Variational Item Response Theory: Fast, Accurate, and Expressive

Understand

Item Response Theory (IRT) is a ubiquitous model for understanding humans based on their responses to questions, used in fields as diverse as education, medicine and psychology.

• Large modern datasets offer opportunities to capture more nuances in human behavior, potentially improving test scoring and better informing public policy.
• Yet larger datasets pose a difficult speed / accuracy challenge to contemporary algorithms for fitting IRT models.
• We introduce a variational Bayesian inference algorithm for IRT, and show that it is fast and scaleable without sacrificing accuracy.