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Clark Quinn’s Learnings about Learning

Work Experiment

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At a  point some days ago, I got the idea to map out different activities by their role as executing versus innovating, and whether it’s in the head or in the world. And I’ve been playing with it since.  I’m mapping some  ways of getting work done, at least the mental aspects, across those dimensions.

LearningSpace

I’m not sure I’ve got things in the right places.  I’m not even sure what it really means. I’ve some ideas, but I think I’m going to try something new, and ask you what  you think it means.  So, what’s interesting and/or important here?

Top 10 Tools for Learning 2016

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It’s that time again: Jane Hart is running her 2016 (and 10th!) Top 100 Tools for Learning poll. It’s a valuable service, and points out some interesting things and it’s interesting to see the changes over time.  It’s also a way to see what others are using and maybe find some new ideas.  She’s now asking that you categorize them as Education, Training & Performance Support, and/or Personal Learning & Productivity.  All  of mine fall in the latter category, because my performance support tools are productivity tools!  So here’re my votes, FWIW:

Google Search is, of course, still my top tool. I’m looking up things several if not many times a day. It’s often a gateway to Wikipedia, which I heavily rely on, but a number of times I find other sources that are equally valuable, such as research or practice sites that have some quality inputs.

Books are still a major way I learn. Yes, I check out books from the library and read them.  I also acquire and read them on my iPad, such as Jane’s great  Modern Workplace Learning.  In my queue is Jane Bozarth’s  Show Your Work.  

Twitter is a  go-to. I am pointed to many serendipitously interesting things, and of course I point to things as well. The learning chats I participate in are another way twitter helps.  Tweetdeck is my twitter tool; columns are a must.

Skype  is a tool I use for communicating with folks to get things done, but also to have conversations (e.g. with my ITA colleagues), whether chat or voice.

Facebook  is also a way I stay in touch with friends and colleagues (those colleagues that I also consider friends; Facebook is more a personal learning tool than  a business tool for me).

LinkedIn  is a way to stay in touch with people, and in particular the  L&D Revolution group is where I want to keep the dialog alive about the opportunity. The articles in LinkedIn  are occasionally of interest too, and it’s always an education to see who wants to link ;).

WordPress is my blogging tool (where you’re at right now), and it’s a way I think ‘out loud’ and the feedback I get is a wonderful way to learn.  Things that eventually appear in presentations and writing typically appear here first, and some of the work I do for others manifests here (typically anonymized).

Word  is my go-to writing tool, and while I use Pages at times too (e.g. if I’m traveling with my iPad), Word is my industrial strength tool.  Writing forces me to get concrete about my thinking.

Omnigraffle  is as always  my diagramming tool, and it’s definitely a way I express and refine my thinking.  Obviously, you’ll see my diagrams here, but also in presentations and articles/chapters/books. And, of course, my mindmaps.

Keynote  is my presentation creating tool. I sometimes  have to export to PowerPoint, but Keynote is where I work natively.  It helps me turn my ideas from diagrams and/or writing into a story to tell with visual support.

So those are my ‘learning’ tools, for now. Some are ‘content’, some are social media, some are personal representational tools, but reading and talking with others and representing my own thinking are  major learning activities for me.

 

Context Rules

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I was watching a blab  (a video chat tool) about the upcoming FocusOn Learning, a new event from the eLearning Guild. This conference combines their previous mLearnCon and Performance Support Symposium with the addition of  video.  The previous events have been great, and I’ll of course be there (offering a workshop on cognition for mobile, a mobile learning 101 session, and one on the topic of this post). Listening to folks talk about the conference led me to ponder the connection, and something struck me.

I find it kind of misleading that it’s FocusOn  Learning, given that performance support, mobile, and even video typically is  more about acting in the moment than developing over time.  Mobile device use tends to be more about quick access than extended experience.  Performance support is more about augmenting our cognitive capabilities. Video (as opposed to animation or images or graphics, and similar to photos) is about showing how things happen  in situ (I note that this is my distinction, and they may well include animation in their definition of video,  caveat emptor).  The unifying element to me is context.

So, mobile is a platform.  It’s a computational medium, and as such is the same sort of computational  augment that a desktop  is.  Except that it can be with you. Moreover, it can have sensors, so not just providing computational capabilities where you are, but  because of when and where you are.

Performance support is about providing a cognitive augment. It can be any medium – paper, audio, digital – but it’s about providing support for the gaps in our mental capabilities.  Our architecture is powerful, but has limitations, and we can provide support to minimize those problems. It’s about support  in the moment, that is, in context.

And video, like photos, inherently captures context.  Unlike an animation that represents conceptual distinctions separated from the real world along one or more dimensions, a video accurately captures what the camera sees happening.  It’s again about context.

And the interesting thing to me is that we can support performance in the moment, whether a lookup table or a howto video, without learning necessarily happening. And that’s  OK!  It’s also possible to use context to support learning, and in fact we can provide least material to augment a context than create an artificial context which so much of learning requires.

What excited me was that there was a discussion about AR and AI. And these, to me, are also about context.  Augmented Reality layers  information on top  of your current context.  And the way you start doing contextually relevant content delivery is with rules tied to content descriptors (content systems), and such rules are really part of an intelligently adaptive system.

So I’m inclined to think this conference is about  leveraging context in intelligent ways. Or that it can be, will be, and should be. Your mileage may vary ;).

