’ve been on a pretty continuous campaign for this, but I’m wondering what other levers we have. And, oddly, I think competencies may be one. Let me make the case for competencies and innovation.

So I’ve gotten involved in standards and competency work. Don’t ask me why, as I have no better answer than a) they asked, and b) the big ‘sucker’ tattoo on my forehead.  Of course, as I’ve said before, the folks that do this stuff (besides me, obviously) are really contributing to the benefit of our org. Maybe I felt I had to walk the talk?

In the course of the one that was just launched, we identified a number of competencies across the suite of L&D activities. This included (in addition the more traditional activities) looking at how to foster innovation. This means understanding culture and the change processes to get there, as well as knowing how to run meetings that get the best outputs. It’s about being prepared for both types of innovation, fast (solve ‘this’ problem) and slow (the steady percolation of ideas).

Thus, the necessary skills are identified as a component of a full suite of L&D capabilities. And the hope, of course, is that people will begin to recognize that there are parts of L&D they’re not addressing, and move to take on this opportunity. I hope that it’s becoming obvious that the ability to facilitate innovation is an organizational imperative, and that there’s a strong argument for L&D to be key. This is on principle, and pragmatically, it’s a no-brainer for L&D to find a way to become central to org success, not peripheral.

However, leaving that to chance would be, well, just silly. What can we do?  Well, two things, I think: one is to help raise awareness, the other is to provide support. A suite of skills aligned to this area is a ‘good thing’ if it known and used. Working on the know has been an ongoing thing (*cough*), but how can we support it?

Again, two things, I think. One are examples where people have put in place programs where they’ve oriented themselves in this direction and documented benefits. The other is to provide scaffolding; support materials that help folks implement these competencies. And I believe that’s coming.

“Systematic creativity is  not an oxymoron” (I may need to make a quip post about that). And this is an example. Think of brainstorming, for example. It can be useful, or  not. When done right, the outcomes are much better. And similarly in lots of ways, the nuances matter. If we define, through competencies, what suites of knowledge matter, we bring awareness to the possible outcomes. And the opportunity to improve them.

It may be an indirect path, to be sure, but it’s a steady, and real one. In fact, to say “we want to innovate, but how” and have a suite of specific sets of knowledge on tap to point people to, is pretty much next to the fastest path.  Showing people the benefits and the path to obtain them is key. It’s even self-referential: let’s innovate on making innovation systematically embedded in organizations! ;)  So, keep on experimenting!

Constraints on activities

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When we design learning activities (per the activity-based learning model), ideally we’re looking to create an integration of a number of constraints around that assignment. I was looking to enumerate them, and (of course) I tried diagramming it.  Thought I’d share the first draft, and I welcome feedback!

Multiple constraints on assignmentsThe goal is an assignment that includes the right type of processing. This must align with what they need to be able to do after the learning experience. Whether at work or in a subsequent class. Of course, that’s factored into the objective for this learning activity (which is part of an overall sequence of learning).

Another constraint is making sure the setting is a context that helps establish the breadth of transfer. The choice should be sufficiently different from contexts seen in examples and other practices to facilitate abstracting the essential elements. And, of course, it’s ideally in the form of a story that the learner’s actions are contributing to (read: resolve). The right level of exaggeration could play an (unrepresented) role in that story.

We also need the challenge in the activity to be in the right range of difficulty for the learner. This is the integration of flow and learning to create meaningful engagement.  And we want to include ways in which learners typically go wrong (read: misconceptions). Learners need to be able to make the mistakes here so we’re trapping and addressing them in the learning situation, not when it could matter.

Finally, we want to make sure there’s enough variation across tasks. While some similarities benefit for both consistency and addressing the objective, variety can maintain interest. We need to strike that balance. Similarly, look at the overall workload: how much are we expecting, and is that appropriate given the other constraints outside this learning goal.

I think you can see that successfully integrating these is non-trivial, and I haven’t even gotten into how to evaluate this, particularly to make it a part of an overall assessment. Yet, we know that multiple constraints help make the design easier (at least until you constrain yourself to an empty solution set ;).  This is probably still a mix of art and science, but by being explicit you’re less likely to miss an element.

We want to align activities with the desired outcome, in the full context.  So, what am I missing?  Does this make sense?

 

Processing

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I’ve been thinking a lot about processing in learning of late; what processing matters, when, and why. I thought I’d share my thinking with you and see what you think.  This is  my processing!  :)

We know processing is useful. You can consider Craik & Lockhart’s Levels of Processing model, or look to the importance of retrieval practice as highlighted in Brown, Roediger, and McDaniel’s Make it Stick. The point is that retrieving information from memory and doing things with it increases the likelihood of learning. One of the questions is  “what sort of retrieval (or processing)?”

I’ve always advocated for  applying the information, doing something with it.  But there are actually a variety of useful things we can do:

  • representing information (a form of reflection) whether rewriting, or mindmapping, or…
  • connecting to other known information, personal or professional
  • considering how it would be applied in practice
  • applying it in practice, real or simulated

Of course, we want there to be scrutiny and feedback for the learning to be optimized, etc.

Now, this is in the individual instance, but I’m also looking at the sequence of processing. What would be a series of activities that would develop understanding. So, for instance, for a problem-solving practice like trouble-shooting a process, what might you do? You might have  (say, after a model of the process, and examples) a sequence of :

  • critique someone else’s performance
  • try a simple example of performing
  • try a more complex example (perhaps in a group)
  • …(more examples of performing)
  • try a very complex (read: typical) example

We could throw in related tasks as well either during or as a summary:

  • create a checklist to follow
  • draw a flow diagram
  • create a representation

On a more categorical task, say determining whether a situation qualifies as this or not (with shades of grey in between), we would have a similar structure, but with different types of tasks (again, after initial content such as definition and examples):

  • review a case where it clearly is (white)
  • review a case where it clearly isn’t (black)
  • group review a case of grey (but not too bad)
  • group review a case of grey (more shady)
  • …

Again, we could have interim or summary tasks:

  • summarize the constraints
  • document a proposed process
  • make a plan for how to do it in the future
  • …

What I’ve explicitly added here is when and why to go ‘social‘.  There are benefits for the same, but should they all be social?  I’ll argue that there’s some initial prep that’s individual, to get everyone on the same page. Since all are different, it helps if this is individual. Then there’s often value in doing it socially, for the reasons in the linked post.  Then, I reckon there’s value in doing  something independently, to consolidate the learning. And, of course, to determine what capability the individual has acquired.

The point I want to make is that the processing  flow, the progression from activity to activity, matters. We want to introduce, diverge, and then converge.  We do need to elaborate across contexts to support transfer, and of course increase complexity until they’ve developed the ability to deal with the typical difficulty of cases.

I’m thinking that, too often, we forget the consolidation phase.  And we’re often doing processing that’s somewhat like what we need them to do, but ultimately tangential. There are multiple constraints here to be acknowledged, cognitive such as depth and breadth as well as pragmatic such as cost and time, but we want to find the right intersection.

And my practical question is: where does this fall apart? Are their situations where this doesn’t make sense?  I realize there are other types of outcomes that I haven’t represented (I’m being indicative, not exhaustive ;), but is this a useful way to think about it?