Exploring the Problem Solving Cycle in Computer Science – Strategies, Techniques, and Tools

  • Post author By bicycle-u
  • Post date 08.12.2023

The world of computer science is built on the foundation of problem solving. Whether it’s finding a solution to a complex algorithm or analyzing data to make informed decisions, the problem solving cycle is at the core of every computer science endeavor.

At its essence, problem solving in computer science involves breaking down a complex problem into smaller, more manageable parts. This allows for a systematic approach to finding a solution by analyzing each part individually. The process typically starts with gathering and understanding the data or information related to the problem at hand.

Once the data is collected, computer scientists use various techniques and algorithms to analyze and explore possible solutions. This involves evaluating different approaches and considering factors such as efficiency, accuracy, and scalability. During this analysis phase, it is crucial to think critically and creatively to come up with innovative solutions.

After a thorough analysis, the next step in the problem solving cycle is designing and implementing a solution. This involves creating a detailed plan of action, selecting the appropriate tools and technologies, and writing the necessary code to bring the solution to life. Attention to detail and precision are key in this stage to ensure that the solution functions as intended.

The final step in the problem solving cycle is evaluating the solution and its effectiveness. This includes testing the solution against different scenarios and data sets to ensure its reliability and performance. If any issues or limitations are discovered, adjustments and optimizations are made to improve the solution.

In conclusion, the problem solving cycle is a fundamental process in computer science, involving analysis, data exploration, algorithm development, solution implementation, and evaluation. It is through this cycle that computer scientists are able to tackle complex problems and create innovative solutions that drive progress in the field of computer science.

Understanding the Importance

In computer science, problem solving is a crucial skill that is at the core of the problem solving cycle. The problem solving cycle is a systematic approach to analyzing and solving problems, involving various stages such as problem identification, analysis, algorithm design, implementation, and evaluation. Understanding the importance of this cycle is essential for any computer scientist or programmer.

Data Analysis and Algorithm Design

The first step in the problem solving cycle is problem identification, which involves recognizing and defining the issue at hand. Once the problem is identified, the next crucial step is data analysis. This involves gathering and examining relevant data to gain insights and understand the problem better. Data analysis helps in identifying patterns, trends, and potential solutions.

After data analysis, the next step is algorithm design. An algorithm is a step-by-step procedure or set of rules to solve a problem. Designing an efficient algorithm is crucial as it determines the effectiveness and efficiency of the solution. A well-designed algorithm takes into consideration the constraints, resources, and desired outcomes while implementing the solution.

Implementation and Evaluation

Once the algorithm is designed, the next step in the problem solving cycle is implementation. This involves translating the algorithm into a computer program using a programming language. The implementation phase requires coding skills and expertise in a specific programming language.

After implementation, the solution needs to be evaluated to ensure that it solves the problem effectively. Evaluation involves testing the program and verifying its correctness and efficiency. This step is critical to identify any errors or issues and to make necessary improvements or adjustments.

In conclusion, understanding the importance of the problem solving cycle in computer science is essential for any computer scientist or programmer. It provides a systematic and structured approach to analyze and solve problems, ensuring efficient and effective solutions. By following the problem solving cycle, computer scientists can develop robust algorithms, implement them in efficient programs, and evaluate their solutions to ensure their correctness and efficiency.

Identifying the Problem

In the problem solving cycle in computer science, the first step is to identify the problem that needs to be solved. This step is crucial because without a clear understanding of the problem, it is impossible to find a solution.

Identification of the problem involves a thorough analysis of the given data and understanding the goals of the task at hand. It requires careful examination of the problem statement and any constraints or limitations that may affect the solution.

During the identification phase, the problem is broken down into smaller, more manageable parts. This can involve breaking the problem down into sub-problems or identifying the different aspects or components that need to be addressed.

Identifying the problem also involves considering the resources and tools available for solving it. This may include considering the specific tools and programming languages that are best suited for the problem at hand.

By properly identifying the problem, computer scientists can ensure that they are focused on the right goals and are better equipped to find an effective and efficient solution. It sets the stage for the rest of the problem solving cycle, including the analysis, design, implementation, and evaluation phases.

Gathering the Necessary Data

Before finding a solution to a computer science problem, it is essential to gather the necessary data. Whether it’s writing a program or developing an algorithm, data serves as the backbone of any solution. Without proper data collection and analysis, the problem-solving process can become inefficient and ineffective.

The Importance of Data

In computer science, data is crucial for a variety of reasons. First and foremost, it provides the information needed to understand and define the problem at hand. By analyzing the available data, developers and programmers can gain insights into the nature of the problem and determine the most efficient approach for solving it.

Additionally, data allows for the evaluation of potential solutions. By collecting and organizing relevant data, it becomes possible to compare different algorithms or strategies and select the most suitable one. Data also helps in tracking progress and measuring the effectiveness of the chosen solution.

Data Gathering Process

The process of gathering data involves several steps. Firstly, it is necessary to identify the type of data needed for the particular problem. This may include numerical values, textual information, or other types of data. It is important to determine the sources of data and assess their reliability.

Once the required data has been identified, it needs to be collected. This can be done through various methods, such as surveys, experiments, observations, or by accessing existing data sets. The collected data should be properly organized, ensuring its accuracy and validity.

Data cleaning and preprocessing are vital steps in the data gathering process. This involves removing any irrelevant or erroneous data and transforming it into a suitable format for analysis. Properly cleaned and preprocessed data will help in generating reliable and meaningful insights.

Data Analysis and Interpretation

After gathering and preprocessing the data, the next step is data analysis and interpretation. This involves applying various statistical and analytical methods to uncover patterns, trends, and relationships within the data. By analyzing the data, programmers can gain valuable insights that can inform the development of an effective solution.

During the data analysis process, it is crucial to remain objective and unbiased. The analysis should be based on sound reasoning and logical thinking. It is also important to communicate the findings effectively, using visualizations or summaries to convey the information to stakeholders or fellow developers.

In conclusion, gathering the necessary data is a fundamental step in solving computer science problems. It provides the foundation for understanding the problem, evaluating potential solutions, and tracking progress. By following a systematic and rigorous approach to data gathering and analysis, developers can ensure that their solutions are efficient, effective, and well-informed.

Analyzing the Data

Once you have collected the necessary data, the next step in the problem-solving cycle is to analyze it. Data analysis is a crucial component of computer science, as it helps us understand the problem at hand and develop effective solutions.

To analyze the data, you need to break it down into manageable pieces and examine each piece closely. This process involves identifying patterns, trends, and outliers that may be present in the data. By doing so, you can gain insights into the problem and make informed decisions about the best course of action.

There are several techniques and tools available for data analysis in computer science. Some common methods include statistical analysis, data visualization, and machine learning algorithms. Each approach has its own strengths and limitations, so it’s essential to choose the most appropriate method for the problem you are solving.

Statistical Analysis

Statistical analysis involves using mathematical models and techniques to analyze data. It helps in identifying correlations, distributions, and other statistical properties of the data. By applying statistical tests, you can determine the significance and validity of your findings.

Data Visualization

Data visualization is the process of presenting data in a visual format, such as charts, graphs, or maps. It allows for a better understanding of complex data sets and facilitates the communication of findings. Through data visualization, patterns and trends can become more apparent, making it easier to derive meaningful insights.

Machine Learning Algorithms

Machine learning algorithms are powerful tools for analyzing large and complex data sets. These algorithms can automatically detect patterns and relationships in the data, leading to the development of predictive models and solutions. By training the algorithm on a labeled dataset, it can learn from the data and make accurate predictions or classifications.

In conclusion, analyzing the data is a critical step in the problem-solving cycle in computer science. It helps us gain a deeper understanding of the problem and develop effective solutions. Whether through statistical analysis, data visualization, or machine learning algorithms, data analysis plays a vital role in transforming raw data into actionable insights.

Exploring Possible Solutions

Once you have gathered data and completed the analysis, the next step in the problem-solving cycle is to explore possible solutions. This is where the true power of computer science comes into play. With the use of algorithms and the application of scientific principles, computer scientists can develop innovative solutions to complex problems.

During this stage, it is important to consider a variety of potential solutions. This involves brainstorming different ideas and considering their feasibility and potential effectiveness. It may be helpful to consult with colleagues or experts in the field to gather additional insights and perspectives.

Developing an Algorithm

One key aspect of exploring possible solutions is the development of an algorithm. An algorithm is a step-by-step set of instructions that outlines a specific process or procedure. In the context of problem solving in computer science, an algorithm provides a clear roadmap for implementing a solution.

The development of an algorithm requires careful thought and consideration. It is important to break down the problem into smaller, manageable steps and clearly define the inputs and outputs of each step. This allows for the creation of a logical and efficient solution.

Evaluating the Solutions

Once you have developed potential solutions and corresponding algorithms, the next step is to evaluate them. This involves analyzing each solution to determine its strengths, weaknesses, and potential impact. Consider factors such as efficiency, scalability, and resource requirements.

It may be helpful to conduct experiments or simulations to further assess the effectiveness of each solution. This can provide valuable insights and data to support the decision-making process.

Ultimately, the goal of exploring possible solutions is to find the most effective and efficient solution to the problem at hand. By leveraging the power of data, analysis, algorithms, and scientific principles, computer scientists can develop innovative solutions that drive progress and solve complex problems in the world of technology.

Evaluating the Options

Once you have identified potential solutions and algorithms for a problem, the next step in the problem-solving cycle in computer science is to evaluate the options. This evaluation process involves analyzing the potential solutions and algorithms based on various criteria to determine the best course of action.

Consider the Problem

Before evaluating the options, it is important to take a step back and consider the problem at hand. Understand the requirements, constraints, and desired outcomes of the problem. This analysis will help guide the evaluation process.

Analyze the Options

Next, it is crucial to analyze each solution or algorithm option individually. Look at factors such as efficiency, accuracy, ease of implementation, and scalability. Consider whether the solution or algorithm meets the specific requirements of the problem, and if it can be applied to related problems in the future.

Additionally, evaluate the potential risks and drawbacks associated with each option. Consider factors such as cost, time, and resources required for implementation. Assess any potential limitations or trade-offs that may impact the overall effectiveness of the solution or algorithm.

Select the Best Option

Based on the analysis, select the best option that aligns with the specific problem-solving goals. This may involve prioritizing certain criteria or making compromises based on the limitations identified during the evaluation process.

Remember that the best option may not always be the most technically complex or advanced solution. Consider the practicality and feasibility of implementation, as well as the potential impact on the overall system or project.

In conclusion, evaluating the options is a critical step in the problem-solving cycle in computer science. By carefully analyzing the potential solutions and algorithms, considering the problem requirements, and considering the limitations and trade-offs, you can select the best option to solve the problem at hand.

Making a Decision

Decision-making is a critical component in the problem-solving process in computer science. Once you have analyzed the problem, identified the relevant data, and generated a potential solution, it is important to evaluate your options and choose the best course of action.

Consider All Factors

When making a decision, it is important to consider all relevant factors. This includes evaluating the potential benefits and drawbacks of each option, as well as understanding any constraints or limitations that may impact your choice.

In computer science, this may involve analyzing the efficiency of different algorithms or considering the scalability of a proposed solution. It is important to take into account both the short-term and long-term impacts of your decision.

Weigh the Options

Once you have considered all the factors, it is important to weigh the options and determine the best approach. This may involve assigning weights or priorities to different factors based on their importance.

Using techniques such as decision matrices or cost-benefit analysis can help you systematically compare and evaluate different options. By quantifying and assessing the potential risks and rewards, you can make a more informed decision.

Remember: Decision-making in computer science is not purely subjective or based on personal preference. It is crucial to use analytical and logical thinking to select the most optimal solution.

In conclusion, making a decision is a crucial step in the problem-solving process in computer science. By considering all relevant factors and weighing the options using logical analysis, you can choose the best possible solution to a given problem.

Implementing the Solution

Once the problem has been analyzed and a solution has been proposed, the next step in the problem-solving cycle in computer science is implementing the solution. This involves turning the proposed solution into an actual computer program or algorithm that can solve the problem.

In order to implement the solution, computer science professionals need to have a strong understanding of various programming languages and data structures. They need to be able to write code that can manipulate and process data in order to solve the problem at hand.

During the implementation phase, the proposed solution is translated into a series of steps or instructions that a computer can understand and execute. This involves breaking down the problem into smaller sub-problems and designing algorithms to solve each sub-problem.

Computer scientists also need to consider the efficiency of their solution during the implementation phase. They need to ensure that the algorithm they design is able to handle large amounts of data and solve the problem in a reasonable amount of time. This often requires optimization techniques and careful consideration of the data structures used.

Once the code has been written and the algorithm has been implemented, it is important to test and debug the solution. This involves running test cases and checking the output to ensure that the program is working correctly. If any errors or bugs are found, they need to be fixed before the solution can be considered complete.

In conclusion, implementing the solution is a crucial step in the problem-solving cycle in computer science. It requires strong programming skills and a deep understanding of algorithms and data structures. By carefully designing and implementing the solution, computer scientists can solve problems efficiently and effectively.

Testing and Debugging

In computer science, testing and debugging are critical steps in the problem-solving cycle. Testing helps ensure that a program or algorithm is functioning correctly, while debugging analyzes and resolves any issues or bugs that may arise.

Testing involves running a program with specific input data to evaluate its output. This process helps verify that the program produces the expected results and handles different scenarios correctly. It is important to test both the normal and edge cases to ensure the program’s reliability.

