Computational Thinking and Social Studies Teacher Education: What, Why, and How

Computational Thinking and Social Studies Teacher Education: What, Why, and How

Thomas C. Hammond (Lehigh University, USA), Julie L. Oltman (Lehigh University, USA) and Meghan M. Manfra (North Carolina State University, USA)
DOI: 10.4018/978-1-7998-1479-5.ch001

Abstract

Computational thinking is highly applicable to social studies education, particularly decision-focused social studies. To better fit the disciplinary needs of social studies and align with social studies standards, we adapt and group computational thinking skills into a heuristic of data, patterns, rules, and questions (DPR-Q). We then propose a four-step model for social studies teachers to follow when planning lessons that integrate computational thinking within their curricular instruction. Both the DPR-Q heuristic and the instructional planning model are explained with worked examples from social studies classrooms. Successful integration of computational thinking into decision-focused social studies can both enrich the social studies curriculum and provide a curricular home for teaching computational thinking, bearing out Wing's claim that computational thinking is ‘everywhere' and ‘for everyone.'
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Why?

For the past two decades, social studies have faced a crisis of relevancy, squeezed first by the pressures of the No Child Left Behind Act, then by the ascendance of perceived high-value fields such as STEM (Fitchett & Heafner, 2010). In the current policy climate, then, social studies are not seen as essential--it is not part of workforce preparation, it does not prepare students for high-stakes assessments, and it does not speak to the other parts of the school curriculum. In fact, in one of our local school districts, social studies have been subsumed under literacy instruction; it has become secondary within the district’s K-12 curriculum and not a priority in its own right. Our concern over this de-emphasis of social studies is further amplified by the fact that our current politics, news, and economy are in a state of tremendous confusion and polarization: Amid so many claims of “fake news” what is real news? In a time of economic mixed signals—low unemployment and high corporate profits versus stagnant wages and rising costs of housing and healthcare—which policies and parties should one choose to support? Do extreme weather events correlate with the onset of catastrophic climate change or are they merely outliers in the distribution of normal weather patterns? The many fault lines of contemporary politics demonstrate the truth of James Russell Lowell’s famous distillation, that American democracy is not “a machine that would go of itself” (Moss, 2017). Social studies is needed now as much or more than ever to help students make sense of the society they are inheriting, and computational thinking can play a vital role.

Key Terms in this Chapter

Computational Thinking: Summarizes habits of mind or skills characterized by using computers to solve complex problems or, borrowing from computer science, developing problem-solving skills such as abstraction, pattern generalization, algorithmic thinking, decomposition, automation, and recursion.

Inquiry Design Model (IDM): Basis of the C3 Framework for developing inquiries focused on compelling questions, supporting questions, and taking informed action.

“Data, Patterns, Rules and Questions” (DPR-Q): A heuristic that applies computational thinking to social studies instruction and guides students through a four-part framework for problem solving.

Decision-focused Social Studies: Social studies instruction organized around student decision-making, whether policy formulation or data interpretation.

College, Career, and Civic Life (C3) Framework: An instructional framework for the social studies that is focused on four dimensions: Dimension 1: Developing Questions and Planning Inquiries, Dimension 2: Applying Disciplinary Concepts and Tools, and Dimension 4: Communicating Conclusions and Taking Informed Action

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