In this case I’m looking at the 4 C’s by work categories.  I see augmenting formal learning as one, providing performance support as a second, social media as a 3rd area, and the unique mobile contribution of context-sensitive support as a 4th area.

I realize there are some problems in this, in that Social and Communicate are hard to discriminate (hence using the catchall phrase social network), and Capture is core to context-sensitivity. Alternate Reality Games (ARGs) don’t have to be social, but can be.  And I hadn’t really thought through what context-sensitive computing and communicating might mean. Certainly you could have a focused directory that knows who knows about this context, and perhaps an app that presents different options for context-sensitive trouble-shooting or repair (e.g. knowing what device you’re liable to be working on), but I could be missing some options.  And I’m not sure I’ve seen socially edited or maintained apps as opposed to content. Anyone?  Anyone?  Bueller?

So, as this is a first shot at this, I welcome feedback. What am I missing?

Talking with my ITA colleague Jay Cross at lunch the other day, it occurred to me that I was seeing the same pattern with social media that I see elsewhere.  When I think through many instances I’ve seen, heard of, or experienced, I see them addressing one issue. “We’ve put in a social media system to use around our formal learning.”  ”We’ll buy a social  media platform to use for our sales force.”  And these aren’t bad decisions, except for the fact that such an initiative has broader ramifications.

What I’m not seeing is folks thinking enough along the lines of “social media is a platform, and we should be looking at how the investment can be leveraged.”  I’m not seeing enough focus on using every tactic as a step on the way to a ‘workscape’ (aka performance ecosystem).  You want to be building the infrastructure for working smarter, and every move should be developing that capability.  You want to be getting closer and closer to workers having tools to hand, the resources they need to get the job done.

To empower workers, you want to have the tools for communication, e.g. video sharing, blogging micro- and macro-, discussion forums, etc as well as the tools for collaboration, e.g. shared documents and expertise finding, arranged around tasks and interests, not around silos.  To free folks up to get the job done, they need to be able to work smarter.

And you want to align what you’re doing with organizational goals, define metrics that will impact key business metrics, provide governance with partners both fundamental and strategic, leverage other organizational initiatives (oh, you’re putting in a CMS?  With just a small additional effort, we can use that to facilitate sharing of information…), etc.  It’s time to start thinking strategically, if you want to really move your organization forward.  There’re a number of steps: advanced ID, performance support, mobile, each taking on another facet, but arguably the biggest benefit will come from bringing together the talent in your organization.  Why not?

Probably the best first step to take is to start using social media in the learning unit, so folks there ‘get it’ (you got to be in it to win it, as they say re: the lottery; guess that’s why I wasn’t one of the 3 winners .  That’s a strategic step that can drive the rest.  And you can take the slow path and figure it out yourself, or accelerate with some assistance, but it’s really time to get going.  So, what’s stopping you?

What occurs to me, however, is that there are two separate places you’ll get to.  If you master formal learning and performance support (and while that’s the only thing many L&D groups do, there’re far fewer that do it well), you’re only going to support execution.  While that used to be ok for a time when we could plan in advance, the increasing turbulence in markets – product cloning happening in weeks, information tsunamis, etc – means that even optimal execution alone isn’t going to be a differentiator.

What’s going to be needed is continual innovation, and that simply won’t, can’t, come from formal learning.  It’s not even going to come from performance support, which while not full courses, is still designed.  What you need to do to get continual innovation going is communication and collaboration. The myth of individual innovation is busted, and it’s talking together, and more importantly working together, that is going to lead to the new ideas, better processes, optimized systems, and more.  Creativity, research, problem-solving are at the core, and those don’t come from formal learning.

You do need to have formal learning, don’t get me wrong, but that’s just the ante.  The real game is going to come from tapping into the power of your people. You’ll have to create the right culture, get a shared vision, and empower your people with the resources to do the job.  It includes the right mindset, skills, and tools.  When things are aligned, you’re going to have the important outcomes: problems solved faster, shorter times to new product and service ideas, better customer relationships, and more.

You can figure it out on your own, but if you want to get there faster, you may want to get some help in accelerating your path to this new way of working, the sustainable path to success.

That may sound trivial, but it’s actually a real issue in terms of freeing up the organization. In one instance, it had been the R&D organization that undertook the cost.  In another case, the cost was attributed to the overhead incurred in dealing with a merger.  These are expedient, but wrong.

It’s increasingly obvious that it’s more than just a ‘nice to have’.  As I’ve mentioned previously, innovation is the only true differentiator.  If that’s the case, then social media is critical. Why?  Because the myth of individual innovation is busted, as clearly told by folks like Keith Sawyer and Steven Berlin Johnson.  So, if it’s not individual, it’s social, and that means we need to facilitate conversations.

If we want people to be able to work together to create new innovations, we don’t want to leave it to chance.  In addition to useful architectural efforts that facilitate in person interactions, we want to put in place the mechanisms to interact without barriers of time or distance.  Which means, we need a social media system.

It’s pretty clear that if you align things appropriately: culture, vision, tools, that you get better outcomes.  And, of course, culture isn’t a line item, and vision’s a leadership mandate.  But tools, well, they are a product/service, and need resources.

Which brings us to the initial point: where does this responsibility lie?  Despite my desire for folks who are most likely to understand facilitating learning (though that’s sadly unlikely in too many L& D departments), it could be IT, operations, or as mentioned above, R&D.  The point is, this is arguably one of the most important investments in the organization, and typically not one of the most expensive (making it the best deal going!). Yet there’s not a unified obvious home!

There are worries if it’s IT. They are, or should be, great at maintaining network uptime, but don’t really understand learning. Nor do the other groups, and yet facilitating the discussion in the network is the most important external role.  But who funds it?

Let’s be real; no one wants to have to own the cost when there’re other things they’re already doing. But I’d argue that it’s the best investment an L&D organization could make, as it will likely have the biggest impact on the organization. Well, if you really are looking to move needles on key business metrics.  So, where do you think it could, and should reside?

In an article in Macrumors (obviously, an Apple info site), they detail part of Adam Lashinsky’s new Inside Apple book that reports on Apple executive development program.  Steve Jobs hired a couple of biz school heavyweights to develop the program, and apparently “Wherever possible the cases shine a light on mishaps…”.  They use examples from other companies, and importantly, Apple’s own missteps.

