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Clark Quinn’s Learnings about Learning

Metacognitive Activity?

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So, as another outcome of the xAPI base camp a few weeks back, I was wondering about tracking not only learning, but meta-learning. That is, not only what activity might mean ‘learning’, but what might mean ‘meta-learning’ is happening?  I started wondering about a vocabulary, but realized that you’d have to have activity that you could actually detect that was evidence of meta-learning.  And I didn’t know what that was. Naturally, I started diagramming.

I started with Harold Jarche’s Personal Knowledge Mastery  model  of Seek-Sense-Share.  This is about how you continue to learn in manageable ways, and it served as an organizing framework.  To each of the elements, I attributed activities that would constitute learning in that model, and then above it I was thinking what would constitute meta-learning.

MetaCogSo, for seek, we start with reading what comes into your feeds, searching about particular topics, and asking questions of your network. Sensing is about reconciling what’s found with your own knowledge. So you could write or present, diagram (see what I  did there?), or experiment. And then to share you can post, or comment, or send a pointer to something.

So what are actions that reflect on those actions?  For seeking, you can adjust your feeds of what you follow, you can try a different search mechanism, or you can follow new people.  These are all detectable, I reckon.

For sensing, I see it as a little harder.  How do we know when you’re annotating a document  with the underlying thinking, not just documenting your progress?  How do we know when you’re explaining the thinking behind a diagram (here it’d be about my choice of vertical dimension, and spreading things below and above)?  How do we know when you’re actually reviewing your experimental approach or the results?

For sharing, it’s a mixed bag. If you choose to use a different media (perhaps it’s relative, like when I created an  animation  after blogging for > 10 years ;), we might know. If you try out a new social media platform/channel, we can probably note that.  If you’re reflecting on your comments from others, how would we know?

And this is just one way of carving it up.  The point being, meta is good, but detecting and tracking it is hard.  We might ask people to annotate it with tags, but that’s problematic too.  I have no obvious answers, but it’s a question I had, and I’m thinking out loud about it.  I welcome your thoughts, too.

Mindmapping

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So, if you haven’t figured it out yet, I do mindmaps.  As I’ve recited before, I started doing it as a way to occupy my brain enough so I could listen to keynotes, but occasionally I use it to other purposes, such as representing structure or even planning. And thru  my esteemed colleague Jane Hart  (who’s Modern Workplace Learning book I’m going through and thoroughly impressed), I’m giving a mindmapping webinar today for a group of several universities in Ireland.  I thought I’d share what I’m presenting.

MindmappingMindmaps are a visual way of representing knowledge.  You use links to show connections between concepts (represented as nodes), developing a structural relationship.  A true semantic network would have those links labeled, as there are many different types of relationships (causal, precedence, hierarchical), but mindmaps typically have unlabeled links.  Still, mindmaps capture structural information in a visual way, that supports tapping into our powerful visual processing system. (This is the one I created for them to advertise the talk, it’s neither the order I ended up for them or am using here. ;)

You can add information to them; as a visual tool, you can add extra graphical information, like tables or charts, to augment the map.  You can similarly add color as a way to layer additional semantic information such as similarity. And the links can be plain or directional.  Importantly, while a mindmap can be essentially equivalent to an outline  if you maintain a strict tree structure, you can create a graph by having more complex links that generate loops.

The process of mindmapping is fairly straightforward: you have a central node, and then generate additional nodes and link them. I tend to go counter-clockwise, and include an arrow indicating that, because I’m capturing a linear presentation, but generating a static representation of information doesn’t have any directional requirement. I find that I have to frequently rearrange to fit the mindmap appropriately to the image, but that’s part of the benefit.

The evidence appears to show that mindmapping is superior to note-taking. I don’t do it all the time, but there are reasons to think you should.  The reasons, I believe, that it  is better is that you’re not just transcribing a presentation, but you’re actively parsing it to represent the structure. If you do take notes, you should be paraphrasing what you hear in your own words, to have active processing of the information. The additional effort to extract the structure as well is a form of valuable cognitive processing that elaborates the information.  Doing both, paraphrasing and extracting structure, would be a great way to really comprehend what you’re hearing.

