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On-the-Fly Assembled Multistage Adaptive Testing.

Yi Zheng1, Hua-Hua Chang2

  • 1Arizona State University, USA.

Applied Psychological Measurement
|June 9, 2018
PubMed
Summary
This summary is machine-generated.

A new adaptive testing design, on-the-fly assembled multistage adaptive testing (OMST), combines the strengths of computerized adaptive testing (CAT) and multistage testing (MST). Simulation results show OMST offers promising features and flexibility for future adaptive tests.

Keywords:
computerized adaptive testingmultistage testingtest assembly

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Area of Science:

  • Educational Measurement
  • Psychometrics
  • Computerized Adaptive Testing

Background:

  • Multistage testing (MST) is increasingly adopted in large-scale assessments, building on computerized adaptive testing (CAT).
  • MST addresses limitations of earlier CAT applications but remains one of several potential solutions.
  • Existing adaptive testing methods have limitations that newer designs aim to overcome.

Purpose of the Study:

  • Introduce a novel adaptive testing design: on-the-fly assembled multistage adaptive testing (OMST).
  • Evaluate OMST's performance by comparing it with traditional CAT and MST.
  • Explore OMST's potential to enhance flexibility and hybrid designs in digital assessments.

Main Methods:

  • Developed the on-the-fly assembled multistage adaptive testing (OMST) framework.
  • Conducted a simulation study to compare OMST against established CAT and MST methods.
  • Analyzed simulation data to assess the performance characteristics of each adaptive testing approach.

Main Results:

  • OMST demonstrated promising features in simulation studies.
  • The new design effectively combines benefits and mitigates limitations of both CAT and MST.
  • OMST offers unique advantages compared to existing CAT and MST approaches.

Conclusions:

  • OMST presents a viable and advantageous alternative for adaptive testing.
  • The OMST framework supports flexible and hybrid adaptive test designs for the future.
  • Further research can explore diverse applications of the OMST model based on specific testing needs.