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Clark Quinn’s Learnings about Learning

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iPads do make sense for schools

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Donald Clark (the UK one) generally writes great posts: insightful and irreverent, and consequently fun. I like that he is willing to counter the prevailing wisdom with good research. I hope to someday meet him. However, his recent post against iPads in the classroom seemed to me to miss a couple of points.  Not that I fully disagree with him, but that I think that some elaboration might shed some light.  Note: I’m starting by focusing on K-6, not middle school or higher ed. He does acknowledge the potential value for young kids, so we’re not quibbling too much, but I still want to make a few points.

He first claims that they don’t support writing.  Yes, that’s true, the touchscreen isn’t the same as a keyboard.  However, my colleague Scott Marvel has filmed lots of kids with iPads and he tells me they don’t have much trouble using the touchscreen (they’re not highly capable with regular keyboards at young ages), they use speech to text as well, and also take freehand notes too.  So writing isn’t horribly impeded on iPads for younger kids.  Further,  writing shouldn’t necessarily be done in the classroom anyway. Learning to type, and heavy writing should be done offline, and shared for feedback in class.  It’s a waste of valuable teacher time, when they could be facilitating meaningful engagement.

I also note that he says they don’t work for creative work, and that they should be creating, not consuming. I generally agree on the creation aspect (while noting that flipping the classroom and getting reading and tutorials done at home isn’t bad and the latter isn’t passive consumption), but note that he’s missed one of the big content creation aspects that smaller devices support: taking pictures and filming videos.  It may be that iPod Touches are even better for K-6, but running around and filming with a tablet (particularly an iPad mini, which may be optimal for K-6) is better than a laptop.  And I’ll bet that the video and photo editing tools on tablets are just the simple tools that kids really need; they just need basic capabilities.

I note that I didn’t buy my iPad for content consumption: when it was announced I wrote it off for just that reason. However, between the time it was announced and became available, I saw how I would use it to be more productive: creating not consuming.  And I bought one the first day it came out for that reason.

Let me also elaborate on the size point.  Elliot Soloway many years ago made the point that laptops were the wrong form-factor for young kids, and he started using Palm Pilots.  I think it’s still the case that a laptop isn’t right for kids, and that touch screens make much more sense than keyboards and touch pads or mice.  There are plenty of people noticing how 2 year olds are able to use iPads!

Donald also talks about coding, and it  is a shame that there isn’t a HyperCard equivalent for the iPad (though Infinite Canvas may be such, tho’ it’d need educational pricing).  However, something like Scratch for the iPad would be a real opportunity (precluded by Apple, unfortunately, I wonder if there’s an Android version).  And coding K-6 other than scratch doesn’t make a lot of sense.

He says that iPads are problems for teachers, and I’m somewhat sympathetic. However, too often I’ve seen instances where teachers weren’t properly prepared.  For instance, something like GoClass (caveat: partner), while still a bit instructivist, could scaffold teachers initially until they began to see the opportunities.  And there needs to be mobile management software to deal with the issues. However, I’m hard pressed to believe iPads are any  more fragile than laptops.

Now, for higher grades, I take the point.  My lad and lass both have MacBook Pros, though they each also have an iPod touch (lad’s is my old iPhone without a sim card) that they use.  Note that they do not take the laptops to school in most cases.  I think that a nice augment for mobile work, getting out of the classroom (please!) is much better facilitated with a tablet or pocketable (smartphone/PDA) than a laptop.  And even for collaborative group work, sharing a tablet is better than hovering around a laptop.  If necessary, they could be using a bluetooth keyboard when needed.  So while I know this is hard to justify on a cost basis, I’d probably argue for an iPad or pocketable for class, and a desktop or laptop for home.

Less related, he makes the side claim that employees don’t use iPads. I’m amazed at the number that turn up at workplace learning conferences, and in meetings.  They seem pretty ubiquitous, so I don’t buy this claim.  Yes, they may be older, and some folks are using netbooks or MacBook Airs, but I see plenty of folks with iPads equipped with keyboard cases. I keep a bluetooth keyboard for when I’m cranking (e.g. writing on an airplane), but frankly just for quick notes the touchscreen keyboard works good enough for meetings, and that ‘all day’ battery really makes a difference.

And I’ll add on one other benefit for mobile devices: the ability to do contextual work. These devices can be context aware, and do things because of where you are.  This is yet to be really capitalized on, but provides a real opportunity.