As suggested, it’s helpful to mindmap talks, but it can also be a thinking tool, to analyze situations and sort out your thoughts or plan activities and add elements as you think of them. No real advantage over an outline, potentially (though the ability to add other graphics and to make non-strict maps may counter that), though I suspect some find the drawing and rearranging to be a nice physical overhead to facilitate reflecting.  And, of course, it can be an evaluation tool, asking someone to create their maps to see their understanding.

While there are dedicated tools for mindmapping, both applications and in the cloud, which will make creating and rearranging easier (I presume), you can use almost any drawing package (I use OmniGraffle). You could use Powerpoint or Keynote, and even pencil and paper (if it’s just for the processing) though it can be harder to revise.

So, that’s my riff on mind mapping.  I welcome your thoughts.

Working wiser?

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Noodling:   I’ve been thinking about Working Smarter, a topic I took up over four years ago.  And while I still think there’s too little talk about it, I wondered also about pushing it further.  I also talked in the past about an interest in wisdom, and what that would mean for learning.  So what happens when they come together?

Working smarter, of course, means recognizing how we  really think, work, and learn, and aligning our processes and tools accordingly. That includes recognizing that we  do use external representations, and ensuring that the ones we want in the world are there, and we also support people being able to create their own. It means tapping into the power of people, and creating ways for them to get together and support one another through both communication and collaboration.  And, of course, it means using Serious learning design.

But what, then, does working  ‘wiser’ mean?  I like Sternberg’s model of wisdom, as it’s actionable (other models are not quite specific enough).  It talks about taking into account several levels of  caring about others, several time scales, several levels of action, and all influenced by an awareness of values.  So how do we work that into practices and tools?

Well, pragmatically, we can provide rubrics for evaluation of ideas that include considerations of others inside and outside your circles of your acquaintances, and in short- and long-term timeframes, and the impacts on existing states of affairs, ultimately focusing on the common good. So we can have job aids that provide guidance,  or bake it into our templates.  These, too, can be shown in collaboration tools, so the outputs will reflect these values.  But there’s another approach.

But, at core, it’s really about what you value, and that becomes about culture.  What values does the organization care about?  Do employees know about the organization’s ultimate goal and role?  Is it about short-term shareholder return, or some contribution to society?  I’m reminded about the old statements about whether you’re about selling candles or providing light.  And do employees know how what they do fits in?

It’s pretty clear that the values implicit in  steps to make workplaces more effective are really about making workplaces more humane, that is: respecting our inherent nature.  And movements like this, that provide real meaning, ongoing support, freedom of approach, and time for reflection, are to me about working not just smarter but also wiser.

We can work smarter with tools and practices, but I think we can work better, wiser, with an enlightened approach to who we are working with and how we work to deliver real value to not only customers but to society.  And, moreover, I think that doing so would yield better organizational  outcomes.

Ok, so have I gone off the edge of the hazy cosmic jive?  I am a native Californian, after all, but I’m thinking that this makes real business sense.  I think we can do this, and that the outputs will be better too, in all respects.  No one says it’d be easy, but my suspicion is it’d be worthwhile.

Model power!

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So, last  night I was talking with my lass, and happened to show her one of my diagrams. I keep a batch of  them around, and it happened to resonate. And (to use a horrible social media ploy): you’ll never believe what happened next!

So, we were talking about her school things, and among the topics was the strategy for studying.  She’s seen a friend who basically studies the night before an exam, and does well.  Of course, she’s forgotten it all within a week. Whereas my lass works hard, doesn’t do quite as well on the test (she has a wee bit of test anxiety, but not much), but will remember it later.  She’s (fortunately) interested in learning for intrinsic interest, rather than to meet the hurdles (but capable and willing to do those too).

