mobile Archives - Page 3 of 23

Microdesign

14 March 2017 by Clark 3 Comments

There’s been a lot of talk about microlearning of late – definitions, calls for clarity, value propositions, etc – and I have to say that I’m afraid some of it (not what I’ve linked to) is a wee bit facile. Or, at least, conceptually unclear. And I think that’s a problem. This came up again in a recent conversation, and I had a further thought (which of course I have to blog about ;). It’s about how to do microdesign, that is, how to design micro learning. And it’s not trivial.

So one of the common views of micro learning is that it’s just in time. That is, if you need to know how to do something, you look it up. And that’s just fine (as I’ve recently ranted). But it’s not learning. (In short: it’ll help you in the moment, but unless you design it to support learning, it’s performance support instead). You can call it Just In Time support, or microsupport, but properly, it’s not micro learning.

The other notion is a learning that’s distributed over time. And that’s good. But this takes a bit more thought. Think about it. If we want to systematically develop somebody over time, it’s not just a steady stream of ‘stuff’. Ideally, it’s designed to optimally get there, minimizing the time taken on the part of the learner, and yet yield reliable improvements. And this is complex.

In principle, it should be a steady development, that reactivates and extends learners capabilities in systematic ways. So, you still need your design steps, but you have to think about granularity, forgetting, reactivation, and development in a more fine-grained way. What’s the minimum launch? Can you do ought but make sure there’s an initial intro, concept, example, and a first practice? Then, how much do we need to reactivate versus how much do we have to expand the capability in each iteration? How much is enough? As Will Thalheimer says in his spaced learning report, the amount and duration of spacing depends on the complexity of the task and the frequency with which it’s performed.

When do you provide more practice, versus another example, versus a different model? What’s the appropriate gap in complexity? We’ll likely have to make our best guesses and tune, but we have to think consciously about it. Just chunking up an existing course into smaller bits isn’t taking the decay of memory over time and the gradual expansion of capability. We have to design an experience!

Microlearning is the right thing to do, given our cognitive architecture. Only so much ‘strengthening’ of the links can happen in any one day, so to develop a full new capability will take time. And that means small bits over time makes sense. But choosing the right bits, the right frequency, the right duration, and the right ramp up in complexity, is non-trivial. So let’s laud the movement, but not delude ourselves either that performance support or a stream of content is learning. Learning, that is systematically changing the reliable behavior of the most complex thing in the known universe, is inherently complex. We should take it seriously, and we can.

Other writings

1 February 2017 by Clark Leave a Comment

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

5 January 2017 by Clark Leave a Comment

I’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?

Collaborative Modelling in AR (and VR)

21 September 2016 by Clark Leave a Comment

A number of years ago, when we were at the height of the hype about Virtual Worlds (computer rendered 3D social worlds, e.g. Second Life), I was thinking about the affordances. And one that I thought was intriguing was co-creating, in particular collaboratively creating models that were explanatory and predictive. And in thinking again about Augmented Reality (AR), I realized we had this opportunity again.

Models are hard enough to capture in 2D, particularly if they’re complex. Having a 3rd dimension can be valuable. Similarly if we’re trying to match how the components are physically structured (think of a model of a refinery, for instance, or a power plant). Creating it can be challenging, particularly if you’re trying to map out a new understanding. And, we know that collaboration is more powerful than solo ideation. So, a real opportunity is to collaborate to create models.

And in the old Virtual Worlds, a number had ways to create 3D objects. It wasn’t easy, as you had to learn the interface commands to accomplish this task, but the worlds were configurable (e.g. you could build things) and you could build models. There was also the overall cognitive and processing overhead inherent to the worlds, but these were a given to use the worlds at all.

What I was thinking of, extending my thoughts about AR in general, that annotating the world is valuable, but how about collaboratively annotating the world? If we can provide mechanisms (e.g. gestures) for people to not just consume, but create the models ‘in world’ (e.g. while viewing, not offline), we can find some powerful learning opportunities, both formal and informal. Yes, there are issues in creating and developing abilities with a standard ‘model-building’ language, particularly if it needs to be aligned to the world, but the outcomes could be powerful.