Companies that can’t learn from mistakes, their own and others’, are doomed to repeat them.  In organizations where it’s not safe to share failures, where anything you say can and will be held against you, the same mistakes will keep getting made.  I’ve worked with firms that have very smart people, but their culture is so aggressive that they can’t admit errors.  As a consequence, the company continues to make them, and gets in it’s own way.  However, you don’t want to celebrate failure, but you do want to tolerate it. What can you do?

I’ve heard a great solution.  Many years ago now, at the event that led to Conner’s & Clawson’s Creating a Learning Culture, one small company shared their approach: they ring a bell not when the mistake is made, but when the lesson’s learned.  They’re celebrating – and, importantly,  sharing – the learning from the event.  This is a beautiful idea, and a powerful opportunity to use social media when the message goes beyond a proximal group.

There’s a lot that goes on behind this, particularly in terms of having a culture where it’s safe to make mistakes  Culture eats strategy for breakfast, as the saying goes..  What is a problem is making the same mistake, or dumb mistakes.  How do you prevent the latter?  By sharing your thinking, or thinking out loud, as you develop your planned steps.

Now, just getting people sharing isn’t necessarily sufficient.  Just yesterday (as I write), Jane Bozarth pointed me towards an article in the New Yorker (at least the abstract thereof) that argues why brainstorming doesn’t work.  I’ve said many times that the old adage “the room is smarter than the smartest person in the room” needs a caveat: if you manage the process right.  There are empirical results that guide what works from what doesn’t, such as: having everyone think on their own first; then share; focus initially on divergence before convergence; make a culture where it’s safe, even encouraged, to have a diversity of viewpoints; etc.

No one says getting a collaborating community is easy, but like anything else, there are ways to do it, and do it right.  And here too, you can learn from the mistakes of others…

The general idea is that there are several levels at which you can evaluate interventions:

1. whether the recipient considered the intervention appropriate or not
2.  whether the recipient can demonstrate new ability after the intervention
3. whether the intervention is being applied in the workplace, and
4. whether the intervention is impacting desired outcomes.

That this is my interpretation became abundantly clear.  But let’s start with what’s wrong in practice.

In practice, first, folks seem to think that just doing level 1 (‘smile sheets’) is enough. Far fewer people take the next logical step and assess level 2. When they do, it’s too often a knowledge test.  Both of these fail to understand the intention: Kirkpatrick (rightly) said you have to start at level 4. You have to care about a business outcome you’re trying to achieve, and then work backwards: what performance change in the workplace would lead to the desired outcome. Then, you can design a program to equip people to perform appropriately and determine whether they can, and finally see if they like it.  And, frankly, level 1 is useless until you finally have had the desired impact, and then care to ensure a desirable user experience.  As a standalone metric, it ranks right up there with measuring learning effectiveness by the pound of learners served.

Now, one of the things my colleagues pointed out to me, beyond the failure in implementation, is that Kirkpatrick assumes that it has to be a course.  If it’s just misused, I can’t lay blame, but my colleagues proceeded to quote chapter and verse from the Kirkpatrick site to document that the Kirkpatricks do think courses are the solution. Consequently, any mention of Kirkpatrick only reinforces the notion that courses are the salve to all ills.

Which I agree is a mindset all too prevalent, and so we have to be careful of any support that could lead a regression to the status quo.  Courses are fine when you’ve determined that a skill gap is the problem.  And then, applying Kirkpatrick starting with Level 4 is appropriate.  However, that’s more like 15% of the time, not 100%.

So where did I go wrong?  As usual, when I look at models, I abstract to a useful level (my PhD focused on this, and Felice Ohrlich did an interesting study that pointed out how the right level of abstraction is critical).  So, I didn’t see it tied to courses, but that it could in principal be used for performance support as well (at least, levels 3 and 4).  I think also at least for some social learning interventions.

Moreover, I was hoping that by starting at level 4, you’d look to the outcome you need, and be more likely to look at other solutions as well as courses.  But I had neglected to note the pragmatic issue that the Kirkpatrick’s imply courses are the only workplace intervention to move the needles, and that’s not good.  So, from now on I’ll have to be careful in my reference to Kirkpatrick.

The model of assessing the change needed and working backward is worthwhile, as is doing so systematically.  Consequently, at an appropriate level of abstraction, the model’s useful.  However, in it’s current incarnation it carries too much baggage to be recommended without a large amount of qualification.

So I’ll stick to talking about impacting the business, and determining how we might accomplish that, rather than talk about levels, unless I fully qualify it.

http://blog.learnlets.com/?p=2390

The problem is that it goes far beyond just a more enlightened instructional design, which of course we need.  We need to think of content architectures, blends between formal and informal, contextual awareness, cross-platform delivery, and more.  It involves technology systems, design processes, organizational change, and more.  We also need to focus on the bigger picture.

Yet the vision driving this is, to me, truly inspiring: augmenting our performance in the moment and developing us over time in a seamless way, not in an idiosyncratic and unaligned way.  And it is strategic, but I’m wondering if architecture doesn’t better capture the need for systems and processes as well as revised design.

This got triggered by an exercise I’m engaging in, thinking how to convey this.  It’s something along the lines of:

The curriculum’s wrong:

• it’s not knowledge objectives, it’s skills
• it’s not current needs, it’s adapting to change
• it’s not about being smart, it’s about being wise

The pedagogy’s wrong:

• it’s not a flood, but a drip
• it’s not knowledge dump, it’s decision-making
• it’s not expert-mandated, instead it’s learner-engaging
• it’s not ‘away from work’, it’s in context

The performance model is wrong:

• it’s not all in the head, it’s distributed across tools and systems.
• it’s not all facts and skill, it’s motivation and confidence
• it’s not independent, it’s socially developed
• it’s not about doing things right, it’s about doing the right thing

The evaluation is wrong:

• it’s not seat time, it’s business outcomes
• it’s not efficiency, at least until it’s effective
• it’s not about normative-reference, it’s about criteria

So what does this look like in practice?   I think it’s about a support system organized so that it recognizes what you’re trying to do, and provides possible help.  On top of that, it’s about showing where the advice comes from, developing understanding as an additional light layer.  Finally, on top of that, it’s about making performance visible and looking at the performance across the previous level, facilitating learning to learn. And, the underlying values are also made clear.

It doesn’t have to get all that right away.  It can start with just better formal learning design, and a bit of content granularity. It certainly starts with social media involvement.  And adapting the culture in the org to start developing meta-learning.  But you want to have a vision of where you’re going.

