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Organizational terms

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Listening to a talk last week led me to ponder the different terms for what it is I lobby for.  The goal is to make organizations accomplish their goals, and to continue to be able to do so.  In the course of my inquiry, I explored and uncovered several different ‘organizational’ terms.  I thought I should lay them out here for my (and your) thoughts.

For one, it seemed to be about organizational  effectiveness. That is, the goal is to make organizations not just efficient, but capable of optimal levels of performance.  When you look at the Wikipedia definition, you find that they’re about “achieving the outcomes the organization intends to produce”.  They do this through alignment, increasing tradeoffs, and facilitating capacity building.  The definition also discusses improvements in decision making, learning, group work, and tapping into the strictures of self-organizing and adaptive systems, all of which sound right.

Interesting, most of the discussion seems to focus on not-for-profit organizations. While I agree on their importance, and have done considerable work with such organizations, I guess I’d like to see a broader focus. Also, and this is purely my subjective opinion, the newer thoughts seem grafted on, and the core still seems to be about producing good numbers. Any time you use the phrase ‘human capital’, I am leery.

Organizational engineering is a phrase that popped to mind (similar to learning engineering). Here, Wikipedia defines it as an offshoot of org development, with a focus on information processing. And, coming from cognitive psychology, that sounds good, with a caveat.  The reality is, we’re flawed as ideal thinkers. And in the definition it also talks about ‘styles’, which are a problem all on their own. Overall, this appears to be more a proprietary suite of approaches under a label. While it uses nice sounding terms, the reality (again, my inferences here) is that it may be designed for an audience that doesn’t exist.

The final candidate is organizational development. Here the definition touts “implementing effective change”. The field is defined as interdisciplinary and drawing on psych, sociology, and more.  In addition to systems thinking and and decision-making, there’s an emphasis on organizational learning and on coaching, so it appears more human-focused. The core values also talk about human beings being valued for themselves, not as resources, and looking at the complex picture.  Overall this approach resonates with me more, not just philosophically, but pragmatically.

As I look at what’s emerging from the scientific study of people and organizations, as summed up in a variety of books I’ve touted here, there are some very clear  lessons. For, one, people respond when you treat the as meaningful parts of a worthwhile endeavor. When you value people’s input and trust them to apply their talents to the goals, things get done. Caring enough to develop them in ways that are supportive, not punitive, and not just your goals but theirs’ too, retains their interest and commitment. And when you provide them with an environment to succeed and improve, you get the best organizational outcomes.

There’s more about how to get started.  Small steps, such as working in a small group (*cough* L&D? *cough* ;), and developing the practices and the infrastructure, then spreading, has been shown to be better than a top-down initiative. Experimenting and reviewing the outcomes, and continually tweaking likewise.  Ensuring that it’s coaching, not ‘managing’ (managers are the primary reason people leave companies).  Etc.

All this shouldn’t be a surprise, but it’s not trivial to do but takes persistence.  And, it flies in the face of much of management and HR practices.  I don’t really care what we label it, I just want to find a way to talk about things that makes it easy for people to know what I’m talking about.  There are goals to achieve, so my main question is how do we get there?  Anyone want to get started?

Simulations versus games

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At the recent Realities 360 conference, I saw some confusion about the difference between a simulation and a game. And while I made some important distinctions in my book on the topic, I realize that it’s possible that it’s time to revisit them. So here I’m talking about some conceptual discriminations that I think are important.

Simulations

As I’ve mentioned, simulations are models of the world. They capture certain relationships we believe to be true about the world. (For that matter, they can represent worlds that aren’t real, certainly the case in games.). They don’t (can’t) capture all the world, but a segment we feel it is important to model. We tend to validate these models by testing them to see if they behave like our real world.  You can also think about simulations as being in a ‘state’ (set of values in variables), and move to others by rules.  Frequently, we include some variability in these models, just as is reflected in the real world. Similarly, these simulations can model considerable complexity.

