In trying to shift from a traditional elearning approach to a more enlightened one, a deeper one, you are really talking about viewing things differently, which is non-trivial. And then, even if you know you want to do better, you still need some associated skills. Take, for example, models.

I’ve argued before that models are a better basis for action, for making better decisions.  Arbitrary knowledge is hard to recollect, and consequently brittle.  We need a coherent foundation upon which to base foundations, and arbitrary information doesn’t help.  If I see a ‘click to learn more’, for instance, I have good clue  that someone’s presenting arbitrary information.  However, as I concluded in the models article, “It‘s not always there, nor even easily inferable.”  Which is a problem that I’ve been wrestling with.  So here’re my interim thoughts.

Others have counseled that not just any Subject Matter Expert (SME) will do.  They may be able to teach material with their stories and experience, and they can certainly do the work, but they may not have a conscious model that’s available to guide novices.  So I’ve head that you have to find one capable. If you don’t, and you don’t have good source material, you’re going to have to do the work yourself.  You might be able to find one in a helpful place like Wikipedia (and please join us  in donating to help keep it going, would you please?), but otherwise you’re going to have to do the hard yards.

Say  you’re wrestling with a list of things, like attacks on networks, or impacts on blood pressure.  There is a laundry list of them, and there may seem to be no central order.  So what do you do?  Well, in these cases where I don’t have one, I make one.

For instance, in attacks on networks, it seems that the inherent structure of the network provides an overarching framework for vulnerabilities.  Networks can be attacked digitally through  password cracking or software vulnerabilities.  The data streams could also be hacked either physically connecting to wires or intercepting wireless signals.  Socially, you can trick people into doing wrong things too.  Similarly with blood pressure, the nature of the system tells us that constricted or less flexible vessels (e.g. from aging) will increase blood pressure. Decreased volume in the system will decrease, etc.

The point is, I’m using the inherent structure to provide a framework that wasn’t given. Is it more than the minimum?  Yes.  But I’ll argue that if you want the information to be available when necessary, or rather that learners will be able to make the right decisions, this is the most valuable thing you can do. And it might take less effort overall, as you can teach the model and support making good inferences more efficiently than teaching all the use cases.

And is this a sufficient approach?  I can’t say that; I haven’t spent enough time on other content. So at this point treat it like a heuristic.  However, it gives you something you can at least take to a SME and have them critique and improve it (which is easier than trying to extract a model whole-cloth ;).

Now there might also be the case that there just isn’t an organizing principle (I’m willing to concede that, for now…). Then, you may  need simply to ask your learners  to do some meaningful processing on the material.  Look, if you’re presenting it, then you’re expecting them to remember it. Presenting arbitrary information isn’t going to do that. If they need to remember it, have them process it.  Otherwise, why present it at all?

Now, this is only necessary when you’re trying to do formal learning; it might be that you don’t have to get it in folks heads and can put it in the world. Do it if you can.   But I believe that what will make a bigger difference for learners, for  performers, will be the ability to make better decisions. And, in our increasingly turbulent times that will come from models, not rote information.  So please, if you’re doing formal learning, do it right, and get the models you need. Beg, borrow, steal, or make, but get them.  Please?

In an previous post, I argued for different types and ratios for  worthwhile learning activities. I’ve been thinking about this (and working on it) quite a bit lately. I know there are other resources that I should know about (pointers welcome), but I’m currently wrestling with several types of situations and wanted to share my thinking. This is aside from scenarios/simulations (e.g. games) that are the first, best, learning practice you can engage in, of course. What I’m looking for is ways to get learners to do processing in ways that will assist their ability to  do.  This isn’t recitation, but application.

So one situation is where the learner has to execute  the right procedure. This seems easy, but the problem is that they’re liable to get it right  in practice.  The problem is that they still can get it wrong when in real situations. An idea I had heard of before, but was reiterated through Socratic Arts  (Roger Schank & cohorts) was to have learners observe (e.g. video) of someone performing it and identifying whether it was right or not. This is a more challenging task than  just doing it right for many routine but important tasks (e.g. sanitation). It has learners monitor the process, and then they can turn that on themselves to become self-monitoring.  If the selection of mistakes is broad enough, they’ll have experience that will transfer to their whole performance.

Another task that I faced earlier was the situation where people had to interpret guidelines to make a decision. Typically, the extreme cases  are obvious, and instructors argue that they all are, but in reality there are many ambiguous situations.  Here, as I’ve argued before, the thing to do is have folks work in groups and be presented with increasingly ambiguous situations. What emerges from the discussion is usually a rich unpacking of the elements.  This processing of the rules in context exposes the underlying issues in important ways.

Another type of task is helping people understand applying models to make decisions. Rather than present them with the models, I’m again looking for more meaningful processing.  Eventually I’ll expect learners to make decisions with them, but as a scaffolding step, I’m asking them to interpret the models in terms of their recommendations for use.  So before I have them engage in scenarios, I’ll ask them to use the models to create, say, a guide to how to use that information. To diagnose, to remedy, to put in place initial protections.  At other times, I’ll have them derive subsequent processes from the theoretical model.

One other example I recall came from a paper  that Tom Reeves wrote (and I can’t find) where he had learners pick from a number of options that indicated problems or actions to take. The interesting difference was then there was a followup question about why. Every choice was two stages: decision and then rationale. This is a very clever way to see if they’re not just getting the right answer but can understand why it’s right.  I wonder if any of the authoring tools on the market right now include such a template!

