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This study analyzes the scholarly landscape of data literacy through citation and co-citation analyses of documents, sources, and authors. Using Scopus data and VOSviewer, the study identifies significant contributions and thematic trends. A minimum criterion of 10 citations per document was applied, filtering the dataset to 205 documents, with a focus on 81 interlinked documents. Citation analysis covered document, source, and author metrics, while co-citation analysis examined cited authors, sources, and references. The study found 997 documents on data literacy, narrowed down to 205 significant ones, with 81 interlinked documents showing a high average citation rate. Key sources included the ACM International Conference Proceeding Series and Teachers College Record, and prominent authors like Ellen B. Mandinach and Kim Schildkamp emerged as central figures. Data literacy research spans fields like education, sociology, and information science, highlighting its interdisciplinary nature. The studys focus on citation metrics may introduce selection bias, emphasizing widely cited works. Future research could explore less-cited but influential works and broader datasets to mitigate biases. Policymakers can use these insights to integrate data literacy into educational curricula and design targeted professional development programs. Promoting interdisciplinary collaboration and supporting open access to scholarly literature can enhance data literacy initiatives. This study provides a comprehensive citation and co-citation analysis of data literacy research, offering valuable insights into key contributions and thematic trends, informing policy and practice, and underscoring the importance of data literacy in contemporary education and society.
Williams E. Nwagwu (Tue,) studied this question.