Learnlets
Clark Quinn's Learnings about Learning
(The Official Quinnovation blog)

24 February 2015

Making ‘sense’

Clark @ 8:19 am

I recently wrote about wearables, where I focused on form factor and information channels.  An article I recently read talked about a guy who builds spy gear, and near the end he talked about some things that started me thinking about an extension of that for all mobile, not just wearables.  The topic is  sensors.

In the article, he talks about how, in the future, glasses could detect whether you’ve been around bomb-making materials:

“You can literally see residue on someone if your glasses emit a dozen different wavelengths of microlasers that illuminate clothing in real time and give off a signature of what was absorbed or reflected.”

That’s pretty amazing, chemical spectrometry on the fly.  He goes on to talk about distance vision:

“Imagine you have a pair of glasses, and you can just look at a building 50 feet away, 100 feet away, and look right through the building and see someone moving around.”

 Now, you might nor might not like what he’s doing with that, but imagine applying it elsewhere: identifying where people are for rescue, or identifying materials for quality control.

Heck, I’d find it interesting just to augment the camera with infrared and ultraviolet: imagine being able to use the camera on your phone or glasses to see what’s happening at night, e.g. wildlife (tracking coyotes or raccoons, and managing to avoid skunks!).  Night vision, and seeing things that fluoresce under UV would both be really cool additions.

I’d be interested too in having them able to work to enlarge as well, bring small things to light like a magnifying glass or microscope.

It made me think about all the senses we could augment. I was thinking about walking our dogs, and how their olfactory life is much richer than ours.  They are clearly sensing things beyond our olfactory capabilities, and it would be interesting to have some microscent detectors that could track faint traces to track animals (or know which owner is not adequately controlling a dog, ahem).  They could potentially serve as smoke or carbon monoxide detectors also.

Similarly, auditory enhancement: could we hear things fainter than our ears detect, or have them serve as a stethoscope?  Could we detect far off cries for help that our ears can’t? Of course, that could be misused, too, to eavesdrop on conversations.  Interesting ethical issues come in.

And we’ve already heard about the potential to measure one’s movement, blood pressure, pulse, temperature, and maybe even blood sugar, to track one’s health.  The fit bands are getting smarter and more capable.

There is the possibility for other things we personally can’t directly track: measuring ambient temperatures quantitatively, and air pressure are both already possible and in some devices.  The thermometer could be a health and weather guide, and a barometer/altimeter would be valuable for hiking in addition to weather.

The combination of reporting these could be valuable too.  Sensor nets, where the data from many micro sensors are aggregated have interesting possibilities. Either with known combinations, such as aggregating temperature and air pressure  help with weather, or machine learning  where for example we include sensitive motion detectors,  and might be able to learn to predict earthquakes like supposedly animals can.  Sounds too could be used to triangulate on cries for help, and material detectors could help locate sources of pollution.

We’ve done amazing things with technology, and sensors are both shrinking and getting more powerful. Imagine having sensors scattered about your body in various wearables and integrating that data in known ways, and agreeing for anonymous aggregation for data mining.  Yes, there are concerns, but benefits too.

We can put these together in interesting ways, notifications of things we should pay attention to, or just curiosity to observe things our natural senses can’t detect.  We can open up the world in powerful ways to support being more informed and more productive.  It’s up to us to harness it in worthwhile ways.

27 January 2015

70:20:10 and the Learning Curve

Clark @ 8:09 am

My colleague Charles Jennings recently posted on the value of autonomous learning (worth reading!), sparked by a diagram provided by another ITA colleague, Jane Hart (that I also thought was insightful). In Charles’ post he also included an IBM diagram that triggered some associations.

So, in IBM’s diagram, they talked about: the access phase where learning is separate, the integration where learning is ‘enabled’ by work, and the on-demand phase where learning is ‘embedded’. They talked about ‘point solutions’ (read: courses) for access, then blended models for integration, and dynamic models for on demand. The point was that the closer to the work that learning is, the more value.