Debugging is the process of identifying and fixing errors or bugs in a program. When a program does not produce the expected results or crashes, it is necessary to go through the code to find and fix the problem. This can involve analyzing the program’s logic, checking for syntax errors, and using debugging tools to trace the flow of data and identify the source of the issue.

Data analysis plays a crucial role in both testing and debugging. It helps to identify patterns, anomalies, or inconsistencies in the program’s behavior. By analyzing the data, developers can gain insights into potential issues and make informed decisions on how to improve the program’s performance.

In conclusion, testing and debugging are integral parts of the problem-solving cycle in computer science. Through testing and data analysis, developers can verify the correctness of their programs and identify and resolve any issues that may arise. This ensures that the algorithms and programs developed in computer science are robust, reliable, and efficient.

Iterating for Improvement

In computer science, problem solving often involves iterating through multiple cycles of analysis, solution development, and evaluation. This iterative process allows for continuous improvement in finding the most effective solution to a given problem.

The problem solving cycle starts with problem analysis, where the specific problem is identified and its requirements are understood. This step involves examining the problem from various angles and gathering all relevant information.

Once the problem is properly understood, the next step is to develop an algorithm or a step-by-step plan to solve the problem. This algorithm is a set of instructions that, when followed correctly, will lead to the solution.

After the algorithm is developed, it is implemented in a computer program. This step involves translating the algorithm into a programming language that a computer can understand and execute.

Once the program is implemented, it is then tested and evaluated to ensure that it produces the correct solution. This evaluation step is crucial in identifying any errors or inefficiencies in the program and allows for further improvement.

If any issues or problems are found during testing, the cycle iterates, starting from problem analysis again. This iterative process allows for refinement and improvement of the solution until the desired results are achieved.

Iterating for improvement is a fundamental concept in computer science problem solving. By continually analyzing, developing, and evaluating solutions, computer scientists are able to find the most optimal and efficient approaches to solving problems.

Documenting the Process

Documenting the problem-solving process in computer science is an essential step to ensure that the cycle is repeated successfully. The process involves gathering information, analyzing the problem, and designing a solution.

During the analysis phase, it is crucial to identify the specific problem at hand and break it down into smaller components. This allows for a more targeted approach to finding the solution. Additionally, analyzing the data involved in the problem can provide valuable insights and help in designing an effective solution.

Once the analysis is complete, it is important to document the findings. This documentation can take various forms, such as written reports, diagrams, or even code comments. The goal is to create a record that captures the problem, the analysis, and the proposed solution.

Documenting the process serves several purposes. Firstly, it allows for easy communication and collaboration between team members or future developers. By documenting the problem, analysis, and solution, others can easily understand the thought process behind the solution and potentially build upon it.

Secondly, documenting the process provides an opportunity for reflection and improvement. By reviewing the documentation, developers can identify areas where the problem-solving cycle can be strengthened or optimized. This continuous improvement is crucial in the field of computer science, as new challenges and technologies emerge rapidly.

In conclusion, documenting the problem-solving process is an integral part of the computer science cycle. It allows for effective communication, collaboration, and reflection on the solutions devised. By taking the time to document the process, developers can ensure a more efficient and successful problem-solving experience.

Communicating the Solution

Once the problem solving cycle is complete, it is important to effectively communicate the solution. This involves explaining the analysis, data, and steps taken to arrive at the solution.

Analyzing the Problem

During the problem solving cycle, a thorough analysis of the problem is conducted. This includes understanding the problem statement, gathering relevant data, and identifying any constraints or limitations. It is important to clearly communicate this analysis to ensure that others understand the problem at hand.

Presenting the Solution

The next step in communicating the solution is presenting the actual solution. This should include a detailed explanation of the steps taken to solve the problem, as well as any algorithms or data structures used. It is important to provide clear and concise descriptions of the solution, so that others can understand and reproduce the results.

Overall, effective communication of the solution in computer science is essential to ensure that others can understand and replicate the problem solving process. By clearly explaining the analysis, data, and steps taken, the solution can be communicated in a way that promotes understanding and collaboration within the field of computer science.

Reflecting and Learning

Reflecting and learning are crucial steps in the problem solving cycle in computer science. Once a problem has been solved, it is essential to reflect on the entire process and learn from the experience. This allows for continuous improvement and growth in the field of computer science.

During the reflecting phase, one must analyze and evaluate the problem solving process. This involves reviewing the initial problem statement, understanding the constraints and requirements, and assessing the effectiveness of the chosen algorithm and solution. It is important to consider the efficiency and accuracy of the solution, as well as any potential limitations or areas for optimization.

By reflecting on the problem solving cycle, computer scientists can gain valuable insights into their own strengths and weaknesses. They can identify areas where they excelled and areas where improvement is needed. This self-analysis helps in honing problem solving skills and becoming a better problem solver.

Learning from Mistakes

Mistakes are an integral part of the problem solving cycle, and they provide valuable learning opportunities. When a problem is not successfully solved, it is essential to analyze the reasons behind the failure and learn from them. This involves identifying errors in the algorithm or solution, understanding the underlying concepts or principles that were misunderstood, and finding alternative approaches or strategies.

Failure should not be seen as a setback, but rather as an opportunity for growth. By learning from mistakes, computer scientists can improve their problem solving abilities and expand their knowledge and understanding of computer science. It is through these failures and the subsequent learning process that new ideas and innovations are often born.

Continuous Improvement

Reflecting and learning should not be limited to individual problem solving experiences, but should be an ongoing practice. As computer science is a rapidly evolving field, it is crucial to stay updated with new technologies, algorithms, and problem solving techniques. Continuous learning and improvement contribute to staying competitive and relevant in the field.

Computer scientists can engage in continuous improvement by seeking feedback from peers, participating in research and development activities, attending conferences and workshops, and actively seeking new challenges and problem solving opportunities. This dedication to learning and improvement ensures that one’s problem solving skills remain sharp and effective.

In conclusion, reflecting and learning are integral parts of the problem solving cycle in computer science. They enable computer scientists to refine their problem solving abilities, learn from mistakes, and continuously improve their skills and knowledge. By embracing these steps, computer scientists can stay at the forefront of the ever-changing world of computer science and contribute to its advancements.

Applying Problem Solving in Real Life

In computer science, problem solving is not limited to the realm of programming and algorithms. It is a skill that can be applied to various aspects of our daily lives, helping us to solve problems efficiently and effectively. By using the problem-solving cycle and applying the principles of analysis, data, solution, algorithm, and cycle, we can tackle real-life challenges with confidence and success.

The first step in problem-solving is to analyze the problem at hand. This involves breaking it down into smaller, more manageable parts and identifying the key issues or goals. By understanding the problem thoroughly, we can gain insights into its root causes and potential solutions.

For example, let’s say you’re facing a recurring issue in your daily commute – traffic congestion. By analyzing the problem, you may discover that the main causes are a lack of alternative routes and a lack of communication between drivers. This analysis helps you identify potential solutions such as using navigation apps to find alternate routes or promoting carpooling to reduce the number of vehicles on the road.

Gathering and Analyzing Data

Once we have identified the problem, it is important to gather relevant data to support our analysis. This may involve conducting surveys, collecting statistics, or reviewing existing research. By gathering data, we can make informed decisions and prioritize potential solutions based on their impact and feasibility.

Continuing with the traffic congestion example, you may gather data on the average commute time, the number of vehicles on the road, and the impact of carpooling on congestion levels. This data can help you analyze the problem more accurately and determine the most effective solutions.

Generating and Evaluating Solutions

After analyzing the problem and gathering data, the next step is to generate potential solutions. This can be done through brainstorming, researching best practices, or seeking input from experts. It is important to consider multiple options and think outside the box to find innovative and effective solutions.

For our traffic congestion problem, potential solutions can include implementing a smart traffic management system that optimizes traffic flow or investing in public transportation to incentivize people to leave their cars at home. By evaluating each solution’s potential impact, cost, and feasibility, you can make an informed decision on the best course of action.

Implementing and Iterating

Once a solution has been chosen, it is time to implement it in real life. This may involve developing a plan, allocating resources, and executing the solution. It is important to monitor the progress and collect feedback to learn from the implementation and make necessary adjustments.

For example, if the chosen solution to address traffic congestion is implementing a smart traffic management system, you would work with engineers and transportation authorities to develop and deploy the system. Regular evaluation and iteration of the system’s performance would ensure that it is effective and making a positive impact on reducing congestion.

By applying the problem-solving cycle derived from computer science to real-life situations, we can approach challenges with a systematic and analytical mindset. This can help us make better decisions, improve our problem-solving skills, and ultimately achieve more efficient and effective solutions.

Building Problem Solving Skills

In the field of computer science, problem-solving is a fundamental skill that is crucial for success. Whether you are a computer scientist, programmer, or student, developing strong problem-solving skills will greatly benefit your work and studies. It allows you to approach challenges with a logical and systematic approach, leading to efficient and effective problem resolution.

The Problem Solving Cycle

Problem-solving in computer science involves a cyclical process known as the problem-solving cycle. This cycle consists of several stages, including problem identification, data analysis, solution development, implementation, and evaluation. By following this cycle, computer scientists are able to tackle complex problems and arrive at optimal solutions.

Importance of Data Analysis

Data analysis is a critical step in the problem-solving cycle. It involves gathering and examining relevant data to gain insights and identify patterns that can inform the development of a solution. Without proper data analysis, computer scientists may overlook important information or make unfounded assumptions, leading to subpar solutions.

To effectively analyze data, computer scientists can employ various techniques such as data visualization, statistical analysis, and machine learning algorithms. These tools enable them to extract meaningful information from large datasets and make informed decisions during the problem-solving process.

Developing Effective Solutions

Developing effective solutions requires creativity, critical thinking, and logical reasoning. Computer scientists must evaluate multiple approaches, consider various factors, and assess the feasibility of different solutions. They should also consider potential limitations and trade-offs to ensure that the chosen solution addresses the problem effectively.

Furthermore, collaboration and communication skills are vital when building problem-solving skills. Computer scientists often work in teams and need to effectively communicate their ideas, propose solutions, and address any challenges that arise during the problem-solving process. Strong interpersonal skills facilitate collaboration and enhance problem-solving outcomes.

  • Mastering programming languages and algorithms
  • Staying updated with technological advancements in the field
  • Practicing problem solving through coding challenges and projects
  • Seeking feedback and learning from mistakes
  • Continuing to learn and improve problem-solving skills

By following these strategies, individuals can strengthen their problem-solving abilities and become more effective computer scientists or programmers. Problem-solving is an essential skill in computer science and plays a central role in driving innovation and advancing the field.

Questions and answers:

What is the problem solving cycle in computer science.

The problem solving cycle in computer science refers to a systematic approach that programmers use to solve problems. It involves several steps, including problem definition, algorithm design, implementation, testing, and debugging.

How important is the problem solving cycle in computer science?

The problem solving cycle is extremely important in computer science as it allows programmers to effectively tackle complex problems and develop efficient solutions. It helps in organizing the thought process and ensures that the problem is approached in a logical and systematic manner.

What are the steps involved in the problem solving cycle?

The problem solving cycle typically consists of the following steps: problem definition and analysis, algorithm design, implementation, testing, and debugging. These steps are repeated as necessary until a satisfactory solution is achieved.

Can you explain the problem definition and analysis step in the problem solving cycle?

During the problem definition and analysis step, the programmer identifies and thoroughly understands the problem that needs to be solved. This involves analyzing the requirements, constraints, and possible inputs and outputs. It is important to have a clear understanding of the problem before proceeding to the next steps.

Why is testing and debugging an important step in the problem solving cycle?

Testing and debugging are important steps in the problem solving cycle because they ensure that the implemented solution functions as intended and is free from errors. Through testing, the programmer can identify and fix any issues or bugs in the code, thereby improving the quality and reliability of the solution.

What is the problem-solving cycle in computer science?

The problem-solving cycle in computer science refers to the systematic approach that computer scientists use to solve problems. It involves various steps, including problem analysis, algorithm design, coding, testing, and debugging.

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What Is Problem Solving? How Software Engineers Approach Complex Challenges

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From debugging an existing system to designing an entirely new software application, a day in the life of a software engineer is filled with various challenges and complexities. The one skill that glues these disparate tasks together and makes them manageable? Problem solving . 

Throughout this blog post, we’ll explore why problem-solving skills are so critical for software engineers, delve into the techniques they use to address complex challenges, and discuss how hiring managers can identify these skills during the hiring process. 

What Is Problem Solving?

But what exactly is problem solving in the context of software engineering? How does it work, and why is it so important?

Problem solving, in the simplest terms, is the process of identifying a problem, analyzing it, and finding the most effective solution to overcome it. For software engineers, this process is deeply embedded in their daily workflow. It could be something as simple as figuring out why a piece of code isn’t working as expected, or something as complex as designing the architecture for a new software system. 

In a world where technology is evolving at a blistering pace, the complexity and volume of problems that software engineers face are also growing. As such, the ability to tackle these issues head-on and find innovative solutions is not only a handy skill — it’s a necessity. 

The Importance of Problem-Solving Skills for Software Engineers

Problem-solving isn’t just another ability that software engineers pull out of their toolkits when they encounter a bug or a system failure. It’s a constant, ongoing process that’s intrinsic to every aspect of their work. Let’s break down why this skill is so critical.

Driving Development Forward

Without problem solving, software development would hit a standstill. Every new feature, every optimization, and every bug fix is a problem that needs solving. Whether it’s a performance issue that needs diagnosing or a user interface that needs improving, the capacity to tackle and solve these problems is what keeps the wheels of development turning.

