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A Comparison of Latent Semantic Analysis and Latent Dirichlet Allocation in Educational Measurement

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Title: A Comparison of Latent Semantic Analysis and Latent Dirichlet Allocation in Educational Measurement
Language: English
Authors: Jordan M. Wheeler, Allan S. Cohen, Shiyu Wang
Source: Journal of Educational and Behavioral Statistics. 2024 49(5):848-874.
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: 27
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Descriptors: Semantics, Educational Assessment, Evaluators, Reliability, Responses, Mathematical Models, Correlation, Language Usage, Item Analysis, Test Items, Measurement Techniques, Algorithms, Scoring, Thinking Skills, Simulation, Comparative Analysis
DOI: 10.3102/10769986231209446
ISSN: 1076-9986
1935-1054
Abstract: Topic models are mathematical and statistical models used to analyze textual data. The objective of topic models is to gain information about the latent semantic space of a set of related textual data. The semantic space of a set of textual data contains the relationship between documents and words and how they are used. Topic models are becoming more common in educational measurement research as a method for analyzing students' responses to constructed-response items. Two popular topic models are latent semantic analysis (LSA) and latent Dirichlet allocation (LDA). LSA uses linear algebra techniques, whereas LDA uses an assumed statistical model and generative process. In educational measurement, LSA is often used in algorithmic scoring of essays due to its high reliability and agreement with human raters. LDA is often used as a supplemental analysis to gain additional information about students, such as their thinking and reasoning. This article reviews and compares the LSA and LDA topic models. This article also introduces a methodology for comparing the semantic spaces obtained by the two models and uses a simulation study to investigate their similarities.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1442196
Database: ERIC
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  Data: A Comparison of Latent Semantic Analysis and Latent Dirichlet Allocation in Educational Measurement
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Educational+and+Behavioral+Statistics%22"><i>Journal of Educational and Behavioral Statistics</i></searchLink>. 2024 49(5):848-874.
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  Data: 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
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  Data: Topic models are mathematical and statistical models used to analyze textual data. The objective of topic models is to gain information about the latent semantic space of a set of related textual data. The semantic space of a set of textual data contains the relationship between documents and words and how they are used. Topic models are becoming more common in educational measurement research as a method for analyzing students' responses to constructed-response items. Two popular topic models are latent semantic analysis (LSA) and latent Dirichlet allocation (LDA). LSA uses linear algebra techniques, whereas LDA uses an assumed statistical model and generative process. In educational measurement, LSA is often used in algorithmic scoring of essays due to its high reliability and agreement with human raters. LDA is often used as a supplemental analysis to gain additional information about students, such as their thinking and reasoning. This article reviews and compares the LSA and LDA topic models. This article also introduces a methodology for comparing the semantic spaces obtained by the two models and uses a simulation study to investigate their similarities.
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        Value: 10.3102/10769986231209446
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      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 27
        StartPage: 848
    Subjects:
      – SubjectFull: Semantics
        Type: general
      – SubjectFull: Educational Assessment
        Type: general
      – SubjectFull: Evaluators
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      – SubjectFull: Reliability
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      – SubjectFull: Responses
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      – SubjectFull: Mathematical Models
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      – SubjectFull: Correlation
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      – SubjectFull: Language Usage
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      – SubjectFull: Item Analysis
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      – SubjectFull: Test Items
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      – SubjectFull: Measurement Techniques
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      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Scoring
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      – SubjectFull: Thinking Skills
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      – SubjectFull: Simulation
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      – SubjectFull: Comparative Analysis
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      – TitleFull: A Comparison of Latent Semantic Analysis and Latent Dirichlet Allocation in Educational Measurement
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            NameFull: Shiyu Wang
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