Optimization of human learning via cognitive models
We are developing computer-aided instructional tools to help a person learn and remember more effectively. The tools leverage computational models of human memory. Just as physical processes such as the weather can be modeled via computer simulation, so can internal cognitive processes such as memory storage and forgetting. With an accurate model of memory, retention can be forecasted, much in the same way that weather is forecasted. In particular, our memory model (MCM) allows us to predict the rate of forgetting for some particular item given its study schedule--the set of times at which it was studied. We use this model for optimization by searching for the study schedule that achieves durable memories.
We are currently developing a web-based tutor, called the Colorado Optimized Language Tutor, that leverages MCM to determine the particular subset of facts that would most benefit from further study if the goal is long-term retention. The system is being used by advanced business Spanish courses at the University of Colorado, and we are currently performing an evaluation of its benefits. We are also applying the system for training individuals to learn and retain survival skills, using mobile-phone apps.
One cool thing about using cognitive models of memory is that we can predict for a student how much better they'll perform--say on a test at the end of the semester--if they increase their study time, or if they pace their study over a longer time period.
Rob Lindsey (Computer Science, Colorado)
Owen Lewis (Applied Math, Colorado; now at MIT)
Nicholas Cepeda (Psychology, York)
Hal Pashler (Psychology, UCSD)