For decades, neuroscience, learning science, experience design and even cybernetics have provided a steady drumbeat of insights for us in the field of learning and development to apply. We’ve taken these lessons as they’ve come, implementing them individually and, often, imperfectly. This has led to a wide segmentation of learning approaches – a massive (and ever-growing) pallet of options we sometimes present to our stakeholders as equally effective, from “bite-sized,” passive “learning nuggets” to fully immersive virtual reality simulations. The problem is, of course, that this approach is completely wrong – learning strategies are not equally effective.
We are in the midst of a significant learning junction. Half of the industry is driven by research into how people currently use the web. These people see the goal of learning and development as matching the modern user’s expectations of the web. These folks tend to focus on sustaining attention, increasing motivation and improving discovery. These goals make sense to pursue as a part of a larger strategy, but the problem is that the modern web was not designed to be an ideal learning platform, and much of what learners do in their non-learning time online has very little to do with learning. To put it more bluntly, observational data compiled from non-learning activities hardly makes a strong foundation for learning.
The other half of the industry is following the explosion of new technologies with rapt attention. This group sees the exciting things happening in the realm of augmented reality, virtual reality and real-time 3D and recognizes that something big is happening. This part of the industry is reaching out to anyone these capabilities, asking for a virtual reality solution to whatever problem they’re facing today. The problem here is probably obvious: Technology is never a solution; it’s only a vehicle for a solution. Few see these new digital reality technologies for what they are: a way of more accurately modeling our learners’ decisions.
The real problem is that we’re not anchoring our approaches to sound learning science. Virtually all credible, researched theories of learning agree on something like this: People learn best by doing. We may disagree on our definitions of “doing” and how it fits into a learning experience, but most agree that it involves a natural goal, a prediction of how that goal can be achieved, an action that attempts to realize the predication, a result that violates the prediction, an analysis of the expectation failure and the long-term encoding of modified predication into long-term memory. Neuroscience is pouring in to support this thesis from every angle. For instance, 2000 research by Wolfram Schultz and Anthony Dickinson showed that our brains have dedicated neuronal structures dedicated to measuring the delta between what we expect and what we achieve and that memories marked with a higher delta are prioritized for long-term retention in the amygdala.
This learning research tells us several key things. First, we know that to make effective learning simulations, we’re going to need to move from designing courses to designing authentic experiences. These experiences need to be engaging and dramatic, and they need to effectively model the decisions that learners will find themselves making in the real world. If we’re teaching workers on an oil rig how to properly situate piping, they should spend their learning time situating piping on an oil rig. Similarly, if we’re training accountants on how to properly account for international currency fluctuations, their learning time should be spent accounting for international currency fluctuations.
Second, we know that real-time 3D technologies are becoming commonplace and that they allow for the modeling of virtually any kind of digital reality that we can imagine. What’s more, we know that the price of these technologies is dropping dramatically. Real-time 3D is the technology that the new buzzwords are built on, so getting some real-time 3D simulations into the learning mix not only opens the door to screen-based learning-by-doing but also serves as the right first step on the path to offering solutions in augmented, virtual, mixed and other digital realities.
How do we transform from people who make training to people who design effective learning experiences?
Firstly, we need to understand that the waterfall models that have helped us succeed are holding us back. Methods like ADDIE don’t help us iterate and adapt to our users, so we need to migrate to the test-driven design methods offered up by agile and design thinking. Secondly, we need to become far more curious about what our learners are struggling with and much faster at determining what our learners really should be doing on the job. Thirdly, we need to work harder to understand which portions of our learners’ context are critical to a learning experience, and we need to be better at simulating that context. Finally, we need to look to professionals in the gaming industry to help us better understand and apply real-time 3D technologies to the task of simulation.
If we manage to make all of that happen, we’ll be well-positioned as an industry to deliver massive results to the business while creating experiences that our users love. If we fail to adapt, it’s unlikely that we’ll find places for ourselves in the future of learning. The web will always out-web us, and technologists will always have a better general application of the latest trend.