Measurement of Computational Thinking in K-12 Education: The Need for Innovative Practices



We are currently living in a period where computational thinking (CT) will influence everyone in every field of endeavor (Wing, 2006, 2008). While its definition as well as its place in school curricula are still not clear, the process of integrating CT within the K-12 school system is underway. In New Brunswick, Canada, as in other parts of the world, more and more students are being exposed to a different programming and coding activities that seek to introduce CT skills such as abstraction, decomposition, algorithmic thinking, as well as pattern recognition while solving problems in a variety of technology-rich environments. Our 3-year study of innovative practices targeting the development of CT consists of three main stages: (1) the research and development of a visual data flow programming language for development of CT skills in K-12, (2) the development of a testing method based on a selection of tasks and its application to measuring CT in middle and high school students, (3) deeper investigation into the process of CT development in students, along with the elaboration of a novel testing suite in order to better detect students’ progress for each of four components of CT skills. Our findings demonstrate, along with students’ engagement and interest in solving challenging tasks, the complexity of issues that emerge from this process as well as possible paths for future investigations.


Computational thinking Innovative practices Skill assessment Visual programming Technology-rich learning environments 



This ongoing study is being conducted with the help of the Canadian Social Sciences and Humanities Research Council (Partnership Development Grant #890-2013-0062), New Brunswick Innovation Foundation (2016 Research Assistantship Program), and le Secrétariat aux Affaires Intergouvernementales Canadiennes du Québec (Programme de soutien à la Francophonie Canadienne).