Which, as I explained to her, is exactly predicted by the research. Spacing out studying means you don’t have it quite as accessible right afterward, but it’s there much later.  It’s a known phenomena of our cognitive architecture (and not neuroscience, citing my colleague Will who pointed me to the original research).  So, if you want to do well on the test, massed practice is just fine. If you want people to retain it, well, space it out!

Now, I have argued the power of models before, and that’s why I keep a suite of diagrams (essentially a quiver of models) on my devices (the qPhone and the qPad).  I can share them at relevant times as a tool for explanation or prediction, and together we can figure out what it  means. And that’s what I was doing, connecting her experience to frameworks that make sense.

What I heard today is that she’s gone and mapped out her study schedule for her finals, spacing it out to get the best results.  She’s applying the model to her own life!  So she’s comprehended the model and the implications. And I’m not touting this to show off my daughter (yes, I’m proud of her, but that’s between me and her), but to show how models serve as the guidance for making important decisions.

Models are powerful guides to making decisions. So are you baking models into your learning, personally and for others?

 

Meta-Learning Manifestations

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I recently mentioned that one of my reflections on the past year was that learning to learn, aka meta-learning, is emerging.  And this has come about in several ways recently, and I think it’s a relatively ‘meta’ thing to do ;) to look at the principles across these areas.

So, yesterday I was talking with a colleague about libraries. And one of the things that I noted was that in talking about the future of libraries, I hadn’t discussed a particular role they could and should play.  The reflection was that even in the future role, librarians are more than just the conduits to the information (or people or equipment), but also demonstrating  how they served that role. That is, don’t just show me the results of the search, show me how you thought about the search, and why you chose the search tool you used, and how you created your query, and…

And he assured me that indeed librarians were being taught this. Moreover,  at San Francisco Public Libraries they actually had dual monitors where the staff member could look, but the patron could  also view the activity, and the staff member could work ‘out loud‘.

And this is important.  Because until our schools start doing a better job of this, we’re not going to  be able to assume that our employees and citizens are actually good at learning.  You can only teach meta-learning on top of real goals, and we (should) have those in schools, so it’s the ideal place and arguably the best contribution schools can provide in this rapidly changing environment.

And it’s not like the investments in learning technology are addressing this either.  As I mentioned when I talked about AI for learning, we’re not really seeing the extra layer that will address that (though it’s doable).  As it is, we’re creating adaptive systems that replicate the existing curricula, which would be ok  if our curricula were defensible (hint: it isn’t). Advanced pedagogy can be great, but it is wasted on the existing curricula.

So, there’re are opportunities for learning to learn (which have real benefits) to be enabled across organizational work, library work, schools, and systems.  And we’re really not seeing anywhere near the uptake that would benefit our efforts.

However, we  are seeing more discussion. And I’m imploring you to start thinking about it, talking about it, and beginning to  do it! It’s doable, and  arguably the best investment we could and should be making.  Are you ready?

2015 Reflections

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It’s the end of the year, and given that I’m an advocate for the benefits of reflection, I suppose I better practice what I preach. So what am I thinking I learned as a consequence of this past year?  Several things come to mind (and I reserve the right for more things to percolate out, but those will be my 2016 posts, right? :):

