Evaluating a National Traditional Chinese Medicine Examination via Cognitive Diagnostic Approaches

Lingling Xu, Zhehan Jiang () and Yuting Han
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Lingling Xu: Institute of Medical Education, Peking University, Beijing 100191, China
Zhehan Jiang: Institute of Medical Education, Peking University, Beijing 100191, China
Yuting Han: School of Psychology, Beijing Language and Culture University, Beijing 100083, China

Sustainability, 2024, vol. 16, issue 13, 1-12

Abstract: The current research utilized diagnostic classification models (DCMs), an advanced psychometric theory, to evaluate the examination’s quality using psychometric methods for a more precise and comprehensive understanding of health professionals’ competence. Data was gathered from 16,310 fourth-year Traditional Chinese Medicine undergraduates who completed the Standardized Competence Test for Traditional Chinese Medicine Undergraduates (SCTTCMU) comprising 300 multiple-choice items. The study examined the fundamental assumptions, model-data fit, and cognitive diagnostic theory models’ item and test properties. The generalized deterministic input, noisy, “and” gate model applied in this research demonstrated a strong alignment with the real response data, meeting all the necessary assumptions. Cognitive diagnostic analysis indicated that all items exhibited satisfactory psychometric characteristics, and the reported scores offered insights into candidates’ proficiency in cognitive skills. It is expected that the advent of modern psychometric technology will contribute to the improvement of refined diagnostic information for health professional candidates. Furthermore, this research holds the potential to significantly enhance sustainability in healthcare practices, knowledge, economics, resource use, and community resilience.

Keywords: traditional Chinese medicine; health professions education; cognitive diagnostic analysis; assessment (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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