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Multistage Testing for Cognitive Diagnosis Based on Skill-Space Partitioning
1Korea Institute for Curriculum and Evaluation, Jincheon-gun, Chungcheongbuk-do, Korea.
Applied Psychological Measurement
|May 25, 2026
Summary
This study introduces a new cognitive diagnosis in multistage testing (CD-MST) design by partitioning skill-space for personalized adaptive testing. This approach enhances diagnostic accuracy and item utilization in educational assessments.
Area of Science:
- Educational Measurement
- Psychometrics
- Computerized Adaptive Testing
Background:
- Personalized online learning requires tailored diagnostic assessments.
- Cognitive diagnostic models (CDMs) combined with computerized multistage testing (MST) offer potential but face integration challenges.
- Limited research exists on cognitive diagnosis in multistage testing (CD-MST) due to module construction and routing rule difficulties.
Purpose of the Study:
- To introduce an innovative design for CD-MST using skill-space partitioning.
- To address challenges in module construction and routing rules for CD-MST.
- To improve the accuracy and efficiency of diagnostic assessments in adaptive testing environments.
Main Methods:
- Developed a skill-space partitioning strategy to group attribute profiles.
- Proposed item information functions conditional on attribute profiles for CD-MST.
- Created an automated module assembly approach for CD-MST to maximize information and meet constraints.
Main Results:
- The proposed CD-MST design improves estimation accuracy compared to traditional linear tests.
- The method effectively utilizes a broader range of item types from item banks.
- Simulation results demonstrate the viability of the skill-space partitioning approach.
Conclusions:
- The novel CD-MST design effectively addresses prior integration challenges.
- Skill-space partitioning enables tailored module construction and adaptive routing.
- This approach advances personalized diagnostic assessment in educational technology.
