Computational thinking

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Wing (2006, 2008 and see National Research Council, 2010) has described computational thinking as a general analytic approach to problem-solving, designing systems, and understanding human behaviours. While computational thinking draws on concepts that are fundamental to computing and computer science, it also includes practices such as problem representation, abstraction, decomposition, simulation, verification, and prediction. These practices, in turn, are also central to modelling, reasoning and problem-solving in many sciences- and mathematics-based disciplines, including sustainability, biodiversity, and climate studies.

With respect to science education, our approach is grounded in the science as practice perspective (Duschl, 2008; Lehrer & Schauble, 2006; National Research Council, 2008). In this perspective, the development of scientific expertise is intertwined with the development of epistemic and representational practices (e.g., Giere, 1988; Lehrer & Schauble, 2006); modelling is viewed as the “language” of science (Giere, 1988), and is therefore identified as the core scientific representational practice.

Modelling, as an important variant of computational thinking, is not only a key epistemic practice in the sciences, but equally important in the applied sciences and in engineering, and in some social sciences. For instance, researchers have developed models of water usage in Australia that can be used to predict (to some extent) how changes to water use policies will affect farming. Models are frequently used for managing natural (and other) resources, for planning and decision-making (e.g., Ticehurst et al., 2007), and for documenting natural phenomena over time, particularly over extremely long durations (e.g., Turney et al., 2017). When used in this way, models are directly relevant for the futures orientation that is essential to Education for Sustainability because they afford answers to “what-if” questions, questions about possible futures, and the likely time-delayed consequences of actions.

Modelling is a futures-oriented epistemic activity in a second sense: as a form of citizen and stakeholder involvement and as a form of social learning. In this context, computational models (of water use, for instance) are developed together with stakeholders and other parties concerned, with the goal of creating shared understandings of (a) the problem and (b) anticipated consequences of lines of action (e.g., Wehn et al., 2018). Models then not only function as an explanatory device and as a management tool, but also as a boundary object between people with different views and interests. They pave the way for relational dialogue and participatory decision-making. (This is also relevant to argumentation below.)

There are two immediate practicalities: First, schools need to find out how to teach computational thinking, and coding, as part of the new Digital Technologies curriculum; they also need to address sustainability, biodiversity, and climate across the curriculum. Both have a cross-curriculum aspect. Exploiting synergies would clearly be advantageous.

Second, EfS is widely practiced in the F-10 years of schooling in Australia; teachers are more likely to engage with new ideas if they can build on what they are already practising. Enriching ‘hands-on’ sustainability and environment education with computational / modelling activities seems a reasonable strategy.

For these reasons, we see the relation between computational thinking and sustainability education as reciprocal and mutually reinforcing: the thoughtful use of computational tools and skillsets can deepen learning of sustainability and science content (Guzdial, 1994; National Research Council, 2011a, b; Repenning et al., 2010; Wilensky et al., 2014). The reverse is also true, namely that sustainability topics provide a meaningful context (and set of problems) within which computational thinking can be applied (Hambrusch et al., 2009; Jona et al., 2014; Lin et al., 2009; Wilensky et al., 2014).

This approach differs markedly from teaching computational thinking as part of a standalone course in which the assignments that students are given tend to be divorced from real-world problems and applications. A sense of authenticity and real-world applicability is important in the effort to motivate diverse and meaningful participation in computational and scientific activities (Blikstein, 2013; Chinn and Malhotra, 2002; Confrey, 1993; Margolis and Fisher, 2003; Margolis, 2008; Ryoo et al., 2013). This reciprocal relationship — using computation to enrich sustainability and science learning and using sustainability and science contexts to enrich computational learning — is at the heart of our motivation to bring computational thinking and science and sustainability concepts together. A curriculum model could comprise taxonomic elements such as those identified in Weintrop and others (2016): 1) data practices, 2) modelling and simulation practices, 3) computational problem solving practices, and 4) systems thinking practices.