Science, technology and society in chemistry education through the lens of topic modeling

This study investigates how the principles of Science, Technology, and Society (CTS) are reflected in chemistry education research. A corpus of 2,468 peer-reviewed abstracts from the Scopus database, retrieved in May 2025, was analyzed using Latent Dirichlet Allocation (LDA) to identify thematic patterns. Five main topics emerged: (1) Pedagogical Models and Conceptual Understanding, (2) Experimental Activities and Laboratory Learning, (3) Green Chemistry and Critical Thinking, (4) Educational Data and Learning Outcomes, and (5) Institutional Frameworks and Scientific Divisions. Temporal analysis revealed shifts from sustainability-focused discourse to data-driven evaluation. Gender-based analysis showed that female authors are more associated with laboratory-oriented research, while male authors are more prominent in data-focused studies. The results suggest that CTS-related themes are implicitly present in chemistry education literature and provide insights for future curricular development, policy, and teacher training. The study demonstrates the value of text mining for mapping academic discourse and offers methodological and theoretical contributions to science education research.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-07-30
DOI
https://doi.org/10.5281/zenodo.17263464
Primary Topic
Bacterial Genetics and Biotechnology
Type
article
Field-Weighted Citation Impact
0.00
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article

Science, technology and society in chemistry education through the lens of topic modeling

Zenodo (CERN European Organization for Nuclear Research)
Bacterial Genetics and Biotechnology
article

Science, technology and society in chemistry education through the lens of topic modeling

article en

Abstract

This study investigates how the principles of Science, Technology, and Society (CTS) are reflected in chemistry education research. A corpus of 2,468 peer-reviewed abstracts from the Scopus database, retrieved in May 2025, was analyzed using Latent Dirichlet Allocation (LDA) to identify thematic patterns. Five main topics emerged: (1) Pedagogical Models and Conceptual Understanding, (2) Experimental Activities and Laboratory Learning, (3) Green Chemistry and Critical Thinking, (4) Educational Data and Learning Outcomes, and (5) Institutional Frameworks and Scientific Divisions. Temporal analysis revealed shifts from sustainability-focused discourse to data-driven evaluation. Gender-based analysis showed that female authors are more associated with laboratory-oriented research, while male authors are more prominent in data-focused studies. The results suggest that CTS-related themes are implicitly present in chemistry education literature and provide insights for future curricular development, policy, and teacher training. The study demonstrates the value of text mining for mapping academic discourse and offers methodological and theoretical contributions to science education research.

Zenodo (CERN European Organization for Nuclear Research)
Instituto Federal do Rio Grande do Norte (BR), Universidade Federal do Rio Grande do Norte (BR)
Openalex Percentile: Top 35%
Bacterial Genetics and Biotechnology
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