Mapping the pedagogical–affective coupling in computational thinking research: a bibliometric analysis
Abstract
Computational thinking (CT) skills have developed rapidly across various fields. CT skills have even become a fundamental competency akin to basic literacy. The development of CT skills is not determined solely by the mastery of cognitive concepts, but also by the role of pedagogy in relation to affective aspects. However, existing literature has not yet reached a consensus on integrating pedagogical (learning) and affective aspects to enhance CT skills, and research in this area remains limited. This study aims to map the global research landscape regarding the relationship between pedagogical (learning) and affective aspects in the development of CT skills. Data were analysed using bibliometric analysis of 103 documents sourced from the Scopus database, the period from 2016 to 2025. Data were processed using RStudio, VOSviewer, OpenRefine, and Excel to focus the analysis on growth trends, the most influential contributors, and to analyse bibliographic coupling, co-citation, and co-occurrence. Findings indicate a significant increase, peaking in 2024. The main contributors were Hsu, T.C., and the most influential countries were Taiwan and the United States. Network analysis revealed five major themes in pedagogical implementation, such as game-based learning, robotics, and collaborative learning, which consistently enhance CT skills and improve affective aspects such as motivation, self-efficacy, and students’ learning attitudes. Data visualisation underscores a shift in research focus away from mere cognitive mastery towards the involvement of attitudes. These findings make a significant contribution to understanding the development and trends in research regarding the relationship between CT skills and pedagogical and affective aspects.
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DOI: https://doi.org/10.17509/ijpe.v10i1.100101
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