It’s estimated that 60% of software development lifecycle costs are related to maintenance tasks, including debugging and problem solving. This highlights how pivotal this skill is to the everyday functioning and advancement of software systems.

Innovation and Optimization

The importance of problem solving isn’t confined to reactive scenarios; it also plays a major role in proactive, innovative initiatives . Software engineers often need to think outside the box to come up with creative solutions, whether it’s optimizing an algorithm to run faster or designing a new feature to meet customer needs. These are all forms of problem solving.

Consider the development of the modern smartphone. It wasn’t born out of a pre-existing issue but was a solution to a problem people didn’t realize they had — a device that combined communication, entertainment, and productivity into one handheld tool.

Increasing Efficiency and Productivity

Good problem-solving skills can save a lot of time and resources. Effective problem-solvers are adept at dissecting an issue to understand its root cause, thus reducing the time spent on trial and error. This efficiency means projects move faster, releases happen sooner, and businesses stay ahead of their competition.

Improving Software Quality

Problem solving also plays a significant role in enhancing the quality of the end product. By tackling the root causes of bugs and system failures, software engineers can deliver reliable, high-performing software. This is critical because, according to the Consortium for Information and Software Quality, poor quality software in the U.S. in 2022 cost at least $2.41 trillion in operational issues, wasted developer time, and other related problems.

Problem-Solving Techniques in Software Engineering

So how do software engineers go about tackling these complex challenges? Let’s explore some of the key problem-solving techniques, theories, and processes they commonly use.


Breaking down a problem into smaller, manageable parts is one of the first steps in the problem-solving process. It’s like dealing with a complicated puzzle. You don’t try to solve it all at once. Instead, you separate the pieces, group them based on similarities, and then start working on the smaller sets. This method allows software engineers to handle complex issues without being overwhelmed and makes it easier to identify where things might be going wrong.


In the realm of software engineering, abstraction means focusing on the necessary information only and ignoring irrelevant details. It is a way of simplifying complex systems to make them easier to understand and manage. For instance, a software engineer might ignore the details of how a database works to focus on the information it holds and how to retrieve or modify that information.

Algorithmic Thinking

At its core, software engineering is about creating algorithms — step-by-step procedures to solve a problem or accomplish a goal. Algorithmic thinking involves conceiving and expressing these procedures clearly and accurately and viewing every problem through an algorithmic lens. A well-designed algorithm not only solves the problem at hand but also does so efficiently, saving computational resources.

Parallel Thinking

Parallel thinking is a structured process where team members think in the same direction at the same time, allowing for more organized discussion and collaboration. It’s an approach popularized by Edward de Bono with the “ Six Thinking Hats ” technique, where each “hat” represents a different style of thinking.

In the context of software engineering, parallel thinking can be highly effective for problem solving. For instance, when dealing with a complex issue, the team can use the “White Hat” to focus solely on the data and facts about the problem, then the “Black Hat” to consider potential problems with a proposed solution, and so on. This structured approach can lead to more comprehensive analysis and more effective solutions, and it ensures that everyone’s perspectives are considered.

This is the process of identifying and fixing errors in code . Debugging involves carefully reviewing the code, reproducing and analyzing the error, and then making necessary modifications to rectify the problem. It’s a key part of maintaining and improving software quality.

Testing and Validation

Testing is an essential part of problem solving in software engineering. Engineers use a variety of tests to verify that their code works as expected and to uncover any potential issues. These range from unit tests that check individual components of the code to integration tests that ensure the pieces work well together. Validation, on the other hand, ensures that the solution not only works but also fulfills the intended requirements and objectives.

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Evaluating Problem-Solving Skills

We’ve examined the importance of problem-solving in the work of a software engineer and explored various techniques software engineers employ to approach complex challenges. Now, let’s delve into how hiring teams can identify and evaluate problem-solving skills during the hiring process.

Recognizing Problem-Solving Skills in Candidates

How can you tell if a candidate is a good problem solver? Look for these indicators:

  • Previous Experience: A history of dealing with complex, challenging projects is often a good sign. Ask the candidate to discuss a difficult problem they faced in a previous role and how they solved it.
  • Problem-Solving Questions: During interviews, pose hypothetical scenarios or present real problems your company has faced. Ask candidates to explain how they would tackle these issues. You’re not just looking for a correct solution but the thought process that led them there.
  • Technical Tests: Coding challenges and other technical tests can provide insight into a candidate’s problem-solving abilities. Consider leveraging a platform for assessing these skills in a realistic, job-related context.

Assessing Problem-Solving Skills

Once you’ve identified potential problem solvers, here are a few ways you can assess their skills:

  • Solution Effectiveness: Did the candidate solve the problem? How efficient and effective is their solution?
  • Approach and Process: Go beyond whether or not they solved the problem and examine how they arrived at their solution. Did they break the problem down into manageable parts? Did they consider different perspectives and possibilities?
  • Communication: A good problem solver can explain their thought process clearly. Can the candidate effectively communicate how they arrived at their solution and why they chose it?
  • Adaptability: Problem-solving often involves a degree of trial and error. How does the candidate handle roadblocks? Do they adapt their approach based on new information or feedback?

Hiring managers play a crucial role in identifying and fostering problem-solving skills within their teams. By focusing on these abilities during the hiring process, companies can build teams that are more capable, innovative, and resilient.

Key Takeaways

As you can see, problem solving plays a pivotal role in software engineering. Far from being an occasional requirement, it is the lifeblood that drives development forward, catalyzes innovation, and delivers of quality software. 

By leveraging problem-solving techniques, software engineers employ a powerful suite of strategies to overcome complex challenges. But mastering these techniques isn’t simple feat. It requires a learning mindset, regular practice, collaboration, reflective thinking, resilience, and a commitment to staying updated with industry trends. 

For hiring managers and team leads, recognizing these skills and fostering a culture that values and nurtures problem solving is key. It’s this emphasis on problem solving that can differentiate an average team from a high-performing one and an ordinary product from an industry-leading one.

At the end of the day, software engineering is fundamentally about solving problems — problems that matter to businesses, to users, and to the wider society. And it’s the proficient problem solvers who stand at the forefront of this dynamic field, turning challenges into opportunities, and ideas into reality.

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Problem Solving

Solving problems is the core of computer science. Programmers must first understand how a human solves a problem, then understand how to translate this "algorithm" into something a computer can do, and finally how to "write" the specific syntax (required by a computer) to get the job done. It is sometimes the case that a machine will solve a problem in a completely different way than a human.

Computer Programmers are problem solvers. In order to solve a problem on a computer you must:

Know how to represent the information (data) describing the problem.

Determine the steps to transform the information from one representation into another.

Information Representation

A computer, at heart, is really dumb. It can only really know about a few things... numbers, characters, booleans, and lists (called arrays) of these items. (See Data Types). Everything else must be "approximated" by combinations of these data types.

A good programmer will "encode" all the "facts" necessary to represent a problem in variables (See Variables). Further, there are "good ways" and "bad ways" to encode information. Good ways allow the computer to easily "compute" new information.

An algorithm (see Algorithm) is a set of specific steps to solve a problem. Think of it this way: if you were to tell your 3 year old neice to play your favorite song on the piano (assuming the neice has never played a piano), you would have to tell her where the piano was, and how to sit on the bench, and how to open the cover, and which keys to press, and which order to press them in, etc, etc, etc.

The core of what good programmers do is being able to define the steps necessary to accomplish a goal. Unfortunately, a computer, only knows a very restricted and limited set of possible steps. For example a computer can add two numbers. But if you want to find the average of two numbers, this is beyond the basic capabilities of a computer. To find the average, you must:

  • First: Add the two numbers and save this result in a variable
  • Then: Divide this new number the number two, and save this result in a variable.
  • Finally: provide this number to the rest of the program (or print it for the user).

We "compute" all the time. Computing is the act of solving problems (or coming up with a plan to solve problems) in an organized manner. We don't need computers to "compute". We can use our own brain.

Encapsulation and Abstraction and Complexity Hiding

Computer scientists like to use the fancy word "Encapsulation" to show how smart we are. This is just a term for things we do as humans every day. It is combined with another fancy term: "Abstraction".

Abstraction is the idea of "ignoring the details". For example, a forest is really a vastly complex ecosystem containing trees, animals, water paths, etc, etc, etc. But to a computer scientist (and to a normal person), its just "a forest".

For example, if your professor needs a cup of coffee, and asks you the single item: "Get me a cup of coffee", he has used both encapsulation and abstraction. The number of steps required to actually get the coffee are enumerable. Including, getting up, walking down the hall, getting in your car, driving to a coffee stand, paying for the coffee, etc, etc, etc. Further, the idea of what a cup of coffee is, is abstract. Do you bring a mug of coffee, or a Styrofoam cup? Is it caffeinated or not? Is it freshly brewed or from concentrate? Does it come from Africa or America?

All of this information is TOO MUCH and we would quickly be unable to funciton if we had to remember all of these details. Thus we "abstract away" the details and only remember the few important items.

This brings us to the idea of "Complexity Hiding". Complexity hiding is the idea that most of the times details don't matter. In a computer program, as simple an idea as drawing a square on the screen involves hundreds (if not thousands) of (low level) computer instructions. Again, a person couldn't possible create interesting programs if every time they wanted to do something, they had to re-write (correctly) every one of those instructions. By "ecapsulating" what is meant by "draw square" and "reusing" this operation over and over again, we make programming tractable.


The idea behind encapsulation is to store the information necessary to a particular idea in a set of variables associated with a single "object". We then create functions to manipulate this object, regardless of what the actual data is. From that point on, we treat the idea from a "high level" rather than worry about all the parts (data) and actions (functions) necessary to represent the object in a computer.

Brute Force

Brute force is a technique for solving problems that relies on a computers speed (how fast it can repeat steps) to solve a problem. For example, if you wanted to know how many times the number 8 goes into the number 100, you could do the following:

Of course this is a silly way for a computer (or a human) to solve this problem. The real way we would do it is:

When in doubt, you can often use "brute force" to solve a problem, but it often saves time (at least computer time) to think about the problem and solve it in an elegant manner.

How to think like a programmer — lessons in problem solving

How to think like a programmer — lessons in problem solving

by Richard Reis


If you’re interested in programming, you may well have seen this quote before:

“Everyone in this country should learn to program a computer, because it teaches you to think.” — Steve Jobs

You probably also wondered what does it mean, exactly, to think like a programmer? And how do you do it??

Essentially, it’s all about a more effective way for problem solving .

In this post, my goal is to teach you that way.

By the end of it, you’ll know exactly what steps to take to be a better problem-solver.

Why is this important?

Problem solving is the meta-skill.

We all have problems. Big and small. How we deal with them is sometimes, well…pretty random.

Unless you have a system, this is probably how you “solve” problems (which is what I did when I started coding):

  • Try a solution.
  • If that doesn’t work, try another one.
  • If that doesn’t work, repeat step 2 until you luck out.

Look, sometimes you luck out. But that is the worst way to solve problems! And it’s a huge, huge waste of time.

The best way involves a) having a framework and b) practicing it.

“Almost all employers prioritize problem-solving skills first.
Problem-solving skills are almost unanimously the most important qualification that employers look for….more than programming languages proficiency, debugging, and system design.
Demonstrating computational thinking or the ability to break down large, complex problems is just as valuable (if not more so) than the baseline technical skills required for a job.” — Hacker Rank ( 2018 Developer Skills Report )

Have a framework

To find the right framework, I followed the advice in Tim Ferriss’ book on learning, “ The 4-Hour Chef ”.

It led me to interview two really impressive people: C. Jordan Ball (ranked 1st or 2nd out of 65,000+ users on Coderbyte ), and V. Anton Spraul (author of the book “ Think Like a Programmer: An Introduction to Creative Problem Solving ”).

I asked them the same questions, and guess what? Their answers were pretty similar!

Soon, you too will know them.

Sidenote: this doesn’t mean they did everything the same way. Everyone is different. You’ll be different. But if you start with principles we all agree are good, you’ll get a lot further a lot quicker.

“The biggest mistake I see new programmers make is focusing on learning syntax instead of learning how to solve problems.” — V. Anton Spraul

So, what should you do when you encounter a new problem?

Here are the steps:

1. Understand

Know exactly what is being asked. Most hard problems are hard because you don’t understand them (hence why this is the first step).

How to know when you understand a problem? When you can explain it in plain English.

Do you remember being stuck on a problem, you start explaining it, and you instantly see holes in the logic you didn’t see before?

Most programmers know this feeling.

This is why you should write down your problem, doodle a diagram, or tell someone else about it (or thing… some people use a rubber duck ).

“If you can’t explain something in simple terms, you don’t understand it.” — Richard Feynman

Don’t dive right into solving without a plan (and somehow hope you can muddle your way through). Plan your solution!

Nothing can help you if you can’t write down the exact steps.

In programming, this means don’t start hacking straight away. Give your brain time to analyze the problem and process the information.

To get a good plan, answer this question:

“Given input X, what are the steps necessary to return output Y?”

Sidenote: Programmers have a great tool to help them with this… Comments!

Pay attention. This is the most important step of all.

Do not try to solve one big problem. You will cry.

Instead, break it into sub-problems. These sub-problems are much easier to solve.

Then, solve each sub-problem one by one. Begin with the simplest. Simplest means you know the answer (or are closer to that answer).

After that, simplest means this sub-problem being solved doesn’t depend on others being solved.

Once you solved every sub-problem, connect the dots.

Connecting all your “sub-solutions” will give you the solution to the original problem. Congratulations!

This technique is a cornerstone of problem-solving. Remember it (read this step again, if you must).