  1. The Revolution  is real: the evidence mounts that there is a need for change in L&D, and when those steps are taken, good things happen. The latest  Towards Maturity report shows that the steps taken by their top-performing organizations are very much about aligning with business,  focusing on performance, and more.  Similarly, Chief Learning Officer‘s Learning Elite Survey similarly point out to making links across the organization and measuring outcomes.  The data supports the principled observation.
  2. The barriers are real: there is continuing resistance to the most obvious changes. 70:20:10, for instance, continues to get challenged on nonsensical issues like the exactness of the numbers!?!?  The fact that a Learning Management System is not a strategy still doesn’t seem to have penetrated.  And so we’re similarly seeing that other business units are taking on the needs for performance support, social media, and ongoing learning. Which is bad news for L&D, I reckon.
  3. Learning design is  rocket science: (or should be). The perpetration of so much bad elearning continues to be demonstrated at exhibition halls around the globe.  It’s demonstrably true that tarted up information presentation and knowledge test isn’t going to lead to meaningful behavior change, but we still are thrusting people into positions without background and giving them tools that are oriented at content presentation.  Somehow we need to do better. Still pushing the Serious eLearning Manifesto.
  4. Mobile is well on it’s way: we’re seeing mobile becoming mainstream, and this is a good thing. While we still hear the drum beating to put courses on a phone, we’re also seeing that call being ignored. We’re instead seeing real needs being met, and new opportunities being explored.  There’s still a ways to go, but here’s to a continuing awareness of good mobile design.
  5. Gamification is still being confounded: people aren’t really making clear conceptual differences around games. We’re still seeing linear scenarios confounded with branching, we’re seeing gamification confounded with serious games, and more.  Some of these are because the concepts are complex, and some because of vested interests.
  6. Games  seem to be reemerging: while the interest in games became mainstream circa 2010 or so, there hasn’t been a real sea change in their use.  However, it’s quietly feeling like folks are beginning to get their minds around Immersive Learning Simulations, aka Serious Games.   There’s still ways to go in really understanding the critical design elements, but the tools are getting better and making them more accessible in at least some formats.
  7. Design is becoming a ‘thing’: all the hype around Design Thinking is leading to a greater concern about design, and this is a good thing. Unfortunately there will probably be some hype and clarity to be discerned, but at least the overall awareness raising is a good step.
  8. Learning to learn seems to have emerged: years ago the late great Jay Cross and I and some colleagues put together the Meta-Learning Lab, and it was way too early (like so much I touch :p). However, his passing has raised the term again, and there’s much more resonance. I don’t think it’s necessarily a  thing yet, but it’s far greater resonance than we had at the time.
  9. Systems are coming: I’ve been arguing for the underpinnings, e.g. content systems.  And I’m (finally) beginning to see more interest in that, and other components are advancing as well: data  (e.g. the great work Ellen Wagner and team have  been doing on Predictive Analytics), algorithms (all the new adaptive learning systems), etc. I’m keen to think what tags are necessary to support the ability to leverage open educational resources as part of such systems.
  10. Greater inputs into learning: we’ve seen learning folks get interested in behavior change, habits, and more.  I’m thinking we’re going to go further. Areas I’m interested in include myth and ritual, powerful shapers of culture and behavior. And we’re drawing on greater inputs into the processes as well (see 7, above).  I hope this continues, as part of learning to learn is to look to related areas and models.

Obviously, these are things I care about.  I’m fortunate to be able to work in a field that I enjoy and believe has real potential to contribute.  And just fair warning, I’m working on a few areas  in several ways.  You’ll see more about learning design and the future of work sometime in the near future. And rather than generally agitate, I’m putting together two specific programs – one on (e)learning quality and one on L&D strategy – that are intended to be comprehensive approaches.  Stay tuned.

That’s my short list, I’m sure more will emerge.  In the meantime, I hope you had a great 2015, and that your 2016 is your best year yet.

Confounding generations?

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At the recent Online Educa Berlin, Laura Overton of Towards Maturity  presented some stats in our joint session.  While she mentioned that she really had to look for results where there were differences by age, she  of course found some. (Which already is a problem;  5% of results are likely to be significant by random chance!). However, in at least one case I think the results is explained by another factor than generations (not that she was making the claim).  In those statistics was an interesting result that I want to look at from two different perspectives.

So, this result, one of the most striking, was that 64% of those 21-30 were motivated to learn to obtain certification, while only 22% of those over 50 were so motivated.  That really seems like to might fit the generational differences story, where over 50s, the baby boomers, differ from the millennials.  Here, the millennials are worried that the world is not a safe place, and want accreditation to help preserve their access (my rough story based upon millennial descriptions). And the baby boomers are more positive and trusting, so consequently feel less drive for certification. Or create your own explanation for the divergence based upon the differences between the generations.

