Computational thinking in mathematics education: A systematic review of research trends and pedagogical implementations
DOI:
https://doi.org/10.31629/jg.v11i1.8309Keywords:
computational thinking, mathematics education, systematic literature reviewAbstract
Although computational thinking has become a key competency in mathematics education, a comprehensive understanding of research trends in this field remains limited. This study examines research trends on computational thinking in mathematics education published between 2019 and 2024. A Systematic Literature Review (SLR) was conducted using qualitative studies retrieved through systematic searches in the Scopus bibliographic database and the Google Scholar academic search engine. The review followed predefined inclusion criteria and the PRISMA protocol. The analysis examined publication year, research design, educational level, research location, mathematical topics, instructional models, and computational thinking indicators. The findings show that publications increased substantially between 2021 and 2023, peaking in 2023 (48%). Most studies employed qualitative methods (56%), focused on junior high schools (44%), and investigated number patterns and two-variable linear systems of equations. Problem-Based Learning and Project-Based Learning were the dominant instructional models, while decomposition, pattern recognition, abstraction, and algorithmic thinking were the most frequently examined indicators of computational thinking. These findings provide an updated synthesis of current research trends and offer valuable references for future research and instructional development in mathematics education.
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