LTC Ho Mee Yin told the mstory of rethinking the learning design for the Singapore Armed Forces. She talked about some new frameworks that helped move to a more enlightened learning design that was more activity-centric, and a performance support tool for instructors.
I‘ve talked in the past about the importance of engaging emotionally before beginning learning. And I‘ve talked about the importance of understanding what makes a topic intrinsically interesting. But I haven‘t really separated them out, as became clear to me in a client meeting. So let me remedy that here.
I‘ve argued, and believe, that we should open up learners emotionally before we address them cognitively. Before we tell them what they‘ll learn, before we show them objectives, we should create a visceral reaction, a wry recognition of “oh, yes, I do need to know thisâ€. It can be a dramatic or humorous exaggeration of the positive consequences of having the knowledge or the negative consequences of not. I call this a ‘motivating example‘ different than the actual reference examples used to illustrate the model in context. In previous content we‘ve used comics to point out the problems of not knowing, and similarly Michael Allen had a fabulous video that dramatized the same. Of course, you also have a graphic novel introduction of someone saving the day with this knowledge. It of course depends on your audience and what will work for them.
Another story I tell is when a colleague found out I did games, and asked if I wanted to assist him and his team. The task was, to me and many, not necessarily a source of great intrinsic interest, but he pointed out that he‘d discovered that to practitioners, it was like playing detective. Which of course gave him a theme, and a overarching hook. And this is the second element of engagement we can and should lever.
Once we‘ve hooked them into why this learning is important, we then want to help maintain interest through the learning experience. If we can find out what makes this particular element interesting, we should have it represented in the examples and practice tasks. This will help illuminate the rationale and develop learner abilities by integrating the inherent nature of the task into the learning experience.
Often SMEs are challenging, particularly to get real decisions out of, but here‘s where they‘re extremely valuable. In addition to stories illustrating great wins and losses that can serve as examples (and the motivating example I mentioned above), they can help you understand why this is intrinsically interesting to them. They‘ve spent the time to become experts in this, we want to unpack why this was worth such effort. You may have to drill a bit below “make the world a better placeâ€, but you could and should be able to.
By hooking them in initially by making them aware of the role of this knowledge, and then maintaining interest through the learning experience, you have a better chance of your learning sticking. And that‘s what we want to achieve, right?
Gary presented a passionate and compelling argument for the value of using the maker movement as a vehicle for education reform.
Roger gave his passioned, opinionated, irreverent, and spot-on talk to kick off LearnTechAsia. He covered the promise (or not) of AI, learning, stories, and the implications for education.
Changing behavior is hard. The brain is arguably the most complex thing in the known universe. Simplistic approaches aren‘t likely to work. To rewire it, one approach is to try surgery. This is problematic for a several reasons: it‘s dangerous, it‘s messy, and we really don’t understand enough about it. What‘s a person to do?
Well, we do know that the brain can rewire itself, if we do it right. This is called learning. And if we design learning, e.g. instruction, we can potentially change the brain without surgery. However, (and yes, this is my point) treating it as anything less than brain surgery (or rocket science), isn‘t doing justice to what‘s known and what‘s to be done.
The number of ways to get it wrong is long. Information dump instead of skills practice. Massed practice instead of spaced. Rote knowledge assessment. Lack of emotional engagement. The list goes on. (Cue the Serious eLearning Manifesto.) In short, if you don‘t know what you‘re doing, you‘re likely doing it wrong and are not going to have an effect. Sure, you‘re not likely to kill anyone (unless you‘re doing this where it matters), but you‘ll waste money and time. Scandalous.
Again, the brain is complex, and consequently so is learning design. So why, in the name of sense and money, do we treat it as trivial? Why would anyone buy a story that we can achieve anything meaningful by taking content and adding a quiz (read: rapid eLearning)? As if a quiz is somehow going to make people do better. Who would believe that just anyone can present material and learning will occur? (Do you know the circumstances when that will work?) And really, throwing fuzzy objects around the room and ice-breakers will somehow make a difference? Please. If you can afford to throw money down the drain (ok, if you insist, throw it here ;), and don‘t care if any meaningful change happens, I pity you, but I can‘t condone it.
