Computational ThinkingThis pocket-sized introduction to computational thinking and problem-solving traces its genealogy centuries before the digital computer. A few decades into the digital era, scientists discovered that thinking in terms of computation made possible an entirely new way of organizing scientific investigation. Eventually, every field had a computational branch: computational physics, computational biology, computational sociology. More recently, “computational thinking” has become part of the K–12 curriculum. But what is computational thinking? This volume in the MIT Press Essential Knowledge series offers an accessible overview—tracing a genealogy that begins centuries before digital computers and portraying computational thinking as the pioneers of computing have described it. The authors explain that computational thinking (CT) is not a set of concepts for programming; it is a way of thinking that is honed through practice: the mental skills for designing computations to do jobs for us, and for explaining and interpreting the world as a complex of information processes. Mathematically trained experts (known as “computers”) who performed complex calculations as teams engaged in CT long before electronic computers. In each chapter, the author identify different dimensions of today's highly developed CT: • Computational Methods • Computing Machines • Computing Education • Software Engineering • Computational Science • Design Along the way, they debunk inflated claims for CT and computation while making clear the power of CT in all its complexity and multiplicity. |
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abstract machine Alan Turing algebra algorithms apps arithmetic artificial intelligence aspects automatic computers automation Babbage basic bits build calculations chapter chess chips complex components computational models computational steps computational thinking computer revolution computer science computer scientists computing education computing machines computing's concerns curriculum Design software early electronic computer ENIAC equations errors example experience fields function gramming grid hardware human computers idea implemented information processes input instructions interaction interface K-12 schools large number learning machine code mathematical mathematicians mechanical memory modules Moore's law Neumann architecture neural networks operating systems output patterns perform phenomena physical pioneer practices principles problems procedures processors produce programming languages puter puting quantum computer reliable simulation skills software developers software engineering software systems solve stored structures teachers teaching tion Turing understanding universities users virtual machine von Neumann architecture