Such simulations are built out of sets of variables that represent the state of the world, and rules that represent the relationships present. There are several ways things change. Some variables can be changed by rules that act on the basis of time (while countdown timer = on, countdown = countdown -1). Variables can also interact (if countdown=0: if 1 g adamantium and 1 g dilithium, Temperature = Temperature +1000, adamantium = adamantium – 1g, dilithium = dilithium – 1g).  Other changes are based upon learner actions (if learner flips the switch, countdown timer = on).

Note that you may already have a simulation. In business, there may already exist a model of particular processes, particularly if they’re proprietary systems.

From a learning point of view, simulations allow motivated and self-effective learners to explore the relationships they need to understand. However, we can’t always assume motivated and self-effective learners. So we need some additional work to turn a simulation into a learning experience.

Scenarios

One effective way to leverage simulations is to choose an initial state (or ‘space of states’, a start point with some variation), and a state (or set) that constitutes ‘win’. We also typically have states that also represent ‘fail’.  We choose those states so that the learner can’t get to ‘win’ without understanding the necessary relationships.   The learner can try and fail until they discover the necessary relationships.  These start and goal states serve as scaffolding for the learning process.    I call these simulations with start and stop states ‘scenarios’.

This is somewhat complicated by the existence of ‘branching scenarios’. There are initial and goal states and learner actions, but they are  not represented by variable and rules. The relationships in branching scenarios are implicit in the links instead of explicit in the variables and rules. And they’re easier to build!  Still, they don’t have the variability that typically is possible in a simulation. There’s an inflection point (qualitative, not quantitative) where the complexity of controlling the branches renders it more sensible to model the world as a simulation rather than track all the branches.

Games

The problem here is that too often people will build a simulation and call it a game. I once reviewed a journal submission about a ‘game’ where the authors admitted that players thought it was boring. Sorry, then it’s not a game!  The difference between a simulation and a game is a subjective experience of engagement on the part of the player.

So how do you get from a simulation to a game?  It’s about tuning.  It’s about adjusting the frequency of events, and their consequences, such that the challenge moves to fall into the zone between boring and frustrating. Now, for learning, you can’t change the fundamental relationships you’re modeling, but you can adjust items like how quickly events occur, and the importance of being correct. And it takes testing and refinement. Will Wright, a game designers’ game designer, once proposed that tuning is 9/10’s of the work!  Now that’s for a commercial game, but it gives you and idea.

You can also use gamification, scores to add competition, but, please,  only after you first expend the effort to make the game intrinsically interesting. Tap into why they  should care about the experience, and bake that it.

Is it worth it to actually expend effort to make the experience engaging?  I believe that the answer is yes. Perhaps not to the level of a game people will pay $60 to play, but some effort to manifest the innate meaningfulness is worth it. Games minimize the time to obtain competency because they optimize the challenge.  You will have sticks as well as carrots, so you don’t need to put in $M budgets, but do tune until your learners have an engaging and effective experience.

So, does this help? What questions do you still have?

Realities 360 Reflections

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So, one of the two things I did last week was attend the eLearning Guild‘s Realities 36o conference.  Ostensibly about Augmented Reality (AR)  and Virtual Reality (VR), it ended up being much more about VR. Which isn’t a bad thing, it’s probably as much a comment on the state of the industry as anything.  However, there were some interesting learnings for me, and I thought I’d share them.

First, I had a very strong visceral exposure to VR. While I’ve played with Cardboard on the iPhone (you can find a collection of resources for Cardboard  here), it’s not quite the same as a full VR experience.  The conference provided a chance to try out apps for the HTC Vive, Sony Playstation VR, and the Oculus.  On the Vive, I tried a game where you shot arrows at attackers.  It was quite fun, but mostly developed some motor skills. On the Oculus, I flew an XWing fighter through an asteroid field and escorted a ship and shoot enemy Tie-fighters.  Again, fun, but mostly about training my motor skills in this environment.

It was the one I think on the Vive that gave me an experience.  In it, you’re floating around the International Space Station. And it was very cool to see the station and experience the immersion of 3D, but it was very uncomfortable.  Partly because I was trying to fly around (instead of using handholds), my viewpoint would fly through the bulkhead doors. However, the positioning meant that it gave the visual clues that my chest was going through the metal edge.  This was extremely disturbing to me!  As I couldn’t control it well, I was doing this continually, and I didn’t like it. Partly it was the control, but it was also the total immersion. And that was impressive!

