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Clark Quinn’s Learnings about Learning

Email a ’rounding error’?

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“Education over the Internet is going to be so big it is going to make email usage look like a rounding error.” – John Chambers, CEO of Cisco

This bold pronouncement of John Chambers a number of years ago hasn’t really played out as promised.  I would argue that elearning has begun to grow, what with the rise of online education and the recent interest in MOOCs.  And if we take a performance ecosystem view of elearning, including performance support and social, we can begin to think much more broadly about the relationship.  I don’t think John thought of self-learning via Google or YouTube, or learning together via LinkedIn and Twitter, but if we give him the benefit of the doubt, we can begin to think that elearning may be of a substantial bulk in proportion to email, though not yet rounding error size.

However, I want to consider another elearning view that could propose such a relationship.  If we take a performance ecosystem view of mobile, we may well have that sort of ratio.  Think about it, mobile can claim large numbers around:

  • people with mobile phones who have no email or real internet, but voice and text messages give them reach
  • using and/or sharing photos or videos for help
  • accessing the internet through their phones to learn and perform
  • using apps to help them do things, calculating things, supporting their performance
  • connecting to social networks on a variety of platforms: FaceBook, Twitter, LinkedIn, …
  • people using context-sensitive apps to solve problems where they are and tell them what’s around
  • the growth in all the above

If we consider all those (and using mobile devices  for email :)  we actually come up with a pretty big number!  We use mobile personally to learn and perform better in increasing ways, and we’ll start doing it more and more for work as well.  In this way, mobile learning  performance is becoming the massive shift that will make email seem like a rounding error. And that is big.

 

Signs of hope?

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Attending the SolutionsFest at the Learning Solutions conference last week, despite my earlier rant, I saw signs of hope.  A quick spot check revealed a number of approaches going above and beyond.

One direction I was pleased to see was a move to more performance support. I saw several solutions that were more focused on providing information as needed, or letting you  navigate to it, rather than thinking everything had to be ‘in the head’. This is definitely a promising sign.  They’re not hard to build, either.

The second promising sign was the use of scenarios. Several different solutions were focused on putting learners into contexts and asking them to perform. This is definitely the direction we need to see more of.  And, again, it’s not that hard to do!

One interesting takeaway was that the innovative solutions seemed to come more from small or internal groups rather than the big teams.  Which only reinforces my overall concern with the industry as a whole.  I wonder if it’s easier for small teams to adapt to advice of folks like Michael Allen (no more boring elearning), Julie Dirksen (Design for How People Learn) and Will Thalheimer than it is for big teams, who not only have to change processes but also educate their customers.

This is an unscientific sample; I did a quick tour of the displays, but couldn’t see all as there were some that were just too crowded.  I also looked at them relatively briefly and didn’t make comprehensive notes, so this is just a read of my state of mind as I finished.  It doesn’t ameliorate the overall concern, but it does provide some hope that things are changing in small pockets.

Yes, you do have to change

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Of late, I’ve seen a disturbing trend.  Not only are the purveyors of existing solutions preaching caution and steadiness, but it even seems like some of the  ‘names’ of the field are talking in ways that make it easy to think that the industry is largely doing ok.  And I do  not understand this, because it’s demonstrably wrong.  The elearning industry, and the broader learning industry, is severely underperforming the potential (and I’m being diplomatic).

We  know what leads to effective learning outcomes.  And we’ve known it for decades (just because MOOCs are new doesn’t mean their pedagogies are): clear models, annotated examples, and most importantly deep and meaningful practice focused on significant skill shifts (let alone addressing the emotional side of the equation).  Learners need to perform, repeatedly, with guidance, over more and more complex contexts until they achieve the level of performance they need.  However, that’s no where near what we’re seeing.

What we see are knowledge dump/test tarted up with trivial interactions.  People will pass a test, but they will  not have achieved the ability to affect any meaningful business outcomes.  If it’s knowledge that performers need, create a job aid, not a ‘spray and pray’.  And facilitate people in self-helping.  As things get more complex and moving faster, there’s no way everything  can be kept up with by new course development, even if it were a useful approach, and mostly it’s not.

