EXPERT CONSENSUS ON COMPUTATIONAL THINKING LEARNING SEQUENCES FOR KINDERGARTEN USING FUZZY DELPHI METHOD

Abstract

This study aims to develop a structured and developmentally appropriate sequence for teaching Computational Thinking (CT), specifically designed for kindergarten. In response to the increasing importance of 21st-century skills and the global push to integrate CT into early childhood education, this research addresses the gap in pedagogical strategies suitable for young learners. The Fuzzy Delphi Method was employed, involving 12 experts in early childhood education, CT, and curriculum development. The expert panel evaluated six core elements of CT: logical reasoning, abstraction, decomposition, pattern recognition, algorithm design, and evaluation. The results showed a high level of consensus (≥90.91%) with threshold values ranging from 0.101 to 0.197 and fuzzy values between 0.509 and 0.564, indicating strong agreement on the relevance and feasibility of implementing CT in kindergartens. These elements were contextualized through screen-free, interactive, and play-based activities tailored to young children's cognitive characteristics. This study contributes to early childhood education by offering a CT learning sequence grounded in empirical data and contemporary educational theory. It also addresses contextual challenges in Indonesia, such as limited digital infrastructure, by proposing cost-effective and culturally relevant pedagogical strategies. The findings demonstrate that early exposure to CT can foster foundational skills in logical thinking, creativity, and problem-solving—essential for lifelong learning. The study highlights the need for policy support, teacher training, and curriculum development to effectively integrate CT into early childhood education.