There are empirical results that demonstrate better learning outcomes for VR, and certainly  I can see that particularly, for tasks inherently 3D. There’s also another key result, as was highlighted in the first keynote: that VR is an ’empathy’ machine. There have been uses for things like understanding the world according to a schizophrenic, and a credit card call center helping employees understand the lives of card-users.

On principle, such environs should support near transfer when designed to closely mimic the actual performance environment. (Think: flight or medicine simulators.)  And the tools are getting better. There’s an app that allows you to take photos of a place to put into Cardboard, and game engines (Unity or Unreal or both) will now let you import AutoCAD models.  There was also a special camera that could sense the distances in a space and automatically generate a model of it.  The point being that it’s getting easier and easier to generate VR environments.

That, I think, is what’s holding AR back.  You can fairly easily use it for marker or location based information, but actually annotating the world visually is still challenging.  I still think AR is of more interest, (maybe just to me), because I see it eventually creating the possibility to see the causes and factors  behind the world, and allow us to understand it better.  I could argue that VR is just extending sims from flat screen to surround, but then I think about the space station, and…I’m still pondering that. Is it revolutionary or just evolutionary?

One session talked about trying to help folks figure out when VR and AR made sense, and this intrigued me. It reminded me that I had tried to characterize the affordances of virtual worlds, and I reckon it’s time to take a stab at doing this for VR and AR.  I believed then that I was able to predict when virtual worlds would continue to find value, and I think results have borne that out.  So, the intent is to try to get on top of when VR and AR make sense.  Stay tuned!

What is the Future of Work?

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which is it?Just what is the Future of Work  about? Is it about new technology, or is it about how we work with people?  We’re seeing  amazing new technologies: collaboration platforms, analytics, and deep learning. We’re also hearing about new work practices such as teams, working (or reflecting) out loud, and more.  Which is it? And/or how do they relate?

It’s very clear technology is changing the way we work. We now work digitally, communicating and collaborating.  But there’re more fundamental transitions happening. We’re integrating data across silos, and mining that data for new insights. We can consolidate platforms into single digital environments, facilitating the work.  And we’re getting smart systems that do things our brains quite literally can’t, whether it’s complex calculations or reliable rote execution at scale. Plus we have technology-augmented design and prototyping tools that are shortening the time to develop and test ideas. It’s a whole new world.

Similarly, we’re seeing a growing understanding of work practices that lead to new outcomes. We’re finding out that people work better when we create environments that are psychologically safe, when we tap into diversity, when we are open to new ideas, and when we have time for reflection. We find that working in teams, sharing and annotating our work, and developing learning and personal knowledge mastery skills all contribute. And we even have new  practices such as agile and design thinking that bring us closer to the actual problem.  In short, we’re aligning practices more closely with how we think, work, and learn.

Thus, either could be seen as ‘the Future of Work’.  Which is it?  Is there a reconciliation?  There’s a useful way to think about it that answers the question.  What if we do either without the other?

If we use the new technologies in old ways, we’ll get incremental improvements.  Command and control, silos, and transaction-based management can be supported, and even improved, but will still limit the possibilities. We can track closer.  But we’re not going to be fundamentally transformative.

On the other hand, if we change the work practices, creating an environment where trust allows both safety  and accountability, we can get improvements whether we use technology or not. People have the capability to work together using old technology.  You won’t get the benefits of some of the improvements, but you’ll get a fundamentally different level of engagement and outcomes than with an old approach.

Together, of course, is where we really want to be. Technology can have a transformative amplification to those practices. Together, as they say, the whole is greater than the some of the parts.

I’ve argued that using new technologies like virtual reality and adaptive learning only make sense  after you first implement good design (otherwise you’re putting lipstick on a pig, as the saying goes).  The same is true here. Implementing radical new technologies on top of old practices that don’t reflect what we know about people, is a recipe for stagnation.  Thus, to me, the Future of Work starts with practices that align with how we think, work, and learn, and are augmented with technology, not the other way around.  Does that make sense to you?

