Algorithms are central to both computer science and computational thinking. Algorithms underlie the most basic tasks everyone engages in, from following a simple cooking recipe to providing complicated driving directions. Because there is a general misconception that algorithms are used only to solve mathematical problems and are not applicable in other disciplines, 29 it is important to introduce students to algorithms by first using examples from their daily lives. For example, in early grades, teachers could highlight the steps involved in brushing teeth, while in later grades, students could engage in following steps during a lab experiment. Understanding algorithms as a set of precise steps provides the basis for understanding how to develop an algorithm that can be implemented in a computing program. Students can be exposed to the computational thinking concept of abstraction by creating models of physics entities (such as a model of the solar system). 3 Abstraction helps students learn to strip away complexity in order to reduce an artifact to its essence and still be able to know what the artifact is. In another example, Barr and Stephenson3 suggested students can learn about parallelization by simultaneously running experiments with different parameters. A number of leading research, educational, and funding organizations have argued for the need to introduce K– 12 students to these core constructs and practices.

Computational Thinking
in K– 12 Education

The National Research Council (NRC) 22 highlighted the importance of exposing students to computational thinking notions early in their school years and helping them to understand when and how to apply these essential skills. 3, 22

The NRC report22 on the scope and na-
ture of computational thinking high-
lighted the need for students to learn
the related strategies from knowledge-
able educators who model these strat-
egies and guide their students to use
them independently. Similarly, Barr
and Stephenson3 argued that, given
that students will go into a workforce
heavily influenced by computing, it is
important for them to begin to work
with computational thinking ideas and
existing learning outcomes. Finally, we
discuss ways to implement computa-
tional thinking into pre-service teacher
training, including how teacher educa-
tors and computer science educators
can collaborate to develop pathways to
help pre-service teachers become com-
putationally educated.

What Is Computational Thinking?

How do we define computational thinking and use the definition as a framework to embed it in K– 12 classrooms? Wing’s seminal column28 offered a promising definition of computational thinking: “… breaking down a difficult problem into more familiar ones that we can solve (problem decomposition), using a set of rules to find solutions (algorithms), and using abstractions to generalize those solutions to similar problems.” Finally, automation is the ultimate step in computational thinking that can be implemented through computing tools. These concepts cut across disciplines and could be embedded across subjects in elementary and secondary schools. Based on this definition, a steering committee formed by the Computer Science Teachers Association (CSTA https://www.csteachers. org/) and the International Society for Technology in Education (ISTE https:// www.iste.org/) presented a computational thinking framework for K– 12 schools in 2011 with nine core computational thinking concepts and capabilities, including data collection, data analysis, data representation, problem decomposition, abstraction, algorithms and procedures, automation, parallelization, and simulation. They were also discussed in 2015 in the Computing at School (CAS) framework and guide for teachers to enable teachers in the U.K. to incorporate computational thinking into their teaching work. 10 CSTA/ISTE and CAS also provide pedagogical approaches to embed these capabilities across the curriculum in elementary and secondary classes. For example, CSTA/ISTE describes how the nine core computational thinking concepts and capabilities could be practiced in science classrooms by collecting and analyzing data from experiments (data collection and data analysis) and summarizing that data (data representation).

Computational
thinking is often
mistakenly
equated with
using computer
technology.