Let‘s get real. Let‘s be honest. There‘s a lot (a lot) of things being done in the name of learning that are just nonsensical. I could laugh, if I didn‘t care so much. But I care about learning. And we know what leads to learning. It‘s not easy. It‘s not even cheap. But it will work. It requires good analysis, and some creativity, and attention to detail, and even some testing and refinement, but we know how to do this.
So let‘s stop pretending. Let‘s stop paying lip-service. Let‘s treat learning design as the true blend of art and science that it is. It‘s not the last refuge of the untalented, it‘s one of the most challenging, and rewarding, things a person can do. When it‘s done right. So let‘s do it right! We‘re performing brain surgery, non-invasively, and we should be willing to do the hard yards to actually achieve success, and then reap the accolades.
OK, that‘s my rant, trying to stop what‘s being perpetrated and provide frameworks that might help change the game. What‘s your take?
One of the ways I’ve been thinking about the role mobile can play in design is thinking about how our brains work, and don’t. It came out of both mobile and the recent cognitive science for learning workshop I gave at the recent DevLearn. This applies more broadly to performance support in general, so I though I’d share where my thinking is going.
To begin with, our cognitive architecture is demonstrably awesome; just look at your surroundings and recognize your clothing, housing, technology, and more are the product of human ingenuity. We have formidable capabilities to predict, plan, and work together to accomplish significant goals. On the flip side, there’s no one all-singing, all-dancing architecture out there (yet) and every such approach also has weak points. Technology, for instance, is bad at pattern-matching and meaning-making, two things we’re really pretty good at. On the flip side, we have some flaws too. So what I’ve done here is to outline the flaws, and how we’ve created tools to get around those limitations. And to me, these are principles for design:
So, for instance, our senses capture incoming signals in a sensory store. Which has interesting properties that it has almost an unlimited capacity, but for only a very short time. And there is no way all of it can get into our working memory, so what happens is that what we attend to is what we have access to. So we can’t recall what we perceive accurately. However, technology (camera, microphone, sensors) can recall it all perfectly. So making capture capabilities available is a powerful support.
Similar, our attention is limited, and so if we’re focused in one place, we may forget or miss something else. However, we can program reminders or notifications that help us recall important events that we don’t want to miss, or draw our attention where needed.
The limits on working memory (you may have heard of the famous 7 ±2, which really is <5) mean we can’t hold too much in our brains at once, such as interim results of complex calculations. However, we can have calculators that can do such processing for us. We also have limited ability to carry information around for the same reasons, but we can create external representations (such as notes or scribbles) that can hold those thoughts for us. Spreadsheets, outlines, and diagramming tools allow us to take our interim thoughts and record them for further processing.
We also have trouble remembering things accurately. Our long term memory tends to remember meaning, not particular details. However, technology can remember arbitrary and abstract information completely. What we need are ways to look up that information, or search for it. Portals and lookup tables trump trying to put that information into our heads.
We also have a tendency to skip steps. We have some randomness in our architecture (a benefit: if we sometimes do it differently, and occasionally that’s better, we have a learning opportunity), but this means that we don’t execute perfectly. However, we can use process supports like checklists. Atul Gawande wrote a fabulous book on the topic that I can recommend.
Other phenomena include that previous experience can bias us in particular directions, but we can put in place supports to provide lateral prompts. We can also prematurely evaluate a solution rather than checking to verify it’s the best. Data can be used to help us be aware. And we can trust our intuition too much and we can wear down, so we don’t always make the best decisions. Templates, for example are a tool that can help us focus on the important elements.
This is just the result of several iterations, and I think more is needed (e.g. about data to prevent premature convergence), but to me it’s an interesting alternate approach to consider where and how we might support people, particularly in situations that are new and as yet untested. So what do you think?
