Abstract
This chapter examines the foundational principles of computational thinking (CT) and their application across diverse academic disciplines, particularly in non-STEM fields such as humanities and social sciences. This chapter adopts a systematic approach and addresses the central research question: “How do CT skills enhance problem-solving methodologies in non-STEM fields, and what are the challenges of integrating these skills into various curricula?” The core components of CT—decomposition, pattern recognition, abstraction, and algorithmic thinking—are examined in this chapter. The chapter also identifies significant barriers to CT integration, including faculty resistance, curriculum rigidity, and resource limitations. This chapter presents a comprehensive literature review and qualitative analysis, with the findings providing insights into the benefits of CT. This chapter also offers practical recommendations for future research and practice.
TopBackground
Logic and problem-solving are core components across various disciplines, each using these tools according to its specific paradigms and methodologies (Ohlsson, 2012). For instance, in mathematics, mathematical logic began to take shape distinctly in the late 19th and early 20th centuries with pioneers like Frege and Russell (Agassi and Agassi, 2018). Mathematical logic involved the study of formal systems in relation to the way we think about numbers, shapes, and logical deduction applying logical operators and set theories to solve equations and proofs (Dutilh Novaes, 2011). In computer science, logic formed the basis for algorithmic design and computation, especially in the development of algorithms that solve complex problems (Lewis and Papadimitriou, 1998). For instance, Boolean logic is quite important in the development of circuits and software, while fuzzy logic is used for systems dealing with uncertain or incomplete information (Wing, 2008). Problem-solving in computer science involves breaking down a problem into smaller, manageable parts and using iterative approaches to develop solutions (Wing, 2006).
Key Terms in this Chapter
Cognitive Development: The progression of learning and acquisition of problem-solving abilities and understanding from childhood through adulthood.
Pattern Recognition: The ability to identify and analyze trends or regularities in data.
Digital Humanities: An interdisciplinary field of study that uses computational tools and methods to analyze and interpret humanities data.
Interdisciplinary Integration: The combination of methods and insights from different academic disciplines to advance understanding or solve problems.
Computational Thinking: A problem-solving process involving logical analysis, pattern recognition, abstraction, and algorithm design, applicable across various disciplines.
Abstraction: The process of reducing complexity by focusing on the main idea and ignoring specific details to create a simplified model.
Problem Decomposition: The process of breaking down a complex problem into smaller, more manageable components.
Decomposition: The practice of breaking down a complex problem or system into smaller, more manageable parts.
Simulation Modeling: The use of models to simulate and analyze the behavior of systems in order to predict and understand real-world phenomena.
Algorithm Design: The process of defining a step-by-step solution to a problem or a set of rules to be followed in calculations or problem-solving operations.