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Updated: Sep 24, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Reducing the Misclassification Costs of Cognitive Diagnosis Computerized Adaptive Testing: Item Selection With

Chia-Ling Hsu^1,2, Wen-Chung Wang¹

¹The Education University of Hong Kong, New Territories, China.

Applied Psychological Measurement

|May 9, 2022

Summary

This summary is machine-generated.

Minimum Expected Risk (MER) in cognitive diagnosis computerized adaptive testing (CD-CAT) improves classification accuracy for specific attribute profiles. MER enhances measurement efficiency, especially for identifying examinees with no or full mastery.

Keywords:

bayesian decision theory cognitive diagnosis computerized adaptive testing item selection minimum expected cost

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Last Updated: Sep 24, 2025

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Area of Science:

Educational Measurement
Psychometrics
Cognitive Science

Background:

Cognitive diagnosis computerized adaptive testing (CD-CAT) identifies examinee strengths/weaknesses on latent attributes.
Misclassification costs vary, necessitating efficient item selection in CD-CAT.

Purpose of the Study:

Propose and evaluate Minimum Expected Risk (MER) for item selection in CD-CAT.
Incorporate varying misclassification costs to enhance measurement efficiency.

Main Methods:

Developed MER based on Bayesian decision theory.
Conducted simulations comparing MER with other item selection criteria (MPWKL, PWCDI, SHE, PWACDI).
Evaluated classification accuracy and test efficiency for different attribute profiles.

Main Results:

MER-U0 and MER-U1 demonstrated superior classification accuracy and efficiency for identifying no/full mastery profiles, especially with short tests or low-quality item banks.
MER, MPWKL, PWCDI, and SHE showed similar performance for other profiles, outperforming PWACDI.
MER, MER-U0, MER-U1, and PWACDI showed more effective item bank utilization.

Conclusions:

MER is a feasible approach for CD-CAT item selection.
MER enhances accuracy for specific attribute profiles, addressing diverse user needs.
MER improves measurement efficiency in CD-CAT, particularly in challenging conditions.