A Scoping Review of Studies on Computational Thinking in K–12 Mathematics Classrooms

Abstract

Since the 1960s, a few, yet very influential, educational researchers have investigated how computer programming can be used to foster mathematics learning. However, since the term ‘computational thinking’ was popularised by Jeannette Wing in 2006, the number of studies in this area has grown substantially. In this article, we present a systematic analysis of literature linking mathematics education to computational thinking in an attempt to quantify the breadth and depth of existing work in the area. Our analysis indicates that many studies: (1) originate from computer science academics rather than education experts; (2) involve mathematics but mainly concentrate on teaching programming skills; (3) present small-scale research designs on self-reported attitudes or beliefs; (4) rarely deal with concepts in mathematical domain areas such as probability, statistics, measurement or functions. Thus, we conclude that there are opportunities for rigorous research designs reporting on observable learning outcomes, explicitly targeting mathematics, conducted by multidisciplinary teams, and focusing on less-explored domain areas. We believe that these opportunities should be investigated, in order to provide a broader evidence base for developing meaningful digital learning experiences in mathematics for school-aged children.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

References

  1. Ackermann, E. (2001). Piaget’s constructivism, Papert’s constructionism: What’s the difference. Future of Learning. Group, 5(3), 438–449.

    Google Scholar 

  2. Agalianos, A., Whitty, G., & Noss, R. (2006). The social shaping of Logo. Social Studies of Science, 36(2), 241–267.

    Article  Google Scholar 

  3. Ahamed, S., Brylow, D., Ge, R., Madiraju, P., Merrill, S., Struble, C., & Early, J. (2010). Computational thinking for the sciences: A three-day workshop for high school science teachers. In Proceedings of the 41st ACM technical symposium on computer science education (pp. 42–46). Milwaukee: SIGSCE.

    Google Scholar 

  4. Aiken, J., Caballero, M., Douglas, S., Burk, J., Scanlon, E., Thoms, B. & Schatz, M. (2013). Understanding student computational thinking with computational modeling. In Engelhardt, P. Chunkin, A., & Rebello, S. (eds.), AIP Conference Proceedings (vol. 1513 issue 1, pp. 46–49). Melville: AIP Publishing.

  5. Akcaoglu, M. (2014). Learning problem solving through making games at the game design and learning summer program. Educational Technology, Research and. Development, 62(5), 583–600.

    Google Scholar 

  6. Al-Duwis, M., Al-Khalifa, H., Al-Razgan, M., Al-Rajebah, N., & Al-Subaihin, A. (2013). Increasing high school girls’ awareness of computer science through summer camp. Paper presented at the 2013 Global Engineering Education Conference. Berlin: IEEE.

  7. Al-Humoud, S., Al-Khalifa, H., Al-Razgan, M., & Alfaries, A. (2014). Using App Inventor and LEGO mindstorm NXT in a summer camp to attract high school girls to computing fields. Paper presented at the 2014 Global Engineering Education Conference. Istanbul: IEEE.

  8. Arksey, H., & O'Malley, L. (2005). Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19–32.

    Article  Google Scholar 

  9. Atmatzidou, S., & Demetriadis, S. (2016). Advancing students’ computational thinking skills through educational robotics: A study on age- and gender-relevant differences. Robotics and Autonomous Systems, 75(Part B), 661–670.

    Article  Google Scholar 

  10. Babbitt, W., Lachney, M., Bulley, E., & Eglash, R. (2015). Adinkra mathematics: A study of ethnocomputing in Ghana. REMIE Multidisciplinary. Journal of Educational Research, 5(2), 110–135.

    Google Scholar 

  11. Barr, V., & Stephenson, C. (2011). Bringing computational thinking to K–12: What is involved and what is the role of the computer science education community? ACM Inroads, 2(1), 48–54.

    Article  Google Scholar 

  12. Bell, T., Newton, H., Andreae, P., & Robins, A. (2012a). The introduction of computer science to NZ high schools: An analysis of student work. In Proceedings of the 7th Workshop on Primary and Secondary Comupting education (pp. 5–15). Hamburg: WIPSCE.

