Scalable Learning of Item Response Theory Models

arXiv:2403.00680v1 Announce Type: new
Abstract: Item Response Theory (IRT) models aim to assess latent abilities of $n$ examinees along with latent difficulty characteristics of $m$ test items from categorical data that indicates the quality of their corresponding answers. Classical psychometric assessments are based on a relatively small number of examinees and items, say a class of $200$ students solving an exam comprising $10$ problems. More recent global large scale assessments such as PISA, or internet studies, may lead to significantly …

abstract aim arxiv categorical cs.ds cs.lg data quality scalable small stat.ml test theory type