“If I could teach every beginning programmer one problem-solving skill, it would be the ‘reduce the problem technique.’
For example, suppose you’re a new programmer and you’re asked to write a program that reads ten numbers and figures out which number is the third highest. For a brand-new programmer, that can be a tough assignment, even though it only requires basic programming syntax.
If you’re stuck, you should reduce the problem to something simpler. Instead of the third-highest number, what about finding the highest overall? Still too tough? What about finding the largest of just three numbers? Or the larger of two?
Reduce the problem to the point where you know how to solve it and write the solution. Then expand the problem slightly and rewrite the solution to match, and keep going until you are back where you started.” — V. Anton Spraul

By now, you’re probably sitting there thinking “Hey Richard... That’s cool and all, but what if I’m stuck and can’t even solve a sub-problem??”

First off, take a deep breath. Second, that’s fair.

Don’t worry though, friend. This happens to everyone!

The difference is the best programmers/problem-solvers are more curious about bugs/errors than irritated.

In fact, here are three things to try when facing a whammy:

  • Debug: Go step by step through your solution trying to find where you went wrong. Programmers call this debugging (in fact, this is all a debugger does).
“The art of debugging is figuring out what you really told your program to do rather than what you thought you told it to do.”” — Andrew Singer
  • Reassess: Take a step back. Look at the problem from another perspective. Is there anything that can be abstracted to a more general approach?
“Sometimes we get so lost in the details of a problem that we overlook general principles that would solve the problem at a more general level. […]
The classic example of this, of course, is the summation of a long list of consecutive integers, 1 + 2 + 3 + … + n, which a very young Gauss quickly recognized was simply n(n+1)/2, thus avoiding the effort of having to do the addition.” — C. Jordan Ball

Sidenote: Another way of reassessing is starting anew. Delete everything and begin again with fresh eyes. I’m serious. You’ll be dumbfounded at how effective this is.

  • Research: Ahh, good ol’ Google. You read that right. No matter what problem you have, someone has probably solved it. Find that person/ solution. In fact, do this even if you solved the problem! (You can learn a lot from other people’s solutions).

Caveat: Don’t look for a solution to the big problem. Only look for solutions to sub-problems. Why? Because unless you struggle (even a little bit), you won’t learn anything. If you don’t learn anything, you wasted your time.

Don’t expect to be great after just one week. If you want to be a good problem-solver, solve a lot of problems!

Practice. Practice. Practice. It’ll only be a matter of time before you recognize that “this problem could easily be solved with <insert concept here>.”

How to practice? There are options out the wazoo!

Chess puzzles, math problems, Sudoku, Go, Monopoly, video-games, cryptokitties, bla… bla… bla….

In fact, a common pattern amongst successful people is their habit of practicing “micro problem-solving.” For example, Peter Thiel plays chess, and Elon Musk plays video-games.

“Byron Reeves said ‘If you want to see what business leadership may look like in three to five years, look at what’s happening in online games.’
Fast-forward to today. Elon [Musk], Reid [Hoffman], Mark Zuckerberg and many others say that games have been foundational to their success in building their companies.” — Mary Meeker ( 2017 internet trends report )

Does this mean you should just play video-games? Not at all.

But what are video-games all about? That’s right, problem-solving!

So, what you should do is find an outlet to practice. Something that allows you to solve many micro-problems (ideally, something you enjoy).

For example, I enjoy coding challenges. Every day, I try to solve at least one challenge (usually on Coderbyte ).

Like I said, all problems share similar patterns.

That’s all folks!

Now, you know better what it means to “think like a programmer.”

You also know that problem-solving is an incredible skill to cultivate (the meta-skill).

As if that wasn’t enough, notice how you also know what to do to practice your problem-solving skills!

Phew… Pretty cool right?

Finally, I wish you encounter many problems.

You read that right. At least now you know how to solve them! (also, you’ll learn that with every solution, you improve).

“Just when you think you’ve successfully navigated one obstacle, another emerges. But that’s what keeps life interesting.[…]
Life is a process of breaking through these impediments — a series of fortified lines that we must break through.
Each time, you’ll learn something.
Each time, you’ll develop strength, wisdom, and perspective.
Each time, a little more of the competition falls away. Until all that is left is you: the best version of you.” — Ryan Holiday ( The Obstacle is the Way )

Now, go solve some problems!

And best of luck ?

Special thanks to C. Jordan Ball and V. Anton Spraul . All the good advice here came from them.

Thanks for reading! If you enjoyed it, test how many times can you hit in 5 seconds. It’s great cardio for your fingers AND will help other people see the story.

If this article was helpful, share it .

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1.3. What Is Computer Science? ¶

Computer science is difficult to define. This is probably due to the unfortunate use of the word “computer” in the name. As you are perhaps aware, computer science is not simply the study of computers. Although computers play an important supporting role as a tool in the discipline, they are just that–tools.

Computer science is the study of problems, problem-solving, and the solutions that come out of the problem-solving process. Given a problem, a computer scientist’s goal is to develop an algorithm , a step-by-step list of instructions for solving any instance of the problem that might arise. Algorithms are finite processes that if followed will solve the problem. Algorithms are solutions.

Computer science can be thought of as the study of algorithms. However, we must be careful to include the fact that some problems may not have a solution. Although proving this statement is beyond the scope of this text, the fact that some problems cannot be solved is important for those who study computer science. We can fully define computer science, then, by including both types of problems and stating that computer science is the study of solutions to problems as well as the study of problems with no solutions.

It is also very common to include the word computable when describing problems and solutions. We say that a problem is computable if an algorithm exists for solving it. An alternative definition for computer science, then, is to say that computer science is the study of problems that are and that are not computable, the study of the existence and the nonexistence of algorithms. In any case, you will note that the word “computer” did not come up at all. Solutions are considered independent from the machine.

Computer science, as it pertains to the problem-solving process itself, is also the study of abstraction . Abstraction allows us to view the problem and solution in such a way as to separate the so-called logical and physical perspectives. The basic idea is familiar to us in a common example.

Consider the automobile that you may have driven to school or work today. As a driver, a user of the car, you have certain interactions that take place in order to utilize the car for its intended purpose. You get in, insert the key, start the car, shift, brake, accelerate, and steer in order to drive. From an abstraction point of view, we can say that you are seeing the logical perspective of the automobile. You are using the functions provided by the car designers for the purpose of transporting you from one location to another. These functions are sometimes also referred to as the interface .

On the other hand, the mechanic who must repair your automobile takes a very different point of view. She not only knows how to drive but must know all of the details necessary to carry out all the functions that we take for granted. She needs to understand how the engine works, how the transmission shifts gears, how temperature is controlled, and so on. This is known as the physical perspective, the details that take place “under the hood.”

The same thing happens when we use computers. Most people use computers to write documents, send and receive email, surf the web, play music, store images, and play games without any knowledge of the details that take place to allow those types of applications to work. They view computers from a logical or user perspective. Computer scientists, programmers, technology support staff, and system administrators take a very different view of the computer. They must know the details of how operating systems work, how network protocols are configured, and how to code various scripts that control function. They must be able to control the low-level details that a user simply assumes.

The common point for both of these examples is that the user of the abstraction, sometimes also called the client, does not need to know the details as long as the user is aware of the way the interface works. This interface is the way we as users communicate with the underlying complexities of the implementation. As another example of abstraction, consider the Python math module. Once we import the module, we can perform computations such as

This is an example of procedural abstraction . We do not necessarily know how the square root is being calculated, but we know what the function is called and how to use it. If we perform the import correctly, we can assume that the function will provide us with the correct results. We know that someone implemented a solution to the square root problem but we only need to know how to use it. This is sometimes referred to as a “black box” view of a process. We simply describe the interface: the name of the function, what is needed (the parameters), and what will be returned. The details are hidden inside (see Figure 1 ).


Figure 1: Procedural Abstraction ¶

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One of the most important skills you learn in your computer science courses is how to problem solve. Although we cover some general problem solving paradigms in class, the best way to improve these skills is to get practice, practice, and more practice. Different people have different techniques that work best for them; below are some general tips that work for most people.

Please read these suggestions carefully.

Questions the Helpers May Ask You

When you ask a lab helper for their assistance, they will assume you have tried to solve the problem yourself. They will (reasonably) expect that you have tried out the steps outlined in this document; you should therefore be prepared to answer the following questions:

  • Did you re-read the prelab and lab?
  • Do you understand the problem?
  • Have you tried solving some examples by hand?
  • (For problems designing a solution) What have you tried? What topic from class does this most ressemble?
  • If you can’t solve the problem whole-hog, what small case can you solve?
  • (For syntax errors) What line of your code is causing the error? What do you think the compile error means, and what usually causes this kind of problem?
  • (For logical errors) On what example does your program consistently break? Have you traced through the program? Which line of your program is not doing what it should?

Four Main Problem Solving Steps:

1. understand the problem..

Solving the right problem is the most important part of problem solving. Be sure, absolutely 100% positively sure, that you understand the problem before attempting a solution. This involves:

  • Reading the prelab and lab very carefully (including all bold text, italicized text, and everything else);
  • Reviewing class notes on related topics;
  • Trying some small examples to make sure you understand what is being asked; if examples are given to you, make sure you understand them before continuing, as they are usually there to help clarify some common misconceptions; and
  • Asking someone to help clarify anything that is still confusing.

2. Design a Solution.

Formulate an algorithm to solve your problem. This involves:

  • Understanding what is being asked of you. See step 1.
  • Draw out some examples. Use paper . How would you solve these small cases, by hand? Is there a method to what you are doing? Try to formalize the steps you are taking, and try to think about whether they would work more generally, in bigger cases. Then try some bigger cases and convince yourself.
  • Reread the prelab . Did you already run some examples by hand? Did you have trouble with it then?
  • Write down the stuff you know about the problem and the examples you’ve tried, so that you can more easily find patterns .
  • Might a recent topic from class help? Usually at least some, if not most, of the lab will make use of recently covered material . Go over that topic, make sure you understand it, then try to make connections to lab.
  • Split the problem into smaller (more manageable) chunks, and try to solve the simpler problems. Go as small as you need in order to find some solution. Once you have the smaller problem solved, worry about how to generalize it to a slightly larger problem.
  • Just try something , anything, even if it is completely random and obviously wrong. When/if your attempt doesn’t work, it may still give you insight into what may work. It is not as crazy as it initially sounds!
  • Use a friend, lab helper, puppet, etc. as a sounding board ; sometimes, just voicing your problem will lead you to the “aha!” moment you need.
  • If you are still stuck, step away from the keyboard . Take a walk, go eat dinner or have a coffee. Sleep on it. Not literally. Taking a break is sometimes the most productive thing you can do, trust me.
  • Finally, stay positive . Even when things don’t work, you can still gain a better understanding of the problem. Don’t give up, just go with the flow and see where it takes you. Struggling is part of the process!

3. Implement your Solution.

Write the code to solve your problem. This involves

  • Understanding the problem, and designing a solution on paper. See steps 1 and 2.
  • Translating your design into actual code. Rather than doing this linearly, implement small chunks at a time. Break your code into subroutines, and make sure that each subroutine works before proceeding to the next. Compile and save often .
  • If you run into syntax errors, determine which line of your code is causing the problem. You can do this by systematically commenting out blocks of code until you find the block that causes the problem.
  • If you run into logical errors (as in, the program compiles but does not do what it is supposed to), find some examples on which your problem consistently fails. Trace through the program line by line, with one of these examples, to figure out exactly which line is not doing what you intend it to.
  • If the output doesn’t match what you expect, use print statements to trace through what your program is doing, and compare that to what your program should be doing. Even better, if you know how to use a debugger (in eclipse, for example, use it!)

4. Check your Solution.

This step is often overlooked, but is absolutely crucial. Your program does not necessarily work because it works on the given test cases on the lab. You have to think critically about what you code. This involves

  • Certainly check your program on all test cases given to you on the lab and prelab. The prelab often specifically contains hand-solved test cases precisely for this purpose!
  • Thinking about the “ boundary cases ,” such as, when would this array go out of bounds? For what indices will this for loop start and end?
  • Think: how would this program break ? Then, that failing: how would I convince my skeptical friend it can’t be broken?

Remember: problem solving is a creative process, which cannot be forced. Don’t get angry if you don’t see the answer right away, or you don’t see it as fast as your friend. You will have different strengths, and you can always improve. You will learn from your mistakes, so that’s always a plus!

Last updated July 3rd, 2012 by asharp

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What is Computational Thinking?

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Computational thinking is an interrelated set of skills and practices for solving complex problems, a way to learn topics in many disciplines, and a necessity for fully participating in a computational world.

Many different terms are used when talking about computing, computer science, computational thinking, and programming. Computing encompasses the skills and practices in both computer science and computational thinking. While computer science is an individual academic discipline, computational thinking is a problem-solving approach that integrates across activities, and programming is the practice of developing a set of instructions that a computer can understand and execute, as well as debugging, organizing, and applying that code to appropriate problem-solving contexts. The skills and practices requiring computational thinking are broader, leveraging concepts and skills from computer science and applying them to other contexts, such as core academic disciplines (e.g. arts, English language arts, math, science, social studies) and everyday problem solving. For educators integrating computational thinking into their classrooms, we believe computational thinking is best understood as a series of interrelated skills and competencies.

A Venn diagram showing the relationship between computer science (CS), computational thinking (CT), programming and computing.

Figure 1. The relationship between computer science (CS), computational thinking (CT), programming and computing.

In order to integrate computational thinking into K-12 teaching and learning, educators must define what students need to know and be able to do to be successful computational thinkers. Our recommended framework has three concentric circles.

