Core Approach

Project Focused Prof-and-Peer Mentoring

Prof peer
To promote cross-disciplinary learning without requiring extensive additional training, trainees work in small cross-department groups to tackle a specific basic or applied research question. Groups include new LUCID trainees, senior graduate mentors, and at least two faculty. Trainees acquire new expertise outside their domain by working with other students on specific problems under faculty guidance.
The team aboveĀ is working to develop new approaches to arithmetic education, by connecting cognitive models of human learning and memory and machine-learning approaches to search. Given a model of the learner, efficient search algorithms are used to discover a practice regimen that optimizes speed of learning and breadth of transfer. Effectiveness of the optimal practice scheme is then assessed empirically in the lab and classroom, providing both a test of the cognitive model and new strategies for teaching.