Skip to main content

Advertisement

Springer Nature Link
Log in

Effect of coding learning on the computational thinking of young Chinese children: based on the three-dimensional framework

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

Abstract

Coding learning can promote the development of computational thinking (CT) in young children. The effect of coding learning on CT may vary between different cultures. However, it lacks studies to evaluate the effect of coding learning on the various dimensions of CT in young Chinese children. To provide insight into this question, we recruited children aged 5–6 years to participate in the quasi-experimental study involving an experimental group and a control group. The experimental group learned collaboration- and robot-based coding for 12 lessons, whereas children in the control group attended school learning activities. The two groups showed significant changes in CT concepts after coding learning, but the changes were not different between the two groups. In addition, coding learning positively influenced the development of CT practices, including algorithm and debugging skills. Finally, qualitative analyses showed that children could express, connect, and question after learning coding, suggesting that coding learning benefits the development of CT perspectives. To summarize, coding learning positively influences the ability to apply coding concepts to solve problems in practice and the perspectives about themselves and the world around them.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data Availability

The datasets in the current study are available from the corresponding author, FG, on reasonable request.

References

  • Adams, C., Cutumisu, M., & Lu, C. (2019). Measuring K-12 Computational Thinking concepts, practices and perspectives: An examination of current CT assessments. In K. Graziano (Ed.), Proceedings of Society for Information Technology & Teacher Education International Conference (pp. 275–285). Las Vegas, NV, United States: Association for the Advancement of Computing in Education (AACE). Retrieved January 23, 2022 from https://www.learntechlib.org/primary/p/207654/.

  • Angeli, C., & Valanides, N. (2020). Developing young children’s computational thinking with educational robotics: An interaction effect between gender and scaffolding strategy. Computers in Human Behavior, 105, Article 105954. https://doi.org/10.1016/j.chb.2019.03.018.

  • Atmatzidou, S., Demetriadis, S., & Nika, P. (2018). How does the degree of guidance support students’ metacognitive and problem solving skills in educational robotics? Journal of Science Education and Technology, 27(1), 70–85. https://doi.org/10.1007/s10956-017-9709-x.

    Article  Google Scholar 

  • Bers, M. U. (2012). Designing digital experiences for positive youth development: From playpen to playground. Oxford University Press.

  • Bers, M. U. (2020). Coding as a playground: Programming and computational thinking in the early childhood classroom. Routledge.

  • Bers, M. U., Flannery, L., Kazakoff, E. R., & Sullivan, A. (2014). Computational thinking and tinkering: Exploration of an early childhood robotics curriculum. Computers & Education, 72, 145–157. https://doi.org/10.1016/j.compedu.2013.10.020.

    Article  Google Scholar 

  • Bers, M. U., González-González, C., & Armas–Torres, M. B. (2019). Coding as a playground: Promoting positive learning experiences in childhood classrooms. Computers & Education, 138, 130–145. https://doi.org/10.1016/j.compedu.2019.04.013.

    Article  Google Scholar 

  • Brennan, K., & Resnick, M. (2012, April). New frameworks for studying and assessing the development of computational thinking. Paper presented at the Proceedings of the 2012 annual meeting of the American educational research association, Vancouver, Canada.

  • Brown, N. C. C., Sentance, S., Crick, T., & Humphreys, S. (2014). Restart: The resurgence of computer science in UK schools. ACM Trans Comput Educ, 14(2), https://doi.org/10.1145/2602484. Article 9.

  • Buitrago Flórez, F., Casallas, R., Hernández, M., Reyes, A., Restrepo, S., & Danies, G. (2017). Changing a generation’s way of thinking: Teaching computational thinking through programming. Review of Educational Research, 87(4), 834–860. https://doi.org/10.3102/0034654317710096.

    Article  Google Scholar 

  • Burleson, W. S., Harlow, D. B., Nilsen, K. J., Perlin, K., Freed, N., Jensen, C. N., Lahey, B., Lu, P., & Muldner, K. (2018). Active learning environments with robotic tangibles: Children’s physical and virtual spatial programming experiences. IEEE Transactions on Learning Technologies, 11(1), 96–106. https://doi.org/10.1109/TLT.2017.2724031.

    Article  Google Scholar 

  • Cao, M., Huang, H., & He, Y. (2017). Developmental connectomics from infancy through early childhood. Trends in Neurosciences, 40(8), 494–506. https://doi.org/10.1016/j.tins.2017.06.003.

    Article  Google Scholar 

  • Caro, D. H., Lenkeit, J., & Kyriakides, L. (2016). Teaching strategies and differential effectiveness across learning contexts: Evidence from PISA 2012. Studies in Educational Evaluation, 49, 30–41. https://doi.org/10.1016/j.stueduc.2016.03.005.

