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Seung W Choi1, Sangdon Lim1, Luping Niu1

  • 1The University of Texas at Austin, Austin, TX, USA.

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PubMed
Summary
This summary is machine-generated.

Multiple Administrations Adaptive Testing (MAAT) enhances computerized adaptive testing (CAT) with periodic assessments. The MAAT approach uses vertically scaled item pools and multiple test phases for improved assessment quality.

Keywords:
CATR packagemultiple administrationsmultistage testingshadow-test approach

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Area of Science:

  • Educational Measurement
  • Psychometrics
  • Computerized Adaptive Testing

Background:

  • Traditional testing lacks flexibility for continuous assessment.
  • Computerized Adaptive Testing (CAT) offers adaptive item selection.
  • Existing CAT models may not fully support periodic, multi-stage assessments.

Purpose of the Study:

  • Introduce Multiple Administrations Adaptive Testing (MAAT) as an extension of the shadow-test approach.
  • Describe the capabilities of the maat package for advanced assessment frameworks.
  • Highlight MAAT's utility in assessments requiring periodic testing throughout the year.

Main Methods:

  • MAAT extends the shadow-test methodology for CAT.
  • The maat package incorporates vertically scaled item pools across grades.
  • It enables multiple phases within each test administration for item pool transitions.

Main Results:

  • MAAT facilitates the use of multiple, vertically scaled item pools.
  • The framework allows for dynamic transitioning between item pools during assessment.
  • This adaptability enhances the precision and quality of measurement.

Conclusions:

  • MAAT provides a robust framework for periodic, adaptive assessments.
  • The maat package supports sophisticated assessment designs with enhanced quality.
  • This approach is valuable for educational measurement requiring longitudinal data.