Modeling Partial Knowledge in Multiple-Choice Cognitive Diagnostic Assessment
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| Title: | Modeling Partial Knowledge in Multiple-Choice Cognitive Diagnostic Assessment |
|---|---|
| Language: | English |
| Authors: | Kentaro Fukushima (ORCID |
| Source: | Journal of Educational and Behavioral Statistics. 2025 50(1):5-43. |
| Availability: | SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com |
| Peer Reviewed: | Y |
| Page Count: | 39 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Diagnostic Tests, Cognitive Measurement, Multiple Choice Tests, Educational Assessment, Objective Tests, Psychometrics, Test Theory, Testing |
| DOI: | 10.3102/10769986241245707 |
| ISSN: | 1076-9986 1935-1054 |
| Abstract: | Diagnostic tests are typically administered in a multiple-choice (MC) format due to their advantages of objectivity and time efficiency. The MC-deterministic input, noisy "and" gate (DINA) family of models, a representative class of cognitive diagnostic models for MC items, efficiently and parsimoniously estimates the mastery profiles of examinees. However, the existing models often overestimate the latent traits of examinees when they respond with partial knowledge, which is often observed in educational assessment. Therefore, the novel models of the MC-DINA family that can appropriately handle such responses were developed in this study. Unlike the existing models, the proposed models placed no restrictions on the Q-vector, which represents attribute specifications. Simulation and empirical studies verified that the proposed approach could resolve the overestimation problem. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1457142 |
| Database: | ERIC |
| Abstract: | Diagnostic tests are typically administered in a multiple-choice (MC) format due to their advantages of objectivity and time efficiency. The MC-deterministic input, noisy "and" gate (DINA) family of models, a representative class of cognitive diagnostic models for MC items, efficiently and parsimoniously estimates the mastery profiles of examinees. However, the existing models often overestimate the latent traits of examinees when they respond with partial knowledge, which is often observed in educational assessment. Therefore, the novel models of the MC-DINA family that can appropriately handle such responses were developed in this study. Unlike the existing models, the proposed models placed no restrictions on the Q-vector, which represents attribute specifications. Simulation and empirical studies verified that the proposed approach could resolve the overestimation problem. |
|---|---|
| ISSN: | 1076-9986 1935-1054 |
| DOI: | 10.3102/10769986241245707 |