For formal, imagine asking learners to express their understanding. Many years ago, I was working with Kathy Fisher on semantic networks, where she had learners express their understanding of the digestive system and was able to expose misconceptions. Imagine asking learners to represent their conceptions of causal and other relationships. They might even collaborate on doing that. They could also just build 3D models not aligned to the world (though that doesn’t necessarily require AR).

And for informal learning, having team or community members working to collaboratively annotate their environment or represent their understanding could solve problems and advance a community’s practices. Teams could be creating new products, trouble-shooting, or more, with their models. And communities could be representing their processes and frameworks.

This wouldn’t necessarily have to happen in the real world if the options weren’t aligned to external context, so perhaps VR could be used. At a client event last week, I was given the chance to use a VR headset (Google Cardboard), and immerse myself in the experience. It might not need to be virtual (instead collaboration could be just through networked computers, but there was data from research into virtual reality that suggests better learning outcomes.

Richer technology and research into cognition starts giving us powerful new ways to augment our intelligence and co-create richer futures. While in some sense this is an extension of existing practices, it’s leveraging core affordances to meet conceptually valuable needs. That’s my model, what’s yours?

China is mobile!

7 September 2016 by Clark 2 Comments

I’ve had the fortune to be here in China speaking on mlearning. And there are a couple of interesting revelations that I hadn’t really recognized when I did the same last year that I thought I’d share.

For one, while mobile is everywhere like many places, it’s more here. It seems many people carry more than one phone, for a variety of reasons (one fellow said that he carried another because the battery wouldn’t last all day!). But they’re all phones, I seem to see few tablets. They vary in size from phones to phablets, but they’re here.

Which leads to a second recognition. They are big into mlearning, and elearning. The culture does respect scholarship (no anti-intellectualism here), so they’re quite keen to continue their education. Companies with mlearning courses do well, and the government is investing in educational technology in a big way. It’s not clear whether their pedagogy is advanced (I can’t read Chinese, I admit), but they do get ‘chunking’ into small bits. And, importantly, the recognition of the value of investment is important.

One other thing struck me as well: QR codes live! They’re everywhere here. They used them during my workshop to run a lottery, and to answer some polling questions on demographics of the audience. They’re in the restaurants as a start to the payment process. And they’re scattered around on most ads. They have an advantage that they seem to have mastered the art of having an app that systematically recognizes them (it’s built into the ubiquitous social media app, WeChat).

Establishing the consistent use of a standard can help build a powerful, and valuable, ecosystem. I can wish that the providers in the US would work and play together a little bit more! There may be better alternatives, but getting consistently behind one standard makes the investment amortize effectively.

I’m pleased to see that mLearning is taking off, and had fun sharing some of the models that I think provide leverage to rally take advantage. Here’s to getting going with mobile!

Learning in Context

4 May 2016 by Clark 1 Comment

In a recent guest post, I wrote about the importance of context in learning. And for a featured session at the upcoming FocusOn Learning event, I’ll be talking about performance support in context. But there was a recent question about how you’d do it in a particular environment, and that got me thinking about the the necessary requirements.

As context (ahem), there are already context-sensitive systems. I helped lead the design of one where a complex device was instrumented and consequently there were many indicators about the current status of the device. This trend is increasing. And there are tools to build context-sensitive helps systems around enterprise software, whether purchased or home-grown. And there are also context-sensitive systems that track your location on mobile and allow you to use that to trigger a variety of actions.

Now, to be clear, these are already in use for performance support, but how do we take advantage of them for learning. Moreover, can we go beyond ‘location’ specific learning? I think we can, if we rethink.

