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Constructing Shadow Tests in Variable-Length Adaptive Testing.

Qi Diao1, Hao Ren1

  • 1ACT, Inc., Monterey, CA, USA.

Applied Psychological Measurement
|September 22, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces an improved Shadow test approach to computerized adaptive testing (CAT) for variable test lengths. The enhanced Shadow CAT method proves superior for content balancing in educational measurement.

Keywords:
adaptive testingshadow test approachtermination rulevariable test length

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Area of Science:

  • Educational Measurement
  • Psychometrics
  • Computerized Adaptive Testing (CAT)

Background:

  • Content constraints are crucial for operational CAT programs.
  • The Shadow test approach to CAT (Shadow CAT) manages constraints by projecting item selection consequences.
  • Existing Shadow CAT models assume fixed test lengths.

Purpose of the Study:

  • To extend the Shadow CAT approach to handle variable test lengths.
  • To evaluate the performance of variable-length Shadow CAT against other content balancing methods.

Main Methods:

  • Developed an extension of the Shadow CAT algorithm for variable test lengths.
  • Implemented and tested the extended Shadow CAT in simulated operational CAT environments.
  • Compared its effectiveness with traditional content balancing techniques.

Main Results:

  • Demonstrated the feasibility of constructing Shadow CAT with variable test lengths.
  • Confirmed the approach's applicability in operational CAT settings.
  • Showed the superiority of the extended Shadow CAT over alternative methods.

Conclusions:

  • Variable-length Shadow CAT is a viable and effective approach for computerized adaptive testing.
  • This method offers improved content balancing in educational measurement.
  • The findings support the adoption of extended Shadow CAT in operational testing programs.