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The accuracy and consistency of mastery for each content domain using the Rasch and deterministic inputs, noisy “and” gate diagnostic classification models: a simulation study and a real-world analysis using data from the Korean Medical Licensing Examination
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Dong Gi Seo
, Jae Kum Kim
- J Educ Eval Health Prof. 2021;18:15. Published online July 5, 2021
- DOI: https://doi.org/10.3352/jeehp.2021.18.15
- 4,334 View
- 288 Download
- 2 Web of Science
- 2 Crossref
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Abstract
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Supplementary Material - Purpose
Diagnostic classification models (DCMs) were developed to identify the mastery or non-mastery of the attributes required for solving test items, but their application has been limited to very low-level attributes, and the accuracy and consistency of high-level attributes using DCMs have rarely been reported compared with classical test theory (CTT) and item response theory models. This paper compared the accuracy of high-level attribute mastery between deterministic inputs, noisy “and” gate (DINA) and Rasch models, along with sub-scores based on CTT.
Methods
First, a simulation study explored the effects of attribute length (number of items per attribute) and the correlations among attributes with respect to the accuracy of mastery. Second, a real-data study examined model and item fit and investigated the consistency of mastery for each attribute among the 3 models using the 2017 Korean Medical Licensing Examination with 360 items.
Results
Accuracy of mastery increased with a higher number of items measuring each attribute across all conditions. The DINA model was more accurate than the CTT and Rasch models for attributes with high correlations (>0.5) and few items. In the real-data analysis, the DINA and Rasch models generally showed better item fits and appropriate model fit. The consistency of mastery between the Rasch and DINA models ranged from 0.541 to 0.633 and the correlations of person attribute scores between the Rasch and DINA models ranged from 0.579 to 0.786.
Conclusion
Although all 3 models provide a mastery decision for each examinee, the individual mastery profile using the DINA model provides more accurate decisions for attributes with high correlations than the CTT and Rasch models. The DINA model can also be directly applied to tests with complex structures, unlike the CTT and Rasch models, and it provides different diagnostic information from the CTT and Rasch models. -
Citations
Citations to this article as recorded by
- Stable Knowledge Tracing Using Causal Inference
Jia Zhu, Xiaodong Ma, Changqin Huang
IEEE Transactions on Learning Technologies.2024; 17: 124. CrossRef - Development of a character qualities test for medical students in Korea using polytomous item response theory and factor analysis: a preliminary scale development study
Yera Hur, Dong Gi Seo
Journal of Educational Evaluation for Health Professions.2023; 20: 20. CrossRef
- Stable Knowledge Tracing Using Causal Inference
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