The reviewed studies suggest that software frameworks may contribute to the development of computational thinking by promoting modular problem decomposition, pattern recognition, abstraction, and structured algorithmic reasoning.
Abstract
Computational thinking has become a fundamental competency in software engineering education due to its contribution to problem-solving, algorithm design, abstraction, and logical reasoning. At the same time, software frameworks have gained increasing relevance as technological environments capable of supporting both software development and educational processes. Despite the growing body of literature in these areas, limited evidence has been synthesized regarding the role of software frameworks in fostering computational thinking skills within software engineering education. The objective of this study was to analyze the role of software frameworks in the development of computational thinking through a systematic review of the scientific literature. The review followed the PRISMA 2020 guidelines and was conducted using publications retrieved from IEEE Xplore, ScienceDirect, SpringerLink, SciELO, Redalyc, and Dialnet. A total of 211 records were initially identified, of which 24 studies met the predefined eligibility criteria and were included in the final analysis. The findings revealed the predominance of widely adopted software frameworks such as Django and Spring, together with the increasing use of educational and adaptive learning frameworks. Among the dimensions of computational thinking, algorithmic thinking and abstraction were the most frequently reported. The reviewed studies suggest that software frameworks may contribute to the development of computational thinking by promoting modular problem decomposition, pattern recognition, abstraction, and structured algorithmic reasoning. In conclusion, software frameworks represent valuable educational resources that may support the development of computational thinking competencies within software engineering education. However, the available evidence remains methodologically heterogeneous, highlighting the need for further empirical and experimental research to better understand the impact of framework-based learning environments on computational thinking development.
The results show that in science education, instead of a focus on programming and coding, CT is used as a pedagogical tool for the scientific practices and models of science and for inquiry-based learning of science.
The integration of CT serves as a vital bridge to transform mathematics pedagogy into a more explorative and meaningful experience, highlighting the urgent need for continuous teacher professional development and standardized CT assessment tools.
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