  • Computational thinking skills , in the outermost circle, are the cognitive processes necessary to engage with computational tools to solve problems. These skills are the foundation to engage in any computational problem solving and should be integrated into early learning opportunities in K-3.
  • Computational thinking practices , in the middle circle, combine multiple computational skills to solve an applied problem. Students in the older grades (4-12) may use these practices to develop artifacts such as a computer program, data visualization, or computational model.
  • Inclusive pedagogies , in the innermost circle, are strategies for engaging all learners in computing, connecting applications to students’ interests and experiences, and providing opportunities to acknowledge, and combat biases and stereotypes within the computing field.

A pie chart extruding from a Venn diagram to illustrate a framework for computational thinking integration.

Figure 2. A framework for computational thinking integration.

What does inclusive computational thinking look like in a classroom? In the image below, we provide examples of inclusive computing pedagogies in the classroom. The pedagogies are divided into three categories to emphasize different pedagogical approaches to inclusivity. Designing Accessible Instruction refers to strategies teachers should use to engage all learners in computing. Connecting to Students’ Interests, Homes, and Communities refers to drawing on the experiences of students to design learning experiences that are connected with their homes, communities, interests and experiences to highlight the relevance of computing in their lives. Acknowledging and Combating Inequity refers to a teacher supporting students to recognize and take a stand against the oppression of marginalized groups in society broadly and specifically in computing. Together these pedagogical approaches promote a more inclusive computational thinking classroom environment, life-relevant learning, and opportunities to critique and counter inequalities. Educators should attend to each of the three approaches as they plan and teach lessons, especially related to computing.

Examples of inclusive pedagogies for teaching computing

Figure 3. Examples of inclusive pedagogies for teaching computing in the classroom adapted from Israel et al., 2017; Kapor Center, 2021; Madkins et al., 2020; National Center for Women & Information Technology, 2021b; Paris & Alim, 2017; Ryoo, 2019; CSTeachingTips, 2021

Micro-credentials for computational thinking

A micro-credential is a digital certificate that verifies an individual’s competence in a specific skill or set of skills. To earn a micro-credential, teachers submit evidence of student work from classroom activities, as well as documentation of lesson planning and reflection.

Because the integration of computational thinking is new to most teachers, micro-credentials can be a useful tool for professional learning and/or credentialing pathways. Digital Promise has created micro-credentials for Computational Thinking Practices . These micro-credentials are framed around practices because the degree to which students have built foundational skills cannot be assessed until they are manifested through the applied practices.

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Unlock the secrets of efficient coding, develop an in-depth understanding of different strategies, and learn how decision-making plays a significant role in using problem-solving techniques in Computer Science . This enlightening journey begins with an exploration into the definition of problem-solving techniques and their paramount importance in Computer Science. You further discover the basic problem-solving methods, their practical applications, and how these foundational skills apply directly to coding. 

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Unlock the secrets of efficient coding, develop an in-depth understanding of different strategies, and learn how decision-making plays a significant role in using problem-solving techniques in Computer Science . This enlightening journey begins with an exploration into the definition of problem-solving techniques and their paramount importance in Computer Science. You further discover the basic problem-solving methods, their practical applications, and how these foundational skills apply directly to coding.

Going deeper, you explore seven pivotal problem-solving techniques, understanding their concepts and their indispensable uses in Computer Science . Finally, learn the nuances involved in contrasting problem-solving and decision-making techniques, the subtleties that set them apart, and ways in which they can be combined for the most effective results, in terms of both efficiency and creativity.

Understanding Problem-Solving Techniques

Problem-solving techniques in computer science are the protocols, procedures, or methods employed to identify the root cause of a problem and construct an efficient solution.

Definition of problem-solving techniques in Computer Science

Problem-solving techniques in computer science refer to the methods used to find solutions to complex issues using algorithmic or heuristic approaches. These techniques can be systematic, analytical, or intuitive, encompassing traditional programming, machine learning, or artificial intelligence methods.

These techniques are used in various domains within computer science, including data analysis, software development, network troubleshooting, and cybersecurity. For example, in software development, problem-solving may involve debugging an application. Here, the issue could be a broken functionality within the application, and the solution might be modifying a specific segment of code.

At a software development company, the team notices that their mobile application crashes whenever a user tries to upload a profile picture. By employing problem-solving techniques such as testing , the team identifies that the crash occurs due to a buffer overflow when processing large images. Once identified, they solve this problem by modifying the code to handle large image sizes better.

Importance of problem-solving techniques in Computer Science

Problem-solving techniques are the cornerstone of computer science. From designing efficient algorithms for a given task to optimising or guaranteeing certain performance metrics, these techniques are used daily. Here's why they're important:

  • Mitigating runtime errors and system crashes: By identifying and rectifying coding mistakes effectively.
  • Optimizing software: Problem-solving techniques can help improve the efficiency of software, leading to enhanced user experience and reduced resource consumption.
  • Data analysis: They help in organizing, evaluating, and interpreting complex datasets to derive meaningful insights.
  • Cybersecurity: By identifying potential vulnerabilities and patching them before they can be exploited, thereby safeguarding digital assets.

In the domain of machine learning, problem-solving techniques are even more paramount. Here, problems can include determining the best machine learning model for a specific task, tuning the hyperparameters of a model, or dealing with issues like data imbalance or overfitting. These techniques can guide computer scientists in their quest to develop robust, accurate machine-learning models that can make sense of vast, complex data.

Given the rapidly evolving nature of computer science, mastering various problem-solving techniques is essential to stay ahead in this field. It helps you adapt to new advancements and tackle a wide range of challenges that come your way.

Basic Problem-Solving Techniques

Before diving into advanced, specialized techniques for solving problems, it is essential to become proficient in the fundamentals, which transcend specific problem domains and provide a solid foundation for exploring more complex areas within computer science.

Introduction to basic problem-solving techniques

There are several standard problem-solving techniques that you can employ irrespective of the field of study in computer science. The first step, however, is always understanding the problem, then you can choose the right strategy to solve it. Here are some of the basic problem-solving methods that are particularly useful:

Divide and Conquer: This technique involves breaking a larger problem into smaller, more manageable parts, solving each of them individually, and finally combining their solutions to get the overall answer.

Consider an example in the context of sorting a list of numbers. Using a divide-and-conquer algorithm like Merge Sort , the list is continually split in half, until you reach lists of size one. These lists are inherently sorted, and then you recursively merge these sorted lists, resulting in a fully sorted list.

Algorithm Design: This technique involves formalizing a series of organized steps into an algorithm to solve a specific problem. Common approaches include greedy algorithms, dynamic programming, and brute force .

Heuristics: These are rules of thumb or educated guesses that can help you find an acceptable, if not the perfect, solution when the problem is too complex for a direct mathematical approach, or when computational resources are limited.

Heuristics are not guaranteed to yield the optimal solution but are often good enough for practical purposes and can dramatically reduce the time and resources needed to find a solution.

Recursive Thinking: Recursion is predicated on solving a problem by breaking it down into smaller instances of the same problem. The idea is that, eventually, you will get to a problem that is small enough to solve directly.

Even though these techniques might sound simple, they form a cornerstone and are often cloaked within complex problem-solving techniques used in higher-level computer science.

Practical application of basic problem-solving techniques

The practical application of basic problem-solving techniques in computer science is broad and varied, depending on the specific domain. However, some applications cut across most sectors of computer science:

Each technique has its strengths and weaknesses, and the key is knowing which technique (or combination of techniques) to use for a particular problem. Remember, the goal is not just to find any solution, but to find the most efficient one possible.

Other fields, too, benefit from these problem-solving techniques. For example, bioinformatics implements algorithm design to match genetic sequences, while digital forensics employs divide-and-conquer techniques to sift through large amounts of data during an investigation. Moreover, heuristics play a significant role in the burgeoning field of AI, proving that these problem-solving techniques not only provide a solid foundation for computer science but also have real-world applications.

Coding Problem-Solving Techniques

Delving into the more specific realm of coding within computer science, the arsenal of problem-solving techniques takes on facets best suited for resolving issues related to programming and development.

Importance of coding problem-solving techniques in Computer Science

Coding problem-solving techniques are the tools that software developers use to create, optimise, and manage software applications effectively. These techniques play an instrumental role in many aspects:

  • Enhancing code efficiency: Efficient code is faster to execute, consumes less memory, and results in responsive, user-friendly applications. For instance, choosing an optimal sorting algorithm based on the size of the list can markedly improve runtime.
  • Mitigating errors: Through structured debugging and systematic thinking, developers can track and rectify logic errors, syntax errors , or runtime exceptions, leading to robust, error-free code.
  • Facilitating code readability and maintenance: Good coding practices, such as following a consistent naming scheme and using descriptive comments, make code easier to understand, troubleshoot, and maintain – essential when working in a team.
  • Implementing complex functionalities: Many modern applications require intricate algorithms, use elaborate data structures , and handle large volumes of data. Mastery of coding problem-solving techniques enables developers to tackle these challenges effectively.

Examples of coding problem-solving techniques

There's a myriad of coding problem-solving techniques at a developer's disposal. These methods typically supplement basic problem-solving techniques with practices tailored for the coding environment. Let's delve into a few:

Debugging: Debugging is the process of identifying and rectifying coding errors. It often involves using built-in tools or software debuggers to step through the code line-by-line, track variable values, and uncover where things go awry. A systematic debugging approach is essential for problem-solving in coding.

Suppose you are developing a JavaScript web application, and some functionality isn't working as expected. By using the browser's debugging tools, you can step through your JavaScript code, watch the values assigned to variables, and identify the line creating the issue.

Code Refactoring: Refactoring implies rearranging and improving the structure of existing code without changing its functionality. Refactoring techniques, such as extracting repeated code into functions or simplifying conditional expressions, are integral problem-solving tools aimed at improving code readability and efficiency.

Using Data Structures & Algorithms: Effective use of data structures ( Arrays , LinkedList, Stack, Queue, Tree, Hashtable, etc.) and algorithms (Sorting, Searching, etc.) is fundamental in coding problem-solving. The correct choice and application of such tools can have a dramatic impact on a program’s performance.

Version Control: While writing code, you often need to try out different solutions or collaborate with other team members. Using version control systems , like Git, helps manage changes, track history, and merge code from different branches. This aids in solving and managing complex coding problems.

Apart from these fundamental techniques, advanced paradigms, such as Test-Driven Development (TDD), Behaviour Driven Development (BDD), etc., also exist. In TDD, the developer writes tests for a function before writing the actual function. In BDD, the behaviour of an application from the end user's perspective is the guiding force behind development. These paradigms incorporate problem-solving in their methodologies and guide the development process to create effective, robust applications.

Indeed, coding problem-solving techniques enrich a developer's toolkit and provide avenues to tackle the myriad of challenges that arise in programming. Whether it's minimising bugs, improving code efficiency, or implementing complex functionalities, these techniques are indispensable in daily coding endeavours.

In-depth study of 7 Problem-Solving Techniques

Problem-solving takes centre stage in the realm of computer science, where challenges need methodical approaches for efficient resolution. Let's delve into an in-depth exploration of seven such techniques, with each offering a unique perspective on how to tackle and solve issues effectively.

Conceptual understanding of the 7 problem-solving techniques

Within the realm of computer science, efficient problem-solving techniques can be the key to unlocking streamlined workflows, effective data handling, and improved coding management. These problem-solving methods include:

  • Divide and Conquer: This technique splits larger problems into smaller, more manageable sub-problems, solves the sub-problems individually and combines the solutions to get a complete resolution. This technique is pertinent to a wide range of algorithms in computer science , including sorting and searching algorithms.
  • Greedy Algorithms: Greedy algorithms solve problems by making the best choice at each step, with the hope that these local optimal solutions will lead to a globally optimal solution. They are often used in scenarios where the optimal solution has a 'greedy property', such as in the famous 'travelling salesman' problem.
  • Backtracking : This technique incrementally builds candidates for the solutions and abandons a candidate as soon as it determines that this candidate cannot possibly be extended to a valid solution.
  • Dynamic Programming: This method solves complex problems by breaking them down into simpler sub-problems, but unlike divide and conquer, these sub-problems are not solved independently. Instead, the results of sub-problems are stored and utilised to build up solutions to larger problems.
  • Brute Force : This straightforward approach tries every possible solution until it finds the best one. The simplicity of this method often makes it a practical and easy-to-implement fallback plan, although it may not be the most efficient.
  • Randomised Algorithms: For certain problems, deterministic algorithms may be too slow or complex, and the solution space too large to navigate exhaustively. In such cases, randomised algorithms offer an option where random choices drive the solution process. These algorithms have proven extremely efficient in problems like QuickSort and the Monte Carlo method.
  • Heuristic Methods: Heuristics are problem-solving approaches that are not always guaranteed to provide the perfect solution but will produce a good solution in a reasonable time. Various AI and machine learning techniques, such as genetic algorithms or neural networks, heavily use heuristic methods.

A Greedy Algorithm is one where, at each step, the choice that looks the best at that moment is selected with the belief that this choice will lead to an optimal global solution.

Understanding the foundations of these techniques provides a comprehensive toolset to approach a wide array of problems in computer science. It's important to remember that a technique's effectiveness largely depends on the nature of the problem.

Uses of the 7 problem-solving techniques in Computer Science

Each problem-solving method can be coupled with different facets within computer science. For example, encryption techniques, compression algorithms, network routing strategies, and database searches all rely on precise problem-solving methodologies. Here are just a few of the potential uses for each method:

The flexibility and variety of these problem-solving techniques enable a far-reaching applicability across the vast landscape of computer science. By understanding and mastering these techniques, you can tackle a wide array of complex problems more efficiently.

