Automated Test Assembly for Multistage Testing With Cognitive Diagnosis

Computer multistage adaptive test (MST) combines the advantages of paper and pencil-based test (P&P) and computer-adaptive test (CAT). As CAT, MST is adaptive based on modules; as P&P, MST can meet the need of test developers to manage test forms and keep test forms parallel. Cognitive diagn...

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Published inFrontiers in psychology Vol. 12; p. 509844
Main Authors Li, Guiyu, Cai, Yan, Gao, Xuliang, Wang, Daxun, Tu, Dongbo
Format Journal Article
LanguageEnglish
Published Frontiers Media S.A 06.05.2021
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ISSN1664-1078
1664-1078
DOI10.3389/fpsyg.2021.509844

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Summary:Computer multistage adaptive test (MST) combines the advantages of paper and pencil-based test (P&P) and computer-adaptive test (CAT). As CAT, MST is adaptive based on modules; as P&P, MST can meet the need of test developers to manage test forms and keep test forms parallel. Cognitive diagnosis (CD) can accurately measure students’ knowledge states (KSs) and provide diagnostic information, which is conducive to student’s self-learning and teacher’s targeted teaching. Although MST and CD have a lot of advantages, many factors prevent MST from applying to CD. In this study, we first attempt to employ automated test assembly (ATA) to achieve the objectives of MST in the application of CD (called CD-MST) via heuristic algorithms. The mean correct response probability of all KSs for each item is used to describe the item difficulty of CD. The attribute reliability in CD is defined as the test quantitative target. A simulation study with the G-DINA model (generalized deterministic input noisy “and” gate model) was carried out to investigate the proposed CD-MST, and the results showed that the assembled panels of CD-MST satisfied the statistical and the non-statistical constraints.
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Reviewed by: Manqian Liao, Duolingo, United States; Fabrizio Stasolla, Faculty of Law, Giustino Fortunato University, Italy
This article was submitted to Quantitative Psychology and Measurement, a section of the journal Frontiers in Psychology
Edited by: Hong Jiao, University of Maryland, College Park, United States
ISSN:1664-1078
1664-1078
DOI:10.3389/fpsyg.2021.509844