Memory and Practice Scheduling
How should practice be spaced, revisited, and sequenced so that performance later is stronger and more durable?
Themes
Core lines of inquiry
Knowledge Tracing and Prediction
How can learner histories be translated into interpretable models that predict performance and support instructional decisions?
Learning With Connected Knowledge
How should adaptive systems handle material with dependencies across concepts, propositions, and mental models instead of isolated facts alone?
Approach
From formal theory to instructional software
Model the learner
We develop models that use prior practice history, timing, success, failure, and content structure to estimate how learning changes over time.
Use models to decide
We use those models to choose what a learner should practice next, when review should happen, and how practice should be organized.
Test in realistic settings
We evaluate whether model-based decisions improve learning in applied educational contexts, not just in abstract simulations.
Build tools others can use
Software and documentation such as MoFaCTS and LKT help turn the lab's methods into reusable resources for research and application.
Research Questions
Representative questions
When is it worth practicing one item instead of another?
We study how to compare competing practice opportunities so educational systems can allocate limited time more effectively.
What kinds of learner data lead to useful, interpretable models?
We examine feature design, knowledge tracing structure, and the tradeoff between predictive accuracy and explanatory clarity.
How should systems adapt when knowledge is interconnected?
Many domains involve dependencies among concepts. The lab studies how those dependencies should alter practice decisions and model structure.
Working principle
A learning system should be able to explain why a practice decision is appropriate for this learner, at this moment, for this content.That requirement links theory development to software design throughout the lab's work.