I’m an advocate of meta-learning, that is: learning to learn. Not just because it’s personally empowering, but because it can and should be organizationally empowering. The problem is, little is talked about how to develop it. And I have to say that what I do see, seems inadequate. So I thought I’d rant, for a post, on what is involved in developing learning to learn skills.

First, of course, you have to identify what they are!  What are learning to learn skills?  Harold Jarche’s PKM is a good start, talking about seek > sense > share. Obviously, there’s more to it than just that, so it’s about seeking actively but also setting up systems to continually feed you new, potentially tangential thoughts. And how to evaluate what you get. Then, it’s about being able to process the inputs in ways that help you understand, or do, something new. What does it mean, in practice?  Finally, of course, it’s about sharing, in two ways. For one, contributing to others’ questions and work. Then it’s also sharing your own thoughts and work.

That’s (largely) working alone, but there are also specifics about how you work and play well with others. Do you know how to best manage the process of solving a problem together?  How can you ask questions, and answer them, in ways that people will recognize and participate?   People need models and frameworks that guide performance.

Of course, just knowing this isn’t enough.  There are some necessary additional steps. The first is evangelizing and sharing the best principles for working together. So, people have to know about the principles, and be encouraged to use them.  And even be rewarded, whether just with praise or actual promotion of their successes. There should also be models, examples. So L&D should be practicing what they preach, and working and learning ‘out loud’.  Show, and narrate, your own work!  And, this is still not enough.

Most importantly, you have to develop the skills. Actively. So, content about them, and examples are good. But learning is, at core, about mentored practice.  And it can’t be in the abstract, it’s about doing it with real tasks. You can set up such opportunities in your formal learning (and should), but you should also be coaching around real work.

At least, you should be facilitating proper approaches in public forums, like social media.  You can quietly coach individuals about good practices if they’re off target.  You can point out, as a meta-discussion, when people are learning effectively.  Annotate the thinking behind what learners can and should be doing.

The worst thing is to leave it to chance, or assume your learners are effective self-learners. The evidence is that they’re not. Sadly, our education system doesn’t do a good job of this. Nor do our organizations. But we could. This is about more effective innovation, really. Learning manifests as new ways of doing things. Innovation is about better ways of doing things. If we evaluate our learnings and apply the ones that are improvements, we’re innovating. Both for specific needs and as a ongoing background process.  And if indeed innovation is the only sustainable differentiator, this is the best investment you can make for the organization.

And, if you’re truly contributing to the central success factor in the organization, you’re becoming essential to the organization. As you should be. So seize the opportunity, and make meta-learning a priority. Develop learning to learn skills consciously, and conscientiously.  It’s an innovative, and valuable, thing to do :).

When we design learning activities (per the activity-based learning model), ideally we’re looking to create an integration of a number of constraints around that assignment. I was looking to enumerate them, and (of course) I tried diagramming it.  Thought I’d share the first draft, and I welcome feedback!

The goal is an assignment that includes the right type of processing. This must align with what they need to be able to do after the learning experience. Whether at work or in a subsequent class. Of course, that’s factored into the objective for this learning activity (which is part of an overall sequence of learning).

Another constraint is making sure the setting is a context that helps establish the breadth of transfer. The choice should be sufficiently different from contexts seen in examples and other practices to facilitate abstracting the essential elements. And, of course, it’s ideally in the form of a story that the learner’s actions are contributing to (read: resolve). The right level of exaggeration could play an (unrepresented) role in that story.

We also need the challenge in the activity to be in the right range of difficulty for the learner. This is the integration of flow and learning to create meaningful engagement.  And we want to include ways in which learners typically go wrong (read: misconceptions). Learners need to be able to make the mistakes here so we’re trapping and addressing them in the learning situation, not when it could matter.

Finally, we want to make sure there’s enough variation across tasks. While some similarities benefit for both consistency and addressing the objective, variety can maintain interest. We need to strike that balance. Similarly, look at the overall workload: how much are we expecting, and is that appropriate given the other constraints outside this learning goal.

I think you can see that successfully integrating these is non-trivial, and I haven’t even gotten into how to evaluate this, particularly to make it a part of an overall assessment. Yet, we know that multiple constraints help make the design easier (at least until you constrain yourself to an empty solution set ;).  This is probably still a mix of art and science, but by being explicit you’re less likely to miss an element.

We want to align activities with the desired outcome, in the full context. So, what am I missing?  Does this make sense?

I’ve been thinking a lot about processing in learning of late; what processing matters, when, and why. I thought I’d share my thinking with you and see what you think.  This is my processing!  :)

We know processing is useful. You can consider Craik & Lockhart’s Levels of Processing model, or look to the importance of retrieval practice as highlighted in Brown, Roediger, and McDaniel’s Make it Stick. The point is that retrieving information from memory and doing things with it increases the likelihood of learning. One of the questions is “what sort of retrieval (or processing)?”

