It actually happened a while ago, but I was pleased to learn that Designing mLearning has been translated into Korean. That’s kind of a nice thing to have happen! A slightly different visual treatment, presumably appropriate to the market. Who knows, maybe I’ll get a chance to visit instead of just transferring through the airport. Anyways, just had to share ;).
So, I found an interesting inconsistency. I had to submit my deck for my DevLearn workshop on Cognitive Science for Learning Design last week, but oddly, for every thing I was recommending I had a diagram, except for the notion of using models. This is ironic, since diagrams can be used to convey models. It bugged me, so I pondered.
And then I remembered that I gave a presentation years ago specifically on diagrams. Moreover, in that presentation I had a diagram for a process for creating a diagram (Department of Redundancy Department). So, I finally got around to trying to apply my own process to my lack of a model. And voilà :
The process is to identify the elements, and the relationships, and then additional dimensions. Then you represent each, place them (elements first, relationships second, dimensions last), and tune.
Here the notion is that you have a mental model of a concept, capturing elements and causal relationships. When you see a situation, you select a model where you can map the elements in the model to elements in the context. Then you can use the model to predict what will happen or explain what happened. Which gives you a basis for making decisions, and adapting decisions to different contexts in principled ways.
Models are a powerful concept I’ve harped on before, but now I’ve an associated diagram. And I like diagrams. I find mapping the conceptual dimensions to spatial dimensions both helps me get concrete about the models and then gives a framework to share with others. Does this make sense to you, both the concept behind it, and the diagram to represent it?
I’ll be presenting this in the workshop, amongst many other implications from how our brains work (and learn) to the design of learning experiences. Would love to see you there.
In a conversation I had recently, specifically about a community focused on research, I used the term ‘community of improvement’, and was asked how that was different than a community of practice. It caused me to think through what the differences might be. (BTW, the idea was sparked by conversations with Lucian Tarnowski from BraveNew.)
First, let me say that a community of practice could be, and should be, a community of improvement. One of the principles of practice is reflection and improvement. But that’s not necessarily the case. A community of practice could just be a place where people answer each other’s questions, collaborate on tasks, and help one another with issues not specifically aligned with the community. But there should be more.
What I suggested in the conversation was that a community should also be about documenting practice, applying that practice through action or design research, and reflecting on the outcomes and the implications for practice. The community should be looking to other fields for inspiration, and attempting experiments. It’s the community equivalent of Schön’s reflective practitioner. And it’s more than just cooperation or collaboration, but actively engaging and working to improve.
Basically, this requires collaboration tools, not just communication tools. It requires: places to share thoughts; ways to find partners on the documentation, experimentation, and reflection; and support to track and share the resulting changes on community practices.
Yes, obviously a real community of practice should be doing this, but too often I see community tools without the collaboration tools. So I think it’s worth being explicit about what we would hope will accompany the outcomes. So, where do we do this, and how?
#itashare
In thinking through what makes experiences engaging, and in particular making practice engaging, I riffed on some core elements. The three terms I came up with were Challenge, Choices, & Consequences. And I realized I had a nice little alliteration going, so I’m going to elaborate and see if it makes sense to me (and you).
In general, good practice is having the learner make decisions in context. This has to be more than just recognizing the correct knowledge option, and providing a ‘right’ or ‘wrong’ feedback. The right decision has to be made, in a plausible situation with plausible alternatives, and the right feedback has to be provided.
So, the first thing is, there has to be a situation that the learner ‘gets’ is important. It’s meaningful to them and to their stakeholders, and they want to get it right. It has to be clear there’s a real decision that has outcomes that are important. And the difficulty has to be adjusted to their level of ability. If it’s too easy, they’re bored and little learning occurs. If it’s too difficult, it’s frustrating and again little learning occurs. However, with a meaningful story and the right level of difficulty, we have the appropriate challenge.
Then, we have to have the right alternatives to select from. Some of the challenge comes from having a real decision where you can recognize that making the wrong choice would be problematic. But the alternatives must require an appropriate level of discrimination. Alternatives that are so obvious or silly that they can be ruled out aren’t going to lead to any learning. Instead, they need to be ways learners reliably go wrong, representing misconceptions. The benefits are several: first, you can find out what they really know (or don’t), and you have the chance to address them. Also, this assists in having the right level of challenge. So you must have the right choices.
Finally, once the choice is made, you need to have feedback. Rather than immediately have some external voice opine ‘yes’ or ‘no’, let the learner see the consequences of that choice. This is important for two reasons. For one, it closes the emotional experience, as you see what happens, wrapping up the experience. Second, it shows how things work in the world, exposing the causal relationships and assists the learner understanding. Then you can provide feedback (or not, if you’re embedding this single decision in a scenario or game where other choices are precipitated by this choice). So, the final element are consequences.
