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Clark Quinn’s Learnings about Learning

Pidgin Learning

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The other day, my colleague Jane Hart wrote an interesting post comparing getting comfortable with learning in the real world to the experience of learning a new language. In my recent experience visiting my aunt in München and then family friends in a small village outside Bayreuth, I had a chance to experience the intermediate stages of transitioning from one language, really one culture, to another.

As I sit on a train watching the landscape change from snowy villages to rainy towns, one of my learnings was that the ‘camaraderie‘ of the people trying to communicate means a lot. While my German is pretty bad, and my aunt‘s and the friends English is better but unpracticed, we could communicate. This was because there were good intentions all around. We were not looking for ways to misinterpret, or to avoid on the grounds that we could not comprehend. We instead were looking for ways to understand.

It‘s clear that we were speaking a ‘pidgin‘ language, a simplified hybrid of the two, and actually both the German and the English were butchered as a result. We would mix words from both languages when our vocabulary failed us, and find creative ways to express our thoughts. And express our thoughts we did. Between getting directions to the post office to mail the package of goodies my aunt had expected me to bring back in my streamlined luggage, running her errands, and getting my glasses fixed, all went well.

Similarly with less-familiar folks; the wonderfully warm people who hosted me in the small village were family friends, but hadn‘t seen me for 20 years, even though they know my mother. Regardless, it was easy to help her buy a computer, and for them to take me through a dark and snow-covered village in a small valley to the once a year weekend Christmas Market, where I met their friends and we drank glühwein, sang some Xmas songs, translated questionable jokes into English, and had a truly magical time.

The implications are clear: when people are committed to the process, they can be incredibly productive despite challenges. Conversely, when they‘re not, even insignificant obstacles can become complete barriers.

the Power of Pull

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John Seely Brown has given the leading keynote to the DevLearn conference with an inspiring talk about how the world needs to move to scalable capacity building using collaboration (we’re totally in synch!)

John Seely Brown Keynote Power of Pull

Don’t take learning skills for granted!

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In all the excitement about empowering learners by providing rich information and social environments, it’s too easy to think that “if you build it, they will learn”.   Yet the evidence is to the contrary. While there are numerous components, including a culture that tolerates diversity and doesn’t punish honest mistakes, one that is easy to neglect the actual learning skills of employees. My ITA colleague Charles Jennings made a nice first pass at a list of useful skills.

Individual learning skills include the ability to know where to look for what, and how to write good search queries and evaluate search results. While you would think that at least the so-called ‘digital natives’ (a myth) would have these skills down, a UK study   found to the contrary that they were “anything but expert searchers”. On the contrary, there was a gap between performance and self-estimates of skill (a general trend when 80% of people think they’re above average :), and little time spent evaluating the quality of the information.

Social learning skills similarly should not be assumed.   As I mentioned in my previous screed on social learning design, my experience showed that learners don’t necessarily know how to work together.   The full suite of how to: be trustworthy, be appropriate, ask for help, give help, discuss intelligently, collaborate usefully and more are all not necessarily in the competency set of your audience.

Back when Jay Cross and I were pushing Meta-Learning, we argued, and still believe, that one of the best investments you could make would be to focus on the learning skills of your team, ensuring they’re optimally capable of learning new things.   That’s certainly true for information/knowledge/concept workers.   Coupled with a similarly light and strategic investment in social learning infrastructure, it seems like the biggest bang you can get for your buck.

I suggest identifying the necessary skills, making them explicit in the organization, and even assessing and developing those skills.   In a time of increasing complexity, helping learners address complexity seems like an obviously valuable investment!

My Top 10 Learning Tools

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My ITA colleague Jane Hart regularly collects the top 100 learning tools via contributions from lots of folks.   It’s a fascinating list, worth looking at. I couldn’t use her submission sheet (some sort of system bug), so I thought I’d make an annotated post.

There are several categories of tools here.   Harold Jarche talks about our personal knowledge management task, and in that, there are the tools I use to capture and share my own thinking (like this), and tools I use to go out and find or follow information.