And what does it take to get here?  It needs a new design that starts from the performance gap and looks at root causes. The design process then onsiders what sort of experience would both achieve the end goal and the gaps in the performer equation (including both technology aids and knowledge and skill upgrades), and consider how that develops over time recognizing the capabilities of both humans and technology, with a value set that emphasis letting humans do the interesting work.  It’ll also take models of content, users, context, and goals, with a content architecture and a flexible delivery model with rich pictures of what a learning experience might look like and what learning resources could be.  And an implementation process that is agile, iterative, and reflective, with contextualized evaluation.  At least, that sounds right to me.

Now, what sounds right to you: learning experience design strategy, performance system design, performance architecture, <your choice here>?

http://blog.learnlets.com/?p=2375

What that’s saying is that at least half of the money you invest in new things could be conceived of as being wasted.  You might be considered a very ineffective manager if 50% of your investments don’t yield returns!  Now, first of all, I’m sure you recognize that failed experiments aren’t a complete waste, as long as you learning something (“when you lose, don’t lose the lesson” as the saying goes).  Still, 50%  might still seem like a high failure rate.  But is low risk really good?

I remember hearing a talk by a Canadian AI researcher (who’s name escapes me after all these years) who had studied the optimal ratio of success to failures in helping a system learn. Now this was particular to the learning algorithm he’d chosen, but his result was roughly that you learned fastest if you failed two-thirds of the time, or around 67% failure.  Now that’d be pretty disheartening, but if you could take emotion out of the equation, e.g. made it safe to fail, would learning faster be a big enough argument to support bigger failure?

It depends on a lot: on how well you discern the lessons from failure, how well you tolerate failure, how much social scrutiny and how tolerant that public viewpoint is, but it’s interesting to contemplate what might be an optimal context for failure, and given that, what would be the fastest way to learn, and capitalize on that learning.  You want your experiments to be designed in the first place to yield maximum information, but if they do, what would a valuable success rate be?

I do believe that they who adapt fastest will be the survivors.  That adaptation may be subconscious, but I think conscious reflection is a valuable component.  Certainly for sharing the learning, so no one else has to make the same mistakes.  So are you learning just as fast as you can?

http://blog.learnlets.com/?p=2371

I’m going through this diagram line by line, from the top.

If you go from just having an idea (first line) to trying to capture it as a product (next), whether a diagram or a screed, to communicate to some hypothetical reader, you find out that you might not have thought it out fully (the first benefit to having a personal journal, e.g. blogging).  And you do some processing to generate that product.

Then, if someone actually reads it, they do some processing.

If they write a response, they do more processing to crystalize their thoughts.

Then, the author, when reading it, also does some more processing.

If someone else reads it, that person does some processing, and if they write  a comment, well, the process continues.

The author could then write a reply to one or both, and that causes even more processing. And so on.

And this is good.  Processing is part of learning, and focused processing is part of good learning design.  So, having learners capture and communicate their thoughts is a valuable learning activity.  It can be personal reflections, e.g. “what does this explain in my past” or “what will I do differently in the future”, or responses to a question.

If other learners are asked to read and constructively comment (not just “great post”), you can get valuable learning outcomes.

This extends to the social learning situation. Here, you have every learner contribute their initial  thoughts on a group assignment (recommended).

Then, every learner reads the other proposals, and they start to put out their integrated ideas.

As they negotiate a shared understanding as a group response, some great processing is happening.

Ultimately, they create an outcome that’s richer than what they’d create on their own.

If you’ve created the right amount of ambiguity in the project, you’ll get some great discussions.  The processing benefits here are because the learners will bring somewhat different interpretations and experiences to the project, and that diversity allows a mre robust understanding to emerge.

Consequently, I suggest that social learning adds benefits to the learning experience beyond what individual assignments can achieve. You can mimic some of these effects by staging additional information, but it’s not quite as effective as individual learning (nor near as engaging).

So, does this make sense?  And, hopefully, inspire you to find ways to add social interaction into your learning experiences? It’s not unique to social media, but social media give you a channel to bring these benefits to learning whenever and wherever.

http://blog.learnlets.com/?p=2193

Think about the sort of ideal learning experience you might have.  As you traverse the ‘rocky road’ of life, imagine having a personal coach who would observe the situation, understand the context of the task and the desired goal, and could provide some aid (from some sack of resources) that could assist you in immediate performance.  Your performance would improve.

Let’s go further. This sage, moreover, could draw from some curricula (learning trajectories) and prepare you beforehand and guide reflection afterward so that real performance event now becomes a learning opportunity as well, helping you understand why this particular approach makes sense, how to adapt it, and more.  In this way, the sage moves from performance coach to learning mentor.

One step further would be to have learning trajectories not only about the domain (e.g. engineering) but also about quality, management, learning, and more.  So learners could be developed as learners, and as persons, not just as performers.

Now this would be ideal, but individual mentors don’t scale very well.  But here’s the twist: we can build this.  We can have curricula, learning objects, and build a sage via rules that can do this.  Imagine going through your workday with a device (e.g. an app phone or a small tablet) that knows what you’re doing (from your calendar), which triggers content to be served up before, during, and after tasks, that develops you over time.  We can build the tutor, develop and access the curricula and content, deliver it, track it.

I hope this is clear.  There are other ways to think about this, and I’ll see if I can’t capture them in some way; stay tuned.  The limitations are no longer the technology, the limits are between our ears.  Reckon?

http://blog.learnlets.com/?p=2285

Have you heard that?  I did, in my first job out of college; my boss said it, but it wasn’t clear what it meant.  What does ‘work smarter’ mean?  I already thought I was working smarter.  Well, as I’ve learned (in conjunction with my ITA colleagues), it means a number of things that organizations can, and should, do.

So, what is known about when we work smarter? We work smarter under a number of conditions: when we have a clear goal of what we’re supposed to achieve and we recognize it’s importance; when we’re free to experiment, explore, and even fail; when we have colleagues to collaborate with; and when we have the resources we need available ‘to hand’.  This provides some guidance about what an organization should be doing to optimize the likelihood of success.

We need to be doing meaningful work that we’re excited about.  We need to be connected to a vision, and understand how our role contributes.  There needs to be transparency above and below as well as ahead, so we can see how the parts are working together.

We also need a culture where that transparency is empowering, not threatening. It has to be safe to perform in public, to share our thoughts, and to both provide and receive help to others.  Where, when mistakes are made, the lessons are learned and shared.