We’re even  measuring  the wrong things.  Cost per seat hour is secondary (at best).  That’s ‘fine-tuning’, not the core issue.  What’s primary is business impact.  Are you measurably improving key performance indicators as outcomes?

And that’s assuming courses are all the learning unit should be doing, but increasingly we recognize that that’s only a small proportion of what makes important business outcomes, and increasingly we’re recognizing that the role needs to move from instructional designer to performance consultant.  More emphasis can and should be on providing performance resources and facilitating useful interactions rather than creating courses.  Think performance support first, and communities of practice, only resorting to courses as a last result.

Tools that make turning Powerpoint presentations into page-turning content aren’t going to fix this, nor are tools that provide prettified drill-and-kill, nor ones that let you host and track courses.  There are places for those, but they’re not the bulk of the opportunity, and shouldn’t be the dominant solutions we see.  There’s  so much more: deeply engaging scenarios and simulation-driven interactions on the formal side, powerful job aid tools for performance support (particularly mobile), coaching and mentoring as a better solution than courses in many (most) cases, performer-focused portals of tools, underlying powerful content management suites, and rich social environments to support performers making each other smarter and more effective.

I’m not quite sure why the easy tools dominate the expo halls, except perhaps because anyone can build them.  More worrisome is that it can let designers off the hook in terms of thinking deeper.  We need to focus first on rich outcomes, and put the tools secondary.

While the industry congratulates itself on how they make use of the latest technology, the lack of impact is leading a drive to irrelevancy.  Learners tolerate the courses, at best.  Operations groups and others are beginning to focus on the performance solutions available.  Executives are beginning to hear a message that the old approach is a waste of resources.

Hiding your head in the sand isn’t going to cut it. The industry is going to have to change.  And that means you will have to change.  But you’re a professional in learning, right?  So lead the way.  The best way to change is to take that first step.

 

Barrier to scale?

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I was part of a meeting about online learning for an institution, and something became clear to me.  We were discussing MOOCs (naturally, isn’t everyone?), and the opportunities for delivering quality learning online.  And that’s where I saw a conflict that suggested a fundamental barrier to scale.

When I think about quality learning, the core of it is, to me, about the learning activity or experience.  And that means meaningful problems with challenge and relevance, more closely resembling those found in the real world than ones typically taught in schools and training.  There’s more.

The xMOOCs that I’ve seen have a good focus on quality assessment aligned to the learning goal, but there’s a caveat. Their learning goals have largely been about cognitive skills, about how to ‘do’. And I’m a big fan of focusing on ‘do’, not know.  But I recognize there’s more, there’s to ‘be’.  That is, even if you have acquired skills in something like AI programming, that doesn’t mean you’re ready to be employed as an AI programmer. There’s much more.  For instance, how to keep yourself up to date, how to work well with others, what are the nature of AI projects, etc.

It also came up that when polled, a learned committee suggested top things to learn were to lead, to work well on a team, communicate, etc.  These are almost never developed by working on abstract problems.  In fact, I’d suggest that the best activities are meaningful, challenging, and collaborative.  The power of social learning, of working together to receive other viewpoints and negotiate a shared understanding, and creating a unique response to the challenge, is arguably the best way to learn.

Consequently, it occurs to me, that you simply cannot make a quality learning experience that can be auto-assessed.  It needs to be rich, and mentored, scaffolded, and evaluated.  Which means that you have real trouble scaling a quality learning experience.  Even with peer assessment, there’s some need for human intervention with every group’s process and product.  Let alone generating the beneficial meta-learning aspects that could come from this.

So, while there are real values to be developed from MOOCs, like developing perhaps some foundation knowledge and skills, ultimately a valuable education will have to incorporate some mechanism to handle meaningful activities to develop the desirable deep understanding.  A tiered model, perhaps?  This is still embryonic, but it seems to me that this is a necessary step on the way to a real education in a domain.

Leadership for Complexity

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The other meme from the retreat event  last weekend was the notion of leadership for complexity.   A few of us decided to workshop a topic around performance, leadership, and technology.   We realized technology was only a means to an end, and the real issue was how to move organizations to optimal performance (e.g. the Coherent Organization).