Brainstorming Problem-Solving Techniques

In the context of problem-solving techniques, brainstorming is an invaluable tool. Brainstorming offers a creative, open-ended approach well-suited for troubleshooting challenges, stimulating new ideas, and tackling issues from fresh angles.

Role of brainstorming in problem-solving techniques

Brainstorming's emphasis on exploratory thinking and collaborative problem-solving makes it an excellent tool in computer science. This interactive technique encourages you to think outside the box, ushering a wealth of ideas and potential problem-solving approaches. Here's why brainstorming plays a pivotal role in problem-solving techniques:

  • Encourages Creative Thinking: Brainstorming breaks down the barriers of conventional thought, promoting imaginative solutions that may not be immediately evident. This out-of-the-box thinking can generate unique problem-solving methods for complex computer science problems.
  • Fosters Collaboration: Brainstorming is fundamentally a collective effort. By combining the expertise and viewpoints of multiple individuals, it can foster innovative problem-solving approaches that would not surface in isolated thinking.
  • Aids in Problem Understanding: In the process of brainstorming, not only are solutions discussed, but the problem itself is dissected from different angles. This aids in gaining a deeper understanding of the problem, essential to uncover the most effective solutions.

Consider a team of developers brainstorming to develop a feature for a software application. One developer might suggest a direct approach that, although simple, may not be the most efficient. Another team member could propose a more complex, but efficient, algorithm for the feature. A third might contribute an innovative approach that balances both performance and simplicity.

Through this collective brainstorming, the team converges on the most well-rounded approach, emphasising the critical role that brainstorming plays in problem-solving methodologies.

Applying brainstorming in problem-solving techniques

Brainstorming is not just about generating as many ideas as possible; it's also about creating an organized framework for synthesizing and evaluating those ideas.

For effective brainstorming in problem-solving and decision-making techniques, you can follow the steps below:

  • Define the Problem: Clearly understand and define the problem that needs solving. The more accurately the problem is described, the more targeted the brainstorming will be.
  • Set Guidelines: Establish rules for the brainstorming session to keep it focused and productive. These might include encouraging free thinking, postponing judgment, welcoming wild ideas, building on other ideas, and setting a time limit.
  • Idea Generation: Begin brainstorming, inviting everyone involved to share their ideas. The key is to promote creativity and diversity of thought. No idea is too outlandish; often, the most unconventional suggestions lead to the most innovative solutions.
  • Categorise and Consolidate: Once all the ideas are documented, start to group related ideas together and consolidate overlapping ideas.
  • Analyse and Evaluate: It's time to analyse each idea based on its feasibility, potential impact, and resource requirement. Ideas that might not appear effective initially can be valuable when combined with other ideas.
  • Select and Implement: After thorough analysis and discussion, decide on the best solution(s) to implement, based on the resources and time available, instantly making the brainstorming session instrumental in decision making as well.

Remember: Brainstorming is not just a one-time activity. It can and should be done iteratively. Often, implementation of an idea will bring forward new challenges, requiring another round of brainstorming. The strength of brainstorming lies in its fluid nature, allowing it to adapt and iterate until the problem at hand is fully resolved.

All in all, brainstorming is a powerful problem-solving and decision-making technique in computer science. By cultivating creativity, encouraging collaboration, and fostering a deeper understanding of problems, it holds the potential to tackle complex issues effectively.

Problem Solving and Decision Making Techniques

In computer science, problem-solving and decision-making form the core techniques widely employed in managing software development, debugging, data analysis, network operations, and more. Incorporating these methodologies in a concerted, structured manner can significantly enhance the outcomes in various fields of technology.

Difference between problem-solving and decision-making techniques

While it might appear that problem-solving and decision-making are interchangeable terms, they signify distinct aspects of addressing challenges in computer science.

  • Problem-solving: Within a computer science context, problem-solving involves identifying an issue within a system, application, or theory and resolving it effectively. This process often includes defining the problem, identifying root causes, generating alternative solutions, selecting a solution, and implementing it. Problem-solving often utilises techniques like debugging, algorithmic design, divide and conquer, dynamic programming, recursive thinking, heuristic methods, and more.
  • Decision-making: Decision-making, on the other hand, is a process of choosing between different alternatives. It often follows problem-solving whereby, after identifying potential solutions to a problem, the best option needs to be chosen. Decision-making techniques might include tools like decision matrices, cost-benefit analyses, or simple pros-and-cons lists. In computer science, decision-making can involve choosing the right data structure, deciding which algorithm to use, or selecting a coding methodology.

For instance, problem-solving might involve identifying a bottleneck in a software's performance and brainstorming different ways to enhance the efficiency. However, decision-making comes into play when you need to choose one of the generated solutions based on various factors like resource availability, time constraints, the impact of the solution, etc. Thus, while both techniques cater to overcoming challenges, problem-solving is more focused on creating solutions, whereas decision-making prioritises choosing the most optimal one from these solutions.

Combining problem-solving and decision-making for effective results

Effective results in computer science often stem from an amalgamation of both problem-solving and decision-making techniques. Combining these approaches ensures a comprehensive solution to challenges, complete with a thorough understanding of the problem, an array of possible solutions, and a well-thought-out decision on implementing the best solution.

Consider a situation where a computer system is repeatedly encountering a fatal error. Here's how problem-solving and decision-making techniques can be combined for effective results:

  • Identification: Firstly, identify the issue affecting the system. This could be established through system monitoring tools or error logs. Once the problem is identified, it sets the base for problem-solving.
  • Problem-Solving: Now, brainstorm for possible solutions to rectify the error. This could involve debugging the system or reviewing the code to find potential bugs. Perhaps the issue might be a memory leak that needs addressing or a race condition in multi-threaded operations. These solutions emanate from problem-solving techniques.
  • Decision-Making: Once a list of possible solutions is generated, use decision-making techniques to select the best course of action. You could create a pros-and-cons list for each solution or use a more formal decision matrix to evaluate effectiveness, resources required, impact on system performance, etc. Finally, implement the solution.
  • Review: After implementation, monitor the system to ensure the solution is working as intended. If the problem persists, the process returns to the problem-solving stage to revisit the issue and generate new solutions.

It's important to keep in mind that real-word scenarios seldom follow a tidy linear sequence. More commonly, problem-solving and decision-making are iterative, cyclical processes that overlap and interrelate. It's a dynamic environment where a bottleneck can stimulate new decision-making criteria, or an unforeseen decisional deadlock might call for fresh problem-solving ideas.

Combining problem-solving with decision-making offers a structured, strategic approach to tackle challenges commonly found in computer science. This conjunction of techniques provides a robust, versatile methodology to drive effective results across the diverse landscape of technology.

Problem Solving Techniques - Key takeaways

  • Problem-solving techniques in Computer Science are techniques which typically use algorithmic or heuristic approaches to resolve complex issues.
  • Problem-solving techniques can be systematic, analytical, or intuitive, and involve traditional programming, machine learning, or artificial intelligence methods. Applied in domains such as data analysis, software development, network troubleshooting, and cybersecurity.
  • Basic problem-solving techniques comprises of methods like divide and conquer, algorithm design, heuristics, and recursive thinking, all aimed at understanding and tackling problems.
  • Practical applications of basic problem-solving techniques include applications spanning across various sectors of computer science, including sorting and searching algorithms, routing protocols for networks, AI game playing, and parsing syntax trees in compilers.
  • Examples of coding problem-solving techniques include Debugging which is essential in identifying and rectifying coding errors, Code Refactoring to improve the structure of existing code without changing its functionality, Using Data Structures & Algorithms to have a dramatic impact on a program’s performance, and Version Control System like Git for managing changes, tracking history and merging code from different branches.

Frequently Asked Questions about Problem Solving Techniques

--> what are some problem-solving techniques.

Some common problem solving techniques include brainstorming, the five whys technique, root cause analysis, lateral thinking, striving for simplicity, the 6 thinking hats and using flow charts or diagrams. Additionally, techniques such as SWOT analysis, Trial and Error, and Decision Trees can also be effective tools in problem-solving. Each technique is employed based on the nature and context of the problem to be solved. It's crucial to understand the problem fully before choosing a technique to apply.

--> What are the four problem-solving techniques?

The four problem solving techniques are: 

1) Defining the problem clearly to understand its nature and scope

2) Generating a range of potential solutions through brainstorming or creative thinking 

3) Evaluating and selecting the most feasible solutions by analysing their pros and cons

4) Implementing the chosen solution and monitoring its effectiveness.

--> How to apply problem-solving techniques?

To apply problem solving techniques, you first need to clearly identify and define the problem. Next, gather as much information as you can related to the problem. Once you have all the details, generate a range of potential solutions and evaluate each for its merits and downsides. Finally, implement the best solution and review its effectiveness, making adjustments as necessary.

--> What are the different problem solving techniques?

Different problem solving techniques include brainstorming, lateral thinking, root cause analysis, the five whys technique, mind mapping, SWOT analysis, "divide and conquer" technique and use of algorithms or heuristics. Additionally, the use of decision trees, fishbone diagrams, and PEST & STEEPLE analysis are also widely used in strategic problem solving. All these techniques help in breaking down complex problems into manageable parts and finding effective solutions. The choice of technique may vary depending on the nature and complexity of the problem.

--> How to choose problem-solving techniques?

Choosing problem-solving techniques involves understanding the nature and scope of the problem, identifying all potential methods for resolution, and then carefully evaluating each one in terms of its appropriateness, feasibility, and probable effectiveness, selecting the most promising one. Take into consideration multidisciplinary insights, and factor in resources available, time constraints, and potential risks. It can also be useful to bring in outside perspectives or utilise brainstorming techniques. The chosen method should ideally be both effective and efficient in resolving the problem at hand.

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Problem-solving techniques in computer science refer to the methods used to find solutions to complex issues using algorithmic or heuristic approaches, which can be systematic, analytical, or intuitive. They encompass traditional programming, machine learning, or artificial intelligence methods.

Problem-solving techniques in computer science are important for mitigating runtime errors and system crashes, optimizing software, organizing, evaluating, and interpreting complex datasets, and identifying potential cybersecurity vulnerabilities and patching them.

What is the 'Divide and Conquer' problem-solving technique in computer science?

The 'Divide and Conquer' technique involves breaking a larger problem into smaller, more manageable parts, solving each individually, and combining their solutions to get the overall answer. This is often used in sorting algorithms like Merge Sort.

What is the purpose of the 'Heuristics' problem-solving technique?

'Heuristics' are educated guesses that can help find an acceptable solution when the problem is too complex for a direct mathematical approach, or when computational resources are limited. They are often used in AI and language translations.

What are some of the important functions of coding problem-solving techniques in computer science?

Coding problem-solving techniques aid in enhancing code efficiency, mitigating errors, facilitating code readability and maintenance, and implementing complex functionalities.

What are some examples of coding problem-solving techniques?

Examples include debugging, code refactoring, using appropriate data structures and algorithms, and implementing version control.


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December 22, 2023

The Most Important Unsolved Problem in Computer Science

Here’s a look at the $1-million math problem at the heart of computation

By Jack Murtagh

mathematic formulas on a computer display, blue text on black screen

alengo/Getty Images

When the Clay Mathematics Institute put individual $1-million prize bounties on seven unsolved mathematical problems , they may have undervalued one entry—by a lot. If mathematicians were to resolve, in the right way, computer science's “P versus NP” question, the result could be worth worlds more than $1 million. They'd be cracking most online-security systems, revolutionizing science and even, in effect, solving the other six of the so-called Millennium Problems, all of which were chosen in the year 2000. It's hard to overstate the stakes surrounding the most important unsolved problem in computer science .

P versus NP concerns the apparent asymmetry between finding solutions to problems and verifying solutions to problems. For example, imagine you're planning a world tour to promote your new book. You pull up Priceline and start testing routes, but each one you try blows your total trip budget. Unfortunately, as the number of cities grows on your worldwide tour, the number of possible routes to check skyrockets exponentially, making it infeasible even for computers to exhaustively search through every case. But when you complain, your book agent writes back with a solution sequence of flights. You can easily verify whether their route stays in budget by simply checking that it hits every city and summing the fares to compare against the budget limit. Notice the asymmetry here: finding a solution is hard, but verifying a solution is easy.

The P versus NP question asks whether this asymmetry is real or an illusion. If you can efficiently verify a solution to a problem, does that mean you can also efficiently find a solution? It might seem obvious that finding a solution should be harder than verifying one. But researchers have been surprised before. Problems can look similarly difficult—but when you dig deeper you find shortcuts to some and hit brick walls on others. Perhaps a clever shortcut can circumvent searching through zillions of potential routes in the book tour problem. For example, if you instead wanted to find a sequence of flights between two specific remote airports while abiding by the budget, you might also throw up your hands at the immense number of possible routes to check. In fact, this problem contains enough structure that computer scientists have developed a fast procedure (or algorithm) for it that bypasses the need for an exhaustive search.

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The P versus NP question rears its head everywhere we look in the computational world well beyond the specifics of our travel scenario—so much so that it has come to symbolize a holy grail in our understanding of computation. Yet every attempt to resolve it only further exposes how monumentally difficult it is to prove one way or another.

In the subfield of theoretical computer science called complexity theory, researchers try to pin down how easily computers can solve various types of problems. P represents the class of problems they can solve efficiently, such as sorting a column of numbers in a spreadsheet or finding the shortest path between two addresses on a map. In contrast, NP represents the class of problems for which computers can verify solutions efficiently. Our book tour problem, which academics call the Traveling Salesperson Problem , lives in NP because we have an efficient procedure for verifying that the agent's solution worked.

