论文标题
变分项目响应理论：快速，准确和表现力
Variational Item Response Theory: Fast, Accurate, and Expressive
论文作者
论文摘要
项目反应理论（IRT）是一个无处不在的模型，可以根据人类对问题的回答理解人类，该问题用于教育，医学和心理学等多样化的领域。大型现代数据集提供了机会，以捕捉人类行为的更多细微差别，可能改善考试评分并更好地告知公共政策。然而，较大的数据集对适合IRT模型的现代算法构成了困难的速度 /准确性挑战。我们引入了一种用于IRT的变异贝叶斯推理算法，并表明它在不牺牲准确性的情况下快速可伸缩。然后，使用这种推论方法，我们通过表现力的贝叶斯响应模型扩展了经典的IRT。将此方法应用于认知科学和教育的五个大规模项目响应数据集，从而在推出丢失的数据时会产生更高的日志可能性和改进。该算法实现是开源的，易于使用。
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.
