Implementation of personalized frameworks in computational thinking development: implications for teaching in software engineering - Guevara-Reyes, Josué; Vinueza-Morales, Mariuxi; Ruano-Lara, Erick; Vidal-Silva, Cristian (Frontiers in Education, 2025)
The development of computational thinking (CT) is crucial in software engineering education, as it enables students to analyze complex problems, design algorithmic solutions, and adapt to an evolving digital landscape. However, traditional teaching methods often fail to accommodate diverse cognitive profiles, limiting students' ability to engage effectively with CT concepts. This study investigates the implementation of personalized frameworks to enhance CT instruction by adapting learning methodologies to students' cognitive characteristics. A Systematic Literature Review (SLR) was conducted, analyzing 3,718 sources from Scopus, IEEE Xplore, and ACM Digital Library databases. After applying rigorous inclusion criteria, 73 empirical studies were selected for in-depth analysis. The review focused on personalized learning strategies, the role of adaptive frameworks, and their impact on academic performance in CT education. Findings indicate that only 37% of studies report using adaptive frameworks, yet these demonstrate significant improvement in learning outcomes. Effective methodologies include project-based learning, visual programming tools, and continuous assessment, which enhance engagement and problem-solving skills. Additionally, frameworks incorporating diagnostic assessments and tailored instructional content show promise in improving CT proficiency among students with logical-mathematical and spatial intelligence. In conclusion, integrating adaptive frameworks into CT education provides a promising avenue for improving student performance and fostering individualized learning experiences. Despite their potential, widespread adoption remains limited due to challenges such as a lack of faculty training, institutional resistance, and technological constraints. Future research should explore scalable implementation strategies and assess the long-term impact of personalized frameworks on computational thinking education.