I think tablets are only going to get more capable, and already make more sense in the classroom than laptops.  Teachers should be seeing how to use them, even at higher levels, and save the high-powered writing and editing out of the classroom.  Laptops make sense for learners, but not in the classroom. In the classroom, smaller and more versatile devices make more sense.

Learning There

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A respected colleague recently suggested Andy Clark’s Being There as a read to characterize the new views of cognition, so I checked it out.  The book covers the new emerging views of cognition, grounded in the connectionist revolution and incorporating a wide variety of neural and robotic studies. The interesting thing to me are the implications for learning and instruction.

The book makes the case that the way we think is not only heavily tied to our contexts, but that we co-construct the world in ways that affect our thinking in profound ways.  Studies across economic behavior, animal cognition, simulation studies, and more are integrated to make the point that they way we think is very different than the models of conscious minds sitting in meat vehicles.  Instead, we’re very driven from below and outside, and our conscious thinking is rare, hard, and language based.  Moreover, the constructs we create to think affect our thinking, making it easier. We automate much not only through learning, but we externalize.  And, our representations and understanding are very much constructed ‘on the fly’ in each new situations, as opposed to existing abstract and robust.

This isn’t easy reading.  Clark is a philosopher of mind, and covers much complex research and deep neuroscience.  The emergent picture, however, is of a mind very different than the cognitivist model.  I’m grateful that while I pursued my PhD in Cog Psych, the research going on in our co-shared lab by Rumelhart and McClelland on connectionist networks sensitized me to this viewpoint, and Hutchins work on Cognition in the Wild was similarly taking place at the same time.   Despite the challenge, there are important reasons to get our minds around this way of thinking.

The notion that providing abstract knowledge will lead to any meaningful outcome has already pretty much been debunked both empirically and theoretically.  What these models seem to suggest is that what can and will work is deep scaffolded practice and guided reflection, based upon a situated cognition. For other reasons, this is the model that Collins and Brown had in Cognitive Apprenticeship, and now we’ve a more solid philosophical basis for it.  (I also think that there are rejoinders to Kirschner, Sweller, & Clark, and Anderson, Reder, & Simon; discussing how language, including writing, is social, and that iterations between abstract models and meaningful practice is guided reflection.)

This model suggests that language is our differentiator, and that much of our higher level cognition is mediated through language.  There’s a reason consciousness feels like a dialog.  Much of our processing is beneath consciousness, and things we monitor and develop through language become compiled away inaccessible to language.

The point, to me, is that the activity-based learning model I’ve proposed has both bottom up grounding in new cognitive models, theoretical framing from anchored instruction and social constructivism, as well as empirical validity from apprenticeships and work-place learning. We need to start aligning our learning design to the cognitive realities.

Mentoring

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I was talking today with an organization that has mentoring as a very core feature of their culture, and it got me reflecting on the fantastic luck I’ve had in my career.  Even before working, I had some great teachers, and then many folks have helped shape me through my job experience.

Dick Bergeron was a 12th grade teacher at SPHS who really helped me understand a different path to learning and thinking. He did what I now know to be reciprocal teaching, had us take turns talking about what we were learning from our reading, and discussing it, with him facilitating our reflection.

I got a job while I was in college at UCSD, maintaining the computer records for the office that did tutoring on campus, and Carmel Myers and Ken Majer helped me learn how to be professional (particularly when I screwed up).

Seeing the connection between computers and learning via that job, I designed my own major in college, and with the guidance of Hugh Mehan and Jim Levin learned a lot about what constituted good research. They also lived what great student development was.

In my first job, designing and programming educational computer games for DesignWare, Jim Schuyler and Lesley Czechowicz helped me learn quite a bit about how organizations run and what good management is.

I returned to grad school at UCSD after a summer working with Ken Majer again, and Don Norman patiently helped me rediscover and expand my understanding of research and cognition, particularly the application thereof.  Don is also a great role model for top-notch critical and innovative thinking.  At the Learning Research & Development Center at the University of Pittsburgh, Leona Schauble furthered my understanding of detailed research and development while guiding my post-doctoral fellowship.

At the University of New South Wales, where I took up an academic position, Paul Compton served to model what a wise leader really looks like.  When I stepped away from the University, first Ron Watts at Open Net and then Rim Keris at Access CMC helped me take steps in understanding a strategic approach to business.