However, I was reminded of Fits & Posner’s model of skill acquisition, which has 3 phases of cognitive, associative, and autonomous learning. The first, cognitive, is when you benefit from formal instruction: giving you models and practice opportunities to map actions to an explicit framework. (Note that this assumes a good formal learning design, not rote information and knowledge test!)  Then there’s an associative stage where that explicit framework is supported in being contextualized and compiled away.  Finally, the learner continues to improve through continual practice.

I was initially reminded of Norman & Rumelhart’s accretion, restructuring, and tuning learning mechanisms, but it’s not quite right. Still, you could think of accreting the cognitive and explicitly semantic knowledge, then restructuring that into coarse skills that don’t require as much conscious effort, until it becomes a matter of tuning a finely automated skill.

721LearningCurveThis, to me, maps more closely to 70:20:10, because you can see the formal (10) playing a role to kick off the semantic part of the learning, then coaching and mentoring (the 20) support the integration or association of the skills, and then the 70 (practice, reflection, and personal knowledge mastery including informal social learning) takes over, and I mapped it against a hypothetical improvement curve.

Of course, it’s not quite this clean. While the formal often does kick off the learning, the role of coaching/mentoring and the personal learning are typically intermingled (though the role shifts from mentee to mentor ;). And, of course, the ratios in 70:20:10 are only a framework for rethinking investment, not a prescription about how you apply the numbers.  And I may well have the curve wrong (this is too flat for the normal power law of learning), but I wanted to emphasize that the 10 only has a small role to play in moving performance from zero to some minimal level, that mentoring and coaching really help improve performance, and that ongoing development requires a supportive environment.

I think it’s important to understand how we learn, so we can align our uses of technology to support them in productive ways. As this suggests, if you care about organizational performance, you are going to want to support more than the course, as well as doing the course right.  (Hence the revolution. :)

#itashare

21 January 2015

Wearables?

Clark @ 8:22 am

In a discussion last week, I suggested that the things I was excited about included wearables. Sure enough, someone asked if I’d written anything about it, and I haven’t, much. So here are some initial thoughts.

I admit I was not a Google Glass ‘Explorer’ (and now the program has ended).  While tempted to experiment, I tend not to spend money until I see how the device is really going to make me more productive.  For instance, when the iPad was first announced, I didn’t want one. Between the time it was announced and it was available, however, I figured out how I’d use it produce, not just consume.   I got one the first day it came out.  By the same rationale, I got a Palm Pilot pretty early on, and it made me much more effective.   I haven’t gotten a wrist health band, on the other hand, though I don’t think they’re bad ideas, just not what I need.

The point being that I want to see a clear value proposition before I spend my hard earned money.  So what am I thinking in regards to wearables? What wearables do I mean?  I am talking wrist devices, specifically.  (I  may eventually warm up to glasses as well, when what they can do is more augmented reality than they do now.)  Why wrist devices?  That’s what I’m wrestling with, trying to conceptualize what is a more intuitive assessment.

Part of it, at least, is that it’s with me all the time, but in an unobtrusive way.  It supports a quick flick of the wrist instead of pulling out a whole phone. So it can do that ‘smallest info’ in an easy way. And, more importantly, I think it can bring things to my attention more subtly than can a phone.  I don’t need a loud ringing!

I admit that I’m keen on a more mixed-initiative relationship than I currently have with my technology.  I use my smartphone to get things I need, and it can alert me to things that I’ve indicated I’m interested in, such as events that I want an audio alert for.  And of course, for incoming calls.  But what about for things that my systems come up with on their own?  This is increasingly possible, and again desirable.  Using context, and if a system had some understanding of my goals, it might be able to be proactive. So imagine you’re out and about, and your watch reminds you that while you were  here you wanted to pick up something nearby, and provide the item and location.  Or to prep for that upcoming meeting and provide some minimal but useful info.   Note that this is not what’s currently on offer, largely.  We already have geofencing to do some, but right now for it to happen you largely have to pull out your phone or have it give a largely intrusive noise to be heard from your pocket or purse.

