Abstract
The proliferation of technology has profoundly impacted every segment of society and the way humans think. The gradual growth of information technology has significantly influenced our approach to problem-solving. Computational thinking, a key aspect of IT, supports and encourages users to think logically, create, analyze, develop, and deliver solutions. This chapter explores various aspects of computational thinking and the essential tools in this field. The findings in this chapter demonstrate how computational thinking enables us to tackle complex problems by understanding them and developing viable solutions. Identifying useful patterns and the underlying details necessary for solving problems is facilitated by these tools. This chapter provides a descriptive study of relevant computational thinking tools and discusses the future tools needed to enhance the subject's importance.
TopLiterature Review
The journey of tools and their use in computational thinking dates to 2015 when Slaviero and Hæusler (2015) first highlighted various tools. The authors discussed the designers' standpoint regarding the introduction of a chosen set of computer science concepts, emphasizing the tendency to conceal intricate details for the sake of simplicity. Their paper specifically examined the treatment of concurrency by five widely recognized tools within this domain: Scratch, Alice, AgentSheets, NetLogo, and Greenfoot. Repenning et al. (2019), delineated three principles and utilized AgentCubes Online as an illustrative example to demonstrate how a computational thinking tool can assist these stages by integrating human capabilities with the functionalities offered by computers.
Nomukasa et al. (2021) reported the use of tools in the educational industry, particularly in mathematics. The authors insisted that computational tools must be used in education so that learning up to the 5th grade can be done in the best manner, paving the way for a better understanding of the complex scenarios in mathematics. Fluently navigating through different domains of computer science and computation, scholars' interpretations of Computational Thinking (CT) hinge on their specific focuses. These areas include programming, digital design, hardware, big data, data structures, complex systems, networks, computational modeling, design, and simulations, among others. Grover and Pea (2013), Pollak and Ebner, and various other researchers note that initial definitions of CT, exemplified in NRC (2010), predominantly emphasized procedural thinking and programming. However, more recent definitions, like those outlined in NRC (2011), depict CT in a more expansive and inclusive light. Yim and Su (2023) reviewed different computational tools and demonstrated that even in the absence of prior programming knowledge, tools like Popbots, Teachable Machine, and Scratch can effectively cater to the varied requirements of students spanning K-12 educational levels (Kumar and Mohammed, 2024).
Key Terms in this Chapter
Decomposition: The practice of breaking down a complex problem or system into smaller, more manageable parts.
Digital Literacy: The ability to use information and communication technologies to find, evaluate, create, and communicate information, requiring both cognitive and technical skills.
Computational Thinking: A problem-solving process that includes characteristics such as logical analysis, pattern recognition, abstraction, and algorithmic thinking, typically used to solve complex problems.
Systems Thinking: An approach to understanding the complex interactions within a whole system by examining the linkages and interactions between the components that comprise the entirety of that system.
Simulation: The imitation of the operation of a real-world process or system over time, often used in computational models to test theories or understand phenomena.
Abstraction: The process of reducing complexity by focusing on the main idea and ignoring specific details to create a simplified model.
Programming: The act of writing instructions for a computer to execute, which involves designing and implementing algorithms.
Algorithmic Thinking: The ability to develop a step-by-step solution to a problem or to understand the process of solving a problem.
Problem-Solving: The process of finding solutions to difficult or complex issues by using a systematic approach.
Pattern Recognition: The ability to identify and analyze patterns within data to predict outcomes or understand the underlying structure.