Computational thinking

In education, computational thinking (CT) is a set of problem-solving methods that involve expressing problems and their solutions in ways that a computer could execute.. It involves the mental skills and practices for 1) designing computations that get computers to do jobs for us, and 2) explaining and interpreting the world as a complex of information processes. Those ideas range from basic CT for beginners to advanced CT for experts. The history of computational thinking dates back at least to the 1950s but most ideas are much older. Computational thinking involves ideas like abstraction, data representation, and logically organizing data, which are also prevalent in other kinds of thinking, such as scientific thinking, engineering thinking, systems thinking, design thinking, model-based thinking, and the like. Neither the idea nor the term are recent: Preceded by terms like algorithmizing, procedural thinking, algorithmic thinking, and computational literacy by computing pioneers like Alan Perlis and Donald Knuth, the term computational thinking was first used by Seymour Papert in 1980 and again in 1996. Computational thinking can be used to algorithmically solve complicated problems of scale, and is often used to realize large improvements in efficiency. The phrase computational thinking was brought to the forefront of the computer science education community in 2006 as a result of an ACM Communications essay on the subject by Jeannette Wing. The essay suggested that thinking computationally was a fundamental skill for everyone, not just computer scientists, and argued for the importance of integrating computational ideas into other subjects in school. The characteristics that define computational thinking are decomposition, pattern recognition / data representation, generalization/abstraction, and algorithms. By decomposing a problem, identifying the variables involved using data representation, and creating algorithms, a generic solution results. The generic solution is a generalization or abstraction that can be used to solve a multitude of variations of the initial problem.