I know there are  more categories of learning and associated tasks that require useful processing (towards do, not  know, mind you ;), but here are a couple that are ‘top of mind’ right now. Thoughts?

A commenter on last week’s post asked an implicit question that caused me to think. The issue was whether the solutions I was proposing are having the learners be self directed or whether it was ‘push’ learning.  And I reckon there’s a bit of both, but I’m fighting for more of a constuctivist approach   than the instructivist model.

I’ve argued in the past for a more active learning, and I think the argument for pure instructivism  sets up a straw man (Feuerzeig argued for guided discovery back in ’85!).  Obviously, I think that pure exploration is doomed to failure, as we know that learners can stay in one small corner of a search space without support (hence the coaching in Quest).  However, a completely guided experience doesn’t ‘stick’ as well, either.

Another factor is our target learners.  In my experience, more constructivist approaches can be disturbing to learners who have had more instructivist approaches.  And the learners we are dealing with haven’t been that successful in school, and typically  need a lot of scaffolding.

Yet our goals are fairly pragmatic overall (and in general we should be looking for ways to pragmatic in more of our learning). We’re focused on meaningful skills, so we should leverage this.

In this case, I’m moving the design to more and more “here’s a goal, here’re some resources” type of approach where the goal is to generate a work-related integration (requiring relevant cognitive processing).  Even if it’s conceptual material, I want learners to be doing this, and of course the main focus is on real contextualized practice.

I’m pushing a very activity-based pedagogy (and curriculum). Yes, the tasks are designed, but they’re expected to take some responsibility for processing the information to produce outputs. The longer term goal is to increase the challenge and variety as we go through the curriculum, developing learner’s ability to  learn to learn and ability to adapt as well. Make sense?

I’ve been on quite the roll of late, calling out some bad practices and calling for learning science. And it occurs to me that there could be some pushback.  So let me be clear, I strongly suggest that the types of learning that are needed are not info dump and knowledge test,  by and large.  What does that mean? Let’s break it down.

First, let me suggest that what’s going to make a difference to organizations is not better fact-remembering. There are times when fact remembering is needed, such as medical vocabulary (my go-to example). When that needs to happen, tarted up drill-and-kill (e.g .quiz show templates, etc)  are the way to do it.   Getting people to remember rote facts or arbitrary things (like part names) is very difficult. And largely unnecessary if people can look it up, e.g. the information is  in the world  (or can be).  There are some things that need to be known cold, e.g. emergency procedures, hence the tremendous emphasis on drills  in aviation and  the military. Other than that, put it in the world, not the head.  Look up tables, info sheets, etc are the solution.    And I’ll argue that the need for this is less than 5-10% of the time.

So what  is  useful?  I’ll argue that what is useful is making better decisions.  That is, the ability to explain what’s happened and react, or predict what will happen and make the right choice as as consequence.  This comes from model-based reasoning.  What sort of learning helps model-based reasoning? Two types, in a simple framework. You need to process the models to help them be comprehended, and use them in context to make decisions with the consequences providing feedback.  Yes, there likely will be some content presentation, but it’s not  everything, and  instead is the core model with examples of how it plays out in context. That is, annotated diagrams or narrated animations for the models; comic books, cartoons, or videos for the examples.  Media, not bullet points.

The processing that helps make models stick includes having learners generate products: giving them data or outcomes and having them develop explanatory models. They can produce summary charts and tables that serve as decision aids. They can create syntheses and recommendations.  This really leads to internalization and ownership, but it may be more time-consuming than worthwhile. The other approach is to have learners make predictions using the models, explaining things.  Worst case, they can answer questions about what this model implies in particular contexts.  So this is a knowledge question, but not a “is this an X or a Y”, but rather “you have to achieve Z, would you use approach X, or approach Y”.

Most importantly, you need people to use the models to make decisions like they’ll be making in the workplace.  That means scenarios and simulations.  Yes, a mini-scenario of one question is essentially a multiple choice (though better written with a context and a decision), but really things tend to be bundled up, and you at least need branching scenarios. A series of these might be enough if the task isn’t too complex, but if it’s somewhat complex, it might be worth creating a model-based simulation and giving the learners lots of goals with it (read: serious game).

And, don’t forget, if it matters (and why are you bothering if it doesn’t), you need to practice until they can’t get it wrong.  And you need to be facilitating reflection.  The alternatives to the right answer should reflect ways learners often go wrong, and address them individually. “No, that’s not correct, try again” is a really rude way to respond to learner actions.  Connect their actions to the model!

What this also implies is that learning is much more practice than content presentation.  Presenting content and drilling knowledge (particularly in about an 80/20 ratio), is essentially a waste of time.  Meaningful practice should be more than half the time.  And you should consider putting the practice up front and driving them to the content, as opposed to presenting the content first.  Make the task make the content meaningful.

Yes, I’m making these numbers up, but they’re a framework for thinking. You should be having lots of meaningful practice.  There’s essentially  no role for bullet points or prose and simplistic quizzes, very little role for tarted up quizzes, and lots of role for media on the content side and  branching scenarios and model-driven interactions on the interaction side.  This kind of is an inverse of the tools and outputs I see.  Hence my continuing campaign for better learning.  Make sense?