We need to see it as important to contribute, and be enabled to communicate to the right people, and be able to work together to get the job done.  We need time to reflect as well, to take time to think about what we’re doing. We should be doing that publicly too. We need to learn out loud and together.

Finally, we need the tools available. We shouldn’t have to take time to go multiple places to get what we need, and use inconsistent interfaces to use them. We should have an environment where we’re focused on our tasks, and can get who and what we need to stay focused.

How to work smarter isn’t a mystery. The mystery is why  more organizations aren’t systematically breaking down the barriers to working smarter.  Are you ready to get going?

http://blog.learnlets.com/?p=2198

The indicators are clear: the world is going mobile.  Mobile subscriptions in the developed world are flattening out, not from lack of interest, but from saturation.  People are accessing the internet more from mobile devices than desktops, and some people only access the internet via mobile!  And when a small company like Google says that they’re designing for mobile first and the desktop is an afterthought, it is safe to say mobile is on the move.

And the opportunities are huge.  Through the centuries, we have continually extended our physical capabilities with tools: we’ve developed more capable clothing to let us go to further extremes, more powerful transportation that can let us travel thousands of miles in a matter of hours, tools that can let us work on the scale of mountains or of molecules.  We’ve also used tools to augment our brains: books to serve as external memory, calculators to support our computational capability, phones to allow us to communicate at distances.  Digital technology has proven to be the ultimate cognitive augment, doing exactly the things that our brains don’t do well, so together we’re truly formidable problem-solvers. And now, we have that capability wherever and whenever we need it.  Which has important implications by itself, but there is more potential, too.

Don’t be mislead by the label, mLearning is about so much more than courses on a phone.  In fact, that’s almost contra-indicated.  What mobile learning really is about is augmenting formal learning, and augmenting performance regardless.  The old ‘event’ model of learning really doesn’t work very well, as our brains only can handle so much at a time. With mobile, however, we can extend that learning over time. And over space: we can turn the entire world into part of our learning environment, or to think about it another way, we can spread our learning environment over the world.  Beyond learning, we can bring specific support to wherever we are: accessing information to make our shopping more effective, our understanding deeper, our interactions richer. We can access information, support our decisions, and share our experiences.

But there’s also something unique to mobile, beyond a pocketable desktop. As the devices get more capable, they begin to know where we are, even which way we’re facing, and they can start adding unique information specific to our current context, location-aware.  We’re just beginning to explore the possibilities, and you really do have to think differently to take advantage, but the potential is exciting.  Are you mobilizing?

http://blog.learnlets.com/?p=2203

Increasingly, organizations and providers are creating APIs to their environments, which allow other organizations to add value in ways that expand their ecosystem.   This is of benefit to both parties and the users of the environment, with appropriate caveats about how the information is used.  From the user point of view, there are extensions to environments and tools you use that can give you unique capabilities.  And, from the personal efficacy department, being able to find and use these extensions is a new skill.  In the Personal Knowledge Management framework of my colleague Harold Jarche, it’s be a new component of improving personal productivity.

First, as an overarching component, you need to understand that platforms can, if properly developed, allow others to add new capabilities.  Then, you need to be aware of the ways in which you’d like to augment your capabilities (accessorize your brain), know which platforms you’re on, choose the most plausible platform and channel (while there’s a Facebook app available for your app phone, it may  not support the app you need, and it may need to be desktop or mobile web), be able to search for the app you need (which may require tapping into other PKM skills like leveraging your network), and be able to hook into it, use it, and keep it handy.

Personal efficacy seems to me to be a growing differentiator.  Jay Cross cites how the exceptional Google engineer is estimated to be 200 times more valuable than the average engineer.  While some of this will come from skills, I suspect that a lot, and a growing component, of success will come from continual improvement both organizationally and individually.  Watts Humphrey makes a compelling case for the benefits of self-improvement process in software engineering, and it’s clear the process generalizes to other tasks.  Jay and I have previously argued (PDF) that improving the ability to learn might be the best investment you could make, and this is a component of being effective: knowing when to augment your capabilities and how.

New capabilities are emerging rapidly.  Understanding them conceptually and clarifying their unique capabilities gives you a handle on generating the skills you need to take advantage of them in a generalizable way.  I reckon apps meet the criteria.

http://blog.learnlets.com/?p=2059

Frankly, the problem with Kirkpatrick (sort of like with LMS’ and ADDIE, *drink*) is not in the concept, but in the execution.  As he would say, stopping at level 1 or 2 is worthless.  You need to start with Level 4, and work back.  This is true whether you’re talking about formal learning, informal learning, or whatever. Then, I’m not feeling like you have to be anal about levels 1-3, it’s level 4 that matters, but there’s plausibility that making the link makes your case stronger.  And I also like what I heard added at a client meeting: level 0, are they even taking the course/accessing the system?  But I digress…

So, let’s say you are interested in seeing what social media can do for your organization: what are you not seeing but need to?  If you’re putting in a social media system into a call center, maybe you want reduced time to problem solution, fewer customer return calls on the same problem, etc.  If you’re into an operations group, maybe you want more service or product ideas.  What is it you’re trying to achieve?  What would indicate the innovation that you’re looking to spark?

Then, you need to find ways to measure those outcomes. You have three basic decisions to make in terms of a strategic initiative:

• it’s working, yay, let’s keep it.
• hmm, it’s kinda working, but we need to tweak it
• oh oh, this is bleeding money, let’s kill it

You should set parameters before you launch the initiative that you think indicate the thresholds you are talking about.  The keep and kill thresholds likely have to do with the costs versus the benefits.  You may change those parameters on inspection of the results at any time, but at least you are doing it consciously.  And gradually your patience will or should fade.  Eventually you end up with either a leave or kill decision.

Frankly, even activity is a metric.  A vendor of a social media system uses that as a metric for billing (though I don’t think that two touches a month constitutes a meaningful interaction by a user), and if people are talking productively and getting value, you’ve got at least an argument that intangible benefits are being generated.  You could couple that with subjective evaluation of value, but overall I would like to argue for more meaningful outcomes.

And don’t think that you have to have only one. Depending on the size of the initiative and the different silos that are being integrated, you might have more.  You might check not only key business metrics, but look for impacts on retention and morale as well, if the benefits of improving work environments are to believed (and I do).  And, of course, there’s more than the installation and measuring: the tweaking for instance could involve messaging, culture, interface design, or more.

