Deep Computerized Adaptive Testing
Jiguang Li, Robert Gibbons, Veronika Rockova

TL;DR
This paper introduces a novel Bayesian multivariate IRT-based adaptive testing system that accelerates item selection and employs reinforcement learning to optimize test strategies, addressing complex real-world data structures.
Contribution
It develops a multivariate latent trait CAT framework with faster sampling and a deep Q-learning approach for optimal item selection, advancing adaptive testing methodologies.
Findings
Accelerates item selection by direct sampling from posterior distributions.
Demonstrates improved efficiency and effectiveness in simulation and real-data studies.
Highlights the potential of reinforcement learning in adaptive testing.
Abstract
Computerized adaptive tests (CATs) play a crucial role in educational assessment and diagnostic screening in behavioral health. Unlike traditional linear tests that administer a fixed set of pre-assembled items, CATs adaptively tailor the test to an examinee's latent trait level by selecting a smaller subset of items based on their previous responses. Existing CAT frameworks predominantly rely on item response theory (IRT) models with a single latent variable, a choice driven by both conceptual simplicity and computational feasibility. However, many real-world item response datasets exhibit complex, multi-factor structures, limiting the applicability of CATs in broader settings. In this work, we develop a novel CAT system that incorporates multivariate latent traits, building on recent advances in Bayesian sparse multivariate IRT. Our approach leverages direct sampling from the latent…
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