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The highly adaptive testing design (HAT) maximizes item selection adaptivity for assessments like PISA. This method uses computer algorithms to balance adaptivity with testing constraints, improving the student experience.

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Area of Science:

  • Educational measurement
  • Computerized adaptive testing
  • Psychometrics

Background:

  • The Programme for International Student Assessment (PISA) requires efficient and adaptive testing methods.
  • Existing computerized adaptive testing (CAT) methods have limitations when dealing with complex constraints and nested item structures.

Purpose of the Study:

  • To describe the methodological and statistical foundations of the Highly Adaptive Testing (HAT) design.
  • To present a novel algorithm for maximizing adaptivity in item selection within assessment constraints.

Main Methods:

  • Developed a HAT algorithm using R programming language.
  • Integrated established CAT methods to address nested items, dimensional correlations, constraint management, and item position effects.
  • Focused on enhancing the student test-taking experience.

Main Results:

  • The HAT design allows for maximum adaptivity in item selection.
  • The algorithm effectively manages PISA's specific constraints.
  • The methodology improves upon standard CAT approaches by incorporating multiple factors for optimal test construction.

Conclusions:

  • The HAT design provides a robust framework for adaptive testing in large-scale assessments.
  • The provided R code facilitates the implementation and adaptation of HAT for future research and assessments.
  • This approach can inspire the development of new adaptive testing designs that balance adaptivity with practical constraints.