Metrics for informal learning aren’t rocket science, but instead mapping of best principles into specific contexts.  Your organization needs to find ways to facilitate social learning, as the innovation outcomes are the key differentiator going forward, as so many say.  You should be experimenting, but with impacts you’d like to have, not just on faith.

I’ve riffed before about virtual mentorship, and it resonated again today.  We were getting a tour of one of the social platforms, organized as many are around tasks, questions, and dialog.  While implicitly it could support tracking a group’s progress, separate thoughts as recorded through blogs and tweets aren’t a natural feature. Yes, there’s integration with wikis and blogging tools, but it’s not quite the same.  And seeing these meme tracks or thought trails can be a valuable way to understand how someone thinks in context, which can develop others’ thinking.

Don’t get me wrong, task oriented discussions are the real new opportunity for business, but I’m looking at a separate level that’s also valuable.  The 70/20/10 model that Charles Jennings so effectively promotes (on the job, mentor/coaching, formal, respectively), suggests that mentorship is a valuable component of overall development.  What if we could make it lower overhead for higher impact?

The notion is that learners ‘follow’ potential leaders.  They can do for external thought leaders by reading their blog and following their tweets.  But there are more immediate people also worth being mentored by.  What if employees could follow their bosses and executives within the organization? Transparency is valuable, and if these leaders can be convinced to share their thoughts, more folks can take advantage of them without needing specific meetings (and, of course, making those meetings more valuable). Naturally, other representations could also be valuable: if they record thoughts while driving, podcasts could be ok too, or recorded video messages (tho’ perhaps harder to edit). Even recording meetings where leaders speak could be a low-overhead mechanism.

The tough part, really, is getting the leaders to share their thoughts.  Making it a recommendation, and making it easy is important.  Sharing the value of reflecting is also important (people who take time to reflect outperform those who don’t, despite corporate mythology to the contrary).  You also have to make it ‘safe’, so that mistakes can be shared and learned from.

The goal is to make thinking visible; leading out loud.   It might seem onerous, but the outcomes of better communication and developing potential new leaders are big.  What do you think are the potential benefits of more people knowing what is important?  What if more people could start thinking strategically?  These are on the table, and potentially on tap.  Are you missing this opportunity?

http://blog.learnlets.com/?p=1910

Increasingly, workers are mobile.  When we look not only at field-deployed individuals, but also those who occasionally must travel to meetings, make site-visits, are away at conferences and workshops, or even are commuting, the number of mobile workers can be considered from half to most of the workforce. When we consider how many have a mobile device of some sort, and that these devices are increasingly powerful, we have a big opportunity to have a business impact.

To examine the opportunities, we must first consider the range of activities mobile can support. Let us be clear, mobile learning is not about courses on a phone, at least in large measure.  There are circumstances where this makes sense, but it is not the main opportunity on tap. First and foremost, mobile is about quick access; just-in-time, just enough. The prescient Zen of Palm documented how desktop computers are accessed not that many times a day, but for long periods, whereas mobile devices are the reverse, accessed many times for short periods. This suggests a different model of use.

Think: how does your mobile device make you smarter? If you are typical, you may use it to keep information you want to look up, like contact details or your calendar. You may also use it to capture data: a note, a photo or video, or a voice memo.  You may calculate something like the tip due the waitstaff or how to split the bill. And, of course, you may reach out to someone like a friend or colleague through voice or text.  These are what I call the Four C’s of mobile: Content, Capture, Compute, and Communicate. This maps much more closely to performance support than formal learning, and indeed mobile likely plays more of a role in performance support and social/informal learning, the companions for formal learning.

It is useful to view computation conceptually as a complement to our cognitive systems. Our brains are really good at pattern matching and executive decision-making, but really bad at remembering rote information and completing complex calculations. Over our history, we have developed many physical and cognitive tools to augment our capabilities.  Computers, in a sense, are the ultimate cognitive adjunct, with limitations due more to our imaginations (and pocketbooks) than to the technology.  When we have mobile computational capabilities, we are now able to augment our thinking wherever and whenever we are. We can respond in the moment, not with a delay.  This makes us both more effective and more efficient.

Which brings us to the business impact of mobile tools. We can augment performance in ways that can address barriers that have arisen in the past. We should, indeed, start with those situations where there have been performance barriers.  Where, with a small bit of support, could we get sizable improvements?  And realize that small improvements, when aggregated, can mean big returns.  For example, cutting down on one extra visit to get information on an unanticipated problem, when multiplied by a lot of calls for a sizable workforce, becomes a substantial savings.  That could come from accessing a job aid, a colleague, or even sharing a picture of the situation. Similarly, sales would likely increase if a quick calculation could show the immediate cost versus benefit relationship, and orders could be placed immediately.  More importantly, employees could be made productive earlier if specific information on a client or situation is scaffolded to support the novice practitioner.

Optimizing performance is a marginal game, but margins are the difference between success and failure.

Let us not forget, however, to also consider how mobile tools can augment learning as well as performance.  Those same Four C’s can be applied to extending and enriching the learning experience just as they can support in-the-moment performance. The activities that support fostering retention and transfer, our learning goals, can have mobile support. For example, you can reactivate knowledge by delivering content, or having learners apply their knowledge to problems and challenges at times other than a learning event. You can have learners capture data from the field and bring back to the discussion. And, of course, learners can discuss and collaborate with one another.

The key to business impact from mobile devices is to think performance; what small tweaks will change our key business metrics in big ways.  While mobile does provide transformative opportunities, the near-term impact will come from optimizing current opportunities for performance support and social communication and collaboration, with resulting aggregate outcomes that provide tangible return on investment.

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, howcan 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.

http://blog.learnlets.com/?p=1502

The opportunity we now have is to use technology to move from an event-based learning model that we know to be ineffective, to a more distributed and contextualized environment that elegantly spans the continuum from formal learning to performance support. And this is not science fiction – we have the tools we need now. Even if we didn’t, we should be preparing our thinking for this capability.

For all the sophistication of our technology, our view of learning has not really changed. In an era of semantic Web, augmented reality, virtual worlds, and more, we are still talking about courses!

But in business, our goals are not learning, our goals are improving performance. And courses, particularly the “event” model for courses, are one of the worst ways to go about achieving learning for performance!

