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Comparing computer adaptive testing stopping rules under the generalized partial-credit model.

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Summary
This summary is machine-generated.

The standard error stopping rule offers the best balance for computer adaptive testing (CAT), providing precise measurement with fewer items. This rule, especially with a maximum item limit, ensures efficient and accurate examinee assessment.

Keywords:
CATComputer adaptive testingStopping rulesTermination criteria

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Computerized Adaptive Testing

Background:

  • Computer adaptive testing (CAT) relies on effective stopping rules to ensure consistent examinee assessment.
  • Previous research has not compared various stopping rules under the generalized partial-credit model.
  • Key stopping rules include standard error (SE), minimum information (MI), and change in theta (CT).

Purpose of the Study:

  • To compare the performance of three variable-length stopping rules (SE, MI, CT) in CAT.
  • To evaluate stopping rules in isolation and combination with item count constraints (min/max items).
  • To assess stopping rules under lenient and stringent termination criteria within a simulation study.

Main Methods:

  • A simulation study was conducted to compare the performance of SE, MI, and CT stopping rules.
  • Rules were tested individually and with minimum/maximum item requirements, alongside a fixed-length rule.
  • Two termination criteria (lenient and stringent) were applied, incorporating content-balancing and exposure controls.

Main Results:

  • The minimum information (MI) stopping rule resulted in biased theta estimates and inconsistent measurement quality.
  • The absolute change in theta (CT) stopping rule performed well with a lenient criterion and minimum test length.
  • The standard error (SE) stopping rule demonstrated the best balance of precision and efficiency, requiring fewer items.

Conclusions:

  • The standard error (SE) stopping rule is recommended for CAT due to its superior balance of measurement precision and operational efficiency.
  • Pairing the SE stopping rule with a maximum-number-of-items constraint further enhances its effectiveness for accurate theta estimation.
  • This study provides valuable insights into optimizing stopping rules for robust and efficient computer adaptive testing.