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Predicting item exposure parameters in computerized adaptive testing.

Shu-Ying Chen1, Shing-Hwang Doong

  • 1Department of Psychology, National Chung-Cheng University, Taiwan.

The British Journal of Mathematical and Statistical Psychology
|May 17, 2008
PubMed
Summary
This summary is machine-generated.

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Genetic programming (GP) discovered a formula linking item exposure and parameters in computerized adaptive tests. This formula accurately predicts exposure rates for new item pools, reducing the need for simulations.

Area of Science:

  • Psychometrics
  • Artificial Intelligence
  • Computerized Adaptive Testing

Background:

  • Computerized adaptive tests (CAT) require careful management of item exposure to prevent over-use.
  • Predicting item exposure parameters is crucial for designing effective CAT item pools.
  • Current methods for estimating item exposure parameters can be computationally intensive.

Purpose of the Study:

  • To develop a predictive formula for item exposure parameters in CAT using genetic programming (GP).
  • To enable accurate prediction of item exposure rates for new item pools without iterative simulations.
  • To establish a knowledge-based approach for determining item exposure parameters.

Main Methods:

  • Utilized genetic programming (GP), a biologically inspired artificial intelligence technique.

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  • Developed a formula to describe the relationship between item exposure parameters and item parameters.
  • Validated the GP-derived formula against established procedures like Sympson and Hetter (1985) and the Stocking and Lewis (1998) model.

    • Main Results:

    • GP successfully identified a formula linking item exposure and item parameters.
    • Predicted item exposure parameters closely matched those from the Sympson and Hetter procedure.
    • The GP approach effectively controlled item exposure rates across different CAT procedures.

    Conclusions:

    • Genetic programming offers an efficient and effective method for deriving item exposure parameters in CAT.
    • The discovered formula provides a valuable tool for predicting and managing item exposure in new item pools.
    • This AI-driven approach enhances the design and implementation of computerized adaptive tests.