We need to review recent advances in technology to ascertain the new capabilities they can provide for learning, and then we can revisit how people think and perform. We need to look at models such as spaced practice,social learning, meta-learning, and distributed cognition. With these new technologies and these ways of thinking, we can envision and deliver a richer learning experience that will lead to persistent change in abilities. And that persistent change in our ability to do, is, and has to be, our goal.

Technologies

We need to consider the changing nature of our technology infrastructure. In 2005, Marc Rosenberg’s Beyond e-Learning brought a much broader (and appropriate) perspective to e-Learning, by including eCommunity, performance support, and knowledge management. Several directions were not really part of the picture then, however. While we did have the Internet and mobile devices, the smartphone had not really taken hold, virtual worlds were still in the “too hard” basket, and “the cloud” hadn’t yet been coined as a term. The first two of these are worth a little more thought here, along with a fourth meme, the semantic Web.

Mobile devices

Mobile learning is not new, but it has only recently “crossed the chasm” from the Innovators and Early Adopters to the Early Majority (or at least to the awareness of the Early Majority). Though I, along with others including David Metcalf and Judy Brown, had felt mobile learning was ready for “prime time,” the realities until the last year or two were that mobile technology providers had maintained their protectionist approach, and furthermore the tools were not yet really available. Now those barriers have eroded, and we have a robust infrastructure for mobile delivery.

Mobile is about augmenting our performance, whether by supplementing formal learning, or by providing performance support. Our brains are really good at pattern matching (although not particularly good at remembering details or arbitrary information), at performing rote tasks repeatedly without flaws, and at doing complex calculations without support. The problem is that our cognitive architecture creates certain fixed barriers – everything from limited working memory and stereotyping to confirmation bias and retaining meaning rather than specifics.

On the other hand, we have these new mobile devices with us whenever and wherever we are, and they are increasingly capable of rich interactions and processing. We have the four C’s of mobile available:

• Content delivery to the learner
• Compute capability for complex processing and data access
• Capture of the world, often in ways difficult for us
• Communicate with others

With these, we can accessorize our brains to make us more capable than we are without support. How can we take advantage of these capabilities for learning?

Virtual worlds

Virtual worlds are, in many ways, related to mobile learning. Virtual worlds are now reaching the mainstream, as Karl Kapp and Tony O’Driscoll have signaled. In both mobile and virtual worlds, our normal capabilities are augmented through technology. The difference is that mobile devices augment our capabilities in the real world, whereas virtual worlds provide an artificial world where we can interact, perhaps with augmented capabilities.

There are fundamental affordances in these virtual worlds, particularly in their 3-D nature, and through the inclusion of other people via “presence.” Emergent affordances include the ability to navigate through the virtual world, and to create new 3-D structures. The social capability means that we can appear as we wish (rather than as nature endowed us), and we can share these spaces and communicate with others. The ability to co-create 3-D representations provides a real opportunity for learning.

While the ability to share space is not new, the ability to change that space, to share space with others who are not geographically proximal, not to mention the ability to have a new appearance, are new possibilities.

Semantic Web

One other category of technology that offers powerful new affordances is the area of semantic technologies. While still in its infancy, the use of semantics already provides content creators with valuable outcomes when managing large quantities of content and when customizing content delivery. However, we also have the capability of doing things at an individual granularity.

What we are talking about here are ways to add meaning to system-tracked content. Currently, we have to prescribe how content appears, but if we can provide information about the topic of content, and a variety of descriptors, we can devise rules that extract information on the fly. Those rules can draw both upon contextual information (“person X is looking for information Y at place Z”) and upon what’s available (“we have information A, both examples and concept presentations”) to do customized combinations.

As a result, what we have on tap is the ability for the system to react to situations and create customized content. So-called mass-customization already exists (e.g., Amazon recommendations), using aggregate information, but we could also use best principles of learning to do targeted interventions based upon particular characteristics of a context. And that’s potentially powerful.

The learning it’s not about

When I say, “our goals are not learning,” I mean that our focus needs to be on the ultimate performance needed, not on the associated learning. There are times when the appropriate support will be from peers or mentors, or from job aids, not just from courses.

Consequently, after the technologies we have to hand, we need to think about what we know about how people perform. What do we know about supporting individuals in learning and performance?

From looking at kids (before schooling extinguishes their love of learning), we see what I call the seven C’s of natural learning:

• Choose what we are interested in
• Commit to do what is necessary to learn about it
• Create expressions of our understanding as application
• Crash when our expressions sometimes fail
• Copy others’ performances
• Converse with others about the topic
• Collaborate to co-create a shared understanding as well as an artifact

This does not much represent what we do with learning. We need to visit some of the more esoteric areas of cognitive science to think more powerfully about learning and performance. We start with formal learning.

Spaced practice

One of the robust results of looking at making learning “stick” is that so-called “mass practice” does not work as well as spaced practice. Our goals for learning are retention over time until needed, and transfer to all appropriate (and no inappropriate) situations. Will Thalheimer’s review of the research has revealed that spacing practice over time, so that we revisit applying the knowledge to problems again several times, with periods of sleep in between, gives far better results (in terms of retention) than when all the practice occurs at one time, on one day.

Our “event” model of learning, in which we bring people together, physically or virtually, and have a unitary learning experience, is one of the least effective learning mechanisms we know. If we reactivate that knowledge, and have the learner reapply that knowledge again over a period of days, the knowledge will last much longer. The actual quantity of practice, and spacing between practices, is dependent on the time between real application of the knowledge and desired level of competency, but spacing is far better overall.

Social learning

As I said in an earlier article in Learning Solutions (http://www.learningsolutionsmag.com/articles/57/social-networking-bridging-formal-and-informal-learning), there are reasons why social learning should be considered. Learning on your own typically is not quite as effective as learning together. It may just be reprocessing, but when we need to work with others to negotiate a shared understanding, we develop a richer understanding (unless only one person is actually contributing). Usually, we have to express our thoughts clearly, and listen to others, and then if others have different understandings, we have to work to reconcile them.

Social learning is not only for formal learning, but also informal. There is not a binary division, but instead a continuum between novices exposed to received wisdom and experts co-creating shared understanding, a steady transition from the periphery of legitimate participation to the center of a community of practice. When we ask questions of a community, share our knowledge, or co-create new understandings (new products, services, or problems solved), we are learning in the broad sense of the term.

As my colleagues in the Internet Time Alliance and I argue, social learning needs to be as much a part of the infrastructure of effective companies as does any other form of IT. Enabling sharing information will be the only sustainable advantage.