So two things about this: one why the watch and not the phone, and the other, why not the glasses? The watch form factor is, to me, a more accessible interface to serve as a interactive companion. As I suggested, pulling it out of the pocket, turning it on, going through the security check (even just my fingerprint), adds more of an overhead than I necessarily want.  If I can have something less intrusive, even as part of a system and not fully capable on it’s own, that’s OK.  Why not glasses? I guess it’s just that they seem more unnatural.  I am accustomed to having information on my wrist, and while I wear glasses, I want them to be invisible to me.  I would love to have a heads-up display at times, but all the time would seem to get annoying. I’ll stretch and suggest that the empirical result that most folks have stopped wearing them most of the time bears up my story.

Why not a ring, or a pendant, or?  A ring seems to have too small an interface area.  A pendant isn’t easily observable. On my wrist is easy for a glance (hence, watches).  Why not a whole forearm console?  If I need that much interface, I can always pull out my phone.  Or jump to my tablet. Maybe I will eventually will want an iBracer, but I’m not yet convinced. A forearm holster for my iPhone?  Hmmm…maybe too geeky.

So, reflecting on all this, it appears I’m thinking about tradeoffs of utility versus intrusion.  A wrist devices seems to fit a sweet spot in an ecosystem of tech for the quick glance, the pocket access, and then various tradeoffs of size and weight for a real productivity between tablets and laptops.

Of course, the real issue is whether there’s sufficient information available through the watch that it makes a value proposition. Is there enough that’s easy to get to that doesn’t require a phone?  Check the temperature?  Take a (voice) note?  Get a reminder, take a call, check your location? My instinct is that there is.  There are times I’d be happy to not have to take my phone (to the store, to a party) if I could take calls on my wrist, do minimal note taking and checking, and navigating.  For the business perspective, also have performance support whether push or pull.  I don’t see it for courses, but for just-in-time…  And contextual.

This is all just thinking aloud at this point.  I’m contemplating the iWatch but don’t have enough information as of yet.  And I may not feel the benefits outweigh the costs. We’ll see.

31 December 2014

Reflections on 15 years

Clark @ 7:32 am

For Inside Learning & Technologies 50th edition, a number of us were asked to provide reflections on what has changed over the past 15 years.  This was pretty much the period in which I’d returned to the US and took up with what was kind of a startup and led to my life as a consultant.  As an end of year piece, I have permission to post that article here:

15 years ago, I had just taken a step away from academia and government-sponsored initiatives to a new position leading a team in what was effectively a startup. I was excited about the prospect of taking the latest learning science to the needs of the corporate world. My thoughts were along the lines of “here, where we have money for meaningful initiatives, surely we can do something spectacular”. And it turns out that the answer is both yes and no.

The technology we had then was pretty powerful, and that has only increased in the past 15 years. We had software that let us leverage the power of the internet, and reasonable processing power in our computers. The Palm Pilot had already made mobile a possibility as well. So the technology was no longer a barrier, even then.

And what amazing developments we have seen! The ability to create rendered worlds accessible through a dedicated application and now just a browser is truly an impressive capability. Regardless of whether we overestimated the value proposition, it is still quite the technology feat. And similarly, the ability to communicate via voice and video allows us to connect people in ways once only dreamed of.

We also have rich new ways to interact from microblogs to wikis (collaborative documents). These capabilities are improved by transcending proximity and synchronicity. We can work together without worrying about where the solution is hosted, or where our colleagues are located. Social media allow us to tap into the power of people working together.

The improvements in mobile capabilities are also worth noting. We have gone from hype to hyphens, where a limited monochrome handheld has given way to powerful high-resolution full-color multi-channel always-connected sensor-rich devices. We can pretty much deliver anything anywhere we want, and that fulfills Arthur C. Clarke’s famous proposition that a truly advanced technology is indistinguishable from magic.

Coupled with our technological improvements are advances in our understanding of how we think, work, and learn. We now have recognition about how we act in the world, about how we work with others, and how we best learn. We have information age understandings that illustrate why industrial age methods are not appropriate.

