While there are many different processes of problem solving taught to students, one stands out as being applicable not only in current educational scenarios but in long-term applications as well: computational thinking. Computational thinking is an integral form of problem solving in which a student will create a clear steep-by-step solution to a problem or challenge that can be replicated by systems, computers or humans. The result is not only the answer to a single problem or question, but a process that can aid in resolving similar challenges in the future.
Computational thinking has become important to teaching problem solving in education because it empowers students with processes to develop strategic solutions to complex problems, essentially “leveling up” their skills, or creating algorithms that can make future processes more effective. This enables students to take on more complex challenges and prepare for real-world applications.
Decomposition in Computational Thinking
While the process of computational thinking is multifaceted, there is one key to computational thinking that is essential for success: decomposition. Decomposition is the process of breaking a complex task or problem into smaller, more manageable pieces. From this decomposition, students can eliminate unnecessary information, identify patterns and begin the process of identifying which parts of the problem are most important, how to solve or complete each step, and how those parts can be put together for a clear and unified step-by-step solution.
Why is the Decomposition Technique Required in Computational Thinking?
The technique of decomposition is required in computational thinking because it breaks complex tasks into subtasks while developing a sequentially-based understanding of the problem. This allows unnecessary information to be discarded, patterns to be identified, relevant information to be extracted and the process of step-by-step resolution to be defined for a more effective problem-solving process.
By definition, computational thinking is the process of tackling complex problems and finding a clear, step-by-step solution that can be replicated. Decomposition, therefore, is essential to this process as it includes breaking a task into several sub-tasks, helping to align the task to a step-by-step solution.
Another reason decomposition in computational thinking is important is because it allows each subtask to be examined more closely. This not only helps to categorize information as essential or non-essential, but also empowers the problem-solver with a better ability to analyze each specific part of the task or challenge to more effectively develop a reasonable solution.
Examples of Decomposition in Computational Thinking
Below are some real-world examples of utilizing decomposition in computational thinking:
- You have to clean your house. Instead of facing the task as a whole, you practice decomposition by breaking the task into a to-do list with multiple subtasks.
- To build shelves for a storage space, you practice decomposition by measuring the space, creating a blueprint or plan to determine the size of the shelves and the materials you need to accomplish the task. Next, you purchase the supplies and cut the lumber to size, then install one wall of shelves at a time.
- In science, to prove or disprove a hypothesis, you practice decomposition by creating subtasks including background research, observation, generating a hypothesis, determining which variables to test, performing experiments and drawing a conclusion.
Final Thoughts
Consider how often computational thinking appears in your everyday life. Do you naturally use decomposition and computational thinking to resolve a challenge? For more information about computational thinking and how they apply to students, explore some of our most recent articles:
