Pre-Service Teacher Preparation to Integrate Computational Thinking

Pre-Service Teacher Preparation to Integrate Computational Thinking

Lesley S. J. Farmer (California State University, Long Beach, USA) and Shuhua An (California State University, Long Beach, USA)
DOI: 10.4018/978-1-7998-1479-5.ch015


United States education has experienced a big push for students to learn coding as part of computer science and more explicitly address computational thinking (CT). However, CT remains a challenging subject for many students, including pre-service teachers. CT, which overlaps mathematics and computer science, tends to be offered as an elective course, at best, in P-16 education. Pre-service teaching profession students usually do not have foundational knowledge to guide them in integrating computational thinking into the curriculum that they will eventually teach as instructors themselves. This chapter explains computational thinking in light of K-8 education, discusses issues and needs in integrating CT into K-8 curriculum, identifies relevant theories and models for teaching CT, describes current practice for integrating computational thinking into K-8 curriculum, and discusses pre-service teachers' preparation that can lead to their successful incorporation of CT into the curriculum.
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Computational Thinking


The underlying idea of computational thinking is centuries old. The term “computational” appeared as early as the 14th century, based on the ancient Latin word “computation,” meaning to calculate or reckon. Similarly, the first computers were humans: individuals who computed. As such, computational thinking was traditionally associated with mathematical reasoning. Along the way, mathematical reasoning was sometimes relegated to solving textbook math problems with little reference to the humanities.

With the advent of computing machines, computational thinking shifted to computer science, particularly in terms of computer programming. Computers manipulate data according to directions or algorithms that can be applied to different inputs and different kinds of problems. The advantage of such machines is their processing speed of such data. The Turing machine in World War II is an early example of machine computing power that could decipher a sophisticated coding algorithm more quickly than a room of humans. In the 1940s, John von Neumann posited that computational thinking was a scientific method, and computational thinking was labelled algorithmic thinking in the 1950s (Demir et al., 2018).

Seymour Papert was one of the early educators who saw the possibility of using a simple programming language to help children learn how to think and solve problems in a constructionist manner. Papert used the term “computational thinking” in his 1980 book Mindstorms as he explained how children, in particular, could represent problems mathematically and then explore those problems dynamically in computer-based microworlds. The concept of algorithmic thinking was central to this process.

In the 1980s, many schools offered elective courses in computer programming, such as Basic or Pascal. However, these courses were usually disconnected from other curricula and did not seem relevant to most students. 1990s programming courses increasingly came pre-packaged so students did less debugging and original designing. In the last ten years, computer programming education, often renamed as “coding,” has made a comeback, to some degree out of industry need for technically skilled employees (Kafai, 2016).

Key Terms in this Chapter

Decomposition: Breaking down data, processes, or problems into smaller, manageable parts.

Algorithm Design: Developing the step by step instructions for solving problems.

Constructionism and Constructivist learning: A learning theory based on the idea that knowledge is constructed by the learner based on mental activity; learners actively seek meaning.

Activity Theory: A framework describing a system of goal-directed human action that accounts for the environment.

Computational Thinking: Thought processes involved in formulating problems and their solutions so that the solutions are represented in a form that can be effectively carried out by an information-processing agent.

Instructional Design: A systematic analysis of training needs and the development of aligned instruction.

STEM: Science, technology, engineering, and mathematics curriculum.

Abstraction: Identifying the general principles that generate discerned patterns.

Common Core State Standards (CCSS): A set of mathematics and language arts standards for what every student is expected to learn in K-12 U.S. education.

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