A new ℓ1-regularized mixture asymmetric IRT framework jointly recovers latent classes for impact and selects DIF items without group labels or anchors, as shown in simulations and two educational datasets.
Marginal maximum likelihood estimation of item parameters: Application of an
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A persona-induced latent variable model with LLM response distributions enables closed-form Bayesian updates and finite-mixture predictions for scalable adaptive querying of user-dependent quantities.
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Latent Impact and Differential Item Functioning Analysis for Asymmetric IRT Models
A new ℓ1-regularized mixture asymmetric IRT framework jointly recovers latent classes for impact and selects DIF items without group labels or anchors, as shown in simulations and two educational datasets.