DOI
https://doi.org/10.1007/s10956-015-9581-5

Summary
The article presents an approach of computational thinking for the application to science and mathematics disciplines. The authors introduce the process of creating taxonomy for computation thinking application. The process includes review of literature, interviews and discussions with science and mathematics teachers, STEM researchers in industry, STEM graduate students The introduced cross disciplinary computational thinking framework consists of four main parts, that is, data processing, modeling and simulation practices, computational problem-solving practices, systems thinking practices. Each part and its subparts are explained in detail. The authors provide the descriptions of three lessons to demonstrate the ways to include computational thinking in science and mathematics disciplines. The lessons cover topics of physics in video games, pseudocode for assembling DNA sequence, interactive simulation of gas experiments. It is concluded that incorporating computational thinking to mathematics and science classes contributes to bringing the education closer to current professional practices and must be supported by various stakeholders, namely, teachers, administrators, policy makers, and other community members.

Relevance for Complex Systems Knowledge
In recent years, competence in computational field is required to solve complex problems as there are many computational tools which can help to find a proper solution. It is mentioned in the article, that students learn to determine system limitations, to identify its parts and their interactions, to decompose a problem to subproblems and formulate them in a computational form, to combine solutions of the subproblems and to interpret them while developing computational thinking skills. Examples of complex problems in high school mathematics, physics, biology and chemistry are provided in the article. The authors state that developed skills in computational thinking enable students to prepare problems for computational solutions, design computational models based on reasoned assumptions and assess their feasibility. They also emphasize that system thinking approach is necessary to construct a proper computational model and choose a reasonable size, complexity and goal in order to achieve the desired goal.

Point of Strength
The strength of the article is suggested ideas how computational thinking can be developed in high school mathematics and science. Another point of strength is a review of systems thinking practices and computational problem-solving practices.