Archives for August 2015

3 C’s of Engaging Practice

26 August 2015 by Clark Leave a Comment

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?

Aligning

25 August 2015 by Clark 2 Comments

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

Concrete and Contextual

19 August 2015 by Clark 3 Comments

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.

Concrete deliverables and multiple contexts 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?

Designing Learning Like Professionals

12 August 2015 by Clark 4 Comments

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.

Content engineering

11 August 2015 by Clark 2 Comments

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!

Meta-learn what?

6 August 2015 by Clark Leave a Comment

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…

Teasing apart cooperation and collaboration

4 August 2015 by Clark 6 Comments

There have been a couple of recent proposals about the relative role of cooperation and collaboration, and I’m trying to make sense of them. Here are a couple of different approaches, and my first take at teasing them apart.

Dion Hinchcliffe of Adjuvi tweeted a diagram about different types of working together that shows his take. He has coordination as a subsidiary to cooperation and on to collaboration. So coordination is when we know what needs to be done, but we can’t do it alone. Cooperation is when we’re doing things that need to have a contribution from each of us, and requires some integration. And collaboration is when we’re working together with a goal but not clear how we’ll get there. I think what’s core here is how well defined the task is and how much we contribute.

In the meantime, Harold Jarche, my ITA colleague, as a different take. He sees collaboration as working together to achieve a goal that’s for the organization, whereas cooperation goes beyond. Cooperation is where we participate and assist one another for our own goals. It’s contribution that’s uncoupled from any sense of requirement, and is freely given. I see here the discussion is more about our motives; why are we engaged.

With those two different takes, I see them as different ways of carving up the activities. My initial reaction is closer to Dion’s; I’ve always seen cooperation as willingness to assist when asked, or to provide pointers. To me collaboration is higher; it’s willing to not just provide assistance in clearly defined ways such as pointers to relevant work, answering questions, etc, but to actively roll up sleeves and pitch in. (Coordination is, to me I guess, a subset of cooperation.) With collaboration I’ve got a vested interest in the outcome, and am willing to help frame the question, do independent research, iterate, and persist to achieve the outcome.

I see the issue of motivation or goal as a different thing. I can cooperate in a company-directed manner, as expected, but I also can (and do) cooperate in a broader sense; when people ask for help (my principles are simple: talk ideas for free; help someone personally for dinner/drinks; if someone’s making a quid I get a cut), I will try to assist (with the Least Assistance Principle in mind). I can also collaborate on mutual goals (whether ITA projects or client work), but then I can also collaborate on things that have no immediate outcome except to improve the industry as a whole (*cough* Serious eLearning Manifesto *cough*).

So I see two independent dimensions: one on the effort invested, just responding to need or actively contributing; and the other on the motivation, whether for a structured goal or for the greater good.

Now I have no belief that either of them will necessarily agree with my take, but I’d like to reconcile these interpretations for the overall understanding (or at least my own!). That’s my first take, feedback welcome!