第2章計算論的思考と設計の再利用性

By the end of this section, you will be able to:

• Define computational thinking

• Discuss computational thinking examples

This chapter presents key aspects of computational thinking, including logical thinking, assessment, decomposition, pattern recognition, abstraction, generalization, componentization, and automation. These elements guide how computer scientists approach problems and create well-designed solution building blocks at both the business and technical levels. Computational thinking often involves a bottom-up approach, focusing on computing in smaller contexts, and seeks to generate innovative solutions by utilizing data structures and algorithms. Additionally, it may make use of existing design building blocks like design patterns and abstract data types to expedite the development of optimal combinations of data structures and algorithms.

The problem-solving and cognitive process, known as computational thinking, is rooted in principles derived from computer science. It involves breaking down complex problems into smaller, more manageable parts and devising systematic approaches to solve them. Complex problems are situations that are difficult because they involve many different interrelated parts or factors. These problems can be hard to understand and often don’t have simple solutions.

While “computational thinking” is still perceived by some as an abstract concept without a universally accepted definition, its core value is to facilitate the application of separate strategies and tools to address complex problems. In problem-solving, computers play a central role, but their effectiveness centers on a prior comprehension of the problem and its potential solutions. Computational thinking serves as the bridge between the problem and its resolution. It empowers solution designers to navigate the complexity of a given problem, separate its components, and formulate possible solutions. These solutions, once developed, can be communicated in a manner that is comprehensible to both computers and humans, adopting effective problem-solving.

Computational thinking serves as the intermediary that helps us read complex problems, formulate solutions, and then express those solutions in a manner that computers, humans, or a collaboration of both can implement. Read this article for a good introduction to computational thinking (https://openstax.org/r/ 76CompThinking) from the BBC.

To further qualify computational thinking, Al Aho of the Columbia University Computer Science Department describes computational thinking as “the thought processes involved in formulating problems so their solutions can be represented as computational steps and algorithms.” Jeannette Wing, also of Columbia University, brought the idea of computational thinking to prominence in a paper she wrote in 2006 while at Carnegie Mellon University. She believes that computational thinking details the mental acts needed to compute a solution to a problem either by human actions or machine. Computational thinking encompasses a collection of methods and approaches for resolving (and acquiring the skills to resolve) complex challenges, closely aligned with mathematical thinking through its utilization of abstraction, generalization, modeling, and measurement (Figure 2.2). However, it differentiates itself by being more definitely aware than mathematics alone in its capacity for computation and the potential advantages it offers.

Critical thinking is an important skill that can help with computational thinking. It boils down to understanding concepts rather than just mastering technical details for using software, prioritizing comprehension over rote learning. It’s a core skill, not an extra burden on a curriculum checklist, and it uniquely involves humans, not computers, blending problem-solving and critical thinking. Critical thinking focuses on ideas, not tangible items, applying advanced thinking to devise solutions. Critical thinking is essential for everyone and is, comparable to foundational abilities like reading, writing, and arithmetic.

The description provided by the International Society for Technology in Education (ISTE) outlines the key components and dispositions of computational thinking. Let’s explore each characteristic in more detail:

• Decomposition: The analytical process of breaking down complex concepts or problems into smaller parts is called decomposition. This approach helps analyze and solve problems more effectively.

• Pattern recognition (logically organizing and analyzing data): Computational thinking emphasizes the logical organization and analysis of data. This includes the ability to structure information in a way that facilitates effective problem-solving.

• Representing data through abstractions: An abstraction is a simplified representation of complex systems or phenomena. Computational thinking involves representing data through an abstraction, such as a simulation, which uses models as surrogates for real systems.

• Automation through algorithmic thinking: Using a program or computer application to perform repetitive tasks or calculations is considered automation.

• Identification, analysis, and implementation of solutions: Computational thinking emphasizes identification, analysis, and implementation of potential solutions to achieve optimal efficiency and effectiveness through a combination of steps and resources.

• Generalization and transferability: Generalizing and transferring this problem-solving process across a wide variety of problems showcases the adaptability and applicability of computational thinking.

These abilities are supported and enriched by fundamental abilities integral to computational thinking. These abilities involve the following characteristics: confidence in navigating complexity, resilience in tackling challenging problems, an acceptance of ambiguity, adeptness in addressing open-ended issues, and proficiency in collaborating with others to attain shared objectives or solutions. Another illustration of computational thinking is the three As, which is organized into three phases, as visualized in Figure 2.3:

1. Abstraction: The initial step involves problem formulation.

2. Automation: Next, the focus shifts to expressing the solution.

3. Analysis: Finally, the process encompasses solution execution and evaluation.