AI RESEARCH
Prediction of Item Difficulty for Reading Comprehension Items by Creation of Annotated Item Repository
arXiv CS.CL
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ArXi:2502.20663v2 Announce Type: replace Prediction of item difficulty based on its text content is of substantial interest. In this paper, we focus on the related problem of recovering IRT-based difficulty when the data originally reported item p-value (percent correct responses). We model this item difficulty using a repository of reading passages and student data from US standardized tests from New York and Texas for grades 3-8 spanning the years 2018-23.