Jim Schuyler brought me back to the US, where he again mentored me on team leadership.  He also introduced me to Joe Miller, who has served to really help me understand the next level of organizational strategy.

Since then, my business partners, Charlie Gillette at Knowledge Anywhere and Mohit Bhargava at LearningMate, have helped me learn much about business models and the art of the deal.  And my colleagues in the Internet Time Alliance – Jay Cross, Jane Hart, Harold Jarche, and Charles Jennings – have taught me a lot about working as an independent and together.

These are the ones where I had an extended period of time with, but many other folks have served as models and provided assistance: Ellen Wagner and Marcia Conner are two that come to mind, and many more I’m forgetting, as well as many friends who’ve shared good times (and bad).  Naturally, my parents had a wee  bit of influence as well.  And I’ve been doubly fortunate that many of these folks have remained in touch.

There certainly is an art to mentoring, though it can be developed, and I reckon there’re also some skills to being a good mentee.  I hope I’ve been able to do some of the former, and not do the latter too  badly.

I certainly still have lots to learn, but I’m exceptionally grateful to these folks.  Many kudos to them, but of course all faults of the end product remain with the author ;).  I think increasingly we can and should be continually mentoring those we care about, and mutually mentoring each other.  Here’s to learning!

Thinking well and, well, not so well

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A number of books have crossed my path for a variety of reasons, and there’re some lessons to be extracted from three of them.  All have to do with looking at how our brains work, and some lessons therefrom.  There have been quite a bit of kerfuffle about ‘brain-based learning’, of which too much is inappropriate inferences from neuroscience to learning.  What I’m doing here is not that, but instead reporting on three books, only one of which has an explicit discussion of implications for both education and work. Still, valuable insight comes from all three.

Let me get the negative stuff out of the way first, a book that a number of folks have been excited about, Joshua Foer’s  Moonwalking with Einstein, just did nothing for me. It’s a great tale well told, but the lessons were only cautionary. In it, a journalist gets intrigued enough with remembering to train sufficiently to win the US memory championships (apparently, globally, a relatively minor accomplishment).  He reveals many memory tools to accomplish this, and points out some potential fraud along the way.  He also concludes that despite this heightened ability, there is little relevance in the real world.  We have devices that can be our memory now, and the need for these skills is questionable at best.  All in all, little benefit except to be skeptical.

A second book, Daniel Kahnemann’s Thinking Fast and Slow, is a different story. Kahnemann, and his late long-time collaborator Amos Tversky, conducted some seminal research in how we make decisions (essential reading in my grad school career).  And the best way to convey how we do this, as Kahnemann tells us, is to postulate two separate systems. Not surprisingly one is fast, and one is slow.  The book is quite long, as Kahnemann goes through every phenomenon of these outcomes that they’ve discovered (often with collaborators), but each chapter closes with some statements that capture the ways your thinking might be wrong, and ways to compensate. It could use more prescriptions and less description (I started skimming, I confess), but understand the two systems and the implications are important.  It’s a well-written and engaging book, I just wish there was a ‘take home’ version.

The fast system is, essentially, intuition. This comes in many ways from your experience, and experts in a field should trust their intuition (there’s a strong argument here for hiring someone with lots of experience) in their area.  In areas where expertise is needed, and you don’t have it, you should go to the slow system, conscious rational thought.  Which is very vulnerable to fatigue (it taxes your brain), so complex decisions late in a day of decision are suspect.  If your decision is commonplace, you can trust the fast system, and many times you’ll be using the slow system just to explain the decision the fast system came up with, but we’re prone to many forms of bias.  It’s a worthwhile read, and tells us a lot about how we might adapt our learning to develop the fast system when necessary, and when to look to the slow system.

Finally, Cathy Davidson’s written Now You See It, a book that takes an attentional phenomena and builds a strong case for more closely matching learning and work to how we really think.  (I was pointed to it by a colleague who complained that my learning theory references are old; I still take my integration of learning theory as appropriate but nice to see that more recent work reflects my take on the best from the past. :) The phenomena is related to how our attention is limited and we need help focusing it.  For a dramatic demonstration of this phenomena, view this video and follow the instructions.  Her point is that what and how we pay attention does not reflect our current schooling systems nor our traditional work environments.  She uses this and myriad examples to make a compelling case for change in both.  On the learning side, she argues strongly for making learning active and meaningful (a view I strongly support), and start using the technology. On the other side, she talks about the new ways of working consonant with our Internet Time Alliance views.  It’s very readable, as it’s funny, poignant, apt, and more.

