There was a terrific article in the New York Times recently by Anna North. It starts out describing persistent myths about “learning styles” in instruction, and it picks up steam when it generalizes to explore why learning research is not having an impact on students. Although she never uses the term, the article makes it clear we suffer because we have almost no “learning engineering” professionals in our industry – and don’t realize what we’re missing.
Our problem is we keep wishing that learning works as simply as we hope, rather than using evidence to guide our actions. Imagine if we were doing this in medicine: staring at the ceiling, no one would ever design an immune system that attacked itself, so clearly that can’t be how our own immune system works. Umm, but, wait, as we’ve dug in and looked closely at diseases, we’ve discovered our immune system is the source of all kinds of problems, including things like arthritides and lupus and more. So, what kind of physician would you like to be treated by, one who is treating the immune system he imagines you have, or one who treats the immune system that evidence says you have? I know where I’m going.
There was a time when we did this in all areas of human endeavor. We used our best armchair thinking to decide how the world worked and then applied this in our daily lives. Think about the Aristotelian (very appealing) view that “things stay in motion by continuing to be pushed,” which matches common experience, but has been superseded (admittedly only since the 17th century) by the more accurate and ultimately useful view that “things in motion tend to stay in motion.” Or think about views of chemistry, grounded in elemental notions of fire, earth, air, water, with a more modern innovation involving phlogiston theory. I believe there’s been recent (only since the 19th century) progress in that area, too – something about molecules? Atoms? I’m not sure – you can’t see them, so it’s all very suspicious to me. And don’t get me started on “viruses,” “bacteria,” “genes,” and all the rest of that modern biological claptrap. . .
We’ve clearly built out a whole range of useful understanding of the natural world compared with our armchair starting points. In the same way, there’s a rich body of evidence about how learning actually works, and pretty good syntheses now available: check out Clark and Mayer’s E-Learning and the Science of Instruction, or Willingham’s Why Don’t Students Like School (or a little volume by Hess and Saxberg if you’ve really got a sleepless evening). We don’t have to stare at the sky and imagine how learning works – we have more and more direct evidence of how it really works, and how it doesn’t. And, unfortunately, like our understanding of the immune system, sometimes we have to face inconvenient truths, like the idea that minds don’t easily bucket into different learning styles.
As the article by North points out, though, in spite of evidence about how learning works now being available, it seems to not be getting into practice. A teacher, Jose Vilson, is quoted as saying,
“It’s so hard to keep up with the research. . . that a lot of us end up not believing the research.”
Yikes!
What’s missing, in my view, is what is now present in so many other fields that have led to practical benefits at scale: an engineering version of learning science, “learning engineering,” which can take up Mr. Vilson’s challenge of mastering the research for application at scale.
Think about chemists vs. chemical engineers. If you are going to design your next biotech brewing facility, you really want a chemical engineer on board: someone who deeply understands modern chemistry (not talking phlogiston theory here, but the truly new stuff), but is also conversant with health regulations, safety regulations, costs of building, and thinks in an integrated way about designing things for scale. He or she looks up at a pipe in the ceiling and thinks, “Is that pipe built well enough to hold the volume we need?,” and knows a bit of mechanical engineering might be needed, too. Chemists don’t usually think this way – scale is not their focus. You want a chemical engineer, to apply chemistry successfully at scale.
So where is the cadre of professionals trained in modern learning science, but also in areas of regulatory compliance, project management, production methos, usability, data security, big-data analytics, etc.? Where are our “learning engineers?”
As the article by Ms. North points out, schools of education seem not to be doing this yet. They don’t have the same relationship to learning science that a school of medicine has to biological science, or a school of chemical engineering has to chemistry. We don’t yet have a ready source of “learning engineers.”
Indeed, once you apply learning science itself to the problem of creating “learning engineers,” you see the challenge. Learning science shows that to build expertise, you have to provide enough practice and feedback to fuel the development of long-term memory components and to support the complex problem-solving dance between long-term memory and short-term memory, for difficult real-world problems. For teaching and instructional design, real “learning engineering,” this means creating learning environments that allow practice in applying learning science to what professionals will be doing in the field, with good, learning-science-based feedback on how they’re doing. They can’t simply listen to the learning science, or read it – they have to actively work to apply it and get feedback on how they’re doing that they have to react to, just like all engineers. It’s the only way to ensure they really can apply learning scale at scale in the real world – they need to do it during training, first. There are not many places doing this now.
Within Kaplan, we’ve set up training programs for our instructional designers to train how to apply learning science at scale, and for our most senior designers, have provided structured coached opportunities to apply this to specific learning tasks. It’s still hard when those professionals go back to their “day jobs” to apply these principles. (We’re starting to evaluate hundreds of our learning environments against a checklist of learning built from learning science principles to help.) Yet we see this as essential, if we’re going to systematically lift learning performance for our students, and subsequently for all those who rely on our learners’ expertise in the future.
There’s no alternative for the long haul. For large groups of people, whether nation-states, organizations, demographic groups, etc., success over the next decades will depend critically on the ability to shift minds to working at their best, vs. merely providing labor. Figuring out how to do “learning engineering” effectively will be a critical differentiator for these groups – and will be a major value-contributor to the world.
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