Item response theory is a modern framework in educational measurement for examining how students respond to individual test items. Unlike traditional scoring methods, it considers both item characteristics and the respondent’s underlying ability.
By modeling the relationship between ability and the probability of a correct response, item response theory supports more precise assessment, improved test design, and fairer comparisons across different examinations.
Item response theory is a framework in educational measurement for analyzing how learners respond to individual test questions. It connects a student’s response pattern with an unobserved ability, often called a latent trait.
Unlike classical test theory, which primarily evaluates total test scores, item response theory examines each item separately. Its models estimate characteristics such as difficulty, discrimination, and, in some cases, the probability of guessing correctly.
A basic IRT model calculates the probability that a learner with a particular ability level will answer an item correctly. More advanced models are used for polytomous responses, where answers may receive multiple score points rather than being simply correct or incorrect.
IRT supports applications such as adaptive testing, item banking, test equating, and detailed assessment design. Its results depend on assumptions, including an appropriate model, sufficient sample size, and adequate evidence that the measured construct is sufficiently consistent.