Exploring the Key Elements of Computational Thinking (Part 1)

Listen to an AI-generated, text-to-speech version of this article. In the first two installments of our computational thinking series, we introduced the significance of computational thinking (CT) in advancing the architectural, engineering, and construction (AEC) fields. We explored some of the benefits that a company can realize through implementing CT, including heightened profit margins, more fulfilling work for engineers, and marked increases in efficiency and quality. In “Deciphering the Principles of Computational Thinking,” we utilized the practical example of making a PB&J to demonstrate the application of CT principles in everyday scenarios. This example illustrated how CT isn’t just a theoretical concept but a practical tool that can be applied in diverse contexts. In this final two-part instalment, we aim to dissect the key elements of CT with greater detail. We will explore how this collection of cognitive methods was formed and examine how it empowers engineers to conduct rational, skeptical, and unbiased analyses. This article will provide an in-depth exploration of each element, including investigation, decomposition, pattern recognition, abstraction, and logic design. By retracing our cognitive steps and developing an intricate understanding of these elements, we’ll uncover how CT stands out as an unparalleled methodology in engineering. It aligns with the advanced skills engineers possess, offers the flexibility to tackle real-world problems, and organizes analytical methods in a sequence optimal for devising engineering solutions. Join us as we unravel the layers of CT, diving into each element’s nuances and showcasing their interconnectedness in solving complex problems in the AEC industry. Origins of Computational Thinking  Recall that CT is formally defined as “an analytical methodology for modelling a situation and creating executable instruction to achieve an objective within a set of boundary conditions.” To understand how we arrived at this definition, we can work backwards, looking at how this collection of cognitive methods was formed. The goal of CT is ultimately to “achieve an objective,” and as engineers, we are trained to perform rational, skeptical, and unbiased analyses to form our judgements about how to reach this goal. These techniques are integral throughout the process as we make observations, analyze evidence, and begin to formulate arguments. This involves the use of multiple branches of intellectual processes, including reasoning, decision-making, and problem solving. We can visualize our arrival at CT by starting with the innate first-order cognitive skills and examining how they can be cultivated into more potent second-order skills. For instance, memory is a fundamental function of the human brain, and all individuals possess some degree of intelligence. As we learn to integrate and refine these abilities, we begin to develop knowledge. With practice and training, the skill of knowledge can eventually evolve into the higher-order skill of comprehension. Possessing these skills, in turn, unlocks the ability to practice individual cognitive methods. CT encompasses five of these methods: investigation, decomposition, pattern recognition, abstraction, and logic design. While there is some overlap with other cognitive methodologies employing similar skills, CT distinguishes itself as particularly effective for engineering work. This is attributed to its compatibility with the advanced skills engineers have already honed during their training, its adaptability to the multifaceted problems engineers encounter, and the optimal sequence in which it organizes analytical methods for crafting engineering solutions. Investigation  The first key element of CT is investigation. This pivotal step involves a meticulous exploratory process to clearly delineate the conditions that characterize a given situation. These conditions are categorized into three distinct segments: scope, data, and solution.

1. Scope Conditions: These conditions set the boundaries of the situation. They help in defining where the problem commences and concludes, and clarify what elements are included or excluded from consideration. In the context of making a PB&J, the scope might be defined by the ingredients available and the tools at your disposal.
2. Data Conditions: These conditions encompass both the information relevant to the situation and the methods used to represent that information meaningfully. For example, in making a PB&J sandwich, the data could include the type of bread and spreads available, while the representation might involve listing the options or visualizing the combinations.
3. Solution Conditions: These conditions are concerned with the specific requirements necessary for achieving the desired objective. In our sandwich scenario, this could involve determining the optimal spread-to-bread ratio or deciding on the layering order to ensure a satisfactory outcome.

By thoroughly addressing these conditions, we build a foundational understanding that guides our decision-making process. This enables a comprehensive understanding of the problem at hand, enhancing our ability to devise effective solutions. The PB&J example, though seemingly simple, serves as a practical illustration of how these conditions apply to real-world scenarios, making CT a versatile and applicable tool in diverse contexts. Through the lens of investigation, we not only define the parameters of the problem but also collect and represent data in meaningful ways, setting the stage for the development of well-informed, optimal solutions. Balancing technical depth with accessibility, this exploration of investigation showcases the nuanced application of CT in everyday situations and complex engineering tasks alike. Decomposition  Decomposition is the second key element of CT. It fundamentally revolves around the concept of dissecting a convoluted problem or system into its smaller, more digestible counterparts — be it sub-problems or sub-systems. This division is more than just a simplification; it enables a detailed and nuanced analysis of each facet of the issue, turning a complex challenge into a series of straightforward tasks. A vital aspect of this process is maintaining consistency in the level of detail across all sub-components. Such delineation ensures that each fragment can be meticulously analyzed on its own merit, taking into account its unique characteristics and requirements. Take, for instance, the seemingly mundane task of making a PB&J. Here, decomposition translates to recognizing and addressing distinct sub-tasks, such as gathering ingredients and mastering the art of spread application. To fully comprehend these sub-tasks, one must be familiar with what we call ‘process variables.’ These variables act as the pillars that support and define each sub-component. At the forefront are the resources — the dedicated stakeholders and employees whose responsibilities breathe life into the process. They work hand in hand with both input variables, the essential information guiding the process, and output variables, the tangible results that emerge. The journey between these inputs and outputs is punctuated by ‘decision points,’ critical moments where the process trajectory shifts based on potential outcomes. Of course, no process is free from the constraints of time. Thus, understanding both processing and execution time is paramount. While they both touch upon the total duration of a task, execution time also weaves in the nuances of lead times and potential delays, offering a more comprehensive picture. In the grand scheme of decomposition, every sub-problem finds itself tethered to a distinct task. This symbiotic relationship ensures that when the time comes, the solutions crafted for these individual tasks can seamlessly converge, presenting a unified answer to the overarching challenge. Drawing from the insights shared in part two of this series, this methodology shone when dissecting the art of sandwich-making into its foundational problems. Each was scrutinized in isolation, paving a clear path towards embracing the next crucial element of CT: pattern recognition. Final Thoughts  As we wrap up the first part of our deep dive into the key components of CT, it’s evident how crucial CT is in transforming complex issues into manageable tasks, especially in the realm of engineering. By taking a closer look at the initial stages of this process, like investigation and decomposition, we see how it systematically untangles challenges, setting the stage for further analysis. The intricate layers of each element, from defining conditions in investigation to understanding the variables in decomposition, elucidate the structured nature of CT. It’s this methodical approach that makes CT invaluable in various industries, especially in the AEC sector. In our next installment, we will continue to peel back the layers of CT, exploring the remaining elements. We’ll shed light on how pattern recognition, abstraction, and logic design interplay with each other, driving innovative solutions for the multifaceted challenges faced by engineers. Stay with us as we further delve into the transformative power of CT in the AEC industry and its role in shaping the future of engineering practices. More from Henderson on Computational Thinking: Part 1: Computational Thinking in the AEC Industry Part 2: Exploring Computational Thinking in the AEC Industry Part 3: Deciphering the Principles of Computational Thinking