Notice that NP actually contains P as a subset because solving a problem outright is one way to verify a solution to it. For example, how would you verify that 27 × 89 = 2,403? You would solve the multiplication problem yourself and check that your answer matches the claimed one. We typically depict the relation between P and NP with a simple Venn diagram:


Credit: Amanda Montañez

The region inside of NP but not inside of P contains problems that can't be solved with any known efficient algorithm. (Theoretical computer scientists use a technical definition for “efficient” that can be debated, but it serves as a useful proxy for the colloquial concept.) But we don't know whether that's because such algorithms don't exist or we just haven't mustered the ingenuity to discover them. This representation provides another way to phrase the P versus NP question: Are these classes actually distinct? Or does the Venn diagram collapse into one circle? Can all NP problems be solved efficiently?

Here are some examples of problems in NP that are not currently known to be in P:

Given a social network, is there a group of a specified size in which all of the people in it are friends with one another?

Given a varied collection of boxes to be shipped, can all of them be fit into a specified number of trucks?

Given a sudoku (generalized to n × n puzzle grids), does it have a solution?

Given a map, can the countries be colored with only three colors such that no two neighboring countries are the same color?

Ask yourself how you would verify proposed solutions to some of the problems listed and then how you would find a solution. Note that approximating a solution or solving a small instance (most of us can solve a 9 × 9 sudoku ) doesn't suffice. To qualify as solving a problem, an algorithm needs to find an exact solution for all instances, including very large ones.

Each of the problems can be solved via brute-force search (for example, try every possible coloring of the map and see whether any of them work), but the number of cases to try grows exponentially with the size of the problem. This means that if we call the size of the problem n (for example, the number of countries on the map or the number of boxes to pack into trucks), then the number of cases to check looks something like 2 n . The world's fastest supercomputers have no hope against exponential growth. Even when n equals 300, a tiny input size by modern data standards, 2 300 exceeds the number of atoms in the observable universe. After hitting “go” on such an algorithm, your computer would display a spinning pinwheel that would outlive you and your descendants.

Thousands of other problems belong on our list. From cell biology to game theory, the P versus NP question reaches into far corners of science and industry. If P = NP (that is, our Venn diagram dissolves into a single circle, and we obtain fast algorithms for these seemingly hard problems), then the entire digital economy would become vulnerable to collapse. This is because much of the cryptography that secures such things as your credit card number and passwords works by shrouding private information behind computationally difficult problems that can become easy to solve only if you know the secret key. Online security as we know it rests on unproven mathematical assumptions that crumble if P = NP.

Amazingly, we can even cast mathematics itself as an NP problem because we can program computers to efficiently verify proofs. In fact, legendary mathematician Kurt Gödel first posed the P versus NP problem in a letter to his colleague John von Neumann in 1956. Gödel observed that P = NP “would have consequences of the greatest importance. Namely, it would obviously mean that ... the mental work of a mathematician concerning yes-or-no questions could be completely replaced by a machine.”

If you're a mathematician worried for your job, rest assured that most experts believe that P does not equal NP. Aside from the intuition that sometimes solutions should be harder to find than to verify, thousands of the hardest NP problems that are not known to be in P have sat unsolved across disparate fields, glowing with incentives of fame and fortune, and yet not one person has designed an efficient algorithm for a single one of them.

Of course, gut feeling and a lack of counterexamples don't constitute a proof. To prove that P is different from NP, you somehow have to rule out all potential algorithms for all of the hardest NP problems, a task that appears out of reach for current mathematical techniques. Indeed, the field has coped by proving so-called barrier theorems, which say that entire categories of tempting proof strategies to resolve P versus NP cannot succeed. Not only have we failed to find a proof, but we also have no clue what an eventual proof might look like.

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Problem Solving in Computer Science: What Does It Really Mean?

Teacher scholar voices.

what is a problem solving in computer science

A Daunting Challenge

Many of my students complain when asked to read technical literature, despite my efforts to make it fun and relevant. Especially in my most advanced computer science class, it becomes difficult to use material that is exciting and topical for my students because the choice of reading is ultimately out of my hands. By the end of the year, on the Advanced Placement Computer Science A exam, students must be able to read and respond to approximately 8 pages of obscure (and sometimes bizarre) scenarios related to programming in Java. The first time I asked my students to practice responding to one such Free Response Question (FRQ), one in five students simply left it blank and took a nap. 

Weeks of practicing FRQs went by but there were no changes in how my students responded to the task. I was confused by their lackluster efforts, and my students were frustrated that they were continually asked to engage in “boring” assignments. However, I knew that if my students were going to be successful on the exam, they would need to attempt to respond to these questions and respond to them correctly. More importantly, through the regular practice of responding to such questions, they would begin to develop critical abstract reasoning skills, whose importance extends well beyond the AP exam. Simple, right?

Reading Comprehension: the Root of the Problem

Honestly, I had no idea how to tackle this problem at first. However, with the support of my Mills Teacher Scholars inquiry colleagues, we began to notice that many of my students were not responding to the FRQ prompt in full, even though they appeared to be engaged in the task the entire time. I felt that the problem was rooted in reading comprehension. So, to support my students’ deeper comprehension of these technical texts, I decided to focus my inquiry on building classroom practices that develop annotation skills. I spent the next six months with my inquiry group refining a series of weekly activities that involved circling key vocabulary terms, underlining prompts, jotting down ideas in margins, and then attempting a response to the questions that fell out of each reading. After students individually practiced their annotation skills, they would act as public learners for each other, guiding the class through their thinking about the text. By the end of my inquiry, 86.5% of my students were naturally utilizing these annotation skills for the FRQs. I was delighted to see that they were developing their critical reading abilities. However, their actual scores never changed. 

Confidence Matters Too

I was so confused! How could my students complete our reading comprehension strategies and still exhibit the same response behaviors seen at the beginning of the year? Why was it so difficult to simply put pen to paper and attempt a response? What did they have to lose? I took these mixed feelings back to my inquiry colleagues, who suggested that it might be enlightening to interview three focal students. 

After compiling the interview transcripts, I went back to my inquiry group, hoping that my colleagues might help me fill in this blind spot. Their questions challenged my assumptions, ultimately allowing me to focus on a new culprit: my students did not feel confident enough to try. For example, one of my students said, “I know how to read. I just don’t know how to respond. So what is the point of trying if I know I’m going to get it wrong?”  This was really surprising to hear. I thought I knew how they felt; I thought they were confident in their abilities to solve problems through code. My focus on reading comprehension had not been enough. I needed to figure out how to develop their confidence as problem solvers, too.

A Question Leads to New Questions

The quest to understand students’ learning lasts much longer than one school year. This year’s struggle motivates me to seek out answers to related questions in the coming fall. For instance, what builds confidence over time in a computer science course, and what changes for students’ identities as learners over time? I have started mulling over instructional strategies that balance “engagement” with rigorous exposure to CS. Most of the time, it feels like engagement and rigor are at odds with each other, especially when you begin to integrate notions of an “appropriate” CS education from some of the field’s most prominent minds. In his essay “On the Cruelty of Really Teaching Computer Science,” E.W. Dijkstra wrote that students should spend their first CS class writing formal proofs about the correctness of their programs. While this approach may be academically rigorous, I am not sure it engages students who still need to be sold on the idea of computer science. My most recent inquiry has started me thinking that a student’s confidence is buried in the slim overlap between rigorous exposure to subject matter and engaging learning experiences. This multifaceted question can feel daunting to address as an individual educator. However, I am not alone. I know that I can continue to lean on future cycles of inquiry, as well as the wonderful community of instructors who, like me, are set on figuring it all out.

what is a problem solving in computer science

My colleagues' questions challenged my assumptions, ultimately allowing me to focus on a new culprit: my students did not feel confident enough to try.

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Problem Solving Techniques in Computer Science

what is a problem solving in computer science

Problem-solving is the process of identifying a problem and finding the best solution for it. Problem-solving is a technique that can be developed by following a well-organized approach. Every day we encounter many problems and solve them.

Every problem is different. Some problems are very difficult and are needed more attention to recognize the solution.

A problem may be solved by multiple methods. One solution may be faster, cheaper, and more reliable than others. It is important to choose a suitable worthy solution.

Different strategies, techniques, and tools are used to solve a problem. Computers are used as a tool to solve complex problems by developing computer programs.

Computer programs contain different instructions for computers. A programmer writes instructions and the computer executes these instructions to solve a problem. A person can be a good programmer if he has the skill of solving problems.

Table of Contents

Problem-Solving Techniques.

There are three different types of problem-solving techniques.

A set of instructions given to a computer to solve a problem is called a program.

A computer works according to the given instructions in the program. Computer programs are written in programming languages. A person who develops a program is called a programmer.

The programmer develops programs to instruct the computer on how to process data into information. The programmer uses programming languages or tools to write programs.

 Advantages of Computer Program

Different advantages of computer programs are as follows:

  • A computer program can solve many problems by giving instructions to the computer.
  • A computer program can be used to perform a task again and again and fastly.
  • A program can process a large amount of data easily.
  • It can display the results in different styles.
  • The processing of a program is more efficient and less time-consuming.
  • Different types of programs are used in different fields to perform certain tasks.

   Algorithms & Pseudo Code

An algorithm is a step-by-step procedure to solve a problem. The process of solving

problem becomes simpler and easier with help of algorithm. It is better to write an algorithm

before writing the actual computer program.

Properties of Algorithm

Following are some properties of an algorithm:

  • The given problem should be broken down into simple and meaningful steps.
  • The steps should be numbered sequentially.
  • The steps should be descriptive and written in simple English. 

Algorithms are written in a language that is similar to simple English called pseudocode. There is no standard to write pseudo code. It is used to specify program logic in an English-like manner that is independent of any particular programming language.

Pseudocode simplifies program development by separating it into two main parts.

Logic Design

In this part, the logic of the program is designed. We specify different steps required to solve the problem and the sequence of these steps.

In this part, the algorithm is converted into a program. The steps of the algorithm are

translated into instructions of any programming language.

The use of pseudo-code allows the programmer to focus on the planning of the program. After the planning is final, it can be written in any programming language.

The following algorithm inputs two numbers calculate the sum and then displays the result on the screen.

4. Total A+B

5. Display Total

The following algorithm inputs the radius from the user and calculates the area of a circle.

Hint: Area 3.14* radius* radius)

2. Input radius in r

3. area = 3.14* r* r

4. Print area

Advantages of Algorithm

There are many advantages of an algorithm

Reduce complexity

Writing algorithm and program separately simplifies the overall task by dividing it into two simpler tasks. While writing the algorithm, we can focus on solving the problem instead of concentrating on a particular language.

Increased Flexibility

An algorithm is written so that the code may be written in any language. Using an algorithm, the program could be written in Visual Basic, Java or C++, etc.

Ease of Understanding

It is not necessary to understand a particular programming language to understand an algorithm. It is written in an English-like manner.

A flowchart is a combination of two words flow and chart. A chart consists of different symbols to display information about any program. Flow indicates the direction processing that takes place in the program.

Flowchart is a graphical representation of an algorithm. It is a way of visually presenting the flow of data, operations performed on data, and the sequence of these operations.

Flowchart is similar to the layout plan of a building. A designer draws the layout plan of the building before constructing it. Similarly, a programmer prefers to design the flowchart before writing the computer program. Flowchart is designed according to the defined rule.

Uses of Logic Flowchart

Flowchart is used for the following reasons

  • Flowchart is used to represent an algorithm in a simple graphical manner.
  • Flowchart is used to show the steps of an algorithm easily.
  • Flowchart is used to understand the flow of the program.
  • Flowchart is used to improve the logic for solving a problem.
  • Programs can be reviewed and debugged easily.
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Photo-illustration of a mini AI bot looking at a laptop atop a stock of books, sitting next to human hands on a laptop.

Generative AI is transforming the software development industry. AI-powered coding tools are assisting programmers in their workflows, while jobs in AI continue to increase. But the shift is also evident in academia—one of the major avenues through which the next generation of software engineers learn how to code.

Computer science students are embracing the technology, using generative AI to help them understand complex concepts, summarize complicated research papers, brainstorm ways to solve a problem, come up with new research directions, and, of course, learn how to code.

“Students are early adopters and have been actively testing these tools,” says Johnny Chang , a teaching assistant at Stanford University pursuing a master’s degree in computer science. He also founded the AI x Education conference in 2023, a virtual gathering of students and educators to discuss the impact of AI on education.

So as not to be left behind, educators are also experimenting with generative AI. But they’re grappling with techniques to adopt the technology while still ensuring students learn the foundations of computer science.

“It’s a difficult balancing act,” says Ooi Wei Tsang , an associate professor in the School of Computing at the National University of Singapore . “Given that large language models are evolving rapidly, we are still learning how to do this.”

Less Emphasis on Syntax, More on Problem Solving

The fundamentals and skills themselves are evolving. Most introductory computer science courses focus on code syntax and getting programs to run, and while knowing how to read and write code is still essential, testing and debugging—which aren’t commonly part of the syllabus—now need to be taught more explicitly.

“We’re seeing a little upping of that skill, where students are getting code snippets from generative AI that they need to test for correctness,” says Jeanna Matthews , a professor of computer science at Clarkson University in Potsdam, N.Y.

Another vital expertise is problem decomposition. “This is a skill to know early on because you need to break a large problem into smaller pieces that an LLM can solve,” says Leo Porter , an associate teaching professor of computer science at the University of California, San Diego . “It’s hard to find where in the curriculum that’s taught—maybe in an algorithms or software engineering class, but those are advanced classes. Now, it becomes a priority in introductory classes.”