I highly recommend Cathy Davidson’s book as something everyone should understand.  Like I said, I wish there were a ‘Readers Digest Condensed‘ version of Kahnemann’s book.  It’s worth having a look at if you’re responsible for decisions by folks, however, and at least the first few chapters if you’re at all responsible for helping people make better decisions.

Slow Learning – #change11

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This is a longer post launching my week in the #change11 MOOC (Massively Open Online Course).  

Our formal learning approaches too often don‘t follow how our brains really work.   We have magic now; we can summon up powerful programs to do our bidding, gaze through webcams across distances, and bring anyone and anything to pretty much anywhere. Our limitations are no longer the technology, but our imaginations. The question is, what are we, and should be, doing with this technology?

I like to look at this a couple of ways. For one, I like to ask myself “what would my ideal learning situation be”

Stop and ask yourself that.   Go ahead, I‘ll wait.   And feel free to share!

For me, that would be having a personal mentor traveling with me, looking at my tasks, providing both support in the moment, and developing me slowly over time.   I talked about how we might systematize that in a post titled Sage at the Side.   I also talked about this model as Layered Learning.   That is, layering on learning across our life.

It‘s part of what my colleague Harold Jarche talks about when saying “work is learning and learning is work”, the notion that as organizations start empowering workers to adapt to the increasing complexity, there will be no difference between work and learning, and we‘ll have to move away from the ‘event‘ model of learning and start integrating learning more closely into our activities.   We‘ll need to have a closer coupling between our activities and the resources, creating what Jay Cross calls a workscape and I‘ve termed the performance ecosystem.   That is, having the tools to hand, including job aids, people, and skill development, but in a more systemic way.

Think about that: how would you construct an optimal performance environment for yourself?   What would it look like?   Again, feel free to share.

Would it look like an LMS over here, training away over there, job aids scattered across portals, and social networks hierarchically structured or completely banned?   Would you have spray-and-pray (aka show up and throw up) training?   Online courses that are clicky-clicky bling-bling? Resources accessible by the way the organization is siloed?   Even the simple and well-documented matter of spaced learning is largely violated in most of the learning interventions we propagate.   In short, all of this is in conflict with how the human brain works!

Look at how how we learn naturally, before schooling (what I call the 7 C‘s of natural learning). We see that we learn by being engaged in meaningful activity, and working with others.   It‘s not about knowledge dump and test, but instead about coupling engaged activity with reflection.   I like Collins, Brown, & Holum‘s Cognitive Apprenticeship as a model for thinking more richly about learning.   Other learning models are not static (c.f. Merrill‘s trajectory through CDT to ID2 to Ripples), and I believe they‘ll converge where Cognitive Apprenticeship is (albeit perhaps my slightly adulterated version thereof).   It talks about modeling, scaffolding and release, naturally incorporating social and meaningful activity into the learning process.

Taking a broader look, too many of our systems have a limited suite of solutions to choose from, and ignore a number of features that we need.   The ADDIE process assumes a course, and still doesn‘t have any real support for the emotional engagement aspect. A step above is the HPT approach, which does look at the learning need and checks to see whether the solution might be a course, a job aid, realigning incentives, or some other things. However, it still doesn‘t consider, really, engagement, nor does an adequate job of considering when connecting to a person is a more valuable solution than designing content.

And while Gloria Gery‘s seminal work on Electronic Performance Support Systems suggested that these systems could not only provide support in the moment but also develop the learners‘ understanding, I still don‘t see this in any systems in practice. Even GPSs don‘t help you understand the area, they just get you where you‘re trying to go. So we are still missing something.

I‘m really arguing for the need to come up with a broader perspective on learning.   I‘ve been calling it learning experience design, but really it‘s more.   It‘s a combination of performance support and learning (and it‘s badly in need of some branding help). The notion is a sort-of personal GPS for your knowledge work. It‘s knows where you want to go (since you told it), and it knows where you are geographically and semantically (via GPS and your calendar), and as it recognizes the context it can provide not only support in the moment, but layers on learning along the way.   And I think that we don‘t know really how to look at things this way yet; we don‘t have design models (to think about the experience conceptually), we don‘t have design processes (to go from goal to solution), and we don‘t have tools (to deliver this integrated experience).   Yet the limits are not technological; we have the ability to build the systems if we can conceptualize the needed framework.

