Advantages and Disadvantages of Computational Thinking
Banner link: https://drive.google.com/file/d/1TGzk8hB3WcS_DmG9-xVQPqXtybGc8Q0_/view?usp=sharing
Image link: https://www.pexels.com/photo/black-and-gray-motherboard-2582937/
Computational thinking involves the process involved with the formulation of a problem and finding a solution that humans and computers can equally understand and compute. It’s a process that’s essential to computer science but can be seamlessly integrated into various educational fields like mathematics, physics, and sociology. Users can precisely work out what to tell computer systems, making problem-solving more straightforward.
Computational thinking encompasses the mental activity involved in formulating problems to admit computational solutions, leveraging various principles and concepts, including pattern recognition, decomposition, abstraction, and algorithm design. Considering that computational thinking is research-based and consistently facilitates innovation, it provides its fair share of benefits. However, there are infrastructural and principle-based issues accompanying computational thinking that should equally be considered.
Advantage — The Problem-Solving Capabilities
The most common yet significant advantage of computational thinking is that it enhances problem-solving capabilities by leveraging the above-mentioned principles to aid learning. Using logic, computational thinking deducts new information/data based on current information, informing real-life conclusions rather than reaching assumptions.
With decomposition, you can break down complex problems into more manageable, easy-to-understand parts. Through pattern recognition, you seek similarities within or among difficulties. Additionally, there is an abstraction that solely focuses on the most pertinent details, ignoring any irrelevant information. And some algorithms develop step-by-step solutions to problems or provide rules that must be followed to create an appropriate solution.
Within educational settings, teachers and students can reinforce things like spelling rules through pattern recognition. Algorithms, meanwhile, can be used to create various writing styles, and abstraction can be used to boost research capabilities.
Computational thinking provides a reliable method to cope with different events, regardless of the industry, whether calculating numbers or growing fresh produce. It’s a multi-dimensional problem-solving concept.
Advantage — Computational Thinking is Rooted in Research and Testing
The term computational thinking was coined by famed mathematician Seymour Papert and later emphasized by Jeannette Wing, bringing it to global attention with her research paper identifying the impact on society that computer science, algorithm design, and technology have. As a result of their philosophies, prominent world leaders and educational philosophers outlined computational thinking as a vital skill that opens peoples’ minds to data and resource usage. The skill shifts people from being technology consumers to becoming creators.
Given its deep roots in research and testing, established companies such as Oracle, Google, and Microsoft have long recruited staff to leverage computational thinking, providing them with a competitive edge.
Advantage — Computational Thinking Promotes And Boosts Efficiency
Another key benefit that computational thinking offers is efficiency, minimizing the number of resources used for problem-solving purposes. Within computer science and algorithm design, resource minimization is vital to solving problems properly.
The time it takes for algorithms to solve problems and the memory space necessary to facilitate problem-solving are key to maximizing computational efficiency, with time being a great emphasis for many. Specific thought must go into algorithm designs to best handle specified problems rather than simply speeding up algorithm runtimes to best address time complexities. Efficient algorithms take less time and steps to solve problems, improving the productivity of the computational process.
With optimized algorithmic designs being logical aspects of the computational thinking process, computer instructions can be created using various languages that make machines and computers do things they were never previously capable of doing.
Disadvantage — Difficulties with Prediction and Implementation
While computational thinking provides so many vast problem-solving opportunities for the people that use it, the predictability involved with computational thinking can sometimes be tricky.
With the computational thinking process, it may be difficult to accurately predict markets, trends, users, and all technical influences. As a result, there are too many variables involved that can complicate any given scenario and make it too difficult to model accurately.
Caching, where data is stored in cache memory, is one way to speed up the computational thinking process and make it easier. However, caching can be hard to integrate and necessitates the collection of the most accurate data for whatever the next instruction is.
Also, there are potential problems with the decomposition model in that an event-driven approach may not be possible compared to a procedural approach for programming purposes.
Disadvantage — Knowing How Much Computational Thinking Aids Problem-Solving and Creativity
While applying computational thinking can be helpful in many settings, particularly in educational settings, there isn’t sufficient research that quantifies how much computational thinking helps. As a result, there’s no unequivocal measure of the range of its problem-solving abilities or how much it enhances creativity. Skills don’t automatically transfer, and computational thinking doesn’t definitively make someone better unless something is explicitly being taught to someone or a group of people.
In the end, I think, as more people and companies explore the capabilities and potential limitations of computational thinking, it’s clear that such a concept helps people develop sharper thought processes and connect with computers to solve problems effectively.
