TITLE: The Role of Risk in Learning AUTHOR: Eugene Wallingford DATE: December 15, 2004 7:29 AM DESC: we can learn a lot about teaching by considering how people make decisions in the face of uncertainty. ----- BODY: Brian Marick's latest post discusses the latest developments in his ongoing exploration of agile methods and the philosophy of science. As always, there's plenty of food for thought there. In particular, he links to an article on behavioral economics that reminds us that people aren't rational in the classical sense.
Most people are more strongly affected in their decision-making by vivid examples than by abstract information, no matter how much more accurate the abstract information is.This reminds me of my previous post. Most often, a good example will help students grok an idea better than any abstractions I give them. The abstractions will work better for them after they have a strong foundation of experiences and examples.
For most people, the possibility of a loss greatly outweighs the chance of a win. "People really discriminate sharply between gaining and losing and they don't like losing." ....I think this principle accounts for why students will work from first principles to solve a problem rather than use a more abstract idea they aren't yet comfortable with. They perceive that the risk of failure is smaller by working from small ideas they understand than by working from a bigger idea they don't. I need to think more about risk when I teach.
For most people, first impressions play a remarkably strong role in shaping subsequent judgments.This reminds me not only of my most recent post but even more so of something Alan Kay said in his OOPSLA Educators Symposium keynote:
Kay reminded us that what students learn first will have a huge effect on what they think, on how they think about computing. He likened the effect to that of duck imprinting, the process in which ducklings latch onto whatever object interacts with them in the first two days of their lives -- even if the object is a human. Teaching university students as we do today, we imprint in them that computing is about arcane syntax, data types, tricky little algorithms, and endless hours spent in front of a text editor and compiler. It's a wonder that anyone wants to learn computing.The first three quotes above are drawn from the work of psychologist Daniel Kahneman, whose work I first encountered in one of my favorite grad school courses, Cognitive Psychology. Kahneman won a Nobel Prize in Economics for his work with colleague Amos Tversky that showed how humans reason in the face of uncertainty and risk. This work has tremendous implications for the field of artificial intelligence, where my first computing passions resided, but also for how we teach. Busy, busy, busy. -----