    Article  Google Scholar 

  • Cervera, N., Diago, P. D., Orcos, L., & Yáñez, D. F. (2020). The acquisition of computational thinking through mentoring: An exploratory study. Education Sciences, 10(8), 202. https://doi.org/10.3390/educsci10080202.

    Article  Google Scholar 

  • Chou, P. N. (2020). Using ScratchJr to foster young children’s computational thinking competence: A case study in a third-grade computer class. Journal of Educational Computing Research, 58(3), 570–595.

    Article  Google Scholar 

  • Chu, R. J., & Chu, A. Z. (2010). Multi-level analysis of peer support, internet self-efficacy and e-learning outcomes – the contextual effects of collectivism and group potency. Computers & Education, 55(1), 145–154. https://doi.org/10.1016/j.compedu.2009.12.011.

    Article  Google Scholar 

  • Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). SAGE. https://us.sagepub.com/en-us/nam/designing-and-conducting-mixed-methods-research/book241842

  • Danby, S., Evaldsson, A. C., Melander, H., & Aarsand, P. (2018). Situated collaboration and problem solving in young children’s digital gameplay. British Journal of Educational Technology, 49(5), 959–972. https://doi.org/10.1111/bjet.12636.

    Article  Google Scholar 

  • Davies, D., Jindal-Snape, D., Collier, C., Digby, R., Hay, P., & Howe, A. (2013). Creative learning environments in education—A systematic literature review. Thinking Skills and Creativity, 8, 80–91. https://doi.org/10.1016/j.tsc.2012.07.004.

    Article  Google Scholar 

  • Erb, C. D., & Marcovitch, S. (2019). Tracking the within-trial, cross-trial, and developmental dynamics of cognitive control: Evidence from the Simon task. Child Development, 90(6), 831–848. https://doi.org/10.1111/cdev.13111.

    Article  Google Scholar 

  • Feldman, R., & Eidelman, A. I. (2009). Biological and environmental initial conditions shape the trajectories of cognitive and social-emotional development across the first years of life. Developmental Science, 12, 194–200. https://doi.org/10.1111/j.1467-7687.2008.00761.x.

    Article  Google Scholar 

  • Fuller, B., & Clarke, P. (1994). Raising school effects while ignoring culture? Local conditions and the influence of classroom tools, rules, and pedagogy. Review of Educational Research, 64(1), 119–157. https://doi.org/10.3102/00346543064001119.

    Article  Google Scholar 

  • Geng, F., Canada, K., & Riggins, T. (2018). Age- and performance-related differences in encoding during early childhood: Insights from event-related potentials. Memory (Hove, England), 26(4), 451–461. https://doi.org/10.1080/09658211.2017.1366526.

    Article  Google Scholar 

  • Geng, F., Redcay, E., & Riggins, T. (2019). The influence of age and performance on hippocampal function and the encoding of contextual information in early childhood. Neuroimage, 195, 433–443. https://doi.org/10.1016/j.neuroimage.2019.03.035.

    Article  Google Scholar 

  • Grover, S., & Pea, R. (2018). Computational thinking: A competency whose time has come. In S. Sentance, E. Barendsen, & S. Carsten (Eds.), Computer science education: Perspectives on teaching and learning in school (pp. 19–37). London: Bloomsbury Academic.

    Google Scholar 

  • Harel, I., & Papert, S. (1991). Constructionism. Ablex Publishing.

  • Heffernan, T., Morrison, M., Basu, P., & Sweeney, A. (2010). Cultural differences, learning styles and transnational education. Journal of Higher Education Policy and Management, 32(1), 27–39. https://doi.org/10.1080/13600800903440535.

    Article  Google Scholar 

  • Immordino-Yang, M. H., Darling-Hammond, L., & Krone, C. R. (2019). Nurturing nature: How brain development is inherently social and emotional, and what this means for education. Educational Psychologist, 54(3), 185–204. https://doi.org/10.1080/00461520.2019.1633924.

    Article  Google Scholar 

  • Ioannou, A., & Makridou, E. (2018). Exploring the potentials of educational robotics in the development of computational thinking: A summary of current research and practical proposal for future work. Education and Information Technologies, 23(6), 2531–2544. https://doi.org/10.1007/s10639-018-9729-z.