I think this framework will need to start with considering the experience design, what is the flow of information and activity that will help develop the learner (e.g. “If you get the design right, there are lots of ways to implement it”).   Then we can get into the mechanics of how to distribute the experience across devices, information, people, etc.   But this is embryonic yet, I welcome your thoughts!

Really, I‘m looking to start matching our technology more closely to our brains.   Taking a   page from the slow movement (e.g. slow X, where X = food, sex, travel, …), I‘m talking about slow learning, where we start distributing our learning in ways that match the ways in which our brains work: meaningfulness, activation and reactivation, not separate but wrapped around our lives, etc.

There‘s lots more: addressing the epistemology of learners, mobile technologies, meta-learning & 21st C skills, and deep analytics and semantic systems, to name a few, but I think we need to start with the right conceptions.   Some of my notions of design may be too didactic, after all, and we‘ll need to couple information augmentation with meaning-making to make real progress, but I think this notion of stepping back and reflecting on what we might want to achieve and where we‘re currently inadequate is an initial step.

And now the initiative is over to you. I look forward to your thoughts.

Readings

Collins, A., Brown, J.S., and Holum, A. (1991).   .   Cognitive Apprenticeship: Making Thinking Visible. American Educator, Winter.

Quinn, C. (2004).   Learning at Large.   Educational Technology, 44, 4, 45-49.

Quinn, C. (2009). Populating the LearnScape: e-Learning as Strategy. In M. Allen (Ed.) Michael Allen‘s eLearning Annual 2009. Pfeiffer, San Francisco.

Quinn, C. (2010). Rethinking eLearning.   Learning Solutions Magazine. April.

Quinn, C. (2010). Designing for an uncertain world. Learnlets. April.

Thalheimer, W. (2006).  Spacing Learning Over Time.  Work Learning Research.

Cognitive Task Analysis

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While I argue strongly for stepping away more frequently from formally structured learning, not least because we overuse it, there are times when it is crucial.  As naysayers of informal learning like to point out, you wouldn’t want your pilot or heart surgeon to have picked up the task by reading a book. When performance is critical, you really want to understand what the important elements are, whether to train them or  provide support.

A technique for doing that is Cognitive Task Analysis (CTA).  This is not a shortcut, it’s deep in terms of the knowledge elicitation techniques, the analytical task, and the representation of results.  Based in decades of cognitive research, integrating work on mental models, expertise, and more, it provides a mechanism to try to unearth the tacit understanding experts hold. Because experts compile away their knowledge to the point that they no longer have access to it, it is hard to get at this knowledge, and it takes a rigorous process.

While useful for system design, CTA is also valuable for designing performance support, and training.  The deep elicitation process can derive what the task really is, and what should be in the learner’s head and what support can and should be available.  When I talk about the performance ecosystem, particularly for complex tasks, you want just this sort of support to determine what should be distributed across formal learning and performance support.

One of the problems with CTA is that there have been a number of different approaches, and they tend to be buried in academic papers or proprietary processes. The good news is that there’s now a book about CTA, Working Minds, by Beth Crandall, Gary Klein, & Robert Hoffman, academics and practitioners.  It boils down the divergence into a fairly reasonable set of steps, with techniques that can be used at each stage.  The bad news is, of course, that it still is a daunting read, with considerable depth.

If you’ve got performances that absolutely have to be right, you’ll want to do the analysis ala CTA, and use it to decide what really needs to be in training, checklists, etc.  This goes deeper than HPT even, tho’ I think it’s as weak when it comes to the benefits of social learning, but I reckon it’s for expert *performance*, not innovation. That’s another layer.  Still, a valuable tool in the quiver of supporting performance.

Please at least understand what CTA is, and know when you need it. You may not need to be an expert in it, but you should at least be aware.

Digital Helplessness(?)

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Recently, I’ve been hearing quite a bit of concern over the possibility that reliance on digital, and increasingly mobile, technology may make us stupider.  And I don’t think this is just easy to dismiss.  In a sense, it could be a case of learned helplessness, where folks find themselves helpless  because after using the tools, folks might not have the information they need when they don’t have the tools.