    Chapter  Google Scholar 

  13. Bell, T., Rosamond, F., & Casey, N. (2012b). Computer science unplugged and related projects in math and computer science popularization. In H. Bodlaender, R. Downey, F. Fomin, & D. Marx (Eds.), The multivariate algorithmic revolution and beyond (pp. 398–456). Berlin: Springer-Verlag Berlin Heidelberg.

    Chapter  Google Scholar 

  14. Benton, L., Hoyles, C., Kalas, I., & Noss, R. (2016). Building mathematical knowledge with programming: Insights from the ScratchMaths project. In A. Sipitakiat & N. Tutiyaphunegprasert (Eds.), Proceedings of constructionism 2016 (pp. 25–32). Bangkok: Suksapattana Foundation.

    Google Scholar 

  15. Benton, L., Hoyles, C., Kalas, I., & Noss, R. (2017). Bridging primary programming and mathematics: Some findings of design research in England. Digital Experiences in Mathematics Education, 3(2), 115–138.

    Article  Google Scholar 

  16. Bojic, I. & Arratia, J. (2015). Teaching K–12 students STEM-C-related topics through playing and conducting research. In Proceedings of the 2015 Frontiers in Education Conference (pp. 548–555). El Paso: IEEE.

  17. Borne, K. (2010). Astroinformatics: Data-oriented astronomy research and education. Earth Science Informatics, 3(1), 5–17.

    Article  Google Scholar 

  18. Boyce, A., Campbell, A., Pickford, S., Culler, D., & Barnes, T. (2011). Experimental evaluation of BeadLoom game: How adding game elements to an educational tool improves motivation and learning. In In Proceedings of the 16th annual joint conference on innovation and technology in computer science education. Darmstadt: ACM.

    Google Scholar 

  19. Brady, C., Holbert, N., Soylu, F., Novak, M., & Wilensky, U. (2014). Sandboxes for model-based inquiry. Journal of Science Education and Technology, 24(2), 265–286.

    Google Scholar 

  20. Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. In Paper presented at the 2012 annual meeting of the American Educational Research Association. Vancouver: AERA.

    Google Scholar 

  21. Brown, N., Sentance, S., Crick, T., & Humphreys, S. (2014). Restart: The resurgence of computer science in UK schools. ACM Transactions on Computing Education, 14(2), 1–22.

    Article  Google Scholar 

  22. Calao, L., Moreno-León, J., Correa, H., & Robles, G. (2015). Developing mathematical thinking with scratch: An experiment with sixth-grade students. In G. Conole, T. Klobučar, C. Rensing, J. Konert, & E. Lavoué (Eds.), Design for teaching and learning in a networked world (pp. 17–27). Cham: Springer.

    Chapter  Google Scholar 

  23. Chatzinikolakis, G. & Papadakis, S. (2014). Motivating K–12 students learning fundamental computer science concepts with App Inventor. Paper presented at the 2014 International conference on interactive mobile communication technologies and learning. Thessaloniki: IMCL.

  24. Dagienė, V. (2008). Teaching information technology and elements of informatics in lower secondary schools: Curricula, didactic provision and implementation. In R. Mittermeir & M. Sysło (Eds), Informatics education: Supporting computational thinking (pp. 293–304). Berlin: Springer-Verlag Berlin Heidelberg.

  25. Dorling, M., & White, D. (2015). Scratch: A way to Logo and python. In Proceedings of the 46th ACM technical tymposium on computer science education (pp. 195–196). Kansas City: SIGCSE.

    Google Scholar 

  26. Dukeman, A., Caglar, F., Shekhar, S., Kinnebrew, J., Biswas, G., Fisher, D. & Gokhale, A. (2013). Teaching computational thinking skills in C3STEM with traffic simulation. In A. Holzinger & G. Pasi (Eds), Human–computer interaction and knowledge discovery in complex, unstructured, big data (pp. 350–357). Berlin: Springer-Verlag Berlin Heidelberg.

  27. Falkner, K., Vivian, R., & Falkner, N. (2014). The Australian digital technologies curriculum: Challenge and opportunity. In J. Whalley & D. D’Souza (Eds.), Proceedings of the Sixteenth Australasian Computing Education conference (pp. 3–12). Auckland: Australian Computer Society.

