论文标题
GPIRT：项目响应理论的高斯流程模型
GPIRT: A Gaussian Process Model for Item Response Theory
论文作者
论文摘要
项目响应理论（IRT）模型的目标是提供来自二进制观察到的指标的潜在性状的估计，同时学习从潜在性状到观察到的响应的项目响应函数（IRF）。但是，在许多情况下，观察到的行为可能会显着偏离传统IRT模型的参数假设。非参数IRT模型通过放松IRF形式的假设来克服这些挑战，但是标准工具无法同时估计灵活的IRF并为受访者恢复能力估计。我们提出了一个贝叶斯非参数模型，该模型通过将高斯工艺先验放置在定义IRF的潜在函数上来解决此问题。这使我们能够同时放松有关IRF形状的假设，同时保留估计潜在特征的能力。反过来，这使我们可以轻松地将模型扩展到其他任务，例如主动学习。因此，GPIRT为IRT文献中的几个长期问题提供了简单，直观的解决方案。
The goal of item response theoretic (IRT) models is to provide estimates of latent traits from binary observed indicators and at the same time to learn the item response functions (IRFs) that map from latent trait to observed response. However, in many cases observed behavior can deviate significantly from the parametric assumptions of traditional IRT models. Nonparametric IRT models overcome these challenges by relaxing assumptions about the form of the IRFs, but standard tools are unable to simultaneously estimate flexible IRFs and recover ability estimates for respondents. We propose a Bayesian nonparametric model that solves this problem by placing Gaussian process priors on the latent functions defining the IRFs. This allows us to simultaneously relax assumptions about the shape of the IRFs while preserving the ability to estimate latent traits. This in turn allows us to easily extend the model to further tasks such as active learning. GPIRT therefore provides a simple and intuitive solution to several longstanding problems in the IRT literature.