In the capture and share category, major tools include:

  • WordPress – I blog as a way to reflect and get feedback on my developing thoughts
  • OmniGraffle – I diagram as another way to capture my thinking, trying to map conceptual relationships onto spatial ones

Then, of course, there are the more standard thought capture and share tools:

  • Word – while I like Pages, it’s outlining just does not meet my needs, as I outline as part of my writing process
  • Keynote – while I often have to transfer to PowerPoint, here the Apple product is superior

On the information finding/sharing path, some tools I use include:

  • Google – like everyone else, I’m all over searching
  • Twitter – this has been quite the revelation, seeing pointers and getting support, and of course #lrnchat
  • Feedblitz – this is how I aggregate blogs I track and have them come via email (where I’ll see them)
  • Skype – chats and calls and videochats with folks

Then I use several tools to keep track of information:

  • Evernote – is a place to keep information across my devices (though I use Notes too, when I want it backed up and private)
  • Google Docs – where I collaborate with colleagues on thoughts

The list changes; it’s different than what I put in the last two years, I’m sure, and may be more representative of today versus tomorrow or yesterday.   And it doesn’t really include my mobile tools, where Google’s Maps app becomes quite the help, and Photos to share diagrams, and….   Also, email’s still big, and is not represented Still, it’s a reasonably representative list.

So, what am I missing?

Situated Learning Styles

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I’ve been thrust back into learning styles, and saw an interesting relationship that bears repeating. Now you should know I’ve been highly critical of learning styles for at least a decade; not because I think there’s anything wrong with the concept, but because the instruments are flawed, and the implications for learning design are questionable.

This is not just my opinion; two separate research reports buttress these positions. A report from the UK surveyed 13 major and representative learning style instruments and found all with some psychometric questions. In the US, Hal Pashler led a team that concluded that there was no evidence that adapting instruction to learning styles made a difference.

Yet it seems obvious that learners differ, and different learning pedagogies would affect different learners differently. Regardless, using the best media for the message and an enlightened learning pedagogy seems best.

Even the simple question of whether to match learners to their style, or challenge them against their style has been unanswered. One of the issues that has been that much of the learning styles have been focused on cognitive aspects, yet cognitive science also recognizes two other areas: affective and conative, that is, who you are as a learner and your intentions to learn.

These two aspects, in particular the latter, could have an effect on learners. Affective, typically considered to be your personality, is best characterized by the Big 5 work to consolidate all the different personality characteristics into a unified set. It is easy to see that elements like openness and conscientiousness would have a positive effect on learning outcomes, and neuroticism could have a negative one.

Similarly, your intention to learn would have an impact. I typically think of this as your motivation to learn (whether from an intrinsic interest, a desire for achievement, or any other reason) moderated by any anxiety about learning (again, regardless whether from performance concerns, embarrassment, or other issue). It is this latter, in particular, that manifests in several instruments of interest.   Naturally, I’m also sympathetic to learning skills, e.g learning to learn and domain-independent skills.

In the UK study, two relatively highly regarded instruments were those coming from Entwistle’s program of research, and another by Vermunt. Both result in four characterizations of learners: roughly undirected learners, surface or reproducing learners, strategic or application learners, and meaning/deep learners.   Nicely, the work by Entwistle and Vermunt is funded research and not proprietary, and their work, instruments, and prescriptions are open.

I admit that any time I see a four element model, I’m inclined to want to put it into a quadrant model. And the emergent model from these three (each of which does include issues of motivation as well as learner skills) very much reminds me of the Situational Leadership model.

The situational leadership model talks about characterizing individual employees and adapting your leadership (really, coaching) to their stage. They have two dimensions: whether the learner needs task support and whether they need motivational support.   In short, you tell unmotivated and unskilled employees what to do, but try to motivate them to get them to the stage where they’re willing but unskilled and skill them.   When they’re still skilled but uncertain you support their confidence, and finally you just get out of their way!

This seems to me to be directly analogous to the learning models. If you chose two dimensions as needing learning skills support, and needing motivational support, you could come up with a nice two way model that provides useful prescriptions for learning. In particular, it seems to me to address the issue of when do you match a learners’ style, and when do you challenge; you match until the learner is confident, and then you challenge to both broaden their capabilities and to keep them engaged with challenge.

