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 in | Frontiers in psychology Vol. 12; p. 509844 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Frontiers Media S.A
06.05.2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1664-1078 1664-1078 |
| DOI | 10.3389/fpsyg.2021.509844 |
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| Abstract | 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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| AbstractList | 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.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. 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. |
| Author | Tu, Dongbo Wang, Daxun Cai, Yan Gao, Xuliang Li, Guiyu |
| AuthorAffiliation | 3 School of Psychology, Guizhou Normal University , Guiyang , China 1 School of Psychology, Jiangxi Normal University , Nanchang , China 2 Department of Curriculum and Instruction, East China Normal University , Shanghai , China |
| AuthorAffiliation_xml | – name: 1 School of Psychology, Jiangxi Normal University , Nanchang , China – name: 3 School of Psychology, Guizhou Normal University , Guiyang , China – name: 2 Department of Curriculum and Instruction, East China Normal University , Shanghai , China |
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| Cites_doi | 10.1111/j.1572-0241.2000.01927.x 10.1177/0146621618762739 10.1177/0013164419901292 10.1177/014662168601000406 10.1111/jedm.12174 10.1177/0013164414539162 10.1177/014662168400800311 10.1007/s11336-011-9207-7 10.1111/jedm.12096 10.1007/0-306-47531-6 10.2307/2280760 10.1111/jedm.12196 10.1111/jedm.12063 10.1111/emip.12226 10.1177/0146621605275413 10.2307/1435202 10.1177/01466216980223003 10.1111/j.1745-3984.2011.00158.x 10.1177/014662167700100209 10.1177/0146621614544519 10.1002/j.2333-8504.1977.tb01128.x 10.1007/s00357-013-9129-4 10.3389/fpsyg.2018.00997 10.1177/01466210122032064 10.1787/b9435d4b-en 10.1007/s11336-012-9296-y 10.1007/s11336-015-9471-z 10.1207/s15324818ame1903_2 |
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| Copyright | Copyright © 2021 Li, Cai, Gao, Wang and Tu. Copyright © 2021 Li, Cai, Gao, Wang and Tu. 2021 Li, Cai, Gao, Wang and Tu |
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| SubjectTerms | automated test assembly cognitive diagnosis cognitive diagnosis modules computer multistage test heuristic algorithms Psychology |
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| Title | Automated Test Assembly for Multistage Testing With Cognitive Diagnosis |
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