Ok, what struck me is that there’s a totally different explanation: those in the 21-30 range are young and new. They want certifications to support their advancements, as they don’t have a lot of experience.  Those who are older have real experience to point to, and have less need for external validation of their learning.  Here what we’re seeing is that this is not related to generations, but by age.  And that’s very different explanation for the same phenomena.

The core point is that if the generational explanation would be true,  this would stay true as these generations aged. The millennials, at age 50, would still care more about certifications.   If it’s more a ‘stage of life’ thing, as they aged they’d care less, but those folks who were growing into that younger range would also demonstrate the differences.

The problem is that there are confounding explanations for the same data.   So what else do we look at?  Interestingly, in my research about what the  data says, I’ve found several studies that show that when you ask folks what they value in the workplace,  there is no significant difference by generation.  That is, generations as defined by societal circumstances at the time of growing up doesn’t have an impact on workplaces.

Now, there have been a few exceptions, including the above (and I’ll reiterate, Laura wasn’t make a generational claim for this), but the question then becomes whether there are other explanations for the differences, such as age, not context.  Could other factors, such as natural age differences, create a perception of generational differences that truly isn’t persistent?

Ok, I’ll buy that WWII was a global event and the impacts were clear and measured.  But other than that, sure there were landmark popular culture elements and zeitgeists, but I think most of the other defining characteristics are nowhere near as clearly delineated in impact (I’ve heard claims of divorce, latchkey kids, etc being generational factors), and I  doubt that they’re sufficiently delineated to create the defining characteristics that are proposed.

My take home?  Be suspicious of someone pushing a particular viewpoint without scrutiny of alternate hypotheses (including mine).  There may be a better explanation than the one someone has a vested interest in pushing.  Is there a real millennial difference?  Certainly the so-called ‘digital native’ myth has been debunked (e.g. no better at search queries or evaluating results of same than any others), so maybe we want to be wary of other claims.  I’m willing to be wrong on this, but my research says that the data seems to point to other explanations than defining generations.  What say you?

Useful cognitive overhead

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As I’ve reported before, I started mind mapping keynotes not as a function of filling the blog, but for listening better.  That is, without the extra processing requirement of processing the talk into a structure, my mind was (too) free to go wandering. I only posted it because I thought I should do  something with it!  And I’ve realized there’s another way I leverage cognitive overhead.

As background, I diagram.  It’s one of the methods I use to reflect.  A famous cognitive science article talked about how diagrams are representations that map conceptual relationships to spatial ones, to use the power of our visual system to facilitate comprehension. And that’s what I do, take something I’m trying to understand, some new thoughts I have, and get concrete about them.  If I can map them out, I feel like I’ve got my mind around them.

I use them to communicate, too. You’ve seen them here in my blog (or will if you browse around a bit), and in my presentations.  Naturally, they’re a large part of my workshops too, and even reports and papers.  As I believe models composed of concepts are powerful tools for understanding the world, I naturally want to convey them to support people in applying them themselves.

Now, what I realized (as I was diagramming) is that the way I diagram actually leverages cognitive overhead in a productive way. I use a diagramming tool (Omnigraffle if you must know, expensive but works well for me) to create them, and there’s some overhead in getting the diagram components sized, and located, and connected, and colored, and…  And in so doing, I’m allowing time for my thoughts to coalesce.

It doesn’t  work with paper, because it’s hard to edit, and what comes out isn’t usually right at first.  I move things around, break them up, rethink the elements.  I can use a whiteboard, but usually to communicate a diagram already conceived.  Sometimes I can capture new thinking, but it’s easy to edit a whiteboard. Flip charts are consequently more problematic.