    Google Scholar 

  28. Farris, A. & Sengupta, P. (2013). On the aesthetics of children's computational modeling for learning science. In Proceedings of the 12th International Conference on Interaction Design and Children (pp. 479–482). New York. ACM.

  29. Feurzeig, W., Papert, S., Bloom, M., Grant, R. & Solomon, C. (1969). Programming-languages as a conceptual framework for teaching mathematics. (Final report on the first fifteen months of the LOGO project.) Washington, DC. (http://nla.gov.au/nla.cat-vn5207614).

  30. Fisher, L. (2016). A decade of ACM efforts contribute to computer science for all. Communications of the ACM, 59(4), 25–27.

    Article  Google Scholar 

  31. Haapasalo, L., & Kadijevich, D. (2000). Two types of mathematical knowledge and their relation. Journal für Mathematik-Didaktik, 21(2), 139–157.

    Article  Google Scholar 

  32. Hoyles, C., & Noss, R. (1992). Learning mathematics and Logo. Cambridge: MIT Press.

    Google Scholar 

  33. Jiangjiang, L., Wilson, J., Hemmenway, D., Yingbo, X., & Cheng-Hsien, L. (2015). Oh SNAP! A one-week summer computing workshop for K–12 teachers. In In Paper presented at the 10th international conference on Computer Science and Education. Cambridge: IEEE.

    Google Scholar 

  34. Jimenez, Y., & Gardner-McCune, C. (2015). Using app inventor and history as a gateway to engage African American students in computer science. In In Paper presented at the Research in Equity and Sustained Participation in Engineering, Computing, and Technology (RESPECT). Atlanta: IEEE.

    Google Scholar 

  35. Kafai, Y., & Burke, Q. (2015). Constructionist gaming: Understanding the benefits of making games for learning. Educational Psychologist, 50(4), 313–334.

    Article  Google Scholar 

  36. Kafai, Y., & Harel, I. (1991). Children’s learning through consulting: When mathematical ideas, programming knowledge, instructional design, and playful discourse are intertwined. In I. Harel & S. Papert (Eds.), Constructionism (pp. 85–100). Norwood: Ablex.

    Google Scholar 

  37. Kalelioğlu, F. (2015). A new way of teaching programming skills to K–12 students: Code.org. Computers in Human Behavior, 52, 200–210.

    Article  Google Scholar 

  38. Kalelioğlu, F., Gülbahar, Y., & Kukul, V. (2016). A framework for computational thinking based on a systematic research review. Baltic Journal of Modern Computing, 4(3), 583–596.

    Google Scholar 

  39. Ke, F. (2014). An implementation of design-based learning through creating educational computer games: A case study on mathematics learning during design and computing. Computers & Education, 73, 26–39.

    Article  Google Scholar 

  40. Kim, H. (2016). Inquiry-based science and technology enrichment program for middle-school-aged female students. Journal of Science Education and Technology, 25(2), 174–186.

    Article  Google Scholar 

  41. Koh, K., Repenning, A., Nickerson, H., Endo, Y., & Motter, P. (2013). Will it stick? Exploring the sustainability of computational thinking education through game design. In Proceedings of the 44th ACM technical symposium on computer science education (pp. 597–602). Denver: SIGCSE.

    Google Scholar 

  42. Kurebayashi, S., Aoki, H., Kamada, T., Kanemune, S. & Kuno, Y. (2008). Proposal for teaching manufacturing and control programming using autonomous mobile robots with an arm. In R. Mittermeir & M. Sysło (Eds), Informatics education: Supporting computational thinking (pp. 75–86). Berlin: Springer-Verlag Berlin Heidelberg.

  43. Kyriakides, A., Meletiou-Mavrotheris, M., & Prodromou, T. (2016). Mobile technologies in the service of students’ learning of mathematics: The example of game application a.L.E.X. In the context of a primary school in Cyprus. Mathematics Education Research Journal, 28(1), 53–78.