  1. 1.
    Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35.CrossRefGoogle Scholar
  2. 2.
    Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 366(1881), 3117–3725.CrossRefGoogle Scholar
  3. 3.
    Papert, S. (1980). Mindstorms: Children, computers and powerful ideas. York: New Basic Books Inc.Google Scholar
  4. 4.
    Papert, S. (1996). An exploration in the space of mathematics education. International Journal of Computers for Mathematical Learning, 1(1), 95–123.Google Scholar
  5. 5.
    Gretter, S., & Yadav, A. (2016). Computational thinking and media & information literacy: An integrated approach to teaching twenty-first century skills. TechTrends, 60(5), 510–516.CrossRefGoogle Scholar
  6. 6.
    Bocconi, S., Chioccariello, A., Dettori, G., Ferrari, A., & Engelhardt, K. (2016). Developing computational thinking in compulsory education.Google Scholar
  7. 7.
    Webb, M., Davis, N., Bell, T., Katz, Y. J., Reynolds, N., Chambers, D. P., & Sysło, M. M. (2017). Computer science in K-12 school curricula of the 2lst century: Why, what and when? Education and Information Technologies, 22(2), 445–468.CrossRefGoogle Scholar
  8. 8.
    Yadav, A., Good, J., Voogt, J., & Fisser, P. (2017). Computational thinking as an emerging competence domain. In Competence-based vocational and professional education (pp. 1051–1067). Cham: Springer International Publishing.CrossRefGoogle Scholar
  9. 9.
    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.CrossRefGoogle Scholar
  10. 10.
    Bower, M., Wood, L. N., Lai, J. W., Howe, C., Lister, R., Mason, R., & Veal, J. (2017). Improving the computational thinking pedagogical capabilities of school teachers. Australian Journal of Teacher Education, 42(3), 4.CrossRefGoogle Scholar
  11. 11.
    Csizmadia, A., Curzon, P., Dorling, M., Humphreys, S., Ng, T., Selby, C., & Woollard, J. (2015). Computational thinking: A guide for teachers. Computing at SchoolsGoogle Scholar
  12. 12.
    Faber, H. H., Wierdsma, M. D., Doornbos, R. P., van der Ven, J. S., & de Vette, K. (2017). Teaching computational thinking to primary school students via unplugged programming lessons. Journal of the European Teacher Education Network, 12, 13–24.Google Scholar
  13. 13.
    Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1), 38–43.CrossRefGoogle Scholar
  14. 14.
    Kalelioglu, 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.Google Scholar
  15. 15.
    Román-González, M., Moreno-León, J., & Robles, G. (2017a). Complementary tools for computational thinking assessment.Google Scholar
  16. 16.
    Chen, G., Shen, J., Barth-Cohen, L., Jiang, S., Huang, X., & Eltoukhy, M. (2017). Assessing elementary students’ computational thinking in everyday reasoning and robotics programming. Computers & Education, 109, 162–175.CrossRefGoogle Scholar
  17. 17.
    Gadanidis, G., Hughes, J. M., Minniti, L., & White, J. G. (2017). Computational thinking, grade 1 students and the binomial theorem. Digital Experiences in Mathematics Education, 3(2), 77–96. CrossRefGoogle Scholar
  18. 18.
    Karsenti, T., & Bugmann, J. (2017). Transformer l’éducation avec Minecraft? Résultats d’une recherche menée auprès de 118 élèves du primaire. Montréal: CRIFPE.Google Scholar
  19. 19.
    Freiman, V., Godin, J., Larose, F., Léger, M., Chiasson, M., Volkanova, V., & Goulet, M. J. (2016). Towards the life-long continuum of digital competences: Exploring combination of soft-skills and digital skills development.Google Scholar
  20. 20.
    Gauvin, S., Paquet, M., & Freiman, V. (2015). Vizwik–visual data flow programming and its educational implications. In Proceedings of EdMedia: World Conference on Educational Media and Technology Montréal, Canada (pp. 1602–1608).Google Scholar
  21. 21.
    Sáez-López, J. M., Román-González, M., & Vázquez-Cano, E. (2016). Visual programming languages integrated across the curriculum in elementary school: A two year case study using “scratch” in five schools. Computers & Education, 97, 129–141.CrossRefGoogle Scholar
  22. 22.
    Gauvin, S., & Cox, P. T. (2011). Controlled dataflow visual programming languages, VINCI, August, Hong Kong (pp. 345–352). New York, NY: ACM.Google Scholar
  23. 23.
    Whitley, K. N., Novick, L. R., & Fisher, D. (2006). Evidence in favor of visual representation for the dataflow paradigm: An experiment testing LabVIEWs comprehensibility. International Journal of Human-Computer Studies, 64, 281–303.CrossRefGoogle Scholar
  24. 24.
    Selby, C., & Woollard, J. (2014). Refining an understanding of computational thinking. Author’s Original, 1–23.Google Scholar
  25. 25.
    Brennan, K., & Resnick, M. (2012, April). New frameworks for studying and assessing the development of computational thinking. In Proceedings of the 2012 annual meeting of the American Educational Research Association, Vancouver, Canada (pp. 1–25).Google Scholar
  26. 26.
    Good, J., Yadav, A., & Mishra, P. (2017). Computational thinking in computer science classrooms: Viewpoints from CS educators. In Society for Information Technology & Teacher Education International Conference (pp. 51–59). Association for the Advancement of Computing in Education (AACE)Google Scholar
  27. 27.
    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.CrossRefGoogle Scholar
  28. 28.
    ISTE & CSTA. (2011). Operational definition of computational thinking for K-12 education.Google Scholar
  29. 29.
    Dolgopolovas, V., Jevsikova, T., Savulionienė, L., & Dagienė, V. (2015). On evaluation of computational thinking of software engineering novice students. In Proceedings of the IFIP TC3 Working Conference “A New Culture of Learning: Computing and next Generations” (pp. 90–99).Google Scholar
  30. 30.
    Papert, S. (1991). Situating constructionism. In I. Harel & S. Papert (Eds.), Constructionism: Research reports and essays 1985–1990 by the epistemology and learning research group, MIT. Cambridge, MA: MIT.Google Scholar
