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Item Selection With Collaborative Filtering in On-The-Fly Multistage Adaptive Testing.

Jiaying Xiao1, Okan Bulut2

  • 1College of Education, University of Washington, Seattle, WA, USA.

Applied Psychological Measurement
|October 20, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces collaborative filtering (CF) for item selection in multistage adaptive testing. User-based CF improved accuracy, while item-based CF enhanced item bank utilization compared to traditional methods.

Keywords:
collaborative filteringitem selectionmeasurement accuracymultistage adaptive testing

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Area of Science:

  • Educational Measurement
  • Computerized Adaptive Testing
  • Psychometrics

Background:

  • Item selection is crucial for adaptive testing accuracy and efficiency.
  • Traditional methods like maximum Fisher information have limitations in optimizing item exposure and bank utilization.
  • On-the-fly assembled multistage adaptive testing requires advanced item selection strategies.

Purpose of the Study:

  • To introduce and evaluate collaborative filtering (CF) methods for item selection in on-the-fly assembled multistage adaptive testing.
  • To compare the performance of user-based CF (UBCF) and item-based CF (IBCF) against the maximum Fisher information method.
  • To assess the impact of CF methods on ability estimation accuracy, item exposure, and item bank utilization.

Main Methods:

  • Simulated data under various test conditions (item bank size, test length, data sparsity).
  • Implementation of user-based collaborative filtering (UBCF) and item-based collaborative filtering (IBCF) for item selection.
  • Comparison with the maximum Fisher information method using metrics like measurement accuracy, item exposure rates, and item bank utilization.

Main Results:

  • The UBCF method demonstrated superior performance in measurement accuracy compared to traditional methods.
  • The IBCF method exhibited the most effective item bank utilization.
  • Both CF methods showed potential for improving adaptive testing efficiency and effectiveness.

Conclusions:

  • Collaborative filtering offers a promising alternative for item selection in multistage adaptive testing.
  • UBCF and IBCF provide distinct advantages in accuracy and resource management, respectively.
  • Further research is needed to explore the full potential and limitations of CF in adaptive testing frameworks.