    Article  Google Scholar 

  44. Larkins, D., Moore, J., Rubbo, L., & Covington, L. (2013). Application of the cognitive apprenticeship framework to a middle school robotics camp. In Proceedings of the 44th ACM technical symposium on computer science education (pp. 89–94). Denver: SIGCSE.

    Google Scholar 

  45. Layer, R., Sherriff, M. & Tychonievich, L. (2012). “Inform, experience, implement”: Teaching an intensive high school summer course. In Paper presented at the 2012 Frontiers in Education Conference. Washington, DC: IEEE.

  46. Lewis, C., & Shah, N. (2012). Building upon and enriching grade four mathematics standards with programming curriculum. In Proceedings of the 43rd ACM technical symposium on computer science education (pp. 57–62). Raleigh: SIGCSE.

    Google Scholar 

  47. Lye, S., & Koh, J. (2014). Review on teaching and learning of computational thinking through programming: What is next for K–12? Computers in Human Behavior, 41, 51–61.

    Article  Google Scholar 

  48. McCoid, S., Freeman, J., Magerko, B., Michaud, C., Jenkins, T., McKlin, T., & Kan, H. (2013). EarSketch: An integrated approach to teaching introductory computer music. Organised Sound, 18(2), 146–160.

    Article  Google Scholar 

  49. Mensing, K., Mak, J., Bird, M. & Billings, J. (2013). Computational, model thinking and computer coding for US common Core standards with 6- to 12-year-old students. In Proceedings of the Emerging eLearning Technologies and Applications Conference (pp. 17–22). Stary Smokovec: IEEE.

  50. Nikou, S. & Economides, A. (2014). Transition in student motivation during a scratch and an app inventor course. In Proceedings of the 2014 Global Engineering Education Conference (pp. 1042–1045). Istanbul: IEEE.

  51. Nishida, T., Idosaka, Y., Hofuku, Y., Kanemune, S., & Kuno, Y. (2008). New methodology of information education with “computer science unplugged”. In R. Mittermeir & M. Sysło (Eds.), Informatics education: Supporting computational thinking (pp. 241–252). Berlin: Springer-Verlag Berlin Heidelberg.

    Chapter  Google Scholar 

  52. Oliveira, O., Nicoletti, M., & Cura, L. (2014). Quantitative correlation between ability to compute and student performance in a primary school. In Proceedings of the 45th ACM technical symposium on computer science education (pp. 505–510). Atlanta: SIGCSE.

    Google Scholar 

  53. Papert, S. (1972). Teaching children to be mathematicians versus teaching about mathematics. International Journal of Mathematical Education in Science and Technology, 3(3), 249–262.

    Article  Google Scholar 

  54. Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. New York: Basic Books.

    Google Scholar 

  55. Papert, S. (1990). A critique of technocentrism in thinking about the school of the future. Cambridge: Epistemology and Learning Group, MIT Media Laboratory.

    Google Scholar 

  56. Papert, S. (1993). The children’s machine: Rethinking school in the age of the computer. New York: Basic Books.

    Google Scholar 

  57. Papert, S. (1996). An exploration in the space of mathematics educations. International Journal of Computers for Mathematical Learning, 1(1), 95–123.

    Google Scholar 

  58. Perdikuri, K. (2014). Students’ experiences from the use of MIT app inventor in classroom. In Proceedings of the 18th Panhellenic conference on informatics. Athens: ACM.

  59. Phalke, A., & Lysecky, S. (2010). Adapting the eblock platform for middle school STEM projects: Initial platform usability testing. IEEE Transactions on Learning Technologies, 3(2), 152–164.

    Article  Google Scholar 

  60. Pinto, A., & Escudeiro, P. (2014). The use of scratch for the development of 21st-century learning skills in ICT. In In Paper presented at the 9th Iberian Conference on Information Systems and Technologies. Barcelona: IEEE.

    Google Scholar 

  61. Reeping, D. & Reid, K. (2015). Viewing K–12 mathematics and science standards through the lens of the first-year introduction to engineering course classification scheme. In Paper presented at the 2015 Frontiers in Education Conference. Washington, DC: IEEE.

