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Optimal Reassembly of Shadow Tests in CAT.

Seung W Choi1, Karin T Moellering2, Jie Li2

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Strategies for reassembling shadow tests in computer-adaptive testing reduced reassembly frequency without impacting measurement accuracy. This improves the practicality of large-scale online testing programs.

Keywords:
CAToptimal test designshadow-test approach

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Area of Science:

  • Educational Measurement
  • Computer Science

Background:

  • Large-scale online testing programs face concurrency challenges impacting computer-adaptive test (CAT) delivery.
  • The shadow-test approach, while effective, can be computationally intensive due to frequent test reassemblies.

Purpose of the Study:

  • To reduce the number of shadow test reassemblies in operational CAT programs.
  • To maintain measurement quality while increasing concurrency for large-scale testing.

Main Methods:

  • Explored strategies for reducing shadow test reassemblies, including fixed intervals and minimum change in ability estimates.
  • Investigated a hybrid approach combining fixed intervals and ability estimate changes.
  • Evaluated strategies for their impact on reassembly frequency and measurement accuracy.

Main Results:

  • Substantial reductions in shadow test reassemblies were achieved across tested strategies.
  • No degradation in measurement accuracy was observed with the implemented strategies.
  • Strategies prevented unnecessary reassemblies caused by early-stage estimation noise.

Conclusions:

  • The explored strategies enhance the efficiency and practicality of the shadow-test approach for large-scale CAT.
  • Minimizing computational load associated with mixed-integer programming in shadow testing is feasible.
  • Optimized reassembly strategies improve the robustness of online adaptive testing programs.