The brain has often been compared to that of a computer and that was all because of one mental ability- Computational thinking. In essence, it is a way of solving problems, designing systems, and understanding human behavior that draws on concepts fundamental to computer science. It can also be called a thought process that is applicable to many fields, including science, engineering, medicine, humanities, and business.
And it wouldn’t be wrong to say, computational thinking is a set of skills that enables people to think like computers. Hence, many educators are now more than willing to incorporate this form of thinking in regular classrooms.
Even though there are debates regarding its applicability in the education sector, computation thinking exercises a great deal of influence in our everyday lives, especially in today’s tech-driven world. Hence, the article below discusses some real-life areas that have employed the usage of computational thinking.
Computation thinking: A crucial mental skill?
Computational thinking is a way of solving problems and an efficient approach to understanding the world around us. It is a valuable skill to have in today’s increasingly technology-driven world.
One key aspect of computational thinking is the ability to decompose problems. This means breaking down a large, complex problem into smaller, more manageable pieces that can be tackled individually. By breaking the problem down into smaller parts, we can more easily understand the problem and identify the necessary steps to solve it. An example of using computational thinking to decompose a problem for kids is to have them plan a birthday party.
- Define the problem: The child wants to plan a birthday party for their friend.
- Break down the problem into smaller parts:
- Invitations: Who to invite, how to create and send the invitations.
- Decorations: What decorations to buy or make, how to set up the decorations.
- Food and drinks: What food and drinks to serve, how to make or order the food and drinks.
- Entertainment: What games or activities to plan, how to organize and run the games or activities.
- Identify the inputs and outputs:
- Inputs: guest list, budget, party theme
- Outputs: invitations sent, decorations set up, food and drinks prepared, entertainment organized
- Develop a solution
Another important component or process of computational thinking is the ability to recognize and identify patterns. This involves looking for repeating or predictable behaviors or structures within a problem or system.
In the example of planning a birthday party, computational thinking can also be used to recognize and identify patterns.
- Recognizing patterns in inputs: For example, the child may notice that they always invite the same group of friends to their parties and that they always have a similar budget. This pattern can help them make decisions about who to invite and what decorations to buy.
- Identifying patterns in outputs: After hosting a few parties, the child may notice that certain games or activities are more popular than others, or that certain foods are always a hit. By identifying these patterns, they can make decisions about what entertainment to plan and what food to serve at future parties.
- Recognizing patterns in problem-solving: With experience, the child may also notice patterns in their problem-solving process, such as always starting with invitations or always forgetting to plan for drinks. By recognizing these patterns, they can make a plan to address and correct them in the future.
- Identifying patterns in feedback: After each party, the child may notice certain feedback from guests such as always requesting a certain type of food or activity. By identifying these patterns, they can make a plan to include them in future parties.
Logical reasoning is also a key component of computational thinking. This involves using logical arguments and deductive reasoning to come up with solutions to problems. It requires the ability to make inferences, draw conclusions, and evaluate the validity of arguments. In the context of planning a child’s birthday party, this could involve using logical reasoning to determine the best course of action based on a set of constraints and requirements.
For example, a child can be assisted in logical reasoning to determine the best location for the party based on factors such as cost, size, and proximity to the child’s home. Additionally, they can use logical reasoning to determine the best date and time for the party based on factors such as the availability of guests and his/her schedule. Once the child has determined the best location, date, and time for the party, he/she can then use logical reasoning to make decisions about the party’s theme, decorations, food, and activities based on the preferences of the child and the guests.
Finally, computational thinking involves the ability to analyze and evaluate the results of one’s work. This includes the ability to test and debug solutions, as well as to critically assess the validity and reliability of one’s findings. In the context of planning a child’s birthday party, analyzing and evaluating results can be used to determine the success of the party and identify areas for improvement.
For example, after the party, children can analyze data such as the number of guests who attended, and the total cost, and take feedback from the guests to determine if the party was successful. They can also evaluate the effectiveness of the party by assessing if the party met the goal, for example, the children had fun, the guests were entertained, and the party was within budget.
This information can be used to identify areas for improvement, such as reducing costs or increasing the number of guests. Additionally, it can be used to make decisions about future parties, such as whether to have the party in the same location or to try a different location.
Overall, computational thinking is a crucial mental skill and a valuable asset in today’s technological landscape that can be applied in a wide range of fields and disciplines, including computer science, engineering, business, and more.
Real-life examples of computational thinking
From concrete thinking to abstract thinking, each of these has plenty of practical uses that come into use on a daily basis. Similarly, here are 10 real-life examples of how computational thinking, influences various behaviors and daily activities, but may or may not have caught our attention
1. Planning a vacation
Computational thinking can be used to help in planning a vacation by breaking down the process into manageable tasks, identifying patterns and commonalities, and analyzing and evaluating results. For example, travelers use abstraction to break down the planning process into smaller tasks such as choosing a destination, determining a budget, and researching accommodations.
Further, Generalization is applied by identifying patterns and commonalities between different vacation options, such as cost, climate, and activities. This helps them narrow down their options and make decisions more efficiently. Logical reasoning to determine the best time to go based on factors such as weather, crowds, and cost. After the vacation, analyzing and evaluating results can be done by assessing the vacation’s success and identifying areas for improvement, such as reducing costs or finding more activities.
