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    This study introduces a new stochastic selection method for Computer Adaptive Testing (CAT) to improve item exposure and test accuracy. The novel approach enhances ability estimation efficiency in assessments like the NIH WD-FAB.

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    Area of Science:

    • Psychometrics
    • Educational Measurement
    • Information Theory

    Background:

    • Computer Adaptive Testing (CAT) aims to accurately estimate individual abilities using item response theory (IRT) with fewer items.
    • A key challenge in CAT is ensuring diverse item exposure to avoid stereotypical testing sequences.

    Purpose of the Study:

    • To develop and evaluate a novel stochastic selection procedure for CAT based on Bayesian information theory.
    • To optimize item selection for improved ability estimation accuracy and equitable item exposure.

    Main Methods:

    • Formulated CAT optimization using Bayesian information theory and ability model discrepancy.
    • Developed a stochastic item selection procedure by sampling from a model-averaging ensemble.
    • Evaluated the new method against existing approaches using the NIH Work Disability Functional Assessment Battery (WD-FAB).

    Main Results:

    • The proposed stochastic selector demonstrated superior performance compared to pre-existing methods.
    • Achieved better control over item exposure rates, keeping them sufficiently far from zero.
    • Showcased enhanced test accuracy and efficiency in ability estimation.

    Conclusions:

    • The novel Bayesian information theory-driven stochastic selector offers significant improvements for CAT.
    • This method enhances both the fairness of item usage and the precision of ability estimates.
    • The approach is validated by its performance on the WD-FAB, suggesting broad applicability.