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Computational Thinking (CT) is the thought processes involved in formulating a problem and expressing its solution(s) in such a way that a computer—human or machine—can effectively carry out. Computational Thinking is an iterative process based on three stages: 1) Problem Formulation (abstraction), 2) Solution Expression (automation), and 3) Solution Execution & Evaluation (analyses) captured by the figure to the right. 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.
Contents
Overview
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.
The phrase computational thinking was brought to the forefront of the computer science community as a result of an ACM Communications article on the subject by Jeannette Wing. The article 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 disciplines.
Computational Thinking in K-12 in Education
Jeannette Wing envisioned computational thinking becoming an essential part of every child's education. However, since her article (published in 2006) integrating computational thinking into the K-12 curriculum has faced several challenges including the agreement on the definition of computational thinking. Currently Computational Thinking is broadly defined as a set of cognitive skills and problem solving processes that include (but are not limited to) the following characteristics:
Current integration computational thinking into the K-12 curriculum comes in two forms: in computer science classes directly or through the use and measure of computational thinking techniques in other subjects. Teachers in Science, Technology, Engineering, and Mathematics (STEM) focused classrooms that include computational thinking, allow students to practice problem-solving skills such as trial and error (Barr, et al, 2011). Valerie Barr and Chris Stephenson describe computational thinking patterns across disciplines in a 2011 ACM Inroads article
Center for Computational Thinking
Carnegie Mellon University in Pittsburgh has a Center for Computational Thinking. The Center's major activity is conducting PROBEs or PROBlem-oriented Explorations. These PROBEs are experiments that apply novel computing concepts to problems to show the value of computational thinking. A PROBE experiment is generally a collaboration between a computer scientist and an expert in the field to be studied. The experiment typically runs for a year. In general, a PROBE will seek to find a solution for a broadly applicable problem and avoid narrowly focused issues. Some examples of PROBE experiments are optimal kidney transplant logistics and how to create drugs that do not breed drug resistant viruses.
Criticism
The concept of Computational Thinking has been criticized as too vague, as it's rarely made clear how it is different from other forms of thinking. Some computer scientists worry about the promotion of Computational Thinking as a substitute for a broader computer science education, as computational thinking represents just one small part of the field. Others worry that the emphasis on Computational Thinking encourages computer scientists to think too narrowly about the problems they can solve, thus avoiding the social, ethical and environmental implications of the technology they create.