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Assessing Computational Thinking in Early Childhood: Evidence from Kindergarten Children Aged 5–6 Years in Surakarta, Indonesia
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Abstract
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.
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References
Aiken, L. R. (1985). Three coefficients for analyzing the reliability and validity of ratings. Educational and Psychological Measurement, 45(1), 131–142. https://doi.org/10.1177/0013164485451012
An, M. Y., & Shin, K. S. (2024). Effects of teachers’ media utilization and computational thinking on sustainable development in early childhood education. Sustainability (Switzerland), 16(13), 1–13. https://doi.org/10.3390/su16135773
Bers, M. U., González-González, C., & Armas-Torres, M. B. (2019). Coding as a playground: promoting positive learning experiences in childhood classrooms. Computers and Education, 138(June 2018), 130–145. https://doi.org/10.1016/j.compedu.2019.04.013
Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. Studies in Computational Intelligence, 727(1), 135–160. https://doi.org/10.1007/978-3-319-64051-8_9
Çiftçi, A., & Topçu, M. S. (2023). Improving early childhood pre-service teachers’ computational thinking skills through the unplugged computational thinking integrated STEM approach. Thinking Skills and Creativity, 49(1), 1–14. https://doi.org/10.1016/j.tsc.2023.101337
Clarke-Midura, J., Lee, V. R., Shumway, J. F., Silvis, D., Kozlowski, J. S., & Peterson, R. (2023). Designing formative assessments of early childhood computational thinking. Early Childhood Research Quarterly, 65(1), 68–80. https://doi.org/10.1016/j.ecresq.2023.05.013
Creswell, J. W., & Creswell, D. (2018). Research design qualitative, quantitative, and mixed methods approaches. In European University Institute (5th ed., Issue 2). Sage.
Creswell, J. W., & Guetterman, T. C. (2019). Educational research planning, conducting, and evaluating quantitative and qualitative research (6th ed.). Pearson.
CSTA (2017). K–12 Computer Science Standards. In Association for Computing Machinery.
George, D., & Mallery, P. (2019). IBM SPSS Statistics 23 Step by Step: A Simple Guide and Reference. In IBM SPSS Statistics 23 Step by Step (14th ed.). Routledge. https://doi.org/10.4324/9781315545899
Guss, S. S., Clements, D. H., Sharifnia, E., Sarama, J., Holland, A., Lim, C., & Vinh, M. (2024). Designing inclusive computational thinking learning trajectories for the youngest learners. Education Sciences, 1(1), 1–24. https://doi.org/10.3390/educsci14070733
Hardiyanti, W. D., Prayitno, Christianti, M., Rambe, A., & Ulfiyani. (2025). Coding games to stimulate computational thinking ability in early childhood. Journal for Lesson and Learning Studies, 8(2), 243–256. https://doi.org/10.23887/jlls.v8i2.92569
Kaya, D., Yaşar, A. Ö., Çetin, İ., & Kutluca, T. (2025). The relationship between the 21st-century skills and computational thinking skills of prospective mathematics and science teachers. Journal of Pedagogical Research, 9(1), 73–95. https://doi.org/10.33902/JPR.202531498
Masarwa, B., Hel-Or, H., & Levy, S. T. (2024). Kindergarten children’s learning of computational thinking with the “sorting like a computer” learning unit. Journal of Research in Childhood Education, 38(2), 165–188. https://doi.org/10.1080/02568543.2023.2221319
Mutoharoh, Hufad, A., Fathurrohman, M., & Rusdiyani, I. (2021). Unplugged coding activities for early childhood problem-solving skills. Journal of Early Childhood Education, 15(1), 121–140. https://doi.org/10.21009/JPUD.151.07
Pratiwi, M. I. (2017). Study on the ability to recognize the abcd-abcd pattern in group B children in kindergarten in group 3 of Kasihan sub-district. Journal of Early Childhood Education, 6(1), 56–64.
Setianingrum, I., & Hidayana, A. F. (2025). The effect of early childhood teachers' understanding on early childhood coding learning. Journal of Care (Children Advisory Research and Education), 13(1), 90–100. https://doi.org/10.25273/jcare.v13i1.22004
Sharma, K., Papavlasopoulou, S., & Giannakos, M. (2019). Coding games and robots to enhance computational thinking: how collaboration and engagement moderate children’s attitudes? International Journal of Child-Computer Interaction, 21, 65–76. https://doi.org/10.1016/j.ijcci.2019.04.004
Singhal, V. (2022). Precoding skills-teaching computational thinking to preschoolers in singapore using unplugged activities. Proceedings of International Conference on Computational Thinking Education, 86–87. https://doi.org/10.34641/ctestem.2022.452
Terroba, M., Ribera, J. M., Lapresa, D., & Anguera, M. T. (2021). Observational analysis of the development of computational thinking in Early Childhood Education -3 years old- through a proposal for solving problems with a ground robot with programmed directionality. Revista de Educación a Distancia, 21(68), 1–20. https://doi.org/10.6018/red.480411
Terroba, M., Ribera, J. M., Lapresa, D., & Anguera, M. T. (2022). Observational analysis of the development of computational thinking in Early Childhood Education (5 years old) through an intervention proposal with a ground robot with programmed directionality. European Early Childhood Education Research Journal, 30(3), 437–455. https://doi.org/10.1080/1350293X.2022.2055102
Triana, D. D., Sabarini, S. S., & Yudha, R. P. (2020). Web-based motion literacy learning and assessment. CV Budi Utama.
Ubaidillah, Setiarini, N., Budiarti, I., & Windarty, K. (2025). The effect of game coding on early childhood problem-solving skills. Jurnal Ar-Raihanah: Early Childhood Islamic Education, 5(1), 17–23. https://doi.org/10.53398/arraihanah.v5i1.632
Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 1(366(1881), 3717–3725.), 1–46. https://doi.org/10.1098/rsta.2008.0118
Wing, J. M. (2011). Research notebook: computational thinking what and why? In The Link Magazine. Carnegie Mellon University School of Computer Science Research. http://www.cs.cmu.edu/link/research-notebook-computational-thinking-what-and-why
Yang, West, J. B., Yang, D., & Bers, M. (2025). The impact of a block-based visual programming curriculum: untangling coding skills and computational thinking. Learning and Instruction, 95(1), 1–14. https://doi.org/10.1016/j.learninstruc.2024.102041
Zeng, Y., Yang, W., & Bautista, A. (2023). Teaching programming and computational thinking in early childhood education: a case study of content knowledge and pedagogical knowledge. Frontiers in Psychology, 14(1), 1–13. https://doi.org/10.3389/fpsyg.2023.1252718
Zhang, X., Chen, Y., Hu, L., Hwang, G. J., & Tu, Y. F. (2025). Developing preschool children’s computational thinking and executive functions: unplugged vs. robot programming activities. International Journal of STEM Education, 12(1), 1--18. https://doi.org/10.1186/s40594-024-00525-z
Zourmpakis, A. I. (2025). Developing computational thinking in early childhood education : long term impacts on ct skills and motivation using the cal approach,scratchjr, and gamification. Advances in Mobile Learning Educational Research, 5(2), 1536–1547. https://doi.org/10.25082/AMLER.2025.02.009
Zurnacı, B., & Turan, Z. (2024). Educational robotics or unplugged coding activities in kindergartens?Comparison of the effects on pre-school children’s computational thinking and executive function skills. Thinking Skills and Creativity, 53(1), 101576. https://doi.org/10.1016/j.tsc.2024.101576