We talked through how things are moving from complicated to complex (and how important it is to recognize the difference), and that organizations need to receive the wake-up call and start moving forward.   Using the Cynefin model, the value will not come from the simple (which should be automated) nor the complicated (which can be outsourced), but from dealing with the complex (and chaotic).   This won’t come from training and top down management. As I’ve said before, optimal execution will only be the cost of entry, and the differentiator (and hence the value) will be continual evaluation. And that comes from a creative and collaborative workforce.   The issue really is to recognize the need to seize new directions, and then execute the change.

One concern was whether we were talking evolution or revolution.   Rather than taking an either or, I was inclined to think that you needed revolutionary thinking (I like Kathy Sierra’s  take on this), but that you fundamentally can’t revolutionize an organization short of total replacement (“blood on the streets” as one colleague gleefully put it :).   I reckoned a committed change initiative to the place the revolutionary thinking pointed was what was needed.

The issue, then, is the vision and guidance to get there.   What’s needed is leadership that can lead the organization to be able to leverage complexity for success.   This will be about equipping and empowering people to work together on shared goals: sharing, commenting, contributing, collaborating, and more.   It will be inherently experimental in an ongoing way.

What that means practically is an exercise I (and we) are continually working on, but we’ve coalesced on the top-level frameworks to form the basis of tools, and what’s needed are some organizations to co-develop the solutions.   Design-based research] if you will. So who’s up for working on the path to the future?

#itashare

Leaving Trails

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So I was away for the weekend at a retreat with like-minded souls, Up to All of Us, thinking deeply about the issues that concern us. I walked away with some new and renewed friendships, relaxed, and with a few new thoughts. Two memes stuck with me, and the first was “leaving trails”.

For context, the event featured designers – graphic, industrial, visual – but mostly learning designers. In a session on supporting the growth of design awareness, we were being led through an exercise on body-storming (using role plays to work through issues), and one of the elements that surfaced was posting your designs on the walls in places where it’s hard to see others’ work. And I had two reactions to this, the first being that the ability to share work was a culture issue, but the other was a transparency issue.

The point that I brought up was that just seeing the work wasn’t enough, ideally you’d want to understand what was the thinking behind it (not just working out loud, but thinking out loud). That can come from a conversation around the work, but that’s not always possible (particularly if it’s a virtual wall).

And I thought the leader of the exercise, an eloquent and experienced designer, said that you couldn’t really annotate your thoughts about the work. Which I fundamentally disagreed with, but he then went on to talk about showing interim work, specs, etc (and I’m filling in here with some inferences because memory’s not perfect).

What emerged in my thinking was the phrase leaving trails, not just your work, but the trajectories, constraints, and more. As I’ve argued before, I think showing the thinking behind decisions is going to be increasingly important at every level. At workgroup level, individuals will be better able to collaborate if their (prior) work is detailed. Communities of practice similarly need such evidence. Another colleague also presented work on B Corps, benefit corporations, in which businesses will move from shareholder returns to missions, and such transparency will be necessary here as well as for eGovernment. I reckon, what with ClueTrain, any org that isn’t being transparent enough will lose trust.

Of course, the comfort level in sharing gets back to the culture issue: people have to be safe to share their work and give and receive feedback in constructive ways to move forward. Which is really the subject of the next meme.

(NB: one of the principles of the event is Chatham House Rule, which basically says you can’t share personal details without prior approval, and I didn’t ask, so the perpetrators and victims shall remain nameless.)

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.

Norman’s Design of Future Things

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Donald Norman’s book, The Design of Everyday Things is a must-read for anyone who creates artifacts or interfaces for humans.  This one goes forward in the same vein, but talking about how new tech in the roughly 20 years since that book came out, and the implications.  There are some interesting thoughts, though few hints for learning.

In the book, Don talks about how new technologies are increasingly smart, e.g. cars are almost self-driving (and since the book was published back in 2007, they’re now already on the cusp).  As a consequence, we have to start thinking deeply about when and where to automate, having technologies make decisions, versus when we’re in the loop.  And, in the latter case, when and how we’re kept alert (pilots lose attention trying to monitor an auto-pilot, even falling asleep).