So first, we obviously can use those same systems to deliver specific learning. We can have a rich model of learning around a system, so a detailed competency map, and then with a rich profile of the learner we can know what they know and don’t, and then when they’re at a point where there’s a gap between their knowledge and the desired, we can trigger some additional information. It’s in context, at a ‘teachable moment’, so it doesn’t necessarily have to be assessed.

This would be on top of performance support, typically, as they’re still learning so we don’t want to risk a mistake. Or we could have a little chance to try it out and get it wrong that doesn’t actually get executed, and then give them feedback and the right answer to perform. We’d have to be clear, however, about why learning is needed in addition to the right answer: is this something that really needs to be learned?

I want to go a wee bit further, though; can we build it around what the learner is doing? How could we know? Besides increasingly complex sensor logic, we can use when they are. What’s on their calendar? If it’s tagged appropriately, we can know at least what they’re supposed to be doing. And we can develop not only specific system skills, but more general business skills: negotiation, running meetings, problem-solving/trouble-shooting, design, and more.

The point is that our learners are in contexts all the time. Rather than take them away to learn, can we develop learning that wraps around what they’re doing? Increasingly we can, and in richer and richer ways. We can tap into the situational motivation to accomplish the task in the moment, and the existing parameters, to make ordinary tasks into learning opportunities. And that more ubiquitous, continuous development is more naturally matched to how we learn.

Learning in context

26 April 2016 by Clark 3 Comments

In preparation for the upcoming FocusOn Learning Conference, where I’ll be running a workshop about cognitive science for L&D, not just for learning but also for mobile and performance support, I was thinking about how context can be leveraged to provide more optimal learning and performance. Naturally, I had to diagram it, so let me talk through it, and you let me know what you think.

What we tend to do, as a default, is to take people away from work, provide the learning resources away from the context, then create a context to practice in. There are coaching resources, but not necessarily the performance resources. (And I’m not even mentioning the typical lack of sufficient practice.) And this makes sense when the consequences of making a mistake on the task are irreversible and costly. E.g. medicine, transportation. But that’s not as often as we think. And there’s an alternative.

We can wrap the learning around the context. Our individual is in the world, and performing the task. There can be coaching (particularly at the start, and then gradually removed as the individual moves to acceptable competence). There are also performance resources – job aids, checklists, etc – in the environment. There also can be learning resources, so the individual can continue to self-develop, particularly in the increasingly likely situation that the task has some ambiguity or novelty in it. Of course, that only works if we have a learner capable of self learning (hint hint).

The problems with always taking people away from their jobs are multiple:

it is costly to interrupt their performance
it can be costly to create the artificial context
the learning has a lower likelihood to make it back to the workplace

Our brains don’t learn in an event model, they learn in little bits over time. It’s more natural, more effective, to dribble the learning out at the moment of need, the learnable moment. We have the capability, now, to be more aware of the learner, to deliver support in the moment, and develop learners over time. The way their brains actually learn. And we should be doing this. It’s more effective as well as more efficient. It requires moving out of our comfort zone; we know the classroom, we know training. However, we now also know that the effectiveness of classroom training can be very limited.

We have the ability to start making learning effective as well as efficient. Shouldn’t we do so?

Context Rules

15 March 2016 by Clark Leave a Comment

I was watching a blab (a video chat tool) about the upcoming FocusOn Learning, a new event from the eLearning Guild. This conference combines their previous mLearnCon and Performance Support Symposium with the addition of video. The previous events have been great, and I’ll of course be there (offering a workshop on cognition for mobile, a mobile learning 101 session, and one on the topic of this post). Listening to folks talk about the conference led me to ponder the connection, and something struck me.

I find it kind of misleading that it’s FocusOn Learning, given that performance support, mobile, and even video typically is more about acting in the moment than developing over time. Mobile device use tends to be more about quick access than extended experience. Performance support is more about augmenting our cognitive capabilities. Video (as opposed to animation or images or graphics, and similar to photos) is about showing how things happen in situ (I note that this is my distinction, and they may well include animation in their definition of video, caveat emptor). The unifying element to me is context.