For the past 6 months, Learnnovators has been hosting a series of posts I’ve done on Deeper eLearning Design that goes through the elements beyond traditional ID. That is, reflecting on what’s known about how we learn and what that implies for the elements of learning. Too often, other than saying we need an objective and practice (and getting those wrong), we talk about ‘content’. Basically, we don’t talk enough about the subtleties.
So here I’ve been getting into the nuances of each element, closing with an overview of changes that are implied for processes:
1. Deeper eLearning Design: Part 1 – The Starting Point: Good Objectives
2. Deeper eLearning Design: Part 2 – Practice Makes Perfect
3. Deeper eLearning Design: Part 3 – Concepts
4. Deeper eLearning Design: Part 4 – Examples
5. Deeper eLearning Design: Part 5 – Emotion
6. Deeper eLearning Design: Part 6 – Putting it All Together
I’ve put into these posts my best thinking around learning design. The final one’s been posted, so now I can collect the whole set here for your convenience.
And don’t forget the Serious eLearning Manifesto! I hope you find this useful, and welcome your feedback.
At the recent DevLearn, Donald Clark talked about AI in learning, and while I largely agreed with what he said, I had some thoughts and some quibbles. I discussed them with him, but I thought I’d record them here, not least as a basis for a further discussion.
Donald’s an interesting guy, very sharp and a voracious learner, and his posts are both insightful and inciteful (he doesn’t mince words ;). Having built and sold an elearning company, he’s now free to pursue what he believes and it’s currently in the power of technology to teach us.
As background, I was an AI groupie out of college, and have stayed current with most of what’s happened. And you should know a bit of the history of the rise of Intelligent Tutoring Systems, the problems with developing expert models, and current approaches like Knewton and Smart Sparrow. I haven’t been free to follow the latest developments as much as I’d like, but Donald gave a great overview.
He pointed to systems being on the verge of auto parsing content and developing learning around it. He showed an example, and it created questions from dropping in a page about Las Vegas. He also showed how systems can adapt individually to the learner, and discussed how this would be able to provide individual tutoring without many limitations of teachers (cognitive bias, fatigue), and can not only personalize but self-improve and scale!
One of my short-term problems was that the questions auto-generated were about knowledge, not skills. While I do agree that knowledge is needed (ala VanMerriënboer’s 4CID) as well as applying it, I think focusing on the latter first is the way to go.
This goes along with what Donald has rightly criticized as problems with multiple-choice questions. He points out how they’re largely used as knowledge test, and I agree that’s wrong, but while there are better practice situations (read: simulations/scenarios/serious games), you can write multiple choice as mini-scenarios and get good practice. However, it’s as yet an interesting research problem, to me, to try to get good scenario questions out of auto-parsing content.
I naturally argued for a hybrid system, where we divvy up roles between computer and human based upon what we each do well, and he said that is what he is seeing in the companies he tracks (and funds, at least in some cases). A great principle.
The last bit that interested me was whether and how such systems could develop not only learning skills, but meta-learning or learning to learn skills. Real teachers can develop this and modify it (while admittedly rare), and yet it’s likely to be the best investment. In my activity-based learning, I suggested that gradually learners should take over choosing their activities, to develop their ability to become self-learners. I’ve also suggested how it could be layered on top of regular learning experiences. I think this will be an interesting area for developing learning experiences that are scalable but truly develop learners for the coming times.
There’s more: pedagogical rules, content models, learner models, etc, but we’re finally getting close to be able to build these sorts of systems, and we should be aware of what the possibilities are, understanding what’s required, and on the lookout for both the good and bad on tap. So, what say you?
Connie Yowell gave a passionate and informing presentation on the driving forces behind digital badges.
At DevLearn next week, I’ll be talking about content systems in session 109. The point is that instead of monolithic content, we want to start getting more granular for more flexible delivery. And while there I’ll be talking about some of the options on how, here I want to make the case about why, in a simplified way.
As an experiment (gotta keep pushing the envelope in a myriad of ways), I’ve created a video, and I want to see if I can embed it. Fingers crossed. Your feedback welcome, as always.