Computer programming is an essential skill in the 21st century and new policies and frameworks aim at preparing students for computer science-related professions. Today, the development of new interfaces and block-programming languages facilitates the teaching of coding and computational thinking starting in kindergarten. However, as new programming languages that are developmentally appropriate emerge, there is a need to explicitly conceptualize pedagogical approaches for teaching computer science in the early years that embrace the maturational stages of children by inviting play and discovery, socialization, and creativity. Thus, it is not enough to copy models developed for older children, which mostly grew out of traditional Science, Technology, Engineering and Math (STEM) disciplines and instructional practices. This paper describes a pedagogical approach for early childhood computer science called “Coding as Another Language” (CAL), as well as six coding stages, or learning trajectories, that young children go through when exposed to CAL curriculum. CAL is grounded on the principle that learning to program involves learning how to use a new language (a symbolic system of representation) for communicative and expressive functions. This paper proposes that, due to the critical foundational role of language and literacy in the early years, the teaching of computer science can be augmented by models of literacy instruction. CAL supports young children in transitioning through different six coding stages. Case studies of young children using either the KIBO robot or the ScratchJr app will be used to characterize each coding stage and to illustrate the instructional practices of CAL curriculum.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Abelson, H., & DiSessa, A. (1981). Turtle geometry: The computer as a medium for exploring mathematics (The mit press series in artificial intelligence). Cambridge, MA: MIT Press.
Alexander, R. J. (2008). Towards dialogic teaching: Rethinking classroom talk. Cambridge: Dialogos.
Allamanis, M., Barr, E. T., Devanbu, P., & Sutton, C. (2018). A survey of machine learning for big code and naturalness. ACM Computing Surveys (CSUR), 51(4), 81.
Barrouillet, P., & Lecas, J. (1999). Mental models in conditional reasoning and working memory. Thinking & Reasoning, 5(4), 289–302.
Bers, M. (2008). Blocks to robots: Learning with technology in the early childhood classroom. New York, NY: Teachers College Press.
Bers, M. (2012). Designing Digital experiences for positive youth development: from playpen to playground. Oxford: Oxford University Press.
Bers, M. (2018a). Coding as a playground: Computational thinking and programming in early childhood. London, UK: Routledge.
Bers, M. U. (2018b). Coding, playgrounds and literacy in early childhood education: The development of KIBO Robotics and ScratchJr. In 2018 IEEE Global Engineering Education Conference (EDUCON), 2100.
Bers, M. (2019). Coding as another language: Why computer science in early childhood should not be stem. In Key Issues in Technology and Early Childhood (Editor Chip Donohue). NY: Routledge.
Bers, M., & Resnick, M. (2015). The official ScratchJr Book: Help your kids learn to code. San Francisco, CA: No Starch Press.
Bialystok, E. (1991). Letters, sounds, and symbols: Changes in children’s understands of written language. Applied Psycholinguistics., 12, 75–89.
Bredekamp, S. (1987). Developmentally appropriate practice in early childhood pro- grams serving children from birth through age 8. Washington, DC: National Association for the Education of Young Children.
Carnine, D. W., Silbert, J., Kame’enui, E., Tarver, S., & Jungjohann, K. (2006). Teaching struggling and at-risk readers: A direct instruction approach. Upper Saddle River, NJ: Pearson.
Chall, J. S. (1983). Stages of reading development. New York: McGraw-Hill.
Clarke, S., Resnick, L. B., & Rosé, C. P. (2015). Dialogic instruction: A new frontier (3rd ed., pp. 378–389). New York: Handbook of Educational Psychology.
Clements, D. H. (2007). Curriculum research: Toward a framework for research-basedCu rricula. Journal for Research in Mathematics Education, 38(1), 35–70.
Clements, D. H., & Sarama, J. (2004). Learning trajectories in mathematics education. Mathematical Thinking and Learning, 6, 81–89.
Code.org. (2018). 2018 annual report. Seattle, WA. Retrieved from https://code.org/files/annual-report-2018.pdf.