Tech and School Problems

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After yesterday’s rant about problems in local schools, I was presented with a recent New York Times article. In it, they talked about how the tech industry was getting involved in schools. And while the initiatives seem largely well-intentioned, they’re off target.   There’s a lack of awareness of what meaningful learning is, and what meaningful outcomes could and should be.  And so it’s time to shed a little clarity.

Tech in schools is nothing new, from the early days of Apple and Microsoft vying to provide school computers and getting a leg up on learners’ future tech choices.  Now, however, the big providers have even more relative leverage. School funds continue to be cut, and the size of the tech companies has grown relative to society. So there’s a lot of potential leverage.

One of the claims in the article is that the tech companies are able to do what they want, and this  is a concern. They can dangle dollars and technology as bait and get approval to do some interesting and challenging things.

However, some of the approaches have issues beyond the political:

One approach is to teach computer science to every student.  The question is: is this worth it?  Understanding what computers do well (and easily), and perhaps more importantly what they don’t, is necessary, no argument. The argument for computer programming is that it teaches you to break down problems and design solutions. But is computer science necessary?  Could it be done with, say, design thinking?  Again, all for helping learners acquire good problem-solving skills.  But I’m not convinced that this is necessarily a good idea (as beneficial as it is to the tech industry ;).

Another initiative is using algorithms, rules like the ones that Facebook uses to choose what ads to show you, to sequence math.  A program, ALEKS, already did this, but this one mixes in gamification. And I think it’s patching a bad solution. For one, it appears to be using the existing curriculum, which is broken (too much rote abilities, too little transferable skills).  And gamification?  Can’t we,  please, try to make math intrinsically interesting by making it useful?  Abstract problems don’t help.  Drilling key skills is good, but there are nuances in the details.

A second approach has students choosing the problems they work on, and teachers being facilitators.  Of course, I’m a fan of this; I’ve advocated for gradually handing off control of learning to learners, to facilitate their development of self-learning. And in a recently-misrepresented announcement, Finland is moving to topics with interleaved skills rapped around them (e.g. not one curricula, but you might intersect math and chemistry in studying ecosystems. However, this takes teachers with skills across both domains, and the ability to facilitate discussion  around projects.  That’s a big ask, and has been a barrier to many worthwhile initiatives.   Compounding this is that the end of a unit is assessed by a 10-point multiple choice question.  I worry about the design of those assessments.

I’m all for school reform. As Mark Warschauer put it, the only things wrong with American education is the curriculum, the pedagogy, and the way we use technology.  I think the pedagogy being funded in the latter description is a good approach, but there are details that need to be worked out to make it a scalable success.  And while problem-solving is a good curricular goal, we need to be thoughtful about how we build it in. Further, motivation is an important component about learning, but intrinsic or extrinsic?

We really could stand to have a deeper debate about learning and how technology can facilitate it. The question is: how do we make that happen?

Evil design?

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This is a rant, but it’s coupled with lessons.  

I’ve been away, and one side effect was a lack of internet bandwidth at the residence.  In the first day I’d used up a fifth of the allocation for the whole time (> 5 days)!  So, I determined to do all I could to cut my internet usage while away from the office.  The consequences of that have been heinous, and  on the principle of “it’s ok to lose, but don’t lose the lesson”, I want to share what I learned.  I don’t think it was evil, but it well could’ve been, and in other instances it might be.

So, to start, I’m an Apple fan.  It started when I followed the developments at Xerox with SmallTalk and the Alto as an outgrowth of Alan Kay‘s Dynabook work. Then the Apple Lisa was announced, and I knew this was the path I was interested in. I did my graduate study in a lab that was focused on usability, and my advisor was consulting to Apple, so when the Mac came out I finally justified a computer to write my PhD thesis on. And over the years, while they’ve made mistakes (canceling HyperCard), I’ve enjoyed their focus on making me more productive. So when I say that they’ve driven me to almost homicidal fury, I want you to understand how extreme that is!

I’d turned on iCloud, Apple’s cloud-based storage.  Innocently, I’d ticked the ‘desktop/documents’ syncing (don’t).  Now, with  every other such system that I know of, it’s stored locally *and* duplicated on the cloud.  That is, it’s a backup. That was my mental model.  And that model was reinforced:  I’d been able to access my files even when offline.  So, worried about the bandwidth of syncing to the cloud, I turned it off.