Recently announced research    shows that folks change what they remember when enabled with search engines: they don’t remember the data, but instead how to find it.  Which could be a problem if they needed to know the data and are not digitally enabled in some context.

As has also been conveyed to me as a concern is whether folks might not engage in learning about their environs (e.g. when traveling), and in other ways miss out on opportunities to learn when dependent on digital devices.  Certainly, as I’ve mentioned before, I’ve been concerned  about how disabled I feel when dissociated from my digital support (my external brain).  Yet is there a concern?

My take is that it might be a concern if people are doing it unconsciously.  I think you could miss out (as m’lady points out when I am reading instead of staring out the window every moment as we take the train through another country :) on some opportunities to learn.

On the other hand, if you are choosing consciously what you want to remember, and what you want to leave to the device, then I think you’re making a choice about how you allocate your resources (a ‘good thing’).  We do this in many ways in our lives already, for instance how much we choose to learn about cooking, and more directly related, how much to learn about how to do formatting in a word processing program.

Yes, I’ve been frustrated without my support when traveling, but that’s chosen (which does not undermine my dismay at the lack of ability to access digital data overseas).  I guess I’m arguing for chosen helplessness :).  So, what are you choosing to learn and what to devolve to resources?

The role of the university?

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Unhappy in many ways with the current status of education, particularly here in the US, I’ve been thinking a lot about what would make sense. What’s the role of K12, and then what’s the role of a university?   Some thoughts recently coalesced that I thought I’d put out and see what reaction I get.

The issue, to me, covers several things.   Now, I talked some time ago about my ongoing search for wisdom, and the notion of a wise curriculum coupled with a wise pedagogy very much permeate my thinking. However, I’m probably going to be a bit more mundane here.   I just want to think what we might want to cover, and how.

Let me start with the premise that what needs to be learned to be a productive member of society needs to be learned before university, as not everyone goes further.   If we truly believe (and we should) that 21st Century skills of learning, research, communication, leadership, etc, are skills everyone needs, then those are K12 goals. Naturally, of course, we also include literacy of many sorts (not just reading and writing), and ideally, thinking like a mathematician and scientist (not science and math).

However, if those are accomplished in K12 (when I’ve previously argued learning how to think might be the role of the university, and now think it’s got to be before then), then what is the role of university?   Given that the half-life of knowledge is less than four years, focusing on preparing for a lifetime of performance is out of the question.   Similarly, pursuing one fixed course of study won’t make sense anymore, as the fields are beginning to change, and the arbitrary categorizations won’t make sense. So what then?

I’m thinking of going back to the original Oxbridge model.   In the old days, you were assigned a tutor (and advisor), and you met with that person regularly. They’d have a discussion with you, recommend some activities (read X, solve Y), and send you on your way. It was a customized solution.   Since then, for a variety of reasons (scale, mostly), the model’s turned into a mass-production model.   However, we now have the power of technology.

What if we moved to a system where individuals could spend some time exploring particular areas (like the first two years or so of college), and then put together a proposal of what they wanted to do, and how they’d pursue it, and the proposal would be vetted. Once approved, there’d be regular updates. Sure, there’d likely be some templates around for learning, but it’d be more self-directed, customizable, and put the appropriate responsibility on the learner.

I may be biased, as I designed my own major (UCSD’s Muir campus had a mechanism to design your own degree, and as they didn’t have a learning technology program…) as an undergraduate, and again you propose your research as a PhD candidate, but I think there’s a lot to recommend a learner taking responsibility for what they’re going to study and why. Granted, universities don’t do a good enough job of articulating why a program sequence has particular courses in it, but I think it’s even better if a learner at least has to review and defend it, if not choose it themselves.

Naturally, some domain-specific learning skills would emerge, but this would provide a more flexible system to match how specializations are changing so dynamically, serve as a model for life, and put the responsibility of faculty members more to mentorship and less to lecture. It would necessitate a change in pedagogy as well.

I think, in the long term, this sort of model has to be adopted.   In the short term, it will wreak havoc with things like accreditation, but that’s not necessarily a bad thing, given the flaws we’re beginning to see in the existing system, both non- and for-profit.   I reckon the for-profits might be able to move quicker, but there will be battles.   And, of course, changing faculty minds reminds me of the old joke: “How many academics does it take to change a lightbulb?”   “Change?” (And I *was* one!)