So, to keep with the result that the UK study found where most purveyors of instruments sell them and have no reason to work together,   I suppose what I ought to do is create an learning assessment instrument and associated prescriptions of my own, label the categories, brand it, and flog it. How about:

Buy: for those not into it, get them doing it
Try: for those willing, get them to develop their learning skills and support the value thereof
My: have them apply those learning skills to their goals and take ownership of the skills
Fly: set them free and resource them

I reckon I’ll have to call it the Quinnstrument!

Ok, I’m not serious about flogging it, but I do think that we can start looking at learning skills, and the conative/intention to learn as important components of learning.   Would you buy that?

Designing for an uncertain world

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My problem with the formal models of instructional design (e.g. ADDIE for process), is that most are based upon a flawed premise.   The premise is that the world is predictable and understandable, so that we can capture the ‘right’ behavior and train it.   Which, I think, is a naive assumption, at least in this day and age.   So why do I think so, and what do I think we can (and should) do about it?   (Note: I let my argument lead where it must, and find I go quite beyond my intended suggestion of a broader learning design.   Fair warning!)

The world is inherently chaotic. At a finite granularity, it is reasonably predictable, but overall it’s chaotic. Dave Snowden’s Cynefin model, recommending various approaches depending on the relative complexity of the situation, provides a top-level strategy for action, but doesn’t provide predictions about how to support learning, and I think we need more.   However, most of our design models are predicated on knowing what we need people to do, and developing learning to deliver that capability.   Which is wrong; if we can define it at that fine a granularity, we bloody well ought to automate it.   Why have people do rote things?

It’s a bad idea to have people do rote things, because they don’t, can’t do them well.   It’s in the nature of our cognitive architecture to have some randomness.   And it’s beneath us to be trained to do something repetitive, to do something that doesn’t respect and take advantage of the great capacity of our brains.   Instead, we should be doing pattern-matching and decision-making.   Now, there are levels of this, and we should match the performer to the task, but as I heard Barry Schwartz eloquently say recently, even the most mundane seeming jobs require some real decision making, and in many cases that’s not within the purview of   training.

And, top-down rigid structures with one person doing the thinking for many will no longer work.   Businesses increasingly complexify things but that eventually fails, as Clay Shirky has noted, and   adaptive approaches are likely to be more fruitful, as Harold Jarche has pointed out.   People are going to be far better equipped to deal with unpredictable change if they have internalized a set of organizational values and a powerful set of models to apply than by any possible amount of rote training.

Now think about learning design.   Starting with the objectives, the notion of Mager, where you define the context and performance, is getting more difficult.   Increasingly you have more complicated nuances that you can’t anticipate.   Our products and services are more complex, and yet we need a more seamless execution.   For example trying to debug problems between hardware device and network service provider, and if you’re trying to provide a total customer experience, the old “it’s the other guy’s fault” just isn’t going to cut it.   Yes, we could make our objectives higher and higher, e.g. “recognize and solve the customer’s problem in a contextually appropriate way”, but I think we’re getting out of the realms of training.

We are seeing richer design models. Van Merrienboer’s 4 Component ID, for instance, breaks learning up into the knowledge we need, and the complex problems we need to apply that knowledge to.   David Metcalf talks about learning theory mashups as ways to incorporate new technologies, which is, at least, a good interim step and possibly the necessary approach. Still, I’m looking for something deeper.   I want to find a curriculum that focuses on dealing with ambiguity, helping us bring models and an iterative and collaborative approach.   A pedagogy that looks at slow development over time and rich and engaging experience.   And a design process that recognizes how we use tools and work with others in the world as a part of a larger vision of cognition, problem-solving, and design.

We have to look at the entire performance ecosystem as the context, including the technology affordances, learning culture, organizational goals, and the immediate context.   We have to look at the learner, not stopping at their knowledge and experience, but also including their passions, who they can connect to, their current context (including technology, location, current activity), and goals.   And then we need to find a way to suggest, as Wayne Hodgins would have it, the right stuff, e.g. the right content or capability, at the right time, in the right way, …

An appropriate approach has to integrate theories as disparate as distributed cognition, the appropriateness of spaced practice, minimalism, and more.   We probably need to start iteratively, with the long term development of learning, and similarly opportunistic performance support, and then see how we intermingle those together.

