I’ll be giving an upcoming webinar where I make my case for defining microlearning.  And, as part of my usual wrestling with clarity, I created a diagram. I thought I’d share it with you.

What, you want me to walk you through it? :)

Microlearning is a portmanteau (technical term:  mashup) of micro and learning. Thus, it implies small bits of learning.  Here I’m mapping it out in several ways.  I’ve previously argued that there are three main ways, but let’s map the first two out. It’s either

• a series of objects contributing to a learning experience
• a one-off object that creates learning

And there are problems with both. Too often, folks talk about breaking an existing course down into small chunks, and I suggest that won’t work without some significant (!) effort.  Just breaking it up means something seen earlier can be forgotten, so you need to worry about knowledge atrophy and plan reactivation. And that’s just spaced learning!

And I think it’s unlikely that you can have a single object lead to any meaningful learning. However, such an object  can serve as support to succeed in the moment.  How-to videos, job aids, and the like all can be used to achieve an outcome.  And that’s just performance support!

What I think is the real untapped opportunity that could (and I say should) capture the moniker would be contextualized learning. Layering on a bit of learning  because of when and where you are that develops you over time. It’s combining the two, potentially, so you help someone in the moment but add in the bit that also makes it a learning experience.  There’s much more to this, but that’s the core idea.

My main issue here is that people are fast and loose with the term microlearning, and I’d like to make sure people are  either talking about spaced learning  or performance support (both good). And not talking about just breaking up a course into chunks that are nice to consume but not engineered to lead to retention and transfer (not so good).  Of course, better yet if we converge on contextualized learning!

Yesterday, I clarified what I meant about microlearning. Earlier, I wrote about designing microlearning, but what I was really talking about was the design of spaced learning. So how should you design the type of microlearning I really feel is valuable?

To set the stage, here’re we’re talking about layering learning on performance in a context. However, it’s more than just performance support. Performance support would be providing a set of steps (in whatever ways: series of static photos, video, etc) or supporting those steps (checklist, lookup table, etc).  And again, this is a good thing, but microlearning, I contend, is more.

To make it learning, what you really need is to support developing an ability to understand the rationale behind the steps, to support adapting the steps in different situations. Yes, you can do this in performance support as well, but here we’re talking about  models.  

What (causal) models give us is a way to explain what has happened, and predict what will happen.  When we make these available around performing a task, we unpack the rationale. We want to provide an understanding behind the rote steps, to support adaptation of the process in difference situations. We also provide a basis for regenerating missing steps.

Now, we can also be providing examples, e.g. how the model plays out in different contexts. If what the learner is doing now can change under certain circumstances, elaborating how the model guides  performing differently in different context provides the ability to transfer that understanding.

The design process, then, would be to identify the model guiding the performance (e..g.  why  we do things in this order, and it might be an interplay between structural constraints (we have to remove this screw first because…) and causal ones (this is the chemical that catalyzes the process).  We need to identify and determine how to represent.

Once we’ve identified the task, and the associated models, we  then need to make these available through the context. And here’s why I’m excited about augmented reality, it’s an obvious way to make the model visible. Quite simply, it can be layered  on top of the task itself!   Imagine that the workings behind what you’re doing are available if you want. That you can explore more as you wish, or not, and simply accept the magic ;).

The actual task  is the practice, but I’m suggesting providing a model explaining  why it’s done this way is the minimum, and providing examples for a representative sample of other appropriate contexts provides support when it’s a richer performance.  Delivered, to be clear, in the context itself. Still, this is what I think  really constitutes microlearning.  So what say you?

I was honored to learn that a respected professor of educational technology liked my definition of micro-learning, such that he presented it as a recent conference.  He asked if I still agreed with it, and I looked back at what I’d written more recently. What I found was that I’d suggested some alternate interpretations, so I thought it worthwhile to be absolutely clear about it.

So, the definition he cited was:

Microlearning is a small, but complete, learning experience, layered on top of the task learners are engaged in, designed to help learners learn how to perform the task.

And I agree with this, with a caveat. In the article, I’d said that it could  also be a small complete learning experience, period. My clarification on this is that those are unlikely, and the definition he cited was the most likely, and likely most valuable.

So, I’ve subsequently said  (and elaborated on the necessary steps):

What I really think microlearning could and should be is for spaced learning.

Here I’m succumbing to the hype, and trying to put a positive spin on microlearning. Spaced learning is a good thing, it’s just not microlearning. And microlearning really isn’t helping them perform the task in  the moment (which is a good thing too), but instead leveraging that moment to also extend their understanding.

No, I like the original definition, where we layer learning on top of a task, leveraging the context and requiring the minimal content to take a task and make it a learning opportunity. That, too, is a good thing. At least I think so. What do you think?