Meta-learning

Assuming that learners are both capable self-learners and capable social learners is a mistake. Some may be good at one or the other, or both, but an individual having a full suite of learning skills is not a safe bet. Instead, we need to be explicit about learning skills, looking at learning to learn, or meta-learning.

This means identifying learning skills, articulating them, assessing them, and developing them. The task of the learning unit staff in the future will be facilitating learning, looking at all opportunities to support communities, including courses, performance support, and community. This nurturing role will include not only providing infrastructure, tools, and knowledge, but also developing learners as learners.

Distributed cognition

Cognitive psychology advanced beyond behaviorism by positing that we actually could try to determine what went on in the head. Situated cognition has gone beyond knowledge in the head to recognize that our brains not only operate “in the wild” (as Ed Hutchins would have it), but use information in the environment as part of the thinking process.

This recognition that our processing uses external representations is an important component in looking at ways to support performance. When should we provide tools, whether representational or computational, instead of trying to put all information in the head? We need a richer picture of how we perform, rather than a simplistic and ineffectual model that posits we can know everything we need.

Slow learning

Where do we go from here? How do we align these different models of thinking to take informed advantage of new technologies? I think there are a couple of really interesting opportunities here: contextualized performance support, and contextualized learning. The former is easier to grasp.

Contextual performance support

We already are seeing Augmented Reality applications for mobile learning. These applications know where you are (and even which way you are facing) and layer relevant information onto your world. You can search “nearby” for food, resources, or more. There is another possible way to provide support, and that is through knowingwhen you are. That is, by looking at your calendar and knowing what sort of event you are in, the application is able to provide relevant information such as sidekicks or planners, or a client update before a meeting, or a contract checklist in a vendor meeting.

In short, we are using contextual information captured as semantic tags, and running rules that look at distributed cognition tools to deliver contextualized support. We need models of the content and context, and also of the learner. However, this is doable now, and the organizational performance outcomes should be obvious.

Formalized performance

A broader extension of this model is to do more than facilitate performance, by actually promoting learning as well. Instead of taking learners away from their contexts and simulating performance opportunities, how about not only scaffolding those performance opportunities, but also turning them into learning opportunities? We could provide conceptual material and examples beforehand, reactivate relevant knowledge before performance, provide support during performance, and then close the loop with either self-evaluation rubrics or connection to a mentor. We could also have the device capture performance, and share it with colleagues for feedback!

We can do this in the real world, or provide virtual worlds to practice. Most formal learning provides artificial contexts, but a closer approximation to the real world is through virtual worlds that add the immersion you would experience in the real world. The capability for sharing this space with others becomes powerful for shared learning, with reciprocal performing and critiquing to co-develop ability. Co-creation is an extension to this model, where performers can collaboratively create new understandings that may only be personally new, or indeed may be new contributions to shared understandings.

We are now linking individual learning goals, social learning power, distributed cognition, and spaced practice in a powerful, long-term learning opportunity. I like to draw upon a drip irrigation metaphor to contrast with the typical “flood” approach, or perhaps to the “slow food” movement, to champion a richer view of individual development.

This is the dream that accompanied Gloria Gery’s call twenty years ago for electronic performance supportsystems, that these systems could not only support performance in the moment, but also develop the learner over time. In practice, the systems typically were not built to accommodate this extra goal, but they can be, and now we can do it wherever and whenever needed, not just in the immediate system-supported workflow.

Moving forward

The opportunity we now have is to use technology to move from an event-based learning model that we know to be ineffective, to a more distributed and contextualized environment that elegantly spans the continuum from formal learning to performance support. And this is not science fiction – we have the tools we need now. Even if we didn’t, we should be preparing our thinking for this capability.

There are some prerequisite steps. We need to:

• Start thinking of how to characterize individuals as learners, not just by their courses completed but by preferences, devices, and goals, so we know how to customize what we deliver to person A versus person B in the same place.
• Develop content in discrete chunks that are fully labeled and tagged to facilitate content reuse and access by rules, not by hand-linking, so we can deliver just enough, not everything there is.
• Characterize contexts both geographic and semantic, so that we know both the type of event they are engaged in as well as where they are and what would be useful to them here and now, finally truly delivering on the just-in-time regardless of proximity to the desktop.
• Identify a suite of self- and social-learning skills that we can assess and develop that is relevant to a new and changing world, so we’re developing their ability to perform, not just their knowledge.
• Think more deeply about a learning model that elegantly integrates a long-term view of learning with a broad view of performance and development, and which can integrate those interim steps to create an integrated and coherent whole.
• Look at learners as a longer-term learner proposition, not merely as cogs, but as individuals of worth who are essential to the success of our organizations and society.

A true learning organization is one where the individuals are developed continuously in a holistic way, and seamlessly across technologies and contexts. That’s the goal, and our capabilities are nearer than we think. What we need is the initiative to take a stab at this. So, who’s in?

References

Gery, G. (1995). Electronic Performance Support Systems: How and why to remake the workplace through the strategic application of technology. Toland, MA: Gery Performance Press.

Hutchins, E. (1996). Cognition in the Wild. Boston: MIT Press.

Internet Time Alliance. http://www.internettimealliance.com

Kapp, M. K. & O’Driscoll, T. (2010). Learning in 3D: Adding a New Dimension to Enterprise Learning and Collaboration. San Francisco: Pfeiffer.

Thalheimer, W. (2006). Spacing Learning Events Over Time: What the Research Says. Boston: Work-Learning Research.

Wexler, S., Schlenker, B., Brown, J., Metcalf, D., Quinn, C., Thor, E., Van Barneveld, A., & Wagner, E. (2007). 360º Research Report on Mobile Learning: What it is, why it matters, and how to incorporate it into your learning strategy. Santa Rosa CA: eLearning Guild.

http://www.learningsolutionsmag.com/articles/452/rethinking-e-learning

Organizations have to become more nimble, more agile. Optimal execution is only the cost of entry, and organizations have to be tapping into continual innovation.

Publishers are not exempt from this. There are major pressures coming in a variety of guises. Yet, surprisingly, we’re seeing little innovation in products, services, or business models.

Open Information
One of the major new pressures is the increasingly available content on the internet. From information from organizations establishing credibility and individuals publishing their own understanding, the quantity of information available on the web is increasing dramatically. Consequently, more and more individuals are choosing to live with what they can find rather than spend money.