Overall, however, this is how we go beyond intervention to augmentation.   Clive Thompson, in a recent Wired column, draws from a recent “man+computer” chess competition to conclude “serious cognitive advantages accrue to those who are best at thinking alongside machines”.   We can accessorize our brains, but I’m wanting to look at the other side, how can we systematically support people to be effectively supported by machines?   That’s a different twist on technology support for performance, and one that requires thinking about what the technology can do, but also how we develop people to be able to take advantage.   A mutual accommodation will happen, but just as with learning to learn, we shouldn’t assume ‘ability to perform with technology augmentation’.   We need to design the technology/human system to work together, and develop both so that the overall system is equipped to work in an uncertain world.

I realize I’ve gone quite beyond just instructional design.   At this point, I don’t even have a label for what I’m talking about, but I do think that the argument that has emerged (admittedly, flowing out from somewhere that wasn’t consciously accessible until it appeared on the page!) is food for thought.   I welcome your reactions, as I contemplate mine.

The GPS and EPSS

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It’s not unknown for me to enter my name into a drawing for something, if I don’t mind what they’re doing with it.   It’s almost unknown, however, for me to actually win, but that’s actually the case a month or so ago when I put a comment on a blog prior to the MacWorld show, and won a copy of Navigon turn-by-turn navigation software for my iPhone.   I’d thought a dedicated one might be better, though I’d have to carry two devices, but if I moved from an iPhone to Droid or Pre I’d suffer. But for free…

When I used to travel more (and that’s starting again), I’ve usually managed to get by with Google Maps: put in my desired location (so glad they finally put copy/paste in, such a no-brainer rather than have to write it elsewhere and type it on, or remember, usually imperfectly).   In general, maps are a great cognitive augment, a tool we’ve developed to be very useful.   And I’m pretty good with directions (thankfully), so when a trip went awry it wasn’t too bad.   (Though upper New Jersey…well, it can get scary.)   Still, I’d been thinking seriously about getting a GPS, and then I won one!

And I’m happy to report that Navigon is pretty darn cool.   At first the audio was too faint, but then I found out that upping the iPod volume (?) worked.   (And then it didn’t the last time, at all, with no explanation I can find.   Wish it used the darn volume buttons. We’ll see next time. ) However, it does a fabulous job of displaying where you are, what’s coming up, and recalculating if you’ve made a mistake.   It’s a battery hog, keeping the device on all the time, but that’s why we have charging holders (which I’d already acquired for long trips and music).   It also takes up memory, keeping the maps onboard the device (handy if you’re in an area with bad network coverage), but that’s not a problem for me.

However, my point here is not to extol the virtues of a GPS, but instead to use them as a model for some optimum performance support, as an EPSS (Electronic Performance Support System).   There’s a problem with maps in a real-time performance situation. This goes back to my contention that the major role of mlearning is accessorizing our brain.   Memorizing a map of a strange place is not something our brains do well.   We can point to the right address, and in familiar places choose between good roads, but the cognitive overhead is too high for a path of many turns in unfamiliar territories.   To augment the challenge, the task is ‘real time’, in that you’re driving and have to make decisions within a limited window of recognition.   Also, your attention has to be largely outside the vehicle, directed towards the environment. And to cap it all of, the conditions can be dark, and visibility obscured by inclement weather.   All told, navigation can be challenging.

While the optimal solution is a map-equipped partner sitting ‘shot-gun’, a GPS has been designed to be the next best thing (and in some ways superior).   It has the maps, knows the goal, and often more about certain peculiarities of the environment than a map-equipped but similarly novice partner.   A GPS also typically does not get it’s attention distracted when it should be navigating.   It can provide voice assistance while you’re driving, so you don’t need to look at the device when your attention needs to be on the road, but at safe moments it can display useful guidance about lanes to be in (and avoid) visually, without requiring much screen real estate.