Code.org. (2019). https://code.org/.
Cunha, F., & Heckman, J. (2007). The technology of skill formation. American Economic Review, 97(2), 31–47.
Dalbey, J., & Linn, M. C. (1985). The demands and requirements of computer programming: A literature review. Journal of Educational Computing Research, 1(3), 253–274.
Dehaene, S. (2010). Reading in the Brain: The new science of how we read. New York: Penguin Books.
de Strulle, A., & Shen, C. (n.d.). STEM + Computing K-12 Education (STEM + C). National Science Foundation. Retrieved from https://wwwnsf.gov/funding/pgm_summ.jsp?pims_id=505006.
Duke, N., & Pearson, P. D. (2002). Effective practices for developing reading comprehension. In A. Farstrup & S. Samuels (Eds.), What research has to say about readinginstruction (3rd ed., pp. 205–242). Newark, DE: International Reading Association.
Elkin, M., Sullivan, A., & Bers, M. U. (2016). Programming with the KIBO robotics kit in preschool classrooms. Computers in the Schools, 33(3), 169–186. https://doi.org/10.1080/07380569.2016.1216251.
Fayer, S., Lacey, A., & Watson, A. (2017). BLS spotlight on statistics: STEM occupations-past, present, and future. Washington, DC: U.S. Department of Labor, Bureau of Labor Statistics.
Fedorenko, E., Ivanova, A., Dhamala, R., & Bers, M. U. (In press). The language of programming: A cognitive perspective. Trends in Cognitive Development.
Ferreiro, E., & Teberosky, A. (1982). Literacy before schooling. Exeter, NH: Heinemann.
Floyd, B., Santander, T., & Weimer, W. (2017). Decoding the representation of code in the brain: An fMRI study of code review and expertise. In Proceedings of the 39th International Conference on Software Engineering (pp. 175–186). IEEE Press.
Fox, B., & Saracho, O. (1990). Emergent writing: Young children solving the written language puzzle. Early Child Development and Care., 56, 81–90.
Gadanidis, G. (2017). Five affordances of computational thinking to support elementary mathematics education. Journal of Computers in Mathematics and Science Teaching, 36(2), 143–151.
Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1), 38–43. https://doi.org/10.3102/0013189X12463051.
Guzdial, M. (2008). Education: Paving the way for computational thinking. Communications of the ACM, 51(8), 25–27. https://doi.org/10.1145/1378704.1378713.
Guzdial, M., & Morrison, B. (2016). Seeking to making computing education as available as mathematics or science education. Communications of the ACM, 59(11), 31–33.
Heckman, J., & Masterov, D. (2007). The productivity argument for investing in young children. Review of Agricultural Economics, 29(3), 446–493.
Hubwieser, P., Armoni, M., Giannakos, M. N., & Mittermeir, R. T. (2014). Perspectives and visions of computer science education in primary and secondary (K-12) schools. ACM Transactions on Computing Education, 14(2), 7.
Janveau-Brennan, G., & Markovits, H. (1999). The development of reasoning with causal conditionals. Developmental Psychology, 35(4), 904–911.
Jenkins, T. (2002). On the difficulty of learning to program. In Proceedings of the 3rd Annual. Conference of the LTSN Centre for Information and Computer Sciences (pp. 53–58). Leeds, UK. Retrieved from http://www.psy.gla.ac.uk/~steve/localed/jenkins.html.
K-12 Computer Science Framework Steering Committee. (2016). K–12 computer science framework. Retrieved from https://k12cs.org.
Kafai, Y. B., & Resnick, M. (1996). Constructionism in practice: Designing, thinking, and learning in a digital world. Mahwah, NJ: Erlbaum.
Littleton, K., & Howe, C. (2010). Educational dialogues: understanding and promoting productive interaction. London: Routledge.
