Journal article
Modeling Hierarchical Attribute Structures in Diagnostic Classification Models with Multiple Attempts
Journal of educational measurement, Vol.61(2), pp.198-218
03/30/2024
DOI: 10.1111/jedm.12387
Abstract
In classroom assessments, examinees can often answer test items multiple times, resulting in sequential multiple-attempt data. Sequential diagnostic classification models (DCMs) have been developed for such data. As student learning processes may be aligned with a hierarchy of measured traits, this study aimed to develop a sequential hierarchical DCM (sequential HDCM), which combines a sequential DCM with the HDCM, and investigate classification accuracy of the model in the presence of hierarchies when multiple attempts are allowed in dynamic assessment. We investigated the model's impact on classification accuracy when hierarchical structures are correctly specified, misspecified, or overspecified. The results indicate that (1) a sequential HDCM accurately classified students as masters and nonmasters when the data had a hierarchical structure; (2) a sequential HDCM produced similar or slightly higher classification accuracy than nonhierarchical sequential LCDM when the data had hierarchical structures; and (3) the misspecification of the hierarchical structure of the data resulted in lower classification accuracy when the misspecified model had fewer attribute profiles than the true model. We discuss limitations and make recommendations on using the proposed model in practice. This study provides practitioners with information about the possibilities for psychometric modeling of dynamic classroom assessment data.
Details
- Title: Subtitle
- Modeling Hierarchical Attribute Structures in Diagnostic Classification Models with Multiple Attempts
- Creators
- Tae Yeon Kwon - University of FloridaA. Corinne Huggins-Manley - University of FloridaJonathan Templin - Univ Iowa, Educ Measurement & Stat, S210B Lindquist Ctr, Iowa City, IA 52242 USAMingying Zheng - Univ Iowa, Educ Measurement & Stat, 240 S Madison St, Iowa City, IA USA
- Resource Type
- Journal article
- Publication Details
- Journal of educational measurement, Vol.61(2), pp.198-218
- Publisher
- Wiley
- DOI
- 10.1111/jedm.12387
- ISSN
- 0022-0655
- eISSN
- 1745-3984
- Number of pages
- 21
- Grant note
- Institute of Education Sciences, U.S. Department of Education; US Department of Education R305A190079; R324A170135 / Institute of Education Sciences; US Department of Education; Institute of Education Sciences (IES)
- Language
- English
- Electronic publication date
- 03/30/2024
- Academic Unit
- Psychological and Quantitative Foundations
- Record Identifier
- 9984586360202771
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