pp 1–8 | Cite as

Unplugged Approaches to Computational Thinking: a Historical Perspective

  • Elisa Nadire CaeliEmail author
  • Aman Yadav
Original Paper


In the recent years, there has been a push to engage primary and secondary students in computer science to prepare them to live and work in a world influenced by computation. One of the efforts involves getting primary and secondary students to think computationally by introducing computational ideas such as, algorithms and abstraction. Majority of this work around computational thinking has focused on the use of digital technologies, in particular programming environments (Yadav, Stephenson, and Hong 2017). In today’s highly digitalized world, we often associate computational problem-solving processes with the use of computers. Yet, solving problems computationally by designing solutions and processing data is not a digital skill, rather a mental skill. Humans have solved problems for eons and before anyone even thought about the types of digital technologies and devices we know today. The purpose of this article is to examine the historical route of computational thinking and how history can inspire and inform initiatives today. We introduce how computational thinking skills are rooted in non-digital (unplugged) human approaches to problem solving, and discuss how mainstream focus changed to digital (plugged) computer approaches, particularly on programming. In addition, we connect past research with current work in computer science education to argue that computational thinking skills and computing principles need to be taught in both unplugged and plugged ways for learners to develop deeper understanding of computational thinking ideas and their relevance in today’s society.


Computational Thinking Unplugged Problem-Solving Primary and Secondary Education Datalogy Algorithms 


Compliance with Ethical Standards

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Conflict of Interest

Author A declares that she has no conflict of interest. Author B declares that he has no conflict of interest.


  1. Bell, T & Roberts, J. (2016). Computational thinking is more about humans than computers, set 2016: no. 1, p. 3–7.
  2. Bocconi, S.; Chioccariello, A.; Dettori, G.; Ferrari, A.; & Engelhardt, K. (2016). Computational thinking in compulsory education. Joint Research Center. European Commission.Google Scholar
  3. Brackmann, C. P.; Román-González, M.; Robles, G.; Moreno-León, J.;Casali, A.; & Barone, D. (2017). Development of Computational Thinking Skills through Unplugged Activities in Primary School. WIPSCE 2017. Proceedings of the 12th workshop in primary and secondary computing education: 65–72.Google Scholar
  4. Caeli, E. N. & Bundsgaard, J. (forthcoming). Computational Thinking in Compulsory Education: A Survey Study on Initiatives and Conceptions. Manuscript submitted.
  5. CS Unplugged (2019). Computer science without a computer. Google Scholar
  6. Denning, P. J. (2017). Remaining trouble spots with computational thinking. Communications of the ACM, 60(6), 33–39. Scholar
  7. Fischer, C.; Frøkjær, E.; & Gedsø, L. (1972). Datalære i skolen. Om data og edb i samfundet. Gads Forlag.Google Scholar
  8. Frandsen, K. (1983) (ed.) EDB i skolens undervisning. Danmarks Skolelederforening.Google Scholar
  9. Greenberger, M. (1962) (ed.). Management and the Computer of the Future. The M.I.T. Press and John Wiley & Sons, inc.Google Scholar
  10. Hermans, F., & Aivaloglou, E. (2017). To Scratch or not to Scratch? A controlled experiment comparing plugged first and unplugged first programming lessons. WIPSCE 2017. Proceedings of the 12th workshop in primary and secondary computing education (pp. 49–56).Google Scholar
  11. Holt, Lone H. (1988). Datalære integreret i dansk og matematik. Datalære, Årg. 12, nr. 5, pp. 16–19.Google Scholar
  12. Katz, D.L. (1960). The Use of Computers in Engineering Classroom Instruction. Conference Report. College of Engineering. The University of Michigan. The Ford Foundation Computer Project.Google Scholar
  13. Knuth, D. E. (1974). Computer science and its relation to mathematics. The American Mathematical Monthly, 81, 323–343.CrossRefGoogle Scholar
  14. Malmberg, A. C. (1970). Datalogi i skolen: Læreruddannelsen – en flaskehals. I Undervisningsministeriets tidsskrift (pp. 272–276).Google Scholar
  15. Naur, P. (1954). Elektronregnemaskinerne og hjernen. Perspektiv, 1(7), 42–46.Google Scholar
  16. Naur, P. (1965). The Place of Programming in a World of Problems, Tools, and People. Proc. IFIP Congress 65, 165–199Google Scholar
  17. Naur, P. (1966). Plan for et kursus i datalogi og datamatik. Regnecentralen.Google Scholar
  18. Naur, P. (1968). Demokrati i datamatiseringens tidsalder. Kriterium, 3(5):31–32. Nyt Nordisk Forlag Arnold Busck.Google Scholar
  19. Naur, P. (1970). Planer og ideer for datalogisk institut ved Københavns Universitet. Studentlitteratur.Google Scholar
  20. Naur, P. (2005). Computing Versus Human Thinking. A. M. Turing Award Lecture Video. ACM.
  21. NGSS Lead States. (Ed.) (2013). Next generation science standards: For states, by states. National Academies Press.Google Scholar
  22. Papert, S. (1980, 1993). Mindstorms. Children, Computers, And Powerful Ideas. Basic BooksGoogle Scholar
  23. Pea, R., & Kurland, M. (1984). On the cognitive effects of learning computer programming. New Ideas in Psychology, 2(2), 137–168.CrossRefGoogle Scholar
  24. Sands, P., Yadav, A., & Good, J. (2018). Computational Thinking in K-12: In-service teacher perceptions of computational thinking. In M. S Khine. (Ed.). Computational Thinking in the STEM Disciplines (pp. 151–164). Springer.Google Scholar
  25. Sveinsdottir, E. & Frøkjær, E. (1988). Datalogy – The Copenhagen Tradition of Computer Science. BIT Numerical Mathematics, 28(3), 450–472Google Scholar
  26. Undervisningsministeriet. (1972). Betænkning om edb-undervisning i det offentlige uddannelsessystem. In Undervisningsministeriet.Google Scholar
  27. Weizenbaum, J. (1976). Computer power and human reason: From judgment to calculation. W H Freeman & Co.Google Scholar
  28. Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. Scholar
  29. Yadav, A., Mayfield, C., Zhou, N., Hambrusch, S., & Korb, J. T. (2014). Computational thinking in elementary and secondary teacher education. ACM Transactions on Computing Education, 14(1), 1–16.CrossRefGoogle Scholar
  30. Yadav, A., Hong, H., & Stephenson, C. (2016). Computational thinking for all: Pedagogical approaches to embedding a 21st century problem solving in K-12 classrooms. TechTrends, 60, 565–568. Scholar
  31. Yadav, A., Stephenson, C., & Hong, H. (2017). Computational Thinking for Teacher Education. Communications of the ACM, 60(4), 55–62.
  32. Yadav, A., Krist, C., Good, J., & Caeli, E. (2018). Computational thinking in elementary classrooms: Measuring teacher understanding of computational ideas for teaching science. Computer Science Education.

Copyright information

© Association for Educational Communications & Technology 2019

Authors and Affiliations

  1. 1.Danish School of EducationAarhus UniversityCopenhagen NVDenmark
  2. 2.College of EducationMichigan State UniversityEast LansingUSA

Personalised recommendations