And that’s a powerful model to generalize from: what is the task, what are our strengths and limitations, and what is the right distribution of task between device and individual?   What information can a device glean from the immediate and networked environment, from the user, and then provide the user, either onboard or networked?   How can it adapt to a changing state, and continue to guide performance?

Many years ago, Don Norman talked about how you could sit in pretty much any car and know how to drive it, since the interface had time to evolve to a standard.   The GPS has similarly evolved in capabilities to a useful standard.   However, the more we know about how our brains work, the more we can predetermine what sort of support is likely to be useful.   Which isn’t to say that we still won’t need to trial and refine, and use good principles of design across the board, interface, information architecture, minimalism, and more.   We can, and should, be thinking about meeting organizational performance, not just learning needs.   Memorizing maps isn’t necessarily going to be as useful as having a map, and knowing how to read it.   What is the right breakdown between human and tool in your world, for the individuals you want to perform to their best?   What’s their EPSS?

And on a personal note, it’s nice to have the mobile learning manuscript draft put to bed, and be able to get back into blogging and more. A touch of the flu has delayed my ability to think again, but now I’m ready to go.   And off I go to the Learning Solutions conference in Orlando, to talk mobile, deeper learning, and more.   The conference will both interfere with blogging and provide fodder as well.   If you’re there, please do say hello.

Writing and the 4C’s of Mobile

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As I’ve mentioned before, I’m writing a book on mobile learning.   My only previous experience was writing Engaging Learning, where the prose practically exploded from my fingers. This time is different.

The prose actually does flow quite easily from my fingers,   but I find myself restructuring more often than last time.   This is a bigger topic, and I keep uncovering new ways to think about mobile and new facets to try to include.   As a consequence, as the deadline nears (!), I find myself more and more compelled to put all free time into the text.

There’s a consequence, and that is a decreasing frequency of blogging.   I’m coming up with some great ideas, but I’ve got to get them into the book, and I’m not finding time to rewrite them.

When I do have ideas in other areas (and I always do), I’m finding that they disappear under the pressure to meet my deadline. And there are ancillary details still to be taken care of (photos of devices, coordinating a few case studies).

Further, as neither blogging or the book (directly) pay the bills, I’ve still got to meet my client needs.   Also, I’m speaking at the Learning Solutions conference and involved in various ways with several others, and some deliverables are due soon. I’m feeling a tad stretched!

So, in many ways, this is an apology for the lack of blog posts, and the fact that it will likely to be sparse for another month and some.

As a brief recompense, I did want to communicate one framework that I’m finding helpful.   I’ll confess that it’s very similar to Low and O’Connell’s 4 R’s (for which I can’t find a link!?!; from my notes: Record, Recall, Reinterpret , Relate), but I can never remember them, which means they need a new alliteration.   Mine’s a bit simpler:

  • Content: the provision of media (e.g. documents, audio, video, etc) to the learner/performer
  • Compute: taking in data from the learner and processing it
  • Communicate: connecting learners/performers with others
  • Capture: taking in data from sensors including camera, GPS, etc, and saving for sharing or reflection

I find this one of several frameworks that support ‘thinking different’ about mobile capabilities.   I’ll be interested to hear your thoughts.

Is it all problem-solving?

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I’ve been arguing for a while that we need to take a broader picture of learning, that the responsibility of learning units in the organization should be ensuring adequate infrastructure, skills, and culture for innovation, creativity, design, research, collaboration, etc, not just formal learning. As I look at those different components, however, I wonder if there’s an overarching, integrating viewpoint.

When people go looking for information, or colleagues, they have a problem to solve. It may be a known one with an effective solution, or it may be new. It doesn’t matter whether it’s a new service to create, a new product to design, a customer service problem, an existing bug, or what. It’s all really a situation where we need an answer and we don’t have one.

We’ll have some constraints, some information, but we’re going to have to research, hypothesize, experiment, etc. If it’s rote, we ought to have it automated, or we ought to have the solution in a performance support manner. Yes, there are times training is part of the solution. But this very much means that first, all our formal solutions (courses, job aids, etc) should be organized around problem-solving (which is another way of saying that we need the objectives to be organized around doing).

