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Computerized Adaptive Testing System of Functional Assessment of Stroke

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Item Selection Criteria With Practical Constraints in Cognitive Diagnostic Computerized Adaptive Testing.

Chuan-Ju Lin¹, Hua-Hua Chang²

¹National University of Tainan, Tainan, Taiwan.

Educational and Psychological Measurement

|March 27, 2019

Summary

This study introduces new methods for item selection in cognitive diagnostic computerized adaptive testing (CD-CAT). The proposed indices effectively balance attribute coverage, item pool usage, and estimation precision.

Keywords:

cognitive diagnostic computerized adaptive test cognitive diagnostic model item selection criteria the reduced version of RUM

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Area of Science:

Psychometrics
Educational Measurement
Computerized Adaptive Testing

Background:

Item selection in cognitive diagnostic computerized adaptive testing (CD-CAT) ideally requires a single index to manage precision, item exposure, and attribute balancing.
Current methods may not simultaneously optimize these critical aspects for effective diagnostic assessment.

Purpose of the Study:

To propose and evaluate novel item selection indices for CD-CAT.
To develop a criterion that balances attribute coverage, item pool usage, and estimation precision.

Main Methods:

Proposed the standardized weighted deviation global discrimination index (SWDGDI) for attribute-balanced item selection.
Formulated the constrained progressive index (CP_SWDGDI) by integrating SWDGDI into a progressive algorithm.
Conducted a simulation study to assess the performance of the proposed indices.

Main Results:

The SWDGDI method effectively balanced attribute coverage in CD-CAT.
The CP_SWDGDI method achieved simultaneous balancing of attribute coverage and item pool usage.
Both methods maintained acceptable estimation precision during the simulation study.
A lower number of attributes was found advantageous for CD-CAT applications.

Conclusions:

The developed SWDGDI and CP_SWDGDI indices offer improved item selection strategies for CD-CAT.
These indices provide a more integrated approach to managing key parameters in adaptive testing.
The findings highlight the importance of attribute balancing and efficient item pool utilization in diagnostic assessments.