Lockwood, J., & Mooney, A. (2018). Computational thinking in education: Where does it fit? A systematic literary review. International Journal of Computer Science Education in Schools, 2(1), 41–60. https://doi.org/10.21585/ijcses.v2i1.26.
Lonigan, C. J., Schatschneider, C., & Westberg, L. (2008). Developing early literacy: Report of the National Early Literacy Panel. Washington, DC: National Institute for Literacy. Identification of children’s skills and abilities linked to later outcomes in reading, writing, and spelling (pp. 55–106).
Madill, H., Campbell, R. G., Cullen, D. M., Armour, M. A., Einsiedel, A. A., Ciccocioppo, A. L., et al. (2007). Developing career commitment in STEM-related fields: Myth versus reality. In R. J. Burke, M. C. Mattis, & E. Elgar (Eds.), Women and minorities in science, technology, engineering and mathematics: Upping the numbers (pp. 210–244). Northhampton, MA: Edward Elgar Publishing.
Markert, L. R. (1996). Gender related to success in science and technology. The Journal of Technology Studies, 22(2), 21–29.
National Governors Association Center for Best Practices, Council of Chief State School Officers. (2010). Common Core State Standards. National Governors Association Center for Best Practices, Council of Chief State School Officers, Washington, DC.
National Research Council. (2011). Report of a workshop of pedagogical aspects of computational thinking. Washington, DC: National Academy Press.
National Research Council. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. Committee on a Conceptual framework for new K-12 science education standards. Board on Science Education, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.
National Research Council Committee on Early Childhood Pedagogy, Bowman, B., Donovan, S., & Burns, M. (2001). Eager to learn: Educating our preschoolers. Washington, DC: National Academy Press.
Norman, K. L. (2017). Cyberpsychology: An introduction to human-computer interaction. Cambridge: Cambridge University Press.
Ong, W. J. (1982). Orality and literacy: The technologizing of the word. London: Methuen.
Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. New York: Basic Books Inc.
Papert S. (1987). Computer criticism vs. technocentric thinking Educational Researcher (Vol. 16, No. I) January/February 1987.
Pea, R. D., & Kurland, D. M. (1984). On the cognitive effects of learning computer programming. New Ideas in Psychology, 2, 137–168.
Pearson, P. D. (2004). The reading wars. Educational policy, 18(1), 216–252.
Pei, C., Weintrop, D., & Wilensky, U. (2018). Cultivating computational thinking practices and mathematical habits of mind in lattice land. Mathematical Thinking and Learning, 20(1), 75–89. https://doi.org/10.1080/10986065.2018.1403543.
Perlis, A. J. (1962). The computer in the university. In M. Greenberger (Ed.), Computers and the world of the future (pp. 180–219). Cambridge, MA: MIT Press.
Piaget, J. (1952). The origins of intelligence in children (Vol. 8, p. 18). New York: International Universities Press.
Puranik, C., & Lonigan, C. (2011). From scribbles to scrabble: Preschool children’s developing knowledge of written language. Reading and Writing, 24(5), 567–589.
Resnick, M. (2017). Lifelong kindergarten: Cultivating creativity through projects, passion, peers, and play. Cambridge: MIT Press.
Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk, N., Eastmond, E., Brennan, K., et al. (2009). Scratch: programming for all. Communications of the ACM, 52(11), 60–67.
Resnick, L. B., Michaels, S., & O’Connor, C. (2010). How (well-structured) talk builds the mind. In D. Preiss & R. Sternberg (Eds.), Innovations in educational psychology: Perspectives on learning, teaching and human development (pp. 163–194). New York: Springer.
Resnick, M., & Siegel, D. (2015). A different approach to coding. International Journal of People-Oriented Programming, 4(1), 1–4.
Ryan, M. (2011). The encyclopedia of literary and cultural theory. Hoboken, NJ: Wiley-Blackwell.
Shanahan, T., Callison, K., Carriere, C., Duke, N., Pearson, D., Schatschneider, C., & Torgesen, J. (2010). Improving reading comprehension in kindergarten through 3rd grade: ies practice guide. NCEE 2010-4038. What Works Clearinghouse.
