Hands-on learning and delegate-led sessions are much discussed in our industry when it comes to figuring how best to structure a meeting. We know that education from these types of sessions sticks more with attendees after the session is over, but what we don't know is why. Why is self-directed learning more beneficial to participants?
In an article published in Perspectives on Psychological Science, researchers Todd Gureckis and Douglas Markant of New York University address this gap in understanding by examining the issue of self-directed learning from a cognitive and a computational perspective.
Gureckis and Markant say that cognitive research offers many explanations that support the advantages of self-directed learning. For example, self-directed learning helps us optimize our educational experience, allowing us to focus effort on useful information that we don’t already possess and exposing us to information that we don’t have access to through passive observation. The active nature of self-directed learning also helps us in encoding information and retaining it over time.
But we’re not always optimal self-directed learners. The many cognitive biases and heuristics that we rely on to help us make decisions can also influence what information we pay attention to and, ultimately, learn.
Gureckis and Markant note that computational models commonly used in machine learning research can provide a framework for studying how people evaluate different sources of information and decide about the information they seek out and attend to. Work in machine learning can also help identify the benefits—and weaknesses—of independent exploration and the situations in which such exploration will confer the greatest benefit for learners.
Drawing together research from cognitive and computational perspectives will provide researchers with a better understanding of the processes that underlie self-directed learning and can help bridge the gap between basic cognitive research and applied educational research. Gureckis and Markant hope that this integration will help researchers to develop assistive training methods that can be used to tailor learning experiences that account for the specific demands of the situation and characteristics of the individual learner.
(Story materials via the Association for Psychological Science.)