Assessing Computational Thinking in Early Childhood: Evidence from Kindergarten Children Aged 5–6 Years in Surakarta, Indonesia - Hardiyanti, Widya Dwi; Prayitno, Prayitno; Hayati, Nur; Annisa Tsamrotul Fuadah, Puteri (Golden Age: Jurnal Ilmiah Tumbuh Kembang Anak Usia Dini, 2025)
Teachers in kindergartens often lack knowledge on how to assess children's computational thinking abilities, as there is a lack of validated instruments to measure this ability in early childhood. This study aims to analyze children’s levels of computational thinking abilities in Kindergarten Surakarta City and identify aspects of computational thinking that are still low, to inform appropriate pedagogical interventions that provide suitable stimulation. The research method employs descriptive quantitative methods, presenting frequency distributions, means, modes, medians, minimums, maximums, ranges, and standard deviations. Data collection techniques through computational thinking tests using instruments that have been validated through expert judgment and pilot testing. The sampling technique used was cluster random sampling, which involved selecting kindergartens in Surakarta City, resulting in 60 respondents aged 5-6 years. Based on the four aspects of computational thinking ability, the algorithm is the most mastered aspect (80%) because teachers often stimulate children through natural daily routines. Pattern recognition (64, 58%) is the ability to identify repetitive patterns, such as those found in color sequences or shapes. Decomposition (54,58%) is challenging because it trains children to break down large problems into smaller parts to make them easier to solve. Debugging (40,41%) is the lowest level because it involves metacognitive aspects in complex problem-solving. This research provides an overview for teachers to be able to implement innovative learning that can stimulate computational thinking. Teachers can actively integrate play-based learning that engages children and collaboration between classmates. In addition, integrating digital technology media can make the learning process more interesting. Given the small sample size in this study, future research should involve a larger group of participants to improve the generalization of results. Future research should use larger sample sizes and encourage collaboration between institutions in different regions so that it can provide further insight into effective learning strategies in stimulating children's computational thinking.