Shonkoff, J., Phillips, D., & National Research Council (U.S.). Committee on Integrating the Science of Early Childhood Development. (2000). From neurons to neighborhoods: The science of early child development. Washington, DC: National Academy Press.
Siegmund, J., Kästner, C., Apel, S., Parnin, C., Bethmann, A., Leich, T., & Brechmann, A. (2014). Understanding source code with functional magnetic resonance imaging. In Proceedings of the 36th International Conference on Software Engineering (pp. 378–389). ACM.
STEM Education Act of 2015, House of Representatives 1020, 114th Congress. (2015). Retrieved from https://www.congress.gov/bill/114th-congress/house-bill/1020.
Strawhacker, A. L., & Bers, M. U. (2015). I want my robot to look for food: Comparing children’s programming comprehension using tangible, graphical, and hybrid user interfaces. International Journal of Technology and Design Education, 25(3), 293–319.
Sulzby, E. (1989). Assessment of writing and of children’s language while writing. In L. Morrow & J. Smith (Eds.), The role of assessment and measurement in early literacy instruction (pp. 83–109). Prentice-Hal: Englewood Cliffs, NJ.
Sulzby, E., & Teale, W. (1991). Emergent literacy. In R. Barr, M. Kamil, P. Mosenthal, & P. D. Pearson (Eds.), Handbook of reading research (Vol. 2, pp. 727–758). New York: Longman.
Tolchinsky, L. (2003). The cradle of culture and what children know about writing and numbers before being taught. Mahwah, NJ: Lawrence Erlbaum Associates.
Vee, A. (2013). Understanding computer programming as a literacy. Literacy in Composition Studies, 1(2), 42–64. https://doi.org/10.21623/220.127.116.11.
Vizner, M. Z. (2017). Big robots for little kids: Investigating the role of sale in early childhood robotics kits (Master’s thesis). Available from ProQuest Dissertations and Theses database. (UMI No. 10622097).
Vygotsky, L. S. (1978). Mind in society: The Development of higher psychological processes. Cambridge, MA: Harvard University Press.
Whitehurst, G., & Lonigan, C. (2001). Emergent literacy: Development from prereaders to readers. In S. B. Neuman & D. K. Dickensen (Eds.), Handbook of early literacy research (pp. 11–29). New York: Guilford Press.
Wilson, C., Sudol, L. A., Stephenson, C., & Stehlik, M. (2010). Running on empty: The failure to teach K-12 computer science in the digital age. New York, NY: The Association for Computing Machinery and the Computer Science Teachers Association.
Wing, J. (2006a). Computational thinking. Communications of Advancing Computing Machinery, 49(3), 33–36. https://doi.org/10.1145/1118178.1118215.
Wing, J. M. (2006b). Computational thinking. Communications of the ACM, 49(3), 33–35.
Wing, J. (2011). Research notebook: Computational thinking—What and why? The link magazine, Spring. Carnegie Mellon University, Pittsburgh. Retrieved from https://www.cs.cmu.edu/link/research-notebookcomputational-thinking-what-and-why.
Wittgenstein, Ludwig. (1997). Philosophical Investigations. Trans. G.E.M. Anscombe (2nd ed.). Cambridge: Blackwell. Print.
Wolf, M., & Stoodley, C. J. (2007). Proust and the squid: The story and science of the reading brain (1st ed.). New York, NY: HarperCollins.
The author is deeply thankful to members of the DevTech research group at Tufts University, and to Ziva Hassenfeld for discussions of these materials, Amanda Strawhacker and Anne Drescher for help with manuscript editing, and Riva Dhamala for help with table and formatting.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Bers, M.U. Coding as another language: a pedagogical approach for teaching computer science in early childhood. J. Comput. Educ. 6, 499–528 (2019). https://doi.org/10.1007/s40692-019-00147-3
- Young children
- Early childhood