Abstract
Computational thinking has become essential for navigating the complexities of surroundings. The way that technology is used in education must be completely redesigned because it is a part of everything in modern life. The emphasis on creativity, problem-solving, and collaborative transdisciplinary efforts that characterize computational thinking has made it an essential skill set for people and organizations looking to thrive in the digital era. Computational thinking, which includes pattern identification, deconstruction, abstraction, and algorithms, is a flexible skill set that stems from computer science and may be used in various academic settings. It develops creative problem-solving capabilities and strengthens analytical ability. The potential advantages of computational thinking for higher education are discussed in this chapter. This chapter comprehensively explores how it might foster creativity and problem-solving abilities, transforming traditional teaching methods into innovative, transdisciplinary educational models.
TopIntroduction
Computational thinking is a paradigm shift in problem solving that takes cues from computer science's workings to solve complex problems in a variety of domains. Fundamentally, computational thinking is a systematic approach that prioritizes logic, abstraction, pattern recognition, and algorithmic reasoning when generating and resolving problems (Wang & Chao, 2022). This revolutionary cognitive capability extends beyond computer science's conventional bounds, providing a valuable foundation applicable to a wide range of sectors including science, engineering, business, and the arts. As the world grows more networked and technologically driven, the demand for persons skilled in computational thinking has increased, establishing it as a basic talent for the twenty-first century (Singh & Kaunert, 2024).
Computational thinking has its origins in the fundamental concepts of computer science and how programmers approach and solve issues. However, the notion has spread beyond its beginnings, influencing a wide range of disciplines and industries. Recognizing computational thinking's potential to empower individuals to solve complex issues, educators and politicians are increasingly researching methods to include it into mainstream education (Singh et al., 2024). This shift is driven by the understanding that cultivating computational thinking skills early in an individual's educational journey can lead to enhanced problem-solving abilities, critical thinking, and adaptability (Czerkawski, & Lyman, 2015). The traditional educational model lacks practical applications and compartmentalizes education into topics, making it unable to satisfy the demands of the futuristic Revolutionizing Curricula. The shift from traditional education to a cutting-edge, interdisciplinary paradigm for higher education is largely shaped by computational thinking, a powerful approach to problem-solving that has been used for a long time in computer science and engineering and computational thinking is turning out to be a surprising source of comfort. This study aims to explore the landscape of computational thinking in the context of education, envisioning its role in transforming traditional educational approaches into forward-thinking, interdisciplinary models that prepare students for the challenges and opportunities of the future (Romero et al., 2017).
Figure 1.
Landscapes of introduction split sections
Key Terms in this Chapter
Pattern Recognition: Identifying similarities or patterns within data or problems, which is crucial in computational thinking for developing generalized solutions.
Decomposition: The act of breaking down a complex problem into smaller, more manageable parts, which is a fundamental practice in computational thinking.
Algorithm: A step-by-step procedure or formula for solving a problem or completing a task, often used in computational thinking to break down complex problems into manageable steps.
Technology Integration: The use of technology tools and resources in education to enhance learning and teaching processes, pivotal in modernizing education systems.
Collaboration: Working together with others to achieve a common goal, often emphasized in computational thinking to leverage diverse perspectives and skills.
Abstraction: The process of reducing complexity by focusing on the main idea and ignoring specific details, essential in computational thinking to simplify complex systems.
Interdisciplinary Approach: An educational method that integrates concepts and skills from multiple disciplines to provide a more comprehensive understanding and innovative solutions to problems.
Problem-Solving: The process of finding solutions to difficult or complex issues, often enhanced by computational thinking techniques like abstraction and algorithms.
Transdisciplinary Educational Models: Educational frameworks that go beyond integrating disciplines by focusing on problem-solving and innovation across multiple fields, driven by the principles of computational thinking.
Computational Thinking: A problem-solving process that involves various techniques from computer science, such as pattern recognition, decomposition, and algorithmic thinking, to tackle complex issues.