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A New Method to Balance Measurement Accuracy and Attribute Coverage in Cognitive Diagnostic Computerized Adaptive

Xiaojian Sun1,2, Björn Andersson3, Tao Xin4

  • 1School of Mathematics and Statistics, Southwest University, Chongqing, China.

Applied Psychological Measurement
|December 6, 2021
PubMed
Summary
This summary is machine-generated.

A new cognitive diagnostic computerized adaptive testing (CD-CAT) index, the ratio of test length to the number of attributes (RTA), balances measurement accuracy and attribute coverage. RTA performs comparably to existing methods, favoring multi-attribute items.

Keywords:
attribute coveragecognitive diagnostic computerized adaptive testingmeasurement accuracythe ratio of test length to the number of attributes

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Area of Science:

  • Cognitive Psychology
  • Educational Measurement
  • Psychometrics

Background:

  • Cognitive diagnostic computerized adaptive testing (CD-CAT) is crucial for accurate cognitive diagnosis assessment.
  • Measurement accuracy in CD-CAT is influenced by item selection and attribute coverage.
  • Existing methods for balancing attribute coverage require evaluation.

Purpose of the Study:

  • Introduce and evaluate a new attribute coverage index: the ratio of test length to the number of attributes (RTA).
  • Compare the RTA index with existing methods like the original item selection method (ORI) and the attribute balance index (ABI).
  • Assess the impact of RTA on measurement accuracy and attribute coverage.

Main Methods:

  • Simulations were conducted to compare the RTA index against ORI and ABI.
  • The study analyzed measurement accuracy and attribute coverage under different item selection strategies.
  • Item pool characteristics, specifically the number of attributes measured by items, were considered.

Main Results:

  • The RTA method demonstrated comparable measurement accuracy to the ORI method across most item selection strategies.
  • RTA generally yielded higher measurement accuracy than ABI, except when using the mutual information item selection method.
  • RTA favors items measuring multiple attributes, unlike ABI which prefers single-attribute items.
  • RTA showed superior attribute coverage compared to ORI but was outperformed by ABI with longer tests.

Conclusions:

  • The RTA index offers a viable approach for CD-CAT, particularly with item pools containing many multi-attribute items.
  • RTA provides a balance between measurement accuracy and attribute coverage, making it a valuable tool in cognitive diagnosis.
  • The choice of attribute coverage index should consider test length and item pool characteristics.