Once we go beyond that, it seems to me that there’s a plausible case to be made that all our informal learning also needs to be organized from a problem-solving perspective. What does that mean?

One of the things I know about problem-solving is that our thought processes are susceptible to certain traps that are an outcome of our cognitive architecture. Functional fixedness and set-effects are just two of the traps. Various techniques have evolved to overcome these, including problem re-representation, systematicity around brain-storming, support for thinking laterally, and more.

Should we be baking this into the infrastructure? We can’t neglect skills. Assuming that individuals are effective problem-solvers is a mistake. The benefits of instruction in problem-solving skills have been demonstrated. Are we teaching folks how to find and use data, how to design useful experiments and test solutions? Do folks know what sort of resources would be useful? Do they know how to ask for help, manage a problem-solving process, and deal with organizational issues as well as conceptual ones?

Finally, if you don’t have a culture that supports problem-solving, it’s unlikely to happen. You need an environment that tolerates experimentation (and associated failure), that support sharing and reflection, that rewards diverse participation and individual initiative, you’re not going to get the type of pro-active solutions you want.

This is still embryonic, but I’m inclined to believe that there are some benefits from pushing this approach a bit. What say you?

Creating meaningful experiences

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What if the learner’s experience was ‘hard fun’: challenging, but engaging, yielding a desirable experience, not just an event to be tolerated, OR what is learning experience design?

Can you imagine creating a ‘course’ that wins raving fans?   It’s about designing learning that is not only effective but seriously engaging.   I believe that this is not only doable, but doable under real world constraints.

Let me start with this bit of the wikipedia definition of experience design:

the practice of designing…with a focus placed on the quality of the user experience…, with less emphasis placed on increasing and improving functionality

That is, experience design is about creating a user experience, not just focusing on their goals, but thinking about the process as well.     And that’s, to me, what is largely ignored in creating elearning is thinking about process from the learner’s perspective. There are really two components: what we need to accomplish, and what we’d like the learner to experience.

Our first goal still has to look at the learning need, and identify an objective that we’d like learners to meet, but even that we need to rethink.   We may have constraints on delivery environment, resources, and more that we have to address as well, but that’s not the barrier.   The barrier is the mistake of focusing on knowledge-level objectives, not on meaningful skill change.   Let me be very clear: one of the real components of creating a learning experience is ensuring that we develop, and communicate, a learning objective that the learner will ‘get’ is important and meaningful to them.   And we have to take on the responsibility for making that happen.

Then, we need to design an experience that accomplishes that goal, but in a way that yields a worthwhile experience.   I’ve talked before about the emotional trajectory we might want the learner to go through.   It should start with a (potentially wry) recognition that this is needed, some initial anxiety but a cautious optimism, etc.   We want the learner to gradually develop confidence in their ability, and even some excitement about the experience and the outcome.   We’d like them to leave with no anxiety about the learning, and a sense of accomplishment.   There are a lot of components I’ve talked about along the way, but at core it’s about addressing motivation, expectations, and concerns.

Actually, we might even shoot for more: a transformative experience, where the learner leaves with an awareness of a fundamental shift in their understanding of the world, with new perspectives and attitudes to accompany their changed vocabulary and capabilities.   People look for those in many ways in their life; we should deliver.

This does not come from applying traditional instructional design to an interview with a SME (or even a Subject Matter Network, as I’m increasingly hearing and inclined to agree).   As I defined it before, learning design is the intersection of learning, information, and experience design.   It takes a broad awareness of how we learn, incorporating viewpoints behavior, cognitive, constructive, connective, and more.   It takes an awareness of how we experience: media effects on cognition and emotion, and of the dramatic arts.   And most of all, it takes creativity and vision.

However, that does not mean it can’t be developed reliably and repeatably, on a pragmatic basis.     It just means you have to approach it anew.   It take expertise, and a team with the requisite complementary skill sets, and organizational support. And commitment.   What will work will depend on the context and goals (best principles, not best practices), but I will suggest that with good content development processes, a sound design approach, and a will to achieve more than the ordinary.   This is doable on a scalable basis, but we have to be willing to take the necessary steps.   Are you ready to take your learning to the next level, and create experiences?