Listen to an AI-generated, text-to-speech version of this article.

When you hear the term “computational thinking,” it’s easy to assume it’s a complex and intricate subject reserved for computer programmers and software developers. This common misinterpretation of “computational” has led to misconceptions and feelings of intimidation. However, computational thinking (CT) is not a labyrinth of advanced technical skills; it’s simply a structured and systematic way of thinking that many people, especially engineers, unconsciously employ in their everyday lives.  

The preceding article in this series delved into why CT is a pivotal asset in the architecture, engineering, and construction (AEC) industry, shedding light on its significance and multifaceted applications. Now, we aim to further demystify CT by demonstrating that it’s an accessible and natural way of thinking already embedded in our daily lives. 

CT is more than merely a problem-solving tool – it is an adaptable mindset employing analytical methods to transform complex situations into manageable tasks. The applicability of CT stretches far beyond abstract, computational scenarios; it is a universal methodology that can seamlessly integrate into all kinds of everyday activities, such as the mundane task of making a peanut butter and jelly sandwich. By examining this simple culinary exercise through the lens of CT, we will reveal the inherent benefits and widespread accessibility of CT, showing its potential to revolutionize how we navigate and resolve challenges, whether big or small. 

Investigation: Defining the Problem 

The journey of applying CT begins with investigation, thereby crafting a clear and concise definition of the problem’s scope. Think of it as drawing boundaries around what matters and what doesn’t. For example, when making a PB&J sandwich, our main concern isn’t to invent new utensils or explore the historical background of the sandwich. While such explorations may be intriguing and potentially beneficial, they aren’t integral to the immediate goal of putting the sandwich together. To avoid this scope creep, we need a targeted and intentional approach that focuses on practical steps, filters out the non-essentials, and is purpose-driven. In our PB&J example, the requirements are clear: acquiring bread, peanut butter, and jelly and then assembling them accurately into a sandwich. This step lays the groundwork for the remaining phases of the CT process. 

Computational thinking is a universal methodology that can be applied to a wide range of problems, from complex engineering systems to the everyday task of creating a PB&J. Photo credit: “Palette of Palates” by Dauphin Flores, Public Domain.

Decomposition: Breaking Down Complex Problems 

Building upon the understanding we gain through our meticulous investigation, we continue by separating the overarching problem into smaller, more digestible sub-problems via decomposition. Addressing each sub-problem individually guarantees a comprehensive approach with a detailed perspective on every facet of the task and ensures nothing is overlooked. 

In the case of our sandwich, this involves isolating and addressing each step, ranging from procuring the ingredients to the ultimate assembly: 

1. Gathering equipment.
2. Gathering ingredients.
3. Placing slices of bread on a plate.
4. Spreading peanut butter on one slice of bread.
5. Spreading jelly on the other slice of bread.
6. Putting the two slices of bread together.

Pattern Recognition: Enhancing Efficiency & Leveraging Past Knowledge 

Now that we have identified our distinct sub-problems, we can refine them using pattern recognition. This involves diligently analyzing the sub-problems we have identified to find any redundancies and opportunities for improvement. 

In the sandwich-making process, recognizing that the actions of spreading peanut butter and spreading jelly involve similar actions allows us to optimize these steps, enhancing efficiency. Having identified this pattern, we might decide to solve both problems consecutively to avoid redundant actions, such as having to pick up the knife at two different points during the solution. 

This phase also provides an opportunity to link the current task with past experiences, such as spreading butter on toast. Drawing connections between current problems and past experiences enables us to leverage prior knowledge and insights, avoid unnecessary repetition, and streamline the process. By doing so, we expedite the task at hand and build a repository of solutions that can be referenced in future scenarios, fostering a more adaptive and resourceful problem-solving approach. 

