Computational Thinking in Science

Abstract

A quiet but profound revolution has been taking place throughout science. The computing revolution has transformed science by enabling all sorts of new discoveries through information technology. Throughout most of the history of science and technology, there have been two types of characters. One is the experimenter, who gathers data to reveal when a hypothesis works and when it does not. The other is the theoretician, who designs mathematical models to explain what is already known and uses the models to make predictions about what is not known. The two types interact with one another because hypotheses may come from models, and what is known comes from previous models and data. The experimenter and the theoretician were active in the sciences well before computers came on the scene. Computational thinking is generally defined as the mental skills that facilitate the design of automated processes. FULL TEXT Headnote The computer revolution has profoundly affected how we think about science, experimentation, and research. A quiet but profound revolution has been taking place throughout science. The computing revolution has transformed science by enabling all sorts of new discoveries through information technology. Throughout most of the history of science and technology, there have been two types of characters. One is the experimenter, who gathers data to reveal when a hypothesis works and when it does not. The other is the theoretician, who designs mathematical models to explain what is already known and uses the models to make predictions about what is not known. The two types interact with one another because hypotheses may come from models, and what is known comes from previous models and data. The experimenter and the theoretician were active in the sciences well before computers came on the scene. When governments began to commission projects to build electronic computers in the 1940s, scientists began discussing how they would use these machines. Nearly everybody had something to gain. Experimenters looked to computers for data analysis-sifting through large data sets for statistical patterns. Theoreticians looked to them for calculating the equations of mathematical models. Many such models were formulated as differential equations, which considered changes in functions over infinitesimal intervals. Consider for example the generic function / over time (abbreviated f(t)). Suppose that the differences in f(t) over time give another equation, abbreviated g(t). We write this relation as df(t)/dt=g(t). You could then calculate the approximate values of /(f) in a series of small changes in time steps, abbreviated At, with the difference equation f(t+At)=f(t)+Atg(t). This calculation could easily be extended to multiple space dimensions with difference equations that combine values on neighboring nodes of a grid. In his collected works, John von Neumann, the polymath who helped design the first stored program computers, described algorithms for solving systems of differential equations on discrete PDF GENERATED BY SEARCH.PROQUEST.COM Page 1 of 7