Computational Bibliometric Analysis of Research on Bloom Digital Taxonomy and Critical Thinking

Abstract

One important step towards understanding the development, trends, and effects in the context of contemporary education is to conduct a computational bibliometric analysis of research focused on Bloom Digital Taxonomy and elements of critical thinking studies. This research was conducted to perform a bibliometric analysis on the digital Bloom's taxonomy and critical thinking. The research method employed was bibliometric analysis, utilizing machine learning to map the data. The research comprised four stages of bibliometric analysis, namely: (a) data retrieval through the application of Publish or Perish, (b) data processing, (c) data mapping using machine learning, and (d) data analysis of the mapping using the R programming language. The research materials were published between 2015 and collected from the Google Scholar database in 2023. The search process involved the usage of the keywords "Taxonomy Bloom Digital" and "Critical Thinking." The results demonstrated that bibliometric analysis and mapping of 500 publications using machine learning enabled a deeper understanding of the development, trends, and crucial aspects of research in this field. By employing a bibliometric analysis approach and implementing machine learning, this study contributes to the comprehension of digital Bloom's taxonomy and critical thinking while providing an overview of research trends.