So, mobile is a platform. It’s a computational medium, and as such is the same sort of computational augment that a desktop is. Except that it can be with you. Moreover, it can have sensors, so not just providing computational capabilities where you are, but because of when and where you are.

Performance support is about providing a cognitive augment. It can be any medium – paper, audio, digital – but it’s about providing support for the gaps in our mental capabilities. Our architecture is powerful, but has limitations, and we can provide support to minimize those problems. It’s about support in the moment, that is, in context.

And video, like photos, inherently captures context. Unlike an animation that represents conceptual distinctions separated from the real world along one or more dimensions, a video accurately captures what the camera sees happening. It’s again about context.

And the interesting thing to me is that we can support performance in the moment, whether a lookup table or a howto video, without learning necessarily happening. And that’s OK! It’s also possible to use context to support learning, and in fact we can provide least material to augment a context than create an artificial context which so much of learning requires.

What excited me was that there was a discussion about AR and AI. And these, to me, are also about context. Augmented Reality layers information on top of your current context. And the way you start doing contextually relevant content delivery is with rules tied to content descriptors (content systems), and such rules are really part of an intelligently adaptive system.

So I’m inclined to think this conference is about leveraging context in intelligent ways. Or that it can be, will be, and should be. Your mileage may vary ;).

Reactivating Learning

27 January 2016 by Clark Leave a Comment

(I looked because I’m sure I’ve talked about this before, but apparently not a full post, so here we go.)

If we want our learning to stick, it needs to be spaced out over time. But what sorts of things will accomplish this? I like to think of three types, all different forms of reactivating learning.

Reactivating learning is important. At a neural level, we’re generating patterns of activation in conjunction, which strengthens the relationships between these patterns, increasing the likelihood that they’ll get activated when relevant. That’s why context helps as well as concept (e.g. don’t just provide abstract knowledge). And I’ll suggest there are 3 major categories of reactivation to consider:

Reconceptualization: here we’re talking about presenting a different conceptual model that explains the same phenomena. Particularly if the learners have had some meaningful activity from your initial learning or through their work, showing a different way of thinking about the problem is helpful. I like to link it to Rand Spiro’s Cognitive Flexibility Theory, and explain that having more ways to represent the underlying model provides more ways to understand the concept to begin with, a greater likelihood that one of the representations will get activated when there’s a problem to be solved, and will activate the other model(s) so there’s a greater likelihood of finding one that leads to a solution. So, you might think of electrical circuits like water flowing in pipes, or think about electron flow, and either could be useful. It can be as simple as a new diagram, animation, or just a small prose recitation.

Recontextualization: here we’re showing another example. We’re showing how the concept plays out in a new context, and this gives a greater base upon which to abstract from and comprehend the underlying principle, and providing a new reference that might match a situation they could actually see. To process it, you’re reactivating the concept representation, comprehending the context, and observing how the concept was used to generate a solution to this situation. A good example, with a challenging situation that the learner recognizes, a clear goal, and cognitive annotation showing the underlying thinking, will serve to strengthen the learning. A graphic novel format would be fun, or story, or video, anything that captures the story, thinking, and outcome would work.

Reapplication: this is the best, where instead of consuming a concept model or an example, we actually provide a new practice problem. This should require retrieving the underlying concept, comprehending the context, and determining how the model predicts what will happen to particular perturbations and figuring out which will lead to the desired outcomes. Practice makes perfect, as they say, and so this should ideally be the emphasis in reactivation. It might be as simple as a multiple-choice question, though a scenario in many instances would be better, and a sim/game would of course be outstanding.

All of these serve as reactivation. Reactivation, as I’ve pointed out, is a necessary part of learning. When you don’t have enough chance to practice in the workplace, but it’s important that you have the ability when you need it (and try to avoid putting it in the head if you can), reactivation is a critical tool in your arsenal.

« Previous Page