The issue, he proposes, is that tenuous relationship between an aware partner and the human.  He uses the relationship between a horse and rider as an example, talking about loose-rein control and close-rein control. Again, there are times the rider can be asleep (I recall a gent in an Irish pub bemoaning the passing of the days when “the horse knew the way home”).

He covers a range of data points from existing circumstances as well as experiments in new approaches.  This ranges from noise to crowd behavior.  For noise, he looks at  how the way mechanical things made noises were clues to their state and operation, and that we’re losing those clues as we increasingly make things quiet. Engineers are even building in noise as a feature when it’s disappeared via technical sophistication.  For crowd behavior, one example is how the removal of street signs in a couple of cities have reduced accidents.

At the end, he comes up with a set of design principles:

  1. Provide rich, complex, and natural signals
  2. Be predictable
  3. Provide a good conceptual model
  4. Make the output understandable
  5. Provide continual awareness, without annoyance
  6. Exploit natural mapping to make interaction understandable and effective

For learning, he talks about how robots that teach are one place in which such animated and embodied avatars make sense, whereas in may situations they’re more challenging.  He talks about how they don’t need much mobility, can speak, and can be endearing. Not to replace teachers, but to supplement them. Certainly we have the software capability, but we have to wonder what sort of system makes sense to invest in the actual embodiment versus speaking from a mobile device or computer.

As an exercise, I looked at his design principles to see what might transfer over to the design of learning experiences.  The main issue is that in learning, we want the learner facing problems, focusing on the task of creating a solution with overt cognitive awareness, as opposed to an elegant, almost unconscious, accomplishment of a goal.  This suggests that rule 2, ‘be predictable’, might be good in non-critical areas of focus, but not in the main area.  The rest seem appropriate for learning experiences as well.

This is a thoughtful book, weaving a number of elements together to capture a notion, not hammer home critical outcomes.  As such, it is not for the casual designer, but for those looking to take their design to the ‘next level’, or consider the directions that will be coming, and how we might prepare people for them. Just as Don proposed that the interface design folks should be part of the product design team in The Invisible Computer, so too should the product support specialists, sales training team, and customer training designers be part of the design team going forward, as the considerations of what people will have to learn to use new systems are increasingly a concern in the design of systems, not just products.

Performance support-ing learning

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In a post last week, I mentioned how Gloria Gery’s original vision of performance support not only was supposed to help you in the moment, it was also – at least in principle – of developing you over time. And yet I have yet to see it. So what am I talking about?

Let’s use an example. I think of the typical GPS as one of the purest models of performance support: it knows where you’re trying to go (since you tell it), and it helps you every step of the way. It can even adapt if you make a mistake. It will get you there.

However, the GPS will tell you nothing about the rationale it’s using to choose your route, which can seem different than one you might have chosen on your own. Even if it offers you alternatives, or you specify preferences like ‘no toll roads’, the underlying reasoning isn’t clear. Yet this might be an opportunity for navigational learning (e.g. “this route has more lights, so we prefer the slightly longer one with fewer opportunities for stopping”).

Nor does it help you learn anything along the way: geography, political boundaries, even geology, although it could do any of these with only a thin veneer of extra work: “as we cross the river, we are also crossing the boundary between X county and Y; in 1643 the pressure between the two cities of X1 and Y1 jockeying for power led to this settlement that shared the water resource.”

It could go further, using this as an example of a greater phenomena: “geographic features often serve as political boundaries, including mountains and rivers as well as oceans”. This latter would, in a sensible approach, only be used a few times (as the message,nonce known, could become annoying. And, ideally, you could choose what you wanted to learn about.

This isn’t limited to GPS, this could be used in any instance of guided performance. Sometimes you might not care (e.g. I suspect most users of Turbo Tax don’t want to know about the nuances of the tax, they just want it done!), but if you want people to understand the reasoning as a boost to more expert performance, e.g. so they can then start using that model to infer how to deal with things that fall outside of the range of performance support, this is a missed opportunity.

The point is to have even our programs to be ‘thinking out loud‘, both to help us learn, and to serve as a check on validity. Sure, it should be able to be shut off or customized, but the processing going on provides an opportunity for learning to happen in new and meaningful ways. The more we can couple the concept to the context, the more we can create learning that will really stick. And that is, or should be, the real goal.