    Article  Google Scholar 

  • Jiang, Q., Wang, L., Zhao, W., & Pan, X. (2020). Mining the implicit relationship between cognitive level and computational thinking: From the perspective of programming behavioral representation. Modern Distance Education Research, 32(2), 94–103. doi10.3969/j.issn.1009-5195.2020.02.011

  • Jun, S., Han, S., & Kim, S. (2017). Effect of design-based learning on improving computational thinking. Behaviour & Information Technology, 36(1), 43–53. https://doi.org/10.1080/0144929X.2016.1188415.

    Article  Google Scholar 

  • Kopcha, T. J., Ocak, C., & Qian, Y. (2021). Analyzing children’s computational thinking through embodied interaction with technology: A multimodal perspective. Educational Technology Research and Development, 69, 1987–2012. https://doi.org/10.1007/s11423-020-09832-y.

    Article  Google Scholar 

  • Korkmaz, Ö., Çakir, R., & Özden, M. Y. (2017). A validity and reliability study of the computational thinking scales (CTS). Computers in Human Behavior, 72, 558–569. https://doi.org/10.1016/j.chb.2017.01.005.

    Article  Google Scholar 

  • Lin, C. P., Yang, S. J., Lin, K. Y., Looi, C. K., & Chen, Y. H. (2022). Explorations of two approaches to learning CT in a game environment for elementary school students. Journal of Computers in Education, 9, 261–290. https://doi.org/10.1007/s40692-021-00203-x.

    Article  Google Scholar 

  • Lye, S. Y., & Koh, J. H. L. (2014). Review on teaching and learning of computational thinking through programming: What is next for K-12? Computers in Human Behavior, 41, 51–61. https://doi.org/10.1016/j.chb.2014.09.012.

    Article  Google Scholar 

  • McCormick, K. I., & Hall, J. A. (2022). Computational thinking learning experiences, outcomes, and research in preschool settings: A scoping review of literature. Education and Information Technologies, 27, 3777–3812. https://doi.org/10.1007/s10639-021-10765-z.

    Article  Google Scholar 

  • Ministry of Education of the People’s Republic of China (2018). Information Technology Curriculum Standard for Ordinary Senior High Schools (2017 Edition). Retrieved from http://www.moe.gov.cn/srcsite/A26/s8001/201801/t20180115_324647.html. Accessed January 6, 2022

  • Muñoz-Repiso, A. G. V., & González, Y. A. C. (2019). Robótica para desarrollar el pensamiento computacional en Educación Infantil. Comunicar: Revista científica iberoamericana de comunicación y educación, 59, 63–72. https://doi.org/10.3916/c59-2019-06.

    Article  Google Scholar 

  • Noh, J., & Lee, J. (2020). Effects of robotics programming on the computational thinking and creativity of elementary school students. Educational Technology Research and Development, 68(1), 463–484. https://doi.org/10.1007/s11423-019-09708-w.

    Article  Google Scholar 

  • Piaget, J. (1952). The origins of intelligence in children (M. Cook, Trans.). W. W. Norton & Co. https://doi.org/10.1037/11494-000

  • Pugnali, A., Sullivan, A., & Bers, M. U. (2017). The impact of user interface on young children’s computational thinking. Journal of Information Technology Education: Innovations in Practice, 16, 171–193. https://doi.org/10.28945/3768.

    Article  Google Scholar 

  • Qualls, J. A., & Sherrell, L. B. (2010). Why computational thinking should be integrated into the curriculum. Journal of Computing Sciences in Colleges, 25(5), 66–71.

    Google Scholar 

  • Relkin, E., de Ruiter, L. E., & Bers, M. U. (2021). Learning to code and the acquisition of computational thinking by young children. Computers & Education, 169, 104222. https://doi.org/10.1016/j.compedu.2021.104222.

    Article  Google Scholar 

  • Roussou, E., & Rangoussi, M. (2020). On the use of robotics for the development of computational thinking in kindergarten: Educational intervention and evaluation. In M. Merdan, W. Lepuschitz, G. Koppensteiner, R. Balogh, & D. Obdržálek (Eds.), Robotics in Education. RiE 2019. Advances in Intelligent Systems and Computing (1023 vol.). Cham: Springer. https://doi.org/10.1007/978-3-030-26945-6_3.

    Chapter  Google Scholar 

  • Sapounidis, T., Stamovlasis, D., & Demetriadis, S. (2019). Latent class modeling of children’s preference profiles on tangible and graphical robot programming. IEEE Transactions on Education, 62(2), 127–133. https://doi.org/10.1109/TE.2018.2876363.

    Article  Google Scholar 

  • Strawhacker, A., & Bers, M. U. (2015). I want my robot to look for food”: Comparing kindergartner’s programming comprehension using tangible, graphic, and hybrid user interfaces. International Journal of Technology and Design Education, 25(3), 293–319. https://doi.org/10.1007/s10798-014-9287-7.