2. Designing a building
Architects and engineers use computational thinking to design buildings and other structures. They create models and simulations to test the stability and feasibility of different design options. For instance, abstraction can help with breaking down the design process into smaller tasks such as creating a floor plan, determining the structural system, and selecting materials. Generalization can be applied by identifying patterns and commonalities between different design options, such as building codes and regulations, energy efficiency, and aesthetic preferences.
Finally, logical reasoning can be used to make decisions such as choosing between different materials based on factors such as cost, durability, and sustainability. Additionally, logical reasoning is used to determine the best building layout based on factors such as functionality, safety, and accessibility. Once the building is designed, analyzing and evaluating results can be done by assessing the building’s performance and identifying areas for improvement, such as reducing energy consumption or increasing the natural light. This information can be used to make decisions about future building designs and to plan them more efficiently.
3. Predicting the weather
Predicting the weather using computational thinking is a process that involves using data, models, and algorithms to make predictions about future weather conditions. The process starts with Meteorologists collecting a large amount of data from various sources such as weather stations, satellites, and radars. This data is then analyzed and processed to identify patterns and trends that can be used to make predictions.
Next, using the acquired data, mathematical models and algorithms are applied to simulate the weather conditions and make predictions. The outcome is a forecast that predicts the weather for a specific time and location. The predictions are then evaluated for their accuracy using historical data, and any errors or discrepancies are analyzed to identify areas for improvement.
4. Diagnosing diseases
Medical professionals use computational thinking to analyze patient data and make diagnoses based on patterns and trends. For example, generalization is applied by identifying patterns and commonalities between different diseases and their symptoms. Next, in the diagnosis, logical reasoning is used to make decisions such as choosing the best diagnostic test based on factors such as the patient’s symptoms and medical history.
Once the diagnosis is made, analyzing and evaluating results is done by assessing the accuracy of the diagnosis and identifying areas for improvement, such as incorporating more data sources or using more advanced models. This information can be used to make decisions about future diagnoses and to improve their accuracy.
5. Detecting fraud
Financial institutions use computational thinking to analyze data and identify patterns that may indicate fraudulent activity. In the case of fraud, generalization helps with the identification of different types of fraud, while logical reasoning is used to make decisions such as choosing the best method to detect fraud based on factors such as the type of fraud, the data available, and the resources.
Once fraud is detected, analyzing and evaluating results can be done by assessing the effectiveness of the detection method and identifying areas for improvement, such as incorporating more data sources or using more advanced models. This information is now being implemented to make decisions about future fraud detection and to improve their accuracy.
6. Personalizing recommendations
Companies like Netflix and Amazon use computational thinking to analyze customer data and make recommendations for products or content that may be of interest. For instance, AI behind companies like Netflix collects data and then uses logical reasoning in their system to suggest content and products. Such companies are always on the look for better recommendation algorithms that use computational thinking.
7. Analyzing social media trends
Marketing firms use computational thinking to analyze data from social media platforms and identify trends and patterns that can inform marketing strategies. For instance, the recognition of patterns is the most effective strategy in social media campaigns. Whenever a particular song or video shows engagement, more firms jump on the bandwagon. Finally, they use analytics tools to track their engagement and profits, derived through participation in social media trends.
8. Self-driving cars
Self-driving cars are an example of how computational thinking is applied in real-world technology. It uses computational thinking to analyze data from sensors and cameras to navigate roads and make decisions about when to turn, stop, or accelerate.
The problem of safely navigating a self-driving car on the road can be broken down into several smaller problems. Engineers and researchers use a variety of techniques from computer science, such as image processing, machine learning, and control theory to help the car, perceive and understand its environment, including detecting and identifying other vehicles, pedestrians, and obstacles, determining its position and orientation on the road, and plan a safe and efficient path to its destination, and then control its motion to follow that path.
Computational thinking can be used to help robotics or robots in many ways. Robots are complex systems that require a combination of hardware and software to perform a variety of tasks. For example, in order to make a robot capable of performing a task, such as moving from one point to another, several sub-problems need to be solved, such as its ability to perceive and understand its environment, including detecting and identifying obstacles, and its ability to plan a safe and efficient path to its destination, and then control its motion to follow that path. Additionally, computational thinking is also used in the robot’s decision-making process, which is based on the logical reasoning of the machine.
10. Virtual assistants
Virtual assistants, such as Amazon’s Alexa or Google Assistant, use computational thinking in order to understand and respond to user commands and queries. The problem of understanding and responding to user input was broken down into several smaller problems and virtual assistants were equipped with the ability to accurately transcribe spoken words into text, understand the meaning of the user’s input and extract relevant information, determine an appropriate response based on the user’s input and the current context of the conversation, and convert the text-based response into speech. All of these features were added by carefully decomposing the problem and then formulating solutions as per computational thinking.
Computational thinking in real life is getting its due credit after decades. All thanks to the high computational thinkers that have a variety of advantages. Such thinkers apart from having used computational thinking in the above-mentioned examples, are able to identify the key components of a problem, can easily recognize patterns and relationships in data and use them to make predictions and solve problems, and think abstractly and use abstract models to represent and solve problems. Hence, computational thinking is one of the most realistic and problem-oriented types of thinking and in real life computational thinking can be a relevant and important skill to possess.