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Item Response Theory (IRT) is a ubiquitous model for understanding humansbased on their responses to questions, used in fields as diverse as education,medicine and psychology. Large modern datasets offer opportunities to capturemore nuances in human behavior, potentially improving test scoring and betterinforming public policy. Yet larger datasets pose a difficult speed / accuracychallenge to contemporary algorithms for fitting IRT models. We introduce avariational Bayesian inference algorithm for IRT, and show that it is fast andscaleable without sacrificing accuracy. Using this inference approach we thenextend classic IRT with expressive Bayesian models of responses. Applying thismethod to five large-scale item response datasets from cognitive science andeducation yields higher log likelihoods and improvements in imputing missingdata. The algorithm implementation is open-source, and easily usable.

Variational Item Response Theory: Fast, Accurate, and Expressive