It is not truly new, but reaching mainstream awareness in the last decade and more is the recognition that the model of our thinking as formal and logical is being updated. While we can work in such ways, it is the exception rather than the rule. Such thinking is effortful and it turns out both that we avoid it and there is a limit to how much deep thinking one can do in a day. Instead, we use our intuition beyond where we should, and while this is generally okay, it helps to understand our limitations and design around them.

There is also a spreading awareness of how much our thinking is externalized in the world, and how much we use technology to support us being effective. We have recognized the power of external support for thinking, through tools such as checklists and wizards. We do this pretty naturally, and the benefits from good design of technology greatly facilitate our ability to think.

There is also recognition that the model of individual innovation is broken, and that working together is far superior to working alone. The notion of the lone genius disappearing and coming back with the answer has been replaced by iterations on top of previous work by teams. When people work together in effective ways, in a supportive environment, the outcomes will be better. While this is not easy to effect in many circumstances, we know the practices and culture elements we need, and it is our commitment to get there, not our understanding, that is the barrier.

Finally, our approaches to learning are better informed now. We know that being emotionally engaged is a valued component in moving to learning experience design. We understand the role of models in supporting more flexible performance. We also have evidence of the value of performing in context. It is not news that information dump and knowledge test do not lead to meaningful skill acquisition, and it is increasingly clear that meaningful practice can. It is also increasingly clear that, as things move faster, meaningful skills – the ability to make better decisions – is what is going to provide the sustainable differentiator for organizations.

So imagine my dismay in finding that the approaches we are using in organizations are largely still rooted in approaches from yesteryear. While we have had rich technology opportunities to combine with our enlightened understanding, that is not what we are seeing. What we see is still expectations that it is done in-the-head, top-down, with information dump and meaningless assessment that is not tied to organizational outcomes. And while it is not working, demonstrably, there seems little impetus to change.

Truly, there has been little change in our underlying models in 15 years. While the technology is flashier, the buzz words have mutated, and some of the faces have changed, we are still following myths like learning styles and generational differences, we are still using ‘spray and pray’ methods in learning, we are still not taking on performance support and social learning, and perhaps most distressingly, we are still not measuring what matters.

Sure, the reasons are complex. There are lots of examples of the old approaches, the tools and practices are aligned with bad learning practices, the shared metrics reflect efficiency instead of effectiveness, … the list goes on. Yet a learning & development (L&D) unit unengaged with the business units it supports is not sustainable, and consequently the lack of change is unjustifiable.

And the need is now more than ever. The rate of change is increasing, and organizations now have more need to not just be effective, but they have to become agile. There is no longer time to plan, prepare, and execute, the need is to continually adapt. Organizations need to learn faster than the competition.

The opportunities are big. The critical component for organizations to thrive is to couple optimal execution (the result of training and performance support) with continual innovation (which does not come from training). Instead, imagine an L&D unit that is working with business units to drive interventions that affect key KPIs. Consider an L&D unit that is responsible for facilitating the interactions that are leading to new solutions, new products and services, and better relationships with customers. That is the L&D we need to see!

The path forward is not easy but it is systematic and doable. A vision of a ‘performance ecosystem’ – a rich suite of tools to support success that surround the performer and are aligned with how they think, work, and learn – provides an endpoint to start towards. Every organization’s path will be different, but a good start is to start doing formal learning right, begin looking at performance support, and commence working on the social media infrastructure.

An associated focus is building a meaningful infrastructure (hint: one all-singing all-dancing LMS is not the answer). A strategy to get there is a companion effort. And, ultimately a learning culture will be necessitated. Yet these components are not just a necessary component for L&D, they are the necessary components for a successful organization, one that can be agile enough to adapt to the increasing rate of change we are facing.

And here is the first step: L&D has to become a learning organization. Mantras like ‘work out loud’, ‘fail fast’, and ‘reflect’ have to become part of the L&D culture. L&D has to start experimenting and learning from the experiments. Let us ensure that the past 15 years are a hibernation we emerge from, not the beginning of the end.

Here’s to change for the better.  May 2015 be the best year yet!

