8 citations · 11 across the 3 of their papers we have counts for
3 papers
stat.ML2024★ 1 cited
BanditCAT and AutoIRT: Machine Learning Approaches to Computerized Adaptive Testing and Item Calibration
James Sharpnack, Kevin Hao, Phoebe Mulcaire +4
In this paper, we present a complete framework for quickly calibrating and administering a robust large-scale computerized adaptive test (CAT) with a small number of responses. Cal…
cs.LG2024★ 2 cited
AutoIRT: Calibrating Item Response Theory Models with Automated Machine Learning
James Sharpnack, Phoebe Mulcaire, Klinton Bicknell +2
Item response theory (IRT) is a class of interpretable factor models that are widely used in computerized adaptive tests (CATs), such as language proficiency tests. Traditionally,…
cs.CL2021★ 8 cited
Local word statistics affect reading times independently of surprisal
Adam Goodkind, Klinton Bicknell
Surprisal theory has provided a unifying framework for understanding many phenomena in sentence processing (Hale, 2001; Levy, 2008a), positing that a word's conditional probability…