We Are Learning Engineers
I have been heartened by how our society has pivoted to digital for work and learning in the face of COVID-19. Those of us in Learning and Development (L&D) have been thinking for a generation about how to properly leverage technology to support learning at and for work. Lots of ideas around new tools such as artificial intelligence (AI), simulations, games, and virtual reality (VR) had surfaced prior to the pandemic, and there are lots of interesting experiments going on. We also have had some interesting discussions as a community about the affordances learning technologies give us—ideas such as just-in-time performance support, personalized learning, social learning, curated learning, and chunked and linked learning. These are all intriguing ideas—some of which are supported by empirical findings.
But effective learning at and for work is not just a function of good science. L&D professionals are actually more akin to learning engineers. They need to put the science to work in practical ways that enhance performance and retention of people at their companies. This has led many to be skeptical about the importance of things such as academic research and evidence. There is some logic to that sentiment, but it is incomplete. You can think about it like a parabola. On the one hand, we have the “shoot-before-we-aim, just-do-it” mentality that our least informed colleagues endorse. On the other hand are the proverbial deep debates about trivial things that characterize the worst of the academy. L&D professionals need to be at neither end but rather at the apex of the parabola by finding a middle ground between quick action and deep understanding. I have a good friend who teaches at IESE who uses a somewhat cleverer analogy employing Shakespeare on the appropriate role of evidence for the professional. At one end is Othello, who rashly acts with huge negative consequences on rumor and heresy. At the other is Hamlet, who has overwhelming evidence on who murdered his father and spends the whole play mulling rather than acting.
What Do We Mean by Learning Technologies?
The point of this is that our profession is a thinking person’s game. To that end, I want to introduce two notions that need to be woven better into our thinking around how to best incorporate technology into our solutions during and after this pandemic. Every organization only faces two problems:
- What to do?
- How to get everyone to do it?
We can characterize both of these as learning problems (which is why we matter so much as a profession), but let’s focus on the latter at the moment, which is less a strategy question and more an execution problem. Clearly, during this crisis, “learning technologies” used wisely will be the fulcrum. But this begs the question of what we mean by “learning technologies.” I am going to suggest that the two most important learning technologies ever created are the two we use least when coming up with plans.
I am trained as an economist, and we think of technology as anything that can expand the production possibility frontier. In other words, something that humans invent that makes us able to do something better, cheaper, or faster (or some combination thereof). By that definition, the most important learning technology ever invented is language; we couldn’t learn without it. The second most important learning technology is the one that most contributed to the geometric progression of all industrialization—pedagogy. The thoughtful process of designing learning experiences with the purpose, content, and learner in mind. This is the technology that differentiates singlehandedly between good and bad learning programs.
So, fellow L&D professionals, remember that this is a thinking person’s game, and when you are thinking about leveraging tech in a crisis, think ecumenically about what you mean about technology. It will greatly increase your likelihood of efficacy.
Applying Engineering Practices
To make this practical and to distinguish it from instructional design, we note that, like other engineers, we need to pay as much attention to process as outcome. To do this, we can apply the engineering practices to our domain:
- We ask questions and define problems.
- We plan and carry out investigations.
- We analyze and interpret data.
- We use computational thinking.
- We engage in argument for evidence.
How does this practically manifest itself? There are a few easy steps to take. First, design all learning as experiments. Plan on learning something from what you implement. Second, don’t accept either your subject matter experts (SMEs) or vendors prima facie—ask critical questions. Don’t assume that because someone is identified as a SME that he or she is, in fact, an expert in the content (let alone the application of that content by another). And don’t assume that because a vendor says a tool works that it will. Think of Bloom’s Taxonomy and how different tools would address different goals—knowing vs. doing or understanding. Become expert in your data and your processes as much as your learner, and your company will be better off for it.
Doug Lynch is faculty at USC and Entangled Senior Advisor. He created the PennCLO Doctoral Program.