Big Data vs. Ed Psychology: Work harder vs. work smarter
I met with a prospective PhD student recently, who told me that she’s interested in using big data to inform her design of computing education. She said that she disliked designing something, just crossing her fingers hoping it would work. She and the faculty she’s working with are trying to use big data to inform their design decisions.
That’s a fine approach, but it’s pretty work-intensive. You gather all this data, then you have to figure out what’s relevant, and what it means, and how it influences practice. It’s a very computer science-y way of solving the problem, but it’s rather brute force.
There is a richer data source with much more easily applicable design guidelines: educational psychology literature. Educational psychologists have been thinking about these issues for a long time. They know a lot of things.
We’re finding that we can inform a lot of our design decisions by simply reading the relevant education literature:
- Like our work on subgoal labeling,
- And on worked examples,
- And on lower-cognitive load learning,
- And on peer instruction.
I was recently reading a computer science paper in which the author said that we don’t know much about mathematics education, and that’s because we’ve never had enough data to come up with findings. But there were no references to mathematics education literature. We actually know a lot about mathematics education literature. Too often, I fear that we computer scientists want to invent it all ourselves, as if that was a better approach. Why not just talk to and read the work of really smart people who have devoted their lives to figuring out how to teach better?