  62. Repenning, A., Webb, D., & Ioannidou, A. (2010). Scalable game design and the development of a checklist for getting computational thinking into public schools. In Proceedings of the 41st ACM technical symposium on computer science education (pp. 265–269). Milwaukee: SIGCSE.

    Google Scholar 

  63. Repenning, A., Basawapatna, A. & Klymkowsky, M. (2013). Making educational games that work in the classroom: A new approach for integrating STEM simulations. In Paper presented at the Games Innovation Conference. Vancouver: IEEE.

  64. Rodriguez, B. (2015). Assessing computational thinking in Computer Science Unplugged activities. Unpublished Masters thesis. Golden: Colorado School of Mines.

  65. Ruutmann, T. (2014). Optional STEM courses for secondary schools designed and implemented for enhancement of K–12 technology education, in order to excite students’ interest in technology and engineering education. In Paper presented at the 2014 International Conference on Interactive Collaborative Learning (ICL). Dubai.

  66. Scherer, R. (2016). Learning from the past: The need for empirical evidence on the transfer effects of computer programming skills. Frontiers in Psychology, 7, 1390.

    Google Scholar 

  67. Sengupta, P., Kinnebrew, J., Basu, S., Biswas, G., & Clark, D. (2013). Integrating computational thinking with K–12 science education using agent-based computation: A theoretical framework. Education and Information Technologies, 18(2), 351–380.

    Article  Google Scholar 

  68. Sullivan, K., Byrne, J., Bresnihan, N., O'Sullivan, K. & Tangney, B. (2015). CodePlus: Designing an after- school computing programme for girls. In Paper presented at the 2015 Frontiers in Education Conference. Washington, DC: IEEE.

  69. Tisue, S. & Wilensky, U. (2004). NetLogo: A simple environment for modeling complexity. In Minai, A. & Bar-Yam, Y. (eds.), Proceedings of the 5th international conference on complex systems (pp. 16–21). Boston.

  70. Turbak, F., Sandu, S., Kotsopoulos, O., Erdman, E., Davis, E., & Chadha, K. (2012). Blocks languages for creating tangible artifacts. In Proceedings of the 2012 symposium on visual languages and human-centric computing (pp. 137–144). Innsbrucka: IEEE.

    Chapter  Google Scholar 

  71. Vivian, R., Falkner, K., & Falkner, N. (2014). Addressing the challenges of a new digital technologies curriculum: MOOCs as a scalable solution for teacher professional development. Research in Learning Technology, 22. https://doi.org/10.3402/rlt.v22.24691.

  72. Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25(1), 127–147.

    Article  Google Scholar 

  73. Werner, L., Denner, J., Campe, S., & Kawamoto, D. (2012). The fairy performance assessment: Measuring computational thinking in middle school. In Proceedings of the 43rd ACM technical symposium on computer science education (pp. 215–220). Raleigh: SIGCSE.

    Google Scholar 

  74. Wilensky, U., & Resnick, M. (1999). Thinking in levels: A dynamic systems approach to making sense of the world. Journal of Science Education and Technology, 8(1), 3–19.

    Article  Google Scholar 

  75. Wilkerson-Jerde, M. (2014). Construction, categorization, and consensus: Student-generated computational artifacts as a context for disciplinary reflection. Educational Technology Research & Development, 62(1), 99–121.

    Article  Google Scholar 

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

    Article  Google Scholar 

  77. Wing, J. (2016). Computational thinking, 10 years later. www.microsoft.com/en-us/research/blog/computational-thinking-10-years-later/.

  78. Xiaoxia, W. & Zhurong, Z. (2011). The research of situational teaching mode of programming in high school with Scratch. In Proceedings of the 2011 Conference on Information Technology and Artificial Intelligence (pp. 488–492). IEEE.

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Daniel Hickmott.

Electronic supplementary material

ESM 1

(DOCX 138 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hickmott, D., Prieto-Rodriguez, E. & Holmes, K. A Scoping Review of Studies on Computational Thinking in K–12 Mathematics Classrooms. Digit Exp Math Educ 4, 48–69 (2018). https://doi.org/10.1007/s40751-017-0038-8

Download citation

Keywords

  • Computational thinking
  • Programming
  • Mathematics
  • Digital technologies
  • Scoping review