And user-generated content is the next generation of the web, with ever-easier publishing opportunities. The amount of free information isn’t going to go away. The open courseware movement is a further source of information.

Worse, the continuing effort to change textbook offerings with new editions is facing increasing pressure from irate students. Costs are high, and the novelty of the offerings isn’t sufficient to make a compelling value proposition to a learner.

And it’s not that the free information is suspect. When you have brands like MIT, there’s little question of the authority. And Wikipedia has been demonstrated to be equal if not better than the Encyclopedia Britannica. The value of vetted information resources is decreasingly in demand.

(Not) Coping
Charles Jennings, former CLO for Reuters, recently cited the examples ofReader’s Digest and Learning Tree International (offering instructor-led training, but with a library of offerings) suggesting that their problems arise from “a failure to innovate and a failure to adapt.”

Both are publishers with solid content, but have been unable to create a new business model. In my own experience I have seen and learned how they’re hampered by outdated technology and inflexible business models.

Some publishers are making inroads with online offerings, and new companies are coming up with disruptive models. The question is whether publishers as a whole can adapt or are doomed.

The Publisher Value Proposition
The question of whether there’s a value proposition is not the issue. The value of editing, both in choosing what’s worthy of publishing and ensuring that that quality is manifested in the final product, is still worthy. Indeed, in the age of information glut, it may be ever more valuable.

And the value proposition of a book is not diminishing. Individuals still buy—and read—books. The value of text is still high owing to a good data-to-bandwidth ratio, and likely will remain so whether the delivery vehicle becomes a smart phone, a tablet, or e-paper.

The real question is how that experience is revisited in the era of the internet. The online world provides new opportunities, but also requires new thinking. Giving away ancillary sites for textbooks isn’t a valuable business model. Online content alone isn’t sufficient.

The Experience
Pine and Gilmore, in their book The Experience Economy, talk about how the basis of business moves up a value chain. Starting with commodities, you add distinctiveness to create goods, then add activities to create service. The next step, they argue, is the experience. This is where the concept of the total customer interaction comes into play, from pre-sales to after sales support.

The opportunity is clear. The old cliché “it’s not about books, it’s about content” doesn’t go far enough. What’s needed is to make a compelling online experience, based on the content, tapping into the additional capabilities of the digital environment while not abandoning the value add.

For educational publishers, there’s an additional consideration, and a market-differentiating opportunity. Pine and Gilmore suggest that the level beyond the experience is the transformative experience, where you pay for experiences that change you in desired ways. This is the core of education, when done right, and the ability to turn expert knowledge into a meaningful learning experience is a captivating premise. The question then is, how do you take a content business and turn it into an experience business?

Delivering Experiences
There are several components necessary to take a content development organization, and create an experience delivery capability. This includes executing an experience design process, developing and augmenting content for flexible delivery, and the capability to develop, trial, and deliver multiple and flexible business models.

Experience Design
Experience design is a new area, involving information architecture and design, engagement, and diverse media skills. Critically, it’s having someone own the ultimate vision of the experience, and coordinating the elements to create the necessary engagement.

For educational publishers, an extra layer is learning experience design. To truly execute against this vision of an engaging experience and an effective learning experience, you have to understand not only learning, but also the alignment with engagement.

Learning experience design capability needs to be placed as a core competency, and one that is not in most publishers today.

Content Development
Underpinning the experience design is the content model. There are several ways in which an experience can be delivered: a monolithic version can be developed, which delivers the highest quality experience, but at a high cost and with limited flexibility; at the opposite end, compositing loose content is easy, but yields a low quality experience; in between is an approach where tight definition around learning components provides the ability to flexibly create different offerings yet structure compelling experiences and keep a tight rein on costs.

Developing against this model requires new rigor in processes and tools. Having authors write to an outline will no longer be sufficient, and a more coordinated approach to content development is required, developing the different components in synchrony from an agreed-upon vision. The notion of author will, of needs, have a new interpretation.

Tight definition supports flexible delivery. With strict constraints upon the content components, you get dual benefits: quality control upon the individual elements, and the ability to combine the elements into different offerings.

The dynamic market for content will require the ability to semantically define appropriate offerings, and pull together appropriate content packages that meet those needs. That only occurs if you have tight definition around the semantic components, and the ability to combine those elements programmatically through business rules.

Business Model
Adaptive and custom content delivery enables new business models. An initial investment in creating the underpinning understanding, resources, processes, tools, and systems is necessary to enable the creation of new business opportunities. While existing systems should be able to be coupled to the integrated infrastructure with service-oriented architectures, a comprehensive set of capabilities need to be enabled that include content management and governance, and flexible delivery tied to both business models and customer authentication.

The existing infrastructure of publishers is, by and large, not ready to accommodate these changes. The barriers are not technical nor procedural, but organizational, in the ways that financing is on tap to support experimentation, evaluation, and continual innovation.

Publish or Perish
Ultimately, the only persistent success model is continual innovation in experience offerings, combining services with offerings that meet transformation experiences with viable relationships between value and cost. Educational publishers must deliver a variety of cost-effective experience offerings that strike a balance between desire and affordability. Without market-relevant offerings, an organization will perish. Rather than publish paper, organizations must recognize that our technology has changed, and so too must the medium of delivery.

The increasing change we see in information, competition, and customer expectation makes the ability to continually experiment an imperative. The issue is not print versus digital delivery, but instead whether the ability to deliver quality content in any available format that creates a comprehensive learning experience is on tap for creative business relationships.

The need is for an infrastructure and associated processes that create a flexible platform for delivering quality content in meaningful experiences. That comes from integrating experience design, content structure, and experimentation around offerings to derive new offerings. The experience design, content structure, an the associated infrastructure can be defined to support flexible new offering, but the commitment needs to be made.

Educational publishers have a lot of opportunity to change, but little choice. The nature of their business and the changing environment provide little opportunity to do aught but find new ways to capitalize on their core value add of high quality content. Doing that in the digital era means leveraging that content to create a compelling learner experience. Understanding learner experience, content development processes that can deliver flexible learner experiences, and the ability to explore different offerings and exploit those that have market resonance is the fundamental change needed to publish, not perish.

Acknowledgement
While these thoughts developed across many experiences over the years with educational publishers and conversations with Mohit Bhargava of LearningMate, they have recently been reactivated and refined by exchanges with Algis Leveckis; I’m grateful to all.