The final steps of the computational thinking process involve abstraction and generalization of patterns. During the abstraction, relevant information is kept, and information that is irrelevant in the context of the problem is ignored. Abstraction is used to organize things so one can reason without thinking about the details. When generalizing patterns, identify common or shared characteristics between two domains or problems so that models or solutions of one can be adapted or applied to the other. For example, when one tells a story or describes a movie to friends, one does not describe every single detail of the story or movie; they provide a summary.

In social studies, a world map is an abstraction of Earth in terms of longitude and latitude, helping one describe the location and geography of a specific place. In English, a book report is an abstraction because it summarizes and discusses only the theme or key aspects of the book. In math, when studying trends, norms, outliers, and scalability, pattern generalization is used. In science, when studying mammals, students learn they are warm-blooded, have hair, etc. An elephant is a mammal. Therefore, it is warm-blooded, has hair, etc. Pattern generalization can be seen when using search engines, such as when students identify popular keywords.

The final step in computational thinking is to design an algorithm or develop a step-by-step strategy for solving a problem. Algorithmic thinking involves both the creation and the execution of an algorithm. For example, when a grandmother writes down a family recipe for her signature dish, she creates an algorithm that others can follow to replicate the dish. In social studies, when using maps to get directions to a friend’s house, there is a specific sequence of steps to follow. In science investigations, students have a set of instructions to carry out, so one can collect and analyze data. In math, when solving a problem, students are working an algorithm.