  31. 31.
    Kafai, Y. B., & Burke, Q. (2014). Mindstorms 2: Children, programming, and computational participation. Retrieved May, 1, 2016.Google Scholar
  32. 32.
    Turkle, S., & Papert, S. (1990). Epistemological pluralism: Styles and voices within the computer culture. Signs: Journal of Women in Culture and Society, 16(1), 128–157.CrossRefGoogle Scholar
  33. 33.
    Blanchard, S., Freiman, V., & Lirrete-Pitre, N. (2010). Strategies used by elementary schoolchildren solving robotics-based complex tasks: Innovative potential of technology. Procedia-Social and Behavioral Sciences, 2(2), 2851–2857.CrossRefGoogle Scholar
  34. 34.
    Benitti, F. B. V. (2012). Exploring the educational potential of robotics in schools: A systematic review. Computers & Education, 58(3), 978–988.CrossRefGoogle Scholar
  35. 35.
    Román-González, M., Pérez-González, J. C., & Jiménez-Fernández, C. (2017b). Which cognitive abilities underlie computational thinking? Criterion validity of the computational thinking test. Computers in Human Behavior, 72, 678–691.CrossRefGoogle Scholar
  36. 36.
    Mühling, A., Ruf, A., & Hubwieser, P. (2015). Design and first results of a psychometric test for measuring basic programming abilities. In Proceedings of the workshop in primary and secondary computing education (pp. 2–10). New York, NY: ACM.CrossRefGoogle Scholar
  37. 37.
    Weintrop, D., & Wilensky, U. (2015). Using commutative assessments to compare conceptual understanding in blocks-based and text-based programs. In ICER (Vol. 15, pp. 101–110).Google Scholar
  38. 38.
    Meerbaum-Salant, O., Armoni, M., & Ben-Ari, M. (2013). Learning computer science concepts with scratch. Computer Science Education, 23(3), 239–264.CrossRefGoogle Scholar
  39. 39.
    Zur-Bargury, I., Pârv, B., & Lanzberg, D. (2013). A nationwide exam as a tool for improving a new curriculum. In Proceedings of the 18th ACM conference on innovation and technology in computer science education (pp. 267–272). New York, NY: ACM.Google Scholar
  40. 40.
    Dagiene, V., & Futschek, G. (2008). Bebras international contest on informatics and computer literacy: Criteria for good tasks. In Informatics education-supporting computational thinking (pp. 19–30). Berlin, Heidelberg: Springer.CrossRefGoogle Scholar
  41. 41.
    Izu, C., Mirolo, C., Settle, A., Mannila, L., & STUPURIENĖ, G. (2017). Exploring Bebras tasks content and performance: A multinational study. Informatics in Education, 16(1), 39–59.Google Scholar
  42. 42.
    Basawapatna, A., Koh, K. H., Repenning, A., Webb, D. C., & Marshall, K. S. (2011). Recognizing computational thinking patterns. In Proceedings of the 42nd ACM technical symposium on computer science education (pp. 245–250). New York, NY: ACM.Google Scholar
  43. 43.
    Moreno-León, J., & Robles, G. (2015). Analyze your Scratch projects with Dr. Scratch and assess your computational thinking skills. In Scratch conference (pp. 12–15).Google Scholar
  44. 44.
    Koh, K. H., Basawapatna, A., Bennett, V., & Repenning, A. (2010). Towards the automatic recognition of computational thinking for adaptive visual language learning. In Visual languages and human-centric computing (VL/HCC), 2010 IEEE symposium on (pp. 59–66). New York: IEEE.CrossRefGoogle Scholar
  45. 45.
    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.CrossRefGoogle Scholar
  46. 46.
    Grover, S. (2011). Robotics and engineering for middle and high school students to develop computational thinking. In annual meeting of the American Educational Research Association, New Orleans, LAGoogle Scholar
  47. 47.
    Grover, S. (2015). “Systems of Assessments” for Deeper Learning of Computational Thinking in K-12. In Proceedings of the 2015 Annual Meeting of the American Educational Research Association (pp. 15–20).Google Scholar
  48. 48.
    Grover, S., Cooper, S., & Pea, R. (2014). Assessing computational learning in K-12. In Proceedings of the 2014 conference on Innovation & Technology in Computer Science Education (pp. 57–62). New York, NY: ACM.Google Scholar
  49. 49.
    Gunn, C., & Peddie, R. (2008). A design-based research approach for eportfolio initiatives. Hello! Where are you in the landscape of educational technology? Proceedings Ascilite Melbourne 2008 Google Scholar
  50. 50.
    The Design-Based Research Collective. (2003). Design-based research: An emerging paradigm for educational inquiry. Educational Researcher, 5–8.Google Scholar
  51. 51.
    Freiman, V., & Lirette-Pitre, N. (2009). Building a virtual learning community of problem solvers: Example of CASMI community. ZDM, 41(1–2), 245–256.CrossRefGoogle Scholar
  52. 52.
    Vegt, W. (2013). Predicting the difficulty level of a Bebras tasks. Olympiads in Informatics, 7, 132–139.Google Scholar
  53. 53.
    Dagiene, V., & Stupuriene, G. (2015). Informatics education based on solving attractive tasks through a contest. KEYCIT 2014: key competencies in informatics and ICT, 7, 97.Google Scholar
  54. 54.
    Dagiene, V., & Stupuriene, G. (2016). Bebras-a sustainable community building model for the concept based learning of informatics and computational thinking. Informatics in Education, 15(1), 25.CrossRefGoogle Scholar
  55. 55.
    Grover, S. (2017). Assessing algorithmic and computational thinking in K-12: Lessons from a middle school classroom. In Emerging research, practice, and policy on computational thinking (pp. 269–288). Cham: Springer International Publishing.CrossRefGoogle Scholar
  56. 56.
    Shute, V. J., Sun, C., Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review.Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Faculté des Sciences de l’ÉducationUniversité de Moncton, Campus de MonctonMonctonCanada
  2. 2.Agora Mobile Inc.MonctonCanada
  3. 3.Anglophone East School DistrictMENBMonctonCanada

Personalised recommendations