When I did, there was a warning that  said something to the effect of: “you’ll lose your desktop/documents”.  And, I admit, I didn’t interpret that literally (see: model, above).  I figured it would disconnect their syncing. Or I’d lose the cloud version. Because, who would actually steal the files from your hard drive, right?

Well, Apple DID!  Gone. With an option to have them transferred, but….

I turned it back on, but didn’t want to not have internet, so I turned it off again but ticked the box that said to copy the files to my hard drive.  COPY BACK MY OWN @##$%^& FILES!  (See fury, above.)   Of course, it started, and then said “finishing”.  For 5 days!  And I could see that my files weren’t coming back in any meaningful rate. But there was work  to do!

The support  guy I reached had some suggestion that really didn’t work. I did try to drag my entire documents folder from the iCloud drive to my hard drive, but it said it was making the estimate of how long, and hung on that for a day and a half.  Not helpful.

In meantime, I started copying over the files I needed to do work. And continuing to generate the new ones that reflected what I was working on.  Which meant that the folders in the cloud, and the ones on my hard drive that I  had  copied over, weren’t in sync any longer.  And I have a  lot of folders in my documents folder.  Writing, diagrams, client files, lots of important information!

I admit I made some decisions in my panic that weren’t optimal.  However, after returning I called Apple again, and they admitted that I’d have to manually copy stuff back.  This has taken hours of my time, and hours yet to go!

Lessons learned

So, there are several learnings from this.  First, this is bad design. It’s frankly evil to take someone’s hard drive files after making it easy to establish the initial relationship.  Now, I don’t  think Apple’s intention was to hurt me this way, they just made a bad decision (I hope; an argument could be made that this was of the “lock them in and then jack them up” variety, but that’s contrary to most of their policies so I discount it).  Others, however,  do make these decisions (e.g. providers of internet and cable from whom you can only get a 1 or 2  year price which will then ramp up  and unless you remember to check/change, you’ll end up paying them more than you should until you get around to noticing and doing something about it).  Caveat emptor.

Second, models are important and can be used for or against you. We do  create models about how things work and use evidence to convince ourselves of their validity (with a bit of confirmation bias). The learning lesson is to provide good models.  The warning is to check your models when there’s a financial stake that could take advantage of them for someone else’s gain!

And the importance of models for working and performing is clear. Helping people get good models is an important boost to successful performance!  They’re not necessarily easy to find (experts don’t have access to 70% of what they do), but there are ways to develop them, and you’ll be improving your outcomes if you do.

Finally, until Apple changes their policy, if you’re a Mac and iCloud user I  strongly recommend you avoid the iCloud option to include Desktop and Documents in the cloud unless you can guarantee that you won’t have a bandwidth blockage.  I like the idea of backing my documents to the cloud, but not when I can’t turn it off without losing files. It’s a bad policy that has unexpected consequences to user expectations, and frankly violates my rights to  my data.

We now return you to our regularly scheduled blog topics.

 

Innovation Thoughts

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So I presented on innovation to the local ATD chapter a few weeks ago, and they did an interesting and nice thing: they got the attendees to document their takeaways. And I promised to write a blog post about it, and I’ve finally received the list of thoughts, so here are my reflections.  As an aside, I’ve written separate articles on L&D innovation recently for both CLO magazine and the Litmos blog  so you can check those out, too.

I started talking about why  innovation was needed, and then what it was.  They recalled that I pointed out that by definition an innovation is not only a new idea, but one that is implemented  and leads to better results.  I made the point that when you’re innovating, designing, researching, trouble-shooting, etc, you don’t know the answer when you start, so they’re  learning situations, though  informal,  not formal.  And they heard me note that agility and adaptation are premised on informal learning of this sort, and that the opportunity is for L&D to take up the mantle to meed the increasing need.

There was interest but some lack of clarity  around meta-learning. I emphasize that learning to learn may be your best investment, but  given that you’re devolving responsibility you shouldn’t assume that individuals are automatically possessed of optimal learning skills. The focus then becomes developing learning to learn skills, which of needs is done  across some other topic. And, of course, it requires the right culture.

