The purpose of this project is to increase our understanding of how to optimize the features of learning technology systems, which have the potential to be applied widely across domains, settings, and age groups. The team of researchers from the University of California, Los Angeles, and the University of Pennsylvania focuses on learning technology that integrates (1) principles of perceptual learning that accelerate learners' abilities to recognize key structures and relations in science and math domains, and (2) adaptive learning algorithms that use a constant stream of performance data to adapt the learning process to each individual. The collaborating partners include two K-12 schools serving diverse populations and two community colleges.
The researchers will investigate the role of response time data as a novel input into both spacing and the setting of learning criteria in adaptive and perceptual learning systems. Specific hypotheses will be tested in a series of randomized controlled experiments. Students will complete pretests, posttests, and delayed posttests to evaluate gains and long-term durability of learning. The researchers aim (1) to test adaptive sequencing that utilizes learner response times (along with accuracy) to guide spacing in learning; (2) to improve learning systems based on understanding the role of response time in setting learning criteria; (3) to develop integrative adaptive and perceptual learning systems that incorporate best practices for the use of combined speed and accuracy data; and (4) to demonstrate the feasibility and effectiveness of such systems across STEM learning domains and age groups by testing learning modules for elementary mathematics and high school and college chemistry.
This project is a major step in the merging of principles of cognitive science with learning technology in service of STEM learning.