9 December 2014

My thoughts on tech and training

Clark @ 8:27 am

The eLearning Guild,  in queuing up interest in their Learning Solutions/Performance Ecosystem conference, asked for some thoughts on the role of technology and training.  And, of course, I obliged.  You can see them here.

In short, I said that technology can augment what we already do, serving to fill in gaps between what we desired and what we could deliver, and it also gave us some transformative capabilities.  That is, we can make the face to face time more effective, extend the learning beyond the classroom, and move the classroom beyond the physical space.

The real key, a theme I find myself thumping more and more often, is that we can’t use technology in ineffective ways. We need to use technology in ways that align with how we think, work, and learn.  And that’s all too rare.  We can do amazing things, if: we muster the will and resources, do the due diligence on what would be a principled approach, and then do the cycles of develop and iteration to get us to where the solution is working as it should.

Again, the full thoughts can be found on their blog.

 

31 October 2014

Belinda Parmar #DevLearn Keynote Mindmap

Clark @ 11:38 am

Belinda Parmar addressed the critical question of women in tech in a poignant way, pointing out that the small stuff is important: language, imagery, context. She concluded with small actions including new job description language and better female involvement in product development.

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29 October 2014

Neil deGrasse Tyson #DevLearn Keynote Mindmap

Clark @ 9:54 am

Neil deGrasse Tyson opened this year’s DevLearn conference. A clear crowd favorite, folks lined up to get in (despite the huge room). In a engaging, funny, and poignant talk, he made a great case for science and learning.

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28 October 2014

Cognitive prostheses

Clark @ 8:05 am

While our cognitive architecture has incredible capabilities (how else could we come up with advances such as Mystery Science Theater 3000?), it also has limitations. The same adaptive capabilities that let us cope with information overload in both familiar and new ways also lead to some systematic flaws. And it led me to think about the ways in which we support these limitations, as they have implications for designing solutions for our organizations.

The first limit is at the sensory level. Our mind actually processes pretty much all the visual and auditory sensory data that arrives, but it disappears pretty quickly (within milliseconds) except for what we attend to. Basically, your brain fills in the rest (which leaves open the opportunity to make mistakes). What do we do? We’ve created tools that allow us to capture things accurately: cameras and microphones with audio recording. This allows us to capture the context exactly, not as our memory reconstructs it.

A second limitation is our ‘working’ memory. We can’t hold too much in mind at one time. We ‘chunk’ information together as we learn it, and can then hold more total information at one time. Also, the format of working memory largely is ‘verbal’. Consequently, using tools like diagramming, outlines, or mindmaps add structure to our knowledge and support our ability to work on it.

Another limitation to our working memory is that it doesn’t support complex calculations, with many intermediate steps. Consequently we need ways to deal with this. External representations (as above), such as recording intermediate steps, works, but we can also build tools that offload that process, such as calculators. Wizards, or interactive dialog tools, are another form of a calculator.

Processing information in short term memory can lead to it being retained in long term memory. Here the storage is almost unlimited in time and scope, but it is hard to get in there, and isn’t remembered exactly, but instead by meaning. Consequently, models are a better learning strategy than rote learning. But external sources like the ability to look up or search for information is far better than trying to get it in the head.

Similarly, external support for when we do have to do things by rote is a good idea. So, support for process is useful and the reason why checklists have been a ubiquitous and useful way to get more accurate execution.

In execution, we have a few flaws too. We’re heavily biased to solve new problems in the ways we’ve solved previous problems (even if that’s not the best approach. We’re also likely to use tools in familiar ways and miss new ways to use tools to solve problems. There are ways to prompt lateral thinking at appropriate times, and we can both make access to such support available, and even trigger same if we’ve contextual clues.

We’re also biased to prematurely converge on an answer (intuition) rather than seek to challenge our findings. Access to data and support for capturing and invoking alternative ways of thinking are more likely to prevent such mistakes.

Overall, our use of more formal logical thinking fatigues quickly. Scaffolding help like the above decreases the likelihood of a mistake and increases the likelihood of an optimal outcome.

