Unplugged Approaches to Computational Thinking: a Historical Perspective

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.

[1]  Donald E. Knuth,et al.  Computer Science and its Relation to Mathematics , 1974 .

[2]  Aman Yadav,et al.  Computational Thinking in K-12: In-service Teacher Perceptions of Computational Thinking , 2018 .

[3]  Gregorio Robles,et al.  Development of Computational Thinking Skills through Unplugged Activities in Primary School , 2017, WiPSCE.

[4]  Ngss Lead States Next generation science standards : for states, by states , 2013 .

[5]  Erik Frøkjær,et al.  Datalogy — The copenhagen tradition of computer science , 1988, BIT.

[6]  Peter Naur,et al.  Computing versus human thinking , 2007, Commun. ACM.

[7]  Jeannette M. Wing An introduction to computer science for non-majors using principles of computation , 2007, SIGCSE.

[8]  Peter J. Denning,et al.  Remaining trouble spots with computational thinking , 2017, Commun. ACM.

[9]  Deborah A. Fields,et al.  A Crafts-Oriented Approach to Computing in High School , 2014, ACM Trans. Comput. Educ..

[10]  Felienne Hermans,et al.  To Scratch or not to Scratch?: A controlled experiment comparing plugged first and unplugged first programming lessons , 2017, WiPSCE.

[11]  Roy D. Pea,et al.  On the Cognitive Effects of Learning Computer Programming: A Critical Look. Technical Report No. 9. , 1987 .

[12]  Aman Yadav,et al.  Computational Thinking for All: Pedagogical Approaches to Embedding 21st Century Problem Solving in K-12 Classrooms , 2016 .

[13]  Aman Yadav,et al.  Computational thinking in elementary classrooms: measuring teacher understanding of computational ideas for teaching science , 2018, Comput. Sci. Educ..

[14]  J. Weizenbaum Computer Power And Human Reason: From Judgement To Calculation , 1978 .

[15]  Aman Yadav,et al.  Computational thinking for teacher education , 2017, Commun. ACM.

[16]  Aman Yadav,et al.  Computational Thinking in Elementary and Secondary Teacher Education , 2014, ACM Trans. Comput. Educ..

[17]  Stefania Bocconi,et al.  Developing Computational Thinking in Compulsory Education - Implications for policy and practice , 2016 .

[18]  Tim Bell,et al.  Computational thinking is more about humans than computers , 2016 .