Naturally, this has implications for K12 too, as many have articulately argued that the pedagogy needs to change there as well, following the learners’ interests.   Likewise the notion of educational publishing (where is that iPad replacement for my kid’s texts?).   Those are topics for another day.

So, does this make sense? What am I missing?

How I became a learning experience designer

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Not meaning this to be a sudden spate of reflectiveness, given my last post on my experience with the web, but Cammy Bean has asked when folks became instructional designers, and it occurs to me to capture my rather twisted path with a hope of clarifying the filters I bring in thinking about design.

It starts as a kid; as Cammy relates, I didn’t grow up thinking I wanted to be a learning designer.   Besides a serious several years being enchanted with submarines (still am, in theory, but realized I probably wouldn’t get along with the Navy for my own flaws), I always wanted to have a big desk covered with cool technology, exploring new ideas.     I wasn’t a computer geek back then (the computer club in high school sent off programs to the central office to run and received the printout a day or so later), but rather a science geek, reading Popular Science and spending hours on the floor looking at the explanatory diagrams in the World Book (I’m pretty clearly a visual conceptual learner :).   And reading science fiction. I did have a bit of an applied bent, however, with a father who was an engineer and could fix anything, who helped my brother and I work on our cars and things.

When I got to UCSD (just the right distance from home, and near the beach), my ambition to be a marine biologist was extinguished as the bio courses were both rote-memorization and cut-throat pre-med, neither of which inspired me (my mom was an emergency room nurse, and I realized early on that I wasn’t cut out for blood and gore).   I took some computer science classes with a buddy and found I could do the thinking (what with, er, distractions, I wasn’t the most diligent student, but I still managed to get pretty good grades).   I also got a job tutoring calculus, physics, and chemistry with the campus office for some extra cash, and took some learning classes. I also got interested in artificial intelligence, too, and was a bit of a groupie around how we think, and really cool applications of technology.

I somehow got the job of computer support for the tutoring office, and that’s when a light went on about the possibilities of computers supporting learning.   There wasn’t a degree program in place, but I found out my college allowed you to specify your own major and I convinced Provost Stewart and two advisors (Mehan & Levin) to let me create my own program.   Fortunately, I was able to leverage the education classes I’d taken for tutoring, the computer science classes I’d also taken, and actually got out faster than any program I’d already dabbled in! (And got to do that cool ’email for classroom discussion’ project with my advisors, in 1979!)

After calling around the country trying to find someone who needed a person interesting in computers for learning, I finally got hooked up with Jim Schuyler, who had just started a company doing computer games to go along with textbook publisher’s offerings.   I eventually managed to hook DesignWare up with Spinnaker to do a couple of home games for them before Jim had DesignWare start producing it’s own home games (I got to do two cool ones, FaceMaker and Spellicopter as well as several others).

However, I had a hankering to go back to graduate school and get an advanced degree.   As I wrestled with how to design the interfaces for games, I read an article calling for a ‘cognitive engineering’, and contacted the author about where I might study this.   Donald Norman ended up letting me study with him.

The group was largely focused on human-computer interaction, but I maintained my passion for learning solutions.   I did a relatively mainstream PhD but while focusing on the general cognitive skill of analogical reasoning, I also attempted an intervention to improve the reasoning.

Though it was a cognitive group, I was eclectic, and looked at every form of learning.   In addition to the cognitive theories that were in abundance, I took and TA’d for the behavioral learning courses.   David Merrill was visiting nearby, and graciously allowed me to visit him for a discussion (as well as reading Reigeluth’s edited overview of instructional design theories).   Michael Cole was a big fan of Vygotsky, and I was steeped in the social learning theories thereby.   David Rumelhart and Jay McClelland were doing the connectionist/PDP work while I was a student, so I got that indoctrination as well.   And, as an AI groupie, I even looked at machine learning!

I subsequently did a postdoc at the University of Pittsburgh’s Learning Research & Development Center, where I was further steeped in cognitive learning theory, before heading off to UNSW to teach interaction design and start doing my own research, which ended up being very much applied, essentially an action- or design-research approach.   My subsequent activities have also been very much applications of a broad integration of learning theory into practical yet innovative design.