    Article  Google Scholar 

  • Sullivan, A., & Bers, M. U. (2016). Robotics in the early childhood classroom: Learning outcomes from an 8-week robotics curriculum in pre-kindergarten through second grade. International Journal of Technology and Design Education, 26(1), 3–20. https://doi.org/10.1007/s10798-015-9304-5.

    Article  Google Scholar 

  • Sullivan, A., & Bers, M. U. (2018). Dancing robots: Integrating art, music, and robotics in Singapore’s early childhood centers. International Journal of Technology and Design Education, 28(2), 325–346. https://doi.org/10.1007/s10798-017-9397-0.

    Article  Google Scholar 

  • Topping, K. J., & Bryce, A. (2004). Cross-age peer tutoring of reading and thinking: Influence on thinking skills. Educational Psychology, 24(5), 595–621. https://doi.org/10.1080/0144341042000262935.

    Article  Google Scholar 

  • Tsortanidou, X., Daradoumis, T., & Barberá, E. (2021). A K-6 computational thinking curricular framework: Pedagogical implications for teaching practice. Interactive Learning Environments, 1–21. https://doi.org/10.1080/10494820.2021.1986725.

  • Wang, L., Geng, F., Hao, X., Shi, D., Wang, T., & Li, Y. (2021). Measuring coding ability in young children: Relations to computational thinking, creative thinking, and working memory. Current Psychology. https://doi.org/10.1007/s12144-021-02085-9.

    Article  Google Scholar 

  • Weintrop, D., & Wilensky, U. (2015). To block or not to block, that is the question: Students’ perceptions of blocks-based programming. Proceedings of the 14th International Conference on Interaction Design and Children, 199–208. https://doi.org/10.1145/2771839.2771860

  • Whitehead, M. R. (2010). Language & literacy in the early years 0–7. Sage.

  • Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35.

    Article  Google Scholar 

  • Zhang, L., & Nouri, J. (2019). A systematic review of learning computational thinking through scratch in K-9. Computers & Education, 141, 103607. https://doi.org/10.1016/j.compedu.2019.103607.

    Article  Google Scholar 

  • Zhong, B., Wang, Q., Chen, J., & Li, Y. (2016). An exploration of three-dimensional integrated assessment for computational thinking. Journal of Educational Computing Research, 53(4), 562–590. https://doi.org/10.1177/0735633115608444.

    Article  Google Scholar 

  • Zuzovsky, R. (2013). What works where? The relationship between instructional variables and schools’ mean scores in mathematics and science in low-, medium-, and high-achieving countries. Large-Scale Assessments in Education, 1(1), 2. https://doi.org/10.1186/2196-0739-1-2.

    Article  Google Scholar 

Download references

Acknowledgements

We thank the kindergarten and the families which participated in this study. We would also like to thank research assistants for helping with data collection and analyses.

Funding

This work was supported by the National Natural Science Foundation of China (62077042); Fundamental Research Funds for the Central Universities, the MOE (Ministry of Education in China) Project of Humanities and Social Sciences (20YJA190002); and Zhejiang University Education Foundation Global Partnership Fund.

Author information

Authors and Affiliations

  1. Department of Curriculum and Learning Sciences, Zhejiang University, 866 Yuhangtang Road, Zijingang Campus, Hangzhou, 310058, China

    Chanjuan Fu, Xiaoxin Hao, Donglin Shi & Fengji Geng

  2. Early Childhood Education Group of Zhejiang Normal University, 125 Wener Road, Hangzhou, 310012, China

    Lin Wang

  3. Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, 310052, China

    Fengji Geng

Authors
  1. Chanjuan Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaoxin Hao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Donglin Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Fengji Geng
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Chanjuan Fu: Conceptualization, Formal analysis, Investigation, Data Curation, Writing - Original Draft, Visualization. Xiaoxin Hao: Investigation. Donglin Shi: Investigation, Writing - Review & Editing. Lin Wang: Investigation, Resources. Fengji Geng: Conceptualization, Methodology, Resources, Writing - Review & Editing, Supervision, Project administration, Funding acquisition.

Corresponding author

Correspondence to Fengji Geng.

Ethics declarations

Declaration of Competing Interest

All authors declare no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fu, C., Hao, X., Shi, D. et al. Effect of coding learning on the computational thinking of young Chinese children: based on the three-dimensional framework. Educ Inf Technol 28, 14897–14914 (2023). https://doi.org/10.1007/s10639-023-11807-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-023-11807-4

Keywords