“Given that large language models are evolving rapidly, we are still learning how to do this.” —Ooi Wei Tsang, National University of Singapore

As a result, educators are modifying their teaching strategies. “I used to have this singular focus on students writing code that they submit, and then I run test cases on the code to determine what their grade is,” says Daniel Zingaro , an associate professor of computer science at the University of Toronto Mississauga . “This is such a narrow view of what it means to be a software engineer, and I just felt that with generative AI, I’ve managed to overcome that restrictive view.”

Zingaro, who coauthored a book on AI-assisted Python programming with Porter, now has his students work in groups and submit a video explaining how their code works. Through these walk-throughs, he gets a sense of how students use AI to generate code, what they struggle with, and how they approach design, testing, and teamwork.

“It’s an opportunity for me to assess their learning process of the whole software development [life cycle]—not just code,” Zingaro says. “And I feel like my courses have opened up more and they’re much broader than they used to be. I can make students work on larger and more advanced projects.”

Ooi echoes that sentiment, noting that generative AI tools “will free up time for us to teach higher-level thinking—for example, how to design software, what is the right problem to solve, and what are the solutions. Students can spend more time on optimization, ethical issues, and the user-friendliness of a system rather than focusing on the syntax of the code.”

Avoiding AI’s Coding Pitfalls

But educators are cautious given an LLM’s tendency to hallucinate . “We need to be teaching students to be skeptical of the results and take ownership of verifying and validating them,” says Matthews.

Matthews adds that generative AI “can short-circuit the learning process of students relying on it too much.” Chang agrees that this overreliance can be a pitfall and advises his fellow students to explore possible solutions to problems by themselves so they don’t lose out on that critical thinking or effective learning process. “We should be making AI a copilot—not the autopilot—for learning,” he says.

“We should be making AI a copilot—not the autopilot—for learning.” —Johnny Chang, Stanford University

Other drawbacks include copyright and bias. “I teach my students about the ethical constraints—that this is a model built off other people’s code and we’d recognize the ownership of that,” Porter says. “We also have to recognize that models are going to represent the bias that’s already in society.”

Adapting to the rise of generative AI involves students and educators working together and learning from each other. For her colleagues, Matthews’s advice is to “try to foster an environment where you encourage students to tell you when and how they’re using these tools. Ultimately, we are preparing our students for the real world, and the real world is shifting, so sticking with what you’ve always done may not be the recipe that best serves students in this transition.”

Porter is optimistic that the changes they’re applying now will serve students well in the future. “There’s this long history of a gap between what we teach in academia and what’s actually needed as skills when students arrive in the industry,” he says. “There’s hope on my part that we might help close the gap if we embrace LLMs.”

  • How Coders Can Survive—and Thrive—in a ChatGPT World ›
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Rina Diane Caballar is a writer covering tech and its intersections with science, society, and the environment. An IEEE Spectrum Contributing Editor, she's a former software engineer based in Wellington, New Zealand.

Bruce Benson

Yes! Great summary of how things are evolving with AI. I’m a retired coder (BS comp sci) and understand the fundamentals of developing systems. Learning the lastest systems is now the greatest challenge. I was intrigued by Ansible to help me manage my homelab cluster, but who wants to learn one more scripting language? Turns out ChatGPT4 knows the syntax, semantics, and work flow of Ansible and all I do is tell is to “install log2ram on all my proxmox servers” and I get a playbook that does just that. The same with Docker Compose scripts. Wow.

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Title: gold: geometry problem solver with natural language description.

Abstract: Addressing the challenge of automated geometry math problem-solving in artificial intelligence (AI) involves understanding multi-modal information and mathematics. Current methods struggle with accurately interpreting geometry diagrams, which hinders effective problem-solving. To tackle this issue, we present the Geometry problem sOlver with natural Language Description (GOLD) model. GOLD enhances the extraction of geometric relations by separately processing symbols and geometric primitives within the diagram. Subsequently, it converts the extracted relations into natural language descriptions, efficiently utilizing large language models to solve geometry math problems. Experiments show that the GOLD model outperforms the Geoformer model, the previous best method on the UniGeo dataset, by achieving accuracy improvements of 12.7% and 42.1% in calculation and proving subsets. Additionally, it surpasses the former best model on the PGPS9K and Geometry3K datasets, PGPSNet, by obtaining accuracy enhancements of 1.8% and 3.2%, respectively.

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Annual skilled trades competition builds technical and professional skills for Iowa students

  • Wednesday, May 1, 2024
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Skills USA

Southeast Polk senior Simon Frohock (R) competed in the cabinet making contest for a second year.

High-quality career and professional skill development took center stage last week as over 600 high school and college students took part in the annual SkillsUSA State Leadership and Skills Conference . Held in Ankeny at the Des Moines Area Community College campus, this two-day competition featured over 50 different leadership and technical competitions for students to test their technical skills and knowledge, explore career pathways and make valuable connections with local industry leaders.

skills usa

Southeast Polk High School seniors Delvis Kouete and Simon Frohock, both 17, were well-prepared for the competition, which featured timed activities related to industrial technology, carpentry, robotics, automotive repair and job interview techniques, among many others. For this year’s skills competition, Delvis competed in architectural drafting and was a member of the school’s quiz bowl team. Simon, the 2023 state champion in cabinet making, returned for a second year in the cabinet making contest. Both students competed well in their individual competitions, with Delvis placing fifth and Simon serving as this year’s runner-up.

“The skills competition can help you strive for excellence in your work and learning,” Simon said. “Even though it’s a competition and there is pressure to do well, it’s a good, low-risk way to see what an employee in this work has to do every day.”

Both Simon and Delvis noted that the competition not only helps to strengthen a student’s technical skills, but it also engages students in career pathway discovery and professional skill development.

“Being a part of SkillsUSA and competing in the skills competition has helped me learn new skills with my hands and work on teamwork, communication and leadership skills,” Delvis said. “You learn how to work with other people that aren’t like you and get your mind thinking about your future career.”

Along with the individual contests, all competitors at the SkillsUSA State Leadership and Skills Conference were required to submit a resume and take a professional development test that focused on workplace, professional and technical skills as well as overall knowledge of SkillsUSA.

“SkillsUSA helps provide real-world context to the content being taught by classroom educators,” said Kent Storm, state director for SkillsUSA Iowa. “Taking the learning beyond the classroom allows students to grow and learn next to industry partners and gain valuable experience."

As one of Iowa’s career and technical student organizations (CTSO) , SkillsUSA champions the skilled trades industry and provides opportunities for students to apply the skills they have developed in classrooms through conferences, competitions, community service events, worksite visits and other activities.

“Participation in a CTSO like SkillsUSA helps students gain hands-on experience and connect classroom curricula to careers,” said Cale Hutchings, education consultant at the Iowa Department of Education. “Through CTSOs, students can become leaders and strengthen their employability skills, which is valuable as they explore potential next steps in their college and career pathways.”

SkillsUSA boasts a roster of over 400,000 members nationwide. In Iowa, over 1,300 students and advisers in career and technical education programs participate in local SkillsUSA chapters.

At Southeast Polk, 21 student members are a part of their SkillsUSA chapter. Led by industrial technology teachers and chapter advisers Ryan Andersen and Brett Rickabaugh, the students have been involved with several community service projects, employer presentations and opportunities to work closely with instructors.

“Any time a student participates in SkillsUSA, it gives us more time with that student to elaborate on what we’ve learned in class,” Andersen said. “They can connect the idea to the planning, design and completion of a project and how that activity fits into a real career. That’s something we can’t replicate without a CTSO.”

Anderson also stated that students who participate in SkillsUSA and activities like the State Leadership and Skills Conference build confidence through their experiences.

“It really helps students to have the confidence to rely on their skills and what they know,” he said. “The skills competition requires them to use problem-solving skills and build off their knowledge to continue to learn and persevere.”

This year’s first-place winners at the SkillsUSA State Leadership and Skills Conference will move onward to compete with 6,000 other students at the national conference in Atlanta this June.

Skills USA

For Simon and Delvis, the skills competition was another step in building necessary skills and acumen for their futures. Simon, with his penchant for cabinet making, already has a full-time job lined up after graduation with a local cabinet shop. Additionally, Delvis would like to pursue something within the computer science field, perhaps in the coding or software engineering areas, and although he is changing fields, he believes SkillsUSA has helped him feel more prepared for the future.

“It has definitely helped me with skill-building and problem-solving,” he said. “What I’ve learned will be beneficial no matter what I decide to do next.”  

New computer algorithm supercharges climate models and could lead to better predictions of future climate change

Professor Samar Khatiwala , from the University of Oxford’s Department of Earth Sciences, has led a major advance to solve a critical issue in modelling future climate change. The findings have been published in  Science Advances .

Earth System Models – complex computer models which describe Earth processes and how they interact – are critical for predicting future climate change. By simulating the response of our land, oceans and atmosphere to manmade greenhouse gas emissions, these models form the foundation for predictions of future extreme weather and climate event scenarios, including those issued by the UN Intergovernmental Panel on Climate Change (IPCC).

However, climate modellers have long faced a major problem. Because Earth System Models integrate many complicated processes, they cannot immediately run a simulation; they must first ensure that it has reached a stable equilibrium representative of real-world conditions before the industrial revolution. Without this initial settling period – referred to as the “spin-up” phase – the model can “drift”, simulating changes that may be erroneously attributed to manmade factors.

Unfortunately, this process is extremely slow as it requires running the model for many thousands of model years which, for IPCC simulations, can take as much as two years on some of the world’s most powerful supercomputers.

However, a study published today in Science Advances by a University of Oxford scientist funded by the Agile Initiative describes a new computer algorithm which can be applied to Earth System Models to drastically reduce spin-up time. During tests on models used in IPCC simulations, the algorithm was on average 10 times faster at spinning up the model than currently-used approaches , reducing the time taken to achieve equilibrium from many months to under a week.

Study author Samar Khatiwala , Professor of Earth Sciences at the University of Oxford’s Department of Earth Sciences, who devised the algorithm, said: ‘Minimising model drift at a much lower cost in time and energy is obviously critical for climate change simulations, but perhaps the greatest value of this research may ultimately be to policy makers who need to know how reliable climate projections are.’

Currently, the lengthy spin-up time of many IPCC models prevents climate researchers from running their model at a higher resolution and defining uncertainty through carrying out repeat simulations. By drastically reducing the spin-up time, the new algorithm will enable researchers to investigate how subtle changes to the model parameters can alter the output – which is critical for defining the uncertainty of future emission scenarios.

Professor Khatiwala’s new algorithm employs a mathematical approach known as sequence acceleration, which has its roots with the famous mathematician Euler. In the 1960s this idea was applied by D. G. Anderson to speed-up the solution of Schrödinger’s equation, which predicts how matter behaves at the microscopic level. So important is this problem that more than half the world’s supercomputing power is currently devoted to solving it, and ‘Anderson Acceleration’, as it is now known, is one of the most commonly used algorithms employed for it.

Professor Khatiwala realised that Anderson Acceleration might also be able to reduce model spin-up time since both problems are of an iterative nature: an output is generated and then fed back into the model many times over. By retaining previous outputs and combining them into a single input using Anderson’s scheme, the final solution is achieved much more quickly.

Not only does this make the spin-up process much faster and less computationally expensive, but the concept can be applied to the huge variety of different models that are used to investigate, and inform policy on, issues ranging from ocean acidification to biodiversity loss. With research groups around the world beginning to spin-up their models for the next IPCC report, due in 2029, Professor Khatiwala is working with a number of them, including the UK Met Office, to trial his approach and software in their models.

Professor Helene Hewitt OBE, Co-chair for the Coupled Model Intercomparison Project (CMIP) Panel, which will inform the next IPCC report, commented: ‘Policymakers rely on climate projections to inform negotiations as the world tries to meet the Paris Agreement. This work is a step towards reducing the time it takes to produce those critical climate projections.’

Professor Colin Jones Head of the NERC/Met Office sponsored UK Earth system modelling, commented on the findings: ‘Spin-up has always been prohibitively expensive in terms of computational cost and time. The new approaches developed by Professor Khatiwala have the promise to break this logjam and deliver a quantum leap in the efficiency of spinning up such complex models and, as a consequence, greatly increase our ability to deliver timely, robust estimates of global climate change.’

Notes to editors:

For media enquiries and interview requests, contact Dr Charlie Rex, Department of Earth Sciences, University of Oxford: [email protected]

The study ‘Efficient spin-up of Earth System Models using sequence acceleration’ has been published in the journal Science Advances  at https://www.science.org/doi/10.1126/sciadv.adn2839   

This research was conducted as part of the Agile Initiative at the Oxford Martin School, with funding from the Natural Environment Research Council (NERC).

About the University of Oxford

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Oxford is world-famous for research and teaching excellence and home to some of the most talented people from across the globe. Our work helps the lives of millions, solving real-world problems through a huge network of partnerships and collaborations. The breadth and interdisciplinary nature of our research alongside our personalised approach to teaching sparks imaginative and inventive insights and solutions.

Through its research commercialisation arm, Oxford University Innovation, Oxford is the highest university patent filer in the UK and is ranked first in the UK for university spinouts, having created more than 300 new companies since 1988. Over a third of these companies have been created in the past five years. The university is a catalyst for prosperity in Oxfordshire and the United Kingdom, contributing  £15.7 billion to the UK economy  in 2018/19, and supports more than 28,000 full time jobs.

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