When you look at performance gaps, you should look to such approaches first, and look to putting information in the head last. This more closely aligns our support efforts with how our brains really think, work, and learn. This isn’t a complete list, I’m sure, but it’s a useful beginning.

16 October 2014

Sharing pointedly or broadly

Clark @ 8:06 am

In a (rare) fit of tidying, I was moving from one note-taking app to another, and found a diagram I’d jotted, and it rekindled my thinking. The point was characterizing social media in terms of their particular mechanisms of distribution. I can’t fully recall what prompted the attempt at characterization, but one result of revisiting was thinking about the media in terms of whether they’re part of a natural mechanism of ‘show your work’ (ala Bozarth)/’work out loud’ (ala Jarche).

whether person to person or one to manyThe question revolves around whether the media are point or broadcast, that is whether you specify particular recipients (even in a mailing or group list), or whether it’s ‘out there’ for anyone to access.  Now, there are distinctions, so you can have restricted access on the ‘broadcast’ mode, but in principle there’re two different mechanisms at work.

It should be noted that in the ‘broadcast’ model, not everyone may be aware that there’s a new message, if they’re not ‘following’ the poster of the message, but it should be findable by search if not directly.  Also, the broadcast may only be an organizational network, or it can be the entire internet.  Regardless, there are differences between the two mechanisms.

So, for example, a chat tool typically lets you ping a particular person, or a set list. On the other hand, a microblog lets anyone decide to ‘follow’ your quick posts.   Not everyone will necessarily be paying attention to the ‘broadcast’, but they could.  Typically, microblogs (and chat) are for short messages, such as requests for help or pointers to something interesting.  The limitations mean that more lengthy discussions typically are conveyed via…

Formats supporting unlimited text, including thoughtful reflections, updates on thinking, and more tend to be conveyed via email or blog posts. Again, email is addressed to a specific list of people, directly or via a mail list, openly or perhaps some folks receiving copies ‘blind’ (that is, not all know who all is receiving the message.  A blog post (like this), on the other hand, is open for anyone on the ‘system’.

The same holds true for other media files besides text.   Video and audio can be hidden in a particular place (e.g. a course) or sent directly to one person. On the other hand, such a message can be hosted on a portal (YouTube, iTunes) where anyone can see.  The dialog around a file provides a rich augmentation, just as such can be happening on a blog, or edited RTs of a microblog comment.

Finally, a slightly different twist is shown with documents.  Edited documents (e.g. papers, presentations, spreadsheets) can be created and sent, but there’s little opportunity for cooperative development.  Creating these in a richer way that allows for others to contribute requires a collaborative document (once known as a wiki).  One of my dreams is that we may have collaboratively developed interactives as well, though that still seems some way off.

The point for showing out loud is that point is only a way to get specific feedback, whereas a broadcast mechanism is really about the opportunity to get a more broad awareness and, potentially, feedback.  This leads to a broader shared understanding and continual improvement, two goals critical to organizational improvement.

Let me be the first to say that this isn’t necessarily an important, or even new, distinction, it’s just me practicing what I preach.  Also, I  recognize that the collaborative documents are fundamentally different, and I need to have a more differentiated way to look at these (pointers or ideas, anyone), but here’s my interim thinking.  What say you?

#itashare

17 September 2014

Learning in 2024 #LRN2024

Clark @ 8:14 am

The eLearning Guild is celebrating it’s 10th year, and is using the opportunity to reflect on what learning will look like 10 years from now.  While I couldn’t participate in the twitter chat they held, I optimistically weighed in: “learning in 2024 will look like individualized personal mentoring via augmented reality, AI, and the network”.  However, I thought I would elaborate in line with a series of followup posts leveraging the #lrn2024 hashtag.  The twitter chat had a series of questions, so I’ll address them here (with a caveat that our learning really hasn’t changed, our wetware hasn’t evolved in the past decade and won’t again in the next; our support of learning is what I’m referring to here):

1. How has learning changed in the last 10 years (from the perspective of the learner)?

I reckon the learner has seen a significant move to more elearning instead of an almost complete dependence on face-to-face events.  And I reckon most learners have begun to use technology in their own ways to get answers, whether via the Google, or social networks like FaceBook and LinkedIn.  And I expect they’re seeing more media such as videos and animations, and may even be creating their own. I also expect that the elearning they’re seeing is not particularly good, nor improving, if not actually decreasing in quality.  I expect they’re seeing more info dump/knowledge test, more and more ‘click to learn more‘, more tarted-up drill-and-kill.  For which we should apologize!

