Search Results for: align

Ok, so I shared that I was trying to visualize the generative process. Which is, to be fair, aka elaboration. And, I did take a stab. So I thought I’d share what I’d come up with for generative visualized. Two reasons: 1) for you to quibble with what I came up with, and 2) learning out loud. I reckon if you can see my process, you may learn, you may improve it, all the good things that happen from sharing. So…

First, I was looking to do a higher build than the neural level. Yes, at the neural level we’re strengthening patterns across neural networks. We’re taking two patterns and putting them into conjunction, basically, activating both. That activating in conjunction strengthens the links between them, associating them more. (I’ve a diagram on that, actually, emphasizing that they need time to recover before the next strengthening. If you’re interested, let me know and I’ll share that, too.) But the way we activate patterns is by using words or images, semantically. So, I was looking for words.

They also should be ideas that are familiar enough that I’m not requiring knowledge you don’t know. Further, I need to be making associations that you’d get. So, something familiar. In this case, animals and pets. (Perhaps that’s salient to me, for some reason.) And I needed a relationship between them. I chose, rightly or wrongly, the notion of how wild animals don’t make good pets. So, what’s this look like?

Now, I’m showing the final build, not the whole process. You start with something you’re talking about, like how there are good and bad pets (“do raccoons make good pets?”). And you ask the learners to find some personal or conceptual basis for that distinction. They can find examples, like that good pets are domesticated animals, and bad pets are wild, drawing on their previous knowledge. Or, they might talk about someone who had a bad pet (“my friend’s parents had this pet monkey, but it was always doing bad things”). Of course, pets are a subset of animals, so this becomes confusing at the top level, but that’s okay, as the structures are similar but not exactly the same.

The point is that they’re generating this information, actively connecting their own pre-existing knowledge to the information you’d like them to acquire. It’s causing them to elaborate, and so strengthening the links in their mind. Which is what we want. So, that’s my attempt to capture generative visualized. Does it work, or do you have ideas to improve or replace?

FWIW, this is part of what I’ll be presenting at the LDA’s Learning Science Conference, starting Nov 3 to review the presentations and discuss, with live sessions with the presenters Dec 8-12. Same format as last year (with recognition of that in pricing, for those that attended), but with some new content and presenters. It’s still the things we think you need to be an informed learning designer. 

As I continue exploring learning, I’m beginning to realize that the picture’s incomplete. Which shouldn’t be a surprise, but it’s a bit of a concern. Of course, I don’t have the answers, but if I point out some of the elements, maybe we’ll identify the gaps. And, I’m sure, there are likely initiatives and results I’m unaware of, and hopefully folks will help fill in the full story.

So, our endeavor is to start with people, with whatever experience and abilities they bring to the table. Then, ultimately (after our ministrations) have them capable of dealing with the situations we’ve identified are important. That includes what needs to be in long-term memory, and having responses aligned to triggers that activate the appropriate actions. Along the way, we have the analysis, the learning science, the coaching, and …?

I talked earlier about how most of our focus has been on getting folks up to some level of initial speed. But we do want folks to ultimately acquire the full range. And, I think, we too often abandon our efforts before getting the full way.

That makes sense for organizational learning, where we have to balance the costs with the value. When lives are on the line (aviation, medicine, military), we probably need to go a very long way. When it’s just inventory, we may allow some checks along the way to catch mistakes, and hope that people will eventually internalize the elements. (There are practices we spend on that are entirely worthless, of course.) For education, when we have kids in schools for years, it is clear that our pedagogy, driven by a wrong curriculum and values, falls far short of actually applying knowledge. But that’s another rant ;).

And we have pretty good guidance for many elements. We know about retrieval practice, with spacing, deliberate choices of next steps, desirable difficulty. We also know about generative activities. I’m not sure we’ve reconciled, however, how much of each. We also have cognitive load theory, which guides us in many ways, but doesn’t necessarily talk about acquisition versus application. The power law of learning suggests that we go from conscious to unconscious, but it assumes doing the right practice. The list goes on: multimedia learning, four component ID, cognitive apprenticeship, …; we have lots of pieces.

And yet, that’s still getting people up to a certain beginning level. How do we go beyond? The aforementioned is all about formal learning, and not about moving on. So we kind of have a hiccup between learning and coaching. How do they segue? We also have the issue of assessment. I have suggested that maybe we need to consider our commitment to apply as well as our ability. And that’s still before actually starting, yet we’re not done then.

Folks are talking about dispositions, I note, but when I look at it, it’s about the broader picture of persistence, etc, not specific to the particular outcome. Happy to be wrong here. But we really want to understand what we do initially, for instruction, for reactivation and continuation, to the final picture. My intuition is that there’s a more systematic structure that we’re not applying.

I don’t have an answer. Certainly not yet! I just think it’s time that we start thinking about it. Well, I guess that’s an open question: do we need an overarching approach? If so, what do we have. Can we extend something like Cognitive Apprenticeship to coaching on the job, and link to community to continue and ultimately own the final development? I welcome feedback!

 

We write for lots of reasons. It’s all about communication, but with different purposes, there should be different writing. Just for books, the language in a thriller should be different than for thoughtful stories. Writing for ads is different than writing for science. And, writing for learning is different than writing for other purposes. What am I talking about?

What research tells us, as Ruth Clark lets us know, is that we learn better from conversational language. Formal language, such as in an encyclopedia, or a textbook, doesn’t work for elearning or how an instructor talks to an audience. You want to be informal, personal, and more. Yet too often our prose is tedious.

Dialog, in particular, should be authentic to the speaker. I quail when I see characters spouting language straight out of an instructional manual or, worse, a marketing spiel. Good character development goes beyond stereotypes and develops some personality. This should come through in their language. Writing dialog, then, isn’t what most designers have been trained in. Which means that designers shouldn’t write dialog, or at least get external support whether training, even just peer review.

Writing for learning needs to be clear, of course. It also needs to be accurate. And yet, it shouldn’t be onerous to read. If there are barriers to comprehension, you’re putting in unnecessary barriers to your learning outcome. Really, you’re managing cognitive load. Obtuse language impedes processing, and learning is processing-intensive enough!

I’ve talked before about the importance of emotion in learning, for motivation, keeping anxiety under control, building confidence, and more. Writing is one of the most compact forms of media for communicating, and so we want our language to address these issues as well. Conversational language helps reduce anxiety by being familiar, and shows relatedness, part of the Self-Determination Theory of motivation. When folks believe we care about them, they’re more inclined to succumb to our ministrations.

Writing for learning is one of the elements necessary for the appropriate use of media. We should use the right media for the message (with a caveat about the value of novelty), and then we should apply the right media correctly. That is, ensuring we apply the appropriate expertise. We can make changes, such as my common example of Ken Burn’s compelling use of still images in his video documentary of the Civil War, but even then there are accommodations. In short, writing for learning has some particular constraints, and we as designers should be aware of them.

There’s more, of course. What you write in an introduction is different than what’s presented about a model, than the narrative for an example, for the instructions versus the description of the context for retrieval practice, etc. Knowing what the role is, and the appropriate writing, becomes habit with experience, but like all learning, models and feedback help accelerate the path there. You need to know not just what to write, but how and when. Those are my thoughts, what are yours?