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Enhancing Rubric Development in Science Education through Topic Modeling Techniques.

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Title: Enhancing Rubric Development in Science Education through Topic Modeling Techniques.
Authors: Hong, Minju (AUTHOR), You, Hyesun (AUTHOR)
Source: Journal of Experimental Education. 2026, Vol. 94 Issue 2, p347-364. 18p.
Subjects: Science education, Scoring rubrics, Evaluation methodology, Evaluators, Latent semantic analysis, Text mining, Educational evaluation, Quantitative research
Abstract: Content experts conduct traditional approaches to developing rubrics for constructed response (CR) items, which is a labor-intensive process. We aim to illustrate the potential benefits of utilizing topic modeling techniques to improve the efficiency of the rubric workload. The results of this study reveal that over half of the keywords in the rubric corresponded with the top 20 words extracted from the supervised latent Dirichlet allocation (sLDA) model. The rubric-based scores assigned by human raters showed medium-to-high associations with the sLDA-predicted scores. The sLDA approach offers supporting evidence for refining the rubric concerning criteria for word usage and score assignments. It accomplishes this by concurrently incorporating the predetermined rubric of content experts and the actual responses provided by students. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Experimental Education is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Enhancing Rubric Development in Science Education through Topic Modeling Techniques.
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  Data: <searchLink fieldCode="AR" term="%22Hong%2C+Minju%22">Hong, Minju</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22You%2C+Hyesun%22">You, Hyesun</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Experimental+Education%22">Journal of Experimental Education</searchLink>. 2026, Vol. 94 Issue 2, p347-364. 18p.
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  Data: <searchLink fieldCode="DE" term="%22Science+education%22">Science education</searchLink><br /><searchLink fieldCode="DE" term="%22Scoring+rubrics%22">Scoring rubrics</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation+methodology%22">Evaluation methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluators%22">Evaluators</searchLink><br /><searchLink fieldCode="DE" term="%22Latent+semantic+analysis%22">Latent semantic analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Text+mining%22">Text mining</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+evaluation%22">Educational evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Quantitative+research%22">Quantitative research</searchLink>
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  Data: Content experts conduct traditional approaches to developing rubrics for constructed response (CR) items, which is a labor-intensive process. We aim to illustrate the potential benefits of utilizing topic modeling techniques to improve the efficiency of the rubric workload. The results of this study reveal that over half of the keywords in the rubric corresponded with the top 20 words extracted from the supervised latent Dirichlet allocation (sLDA) model. The rubric-based scores assigned by human raters showed medium-to-high associations with the sLDA-predicted scores. The sLDA approach offers supporting evidence for refining the rubric concerning criteria for word usage and score assignments. It accomplishes this by concurrently incorporating the predetermined rubric of content experts and the actual responses provided by students. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Experimental Education is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/00220973.2024.2329094
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      – Code: eng
        Text: English
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      – SubjectFull: Science education
        Type: general
      – SubjectFull: Scoring rubrics
        Type: general
      – SubjectFull: Evaluation methodology
        Type: general
      – SubjectFull: Evaluators
        Type: general
      – SubjectFull: Latent semantic analysis
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      – SubjectFull: Text mining
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      – SubjectFull: Educational evaluation
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      – SubjectFull: Quantitative research
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              Text: 2026
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