2. What is the most significant change technology has made to organizational learning in the past decade?

I reckon there are two significant changes that have happened. One is rather subtle as yet, but will be profound, and that is the ability to track more activity, mine more data, and gain more insights. The ExperienceAPI coupled with analytics is a huge opportunity.  The other is the rise of social networks.  The ability to stay more tightly coupled with colleagues, sharing information and collaborating, has really become mainstream in our lives, and is going to have a big impact on our organizations.  Working ‘out loud’, showing our work, and working together is a critical inflection point in bringing learning back into the workflow in a natural way and away from the ‘event’ model.

3. What are the most significant challenges facing organizational learning today?

The most significant change is the status quo: the belief that an information oriented event model has any relationship to meaningful outcomes.  This plays out in so many ways: order-taking for courses, equating information with skills, being concerned with speed and quantity instead of quality of outcomes, not measuring the impact, the list goes on.   We’ve become self-deluded that an LMS and a rapid elearning tool means you’re doing something worthwhile, when it’s profoundly wrong.  L&D needs a revolution.

4. What technologies will have the greatest impact on learning in the next decade? Why?

The short answer is mobile.  Mobile is the catalyst for change. So many other technologies go through the hype cycle: initial over-excitement, crash, and then a gradual resurgence (c.f. virtual worlds), but mobile has been resistant for the simple reason that there’s so much value proposition.  The cognitive augmentation that digital technology provides, available whenever and wherever you are clearly has benefits, and it’s not courses!  It will naturally incorporate augmented reality with the variety of new devices we’re seeing, and be contextualized as well.  We’re seeing a richer picture of how technology can support us in being effective, and L&D can facilitate these other activities as a way to move to a more strategic and valuable role in the organization.  As above, also new tracking and analysis tools, and social networks.  I’ll add that simulations/serious games are an opportunity that is yet to really be capitalized on.  (There are reasons I wrote those books :)

5. What new skills will professionals need to develop to support learning in the future?

As I wrote (PDF), the new skills that are necessary fall into two major categories: performance consulting and interaction facilitation.  We need to not design courses until we’ve ascertained that no other approach will work, so we need to get down to the real problems. We should hope that the answer comes from the network when it can, and we should want to design performance support solutions if it can’t, and reserve courses for only when it absolutely has to be in the head. To get good outcomes from the network, it takes facilitation, and I think facilitation is a good model for promoting innovation, supporting coaching and mentoring, and helping individuals develop self-learning skills.  So the ability to get those root causes of problems, choose between solutions, and measure the impact are key for the first part, and understanding what skills are needed by the individuals (whether performers or mentors/coaches/leaders) and how to develop them are the key new additions.

6. What will learning look like in the year 2024?

Ideally, it would look like an ‘always on’ mentoring solution, so the experience is that of someone always with you to watch your performance and provide just the right guidance to help you perform in the moment and develop you over time. Learning will be layered on to your activities, and only occasionally will require some special events but mostly will be wrapped around your life in a supportive way.  Some of this will be system-delivered, and some will come from the network, but it should feel like you’re being cared for in the most efficacious way.

In closing, I note that, unfortunately,my Revolution book and the Manifesto were both driven by a sense of frustration around the lack of meaningful change in L&D. Hopefully, they’re riding or catalyzing the needed change, but in a cynical mood I might believe that things won’t change near as much as I’d hope. I also remember a talk (cleverly titled: Predict Anything but the Future :) that said that the future does tend to come as an informed basis would predict with an unexpected twist, so it’ll be interesting to discover what that twist will be.

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