There were some terms they heard that they weren’t necessarily clear on, so per the request, here are the terms (from them) and my definition:

  • Innovation by Design: here I mean deliberately creating an environment where innovation can flourish. You can’t plan for innovation, it’s ephemeral, but you can certainly create a felicitous environment.
  • Adjacent Possible: this is a term Steven Johnson used in his book Where Good Ideas Come From, and my take is that it means that lateral inspiration (e.g. ideas from nearby: related fields or technologies) is where innovation happens, but it takes exposure to those ideas.
  • Positive Deviance:  the idea (which I heard of from Jane Bozarth) is that the best way to find good ideas is to find people who are excelling and figure out what they’re doing differently.
  • Hierarchy and Equality: I’m not quite sure what they were referring to hear (I think more along the lines of  Husband’s Wirearchy versus hierarchy) but the point is to reduce the levels and start tapping into the contributions possible from all.
  • Assigned roles and vulnerability: I’m even less certain what’s being referred to here (I can’t be responsible for everything people take away ;), but I could interpret this to mean that it’s hard to be safe to contribute if you’re in a hierarchy and are commenting on someone above  you.  Which again is an issue of safety (which is why I advocate that leaders ‘work out loud’, and it’s a core element of Edmondson’s Teaming; see below).

I used the Learning Organization Dimensions diagram (Garvin, Edmondson & Gino)  to illustrate the components of successful innovation environment, and these were reflected in their comments. A number mentioned  psychological safety in particular as well as  the other elements of the learning environment. They also picked up on the importance of  leadership.

Some other notes that they picked up on included:

  • best principles instead of best practices
  • change is facilitated when the affected individual choose to  change
  • brainstorming needs individual work before collective work
  • that trust is required to devolve responsibility
  • the importance of coping with ambiguity

One that was provided  that I know I didn’t say because I don’t believe it, but is interesting as a comment:

“Belonging trumps diversity, and security trumps grit”

This is an interesting belief, and I think that’s likely the case if it’s  not safe to experiment and make mistakes.

They recalled some of the books I mentioned, so here’s the list:

  • The Invisible Computer  by Don Norman
  • The Design of Everyday Things  by Don Norman
  • My  Revolutionize Learning and Development  (of course ;)
  • XLR8 by John Kotter (with the ‘dual operating system‘ hypothesis)
  • Teaming to Innovate by Amy Edmondson (I reviewed it)
  • Working Out Loud by John Stepper
  • Scaling Up Excellence by Robert I. Sutton and Huggy Rao (blogged)
  • Organize for Complexity by Niels Pflaeging (though they heard this as a concept, not a title)

It was a great evening, and really rewarding to see that many of the messages stuck.  So, what are your thought around innovation?

 

Human Learning is Not About to Change Forever

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In my inbox was an announcement about a new white paper with the intriguing title  Human Learning is About to Change Forever.  So naturally I gave up my personal details to download a copy.  There are nine claims in the paper, from the obvious to the ridiculous. So I thought I’d have some fun.

First, let’s get clear.  Our learning runs on our brain, our wetware. And that’s not changing in any fundamental way in the near future. As a famous article once had it: phenotypic plasticity triumphs over genotypic plasticity (in short, our human advantage has gained    via  our ability to adapt individually and learn from each other, not through  species evolution).   The latter takes a long time!

And as a starting premise, the “about to” bit implies these things are around the corner, so that’s going to be a bit of my critique. But nowhere near  all of it.  So here’s a digest of the  nine claims and my comments:

