Given the importance of inquiry in science education, various computational tools and emerging 4IR technologies have been embraced in recent years to improve how inquiry is carried out across science disciplines. However, there is growing concern about how schools can better guide students and teachers in leveraging the technological affordances of the 4IR. In response to this, the authors propose that one pertinent approach to the development and preparation of science teachers for the future of 4IR classrooms is to teach them how to think computationally. Based on a recent literature review, this chapter offers insight into how computational thinking can be used as a pedagogical approach to science teaching in a 4IR classroom.
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Schwab (2016) described the fourth industrial revolution (4IR) as a technological era characterised by the fusion of modern knowledge and skills in unprecedented ways to foster and broaden innovative collaboration between humans, the digital, biological and physical worlds. Considering the exponential rate at which the use of scientific and technological innovations such as robots, artificial intelligence, 3D printing, the internet of things, augmented reality, virtual reality, mixed reality, and many other advances and innovative systems are becoming imperative to economic development and globalisation, there is a growing trend of interest on the set of skills, training, expertise, abilities and education that learners require in order to succeed in the changing world of work brought about by 4IR. Research claims that one approach to teaching the essential skills needed for the 4IR is to teach computational thinking (National Research Council (NRC, 2010; Wesse, 2017; Balanskat et al., 2018). Berland and Wilensky (2015) defined computational thinking (CT) as “the ability to think with the computer-as-tool” (p. 630). However, Wing (2006) stated that CT employs computers as a thinking tool, universal mindset, and skill set. She claims CT “draws on the fundamental ideas of computer science to account for solving problems, designing systems, and understanding human behaviour” (p.33). More specifically, CT includes breaking down a complex task into smaller and more manageable components (decomposition), identifying and defining trends or patterns within a problem (pattern recognition), identification of particular similarities and differences between similar problems to work towards a solution (abstraction), development of step by step guidelines for solving a problem and can be used again to answer similar problems (algorithm) and use of technological tools to mechanise problem solutions (automation) (ISTE & CSTA, 2011; Selby, 2013; Yadav et al., 2018). This suggests that components of computational thinking are made up of computational concepts, principles, methods, languages, models, and tools that are commonly found in the study of computer science. Hence, computational thinking may include the following:
“reformulation of difficult problems by reduction and transformation; approximate solutions; parallel processing type checking and model checking as generalisations of dimensional analysis; problem abstraction and decomposition; problem representation; modularisation; error prevention, testing, debugging, recovery, and correction; damage containment; simulation; heuristic reasoning; planning, learning, and scheduling in the presence of uncertainty; search strategies; analysis of the computational complexity of algorithms and processes; and balancing computational costs against other design criteria. Concepts from computer science such as algorithm, process, state machine, task specification, formal correctness of solutions, machine learning, recursion, pipelining, and optimisation also find broad applicability” (NRC, 2010, p.3).