So I was unconsciously leveraging the affordances of the tool to help allow my thinking to ferment/percolate/incubate (pick your metaphor).  Another similar approach is to seed a question you want to answer or a thought you want to ponder before some activity like driving, showering, jogging, or the like.  Our unconscious brain works powerfully in the background, given the right fodder.  So hopefully this gives you some mental fodder too.

When (and not) to crowdsource?

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Will Thalheimer commented on my ‘reconciliation‘ post, and pointed out that there are times when you would be better off going to an expert. His apt observation is that there are times when it makes sense to crowdsource and when not to, but it wasn’t clear to him or me when each was. Naturally that led to some reflection, and this is where I ended up.

As a framework, I thought of Dave Snowden’s Cynefin model.  Here, we break situations into one of four types: simple or obvious, where there are known answers; complicated, where it requires known expertise to solve;  complex, where we’re dealing in new areas; and  chaotic, where things are unstable.

With this model, it’s clear that we’ll know what to do in the simple cases, and we should bring in experts to deal with the complicated. For chaotic systems, the proposal is just to do something, to try to move it to one of the other three quadrants!  It’s the other where we might want to consider social approaches.

The interesting place is the complex.  Here, I suggest, is where innovation is needed. This is the domain of trouble-shooting unexpected problems, coming up with new products or services, researching new opportunities, etc.  Here is where you determine experiments to try, and formulate plans to test.  While when the stakes are low you might do it individually, when the stakes are high you bring together a group.  It may be more than one expert, but here’s where you want to use good processes such as brainstorming (done right), etc.

Here is where the elements of the learning organization come in.  Here is where you want to value diversity, be open to new ideas, make it safe to contribute, and provide time for reflection. Here is where you want to tap into collaboration and cooperation. Here is where you want to find ways to get people to work together effectively.

Will was insightful  in pointing out that you don’t always want to tap into the wisdom of the crowd, not least for pragmatics, so we want to be clear about when you do.  My point is that we want to be able to when it makes sense, and facilitate this as part of the new role for L&D in the revolution. So, as this is new to me, let me tap into the power of the crowd here: does this  make sense to you?

Facilitating Knowledge Work #wolweek

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In the course of some work with a social business agency, was wondering how to represent the notion of facilitating continual innovation.  This representation emerged from my cogitations, and while it’s not quite right, I thought I’d share it as part of Work Out Loud week.

5RsThe core is the 5 R’s: Researching the opportunities, processing your explorations by either Representing them or putting them into practice (Reify) and Reflecting on those, and then Releasing them.  And of course it’s recursive: this is a  release of my representation of some ideas I’ve been researching, right?    This is very much based on Harold Jarche’s Seek-Sense-Share model for Personal Knowledge Mastery (PKM). I’m trying to be concrete about different types of activities you might do in the Sense section as I think representations  such as diagrams are valuable but very different than active application via prototyping and testing.  (And yes, I’m really stretching to keep the alliteration of the R’s.  I may have to abandon that. ;)

What was interesting to me was to think of the ways in which we can facilitate around those activities.  We shouldn’t assume good research skills, and assist individuals in doing understanding what qualifies as good  searches for input and evaluating the hits, as well as  establishing and filtering existing information streams.

We can and should also  facilitate the representations of interpretations, whether informing properties of good diagrams,  prose, or other representation forms.  We can help make the processes of representation clear as well. Similarly, we can  develop understanding of useful experimentation approaches, and how to evaluate the results.

Finally, we can communicate the outcomes of our reflections, and collaborate on all these activities whether research, representation, reification (that R is a real stretch), and reflection.  As I’m doing here, soliciting feedback.

I do believe there’s a role for L&D to look at these activities as well, and ‘training’ isn’t the solution. Here the role is very much facilitation.   It’s a different skill set, yet a fundamental contribution to the success of the organization. If you believe, like I do, that the increasing rate of change means innovation is the only sustainable differentiator for success, then this role is crucial and it’s one I think L&D has the opportunity to take on.  Ok, those are my thoughts, what are yours?