I’ve always advocated for applying the information, doing something with it.  But there are actually a variety of useful things we can do:

• representing information (a form of reflection) whether rewriting, or mindmapping, or…
• connecting to other known information, personal or professional
• considering how it would be applied in practice
• applying it in practice, real or simulated

Of course, we want there to be scrutiny and feedback for the learning to be optimized, etc.

Now, this is in the individual instance, but I’m also looking at the sequence of processing. What would be a series of activities that would develop understanding. So, for instance, for a problem-solving practice like trouble-shooting a process, what might you do? You might have (say, after a model of the process, and examples) a sequence of :

• critique someone else’s performance
• try a simple example of performing
• try a more complex example (perhaps in a group)
• …(more examples of performing)
• try a very complex (read: typical) example

We could throw in related tasks as well either during or as a summary:

• create a checklist to follow
• draw a flow diagram
• create a representation

On a more categorical task, say determining whether a situation qualifies as this or not (with shades of grey in between), we would have a similar structure, but with different types of tasks (again, after initial content such as definition and examples):

• review a case where it clearly is (white)
• review a case where it clearly isn’t (black)
• group review a case of grey (but not too bad)
• group review a case of grey (more shady)
• …

Again, we could have interim or summary tasks:

• summarize the constraints
• document a proposed process
• make a plan for how to do it in the future
• …

What I’ve explicitly added here is when and why to go ‘social‘.  There are benefits for the same, but should they all be social?  I’ll argue that there’s some initial prep that’s individual, to get everyone on the same page. Since all are different, it helps if this is individual. Then there’s often value in doing it socially, for the reasons in the linked post.  Then, I reckon there’s value in doing something independently, to consolidate the learning. And, of course, to determine what capability the individual has acquired.

The point I want to make is that the processing flow, the progression from activity to activity, matters. We want to introduce, diverge, and then converge.  We do need to elaborate across contexts to support transfer, and of course increase complexity until they’ve developed the ability to deal with the typical difficulty of cases.

I’m thinking that, too often, we forget the consolidation phase.  And we’re often doing processing that’s somewhat like what we need them to do, but ultimately tangential. There are multiple constraints here to be acknowledged, cognitive such as depth and breadth as well as pragmatic such as cost and time, but we want to find the right intersection.

And my practical question is: where does this fall apart? Are their situations where this doesn’t make sense?  I realize there are other types of outcomes that I haven’t represented (I’m being indicative, not exhaustive ;), but is this a useful way to think about it?

I’ve called out a number of myths (and superstitions, and misconceptions) in my latest tome, and I’m grateful people appear to be interested.  I take this as a sign that folks are beginning to really pay attention to things like good learning design. And that’s important. It’s also important not to minimize the problems myths can create. I do that in my presentations, but I want to go a bit deeper.  We need to care about why myths matter to limit our mistakes!

It’s easy to think something like “they’re wrong, but surely they’re harmless”.  What can a few misguided intentions matter?  Can it hurt if people are helped to understand if people are different?  Won’t it draw attention to important things like caring for our learners?  Isn’t it good if people are more open-minded?

Would that this were true. However, let me spin it another way: does it matter if we invest in things that don’t have an impact?  Yes, for two reasons.  One, we’re wasting time and money. We will pay for workshops and spend time ensuring our designs have coverage for things that aren’t really worthwhile. And that’s both profligate and unprofessional.  Worse, we’re also not investing in things that might actually matter.  Like, say, Serious eLearning. That is, research-derived principles about what actually works. Which is what we should be getting dizzy about.

But there are worse consequences. For one, we could be undermining our own design efforts. Some of these myths may have us do things that undermine the effectiveness of our work. If we work too hard to accommodate non-existent ‘styles’, for instance, we might use media inappropriately. More problematic, we could be limiting our learners. Many of the myths want to categorize folks: styles, gender, left/right brain, age, etc.  And, it’s true, being aware of how diversity strengthens is important. But too often people go beyond; they’ll say “you’re an XYZ”, and people will self-categorize and consequently self-limit.  We could cause people not to tap into their own richness.

That’s still not the worst thing. One thing that most such instruments explicitly eschew is being used as a filter: hire/fire, or job role. And yet it’s being done. In many ways!  This means that you might be limiting your organization’s diversity. You might also be discriminatory in a totally unjustifiable way!

Myths are not just wasteful, they’re harmful. And that matters.  Please join me in campaigning for legitimate science in our profession. And let’s chase out the snake oil.  Please.