Generating Retention and Transfer: Deeper Learning Science

If we want learning to have an impact, we need two things. First, we should ensure retention of the learning over time until it is needed. In addition, we should engineer transfer of the learning to all appropriate (and no inappropriate) situations. The results of research into learning provide apt guidance about what it takes to achieve these outcomes. What’s surprising, then, is how infrequently we see learning designs that have any likelihood of achieving such lasting results. It’s likely that these designs are a legacy of old beliefs about learning, but it’s time we did better. We need a quick dive into the science of learning. 

In this session we’ll cover the specifics that govern learning, explaining what we need and why. We’ll start by exploring legacy beliefs of how we learn as a jumping off point. From there, we’ll look at the strengthening mechanism that underlies learning. We’ll use this to generate a solid understanding of what leads to retention. From there, we’ll work through what is required to support transfer. We’ll look at how variation in situations can make transfer challenging, and what is required to successfully engineer transfer. We’ll also explore the conflicts that come from expectations versus the reality of learning. We’ll use concrete examples to illustrate the necessities, and explore the practical implications. You’ll leave with the necessary understanding to support instructional designs that will lead to persistent and appropriate learning. Come be part of the solution!

In this session, you will learn:

• What, specifically, learning actually involves
• When learning can be expected to be retained over time
• How to foster transfer and how to limit it
• The parameters that govern appropriate expectations for retention and transfer