  1. Enhanced reality tools will transform the learning environment.  Well, these tools will  certainly augment the learning environment  (pun intended :). There’s evidence that VR leads to better learning outcomes, and I have high hopes for AR, too. Though is that a really fundamental transition? We’ve had VR and virtual worlds for over a decade at least.  And is VR a evolutionary or revolutionary change from simulations? Then they go on to talk about performance support. Is that transforming learning? I’m on record saying contextualized learning (e.g. AR) is the real opportunity to do something interesting, and I’ll buy it, but we’re a long way away. I’m all for AR and VR, but saying that it puts learning in the hands of the students is a design issue, not a technology issue.
  2. People will learn collaboratively, no matter where they are.  Um, yes, and…?  They’re already doing this, and we’ve been social learners for as long as we’ve existed. The possibilities in virtual worlds to collaboratively create in 3D I still think is potentially cool, but even as the technology limitations come down, the cognitive limitations remain. I’m big on social learning, but mediating it through technology strikes me as just a natural step, not transformation.
  3. AI will banish intellectual tedium. Everything is  awesome.  Now we’re getting a wee bit hypish. The fact that software can parse text and create questions is pretty impressive. And questions about semantic knowledge aren’t going to transform education. Whether the questions are developed by hand, or by machine, they aren’t likely on their own to lead to new abilities to do. And AI is not yet to the level (nor will it be soon) where it can take content and create compelling activities that will drive learners to apply knowledge and make it meaningful.
  4. We will maximize our mental potential with wearables and neural implants. Ok, now we’re getting confused and a wee bit silly. Wearables are cool, and in cases where they can sense things about you and the world means they can start doing some very interesting AR. But transformative? This still seems like a push.  And neural implants?  I don’t like surgery, and messing with my nervous system when you still don’t really understand it? No thanks.  There’s a lot more to it than managing to adjust firing to control limbs. The issue is again about the semantics: if we’re not getting meaning, it’s not really fundamental. And given that our conscious representations are scattered across our cortex in rich patterns, this just isn’t happening soon (nor do I want that much connection; I don’t trust them not to ‘muck about’).
  5. Learning will be radically personalized.  Don’t you just love the use of superlatives?  This is in the realm of plausible, but as I mentioned before, it’s not worth it until we’re doing it on  top of good design.  Again, putting together wearables (read: context sensing) and personalization will lead to the ability to do transformative AR, but we’ll need a new design approach, more advanced sensors, and a lot more backend architecture and semantic work than we’re yet ready to apply.
  6. Grades and brand-name schools won‘t matter for employment.  Sure, that MIT degree is worthless! Ok, so there’s some movement this way.  That will actually be a nice state of affairs. It’d be good  if we started focusing on competencies, and build new brand names around real enablement. I’m not optimistic about the prospects, however. Look at how hard it is to change K12 education (the gap  between what’s known and what’s practiced hasn’t significantly diminished in the past decades). Market forces may change it, but the brand names will adapt too, once it becomes an economic necessity.
  7. Supplements will improve our mental performance.  Drink this and you’ll fly! Yeah, or crash.  There are ways I want to play with my brain chemistry, and ways I don’t. As an adult!  I really don’t want us playing with children, risking potential long-term damage, until we have a solid basis.  We’ve had chemicals support performance for a while (see military use), but we’re still in the infancy, and here I’m not sure our experiments with neurochemicals can surpass what evolution has given us, at least not without some pretty solid understanding.  This seems like long-term research, not near-term plausibility.
  8. Gene editing will give us better brains.  It’s  alive!  Yes, Frankenstein’s monster comes to mind here. I do believe it’s possible that we’ll be able to outdo evolution eventually, but I reckon there’s still not everything known about the human genome  or the human brain. This similarly strikes me as a valuable long term research area, but in the short term there are so many interesting gene interactions we don’t yet understand, I’d hate to risk the possible side-effects.
  9. We won‘t have to learn: we‘ll upload and download knowledge. Yeah, it’ll be  great!  See my comments above on neural implants: this isn’t yet ready for primetime.  More importantly, this is supremely dangerous. Do I trust what you say you’re making available for download?  Certainly not the case now with many things, including advertisements. Think about downloading to your computer: not just spam ads, but viruses and malware.  No thank you!  Not that I think it’s close, but I’m not convinced we can ‘upgrade our operating system’ anyway. Given the way that our knowledge is distributed, the notion of changing it with anything less than practice seems implausible.

Overall, this is reads like more a sci-fi fan’s dreams than a realistic assessment of what we should be preparing for.  No, human learning isn’t going to change forever.  The ways we learn, e.g. the tools we learn with are changing, and we’re rediscovering how we really learn.