The point being, I never formally considered myself an instructional designer so much as a learning designer.   Having worked on non-formal education in many ways, as well as teaching in higher education, my applications have crossed formal instruction and informal learning.   As the interface design field was very much exploring   subjective experiences at the time I was a graduate student, and from my game design experience, I very naturally internalized a focus on engaging learning, believing that learning can, and should, be hard fun.

I’ve synthesized the eclectic frameworks into a coherent learning design model that I can apply across technologies, and strongly believe that a solid grounding in conceptual frameworks combined with experiences that span a range of technologies and learning outcomes is the best preparation for a flexible ability to design experiences that are effective and engaging. Passionate as I am about learning, I do think we could do a better job of providing the education that’s needed to help make that happen, and still look for ways to try to help others learn (one of my employees once said that working with me was like going to grad school, and I do try to educate clients, in addition to running workshops and continuing to speak).

And, I’ve ended up, as I dreamed of, with a desk covered with cool technology and I get to explore new ideas: designing solutions that integrate the cutting edge of devices, tools, models, frameworks, all to help people achieve their goals.   I continue to think ahead about what new possibilities are out there, and work to improve what’s happening.     I love learning experience design (and the associated strategic thinking to make it work), believe there’s at least some evidence that I do it pretty well, and hope to keep doing it myself and helping others do it better.   Who’s up for some hard fun?

Ignoring Informal

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I received in the mail an offer for a 3 book set titled Improving Performance in the Workplace.   It’s associated with ISPI, and greatly reflects their Human Performance Technology approach, which I generally laud as going beyond instructional design.   It’s also by Pfeiffer, who is my own publisher, and they’re pretty good as publishers go.   However, I noticed something that really struck me, based upon the work I’ve been doing with my colleagues in the Internet Time Alliance (formerly TogetherLearn).

The first volume is really about assessing needs, and design, and it includes behavioral task analysis and cognitive task analysis, and even talkes about engagement strategies in simulation and gaming, video gaming.   The second volume includes performance interventions, and includes elaerning, coaching, knowledge management, and more (as well as things like incentives, culture, EPSS, feedback, etc.   The third volume’s on measurement and evaluation.

All this is good: these are important topics, and having a definitive handbook about them is a valuable contribution (and priced equivalently, the whole set is bargain-priced at $400).   However, while I don’t have the book to hand to truly evaluate it, it appears that there are some gaps.

In my experience, some issues are not behavioral or cognitive but attitudinal.   Consequently, I’d have thought there might be some coverage.   There was a chapter in Jonassen’s old Handbook on Research in Ed Tech on the topic, and I’ve derived my own approach from that and some other readings. When they get into tools, they seem to miss virtual worlds, and they seem to have a repeat of the straw-man case against discovery environments (many years ago it was recognized that pure discovery wasn’t the go, and guided discovery was developed).   It bugs me that I can’t find the individual authors, but I do recognize the names of one of the editors.     But these aren’t the biggest misses, to me.

Overall, there seems to be no awareness of the whole thrust of social and informal learning.   Ok, so Jay’s book on Informal Learning is relatively new, and the concrete steps may still be being sorted out, but there’s a lot there.   Or perhaps it’s covered in Knowledge Management (after all, Marc Rosenberg’s been deeply involved in ISPI and wrote the Beyond e-Learning book).   Yet it seems a bit buried and muddled, and here’s why:

I’m working with a client now, and one of my tasks is surveying how they’re using social media.   A group responsible for technical training (and they’re an engineering organization) recognized that they weren’t able to keep up with the increasing quantity and quality of changes that were coming.   Rather than do a performance improvement intervention, they realized that another opportunity would be to start putting up information and inviting others to contribute.   They put up a wiki, and first maintained it internally, and then gradually devolved some of the responsibility out to their ‘customers’.

The point is, how does that fit into the traditional paradigm?   And yet, increasingly, we’re seeing and recommending approaches that go beyond the categories that fit here.   I wonder if their metrics include the outputs of enabling innovation.   I wonder if their interventions include expertise finders and collaboration tools. I wonder if their analyses include the benefits of ‘presence’.

Times are changing, faster and faster.   I think these books would’ve been the ideal thing, maybe 5 years ago.   Now, I think they’re emblematic of a training mindset when a larger perspective is needed.   These come into play after you’ve identified that a formal approach is needed.   They use a phrase of a ‘performance landscape’, but their picture doesn’t seem to include the concepts that Jay includes in his ‘learnscape’ and I as the ‘performance ecosystem’.