Making Small Learnings Work
The label “microlearning” refuses to die. https://www.litmos.com/blog/articles/microlearning-boosts-productivity
You’d think, like most hype-driven buzzwords, it would have faded away. Its very persistence suggests, instead, that there’s some legitimate appeal. And, given that I’ve been an advocate of it, I should agree. So, why am I resistant? And, more importantly, what’s involved in making small learnings work?
I get the appeal, really I do. The notion of small chunks of content isn’t just driven by the advent of mobile devices. There’s a tight coupling with how our brains work. It’s just that there’re problems with the “microlearning” label. And, frankly, labels matter.
Why Labels Matter in Learning
For example, mobile learning, or mLearning, was eagerly taken up. Everyone had mobile devices, and learning is important, so together there should be something there. And the label itself implies “learning” (read: courses) on a mobile device. The natural implication is for courses on a phone.
Yet, there’s a fundamental flaw. The pattern of mobile use, as documented in research by Palm after their Pilot became a success, was many uses a day for short periods of time. This was in contrast to laptops, which were instead being used for few times a day but for much longer periods of time.
And, most courses, organizationally at least, typically run an hour or so. In fact, the hours a course must take are often mandated! So, the notion of a course on a phone was the wrong implication. And, in fact, if you look at the broad spread of mobile uses (quick thought experiment: how do you use your phone to make you more effective?), they’re largely not about consuming quantities of content. I’ll suggest mobile is about many different quick things like calendars and search and navigation and photos and communication. It’s not about long-form content except perhaps movies on mass transportation or podcasts while driving or exercising.
The same thing is happening with micro-learning. The use of the term itself implies achieving a learning objective. Yet, in contrast, most of the references are to accessing “how to” videos. Or about reactivating learning over time (which is a requirement of good learning design). And both are great, make no mistake. I’ve previously trumpeted the value of both performance support and spaced learning. Yet there are, clearly, already labels for each.
The reason I care is that you design performance support and spaced formal learning very differently! They meet different cognitive needs, and require separate approaches. If you don’t distinguish between them, how can we be certain you know the specifics? And, yes, I can be a bit pedantic, but I’ve seen too many claims about “cutting up a course into chunks” (which is not recommended).
Leaving aside the performance support topic, it is worth thinking about making small learnings work. And to do that, you need to dig into how you make learning work. If you get that right, you’re going to have a better handle on the rest.
Making Learning Work
Learning happens over time. It turns out that learning is really about strengthening the connections between neurons. It happens by triggering them in conjunction with one another. As the saying goes: “the neurons that fire together, wire together.” While it happens at the neuronal level, the firing for formal learning happens by activating patterns triggered by semantic elements. That is, words or images, not triggering individual neurons.
In fact, we build bigger patterns, or chunks, by activating things together enough that they become connected below consciousness. What we’ve chunked is what we’ve learned, as well as the ability to apply those chunks to make decisions. Practice automates the ability to recall things.
Now it turns out that the mechanism that does the strengthening of the connections gets tired. It can only do so much strengthening before you pretty much literally need sleep before you can strengthen some more. In short, you’ve got to practice a bit one day, and then come back to it another. Again and again.
That’s called “spaced learning,” and it’s a robust phenomenon. You can get people to perform to a certain level at the end of a day, but much will be gone the next one. Think about anything you can do with any reasonable degree of excellence; it’s a safe bet that you’ve done it much over time.
Thus, small chunks distributed over time makes sense. However, nuances matter. Getting them wrong means your learning is not going to work.
Making Small Learnings Work
The point of reactivating over time means you need to do it again and again and again. And, it turns out, that typically it needs to happen over longer gaps each time. That’s what builds up retention, so that the learning is still around when you need it. The answer to how much spacing and for how long depends on how complex the task is, how frequently you’re likely to need it, and how important it is to get it right. Think of airplane trouble; they train for those again and again and again and hope never to have to use it!
And, most learning goals are complex. As a consequence, you need to coordinate a number of steps or elements. And, learners need to master the bits that have to be automated and then pull them together to apply to the types of problems your learners will face. Van Merrienboer’s Four-Component Instructional Design is very much oriented just towards this complexity.
Thus, you likely have a suite of thing to master. And, it turns out, they’re retained better if they’re mixed in together (A – B – A – B – C – A … rather than A – A – A – B – B – B – C …). A further element is practicing the right thing next. Ericsson, in Peak, emphasizes the important of “deliberate” practice. It turns out just repeating the same thing isn’t the trick. It’s about a steady sequence of increasing complexity.
Note that you’re still requiring all the elements: models, examples, and sufficient practice. So you’re actually needing to sequence those elements as well! And, of course, you’re going to want to make it motivating, finding the emotional hooks.
And, when you pull all this together – choosing the right thing at the right time and in the right sequence – you end up with a non-trivial exercise. It’s doable, and worthwhile, but not achievable with a simplistic approach (e.g., just breaking a course up into small chunks isn’t it)!
There is the opportunity for two variations on this. One is a small learning objective. I reckon that’s rarer than proponents would suggest, but it’s viable. That’s different, by the way, than just providing support in the moment for a small task. For example, if I need help creating a pivot table, it’s performance support. Unless I see another pivot table soon, it’s going to be gone. And that’s ok, but it’s not learning.
The other is much more sophisticated. Here it’s the intersection of our learner model and our context model. That is, we know what the learner knows and what they’re doing. Then if they’re doing a task that we know they’re still new to, we can provide learning, and once they’ve done it a few times we can stop providing support. Or less. This, again, is very, very different than typical design and treads across the boundaries into learning engineering.
Making small learnings work is actually deeper in many ways than just designing a course. Of course, done right, it’s going to have a higher likelihood of success. Done wrong, it’ll just be another ineffective eLearning course. I encourage you to think of small learnings, but also implore you to do it right.