There are better guides available to what’s coming in the near term that we should prepare for.  Again, we need to focus on good learning design, and leveraging technology in ways that align with how our brains work, not trying to meld the two.  So, there’re my opinions, I welcome yours.

Other writings

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It occurs to me to mention some of the other places you can find my writings besides here (and how they differ ;).  My blog posts are pretty regular (my aim is 2/week), but tend to have ideas that are embryonic or a bit ‘evangelical’. First, I’ve written four books; you can check them out and get sample chapters at their respective sites:

Engaging Learning: Designing e-Learning Simulation Games

Designing mLearning: Tapping Into the Mobile  Revolution for Organizational Performance

The Mobile Academy: mLearning For Higher Education

Revolutionize Learning &  Development: Performance and Information Strategy for the Information Age

They’re designed to be the definitive word on the topic, at least at the moment.

I’ve also written or co-written a number of chapters in a variety of books.  The books  include The Really Useful eLearning Instruction Manual,  Creating a Learning Culture, Michael Allen’s eLearning Annual 2009,   and a bunch of academic handbooks (Mobile Learning, Experiential Learning, Wiley Learning Technology ;).  These tend to be longer than an article, with a pretty thorough coverage of whatever topic is on tap.

Then  there are articles in a variety of magazines.  These tend to be aggregated thoughts that are longer than a blog post, but not as through as a chapter. In particular, they are things I think need to be heard (or read).  So, my writing has shown up in:

eLearnMag

Learning Solutions

CLO

The topics  vary. (For the eLearnMag ones, you’ll have to search for my name owing to their interface, and they tend to be more like editorials.)

And then there are blog posts for others that are a bit longer than my usual blog post, and close to an article in focus:

The  Deeper eLearning  series for  Learnnovators

A monthly article for Litmos.

These, too, are more like articles in that they’re focused, and deeper than my usual blog post.  For the latter I cover a lot of different topics, so you’re likely to find something relevant there in many different areas.

I’m proud of it all, but for a quick update on a topic, you might be best seeing if there’s a Litmos post on it first.  That’s likely to be relatively short and focused if there is one. And, of course, if it’s a topic you’re interested in advancing in and I can help, do let me know.

Mobile Lesson

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Designing mLearning bookI’m preparing my keynote for a mobile conference, and it’s caused an interesting reflection.  My mlearning  books came out in 2011, and subsequently I’ve written on the revolution.  And I’ve been speaking on both of late, but in some ways the persistent interest in mobile intrigues me.

While my services are pushing the better design of and the bigger picture of elearning, mobile isn’t going away. My trip to China to keynote this past year was on mlearning  (and one the year before), and now again I’m talking on the topic.  What does this mean?

As I wrote before, China is much bigger into mobile than we are. It’s likely because we had more ubiquity of internet access  and computers, but they’re also a highly mobile populace.  And it makes sense that they’re showing a continuing interest. In fact, they specifically asked for a presentation that was advanced, not my usual introduction.

I’m also going to be presenting on  more advanced thinking to the audience coming up, because the entire focus of the event is mlearning  and I infer that they’re already up on the basics.  The focus in my books was to get people thinking differently about mobile (because it’s not about courses on a phone), but certainly that was understood in China. I think it’s also understood by  most of the developers. I’m less certain about the elearning field (corporate and  education), at least not yet.

In many ways, mobile  was a catalyst for the revolution.  I think of mlearning  as much more than courses, and my models focused on performance support and social more than formal learning. That is really one of the two-fold focuses on the revolution (the “L&D isn’t doing near what it could and should”; to complement the “and what it  is doing, it is doing badly” :).  In that way, these devices  can be a wedge in the door for a broader focus.

Yet mobile is just a platform for enabling the type of experiences, the types of cognitive support, as any other platform  from conversation to artificial intelligence.  It is an important one, however, with the unique properties of doing things  whenever &  wherever you are  and  doing things  because of when and where you are.

So I get that mlearning  is of interest because of the ubiquity, but the thinking that goes into mobile really goes  beyond mobile.  It’s about aligning with us, supporting our needs to communicate and collaborate.  That’s still a need, a useful message, and an opportunity.  Are you mobilizing?