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.
31 October 2014
29 October 2014
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.
28 October 2014
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
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).
The 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?
17 September 2014
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.
16 September 2014
Fall always seems to be a busy time, and I reckon it’s worthwhile to let you know where I’ll be in case you might be there too! Coming up are a couple of different events that you might be interested in:
September 28-30 I’ll be at the Future of Talent retreat at the Marconi Center up the coast from San Francisco. It’s a lovely spot with a limited number of participants who will go deep on what’s coming in the Talent world. I’ll be talking up the Revolution, of course.
October 28-31 I’ll be at the eLearning Guild’s DevLearn in Las Vegas (always a great event; if you’re into elearning you should be there). I’ll be running a Revolution workshop (I believe there are still a few spots), part of a mobile panel, and talking about how we are going about addressing the challenges of learning design at the Wadhwani Foundation.
November 12-13 I’ll be part of the mLearnNow event in New Orleans (well, that’s what I call it, they call it LearnNow mobile blah blah blah ;). Again, there are some slots still available. I’m honored to be co-presenting with Sarah Gilbert and Nick Floro (with Justin Brusino pulling strings in the background), and we’re working hard to make sure it should be a really great deep dive into mlearning. (And, New Orleans!)
There may be one more opportunity, so if anyone in Sydney wants to talk, consider Nov 21.
Hope to cross paths with you at one or more of these places!
1 July 2014
At the mLearnCon conference, it became clear it was time to write about wearables. At the same time, David Kelly (program director for t he Guild) asked for conference reflections for the Guild Blog. Long story short, my reflections are a guest post there.
25 June 2014
Karen McGrane evangelized good content architecture (a topic near to my heart), in a witty and clear keynote. With amusing examples and quotes, she brought out just how key it is to move beyond hard wired, designed content and start working on rule-driven combinations from structured chunks. Great stuff!
22 April 2014
Neil Jacobstein gave the keynote for the special Future of Talent event sponsored by SAP and hosted by the Churchill Club. In a wide ranging and inspiring talk, Neil covered how new technologies, models, and methods provide opportunities to transcend our problems and create a world worth living in.
10 April 2014
It’s a well-known phenomena that new technologies get used in the same ways as old technologies until their new capabilities emerge. And this is understandable, if a little disappointing. The question is, can we do better? I’d certainly like to believe so! And a conversation on twitter led me to try to make the case.
So, to start with, you have to understand the concept of affordances, at least at a simple level. The notion is that objects in the world support certain action owing to the innate characteristics of the object (flat horizontal surfaces support placing things on them, levers afford pushing and pulling, etc). Similarly, interface objects can imply their capabilities (buttons for clicking, sliders for sliding). They can be conveyed by visual similarity to familiar real-world objects, or be completely new (e.g. a cursor).
One of the important concepts is whether the affordance is ‘hidden’ or not. So, for instance, on iOS you can have meaningful differences between one, two, three, and even four-fingered swipes. Unless someone tells you about it, however, or you discover it randomly (unlikely), you’re not likely to know it. And there’re now so many that they’re hard to remember. There are many deep arguments about affordances, and they’re likely important but they can seem like ‘angels dancing on the head of a pin’ arguments, so I’ll leave it at this.
The point here being that technologies have affordances. So, for example, email allows you to transmit text communications asynchronously to a set group of recipients. And the question is, can we anticipate and leverage the properties and skip (or minimize) the stumbling beginnings.
Let me use an example. Remember the Virtual Worlds bubble? Around 2003, immersive learning environments were emerging (one of my former bosses went to work for a company). And around 2006-2009 they were quite the coming thing, and there was a lot of excitement that they were going to be the solution. Everyone would be using them to conduct business, and folks would work from desktops connecting to everyone else. Let me ask: where are they now?
The Gartner Hype Cycle talks about the ‘Peak of Inflated Expectations’ and then the ‘Trough of Disillusionment’, followed by the ‘Slope of Enlightenment’ until you reach the ‘Plateau of Productivity’ (such vibrant language!). And what I want to suggest is that the slope up is where we realize the real meaningful affordances that the technology provides.
So I tried to document the affordances and figure out what the core capabilities were. It seemed that Virtual Worlds really supported two main points: being inherently 3D and being social. Which are important components, no argument. On the other hand, they had two types of overhead, the cognitive load of learning them, and the technological load of supporting them. Which means that their natural niche would be where 3D would be inherently valuable (e.g. spatial models or settings, such as refineries where you wanted track flows), and where social would also be critical (e.g. mentoring). Otherwise there were lower-cost ways to do either one alone.
Thus, my prediction would be that those would be the types of applications that’d be seen after the bubble burst and we’d traversed the trough. And, as far as I know, I got it right. Similarly, with mobile, I tried to find the core opportunities. And this led to the models in the Designing mLearning book.
Of course, there’s a catch. I note that my understanding of the capabilities of tablets has evolved, for instance. Heck, if I could accurately predict all the capabilities and uses of a technology, I would be running venture capital. That said, I think that I can, and more importantly, we can, make a good initial stab. Sure, we’ll miss some things (I’m not sure I could’ve predicted the boon that Twitter has become), but I think we can do better than we have. That’s my claim, and I’m sticking to it (until proved wrong, at least ;).