Two-stage polytomous attribute estimation for cognitive diagnostic models: overcoming computational challenges in large-scale assessments with many polytomous attributes
Abstract Cognitive diagnosis models (CDMs) have been advocated as a useful tool in calibrating large-scale assessments, yet the computational challenges are inevitably amplified when the modeling complexity (e.g., the number and the levels of attributes) increases. This study presents a critical sce...
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| Format: | Article |
| Language: | English |
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Springer Nature
2025-05-01
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| Series: | Humanities & Social Sciences Communications |
| Online Access: | https://doi.org/10.1057/s41599-025-04959-w |
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| author | Yuting Han Feng Ji Zhehan Jiang |
| author_facet | Yuting Han Feng Ji Zhehan Jiang |
| author_sort | Yuting Han |
| collection | DOAJ |
| description | Abstract Cognitive diagnosis models (CDMs) have been advocated as a useful tool in calibrating large-scale assessments, yet the computational challenges are inevitably amplified when the modeling complexity (e.g., the number and the levels of attributes) increases. This study presents a critical scenario, a large-scale national medical certification exam, where CDM with many polytomous attributes (mpCDM) is of great utility, but poses great computational challenges to many popular open-source CDM software packages. We developed a novel two-stage estimation method and assessed its performance through a Monte Carlo simulation study under various conditions of attribute number, item number, item quality, and sample size. Results indicate that the proposed method maintains high accuracy in handling large-scale data while effectively overcoming computational capacity limitations, especially in scenarios with many polytomous attributes, large numbers of items, and substantial sample sizes. Furthermore, we applied the proposed method to a large-scale health examination dataset, demonstrating its effectiveness in practice. This study contributes to the field of psychometrics by offering a simple yet effective solution to the computational challenges inherent in implementing mpCDMs for large-scale assessments, providing a practical tool for diagnostic analyses in educational and professional certification contexts. |
| format | Article |
| id | doaj-art-ca7ce93ef6154e07b21863f26079a621 |
| institution | OA Journals |
| issn | 2662-9992 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Humanities & Social Sciences Communications |
| spelling | doaj-art-ca7ce93ef6154e07b21863f26079a6212025-08-20T02:39:02ZengSpringer NatureHumanities & Social Sciences Communications2662-99922025-05-0112111410.1057/s41599-025-04959-wTwo-stage polytomous attribute estimation for cognitive diagnostic models: overcoming computational challenges in large-scale assessments with many polytomous attributesYuting Han0Feng Ji1Zhehan Jiang2Cognitive Science and Allied Health School, Beijing Language and Culture UniversityDepartment of Applied Psychology and Human Development, University of TorontoInstitute of Medical Education, Peking UniversityAbstract Cognitive diagnosis models (CDMs) have been advocated as a useful tool in calibrating large-scale assessments, yet the computational challenges are inevitably amplified when the modeling complexity (e.g., the number and the levels of attributes) increases. This study presents a critical scenario, a large-scale national medical certification exam, where CDM with many polytomous attributes (mpCDM) is of great utility, but poses great computational challenges to many popular open-source CDM software packages. We developed a novel two-stage estimation method and assessed its performance through a Monte Carlo simulation study under various conditions of attribute number, item number, item quality, and sample size. Results indicate that the proposed method maintains high accuracy in handling large-scale data while effectively overcoming computational capacity limitations, especially in scenarios with many polytomous attributes, large numbers of items, and substantial sample sizes. Furthermore, we applied the proposed method to a large-scale health examination dataset, demonstrating its effectiveness in practice. This study contributes to the field of psychometrics by offering a simple yet effective solution to the computational challenges inherent in implementing mpCDMs for large-scale assessments, providing a practical tool for diagnostic analyses in educational and professional certification contexts.https://doi.org/10.1057/s41599-025-04959-w |
| spellingShingle | Yuting Han Feng Ji Zhehan Jiang Two-stage polytomous attribute estimation for cognitive diagnostic models: overcoming computational challenges in large-scale assessments with many polytomous attributes Humanities & Social Sciences Communications |
| title | Two-stage polytomous attribute estimation for cognitive diagnostic models: overcoming computational challenges in large-scale assessments with many polytomous attributes |
| title_full | Two-stage polytomous attribute estimation for cognitive diagnostic models: overcoming computational challenges in large-scale assessments with many polytomous attributes |
| title_fullStr | Two-stage polytomous attribute estimation for cognitive diagnostic models: overcoming computational challenges in large-scale assessments with many polytomous attributes |
| title_full_unstemmed | Two-stage polytomous attribute estimation for cognitive diagnostic models: overcoming computational challenges in large-scale assessments with many polytomous attributes |
| title_short | Two-stage polytomous attribute estimation for cognitive diagnostic models: overcoming computational challenges in large-scale assessments with many polytomous attributes |
| title_sort | two stage polytomous attribute estimation for cognitive diagnostic models overcoming computational challenges in large scale assessments with many polytomous attributes |
| url | https://doi.org/10.1057/s41599-025-04959-w |
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