While this isn’t complete, I think it’s a nice shorthand to guide the design of meaningful and engaging practice. What do you think?
I’m realizing that a major theme of my work and the revolution is that what we do in organizations, and what we do as L&D practitioners, is not aligned with how we think, work, and learn. And to that extent, we’re doomed to failure. We can, and need to, do better.
Let’s start with thinking. The major mismatch here is that our thinking is done rationally and in our head. Results in cognitive science show, instead, that much of our thinking is irrational and is distributed across the world. We use external representations and tools, and unless we’re experts, we make decisions and use our brains to justify them rather than actually do the hard work.
What does this mean for organizations and L&D? It means we should be looking to augment how we think, with tools and processes like performance support, helping us find information with powerful search. We want to have open book learning, since we’ll use the book in the real world, and we want to avoid putting it ‘in the head’ as much as possible. Particularly rote information. We should expect errors, and provide support with checklists, not naively expect that people can perform like robots.
This carries over to how we work. The old view is that we work alone, performing our task, and being managed from above with one person thinking for a number of folks. What we now know, however, is that this view isn’t optimal. The output is better when we get multiple complementary minds working together. Adaptation and innovation work best when we work together.
So we don’t need isolation to do our work, we need cooperation and collaboration. We need ways to work together. We need to give people meaningful tasks and give them space to execute, with appropriate support. We need to create environments where it’s safe to share, to show your work, to work out loud.
And our models of learning are broken. The trend to an event comprised of information dump and knowledge test we know doesn’t work. Rote procedures are no longer sufficient for the increasing ambiguity and unique situations our learners are seeing. And the notion that “practice ’til they get it right” will lead to any meaningful change in ability is fundamentally flawed.
To learn, we need models to guide our behavior and help us adapt. We need to identify and address misconceptions. We need learners to engage concretely and be scaffolded in reflection. And we need much practice. Our learning experiences need to look much more like scenarios and serious games, not like text and next.
We’re in an information age, and industrial models just won’t cut it. I’m finding that we’re hampered by a fundamental lack of awareness of our brains, and this is manifesting in too many unfortunate and ineffective practices. We need to get better. We know better paths, and we need to trod them. Let’s start acting like professionals and develop the expertise we need to do the job we must do.
#itashare
I’m working on the learning science workshop I’m going to present at DevLearn next month, and in thinking about how to represent the implications of designing to account for how we work better when the learning context is concrete and sufficient contexts are used, I came up with this, which I wanted to share.
The empirical data is that we learn better when our learning practice is contextualized. And if we want transfer, we should have practice in a spread of contexts that will facilitate abstraction and application to all appropriate settings, not just the ones seen in the learning experience. If the space between our learning applications is too narrow, so too will our transfer be. So our activities need to be spread about in a variety of contexts (and we should be having sufficient practice).
Then, for each activity, we should have a concrete outcome we’re looking for. Ideally, the learner is given a concrete deliverable as an outcome that they must produce (that mimics the type of outcome we’re expecting them to be able to create as an outcome of the learning, whether decision, work product, or..). Ideally we’re in a social situation and they’re working as a team (or not) and the work can be circulated for peer review. Regardless, then there should be expert oversight on feedback.
With a focus on sufficient and meaningful practice, we’re more likely to design learning that will actually have an impact. The goal is to have practice that is aligned with how our learning works (my current theme: aligning with how we think, work, and learn). Make sense?
It’s time for another game of Where’s Clark? As usual, I’ll be somewhat peripatetic this fall, but more broadly scoped than usual:
Yes, it’s quite the whirl, but with this itinerary I should be somewhere near you almost anywhere you are in the world. (Or engage me to show up at your locale!) I hope to see you at one event or another before the year is out.
I’m increasingly realizing that the ways we design and develop content are part of the reason why we’re not getting the respect we deserve. Our brains are arguably the most complex things in the known universe, yet we don’t treat our discipline as the science it is. We need to start combining experience design with learning engineering to really start delivering solutions.
To truly design learning, we need to understand learning science. And this does not mean paying attention to so-called ‘brain science’. There is legitimate brain science (c.f. Medina, Willingham), and then there’s a lot of smoke.
For instance, there’re sound cognitive reasons why information dump and knowledge test won’t lead to learning. Information that’s not applied doesn’t stick, and application that’s not sufficient doesn’t stick. And it won’t transfer well if you don’t have appropriate contexts across examples and practice. The list goes on.
What it takes is understanding our brains: the different components, the processes, how learning proceeds, and what interferes. And we need to look at the right levels; lots of neuroscience is not relevant at the higher level where our thinking happens. And much about that is still under debate (just google ‘consciousness‘ :).
What we do have are robust theories about learning that pretty comprehensively integrate the empirical data. More importantly, we have lots of ‘take home’ lessons about what does, and doesn’t work. But just following a template isn’t sufficient. There are gaps where have to use our best inferences based upon models to fill in.
