Perception and Cognition: Applications to Learning Technology
In this work we use principles of perception and cognition and results of our research and others to develop and optimize computer-based learning technology. This work focuses on two innovation areas: perceptual learning methods and adaptive learning technology.
Perceptual learning methods lead learners to extract invariant structure from variable instances and transfer their knowledge to the classification of novel instances and structures. This work addresses dimensions of learning that are crucial to expertise in any domain but are poorly addressed by both conventional instructional methods and by existing learning technology.
Our patented adaptive learning methods use the individual learner's speed and accuracy to determine the recurrence of items or categories in learning. Our algorithms produce sequencing of short, interactive trials that implements a number of important laws of learning and tend to optimize the efficiency of learning of whole sets of items or categories.
This work is ideal for students hoping to obtain strong training in cognitive science research but also wishing to apply their training to real-world learning contexts. Opportunities for support under a US Department of Education grant, and opportunities for involvement in a growing learning technology company are available.
Examples of Learning Modules
- Philip J. Kellman
- Everett Mettler
- Carolyn Bufford
- Tim Burke
- Christine Massey (Penn)
- Sally Krasne (UCLA)
- Erik Dutson (UCLA)
- Cheryl Hein (UCLA)