Abstraction: Focusing on the Essentials 

Abstraction might sound like a nebulous term, but it’s easy to think of it as peeling away the layers of an onion. This process requires us to discard the layers of specific details to uncover the commonalities between different tasks and goes hand-in-hand with the previous step of pattern recognition. Together, they allow us to craft solutions that are not only tailored to the immediate problem but are also versatile enough to address future challenges. Abstraction is essential throughout the CT process, including the initial investigation, helping filter out unnecessary information and paving the way for more targeted problem solving. 

When making a PB&J, abstracting the specific substances we are spreading on bread reveals that the sub-problems of spreading peanut butter and spreading jelly can be reduced to the common action of applying a spread to bread. In the future, if we encounter another problem that involves spreading another substance on bread, we can reuse our solution. 

Logic Design: Constructing a Solution 

Logic design represents a synthesis of all the insights and knowledge accrued from the previous stages into a coherent, step-by-step plan that acts as a guiding light, illuminating the path to a successful outcome. 

In the context of our PB&J sandwich, constructing a solution entails formulating a detailed and sequentially organized set of instructions for each sub-problem. This process integrates the essential components identified through abstraction and arranges them in the most efficient sequence. To illustrate, let’s craft a series of instructions where each step is clearly defined, leaving no room for ambiguity and ensuring a seamless transition from ingredients to the final product: 

1. Gathering equipment
   • Locate a clean work surface.
   • Locate a plate.
   • Locate a knife.
   • Move the knife and plate to the work surface.
2. Gathering ingredients
   • Locate the required ingredients (bread, peanut butter, jelly).
   • Ensure you have proper quantities of each ingredient.
   • Move the ingredients to the work surface.
3. Placing slices of bread on a plate
   • Grab two slices of bread.
   • Place them side by side on the plate.
4. Spreading peanut butter on one slice of bread
   • Open the jar of peanut butter.
   • Use the knife to scoop a dollop of peanut butter onto the blade.
   • Use the knife to spread the peanut butter evenly on one side of one slice of bread.
5. Spreading jelly on the other slice of bread
   • Open the jar of jelly.
   • Use the knife to scoop a dollop of jelly onto the blade.
   • Use the knife to spread the jelly evenly on one side of the other slice of bread.
6. Putting the two slices of bread together
   • Pick up the slice of bread with peanut butter on it.
   • Place it on top of the slice of bread with jelly so that the spreads are together on the sandwich’s interior.

In this example, we have chosen to document the logic as a plain English pseudocode. However, documenting the logic design can take various forms, from written instructions to flowcharts. The key is ensuring precision and clarity to create a comprehensive guide addressing every aspect of the problem and guaranteeing a successful solution. 

It is also essential to debug your logic design to confirm its correctness, completeness, and effectiveness. This means reviewing each step and carefully checking every detail to ensure the results meet your expectations. Uncovering any discrepancies or unexpected outcomes is a sign that there might be issues with the logic design. Debugging is not just about identifying these issues; it’s also a chance to refine the design, ensuring the final solution is sturdy, dependable, and actionable. 

Final Thoughts 

Applying the principles of CT to the everyday task of making a PB&J sandwich not only provides an efficient and transparent solution but also dispels the aura of complexity surrounding CT. This exercise underscores the practicality, accessibility, and versatility of CT. It highlights the potential to enhance problem-solving skills in a wide range of contexts, both within and beyond the world of engineering. 

As we wrap up this exploration into the simplicity and applicability of CT, we’ve set the stage for the final installment in this series. In the upcoming article, we plan to delve deeper into the nuanced technical aspects of CT, unravelling its intricate components to provide you with a clearer, more comprehensive understanding. We aim to highlight how these foundational principles can be systematically and effectively applied to unravel more complex, real-world problems. By doing so, we strive to further equip you with the knowledge and skills necessary for mastering CT and enhancing your problem-solving abilities in various contexts. 

More from Henderson on Computational Thinking:

• Part 1: Computational Thinking in the AEC Industry
• Part 2: Exploring Computational Thinking in the AEC Industry