The point I’m trying to make is that we have to stop treating designing learning as something anyone can do. The notion that we can have tools that make it so anyone can design learning has to be squelched. We need to go back to taking pride in our work, and designing learning that matches how our brains work. Otherwise, we are guilty of malpractice. So please, please, start designing in coherence with what we know about how people learn.
If you’re interested in learning more, I’ll be running a learning science for design workshop at DevLearn, and would love to see you there.
We’ve heard about learning engineering and while the focus is on experience design, the pragmatics include designing content to create the context, resources, and motivation for the activity. And it’s time we step beyond just hardwiring this content together, and start treating it as professionals.
Look at business websites these days. You can customize the content you’re searching for with filters. The content reacts to the device you’re on and displays appropriately. There can even be content that is specific to your particular trace of action through the site and previous visits. Just look at Amazon or Netflix recommendations!
This doesn’t happen by hardwired sites anymore. If you look at the conferences around content, you’ll find that they’re talking industrial strength solutions. They use content management systems, carefully articulated with tight definitions and associated tags, and rules that pull together those content elements by definition into the resulting site. This is content engineering, and it’s a direction we need to go.
What’s involved is tighter templates around content roles, metadata describing the content, and management of the content. You write into the system, describe it, and pull it out by description, not by hard link. This allows flexibility and rules that can pull differentially by different contexts: different people, different role, different need, and different device. We also separate out what it says from how it looks, using tags to support rendering appropriately on different devices rather than hard-coding the appearance as well as the content and the assembly.
This is additional work, but the reasons are several. First, being tighter around content definitions provides a greater opportunity to be scientific about the role the content plays. We’re too lax in our content, so that beyond a good objective, we don’t specify what makes a good example, etc. Second, by using a system to maintain that content, we can get more rigorous in content management. I regularly ask audiences whether they have outdated legacy content hanging around, and pretty much everyone agrees. This isn’t effective content governance, and content should have regular cycles of review and expiry dates.
By this tighter process, we not only provide better content design, delivery, and management, but we set the stage for the future. Personalization and customization, contextualization, are hampered when you have to hand-configure every option you will support. It’s much easier to write a new set of rules and then your content can serve new purposes, new business models, and more.
If you want to know more about this, I hope to see you at my session on content at DevLearn!
If, indeed, learning is the new business imperative, what does that mean we need to learn? What are the skills that we want to have, or need to develop? I reckon they fall into two categories; those we do for our own learning, and those for learning with and through others.
When we learn on our own, we need to address what information we want coming in and how we process it. This falls under Harold Jarche’s Personal Knowlege Mastery of Seek – Sense – Share. To me there are two main components: what you actively seek, and what comes to you.
What you actively seek really is your searching abilities. Several things come into play. One is knowing where to look. When do you google, when do you do an internal search, when do you check out a book? And how to look is also a component. Do you know how to make a good search string? Do you know how to evaluate the quality of the responses you get? I see too often that people aren’t critical enough in looking at purveyed information.
Then, you also want to set up a stream of information that comes to you. Who to follow on social media? What streams of information? How do you find what sources others use? How do you track what’s happening in your areas of interest and responsibility without getting overwhelmed? This is personal information management, and it requires active management, as sources change. And there are different strategies for different media, as well.
Note that this crosses over into social, but people don’t necessarily know you’re following them. While there may be a notification, they don’t know how much attention you’re paying. I’ve talked about ‘stealth mentoring’, where you can follow someone’s tweets and blog posts, and they can serve as a mentor for you without even knowing it!
There’s some processing of that information, too. What do you do with it? How do you make sense of it? If you hear X over here, and Y over there, you should try to actively reconcile it (e.g. as I did here with collaboration and cooperation). Do you diagram, write, make a video, ?
Of course, if you do process it, do you share it? Now we’re crossing over into the social space more proactively. There’re good reasons to ‘show your work’; in terms of helping others understand where you’re at in your process and for them to offer help. And sharing your thinking can help others. Your thoughts, even interim, can help you and others sort out your thinking. There are some skills involved in figuring out how to systematically share, and of course some diligence and effort is required too, at least before it becomes a habit.
And, of course, there is explicitly asking for help. There are ways to ask for help that aren’t effective! Similarly, there are ways to offer help that won’t necessarily be taken up. So there are skills involved in communicating.
Similarly, collaboration shouldn’t be taken for granted. Do you know different ways to collaborate on documents, presentations, and spreadsheets? Hint: there are better ways than emailing around files! How do you manage a collaboration process so that it maximizes the outcome? For instance, there are nuances to brainstorming.
There are lots of skills involved, and not only should you develop your own, but you should consider the benefits to the organization to developing them systematically and systemically. So, what did I miss? Wondering if I should try to diagram this…