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Strategies for addressing high-dimensional cognitively diagnostic assessment problems

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TitleInfo
Title
Strategies for addressing high-dimensional cognitively diagnostic assessment problems
Name (type = personal)
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Sun
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Yan
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Yan Sun
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author
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Chiu
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Chia-Yi
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Chia-Yi Chiu
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Advisory Committee
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chair
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de la Torre
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Jimmy
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Jimmy de la Torre
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Advisory Committee
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internal member
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Drew
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Drew Gitomer
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Advisory Committee
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internal member
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Chopade
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Pravin
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Pravin Chopade
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Advisory Committee
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outside member
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Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
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School of Graduate Studies
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school
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Text
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theses
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DateCreated (encoding = w3cdtf); (keyDate = yes); (qualifier = exact)
2019
DateOther (encoding = w3cdtf); (qualifier = exact); (type = degree)
2019-10
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2019
Language
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English
Abstract (type = abstract)
In recent years, cognitive diagnosis models (CDMs) have sparked the interest of educational measurement researchers and practitioners because of its capability to provide formative information on student mastery or nonmastery of a set of fine-grained skills. One of the advantages of CDMs is that, by treating latent variables as discrete, usually binary, CDMs can accommodate higher dimensional latent space than multidimensional latent trait (e.g., multidimensional item response theory) models. Theoretically, the number of attributes that can be estimated by a CDM is unlimited; however, in practice, this number may not exceed 20 due to a number of computational issues. This constraint limits the use of CDMs in scenarios where a comprehensive diagnosis of a complete knowledge space, such as large-scale diagnostic assessments or retrofitting summative assessments using CDMs, is of interest.

In this dissertation, a series of strategies are proposed to address issues in classifying examinees' proficiency profiles for high-dimensional testing data. In particular, these strategies can be used in situations where attributes can be partitioned into non-overlapping knowledge subsets. An approach, called the accordion procedure (AP), is proposed to address the high dimensionality estimation problem by focusing only on the attributes of one particular subset at a time, while the attributes of each of the remaining subsets are collapsed to create composite nuisance attributes. Simulation studies are conducted to examine the performance of AP compared to the complete profile estimation procedure in terms of classification accuracy and computation time. A real data illustration is also provided by retrofitting extant large-scale assessment data using AP.

To provide appropriate actionable feedback, one important prerequisite is ensuring the CDMs fitted to test data yield accurate classifications of examinees' proficiency profiles. However, due to various reasons (e.g., short test, poor item quality), tests sometimes do not provide sufficient information to classify examinees accurately.

When a test is not sufficiently informative, other sources of information might be needed to improve the classification accuracy. Thus, in the second study, covariates are incorporated in the context of AP using a four-step latent regression approach to supplement the information obtained from CDMs. The four-step approach is shown to be computationally more manageable when data are high-dimensional, as well as more flexible when specifications of each step need to be adjusted. Simulation and real-data studies are conducted to examine the performance of the proposed approach.

Cognitive diagnosis computerized adaptive testing (CD-CAT) has been proposed to administer a test more efficiently by selecting the optimal set of items for each examinee. However, when the number of skills of interest is large, practical issues, such as calibration of item pools and item selection method, emerge.

The third study aims to propose a series of strategies to make high-dimensional CD-CAT feasible, namely, an item pool calibration method, item selection method, and examinees' prior distribution estimation method. Simulation studies are conducted to evaluate the performance of the proposed strategies.

In summary, the issues associated with using CDMs in high-dimensional situations are addressed in this dissertation. Several strategies are proposed primarily with the aim of obtaining accurate classification results to ensure that the feedback and remedial procedures are informative and effective.
Subject (authority = RUETD)
Topic
Education
Subject (authority = local)
Topic
Accordion procedure
Subject (authority = LCSH)
Topic
Cognition disorders -- Diagnosis
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Rutgers University Electronic Theses and Dissertations
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ETD_10354
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application/pdf
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text/xml
Extent
1 online resource (xiii, 117 pages) : illustrations
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
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School of Graduate Studies Electronic Theses and Dissertations
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rucore10001600001
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Identifier (type = doi)
doi:10.7282/t3-4p5v-1r13
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Sun
GivenName
Yan
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2019-09-27 13:17:35
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Name
Yan Sun
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Affiliation
Rutgers University. School of Graduate Studies
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Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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Type
Embargo
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2019-10-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2020-05-01
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after May 1st, 2020.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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