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.

Content isn’t a silo

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I mentioned in my previous post that I was talking at the xAPI camp about content strategy, and on the way in I created a new diagram to convey a concept I wanted to discuss.  Of course one of the things I agitate about for the revolution is that L&D can’t hide away but has to start engaging across the business.  And, let me add, that’s only increasing.  Our silos are breaking down. To wit:

ContentStrategyHere I was trying to think of activities that cross silos.  So, of course, the overall role of the business aligns and integrates the separate actions of sales, marketing, IT, etc.  And, to suit my campaign, I looked for others.

Obviously, data is coming out across the organization.  As I mentioned in that last post, we can only look at the impact of L&D on performance if we can start working with data from the business units, but data from customer service influences marketing, and so on.

The web, too, is a channel for many activities. Units that reach customers, for instance,  include customer service, customer education, sales & marketing, and more.  Heck, the supply chain is increasingly connected by the web, and data.

Consequently, so too is content.  Content is used in many ways, whether via  apps, through the web, or print.  And for many purposes: sales, marketing, tech support, and of course learning.  And there’s a point to all this.

L&D, with it’s hard-wired content, needs to pull on the big kids pants, and start getting with content systems: content engineering, governance, and strategy. Truly, if you want to be part of the strategic picture going forward, you have to work with information tools. Industrial age methods won’t cut it. So, are you thinking about how to move  to a content strategy?

xAPI conceptualized

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A couple of weeks ago, I had the pleasure of attending the xAPI Base Camp, to present on content strategy. While I was there, I remembered that I have some colleagues who don’t see the connection between xAPI and learning.  And it occurred to me that I hadn’t seen a good diagram that helped explain how this all worked.  So I asked and was confirmed in my suspicion. And, of course, I had to take  a stab at it.

xAPIWhat I was trying to capture was how xAPI tracked activity, and that could then be used for insight. I think one of the problems people have is that they think xAPI is a solution all in itself, but it is just a syntax for reporting.

So when A might demonstrate a capability at a particular level, say at the end of learning, or by affirmation from a coach or mentor, that gets recorded in a Learning Record Store. We can see that A and B demonstrated it, and C demonstrated a different level of capability (it could also be that there’s no record for C, or D, or…).

From there, we can compare that activity with results.  Our business intelligence system can provide   aggregated data of performance for A (whatever A is being measured on: sales data, errors, time to solve customer problems, customer satisfaction, etc). With that, we can see if there are the correlations we expect, e.g. everyone who demonstrated  this level of capability has reliably better performance than those who didn’t.  Or whatever you’re expecting.

Of course, you can mine the data too, seeing what emerges.  But the point is that there are a wide variety of things we might track (who touched this job aid, who liked this article, etc), and a wide variety of impacts we might hope for.  I reckon that you should plan what impacts you expect from your intervention, put in checks to see, and then see if you get what you intended.  But we can look at a lot more interventions than just courses. We can look to see if those more active in the community perform better, or any other question tied to a much richer picture than we get other ways.

Ok, so you can do this with your own data generating mechanisms, but standardization has benefits (how about agreeing that red means stop?).  So, first, does this align with your understanding, or did I miss something?  And, second does this help, at all?

Organizational Knowledge Mastery?

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I was pointed to a report from MIT Sloan Management talking about how big data was critical to shorten ‘time to insight’. And I think that’s a ‘good thing’ in the sense that knowing what’s happening faster is clearly going to be part of agility.  But I  must be missing something, because unless I’m mistaken, big data can’t  give you the type of insights you  really need.

Ok, I get it. By the ‘test and learn’ process of doing experiments and reading reactions, you can gather data quickly. And I’m all for this.  But this is largely  internal, and I think the insights needed are external. And yes, the experiments can be outside the firewall, trying new things with customers and visitors and reading reactions, but that’s still in the realms of the understood or expected. How can such a process detect the disruptive influences?

Years ago, with friend and colleague  Eileen Clegg, we wrote a chapter based upon her biologist husband’s work in extremophiles, looking for insight into how to survive in tough times.  We made analogies from a number of the biological phenomena, and one was the need to be more integrated with the environment, sensing changes and bringing them in. Which of course, triggered an association.

If we adapt Harold Jarche’s Personal Knowledge Mastery (or PKM), which is about Seek-Sense-Share as a mechanism to grow our own abilities, to organizations, we can see a different model.  Perhaps an OKM?  Here’s organizations seek knowledge sources, sense via experiments and reflection, and share internally (and externally, as appropriate ;).

This is partly at the core of the Coherent Organization model as well, where communities are seeking and sharing outside as ways to continue to evolve and feed the teams whose work is driving the organization forward. It’s about flows of information, which can’t happen if you’re in a Miranda Organization. And so while big data is a powerful tool, I think there’s something more required.

I think the practices and the culture of the organization are more important.  If you don’t have those right, big data won’t give big insights, and if you do, big data is just one of your tools.  Even if you’re doing experiments, it might be small data, carefully instrumented experiments targeted at getting specific outcomes, rather than big data, that will give you what you need.  But more importantly, sensing what’s going on outside, having diverse interests and a culture of curiosity is going to be the driver for the unexpected opportunities.

So yes, use the tools to hand and leverage the power of technology, but focus on motivations and culture so that the tools will be used in the important ways.  At least that was my reaction.  What’s yours?

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.

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.