Variational Item Response Theory: Fast, Accurate, and Expressive
This work addresses the scalability problem for researchers and practitioners in fields like education and psychology using IRT, though it is incremental as it builds on existing IRT models.
The authors tackled the speed and accuracy challenge of fitting Item Response Theory (IRT) models on large datasets by introducing a variational Bayesian inference algorithm, which achieved higher log likelihoods and improved missing data imputation on five large-scale datasets from cognitive science and education.
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. Using this inference approach we then extend classic IRT with expressive Bayesian models of responses. Applying this method to five large-scale item response datasets from cognitive science and education yields higher log likelihoods and improvements in imputing missing data. The algorithm implementation is open-source, and easily usable.